{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "aware-cleveland",
      "metadata": {
        "id": "aware-cleveland"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from scipy import stats\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.lines as mlines\n",
        "import matplotlib.transforms as mtransforms\n",
        "import matplotlib.patches as mpatches\n",
        "import numpy as np\n",
        "import scipy as scp\n",
        "import scipy.stats as stats\n",
        "from matplotlib_venn import venn2_unweighted\n",
        "from pysankey import sankey\n",
        "from matplotlib import rcParams\n",
        "from scipy.stats.contingency import odds_ratio\n",
        "from scipy.stats import fisher_exact\n",
        "from scipy.stats import chi2_contingency\n",
        "import matplotlib.patches as mpatches\n",
        "import matplotlib.ticker as ticker\n",
        "from math import isnan "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cf4781fc",
      "metadata": {},
      "source": [
        "# Color Palette"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "23c4864e",
      "metadata": {},
      "outputs": [],
      "source": [
        "labels = [mpatches.Patch(color='#E69F00', label=\"Code unavailable\"),\n",
        "mpatches.Patch(color='#56B4E9', label=\"Code available\"),\n",
        "mpatches.Patch(color='#EA526F', label=\"Data unavailable\"),\n",
        "mpatches.Patch(color='#009E73', label=\"Data available\"),\n",
        "mpatches.Patch(color='#391463', label=\"Mandatory\"),\n",
        "mpatches.Patch(color='#CA78A6', label=\"Encouraged\"),\n",
        "]"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "c04d91c7",
      "metadata": {},
      "source": [
        "# Helper Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "eed10de8",
      "metadata": {},
      "outputs": [],
      "source": [
        "def as_si(x, ndp):\n",
        "    s = '{x:0.{ndp:d}e}'.format(x=x, ndp=ndp)\n",
        "    m, e = s.split('e')\n",
        "    return r'{m:s}\\times 10^{{{e:d}}}'.format(m=m, e=int(e))\n",
        "def letter_annotation(ax, xoffset, yoffset, letter,fontsize):\n",
        " ax.text(xoffset, yoffset, letter, transform=ax.transAxes,\n",
        "         size=fontsize, weight='bold')"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "34ec7863",
      "metadata": {},
      "source": [
        "# Load Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "distinguished-soldier",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "distinguished-soldier",
        "outputId": "5f1a9994-5ed5-4901-bb3f-8381f41b75bb"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PMC_ID</th>\n",
              "      <th>PMC_Year</th>\n",
              "      <th>Journal</th>\n",
              "      <th>IF</th>\n",
              "      <th>Article Title</th>\n",
              "      <th>Type of paper</th>\n",
              "      <th>Secondary analysis</th>\n",
              "      <th>Data availability statement (yes/no)</th>\n",
              "      <th>Data availability with explaination(yes/no)</th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>...</th>\n",
              "      <th>If yes, notebook/Rmd</th>\n",
              "      <th>If yes, single/ multiple</th>\n",
              "      <th>Summary data (yes/ no/ partial)</th>\n",
              "      <th>Number of citations</th>\n",
              "      <th>Gender of the PI</th>\n",
              "      <th>Experience of PI/ Last author (Check their profile or webpage to see whether they are an associate/ assistant/ professor/ professor emiretus, etc)</th>\n",
              "      <th>PI/ last author's training background - CS/ Bioinformatics/ Other</th>\n",
              "      <th>PI (last author's) education level - PhD/PostDoc/ Master's etc</th>\n",
              "      <th>First author's education level</th>\n",
              "      <th>First author's training background (CS/ Bioinformatics/ Other)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>PMC6686906</td>\n",
              "      <td>2019</td>\n",
              "      <td>Nat_Genet</td>\n",
              "      <td>28.0</td>\n",
              "      <td>Accurate estimation of SNP-heritability from b...</td>\n",
              "      <td>Research article</td>\n",
              "      <td>hybrid</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes for secondary/ no for primary</td>\n",
              "      <td>no</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>yes</td>\n",
              "      <td>47</td>\n",
              "      <td>Male</td>\n",
              "      <td>Associate Professor</td>\n",
              "      <td>CS, Bioinformatics</td>\n",
              "      <td>Postdoc</td>\n",
              "      <td>MS</td>\n",
              "      <td>Bioinformatics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PMC7610512</td>\n",
              "      <td>2020</td>\n",
              "      <td>Nat_Genet</td>\n",
              "      <td>38.3</td>\n",
              "      <td>Regulation of single-cell genome organization ...</td>\n",
              "      <td>Research article</td>\n",
              "      <td>Hybrid</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes for secondary/Available upon request for p...</td>\n",
              "      <td>no</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>yes</td>\n",
              "      <td>39</td>\n",
              "      <td>Male</td>\n",
              "      <td>Research Director</td>\n",
              "      <td>Other</td>\n",
              "      <td>Postdoc</td>\n",
              "      <td>Postdoc</td>\n",
              "      <td>Other, Genetics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>PMC7343611</td>\n",
              "      <td>2020</td>\n",
              "      <td>Nat_Genet</td>\n",
              "      <td>38.3</td>\n",
              "      <td>Lymph node metastases develop through a wider ...</td>\n",
              "      <td>Research article</td>\n",
              "      <td>Hybrid</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>...</td>\n",
              "      <td>notebook</td>\n",
              "      <td>multiple</td>\n",
              "      <td>yes</td>\n",
              "      <td>31</td>\n",
              "      <td>Female</td>\n",
              "      <td>Assistant Professor</td>\n",
              "      <td>Other</td>\n",
              "      <td>Phd</td>\n",
              "      <td>Postdoc</td>\n",
              "      <td>CS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>PMC6767648</td>\n",
              "      <td>2019</td>\n",
              "      <td>Genome_Med</td>\n",
              "      <td>11.0</td>\n",
              "      <td>Identifying Crohn’s disease signal from variom...</td>\n",
              "      <td>Research article</td>\n",
              "      <td>hybrid</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes for secondary/ Available upon request for ...</td>\n",
              "      <td>no</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>yes</td>\n",
              "      <td>58</td>\n",
              "      <td>Female</td>\n",
              "      <td>Associate Professor</td>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>Phd</td>\n",
              "      <td>Phd</td>\n",
              "      <td>Bioinformatics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PMC6862797</td>\n",
              "      <td>2019</td>\n",
              "      <td>Genome_Biol</td>\n",
              "      <td>9.5</td>\n",
              "      <td>Population-level analysis reveals the widespre...</td>\n",
              "      <td>Research article</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>74</td>\n",
              "      <td>Male</td>\n",
              "      <td>Professor</td>\n",
              "      <td>Other</td>\n",
              "      <td>Phd</td>\n",
              "      <td>Masters</td>\n",
              "      <td>Other</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 29 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       PMC_ID  PMC_Year      Journal    IF  \\\n",
              "0  PMC6686906      2019    Nat_Genet  28.0   \n",
              "1  PMC7610512      2020    Nat_Genet  38.3   \n",
              "2  PMC7343611      2020    Nat_Genet  38.3   \n",
              "3  PMC6767648      2019   Genome_Med  11.0   \n",
              "4  PMC6862797      2019  Genome_Biol   9.5   \n",
              "\n",
              "                                       Article Title     Type of paper  \\\n",
              "0  Accurate estimation of SNP-heritability from b...  Research article   \n",
              "1  Regulation of single-cell genome organization ...  Research article   \n",
              "2  Lymph node metastases develop through a wider ...  Research article   \n",
              "3  Identifying Crohn’s disease signal from variom...  Research article   \n",
              "4  Population-level analysis reveals the widespre...  Research article   \n",
              "\n",
              "  Secondary analysis Data availability statement (yes/no)  \\\n",
              "0             hybrid                                  yes   \n",
              "1             Hybrid                                  yes   \n",
              "2             Hybrid                                  yes   \n",
              "3             hybrid                                  yes   \n",
              "4                 no                                  yes   \n",
              "\n",
              "         Data availability with explaination(yes/no)  \\\n",
              "0                  yes for secondary/ no for primary   \n",
              "1  yes for secondary/Available upon request for p...   \n",
              "2                                                yes   \n",
              "3  yes for secondary/ Available upon request for ...   \n",
              "4                                                yes   \n",
              "\n",
              "  Data availability (yes/no)  ... If yes, notebook/Rmd  \\\n",
              "0                         no  ...                  NaN   \n",
              "1                         no  ...                  NaN   \n",
              "2                        yes  ...             notebook   \n",
              "3                         no  ...                  NaN   \n",
              "4                        yes  ...                  NaN   \n",
              "\n",
              "  If yes, single/ multiple Summary data (yes/ no/ partial)  \\\n",
              "0                      NaN                             yes   \n",
              "1                      NaN                             yes   \n",
              "2                 multiple                             yes   \n",
              "3                      NaN                             yes   \n",
              "4                      NaN                              no   \n",
              "\n",
              "  Number of citations Gender of the PI  \\\n",
              "0                  47             Male   \n",
              "1                  39             Male   \n",
              "2                  31           Female   \n",
              "3                  58           Female   \n",
              "4                  74            Male    \n",
              "\n",
              "  Experience of PI/ Last author (Check their profile or webpage to see whether they are an associate/ assistant/ professor/ professor emiretus, etc)  \\\n",
              "0                                Associate Professor                                                                                                   \n",
              "1                                 Research Director                                                                                                    \n",
              "2                                Assistant Professor                                                                                                   \n",
              "3                                Associate Professor                                                                                                   \n",
              "4                                          Professor                                                                                                   \n",
              "\n",
              "  PI/ last author's training background - CS/ Bioinformatics/ Other  \\\n",
              "0                                 CS, Bioinformatics                  \n",
              "1                                              Other                  \n",
              "2                                              Other                  \n",
              "3                                     Bioinformatics                  \n",
              "4                                              Other                  \n",
              "\n",
              "  PI (last author's) education level - PhD/PostDoc/ Master's etc  \\\n",
              "0                                            Postdoc               \n",
              "1                                            Postdoc               \n",
              "2                                                Phd               \n",
              "3                                                Phd               \n",
              "4                                                Phd               \n",
              "\n",
              "  First author's education level  \\\n",
              "0                             MS   \n",
              "1                        Postdoc   \n",
              "2                        Postdoc   \n",
              "3                            Phd   \n",
              "4                        Masters   \n",
              "\n",
              "  First author's training background (CS/ Bioinformatics/ Other)  \n",
              "0                                     Bioinformatics              \n",
              "1                                    Other, Genetics              \n",
              "2                                                 CS              \n",
              "3                                     Bioinformatics              \n",
              "4                                              Other              \n",
              "\n",
              "[5 rows x 29 columns]"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df= pd.read_csv('../data/240_B.txt',sep=\"\\t\") \n",
        "df.head()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "1899dbfd",
      "metadata": {},
      "source": [
        "# Data cleanup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "a6a6a386",
      "metadata": {},
      "outputs": [],
      "source": [
        "#github cleanup\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'github', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'GitHub'\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'GitHub + Zenodo', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'GitHub, Zenodo'\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Zenodo/GitHub', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'GitHub, Zenodo'\n",
        "\n",
        "#Bitbucket cleanup\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'BitBucket', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Bitbucket'\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Other (Bitbucket)', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Bitbucket'\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Other (BitBucket)', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Bitbucket'\n",
        "\n",
        "#Sourceforge cleanup \n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Other(Sourceforge)', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Sourceforge'\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Other (Sourceforge)', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Sourceforge'\n",
        "\n",
        "# Website Cleanup\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Other (Website)', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Website'\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Others (Website)', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Website'\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Other(Website)', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Website'\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Other(Website), GitHub', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Website, Github'\n",
        "\n",
        "# Supplementary Cleanup\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Other (Supplementary)', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Supplementary'\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Others(Supplementary)', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Supplementary'\n",
        "\n",
        "# No,Nan merge \n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'no', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'NA'\n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'nan', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'NA'\n",
        "\n",
        "#gitlab fix \n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'gitlab', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'GitLab'\n",
        "\n",
        "#downloadable file fix \n",
        "df.loc[df['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'downloadable file', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Downloadable File'\n",
        "\n",
        "\n",
        "#GEO Fix \n",
        "df.loc[df['If Data availability = yes, where do they share data? (SRA/GEO/other)'] == 'Geo', 'If Data availability = yes, where do they share data? (SRA/GEO/other)'] = 'GEO'\n",
        "\n",
        "#Other Fix for 'If Data availability = yes, where do they share data? (SRA/GEO/other)'\n",
        "df.loc[df['If Data availability = yes, where do they share data? (SRA/GEO/other)'] == 'other', 'If Data availability = yes, where do they share data? (SRA/GEO/other)'] = 'Other'\n",
        "df.loc[df['If Data availability = yes, where do they share data? (SRA/GEO/other)'] == 'SRA,other', 'If Data availability = yes, where do they share data? (SRA/GEO/other)'] = 'SRA,Other'\n",
        "\n",
        "#cleanup secondary analysis column \n",
        "df.loc[df['Secondary analysis'] == 'hybrid', 'Secondary analysis'] = 'Hybrid'\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "09e9c7ef",
      "metadata": {},
      "source": [
        "# Figure 1 "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "0db468b5",
      "metadata": {},
      "source": [
        "## Figure 1a"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "id": "3af72fbc",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "rcParams['font.family'] = 'sans-serif'\n",
        "#colors=sns.color_palette('pastel')\n",
        "colors = ['#E69F00', '#56B4E9']\n",
        "explode = (0.05, 0) \n",
        "textprops = {\"fontsize\":12}\n",
        "plt.pie(df['Code availability (Yes/No)'].value_counts(), explode=explode, labels=['Code unavailable','Code available'], colors=colors, autopct='%1.1f%%',textprops = textprops)\n",
        "plt.text(0.85, 0.95, \"n=453\", fontsize=12)\n",
        "sns.despine()\n",
        "plt.show()\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "44ced0be",
      "metadata": {},
      "source": [
        "## Figure 1b"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "id": "39150dc4",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>no</td>\n",
              "      <td>227</td>\n",
              "      <td>50.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>yes</td>\n",
              "      <td>226</td>\n",
              "      <td>49.89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Code availability (Yes/No)  Counts  Percentage\n",
              "0                         no     227       50.11\n",
              "1                        yes     226       49.89"
            ]
          },
          "execution_count": 148,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_code_avail=pd.DataFrame({'Counts': df['Code availability (Yes/No)'].value_counts()}).reset_index()\n",
        "#rename columns\n",
        "df_code_avail=df_code_avail.rename(columns={\"index\":\"Code availability (Yes/No)\"})\n",
        "\n",
        "#add percentage column \n",
        "df_code_avail['Percentage'] = (df_code_avail['Counts']/df_code_avail['Counts'].sum()) * 100\n",
        "df_code_avail['Percentage']=df_code_avail['Percentage'].round(2)\n",
        "df_code_avail"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "id": "8034ac60",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>no</td>\n",
              "      <td>244</td>\n",
              "      <td>53.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>yes</td>\n",
              "      <td>209</td>\n",
              "      <td>46.14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Code availability (Yes/No)  Counts  Percentage\n",
              "0                         no     244       53.86\n",
              "1                        yes     209       46.14"
            ]
          },
          "execution_count": 149,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_code_avail_statement=pd.DataFrame({'Counts': df['Code availability statement (Yes/No)'].value_counts()}).reset_index()\n",
        "df_code_avail_statement=df_code_avail_statement.rename(columns={\"index\":\"Code availability (Yes/No)\"})\n",
        "\n",
        "#add percentage column \n",
        "df_code_avail_statement['Percentage'] = (df_code_avail_statement['Counts']/df_code_avail_statement['Counts'].sum()) * 100\n",
        "df_code_avail_statement['Percentage']=df_code_avail_statement['Percentage'].round(2)\n",
        "df_code_avail_statement"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 150,
      "id": "580d236a",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Code availability statement (Yes/No)</th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>Counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Code availability\\nstatement not given\\n(53.86%)</td>\n",
              "      <td>Code unavailable\\n(50.11%)</td>\n",
              "      <td>197</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Code availability\\nstatement not given\\n(53.86%)</td>\n",
              "      <td>Code available\\n(49.89%)</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Code availability\\nstatement given\\n(46.14%)</td>\n",
              "      <td>Code unavailable\\n(50.11%)</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Code availability\\nstatement given\\n(46.14%)</td>\n",
              "      <td>Code available\\n(49.89%)</td>\n",
              "      <td>179</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Code availability statement (Yes/No)  \\\n",
              "0  Code availability\\nstatement not given\\n(53.86%)   \n",
              "1  Code availability\\nstatement not given\\n(53.86%)   \n",
              "2      Code availability\\nstatement given\\n(46.14%)   \n",
              "3      Code availability\\nstatement given\\n(46.14%)   \n",
              "\n",
              "   Code availability (Yes/No)  Counts  \n",
              "0  Code unavailable\\n(50.11%)     197  \n",
              "1    Code available\\n(49.89%)      47  \n",
              "2  Code unavailable\\n(50.11%)      30  \n",
              "3    Code available\\n(49.89%)     179  "
            ]
          },
          "execution_count": 150,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#create dataframe for counts data of data availability and code availability \n",
        "df_temp=pd.DataFrame({'Counts': df.groupby(['Code availability statement (Yes/No)','Code availability (Yes/No)',]).size()}).reset_index()\n",
        "df_temp.loc[df_temp['Code availability statement (Yes/No)'] == 'yes', 'Code availability statement (Yes/No)'] = 'Code availability\\nstatement given\\n(' + str(df_code_avail_statement['Percentage'][1]) + '%)'\n",
        "df_temp.loc[df_temp['Code availability statement (Yes/No)'] == 'no', 'Code availability statement (Yes/No)'] = 'Code availability\\nstatement not given\\n(' + str(df_code_avail_statement['Percentage'][0]) +'%)'\n",
        "df_temp.loc[df_temp['Code availability (Yes/No)'] == 'no', 'Code availability (Yes/No)'] = 'Code unavailable\\n(' + str(df_code_avail['Percentage'][0]) + '%)'\n",
        "df_temp.loc[df_temp['Code availability (Yes/No)'] == 'yes', 'Code availability (Yes/No)'] = 'Code available\\n(' + str(df_code_avail['Percentage'][1]) + '%)'\n",
        "\n",
        "\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 151,
      "id": "67433056",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 151,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 800x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 10))\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "category_labels = [\n",
        "    'Code availability\\nstatement not given\\n(' + str(df_code_avail_statement['Percentage'][0]) +'%)',\n",
        "    'Code availability\\nstatement given\\n(' + str(df_code_avail_statement['Percentage'][1]) + '%)', \n",
        "    'Code unavailable\\n(' + str(df_code_avail['Percentage'][0]) + '%)',\n",
        "    'Code available\\n(' + str(df_code_avail['Percentage'][1]) + '%)'\n",
        "]\n",
        "#colors = [\"#fe2626\",\"#21a0fc\",\"#eef91a\",\"#40ee40\"]\n",
        "colors = [\"#e29d00\",\"#0072B2\",\"#e29d00\",\"#0072B2\"]\n",
        "#colors = sns.color_palette(\"pastel\")\n",
        "color_dict = {}\n",
        "for i, label in enumerate(category_labels):\n",
        "    color_dict[label] = colors[i]\n",
        "\n",
        "sankey(\n",
        "    left=df_temp['Code availability statement (Yes/No)'].values, right=df_temp['Code availability (Yes/No)'].values,  rightWeight=df_temp['Counts'].values.astype(float), leftWeight=df_temp['Counts'].values.astype(float), aspect=20,\n",
        "    fontsize=10,colorDict=color_dict\n",
        ")\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "9b96ec04",
      "metadata": {},
      "source": [
        "## Figure 1c"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "id": "4e609096",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>Code unavailable</th>\n",
              "      <th>Code available</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analysis</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Primary Analysis</th>\n",
              "      <td>130</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Secondary Analysis</th>\n",
              "      <td>97</td>\n",
              "      <td>156</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Code availability (Yes/No)  Code unavailable  Code available\n",
              "Analysis                                                    \n",
              "Primary Analysis                         130              74\n",
              "Secondary Analysis                        97             156"
            ]
          },
          "execution_count": 163,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp=pd.DataFrame({'Counts': df.groupby(['Secondary analysis','Code availability (Yes/No)']).size()}).reset_index()\n",
        "df_temp.loc[df_temp['Secondary analysis'] == 'hybrid', 'Secondary analysis'] = 'Hybrid'\n",
        "df_temp['Analysis']='Unknown'\n",
        "df_temp.loc[df_temp['Secondary analysis'] == 'yes', 'Analysis'] = 'Secondary Analysis'\n",
        "df_temp.loc[df_temp['Secondary analysis'] == 'Hybrid', 'Analysis'] = 'Both'\n",
        "df_temp.loc[df_temp['Secondary analysis'] == 'no', 'Analysis'] = 'Primary Analysis'\n",
        "#df_temp.loc[df_temp['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'yes'\n",
        "df_temp = df_temp.drop('Secondary analysis', axis=1)\n",
        "\n",
        "\n",
        "#dealing with both value for Analysis column\n",
        "df_temp.loc[(df_temp['Code availability (Yes/No)'] == 'yes') & (df_temp['Analysis'] == 'Primary Analysis'),'Counts'] += int(df_temp.loc[(df_temp['Code availability (Yes/No)'] == 'yes') & (df_temp['Analysis'] == 'Both'),'Counts'])\n",
        "df_temp.loc[(df_temp['Code availability (Yes/No)'] == 'yes') & (df_temp['Analysis'] == 'Secondary Analysis'),'Counts'] += int(df_temp.loc[(df_temp['Code availability (Yes/No)'] == 'yes') & (df_temp['Analysis'] == 'Both'),'Counts'])\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Analysis']=='Both'].index[0])\n",
        "\n",
        "\n",
        "#df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "#df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "\n",
        "df_temp=df_temp.pivot(index='Analysis',columns='Code availability (Yes/No)',values='Counts')\n",
        "df_temp=df_temp.rename(columns={\"no\":\"Code unavailable\",\"yes\":\"Code available\"})\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "id": "5ece4eaf",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Chi-squared statistic: 28.106391604188723\n",
            "P-value: 1.1482601063804904e-07\n"
          ]
        }
      ],
      "source": [
        "# Perform chi-squared test\n",
        "chi2, p, dof, expected = chi2_contingency(df_temp)\n",
        "\n",
        "# Display the test results\n",
        "print(\"Chi-squared statistic:\", chi2)\n",
        "print(\"P-value:\", p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "id": "79fcff21",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "colors = ['#E69F00', '#56B4E9']\n",
        "df_temp.plot(kind='bar', stacked=True, color=colors)\n",
        "# statistical annotation\n",
        "y, h, col = 270, 10, 'k'\n",
        "x1, x2 = 0, 1  # columns  (first column: 0, see plt.xticks())\n",
        "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)\n",
        "plt.text((x1+x2)*.5, y+h, r\"$p = {0:s}$\".format(as_si(p,2)), ha='center', va='bottom', color=col, fontsize=12)\n",
        "plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1))\n",
        "plt.xlabel(\"Type of Analysis\",fontsize=12)\n",
        "plt.ylabel(\"Number of manucripts (n=453)\",fontsize=12)\n",
        "#plt.ylim(0,100)\n",
        "plt.xticks(rotation=0)\n",
        "plt.tick_params(axis='both', labelsize=12)\n",
        "sns.despine()\n",
        "#plt.savefig('../Figures/sup2_v2.png', format='png', dpi=300,bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "fcfd85b7",
      "metadata": {},
      "source": [
        "## Figure 1d"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "72b9804e",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Availability</th>\n",
              "      <th>Unavailable</th>\n",
              "      <th>Available</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Analysis</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Code</th>\n",
              "      <td>130</td>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data</th>\n",
              "      <td>62</td>\n",
              "      <td>142</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Availability  Unavailable  Available\n",
              "Analysis                            \n",
              "Code                  130         74\n",
              "Data                   62        142"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#create dataframe for counts data of code availability \n",
        "df_modified = df.copy()\n",
        "#df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Secondary analysis','Code availability (Yes/No)','Data availability (yes/no)']).size()}).reset_index()\n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "df_temp = df_temp.drop('Secondary analysis', axis=1)\n",
        "df_temp=pd.DataFrame({'Analysis': ['Data','Data','Code','Code'], 'Availability': ['No','Yes','No','Yes'], \n",
        "              'Counts':[df_temp.loc[df_temp['Data availability (yes/no)']=='no','Counts'].sum(),\n",
        "                        df_temp.loc[df_temp['Data availability (yes/no)']=='yes','Counts'].sum(),\n",
        "                        df_temp.loc[df_temp['Code availability (Yes/No)']=='no','Counts'].sum(),\n",
        "                        df_temp.loc[df_temp['Code availability (Yes/No)']=='yes','Counts'].sum()]})\n",
        "\n",
        "df_temp=df_temp.pivot(index='Analysis',columns='Availability',values='Counts')\n",
        "df_temp=df_temp.rename(columns={\"No\":\"Unavailable\", \"Yes\":\"Available\"})\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "e1a3d4ca",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Chi-squared statistic: 44.16261574074074\n",
            "P-value: 3.021978769442915e-11\n"
          ]
        }
      ],
      "source": [
        "# Perform chi-squared test\n",
        "chi2, p, dof, expected = chi2_contingency(df_temp)\n",
        "\n",
        "# Display the test results\n",
        "print(\"Chi-squared statistic:\", chi2)\n",
        "print(\"P-value:\", p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "8c78a103",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Analysis\n",
              "Code    130\n",
              "Data     62\n",
              "Name: Unavailable, dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp['Unavailable']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "18db0c2c",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "fontsize=12\n",
        "colors = ['#E69F00', '#56B4E9']\n",
        "ax=df_temp.plot(kind='bar', stacked=True, color=colors,legend=None)\n",
        "# Changing colors of stacks\n",
        "bars = ax.containers\n",
        "# First bar (bottom stack: Yellow, top stack: Blue)\n",
        "bars[0][0].set_facecolor('#E69F00')  # Bottom stack of the first bar\n",
        "bars[1][0].set_facecolor('#56B4E9')    # Top stack of the first bar\n",
        "\n",
        "# Second bar (bottom stack: Green, top stack: Red)\n",
        "bars[0][1].set_facecolor('#EA526F')   # Bottom stack of the second bar\n",
        "bars[1][1].set_facecolor('#009E73')     # Top stack of the second bar\n",
        "\n",
        "# statistical annotation\n",
        "y, h, col = 270, 10, 'k'\n",
        "x1, x2 = 0, 1  # columns  (first column: 0, see plt.xticks())\n",
        "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)\n",
        "plt.text((x1+x2)*.5, y+h, r\"$p = {0:s}$\".format(as_si(p,1)), ha='center', va='bottom', color=col, fontsize=12)\n",
        "\n",
        "#plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1))\n",
        "plt.xlabel(\"Type of Analysis\",fontsize=12)\n",
        "plt.ylabel(\"Number of manuscripts (n=204)\",fontsize=12)\n",
        "#plt.ylim(0,100)\n",
        "plt.xticks(rotation=0)\n",
        "plt.tick_params(axis='both', labelsize=12)\n",
        "labels = [mpatches.Patch(color='#E69F00', label=\"Code unavailable\"),\n",
        "mpatches.Patch(color='#56B4E9', label=\"Code available\"),\n",
        "mpatches.Patch(color='#EA526F', label=\"Data unavailable\"),\n",
        "mpatches.Patch(color='#009E73', label=\"Data available\"),\n",
        "] \n",
        "plt.legend(handles=labels, \n",
        "           loc=\"lower center\",ncol=len(labels),fontsize=fontsize,bbox_to_anchor=(0.5, -.3),frameon=False) \n",
        "\n",
        "sns.despine()\n",
        "#plt.savefig('../Figures/sup2_v2.png', format='png', dpi=300,bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "533813b1",
      "metadata": {},
      "source": [
        "## Figure 1e"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "d3d22a82",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>49</td>\n",
              "      <td>Neither available</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>13</td>\n",
              "      <td>Code available</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>81</td>\n",
              "      <td>Data available</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>61</td>\n",
              "      <td>Code and Data available</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data availability (yes/no) Code availability (Yes/No)  Counts  \\\n",
              "0                         no                         no      49   \n",
              "1                         no                        yes      13   \n",
              "2                        yes                         no      81   \n",
              "3                        yes                        yes      61   \n",
              "\n",
              "                    Labels  \n",
              "0        Neither available  \n",
              "1           Code available  \n",
              "2           Data available  \n",
              "3  Code and Data available  "
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#replace upon request with no in the Data availability column of the data\n",
        "df_modified = df.copy()\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "#create dataframe for counts data of data availability and code availability \n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Data availability (yes/no)','Code availability (Yes/No)']).size()}).reset_index()\n",
        "df_temp['Labels']=['Neither available','Code available','Data available','Code and Data available']\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "7712f4b6",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "rcParams['font.family'] = 'sans-serif'\n",
        "colors = ['#E69F00', '#56B4E9','#009E73', '#CC79A7']\n",
        "explode = (0.05,0.03,0.05,0.03)\n",
        "textprops = {\"fontsize\":12}\n",
        "plt.pie(df_temp['Counts'], explode=explode, labels=df_temp['Labels'], colors=colors, autopct='%1.1f%%',textprops = textprops)\n",
        "sns.despine()\n",
        "plt.show()\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "c233db19",
      "metadata": {
        "id": "c233db19"
      },
      "source": [
        "## Figure 1f"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "c1d7adf1",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>PMC_Year</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percent Increase</th>\n",
              "      <th>Cumulative Percent Increase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>33</td>\n",
              "      <td>7.284768</td>\n",
              "      <td>7.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>yes</td>\n",
              "      <td>2017</td>\n",
              "      <td>28</td>\n",
              "      <td>6.181015</td>\n",
              "      <td>13.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yes</td>\n",
              "      <td>2018</td>\n",
              "      <td>32</td>\n",
              "      <td>7.064018</td>\n",
              "      <td>20.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>yes</td>\n",
              "      <td>2019</td>\n",
              "      <td>40</td>\n",
              "      <td>8.830022</td>\n",
              "      <td>29.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>yes</td>\n",
              "      <td>2020</td>\n",
              "      <td>51</td>\n",
              "      <td>11.258278</td>\n",
              "      <td>40.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>yes</td>\n",
              "      <td>2021</td>\n",
              "      <td>42</td>\n",
              "      <td>9.271523</td>\n",
              "      <td>49.89</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Code availability (Yes/No)  PMC_Year  Counts  Percent Increase  \\\n",
              "0                        yes      2016      33          7.284768   \n",
              "1                        yes      2017      28          6.181015   \n",
              "2                        yes      2018      32          7.064018   \n",
              "3                        yes      2019      40          8.830022   \n",
              "4                        yes      2020      51         11.258278   \n",
              "5                        yes      2021      42          9.271523   \n",
              "\n",
              "   Cumulative Percent Increase  \n",
              "0                         7.28  \n",
              "1                        13.47  \n",
              "2                        20.53  \n",
              "3                        29.36  \n",
              "4                        40.62  \n",
              "5                        49.89  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#create dataframe for counts data of code availability \n",
        "df_modified = df.copy()\n",
        "#df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Code availability (Yes/No)','PMC_Year']).size()}).reset_index()\n",
        "\n",
        "#filter df_temp to only have rows where data and code are available, only data shared, and only code shared \n",
        "#df_temp['Total papers per year published'] = df_temp[df_temp['Code availability (Yes/No)']=='no']['Counts'] + df_temp[df_temp['Code availability (Yes/No)']=='yes']['Counts'] \n",
        "\n",
        "df_temp=df_temp[~((df_temp['Code availability (Yes/No)']=='no'))]\n",
        "\n",
        "#filter df_temp to only have primary studies \n",
        "#create percentage column\n",
        "#create dataframe for counts data of code availability\n",
        "\n",
        "df_temp['Percent Increase'] = (df_temp['Counts']/453) * 100\n",
        "df_temp['Cumulative Percent Increase']=df_temp['Percent Increase'].cumsum()\n",
        "df_temp['Cumulative Percent Increase']=df_temp['Cumulative Percent Increase'].round(2)\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "b532e3ff",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_1007672/1723764177.py:4: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(data=df_temp, x='PMC_Year', y='Cumulative Percent Increase', palette=colors)\n",
            "/tmp/ipykernel_1007672/1723764177.py:4: UserWarning: \n",
            "The palette list has fewer values (1) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
            "  sns.barplot(data=df_temp, x='PMC_Year', y='Cumulative Percent Increase', palette=colors)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_style(\"white\")\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "colors = ['#56B4E9']\n",
        "sns.barplot(data=df_temp, x='PMC_Year', y='Cumulative Percent Increase', palette=colors)\n",
        "\n",
        "for index, row in df_temp.iterrows():\n",
        "    plt.annotate(f'{row[\"Cumulative Percent Increase\"]:.2f}%', xy=(index, row['Cumulative Percent Increase']),\n",
        "                 xytext=(0, 5), textcoords='offset points', ha='center', va='bottom')\n",
        "sns.despine()\n",
        "plt.xlabel(\"Year\")\n",
        "plt.ylabel(\"Cumulative code-sharing\\nmanuscript growth (%)\")\n",
        "plt.ylim([0, 100])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "b71a7c1f",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[-15.151515151515152,\n",
              " 14.285714285714285,\n",
              " 25.0,\n",
              " 27.500000000000004,\n",
              " -17.647058823529413]"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "growth_rate = [(df_temp['Counts'][i] - df_temp['Counts'][i-1]) / df_temp['Counts'][i-1] * 100 \n",
        "               for i in range(1, len(df_temp['Counts']))]\n",
        "growth_rate"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "885abf71",
      "metadata": {},
      "source": [
        "# Multipanel Figure 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "3533acbc",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_21447/1462870723.py:164: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(ax=fig_1f,data=df_temp, x='PMC_Year', y='Cumulative Percent Increase',palette=colors)\n",
            "/tmp/ipykernel_21447/1462870723.py:164: UserWarning: \n",
            "The palette list has fewer values (1) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
            "  sns.barplot(ax=fig_1f,data=df_temp, x='PMC_Year', y='Cumulative Percent Increase',palette=colors)\n"
          ]
        },
        {
          "data": {
            "image/png": 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lT64IROrST7X34xf8ttkiYhXRobcc0Q3lzctW1q7flLlrg+TzST6vDi98XzIsajzslqqeQomy9iZr65Tb5c37Y/VTi8OliHa95GzQXPL5lHN4t05s/kW+/IKJDCeSl2vH9AfV5uYXZbHZSxtakrRn1jN/JGIYFkV17i9Xo9bKTd2rY2sXy+dxy5N5TLtmPqH2d70hw1r2x6G89EM6PP9dSQVfu8SL/lyFswdQ34XYDE3oHkkiBgAAqBV5eXm65ZZb/BIxQkND1atXL7Vs2VIRERHKz8/XsWPHtH37dnOFvpJYLBZzknfhm4kWi6XUZP7ykvwTEhLUvn17tWjRQhEREbLZbDp+/Li2b9+uX375xZzcvnPnTt14442aM2eOoqKiajS2xx57TB988IHftnbt2qlbt26Kj4+Xz+fT3r17tXTpUqWnp0uStm/frmuuuUazZ89WgwYNyjznk3bt2qWnnnpKmZmZstvt6t27t9q3by+Hw6Hdu3dr0aJF5mSIgwcP6p577tG7776rTz/9VE8//bQkKSIiQmeffbZatGghn8+njRs3aunSpWaSxtKlS/Xaa6+VWQFEKrgBO378eK1bt87cZrPZ1L17d3Xs2FFRUVHKzs7Wli1btHz5cvP78t133+mWW27RW2+9FXACwE8//aTHH39cbrdbISEh6tu3r1q1aiWn06k9e/Zo8eLF5o367Oxs3X333Zo3b16xieqlfc8llRpLTSQpVNf5FObz+XTHHXfou+++89vesmVL9e3bV7GxsUpLS9PPP/+snTt3ms9/8MEHOn78uF566aViY5b29TIMo9TEgrIqP1TEyYSDwpo1a6azzz5bcXFxOn78uFauXKmNGzfq4MGDuv322zVw4MBqObZU8D4ZNGiQvvjiC0nSggULlJ6erpiYmID2L1pRpLSVMT0ej/76179q4cKF5jbDMNS5c2d17txZcXFxysvL086dO/XTTz+ZK9GuWrVKY8eO1UcffRTwarSbN2/Wiy++qKysLNntdvXs2VPt27dXaGioDh48qMWLF+vIkSNmXI8++qi6du1aawltAAAAAOq2otdWSrsWVB127Njh165Ixciifbdv316sT+FkipOfxwPVsWPHcpMxtmzZ4pcQ8NJLL+lf//pXwMco6mSSwqkmkAoHJ4WGhprnWV6iz9GjR/X888/rk08+KXXRkbIcP3681OdOJlTs2bNHUsHn+9KSMRYvXuxXTfjyyy8v99gHDhzQpEmTilVnCdTx48cDqhJTndxut9555x1NmTKlWIWcQJS1IERRHTp0CLhv0aorW7durXQyRuEFNSTp7rvv1sSJEwPev2gyytGjR0nGAACgtlgM6c6zIjXx+zR5yl7wCgACdmj+TOUcLLigYIuMk/v4kQrtn7UnWfvmvOK3reGgsWp44fWy2Pwz7DN3/aZd7z6uvLQDkqTDC95TaPNOiu52bhXOoDhPTpZ2vv2IXyJGZKd+anbVfbJHxvn1zTuaoj0fPK0TmwtK/2VuW639n/9bTUfeUer4mTvW6UTy76UCDYtaXv+korv+cQ4Z29do27T/J587Tzn7t+ro2sWKOavs1Tf2fzbFjLfxsFtkdRUvcwkAgfpz53C5bCRiAACA2jFv3jy/ydqXX365HnzwQYWHh5fYPz8/XytWrNCHH34om83/1tE777wjqWA198KrGE6aNEmjRo0KOKYmTZrojjvu0IUXXqi2bduW2i8jI0Ovvfaa3njjDfl8Pu3bt08vvvii/v73vxfrW12xffDBB36JGN27d9cjjzyirl27Fuubk5Oj//znP5oyZYq8Xq9SUlI0ceJEzZgxI6Bjvfnmm8rPz1evXr309NNPF1tt79ChQ7rxxhuVnJwsSVq+fLk+/vhjTZo0SZJ0zTXXaOLEicW+lytXrtTNN99sJgC8+eab+vOf/1zq91ySHnnkEb9EjJEjR+quu+4q8UZ7SkqKnnzySbOCxs8//6ypU6dqwoQJAZ333//+d7ndbl122WV64IEHiiUlpKen66677tJPP/1ktt955x3dddddfv1GjBhhrvQ/duxYLV++XJLUu3dvvfvuuwHFUh2q63wKmzFjhl8iRkREhJ544gldcsklxfp+/vnnevTRR83JDfPmzVPv3r11zTXX+PX7xz/+oX/84x+SpPbt25vb//rXv5abrFMVCxYs0EcffWS2nU6nHnvssRInNfz444+67777tHv3bv33v/+t1jguv/xyMxkjPz9fX3zxhcaOHVvufm63W5999pnZTkpKUsuWLUvs+69//csvEePcc8/Vgw8+WOIKnSeTZk6e59atW/Xkk0+aiVblefbZZ5Wfn68BAwboiSeeUJMmTfyez8rK0qOPPmqu6pmbm6upU6fq+eefD2h8AAAAAKe3osnphVeRr26FJ8tbLJYKVQWMi4uTYRjm5OiSJt4X3lbRioNxcXHl9jm5EMdJlUkYKKwmE19qUkWqohZeAKWsKgupqakaN25cuQkxZSmrSqlhGBo5cqReeaVgzszGjRu1cePGYhP/JemTTz4xH4eFhemiiy4q87jbt2/XuHHjlJKSUsnIy469Jrjdbt199936+uuvKz1GXl5ewH0rkmhStG9ZSTblqe73bKDVXU6qnqVNAACopyyGofgQq65sxwRdANUj5/BuHfquYPKAxeFSo0tuqvAYB+a9Jp/nj1UaGg4aq0aX3FQsEUOSwlp0Utu/TpY15I/JGQe+nCqfp3or/qQu+cRM+JCksDZnqtWf/1EsEUOSHNEJavV/zyq0RWdz25Glnyo3dV+p46ev+mOyQkzSYL9EDEkKb91dCedeWah/2asUnNiyUkfXFNxID23RWTE9h5TZHwDK0j3eof6NXXJYScYAAAC1Y+nSpebjli1b6qmnnipzUr7dble/fv300ksvqUuXLjUS00MPPaTbbrutzEQMSQoPDy+2ctncuXMrtAJbRaSkpPhNhD733HM1c+bMEhMxJMnlcmnChAl65JFHzG3Lli0zJ92XJz8/X927d9dbb71VLBFDKlhx8KWXXvKrTvDII48oKytL48eP1+OPP17i9zIpKUn33HOP2c7Ozta3335bahzfffedvvzyS7N9xx136Jlnnil1xcOEhAS98sorfokBb731VsA3SfPz83XVVVfpn//8Z4mTJGJiYvTKK6/4PVd4MnxdU93nk5GR4bfCpt1u1+uvv15iIoYkXXrppZo6dapf1Y8XXnihUqsb1oSik/9feumlUleX7N+/v9566y2FhIRU6OZ+IPr27avGjRub7aLVLkpTuMKEpFKTuzZu3Kg33njDbF955ZV6/fXXS0zEkKTIyEg99thj+r//+z9z26effuqXPFeW/Px8nXvuuXrttdeKJWJIBauQPv30034/Z7/99ltlZ2cHND4AAACA01vhJH2p+Ery1anw51OXy1VuFdXCLBaLXxJASZ91C1deCLTa4EmBJBhUZVJ4Sao6Mfx08uCDD/olYsTFxekvf/mLpk2bpnnz5mnFihVat26dkpOTzX9FF0EpK9lDKlhwpPC1tdmzZxfrk5aWph9++MFsX3LJJWW+Njwej+644w6/RIwmTZror3/9q9588019++23WrlyZbHYiy7AUF7s1W369Ol+iRgOh0MjR47UCy+8oLlz5+qnn37SmjVrtGnTJr+4KxtzRRJ4ivatynWtiiZPlKei71mSMQAAqCKH1dClrUPVJoqCUwCqxufzac+Hz8nnLrjx3HDw9XLENqrQGDmHd/9RIUKSPSZRiRfdUOY+jpiGfn1yU/YqbWXpEzYqI3VJoQ+3hqFmV94jw1r6z02Lza5mV/wx8cbncevg/94utX/mrj8uFMUmlbxaQWyvi83HWbtKv7Dk87i1b87Lv8dqUdNRd1Xo4hAAFBZiMzThzEjZLPwcAQAAtafwJOJOnTr53Xw8VYwfP16RkZGSChILfv755xo5zowZM5STU1AVMTIyUs8++6wcjuKLGRQ1ZswYdevWzWy///77AR3PMAxNmjSpzGO0adNGPXv2NNter1eNGjXyS1ApyWWXXeZ3E3P16tWl9v3Pf/5jPk5KStKtt94aQPQFiSEnJzlkZmaaK/CXJyEhQQ8//HCZfSIjIzVy5EizvX//fh06dCig8WtbdZ/PJ5984jeR5Prrr1ePHj3KHL9v374aM2aM2T5x4kTAyQY1afny5X6TKi6++GK/yjUladeunW66qeILkpTHYrGYlVSkgolGRScUlKTwJI3Q0FBdfPHFJfY7WcFHkpo3b65HH300oGtId9xxhxo1Krjm5/V6NWvWrHL3kQoqjPzjH/8oVsGoMLvd7ve6yM3N1caNGwMaHwAAAMDprfC1Bklau3ZtjR0rLOyPRW1zcnIqNJnb6/X6JZUXHuukwgkYhT9PByKQhPWik8Qff/xxv4nqFf1XkQqup7O1a9dq8eLFZrtXr1769ttvde+99+r8889XmzZtFBERUey6WUW/x40bN1bfvn3N9hdffKH8/Hy/Pp999pnftvK+R9988402b95sti+++GJ9/fXX+tvf/qYBAwaoRYsWCg8Pr3Ls1SkvL0+vvfaa2U5ISNCnn36qZ555RsOGDVOHDh0UGxtbLGGqKkkRFVkQomjfkt7rgXK5XH7tN954o0rv2T59+lTo+Kfe1XcAAOogi6Q7ekTJzm9WAFVw5Ke5yty+RpLkSmytBueNrvAYJzav8GvH9ryozKQHs1+viyXLH6spHv11foWPXZrsgzuUfzzVbIe16iZXg+bl7hfSpK1Cm/1RKvLYuh/kdeeX2Ldw1Y2QJiWvsups0FwWR8EHMHfGUXlyS/4QmLJktnIO7pAkxfUZqtBm7UvsBwCBuL5TuFw2EjEAAEDtKnxDetOmTafk6nt2u12tW7c222vWrKmR43z66afm42HDhpVY5aA0Q4cONR//8ssvZfT8Q1JSks4444xy+3Xv3t2vPWrUqHKTREJCQtSuXTuzvXXr1hL77dy5U7/++qvZHjt2bMCLEMTGxvrdzF62bFlA+11++eVyOp3l9uvVq5dfe/v27QGNX9uq+3zmz//jOozVatW4ceMCiuOGG27wS7YqPE6wLFq0yK9dODGgLKNHjy4zyaCyRo0a5ff6Lmk1zMLS0tL8JoZcdNFFJU4GKFp9ZvTo0QElckkFq1AOGjTIbAf6PrrwwguVkJBQbr+ir7vCyTEAAAAA6q9u3br5TVhesWKFDh48WCPHOrnAhlSQXJGWlhbwvkeOHPFL3ig8VknbKjL2yfHLEx0d7ddOT0+v0DFqQ0USXIKZEFDYggULzMeGYeiZZ54ps5rvSampqeX2KapwckVaWlqx6xWFrw+0bNmy3EUxFi5caD4OCwsrd7GVkyoTe3VZuXKlX8WIiRMnqk2bNuXuF8h7pDr2Lfq1Kem9HqiYmBi/dm2/Z5kyCgBANbBYDMU4LRrdvvIZmgDqt/xjqdr/xe8Z6YahZldODCiJoqjsvZv92mGtuga0nzUkXK7EVmY7Y+sqebIzKnz8kmPyX3EwrGWXgPcNLdTXm5OpjCLJJid5cv7IzLeGlvwBzTAMWUMi/MYrKv9Emg5+M71gnJAINbqk+ldlBFB/dE9waEATlxxWkjEAAEDt6tr1j8+C27dv10MPPaRjx44FMaI/eDweLVmyRE899ZSuu+46nXfeeerZs6c6d+6sTp06+f0rXNmhJiok7Nq1S4cPHzbbRScwl6dFixbm47S0tIAmMBRdhbI0iYmJfu2kpKQK71f4ZmthRRNHKnreLVu2NB8HuuJ+oMdo0qSJX/v48eMBx1WbqvN8PB6P1q1bZ7a7du2qBg0aBDR+48aN1blzZ7O9Zs2aoCdfFV5Z1eVyBfzajYuLU4cOHao9nmbNmvl9vz7//HO53e5S+we6Muavv/7q168q76Pk5OSAJtFU9nVX2s8CAAAAAPWL0+nUZZddZra9Xq8+/PDDGjlWq1at/Nq//fZbwPsW7Vt4sY6TCk8o37Fjh3JzcwMef9OmTeX2ad26tV9i/4YNGwIevyYVXoClIhUICl//CqYdO3aYj1u3bq2mTZsGtN/69esrfKzBgwcrIuKP+SGFky+KVs68/PLLyx2vcOxJSUkBV3GoTOzVpeiiIOecc05A+1Ul5opU5yz6XmzbtuSFVwNRdN/afs9W//IiAADUUw6roYtbhmrZgVxtOVr6zRwAKMne2S/Jm1OQ/BDX59KAkyiKcmf6T66xR5W/Wt5Jjqh45ewvWDXT53Erc8c6RXbqV6k4qjOmwjK2/VpiTBabXd48jyTJ53bLcFiL9ZEkn+ePm+QlJbsc+GKa+X1IvPj/ZAuPDjhWACgsxmnRnWdFymYhEQMAANS+K664Qq+//ro5AXf27Nn68ssvNWDAAPXv319nnXWW2rdvL6u15M9ONWXhwoV66qmntHfv3grvWxPJJEVvyt19992aOHFiwPsXnTx99OjRYkkURQU6yT4kJMSv3bBhwwrvl5lZfBECqfh5n3vuuQGNfVLhyf5Hjx4NaJ/KxC/VnZUbi6rO8zl06JDf9o4dOxbrU5ZOnTqZyRyZmZk6dOiQGjVqVKExqtOePXvMx23atKnQz5l27drVyCSFyy+/XMuXL5dUsELjokWL/CpTFDZnzhzzcfPmzUtNgCj6PrrmmmsqFFPhnx9ut1sZGRl+k0RKEujrrvDkHKnuvo8AAAAA1L7rr79eH330kfmZ5K233tKIESPUvHnzSo+Zl5dXrEJA0SoD8+fPD3gi+P/+9z+/9llnnVWsT/fu3fX9999LKvhMtWTJEl1wwQXljr1r165SK4kWFhsbqw4dOpgTy3/++WdlZGQEVMWhJhWuHLB///6A9snKyqozySSFFwsItApCfn5+pSqBulwuDR06VB988IEk6fvvv9eRI0cUFxfnl5hhtVo1fPjwcserTOxpaWkBV8OsCUUXZwg07q+++qrSx1y0aJGGDBkSUN+ilVIKLzBUUR07dlR0dLR5rXLhwoW6//77/SrK1iQqYwAAUI0shnRnjyg5a/c+NoBT3NG1i3VsXcGFAlt4jBoNu6XSY/ny/Vd8sNjsAe9r2Jx+7ZxDOysdR2FVisnuf9GmtJisYVHm47z0klci9eRm/5EYYrHKGuJ/oSRz5walrfhakuRq1EbxZ5f/gRsASmJIuqtHJBUxAABA0MTGxmry5Ml+E3tzc3M1f/58PfHEExo5cqR69uyp//u//9M777xTK6vjvfPOO7rlllsqlYghqUIrHAaqaLl6r9crj8cT8L+iFQgCWX2+6OT80hRefVEquIFcmf1KUvS8K3LOHo/HbxJ5oCvuV/a8A6kWEAzVeT5Fq2XEx8cX61OWhAT/RS+CXQWn8PlER0dXaN+K9g/URRdd5LdiZeFJF4X99ttvfqsyjhw5stT3VFXfR7X586Ouvo8AAAAA1L42bdpoxIgRZjs7O1v33ntvqQs6lOfw4cO67rrrim3v1q2b3+fbzz//XKmpqeWOd+jQIX355Zdmu0GDBurWrVuxfkUT7GfOnBlQvO+++25A/SRp2LBh5uOMjAy99dZbAe9bUwqv/r9p06aAKop+/PHHysvLq8mwAlb4s3mgySQfffSRUlJSKnW8whUv3G63PvvsM+Xl5emLL74wtw8YMCCgxQ8Kx75v376Ajv/mm28G9WtftHpHIHEnJydXKvnlpG+++Saga83r16/XqlWrzHa/fv3KXaSiLBaLRZdcconZ3r17t9+CGzWNZAwAAKqRxTAU6bDoxq6BZZICgCc7Q3tnv2S2Gw+fIFto5T9gFE0w8GRnVCAW/5u+OYd2VToO/5j8z6dCMWUFFlNI4z8uOmRsXVVin4ytq6Tfbz67Grb0q4zh83oLvg+/P9901J0yLGTWAaicUW1D1SrKTlUMAAAQVP369dMXX3yh0aNHF7vxJhWsivfDDz/oH//4h84//3w99NBDxSYXV5c1a9bo6aef9ts2YMAAPfbYY/roo4/0ww8/6Ndff9XGjRuVnJxs/uvdu3eNxHNSoIkEgSo6ubquqs7zZpJ31WVk+F8nKVrVoDxF+1d2Ak11KTzJwG4PfEEOScVWUq0uISEhuvjii832999/r7S0tGL9CidpWCwWjRw5stQx6+vPDwAAAACnvkceeUQtW7Y027/++qtuuOEGHTlypELjzJ8/X1dccYXWrFlT7DmHw6Grr77abGdkZOjRRx+Vx+MpdTy3262HH37Yr7rfmDFjSvxs2aFDB7+KGUuXLi018f6kFStW6P333y+zT2FjxoxRbGys2Z42bVqxqh3lOXHihFnNsjoUPuf8/Pxyz2fHjh16+eWXq+34VVU4meTQoUNavHhxmf03btyof/7zn5U+Xrdu3fyOOXv2bC1YsMCv0uuoUaMCGqvwOGvWrFFycnKZ/X/44QdNnz69YgFXs8IxS9KHH35YZv9jx45p4sSJZb5Py5OVlaXHHnuszOscWVlZevTRR/2uK1a04mhJbr75Zr+fF08++aR+/fXXCo2RmpqqzZs3V/jYJGMAAFDNHFZDfROdOrdJYKvVAajf9n/+b7mPF1zUCG/XU7FJF1ZpPFuk/+qJ2Qe3B7xvTpG++UerZ2VUW2RckZh2VCAm/76lxRTRrqf5+PCiWfLm5fg97/N6dOi7P1a5iGjfy+/5I8u+UPbegg/L0WcNUnibMwOOEQAK6xBj16gzwqiKAQAA6oTExEQ98cQTWrp0qd544w3dfPPN6t27d7EqC263Wx9//LFGjhypAwcOVHscr776qnlzzWKxaPLkyXrzzTc1ZswYdevWTQ0aNFBoaGixsvE1Pam86NfhjTfe8EsGqei/Pn361Gi81aXoea9du7ZK542qCQ/3X1ij8KSTQBTtX1LyVW0qfD4VfQ/X5Hu+8OSK/Px8ffbZZ37P5+fn+62M2a9fPzVq1KjU8Yq+j+bNm1el91HTpk2r6UwBAAAAoGxhYWF6+eWX/aoTrl69WhdddJFef/31EpPXT8rNzdXChQt13XXX6bbbbtOhQ4dK7fvnP/9ZTZo0Mdvz58/X3/72txKTPlJTUzVhwgR9//335rYWLVpo3LhxpY5///33+11LeuSRRzRz5swSF4749ttvdcstt8jtdge8EEBoaKiefPJJs/qgx+PR7bffrmeeeabMxBWfz6dVq1bpqaee0nnnnVfs82dVDB482G9RhsmTJ+vrr78use+iRYt07bXXKiMjo8KLJdSUohVN7rvvPq1YsaLEvl9++aXGjx+vrKysCi9cUVjh6wGbN2/WSy/9sVhqdHS0Bg4cGNA4gwcPNh97vV5NmDChxEn7Ho9HM2fO1G233SaPx1Ol2KsqKSnJL6Fo+vTpeu+990p8j6xbt07XXXedNm/eXKWYHQ6HFixYoDvvvLPEhX/27dunG2+8URs2bDC39e7d2+/rW1mJiYm67777zHZ2drbGjRunadOmFVsMpTC3260ff/xRDz74oM4//3wtXbq0wse2ld8FAABUlN1q6KauEdp2LF/7MiqfLQrg9JaxbbWOLCu4yWvYHGp2+d1VHjOsVRel/vCR2T6+4SfF9R5a7n6Zu36TO+Oo3zZPbsVu/pcaU8uufu0Tycvl87j9KlOUxJufqxNbVvpt83nc8rrzZLH5XyCJSbpIB+a9IW9ulvKO7NO2N+5RkxF/k6thS+Ud2af9X76mrF2/f5izWBXf7zJzX3fWCR2Y93rBU44QNb70tsqeKoB6Ltxu6O6kKJGHAQAA6hqXy6Vzzz1X5557rqSCScerVq3SN998o08//dScAH3gwAE98MADevvtt6vt2NnZ2fr555/N9ogRI3ThhYEtRFDRFRkrKiYmxq9dU5VB6pqSzjsxMTFI0SAy0r/KcmpqaoX2T0lJ8WtHRUVVOaaqiImJMVeY3L9/f4X23bdvXw1EVCApKUktW7bUzp07JUlz5szR9ddfbz6/cOFCv58B5a2MWV9/fgAAAAA4PXTo0EGzZs3SjTfeqN27d0sqqOLwwgsv6KWXXlKXLl3Utm1bxcTEyG6368iRIzpw4IBWrlyp7Oxsv7GKLq5xUnh4uP75z3/qhhtuUE5OwWKK3333nZYsWaL+/furdevWkqTt27dryZIlys3NNfcNDQ3V888/X+aCA2eeeaZuvvlmTZ06VVLBhOonn3xSb775pgYMGKD4+HgdO3ZMy5cv15YtWyRJzZs318CBAwO+9jVo0CDde++9eu655+Tz+eTz+TR9+nTNnDlT3bp1U4cOHRQdHS23263jx49rx44d2rhxo44dOxbQ+BUVHh6um266Sf/6178kFVzju+OOO9S1a1f17NlToaGhSk1N1S+//KLt2wsW4+zXr5/i4+P1+eef10hMFdG5c2cNHjzYrDCSnp6ua6+9VklJSerevbtcLpdSU1O1dOlS7d27V1JBtcu77rpLkyZNqtQxhw8frhdffFFut1uSzOsCknTppZcGnJwzaNAgde7c2Uwi2L17t4YPH66zzz5bHTt2lNVq1aFDh7RkyRLzWk18fLzGjh3rlwBSmxwOh2699Vbza+f1evXEE09oxowZOvvssxUTE6Pjx49r9erVfhVcHnzwQT388MOVOub999+vJ554Qt98842+//57nXPOOWrZsqU8Ho+2bNmin376Sfn5+Wb/mJgYTZo0yUx6qqqxY8dq165devfdgsVa8/Ly9NJLL2natGk666yz1KZNG0VFRSk3N1fHjx/X1q1btXHjxgovjlIUyRgAANQQq0W6r2eU7vkhTbnkYwAowuvO056P/in9nnHecNBYOROqvgJexBlJMmwO+dx5kqRjG5Yoe/9WhTRuW+Z+h76bUTzG3OwSelacPSJGoc06KmvPRkmS+0Sajvz8ueL7jyxzv9Qf58iTdbzEuIomY9hCI9T40lu19+MXJEmZ21Zr8ws3lDhuw4HXypnQzGwf/OoNeTILLoY0HDxOjuiEwE8OAAq546wohdiMartYBAAAUFPsdrv69OmjPn366KabbtLo0aN18OBBSdJPP/2kvXv3Vtsq7fv27VNeXp7ZPueccwLaLyUlxYypprRt6/9ZecOGDRo+fHiNHrMuKHre69evJxkjiBo2bKjQ0FDzpu/GjRsrtP9vv/1mPg4LC1PDhg2rNb6K6tSpk3bsKKh0um/fPh05ckRxcXHl7FWwcmjhG/81YdSoUXrxxRclSZs2bdJvv/2mTp06SZJmz55t9ouMjCx3RcaSfn707NmzlN4AAAAAUPe0bNlSH374oV5++WV9/PHH5gRpr9ertWvXau3ateWO0b9/f91zzz2lPp+UlKS3335bt956q5nEnpOTo/nz52v+/Pkl7hMfH69p06apa9euJT5f2J133qmsrCy988475rb9+/frww8/LNa3YcOGmjx5spkIEKgbbrhBzZs31wMPPKDjxwvmL+Tn52vlypVauXJlOXsr4Mn+gbrxxhu1bt06v6/funXrSvxM3aNHD/1/9u47Pqoqf+P4c++dkknvobfQpIP0ogJSRBFEUVHBtoq6iq4FdhXXrujPgrIq6qoINpZVxILYgUVUFOlI772FFNIz8/sjMGYgZRISJuXzfr3cnTNzzrnfhASd5Dz3++KLL+qpp54q1xpOx5NPPqkdO3b4dFst6nMZEhKiKVOmyGYr+1H32NhY9enTRz/88MMpr5V0I4aCDMPQSy+9pGuuucbbWdjtdmvRokVatGjRKfPj4uL0xhtvlPrnPOVtzJgxWrNmjT755BPvc9u2bfMJpZxgGIb+9re/aeTIkWUOY1x99dXaunWrZsyYoYyMDH399ddFzo2Ojtbbb7+tBg0alOlaRZk4caISExP11FNPeUNeGRkZWrx4sV9dL8ryPVt4JA0AAJw20zAUFWTppjZhgS4FQCW0/+t3lHUg/w4TzvgGiu93dbnsawuJUHTXIX8+4c7TtukPKyel6Lsq7v92hlLW/HjK8+6crEJml01c3yt9xns+e1VpW4r+4U3qxqXa++W/C32tqLpiew5XwoBrpWIOQcf0GKZaF/zFO87YvUmHfspvC+qMq6e4c6/wvVZ2lvZ//542vHCzVt5/gVZMOF9/PHWVds1+UdlJRbdcBVDzDGnkUstou+y0xQAAAFVMrVq1dPPNN/s8t27dulPm2e12n3Fenn93HznxS+oT/L1r/7x58/yaJ5W9trPOOkuRkZHe8Q8//CC32+33dauqHj16+IyLOvxQ2RX8Rby/f+aVkWVZateunXe8atUqHThwwK+1e/fu9d6RUZLat29f5B1Jz9Tnq1OnTj7juXPn+rVuyZIlp3T5KG/Dhw+XZVne8YkAxqFDh/S///3P+/yQIUPkdDqL3atbt24+n+tvv/22nKsFAAAAgIoXFRWlhx9+WHPnztXo0aPVsGHDEtfExcXpmmuu0UcffaS33npLZ511VrHzO3bsqK+++ko33nijz89hCqvl5ptv1ldffeVXEOOE+++/X1OnTvV22jiZw+HQRRddpE8++UQtW7b0e9+Czj//fH3//fe68847Vbdu3RLn161bVyNHjtS0adN07733lumaRbHZbHrppZd0zz33KCys8DNp0dHRuuOOOzRjxoyAd9A8WXh4uD788EONGTNGQUFBhc5xuVy66KKLNGfOHPXq1eu0r3nppZee8lzLli29N2jwV7169fTxxx9r+PDhRQZEwsPDNWrUKM2ZM6fE740z5emnn9Y///lPxcUVfmNS0zTVvXt3TZs2TWPHjj3t602cOFHPPvusGjduXOjrDodDI0aM0BdffFHm78mSjBo1St99951uvPHGIj/ugho1aqTRo0dr1qxZuuqqq0p9PTpjAABQgRyWoe61g7TyUI4W7s4MdDkAKomMPZt14If3veN6l90r02YvZkXp1B58o1LW/Kic5PxfYGcd2K71z96ghPPHKKJNb9kj4+TOzlL6jrU6+L//eoMYZlCI3JnHvPuYTle51RTZvq+OtJyr1HW/SJLc2Rna/Opdiu1zqaI7D5IzvoHk8SjzwA4dWfKlDi+eLU9ergzLJhmmt9NHSXXVvuAvCm/ZTQcXfaxjW1cqLz1FlitMwQ3OUkzPYQpv0dVn/q7ZkyV3/gGEusPG+fw5ZB3eo82v3aPsQ7t81mQd3Kmsgzt15Jcv1OjaRxV+VvfT/fQAqOKaRNh09VmhspkEMQAAQNV0cheMgp0sTggJCfEZnxyyKMrJ63bv3l3imvT0dE2bNs2v/U+nNtM0NWTIEL3/fv579B07dmj27NmF/oK2OmnRooWaNWumjRs3SpI+++wz3XTTTUUeWqisCv65+/tnXln1799fP//8s6T8oMT06dP9Oqwxbdo0nwDR+eefX+TckJAQJSfndwatyM/XkCFDNGnSJO8dVd944w2NGDHilO/Tgjwej1588cUKq+mEhIQE9erVSwsXLpSU/7U/fvx4ffrpp8rNzfXO8+fvgIiICPXp00cLFiyQlB8m+fnnn9W9Oz8nAgAAAFD1NGjQwHsn/J07d2rTpk06cuSIjhw5Io/Ho/DwcMXGxqpVq1aqU6dOqfePiIjQ+PHjde+992rlypXasmWLjhw5Iik/OJCYmKi2bdsWeYOBkvTt21d9+/bVH3/8obVr1+rw4cMKCQlRrVq11KVLF4WHh3vn3nHHHbrjjjtKfY2wsDDddtttuu2227Rr1y6tXr1aR44cUUpKiizLUlhYmOrWravExMQyfY5Kw2az6eabb9Z1112npUuXasuWLUpNTVV0dLTq16+vzp07+9y8ZNKkSZo0aVKJ+9arV8+nY0VpfP/9937PDQ4O1gMPPKA777xTv/32m7Zv366MjAzFxMQoPj5enTt39vk5Qrdu3cpclyQNGDDgtNYXFB0draeffloTJkzQb7/9pj179ig7O1sxMTGqU6eOzj77bJ/OCiNGjPC7A8eMGTP8rqO0H8/VV1+tyy+/XCtWrND69euVmpqqsLAwxcfHq127dqd0WvV3/6K+n4YOHaqhQ4dq9erV2rRpkw4cOCCn06k6deqoR48eCg0N9bv20nxeCoqLi9P48eM1fvx4bd68WevWrVNSUpJSUlLkdDoVHh6uevXqqWnTpn4FNopDGAMAgApmtwzd3DZMm5NztDut6t6hDUD58Ljd2vmfp+XJy/8Fb1SXwQpr2rFcr2ELjVSj6x7Xln+PV96x/F+y56YlafcnL2r3J4X/Ytt0Bqv+yHu1fcYj3ucsl/9vfkpiGIYaXv2gNk/9mzJ25x/28OTl6OD8D3Vw/odFrqs38j7t+ewV5Z0IYxiGLGfRv7yXpJDGbRXSuOQ7ZRxZ+rWObVkhSQpv1VPhrf68M6g7O1NbXv8ziOGIrq3Ijv1lOlxK/eMnHdu2Wu7sDG19+wE1/9sbctWuWgdWAJQfl83QvWdHiBwGAACoTFauXOlzp/2S/Pbbbz7jk8MZkhQaGqqIiAjvYe5Vq1b5tXfDhg3lcDi8AY///ve/uuyyy4r85brH49HDDz+sXbt2Ffp6YcpamySNHTtWs2bN8h4cf+yxx9SkSRN17Oj/e/VDhw7pyJEjat68ud9rAskwDN12223629/+JknKycnR7bffrunTpys2NtbvfdatW6f4+HhFR0dXVKnFKngnym3btnl/iVwVjRgxQpMnT9axY/k3yZg2bZr69et3SpeJgpYsWaJ3333XOw4LC9Mll1xS5Py6deuW6XuktKKjozVs2DD997//lSTt379fd999t6ZMmeJzGOEEj8ejSZMmaenSpRVWU0EjRozwhjGOHj2qH374QbNnz/a+3qxZM7///rz99tu9YQxJuueee/Tee++pUaNGftezc+dOSVL9+vX9XgMAAAAAFal+/foV9h7FNE116NBBHTp0qJD9zzrrrDPSjaBevXqF/vzsTHM4HOrRo8cpXVCritDQUJ133nmBLqNMoqOjNXDgwECXUSp2u12dO3dW586dz9g127RpozZt2pyx6xUlMTFRiYmJFbZ/2WJkAACgVExDGt85Qg7+zQvUeIcWfaT0HX9IkqyQCNUd+tcKuU5Iw1ZqPm6qQhqV/KYmqFYTNb39X3JE1fZ53uYq3wMMtpAINb39X4rqMlgyiv8L0QoOV8Mxjyim6xC5szL+fD4oREYZ74ZRUF5muvZ+9qokybA5VHf4OJ/XD/zwgbIO5h/8CW7URi3HT1edC8eq1oAxajbuVcX2zr9Doic3W7tnV/ydGwFUXre0C1OYw5RpkMYAAACVx1133aWLL75YM2bM0L59+4qc53a7NXPmTL311lve52rXrl3kL8gK/qL866+/1vvvv6+kpKRiawkKClLv3r294xUrVuiBBx5QZuapXWQPHDigcePGac6cOZLy75Lnr7LUJkm1atXShAkTvOOMjAyNGTNGU6dOVVpaWpHrcnNz9eOPP+r+++9X3759tXjxYr9rrQwuuOACDRo0yDvevHmzLrnkEn3++efKyyv6hjJpaWn67LPPdOONN2rYsGHav3//mSi3UAX/zHNycnT//fdr8+bNPp0iqorQ0FDdeeed3nFOTo7Gjh2rL7/8stD5X3zxhW655Rafbg733HNPsd8zBT9fK1eu1L/+9S8dOHDg9IsvxL333usT7Jk/f76GDRumOXPm6ODBg3K73Tp69Ki+//57jR49WtOmTZNhGBV2GKeg/v37KzIy0jt+/vnntWHDBu/Y37tVSlK7du10/fXXe8eHDh3SZZddpvfff19ZWVlFrsvKytK3336rO++8U4MGDSq3u3MCAAAAAADgzKMzBgAAZ4BlGooOsnRT2zC9vCI10OUACBB3dpb2fvmGd1xn6K2yhUZW2PWccfXUbNyrSlm/RMkrF+rYlhXKSTksd3ambOHRctVqrMiO5yuyQ1+ZNoeSVy/yWR9Uq1G512Q5g9Vw1AOK73uVkpZ+rbQNS5WdtE95GamygsPljKmjiHbnKrrzINlCo5SXeUyeE10xJAUllE9N+7+ZppyUQ5Kk+POukDO2rs/rh3/61Pu4/mX3ynQE+bxeZ+itOrriB+WmHlHapt+VdXCnnHHcwRCoafrWD9LZ8U7ZLYIYAACg8lm/fr0ef/xxPfHEE2rQoIFatWqlhIQEhYWFKTs7W3v27NGSJUtOOUz/wAMPFNm14vLLL/feBT4vL0+PPPKIHnnkETkcDtlsf/7KaezYsbrlllu847/+9a9auHCh9+D4xx9/rB9++EHnnnuu6tSpo8zMTG3atEk//fSTt0PFxRdfrH379mnJkiV+fbxlrU2SRo8ere3bt3tb3mdnZ+uFF17Q1KlT1bFjRyUmJioiIkJZWVlKSUnRpk2b9Mcffyg9Pd2v2iojwzA0adIk7d+/X8uXL5eUH4a555579Pjjj6tLly6qW7euQkJClJ6eriNHjmj9+vXatGmT988o0Pr166e4uDgdPHhQUn4I5+uvv5ZlWXI6nd55derU0RdffBGoMv02ZswY/fLLL/ruu+8kSSkpKbrrrrv04osvqnv37oqKilJSUpJ+/vlnbd261WftkCFDNGrUqGL3v+yyy/Thhx96wypTpkzRlClTZLfbZbfbvfOGDh2qRx999LQ+lqioKP3rX//SjTfe6O32sWXLFo0fP77INddff72Cg4O9X4+WZZ1WDUVxOBy66KKLvF1Ftm3b5n3NZrPp4osvLtV+9913n3bu3Klvv/1WkpSamqpHHnlEzz33nDp37qyGDRsqLCxMGRkZOnr0qDZu3Kj169cXG9YAAAAAAABA1UEYAwCAM8RhGepRJ0grD+Xof7tPvfMfgOrPnZft0+Vh53/+Tzv/83/FL/J4fIZJv32lpKXfeMfRnQerwZV/L3aL8BZdFd6ia4n1Ze7b4jN21W9Z4pqyctVqLNeFY6ULS6hp78k1nX5L0cwDO3Rw4SxJkj0yXvH9R5/y+omghjO+gVx1Tm1VaNqdimjdW4d/zg9tpG1eThgDqGHqhVq6sXUYQQwAAFDpeTwebd++Xdu3by92nt1u10MPPaQBAwYUOef888/XmDFjNH36dJ/ns7OzlZ39Z5D+5MP6bdq00UMPPaSHHnrIexA8KSlJn3zySaHX6d+/v5544gndeOONxdZcHrWdMHHiRCUmJuqpp57yHpLOyMjQ4sWL/ep64XA4/K61sggODtaMGTP0+OOPa+bMmd7nk5KS9PXXX5e43jAMn0P8Z5rT6dRzzz2n22+/XSkpKd7n8/LyfIIyGRkZhS2vdAzD0EsvvaSJEydq9uzZ3ue3bt16SviioCuuuEIPPfRQifu3bt1a48eP1zPPPOPTPSQnJ8fn+6Lg98vp6Nixo9555x39/e9/16ZNm4qcZ1mW7rzzTt188836v//78+dkoaGh5VJHYUaMGOENYxR0zjnn+HT08IdlWZoyZYpefvllvfrqq97OMmlpaZo/f75fewTy+wgAAAAAAACnhzAGAABnkN00dHPbMG1JztHutLxAlwMg0Nxl+HvA45E8eQWG5fd3SfquDX8ODEMhDVqV295l5VOTpJCGp1/T7tmT5cnLvxtrnaG3yXK6fF7POrTL+7i4ThwFO4dkHdx52nUBqDocpnRf5whZhd8wGgAAIOCeeeYZffPNN/rxxx+1adMmeU4K+hcUHBysgQMH6rbbblPDhg1L3PuBBx7Q4MGDNWfOHK1cuVJ79uxRenp6id0SLr/8ctWvX19PP/20/vjjj0LnNGvWTKNHj9bIkSOL7M5REbWdMGrUKJ1//vl6++239emnn3o7LhSlUaNG6tOnjy6++GK1a9eu1PVWBg6HQ48++qhGjRqlN954Qz/88EOxHT8sy1KbNm103nnnadiwYapbt26Rc8+Ebt26ae7cufroo4/0008/afPmzUpNTVVmZtW8GY7NZtOkSZM0dOhQTZkyRcuXLy/0+9c0TXXs2FF33nmnunXr5vf+119/vXr06KGPP/5Yy5Yt044dO3Ts2LEK63bStm1bffLJJ/r88881b948rV+/XocPH1ZoaKhq1aqlXr166dJLL1Xjxo0l5QeBTqjIMEbr1q3VokULrV+/3uf5Sy+9tEz7maapO+64Q8OGDdO///1vzZs3T8nJyUXONwxDLVq00DnnnKNhw4apadOmZbouAAAAAAAAAs/wFPcTeAAAUO7y3B4dyMjT+IVHlO0ueT6A6iM3I1WrHxhSrntGdRmshqMeOO193NmZWv3Pi+XOzr9bZGhiRzX960unve/p2vTyOKVtXiZJMp0utX74E1nO4DLvd3TVQm17O//zFZLYQc3+OuWUOUeWfq0d7z0mSYrqNEANr/lnoXsd/ulT7ZyVf8fGmO5DVf/y8WWuC0DVcmu7MPWsEyQHXTEAAEAVkJKSog0bNmjnzp1KSkpSRkaGgoKCFBkZqaZNm6ply5ZyOp1ntKYNGzZo5cqVOnLkiBwOh+Li4tSsWTM1b978jNZRks2bN2vdunVKSkpSSkqKnE6nwsPDVa9ePTVt2lRxcXGBLrHc5ebmavXq1dq2bZuOHj2q9PR0BQcHKzIyUo0aNVLTpk0r9JA8fB0+fFi//fabDh06pJSUFIWHhysuLk6dO3dWdHR0oMsrd8OHD/eGtXr37q0333wzwBWVjdvt1rp167Rp0yYdPXpUaWlpcrlcCg8PV8OGDdW0aVNFRkYGusxy485O0e53o/IHRineJ3vckjwKanCJTFtQhdQGAAAABII7N1OZO2ZLMiSjFDebOH6Ut+41STId4RVTHACg3NEZAwCAM8wyDcUEWfpL2zC9siI10OUAOINsrjB1eP5/pVqTummZNr8yzjsur/DFyZKWfu0NYkhSdPeLyv0apZV5YIfStqzwjiM79D+tIIY7J0t75vwrf2BaqnfJXYXOM2127+Oc1CNF7lfwNcPmKHNdAKqWHrWd6lU3SHaTIAYAAKgawsPD1blzZ3Xu3DnQpXg1b9680gUvCpOYmKjExMRAl3FG2Ww2dejQQR06dAh0KZAUExOjQYMGBbqMM2LHjh0+nSratGkTwGpOj2maatWqlVq1CnzXWQAAAAAAAFSs0vd4BgAAp81hGepZJ0i9657Zuw4CQGHyMtK0d96fdxp0xNRVZLtzA1hRvt2zXzx+hzzJsGyKP/fy09rvwPfvK/vIXklSbK9L5KpT+IEaW9ifd5bMOrS7yP0KvmYLizqt2gBUDfVCLd3WPpwgBgAAAACUs8mTJ8vt/rOVdN++fQNYDQAAAAAAAOAfOmMAABAgdtPQ2Lbh2nL0iPYcywt0OQBqKHd2pra8+XflFujyUH/kvTLtxYfFTu7YEZLYQc3+OqXc6to9519KXb/EO47ve5WCajUu835ZR/Zq//fvSZJsoZGqNfiGIue66jaTTEty5yknaZ9SN/ymsOa+d5DNzUhV8soF3nFwvZZlrg1A1RBiN3R/10jZuK0FAAAAAJRo3bp1cjgcatKkSbHzPB6PJk+erC+++ML73FlnnUV3FgAAAAAAAFQJHCEAACCALEP6e5cIuWzcXRlA+dr08jgd/ulT5aanFjkndePv2vDCTTq2ZYX3uZjuF58SPCgvuz56Qfu+eltZh/cUOSfr4C5t+fcEHVww0/tcUJ2mShgw5rSuvWfOv+TJyZIk1R4yVjZXWJFzLWewz+dgz2evKC8jzTv2uN3a8+nLcmdn5M8PiVBo0w6nVR+Ays00pPs6RyjcYco0+O82AAAAACjJqlWrdOGFF+r666/X+++/rzVr1igtLU0ej0cZGRnasmWL/vOf/2j48OGaOnWqd51lWZo4cWIAKwcAAAAAAAD8R2cMAAACyDINRQVZurtThJ5cclSeQBcEoNrI2LdFabP+T7s+fkGuus3lqtNUtrAoefJylZNyWGmblysnaZ/Pmog2fVTv0r9VWE05qYd16MePte+rtxSU0Eiues1lj4iTYdmUm3pE6bs2KGPXep81zrj6avKXZ0rs1FGc1PW/KnnVQklScP2zFN3twhLXJPS/Jr8zh8ejjN0b9cfT1yiyfV+ZjiClrvtFGbs3eufGn3fladUHoPK7rlWoEiPsslsEMQAAAADAX263W4sXL9bixYv9mm8YhiZMmKDOnSvmRiE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",
            "text/plain": [
              "<Figure size 3125x3125 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
          ]
        }
      ],
      "source": [
        "fig = plt.figure(layout='constrained', figsize=(31.25, 31.25))\n",
        "subfigs = fig.subfigures(3, 2, wspace=0.07)\n",
        "sns.set_style(\"white\")\n",
        "sns.set_context(\"paper\")\n",
        "#sns.color_palette(\"pastel\")\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "#sns.set(font_scale=1)\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "\n",
        "#fig 1a \n",
        "fig_1a = subfigs[0,0].subplots(1, 1)\n",
        "textprops = {\"fontsize\":fontsize}\n",
        "explode = (0.05, 0) \n",
        "colors = ['#E69F00', '#56B4E9']\n",
        "fig_1a.pie(df['Code availability (Yes/No)'].value_counts(), explode=explode, labels=['Code unavailable','Code available'], colors=colors, autopct='%1.1f%%',textprops = textprops)\n",
        "fig_1a.text(0.85, 0.95, \"n=\" + str(df['Code availability (Yes/No)'].value_counts().sum()), fontsize=fontsize)\n",
        "\n",
        "# fig 1b\n",
        "fig_1b= subfigs[0,1].subplots(1, 1)\n",
        "df_code_avail=pd.DataFrame({'Counts': df['Code availability (Yes/No)'].value_counts()}).reset_index()\n",
        "#rename columns\n",
        "df_code_avail=df_code_avail.rename(columns={\"index\":\"Code availability (Yes/No)\"})\n",
        "\n",
        "#add percentage column \n",
        "df_code_avail['Percentage'] = (df_code_avail['Counts']/df_code_avail['Counts'].sum()) * 100\n",
        "df_code_avail['Percentage']=df_code_avail['Percentage'].round(1)\n",
        "\n",
        "df_code_avail_statement=pd.DataFrame({'Counts': df['Code availability statement (Yes/No)'].value_counts()}).reset_index()\n",
        "df_code_avail_statement=df_code_avail_statement.rename(columns={\"index\":\"Code availability (Yes/No)\"})\n",
        "\n",
        "#add percentage column \n",
        "df_code_avail_statement['Percentage'] = (df_code_avail_statement['Counts']/df_code_avail_statement['Counts'].sum()) * 100\n",
        "df_code_avail_statement['Percentage']=df_code_avail_statement['Percentage'].round(1)\n",
        "\n",
        "#create dataframe for counts data of data availability and code availability \n",
        "df_temp=pd.DataFrame({'Counts': df.groupby(['Code availability statement (Yes/No)','Code availability (Yes/No)',]).size()}).reset_index()\n",
        "df_temp.loc[df_temp['Code availability statement (Yes/No)'] == 'yes', 'Code availability statement (Yes/No)'] = 'Code availability\\nstatement given\\n(' + str(df_code_avail_statement['Percentage'][1]) + '%)'\n",
        "df_temp.loc[df_temp['Code availability statement (Yes/No)'] == 'no', 'Code availability statement (Yes/No)'] = 'Code availability\\nstatement not given\\n(' + str(df_code_avail_statement['Percentage'][0]) +'%)'\n",
        "df_temp.loc[df_temp['Code availability (Yes/No)'] == 'no', 'Code availability (Yes/No)'] = 'Code unavailable\\n(' + str(df_code_avail['Percentage'][0]) + '%)'\n",
        "df_temp.loc[df_temp['Code availability (Yes/No)'] == 'yes', 'Code availability (Yes/No)'] = 'Code available\\n(' + str(df_code_avail['Percentage'][1]) + '%)'\n",
        "\n",
        "category_labels = [\n",
        "    'Code availability\\nstatement not given\\n(' + str(df_code_avail_statement['Percentage'][0]) +'%)',\n",
        "    'Code availability\\nstatement given\\n(' + str(df_code_avail_statement['Percentage'][1]) + '%)', \n",
        "    'Code unavailable\\n(' + str(df_code_avail['Percentage'][0]) + '%)',\n",
        "    'Code available\\n(' + str(df_code_avail['Percentage'][1]) + '%)'\n",
        "]\n",
        "\n",
        "colors = [\"#e29d00\",\"#0072B2\",\"#e29d00\",\"#0072B2\"]\n",
        "color_dict = {}\n",
        "for i, label in enumerate(category_labels):\n",
        "    color_dict[label] = colors[i]\n",
        "sankey(\n",
        "    left=df_temp['Code availability statement (Yes/No)'], right=df_temp['Code availability (Yes/No)'],  aspect=20, rightWeight=df_temp['Counts'].values.astype(float), leftWeight=df_temp['Counts'].values.astype(float),\n",
        "    fontsize=fontsize,colorDict=color_dict, ax=fig_1b\n",
        ")\n",
        "fig_1b.text(23.1, 510, \"n=\" + str(df_temp['Counts'].sum()), fontsize=fontsize)\n",
        "fig_1b.tick_params(axis='both', labelsize=fontsize)\n",
        "\n",
        "#fig 1c \n",
        "fig_1c=subfigs[1,0].subplots(1, 1)\n",
        "df_temp=pd.DataFrame({'Counts': df.groupby(['Secondary analysis','Code availability (Yes/No)']).size()}).reset_index()\n",
        "df_temp.loc[df_temp['Secondary analysis'] == 'hybrid', 'Secondary analysis'] = 'Hybrid'\n",
        "df_temp['Analysis']='Unknown'\n",
        "df_temp.loc[df_temp['Secondary analysis'] == 'yes', 'Analysis'] = 'Secondary Analysis'\n",
        "df_temp.loc[df_temp['Secondary analysis'] == 'Hybrid', 'Analysis'] = 'Both'\n",
        "df_temp.loc[df_temp['Secondary analysis'] == 'no', 'Analysis'] = 'Primary Analysis'\n",
        "df_temp = df_temp.drop('Secondary analysis', axis=1)\n",
        "#dealing with both value for Analysis column\n",
        "df_temp.loc[(df_temp['Code availability (Yes/No)'] == 'yes') & (df_temp['Analysis'] == 'Primary Analysis'),'Counts'] += int(df_temp.loc[(df_temp['Code availability (Yes/No)'] == 'yes') & (df_temp['Analysis'] == 'Both'),'Counts'])\n",
        "df_temp.loc[(df_temp['Code availability (Yes/No)'] == 'yes') & (df_temp['Analysis'] == 'Secondary Analysis'),'Counts'] += int(df_temp.loc[(df_temp['Code availability (Yes/No)'] == 'yes') & (df_temp['Analysis'] == 'Both'),'Counts'])\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Analysis']=='Both'].index[0])\n",
        "y, h, col = df_temp['Counts'].max() + 120, 15, 'k'\n",
        "df_temp=df_temp.pivot(index='Analysis',columns='Code availability (Yes/No)',values='Counts')\n",
        "df_temp=df_temp.rename(columns={\"no\":\"Code unavailable\",\"yes\":\"Code available\"})\n",
        "chi2, p, dof, expected = chi2_contingency(df_temp)\n",
        "colors = ['#0072B2','#D55E00']\n",
        "df_temp.plot(kind='bar', stacked=True, color=['#E69F00', '#56B4E9'], ax=fig_1c, rot=0,legend=None)\n",
        "#fig_1c.legend(loc='best', bbox_to_anchor=(0.8, 1),fontsize=fontsize,frameon=False)\n",
        "fig_1c.set_xlabel(\"Type of analysis\",fontsize=fontsize)\n",
        "fig_1c.set_ylabel(\"Number of manucripts (n=453)\",fontsize=fontsize)\n",
        "fig_1c.tick_params(axis='both', labelsize=fontsize)\n",
        "\n",
        "# statistical annotation\n",
        "x1, x2 = 0, 1   # columns 'Sat' and 'Sun' (first column: 0, see plt.xticks())\n",
        "fig_1c.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=5, c=col)\n",
        "fig_1c.text((x1+x2)*.5, y+h, r\"$p = {0:s}$\".format(as_si(p,1)), ha='center', va='bottom', color=col, fontsize=fontsize)\n",
        "\n",
        "#fig 1d\n",
        "fig_1d=subfigs[1,1].subplots(1, 1)\n",
        "#create dataframe for counts data of code availability \n",
        "df_modified = df.copy()\n",
        "#df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Secondary analysis','Code availability (Yes/No)','Data availability (yes/no)']).size()}).reset_index()\n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "df_temp = df_temp.drop('Secondary analysis', axis=1)\n",
        "df_temp=pd.DataFrame({'Analysis': ['Data','Data','Code','Code'], 'Availability': ['No','Yes','No','Yes'], \n",
        "              'Counts':[df_temp.loc[df_temp['Data availability (yes/no)']=='no','Counts'].sum(),\n",
        "                        df_temp.loc[df_temp['Data availability (yes/no)']=='yes','Counts'].sum(),\n",
        "                        df_temp.loc[df_temp['Code availability (Yes/No)']=='no','Counts'].sum(),\n",
        "                        df_temp.loc[df_temp['Code availability (Yes/No)']=='yes','Counts'].sum()]})\n",
        "\n",
        "df_temp=df_temp.pivot(index='Analysis',columns='Availability',values='Counts')\n",
        "df_temp=df_temp.rename(columns={\"No\":\"Unavailable\", \"Yes\":\"Available\"})\n",
        "chi2, p, dof, expected = chi2_contingency(df_temp)\n",
        "colors = ['#0072B2','#D55E00']\n",
        "ax=df_temp.plot(kind='bar', stacked=True, color=['#E69F00', '#56B4E9'],ax=fig_1d,rot=0,legend=None)\n",
        "bars = ax.containers\n",
        "# First bar (bottom stack: Yellow, top stack: Blue)\n",
        "bars[0][0].set_facecolor('#E69F00')  # Bottom stack of the first bar\n",
        "bars[1][0].set_facecolor('#56B4E9')    # Top stack of the first bar\n",
        "\n",
        "# Second bar (bottom stack: Green, top stack: Red)\n",
        "bars[0][1].set_facecolor('#EA526F')   # Bottom stack of the second bar\n",
        "bars[1][1].set_facecolor('#009E73')     # Top stack of the second bar\n",
        "\n",
        "# statistical annotation\n",
        "y, h, col = 220, 10, 'k'\n",
        "x1, x2 = 0, 1  # columns  (first column: 0, see plt.xticks())\n",
        "fig_1d.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=5, c=col)\n",
        "fig_1d.text((x1+x2)*.5, y+h, r\"$p = {0:s}$\".format(as_si(p,1)), ha='center', va='bottom', color=col, fontsize=fontsize)\n",
        "\n",
        "#fig_1d.legend(loc='best', bbox_to_anchor=(0.8, 1),fontsize=fontsize,frameon=False)\n",
        "fig_1d.set_xlabel(\"Type of analysis\",fontsize=fontsize)\n",
        "fig_1d.set_ylabel(\"Number of manucripts (n=204)\",fontsize=fontsize)\n",
        "fig_1d.set_ylim(0,250)\n",
        "fig_1d.tick_params(axis='both', labelsize=fontsize)\n",
        "\n",
        "#fig 1e\n",
        "fig_1e=subfigs[2,0].subplots(1, 1)\n",
        "#replace upon request with no in the Data availability column of the data\n",
        "df_modified = df.copy()\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "#create dataframe for counts data of data availability and code availability \n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Data availability (yes/no)','Code availability (Yes/No)']).size()}).reset_index()\n",
        "df_temp['Labels']=['Neither available','Only code available','Only data available','Code and data available']\n",
        "explode = (0.05,0.03,0.05,0.03)\n",
        "textprops = {\"fontsize\":fontsize}\n",
        "colors = ['#E69F00', '#56B4E9','#009E73', '#CC79A7']\n",
        "fig_1e.pie(df_temp['Counts'], explode=explode, labels=df_temp['Labels'], colors=colors, autopct='%1.1f%%',textprops = textprops)\n",
        "fig_1e.text(0.85, 1.2, \"n=\" + str(df_temp['Counts'].sum()), fontsize=fontsize)\n",
        "\n",
        "#fig 1f\n",
        "fig_1f=subfigs[2,1].subplots(1, 1)\n",
        "#create dataframe for counts data of code availability \n",
        "df_modified = df.copy()\n",
        "#df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Code availability (Yes/No)','PMC_Year']).size()}).reset_index()\n",
        "\n",
        "#filter df_temp to only have rows where data and code are available, only data shared, and only code shared \n",
        "df_temp=df_temp[~((df_temp['Code availability (Yes/No)']=='no'))]\n",
        "#filter df_temp to only have primary studies \n",
        "#create percentage column\n",
        "df_temp['Percent Increase'] = (df_temp['Counts']/453) * 100\n",
        "df_temp['Cumulative Percent Increase']=df_temp['Percent Increase'].cumsum()\n",
        "df_temp['Cumulative Percent Increase']=df_temp['Cumulative Percent Increase'].round(2)\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "colors = ['#56B4E9']\n",
        "sns.barplot(ax=fig_1f,data=df_temp, x='PMC_Year', y='Cumulative Percent Increase',palette=colors)\n",
        "\n",
        "for index, row in df_temp.iterrows():\n",
        "    fig_1f.annotate(f'{row[\"Cumulative Percent Increase\"]:.1f}%', xy=(index, row['Cumulative Percent Increase']),\n",
        "                 xytext=(0, 5), textcoords='offset points', ha='center', va='bottom',fontsize=fontsize)\n",
        "plt.ylim([0, 100])\n",
        "#ax[2,1].text(4.9, 99, \"n=453\", fontsize=fontsize)\n",
        "fig_1f.tick_params(axis='both', labelsize=fontsize)\n",
        "fig_1f.set_xlabel('Year', fontsize=fontsize)\n",
        "fig_1f.set_ylabel('Percentage of manuscripts sharing code (%)\\n(n=453)', fontsize=fontsize)\n",
        "fig_1f.set_ylim(0,100)\n",
        "\n",
        "\n",
        "\n",
        "# Add labels to each subplot\n",
        "letter_annotation(fig_1a, -.25, 1, 'a',fontsize)\n",
        "letter_annotation(fig_1b, -.25, 1, 'b',fontsize)\n",
        "letter_annotation(fig_1c, -.25, 1, 'c',fontsize)\n",
        "letter_annotation(fig_1d, -.25, 1, 'd',fontsize)\n",
        "letter_annotation(fig_1e, -.25, 1, 'e',fontsize)\n",
        "letter_annotation(fig_1f, -.25, 1, 'f',fontsize)\n",
        "\n",
        "\n",
        "labels = [mpatches.Patch(color='#E69F00', label=\"Code unavailable\"),\n",
        "mpatches.Patch(color='#56B4E9', label=\"Code available\"),\n",
        "mpatches.Patch(color='#EA526F', label=\"Data unavailable\"),\n",
        "mpatches.Patch(color='#009E73', label=\"Data available\"),\n",
        "] \n",
        "fig.legend(handles=labels, \n",
        "           loc=\"lower center\",ncol=len(labels),fontsize=fontsize,bbox_to_anchor=(0.5, -.03),frameon=False) \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "sns.despine()\n",
        "plt.show()\n",
        "fig.savefig('../Figures/fig1.png', format='png', dpi='figure',bbox_inches='tight')\n",
        "fig.savefig('../Figures/fig1.eps', format='eps', dpi='figure',bbox_inches='tight')\n",
        "#fig.savefig('../Figures/fig1.pdf', format='pdf', dpi=1200,bbox_inches='tight')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "035c8af3",
      "metadata": {},
      "source": [
        "# Figure 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "bd088d1e",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Source</th>\n",
              "      <th>Code Sharing Link Expired?</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10xGenomics</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bitbucket</td>\n",
              "      <td>yes</td>\n",
              "      <td>1</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bioconductor</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CRAN</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Downloadable File</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>GitLab</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Sourceforge</td>\n",
              "      <td>no</td>\n",
              "      <td>3</td>\n",
              "      <td>1.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Bitbucket</td>\n",
              "      <td>no</td>\n",
              "      <td>5</td>\n",
              "      <td>2.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Supplementary</td>\n",
              "      <td>no</td>\n",
              "      <td>6</td>\n",
              "      <td>2.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Zenodo</td>\n",
              "      <td>no</td>\n",
              "      <td>14</td>\n",
              "      <td>5.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Website</td>\n",
              "      <td>no</td>\n",
              "      <td>16</td>\n",
              "      <td>6.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>GitHub</td>\n",
              "      <td>no</td>\n",
              "      <td>180</td>\n",
              "      <td>76.92</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Source Code Sharing Link Expired?  Counts  Percentage\n",
              "0         10xGenomics                         no       1        0.43\n",
              "1           Bitbucket                        yes       1        0.43\n",
              "2        Bioconductor                         no       2        0.85\n",
              "3                CRAN                         no       2        0.85\n",
              "4   Downloadable File                         no       2        0.85\n",
              "5              GitLab                         no       2        0.85\n",
              "6         Sourceforge                         no       3        1.28\n",
              "7           Bitbucket                         no       5        2.14\n",
              "8       Supplementary                         no       6        2.56\n",
              "9              Zenodo                         no      14        5.98\n",
              "10            Website                         no      16        6.84\n",
              "11             GitHub                         no     180       76.92"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_modified = df.copy()\n",
        "df_modified.loc[df_modified['Code availability (Yes/No (reason))'] == 'no (Available upon request)', 'Code availability (Yes/No (reason))'] = 'no'\n",
        "\n",
        "df_modified.loc[df_modified['Code availability (Yes/No (reason))'] == 'no (Link expired)', 'Code availability (Yes/No (reason))'] = 'Link Expired'\n",
        "df_modified.loc[df_modified['Code availability (Yes/No (reason))'] == 'no (Link Expired)', 'Code availability (Yes/No (reason))'] = 'Link Expired'\n",
        "df_modified.loc[df_modified['Code availability (Yes/No (reason))'] == 'no (link does Not work)', 'Code availability (Yes/No (reason))'] = 'Link Expired'\n",
        "df_modified.loc[df_modified['Code availability (Yes/No (reason))'] == 'no (Link not accessible)', 'Code availability (Yes/No (reason))'] = 'Link Expired'\n",
        "df_modified.loc[df_modified['Code availability (Yes/No (reason))'] == 'no (No access on BitBucket)', 'Code availability (Yes/No (reason))'] = 'Link Expired'\n",
        "df_modified['Code Sharing Link Expired?']='no'\n",
        "df_modified.loc[(df_modified['Code availability (Yes/No (reason))'] == 'Link Expired'), 'Code Sharing Link Expired?'] = 'yes'\n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)','Code Sharing Link Expired?']).size()}).reset_index()\n",
        "df_temp=df_temp.rename(columns={\"If Code availability = yes, where do they share code (supplementary/ GitHub/ other)\":\"Source\"})\n",
        "\n",
        "#adding Github,Zenodo to Github and Zenodo Counts \n",
        "df_temp.loc[df_temp['Source']=='GitHub','Counts'] += int(df_temp.loc[df_temp['Source']=='GitHub, Zenodo','Counts'])\n",
        "df_temp.loc[df_temp['Source']=='Zenodo','Counts'] += int(df_temp.loc[df_temp['Source']=='GitHub, Zenodo','Counts'])\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='GitHub, Zenodo'].index[0])\n",
        "\n",
        "#adding Github,Supplementary to Github and Supplementary Counts\n",
        "df_temp.loc[df_temp['Source']=='GitHub','Counts'] += int(df_temp.loc[df_temp['Source']=='GitHub, Supplementary','Counts'])\n",
        "df_temp.loc[df_temp['Source']=='Supplementary','Counts'] += int(df_temp.loc[df_temp['Source']=='GitHub, Supplementary','Counts'])\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='GitHub, Supplementary'].index[0])\n",
        "\n",
        "#adding Website,Github to Github and Website Counts\n",
        "df_temp.loc[df_temp['Source']=='GitHub','Counts'] += int(df_temp.loc[df_temp['Source']=='Website, Github','Counts'])\n",
        "df_temp.loc[df_temp['Source']=='Website','Counts'] += int(df_temp.loc[df_temp['Source']=='Website, Github','Counts'])\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='Website, Github'].index[0])\n",
        "\n",
        "#drop NA row\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='NA'].index[0])\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='NA'].index[0])\n",
        "\n",
        "#add percentage column \n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "\n",
        "#sort data by percentage\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "f17ec699",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 3125x3125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(31.25, 31.25))\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "color=['#009E73','#E69F00']\n",
        "ax=sns.barplot(data=df_temp, y='Source', x='Percentage',hue='Code Sharing Link Expired?',errorbar=None,palette=color,legend=None)\n",
        "sns.despine()\n",
        "ax.set_xlabel(\"Percentage of code sharing links (%)\\n(n=453)\",fontsize=fontsize,loc='center')\n",
        "ax.set_xlim(0,100)\n",
        "ax.set_ylabel(\"Code Sharing Platform\",fontsize=fontsize)\n",
        "ax.tick_params(axis='both', labelsize=fontsize)\n",
        "ax.legend(labels=['Code URL is functional','Code URL is non-functional'],loc='upper right', bbox_to_anchor=(1.1, 1),fontsize=fontsize,frameon=False)\n",
        "\n",
        "plt.savefig('../Figures/fig2.png', format='png', dpi='figure',bbox_inches='tight')\n",
        "plt.savefig('../Figures/fig2.eps', format='eps', dpi='figure',bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f6dd9ba0",
      "metadata": {},
      "source": [
        "# Figure 3"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e5e58e19",
      "metadata": {},
      "source": [
        "## Figure 3a (Supp 13)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "718b35a3",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Journal</th>\n",
              "      <th>Code Policy</th>\n",
              "      <th>Data Policy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMC Bioinformatics</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Encouraged</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Genome Biology</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Genome Medicine</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Encouraged</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Nature Biotechnology</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Nature Genetics</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Nature Methods</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Nucleic Acids Research</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Journal Code Policy Data Policy\n",
              "0          Bioinformatics   Mandatory   Mandatory\n",
              "1      BMC Bioinformatics  Encouraged  Encouraged\n",
              "2          Genome Biology   Mandatory   Mandatory\n",
              "3         Genome Medicine  Encouraged  Encouraged\n",
              "4    Nature Biotechnology   Mandatory   Mandatory\n",
              "5         Nature Genetics   Mandatory   Mandatory\n",
              "6          Nature Methods   Mandatory   Mandatory\n",
              "7  Nucleic Acids Research  Encouraged   Mandatory"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_journal=pd.read_csv(\"../data/journal_data.csv\")\n",
        "df_journal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "61de6a04",
      "metadata": {},
      "outputs": [],
      "source": [
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 323,
      "id": "feb8f2ec",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Journal</th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>no</td>\n",
              "      <td>7</td>\n",
              "      <td>11.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Nucleic Acids Research</td>\n",
              "      <td>yes</td>\n",
              "      <td>11</td>\n",
              "      <td>18.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Nature Biotechnology</td>\n",
              "      <td>yes</td>\n",
              "      <td>18</td>\n",
              "      <td>37.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Nature Methods</td>\n",
              "      <td>no</td>\n",
              "      <td>23</td>\n",
              "      <td>40.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Genome Medicine</td>\n",
              "      <td>yes</td>\n",
              "      <td>22</td>\n",
              "      <td>42.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Genome Biology</td>\n",
              "      <td>yes</td>\n",
              "      <td>28</td>\n",
              "      <td>48.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Nature Genetics</td>\n",
              "      <td>no</td>\n",
              "      <td>29</td>\n",
              "      <td>49.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMC Bioinformatics</td>\n",
              "      <td>no</td>\n",
              "      <td>30</td>\n",
              "      <td>50.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMC Bioinformatics</td>\n",
              "      <td>yes</td>\n",
              "      <td>30</td>\n",
              "      <td>50.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Nature Genetics</td>\n",
              "      <td>yes</td>\n",
              "      <td>30</td>\n",
              "      <td>50.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Genome Biology</td>\n",
              "      <td>no</td>\n",
              "      <td>30</td>\n",
              "      <td>51.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Genome Medicine</td>\n",
              "      <td>no</td>\n",
              "      <td>30</td>\n",
              "      <td>57.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Nature Methods</td>\n",
              "      <td>yes</td>\n",
              "      <td>34</td>\n",
              "      <td>59.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Nature Biotechnology</td>\n",
              "      <td>no</td>\n",
              "      <td>30</td>\n",
              "      <td>62.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Nucleic Acids Research</td>\n",
              "      <td>no</td>\n",
              "      <td>48</td>\n",
              "      <td>81.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>yes</td>\n",
              "      <td>53</td>\n",
              "      <td>88.33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Journal Code availability (Yes/No)  Counts  Percentage\n",
              "2           Bioinformatics                         no       7       11.67\n",
              "15  Nucleic Acids Research                        yes      11       18.64\n",
              "9     Nature Biotechnology                        yes      18       37.50\n",
              "12          Nature Methods                         no      23       40.35\n",
              "7          Genome Medicine                        yes      22       42.31\n",
              "5           Genome Biology                        yes      28       48.28\n",
              "10         Nature Genetics                         no      29       49.15\n",
              "0       BMC Bioinformatics                         no      30       50.00\n",
              "1       BMC Bioinformatics                        yes      30       50.00\n",
              "11         Nature Genetics                        yes      30       50.85\n",
              "4           Genome Biology                         no      30       51.72\n",
              "6          Genome Medicine                         no      30       57.69\n",
              "13          Nature Methods                        yes      34       59.65\n",
              "8     Nature Biotechnology                         no      30       62.50\n",
              "14  Nucleic Acids Research                         no      48       81.36\n",
              "3           Bioinformatics                        yes      53       88.33"
            ]
          },
          "execution_count": 323,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Journal','Code availability (Yes/No)']).size()}).reset_index()\n",
        "#f_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "#replacing journal short names with full names\n",
        "labels = {'Bioinformatics':'Bioinformatics', 'Nucleic_Acids_Res':'Nucleic Acids Research', 'Nat_Biotechnol':'Nature Biotechnology', 'Genome_Med':'Genome Medicine', 'Nat_Methods':'Nature Methods', 'Genome_Biol':'Genome Biology','Nat_Genet':'Nature Genetics','BMC_Bioinformatics':'BMC Bioinformatics'}\n",
        "df_temp['Journal'] = df_temp['Journal'].replace(labels)\n",
        "#add percentage column\n",
        "percentages=[]\n",
        "for index, row in df_temp.iterrows():\n",
        "   value=(row['Counts']/df_temp.loc[(df_temp['Journal'] == row['Journal'])]['Counts'].sum())*100\n",
        "   percentages.append(value)\n",
        "df_temp['Percentage']=percentages\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 324,
      "id": "0379a919",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>no</th>\n",
              "      <th>yes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Journal</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Bioinformatics</th>\n",
              "      <td>11.67</td>\n",
              "      <td>88.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nature Methods</th>\n",
              "      <td>40.35</td>\n",
              "      <td>59.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nature Genetics</th>\n",
              "      <td>49.15</td>\n",
              "      <td>50.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMC Bioinformatics</th>\n",
              "      <td>50.00</td>\n",
              "      <td>50.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Genome Biology</th>\n",
              "      <td>51.72</td>\n",
              "      <td>48.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Genome Medicine</th>\n",
              "      <td>57.69</td>\n",
              "      <td>42.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nature Biotechnology</th>\n",
              "      <td>62.50</td>\n",
              "      <td>37.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nucleic Acids Research</th>\n",
              "      <td>81.36</td>\n",
              "      <td>18.64</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Code availability (Yes/No)     no    yes\n",
              "Journal                                 \n",
              "Bioinformatics              11.67  88.33\n",
              "Nature Methods              40.35  59.65\n",
              "Nature Genetics             49.15  50.85\n",
              "BMC Bioinformatics          50.00  50.00\n",
              "Genome Biology              51.72  48.28\n",
              "Genome Medicine             57.69  42.31\n",
              "Nature Biotechnology        62.50  37.50\n",
              "Nucleic Acids Research      81.36  18.64"
            ]
          },
          "execution_count": 324,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp=df_temp.pivot(index='Journal',columns='Code availability (Yes/No)',values='Percentage')\n",
        "df_temp=df_temp.sort_values('no', ascending=True)\n",
        "order=list(df_temp.index)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 316,
      "id": "8b9f8673",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "color=['#E69F00','#56B4E9']\n",
        "#sns.barplot(data=df_temp, x='Journal', y='Percentage',hue='Code availability (Yes/No)',ci=None,palette=color)\n",
        "df_temp.plot(kind='barh', stacked=True, color=color)\n",
        "#, palette=colors)\n",
        "plt.legend(loc='upper right', bbox_to_anchor=(1.4, 1.2),labels=['Code unavailable','Code available'])\n",
        "#for index, row in df_temp.iterrows():\n",
        "#    plt.annotate(f'{row[\"Cumulative Percent Increase\"]:.2f}%', xy=(index, row['Cumulative Percent Increase']),\n",
        "#                 xytext=(0, 5), textcoords='offset points', ha='center', va='bottom')\n",
        "\n",
        "sns.despine()\n",
        "plt.xlabel(\"Percentage of code shared across all studies by journal\\n(n=453)\",fontsize=12)\n",
        "#plt.ylim(0,100)\n",
        "plt.ylabel(\"Journal\",fontsize=12)\n",
        "plt.xticks(ha='center')\n",
        "plt.tick_params(axis='both', labelsize=12)\n",
        "#plt.savefig('../Figures/sup13.png', format='png', dpi=300,bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "49c97304",
      "metadata": {},
      "source": [
        "## Figure 3b (Supp 14)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "id": "c78f27c0",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Journal</th>\n",
              "      <th>Code Policy</th>\n",
              "      <th>Data Policy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMC Bioinformatics</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Encouraged</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Genome Biology</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Genome Medicine</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Encouraged</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Nature Biotechnology</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Nature Genetics</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Nature Methods</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Nucleic Acids Research</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Journal Code Policy Data Policy\n",
              "0          Bioinformatics   Mandatory   Mandatory\n",
              "1      BMC Bioinformatics  Encouraged  Encouraged\n",
              "2          Genome Biology   Mandatory   Mandatory\n",
              "3         Genome Medicine  Encouraged  Encouraged\n",
              "4    Nature Biotechnology   Mandatory   Mandatory\n",
              "5         Nature Genetics   Mandatory   Mandatory\n",
              "6          Nature Methods   Mandatory   Mandatory\n",
              "7  Nucleic Acids Research  Encouraged   Mandatory"
            ]
          },
          "execution_count": 170,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_journal=pd.read_csv(\"../data/journal_data.csv\")\n",
        "df_journal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "id": "8cd3c99a",
      "metadata": {},
      "outputs": [],
      "source": [
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "id": "64671e3c",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Journal</th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Genome Biology</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Nature Genetics</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>11.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Nature Biotechnology</td>\n",
              "      <td>no</td>\n",
              "      <td>12</td>\n",
              "      <td>31.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMC Bioinformatics</td>\n",
              "      <td>yes</td>\n",
              "      <td>2</td>\n",
              "      <td>33.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Genome Medicine</td>\n",
              "      <td>no</td>\n",
              "      <td>8</td>\n",
              "      <td>33.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Nature Methods</td>\n",
              "      <td>no</td>\n",
              "      <td>14</td>\n",
              "      <td>36.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Nucleic Acids Research</td>\n",
              "      <td>no</td>\n",
              "      <td>18</td>\n",
              "      <td>48.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>50.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>yes</td>\n",
              "      <td>1</td>\n",
              "      <td>50.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Nucleic Acids Research</td>\n",
              "      <td>yes</td>\n",
              "      <td>19</td>\n",
              "      <td>51.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Nature Methods</td>\n",
              "      <td>yes</td>\n",
              "      <td>24</td>\n",
              "      <td>63.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BMC Bioinformatics</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>66.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Genome Medicine</td>\n",
              "      <td>yes</td>\n",
              "      <td>16</td>\n",
              "      <td>66.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Nature Biotechnology</td>\n",
              "      <td>yes</td>\n",
              "      <td>26</td>\n",
              "      <td>68.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Nature Genetics</td>\n",
              "      <td>yes</td>\n",
              "      <td>30</td>\n",
              "      <td>88.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Genome Biology</td>\n",
              "      <td>yes</td>\n",
              "      <td>24</td>\n",
              "      <td>96.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Journal Data availability (yes/no)  Counts  Percentage\n",
              "4           Genome Biology                         no       1        4.00\n",
              "10         Nature Genetics                         no       4       11.76\n",
              "8     Nature Biotechnology                         no      12       31.58\n",
              "1       BMC Bioinformatics                        yes       2       33.33\n",
              "6          Genome Medicine                         no       8       33.33\n",
              "12          Nature Methods                         no      14       36.84\n",
              "14  Nucleic Acids Research                         no      18       48.65\n",
              "2           Bioinformatics                         no       1       50.00\n",
              "3           Bioinformatics                        yes       1       50.00\n",
              "15  Nucleic Acids Research                        yes      19       51.35\n",
              "13          Nature Methods                        yes      24       63.16\n",
              "0       BMC Bioinformatics                         no       4       66.67\n",
              "7          Genome Medicine                        yes      16       66.67\n",
              "9     Nature Biotechnology                        yes      26       68.42\n",
              "11         Nature Genetics                        yes      30       88.24\n",
              "5           Genome Biology                        yes      24       96.00"
            ]
          },
          "execution_count": 172,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Journal','Data availability (yes/no)']).size()}).reset_index()\n",
        "#f_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "labels = {'Bioinformatics':'Bioinformatics', 'Nucleic_Acids_Res':'Nucleic Acids Research', 'Nat_Biotechnol':'Nature Biotechnology', 'Genome_Med':'Genome Medicine', 'Nat_Methods':'Nature Methods', 'Genome_Biol':'Genome Biology','Nat_Genet':'Nature Genetics','BMC_Bioinformatics':'BMC Bioinformatics'}\n",
        "df_temp['Journal'] = df_temp['Journal'].replace(labels)\n",
        "#add percentage column\n",
        "percentages=[]\n",
        "for index, row in df_temp.iterrows():\n",
        "   value=(row['Counts']/df_temp.loc[(df_temp['Journal'] == row['Journal'])]['Counts'].sum())*100\n",
        "   percentages.append(value)\n",
        "df_temp['Percentage']=percentages\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "id": "29eddccd",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>no</th>\n",
              "      <th>yes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Journal</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Bioinformatics</th>\n",
              "      <td>50.00</td>\n",
              "      <td>50.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nature Methods</th>\n",
              "      <td>36.84</td>\n",
              "      <td>63.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nature Genetics</th>\n",
              "      <td>11.76</td>\n",
              "      <td>88.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BMC Bioinformatics</th>\n",
              "      <td>66.67</td>\n",
              "      <td>33.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Genome Biology</th>\n",
              "      <td>4.00</td>\n",
              "      <td>96.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Genome Medicine</th>\n",
              "      <td>33.33</td>\n",
              "      <td>66.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nature Biotechnology</th>\n",
              "      <td>31.58</td>\n",
              "      <td>68.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nucleic Acids Research</th>\n",
              "      <td>48.65</td>\n",
              "      <td>51.35</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Data availability (yes/no)     no    yes\n",
              "Journal                                 \n",
              "Bioinformatics              50.00  50.00\n",
              "Nature Methods              36.84  63.16\n",
              "Nature Genetics             11.76  88.24\n",
              "BMC Bioinformatics          66.67  33.33\n",
              "Genome Biology               4.00  96.00\n",
              "Genome Medicine             33.33  66.67\n",
              "Nature Biotechnology        31.58  68.42\n",
              "Nucleic Acids Research      48.65  51.35"
            ]
          },
          "execution_count": 173,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp=df_temp.pivot(index='Journal',columns='Data availability (yes/no)',values='Percentage')\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp['Journal']=pd.Categorical(df_temp['Journal'],categories=order)\n",
        "df_temp=df_temp.sort_values('Journal')\n",
        "df_temp=df_temp.set_index('Journal')\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "id": "2f13cba9",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "color=['#EA526F','#009E73']\n",
        "df_temp.plot(kind='barh', stacked=True, color=color)\n",
        "#, palette=colors)\n",
        "plt.legend(loc='upper right', bbox_to_anchor=(1.4, 1.2),labels=['Data unavailable','Data available'])\n",
        "#for index, row in df_temp.iterrows():\n",
        "#    plt.annotate(f'{row[\"Cumulative Percent Increase\"]:.2f}%', xy=(index, row['Cumulative Percent Increase']),\n",
        "#                 xytext=(0, 5), textcoords='offset points', ha='center', va='bottom')\n",
        "\n",
        "sns.despine()\n",
        "plt.ylabel(\"Percentage of data shared across all studies by journal\\n(n=204)\",fontsize=12)\n",
        "#plt.ylim(0,100)\n",
        "plt.xlabel(\"Journal\",fontsize=12)\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tick_params(axis='both', labelsize=12)\n",
        "#plt.savefig('../Figures/sup14.png', format='png', dpi=300,bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2855c9a9",
      "metadata": {},
      "source": [
        "## Figure 3c (Supp 11)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 175,
      "id": "e9c4b56c",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Journal</th>\n",
              "      <th>Code Policy</th>\n",
              "      <th>Data Policy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMC Bioinformatics</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Encouraged</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Genome Biology</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Genome Medicine</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Encouraged</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Nature Biotechnology</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Nature Genetics</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Nature Methods</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Nucleic Acids Research</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Journal Code Policy Data Policy\n",
              "0          Bioinformatics   Mandatory   Mandatory\n",
              "1      BMC Bioinformatics  Encouraged  Encouraged\n",
              "2          Genome Biology   Mandatory   Mandatory\n",
              "3         Genome Medicine  Encouraged  Encouraged\n",
              "4    Nature Biotechnology   Mandatory   Mandatory\n",
              "5         Nature Genetics   Mandatory   Mandatory\n",
              "6          Nature Methods   Mandatory   Mandatory\n",
              "7  Nucleic Acids Research  Encouraged   Mandatory"
            ]
          },
          "execution_count": 175,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_journal=pd.read_csv(\"../data/journal_data.csv\")\n",
        "df_journal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "id": "db84f074",
      "metadata": {},
      "outputs": [],
      "source": [
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 177,
      "id": "faedb726",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Secondary analysis</th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>Code Policy</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>14</td>\n",
              "      <td>6.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>53</td>\n",
              "      <td>25.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>60</td>\n",
              "      <td>29.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>77</td>\n",
              "      <td>37.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Secondary analysis Code availability (Yes/No) Code Policy  Counts  \\\n",
              "2                 no                        yes  Encouraged      14   \n",
              "0                 no                         no  Encouraged      53   \n",
              "3                 no                        yes   Mandatory      60   \n",
              "1                 no                         no   Mandatory      77   \n",
              "\n",
              "   Percentage  \n",
              "2        6.86  \n",
              "0       25.98  \n",
              "3       29.41  \n",
              "1       37.75  "
            ]
          },
          "execution_count": 177,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Secondary analysis','Code availability (Yes/No)','Code Policy']).size()}).reset_index()\n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "#add percentage column \n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 178,
      "id": "e35e3d85",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>Code Available</th>\n",
              "      <th>Code Unavailable</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Code Policy</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Encouraged</th>\n",
              "      <td>14</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mandatory</th>\n",
              "      <td>60</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Code availability (Yes/No)  Code Available  Code Unavailable\n",
              "Code Policy                                                 \n",
              "Encouraged                              14                53\n",
              "Mandatory                               60                77"
            ]
          },
          "execution_count": 178,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp_pivot=df_temp.pivot(index='Code Policy',columns='Code availability (Yes/No)',values='Counts')\n",
        "df_temp_pivot=df_temp_pivot.rename(columns={\"no\":\"Code Unavailable\",\"yes\":\"Code Available\"})\n",
        "df_temp_pivot = df_temp_pivot.reindex(columns=['Code Available', 'Code Unavailable'])\n",
        "df_temp_pivot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "ac65c9d4",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Chi-squared statistic: 9.241015886619984\n",
            "P-value: 0.00236653625855266\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Perform chi-squared test\n",
        "chi2, p, dof, expected = chi2_contingency(df_temp_pivot)\n",
        "\n",
        "# Display the test results\n",
        "print(\"Chi-squared statistic:\", chi2)\n",
        "print(\"P-value:\", p)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a40b1b3e",
      "metadata": {},
      "source": [
        "### OR for How likely were articles to share their code if the journal only encouraged sharing code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "id": "9ac15fae",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.934875129238918 0.0018007030647661246\n"
          ]
        }
      ],
      "source": [
        "#stat=odds_ratio([[63,108],[163,119]])\n",
        "stat=odds_ratio([df_temp_pivot.values[1],df_temp_pivot.values[0]])\n",
        "res = fisher_exact(df_temp_pivot.values, alternative='two-sided')\n",
        "print(stat.statistic,res.pvalue)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f33b2809",
      "metadata": {},
      "source": [
        "### OR for How likely were articles to share their code if the journal mandated code sharing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "id": "18852e6e",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.3407299990508732 0.0018007030647661246\n"
          ]
        }
      ],
      "source": [
        "#stat=odds_ratio([[63,108],[163,119]])\n",
        "stat=odds_ratio([df_temp_pivot.values[0],df_temp_pivot.values[1]])\n",
        "res = fisher_exact(df_temp_pivot.values, alternative='two-sided')\n",
        "print(stat.statistic,res.pvalue)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 106,
      "id": "6f1b6781",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>Code Available</th>\n",
              "      <th>Code Unavailable</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Code Policy</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Encouraged</th>\n",
              "      <td>6.86</td>\n",
              "      <td>25.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mandatory</th>\n",
              "      <td>29.41</td>\n",
              "      <td>37.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Code availability (Yes/No)  Code Available  Code Unavailable\n",
              "Code Policy                                                 \n",
              "Encouraged                            6.86             25.98\n",
              "Mandatory                            29.41             37.75"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp_pivot=df_temp.pivot(index='Code Policy',columns='Code availability (Yes/No)',values='Percentage')\n",
        "df_temp_pivot=df_temp_pivot.rename(columns={\"no\":\"Code Unavailable\",\"yes\":\"Code Available\"})\n",
        "df_temp_pivot = df_temp_pivot.reindex(columns=['Code Available', 'Code Unavailable'])\n",
        "df_temp_pivot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "id": "15aae3fd",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 5000x5000 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(50, 50))\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "color=['#56B4E9','#E69F00']\n",
        "#sns.barplot(data=df_temp, x='Code Policy', y='Percentage',hue='Code availability (Yes/No)', ci=None,palette=color)\n",
        "df_temp_pivot.plot(kind='bar', stacked=True, color=color, rot=0)\n",
        "plt.legend(labels=['Code available','Code unavailable'],loc='upper right', bbox_to_anchor=(1.3, 1))\n",
        "# statistical annotation\n",
        "y, h, col = 75, 10, 'k'\n",
        "x1, x2 = 0, 1  # columns  (first column: 0, see plt.xticks())\n",
        "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)\n",
        "plt.text((x1+x2)*.5, y+h, r\"$p = {0:s}$\".format(as_si(p,2)), ha='center', va='bottom', color=col, fontsize=12)\n",
        "sns.despine()\n",
        "plt.ylabel(\"Percentage of code shared in\\nprimary analysis studies\\n(n=204)\",fontsize=12)\n",
        "plt.ylim(0,100)\n",
        "plt.xlabel(\"Journal policy on code sharing\",fontsize=12)\n",
        "plt.tick_params(axis='both', labelsize=12)\n",
        "#plt.savefig('../Figures/sup11.png', format='png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c0788b8a",
      "metadata": {},
      "source": [
        "## Figure 3d (Supp 12)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 179,
      "id": "e0d25c12",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Journal</th>\n",
              "      <th>Code Policy</th>\n",
              "      <th>Data Policy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMC Bioinformatics</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Encouraged</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Genome Biology</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Genome Medicine</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Encouraged</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Nature Biotechnology</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Nature Genetics</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Nature Methods</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Nucleic Acids Research</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Journal Code Policy Data Policy\n",
              "0          Bioinformatics   Mandatory   Mandatory\n",
              "1      BMC Bioinformatics  Encouraged  Encouraged\n",
              "2          Genome Biology   Mandatory   Mandatory\n",
              "3         Genome Medicine  Encouraged  Encouraged\n",
              "4    Nature Biotechnology   Mandatory   Mandatory\n",
              "5         Nature Genetics   Mandatory   Mandatory\n",
              "6          Nature Methods   Mandatory   Mandatory\n",
              "7  Nucleic Acids Research  Encouraged   Mandatory"
            ]
          },
          "execution_count": 179,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_journal=pd.read_csv(\"../data/journal_data.csv\")\n",
        "df_journal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 180,
      "id": "db9cb10f",
      "metadata": {},
      "outputs": [],
      "source": [
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 181,
      "id": "3d92c255",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Secondary analysis</th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>Data Policy</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>12</td>\n",
              "      <td>5.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>18</td>\n",
              "      <td>8.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>50</td>\n",
              "      <td>24.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>124</td>\n",
              "      <td>60.78</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Secondary analysis Data availability (yes/no) Data Policy  Counts  \\\n",
              "0                 no                         no  Encouraged      12   \n",
              "2                 no                        yes  Encouraged      18   \n",
              "1                 no                         no   Mandatory      50   \n",
              "3                 no                        yes   Mandatory     124   \n",
              "\n",
              "   Percentage  \n",
              "0        5.88  \n",
              "2        8.82  \n",
              "1       24.51  \n",
              "3       60.78  "
            ]
          },
          "execution_count": 181,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Secondary analysis','Data availability (yes/no)','Data Policy']).size()}).reset_index()\n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "#add percentage column \n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 182,
      "id": "d2577799",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>Data available</th>\n",
              "      <th>Data unavailable</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Policy</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Encouraged</th>\n",
              "      <td>18</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mandatory</th>\n",
              "      <td>124</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Data availability (yes/no)  Data available  Data unavailable\n",
              "Data Policy                                                 \n",
              "Encouraged                              18                12\n",
              "Mandatory                              124                50"
            ]
          },
          "execution_count": 182,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp_pivot=df_temp.pivot(index='Data Policy',columns='Data availability (yes/no)',values='Counts')\n",
        "df_temp_pivot=df_temp_pivot.rename(columns={\"no\":\"Data unavailable\",\"yes\":\"Data available\"})\n",
        "df_temp_pivot = df_temp_pivot.reindex(columns=['Data available', 'Data unavailable'])\n",
        "df_temp_pivot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "id": "3cf2cb86",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Chi-squared statistic: 1.0484607310156826\n",
            "P-value: 0.30586186082510286\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Perform chi-squared test\n",
        "chi2, p, dof, expected = chi2_contingency(df_temp_pivot)\n",
        "\n",
        "# Display the test results\n",
        "print(\"Chi-squared statistic:\", chi2)\n",
        "print(\"P-value:\", p)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7f2ea470",
      "metadata": {},
      "source": [
        "### OR for How likely were articles to share their code if the journal only encouraged sharing code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 184,
      "id": "c4e9c142",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.6489900609856623 0.28184485090076145\n"
          ]
        }
      ],
      "source": [
        "#stat=odds_ratio([[63,108],[163,119]])\n",
        "stat=odds_ratio([df_temp_pivot.values[1],df_temp_pivot.values[0]])\n",
        "res = fisher_exact(df_temp_pivot.values, alternative='two-sided')\n",
        "print(stat.statistic,res.pvalue)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f9790481",
      "metadata": {},
      "source": [
        "### OR for How likely were articles to share their code if the journal mandated code sharing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 185,
      "id": "05ed4bea",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.6064317934107323 0.28184485090076145\n"
          ]
        }
      ],
      "source": [
        "#stat=odds_ratio([[63,108],[163,119]])\n",
        "stat=odds_ratio([df_temp_pivot.values[0],df_temp_pivot.values[1]])\n",
        "res = fisher_exact(df_temp_pivot.values, alternative='two-sided')\n",
        "print(stat.statistic,res.pvalue)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 186,
      "id": "38115beb",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>Data available</th>\n",
              "      <th>Data unavailable</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Data Policy</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Encouraged</th>\n",
              "      <td>8.82</td>\n",
              "      <td>5.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mandatory</th>\n",
              "      <td>60.78</td>\n",
              "      <td>24.51</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Data availability (yes/no)  Data available  Data unavailable\n",
              "Data Policy                                                 \n",
              "Encouraged                            8.82              5.88\n",
              "Mandatory                            60.78             24.51"
            ]
          },
          "execution_count": 186,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp_pivot=df_temp.pivot(index='Data Policy',columns='Data availability (yes/no)',values='Percentage')\n",
        "df_temp_pivot=df_temp_pivot.rename(columns={\"no\":\"Data unavailable\",\"yes\":\"Data available\"})\n",
        "df_temp_pivot = df_temp_pivot.reindex(columns=['Data available', 'Data unavailable'])\n",
        "df_temp_pivot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 187,
      "id": "66b5355a",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 5000x5000 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(50, 50))\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "color=['#009E73','#EA526F']\n",
        "#sns.barplot(data=df_temp, x='Data Policy', y='Percentage',hue='Data availability (yes/no)',ci=None,palette=color)\n",
        "df_temp_pivot.plot(kind='bar', stacked=True, color=color, rot=0)\n",
        "\n",
        "#, palette=colors)\n",
        "plt.legend(labels=['Data available','Data unavailable',],loc='upper right', bbox_to_anchor=(1.3, 1))\n",
        "#for index, row in df_temp.iterrows():\n",
        "#    plt.annotate(f'{row[\"Cumulative Percent Increase\"]:.2f}%', xy=(index, row['Cumulative Percent Increase']),\n",
        "#                 xytext=(0, 5), textcoords='offset points', ha='center', va='bottom')\n",
        "# statistical annotation\n",
        "y, h, col = 90, 10, 'k'\n",
        "x1, x2 = 0, 1  # columns  (first column: 0, see plt.xticks())\n",
        "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)\n",
        "plt.text((x1+x2)*.5, y+h, r\"$p = {0:s}$\".format(as_si(p,2)), ha='center', va='bottom', color=col, fontsize=12)\n",
        "\n",
        "sns.despine()\n",
        "plt.ylabel(\"Percentage of data shared in\\nprimary analysis studies\\n(n=204)\",fontsize=12)\n",
        "plt.ylim(0,100)\n",
        "plt.xlabel(\"Journal policy on data sharing\",fontsize=12)\n",
        "plt.tick_params(axis='both', labelsize=12)\n",
        "#plt.savefig('../Figures/sup12.png', format='png', dpi=300)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c7037fdb",
      "metadata": {},
      "source": [
        "## Figure 3e (Supp 16)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 188,
      "id": "55c72337",
      "metadata": {},
      "outputs": [],
      "source": [
        "df_journal=pd.read_csv(\"../data/journal_data.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 189,
      "id": "bb2531e1",
      "metadata": {},
      "outputs": [],
      "source": [
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "#replace upon request with no in the Data availability column of the data\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 190,
      "id": "0bce52c5",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Code Policy\n",
              "Encouraged     67\n",
              "Mandatory     137\n",
              "dtype: int64"
            ]
          },
          "execution_count": 190,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_modified[~(df_modified['Secondary analysis']=='yes')].groupby(['Code Policy']).size()\n",
        "#df_modified[~(df_modified['Secondary analysis']=='yes')].groupby(['Code Policy','Code availability (Yes/No)']).size()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fb5e0b8b",
      "metadata": {},
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 191,
      "id": "29332475",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_5119/3784286994.py:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_temp_mandatory_yes['Cumulative Percent Increase'] = 100*(\n",
            "/tmp/ipykernel_5119/3784286994.py:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_temp_encouraged_yes['Cumulative Percent Increase'] = 100*(df_temp_encouraged_yes['Counts'].cumsum() / df_temp[df_temp['Code Policy']==\"Encouraged\"]['Counts'].sum())\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>PMC_Year</th>\n",
              "      <th>Secondary analysis</th>\n",
              "      <th>Code Policy</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Cumulative Percent Increase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>9</td>\n",
              "      <td>6.569343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>yes</td>\n",
              "      <td>2017</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>5</td>\n",
              "      <td>10.218978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yes</td>\n",
              "      <td>2018</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>5</td>\n",
              "      <td>13.868613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>yes</td>\n",
              "      <td>2019</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>13</td>\n",
              "      <td>23.357664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>yes</td>\n",
              "      <td>2020</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>14</td>\n",
              "      <td>33.576642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>yes</td>\n",
              "      <td>2021</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>14</td>\n",
              "      <td>43.795620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>yes</td>\n",
              "      <td>2017</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>3</td>\n",
              "      <td>4.477612</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>yes</td>\n",
              "      <td>2018</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>1</td>\n",
              "      <td>5.970149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>yes</td>\n",
              "      <td>2019</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>3</td>\n",
              "      <td>10.447761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>yes</td>\n",
              "      <td>2020</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>5</td>\n",
              "      <td>17.910448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>yes</td>\n",
              "      <td>2021</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>2</td>\n",
              "      <td>20.895522</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Code availability (Yes/No)  PMC_Year Secondary analysis Code Policy  \\\n",
              "0                         yes      2016                 no   Mandatory   \n",
              "1                         yes      2017                 no   Mandatory   \n",
              "2                         yes      2018                 no   Mandatory   \n",
              "3                         yes      2019                 no   Mandatory   \n",
              "4                         yes      2020                 no   Mandatory   \n",
              "5                         yes      2021                 no   Mandatory   \n",
              "6                         yes      2017                 no  Encouraged   \n",
              "7                         yes      2018                 no  Encouraged   \n",
              "8                         yes      2019                 no  Encouraged   \n",
              "9                         yes      2020                 no  Encouraged   \n",
              "10                        yes      2021                 no  Encouraged   \n",
              "\n",
              "    Counts  Cumulative Percent Increase  \n",
              "0        9                     6.569343  \n",
              "1        5                    10.218978  \n",
              "2        5                    13.868613  \n",
              "3       13                    23.357664  \n",
              "4       14                    33.576642  \n",
              "5       14                    43.795620  \n",
              "6        3                     4.477612  \n",
              "7        1                     5.970149  \n",
              "8        3                    10.447761  \n",
              "9        5                    17.910448  \n",
              "10       2                    20.895522  "
            ]
          },
          "execution_count": 191,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#create dataframe for counts data of code availability \n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Code availability (Yes/No)','PMC_Year','Secondary analysis','Code Policy']).size()}).reset_index()\n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "\n",
        "df_temp_mandatory_yes=df_temp[((df_temp['Code Policy']=='Mandatory') & (df_temp['Code availability (Yes/No)']=='yes') )]\n",
        "\n",
        "\n",
        "df_temp_encouraged_yes=df_temp[((df_temp['Code Policy']=='Encouraged') & (df_temp['Code availability (Yes/No)']=='yes') )]\n",
        "\n",
        "df_temp_mandatory_yes['Cumulative Percent Increase'] = 100*(\n",
        "df_temp_mandatory_yes['Counts'].cumsum() / \n",
        "df_temp[df_temp['Code Policy']==\"Mandatory\"]['Counts'].sum())\n",
        "df_temp_encouraged_yes['Cumulative Percent Increase'] = 100*(df_temp_encouraged_yes['Counts'].cumsum() / df_temp[df_temp['Code Policy']==\"Encouraged\"]['Counts'].sum())\n",
        "\n",
        "df_temp=pd.concat([df_temp_mandatory_yes,df_temp_encouraged_yes])\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "df_temp\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "id": "84b78ee7",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "colors=['#391463','#CA78A6']\n",
        "fontsize=12\n",
        "sns.lineplot(df_temp, x=\"PMC_Year\", y='Cumulative Percent Increase', hue=\"Code Policy\",palette=colors,linewidth=5)\n",
        "plt.xlabel(\"Year\",fontsize=12)\n",
        "plt.ylabel(\"Percentage of code available (%)\",fontsize=12)\n",
        "plt.ylim(0,100)\n",
        "sns.despine()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b38a130c",
      "metadata": {},
      "source": [
        "## Figure 3f (Supp 17)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "id": "62ea6910",
      "metadata": {},
      "outputs": [],
      "source": [
        "df_journal=pd.read_csv(\"../data/journal_data.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "id": "6f395146",
      "metadata": {},
      "outputs": [],
      "source": [
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "#replace upon request with no in the Data availability column of the data\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "f2ac94c7",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Data Policy\n",
              "Encouraged     30\n",
              "Mandatory     174\n",
              "dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_modified[~(df_modified['Secondary analysis']=='yes')].groupby(['Data Policy']).size()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "fc9294c9",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_5119/3917531637.py:9: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_temp_mandatory_yes['Cumulative Percent Increase'] = 100*(\n",
            "/tmp/ipykernel_5119/3917531637.py:12: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_temp_encouraged_yes['Cumulative Percent Increase'] = 100*(df_temp_encouraged_yes['Counts'].cumsum() / df_temp[df_temp['Data Policy']==\"Encouraged\"]['Counts'].sum())\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>PMC_Year</th>\n",
              "      <th>Secondary analysis</th>\n",
              "      <th>Data Policy</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Cumulative Percent Increase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>15</td>\n",
              "      <td>8.620690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>yes</td>\n",
              "      <td>2017</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>22</td>\n",
              "      <td>21.264368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yes</td>\n",
              "      <td>2018</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>20</td>\n",
              "      <td>32.758621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>yes</td>\n",
              "      <td>2019</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>24</td>\n",
              "      <td>46.551724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>yes</td>\n",
              "      <td>2020</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>16</td>\n",
              "      <td>55.747126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>yes</td>\n",
              "      <td>2021</td>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>27</td>\n",
              "      <td>71.264368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>4</td>\n",
              "      <td>13.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>yes</td>\n",
              "      <td>2017</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>4</td>\n",
              "      <td>26.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>yes</td>\n",
              "      <td>2018</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>1</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>yes</td>\n",
              "      <td>2019</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>1</td>\n",
              "      <td>33.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>yes</td>\n",
              "      <td>2020</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>5</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>yes</td>\n",
              "      <td>2021</td>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>3</td>\n",
              "      <td>60.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Data availability (yes/no)  PMC_Year Secondary analysis Data Policy  \\\n",
              "0                         yes      2016                 no   Mandatory   \n",
              "1                         yes      2017                 no   Mandatory   \n",
              "2                         yes      2018                 no   Mandatory   \n",
              "3                         yes      2019                 no   Mandatory   \n",
              "4                         yes      2020                 no   Mandatory   \n",
              "5                         yes      2021                 no   Mandatory   \n",
              "6                         yes      2016                 no  Encouraged   \n",
              "7                         yes      2017                 no  Encouraged   \n",
              "8                         yes      2018                 no  Encouraged   \n",
              "9                         yes      2019                 no  Encouraged   \n",
              "10                        yes      2020                 no  Encouraged   \n",
              "11                        yes      2021                 no  Encouraged   \n",
              "\n",
              "    Counts  Cumulative Percent Increase  \n",
              "0       15                     8.620690  \n",
              "1       22                    21.264368  \n",
              "2       20                    32.758621  \n",
              "3       24                    46.551724  \n",
              "4       16                    55.747126  \n",
              "5       27                    71.264368  \n",
              "6        4                    13.333333  \n",
              "7        4                    26.666667  \n",
              "8        1                    30.000000  \n",
              "9        1                    33.333333  \n",
              "10       5                    50.000000  \n",
              "11       3                    60.000000  "
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#create dataframe for counts data of code availability \n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Data availability (yes/no)','PMC_Year','Secondary analysis','Data Policy']).size()}).reset_index()\n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "\n",
        "df_temp_encouraged_yes=df_temp[((df_temp['Data Policy']=='Encouraged') & (df_temp['Data availability (yes/no)']=='yes') )]\n",
        "df_temp_mandatory_yes=df_temp[((df_temp['Data Policy']=='Mandatory') & (df_temp['Data availability (yes/no)']=='yes') )]\n",
        "\n",
        "\n",
        "df_temp_mandatory_yes['Cumulative Percent Increase'] = 100*(\n",
        "df_temp_mandatory_yes['Counts'].cumsum() / \n",
        "df_temp[df_temp['Data Policy']==\"Mandatory\"]['Counts'].sum())\n",
        "df_temp_encouraged_yes['Cumulative Percent Increase'] = 100*(df_temp_encouraged_yes['Counts'].cumsum() / df_temp[df_temp['Data Policy']==\"Encouraged\"]['Counts'].sum())\n",
        "\n",
        "df_temp=pd.concat([df_temp_mandatory_yes,df_temp_encouraged_yes])\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "df_temp\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "id": "ed5217a9",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "plt.rcParams.update({'font.size': 12})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "colors=['#391463','#CA78A6']\n",
        "fontsize=12\n",
        "sns.lineplot(df_temp, x=\"PMC_Year\", y='Cumulative Percent Increase', hue=\"Data Policy\",palette=colors,linewidth=5)\n",
        "plt.xlabel(\"Year\",fontsize=12)\n",
        "plt.ylabel(\"Percentage of data available (%)\",fontsize=12)\n",
        "plt.ylim(0,100)\n",
        "sns.despine()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Multipanel Figure 3 (Journal Figures)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "f49123fb",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_93938/747091864.py:276: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_temp_mandatory_yes['Cumulative Percent Increase'] = 100*(\n",
            "/tmp/ipykernel_93938/747091864.py:279: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_temp_encouraged_yes['Cumulative Percent Increase'] = 100*(df_temp_encouraged_yes['Counts'].cumsum() / df_temp[df_temp['Code Policy']==\"Encouraged\"]['Counts'].sum())\n",
            "/tmp/ipykernel_93938/747091864.py:333: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_temp_mandatory_yes['Cumulative Percent Increase'] = 100*(\n",
            "/tmp/ipykernel_93938/747091864.py:336: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df_temp_encouraged_yes['Cumulative Percent Increase'] = 100*(df_temp_encouraged_yes['Counts'].cumsum() / df_temp[df_temp['Data Policy']==\"Encouraged\"]['Counts'].sum())\n"
          ]
        },
        {
          "data": {
            "image/png": 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U//73vxPtQw89tNAR7gAAAACwuZBDqRxyKOvJocihFCSHIocCAAAAbH4UQFSik046KR599NGYN29eRET84x//iJNOOinq1q27yXPvvvvucdhhh8UBBxwQ22yzTbH9vv3227jxxhvjP//5T0SsPxb51VdfjSOOOKJQ36lTp0ZE4aOU//WvfxV7lHJVe+ONNyIvLy8aNGgQAwYMiMMOO6xQn7Vr1yYez5s3L/r165dYPKtXr15ceuml0atXr6hevXqhsR988EH0798/vv/++4iIGDBgQHTu3LlKjnJevXp1zJ49O95555145JFHknbU+fOf/1zs8dsb44knnkhauK9Xr14MGDCgyM/Nyy+/HFdffXXiPX311Veje/fucdJJJyX1u+mmm+Kmm26KiOSjws8///y44IILUo7tnXfeib///e+J9o477hjXXHNNkYugubm58eyzz8att94aa9asiZUrV0bfvn1j5MiRRf68IyJGjRqVtHCfmZkZZ511VvzpT3+KevXqFeqfn58f48ePj2eeeSays7NLjX/+/PnxwAMPRLVq1eKvf/1rnHbaaYVimTFjRpxzzjmJz91HH30UH3zwQey7776lzh+x/kjsnJyc2HXXXePmm28ulKzIycmJu+66Kx555JHEa7jnnntKXLyfNGlS3HHHHYl2RkZGnHvuuXHuuecWin/SpElxySWXJBI7X331VVxzzTVFJnU2xpVXXpm0cN+mTZu47bbbCh3Tvnbt2hg4cGAMHjw4ce3mm2+OLl26RPv27Yuce968eXH99dcnLdyffvrp8de//jVq1qyZ1Pfrr7+O//u//4tPP/006TNZnLp160bPnj1j2LBhERHx5ptvxoIFC2LrrbcudeyLL74YOTk5ifbvf//7UscAAAAAQDqTQ6l4cijryaHIoRQkh7KeHAoAAACwOSl6KxAqRI0aNeK8885LtBcvXhyPPfZYucz9yCOPxPHHH1/iwn1ERMuWLeOBBx6I3/zmN4lrQ4YMKZcY0kFeXl5kZ2fHo48+WuTCfUQkLSwOGDAgFi1aFBERDRo0iKeeeipOOOGEYhdy99lnn3jqqacSR3GvXr06HnzwwXJ+Ff8zd+7c2GWXXQr96dChQ+y+++7Rs2fPuPPOOxML91tvvXVcd9110bdv33KLYfny5XH33Xcn2tnZ2TF48OAiF+4jIo4++ugYNGhQ0i5Xd955Z6xYsaLcYvrZmjVr4vLLL08srHbs2DGef/75YneAycrKilNOOSXuvffexM4/X375ZbHH7S5fvjyuvfbaRDsjIyP+/ve/x8UXX1zkwv3Pfbp16xZ33HFHqbviRKxfOM/Pz49bbrklzjrrrCI/ezvvvHOhxeCRI0eWOnfBe3To0CGGDh1a5E5N2dnZcemll8avfvWrxLUJEyYkLYhv6NZbb01aPP6///u/6Nu3b5Hx77bbbvHEE09Es2bNEtdeffXVGDduXMqvoTgffvhhUmKpRYsW8cQTTxRauI9Y/92/+OKLk5JDa9euLbQDU0EPPPBALFu2LNH+wx/+EJdffnmhhfuI9bulPfroo9GuXbukJGFJCn5GcnJy4oUXXih1TH5+fjz33HOJdrt27aJr164p3Q8AAAAA0pUcSsWTQ5FDkUMp+h5yKHIoAAAAwOZFAUQl+93vfhctW7ZMtB9//PHE4nFlyczMjPPPPz/RnjhxYqXHUJHOPPPM2HXXXUvtN2vWrBgzZkyi/X//938p7fjTvHnzpMW/UaNGxdKlSzcu2BTk5eUV+lPwOO2fde7cOe66665y38XkxRdfTDra+owzzoguXbqUOGbvvfdOWpRctmxZvPTSS+UaV0TESy+9FAsWLIiI9ceK33HHHcUuqhd04IEHJiV3ft49ZkPDhg1L+tmeccYZcfjhh6ccX6rHK/fo0SOOOeaYEvt06dIldtttt0R74sSJKccRsX6Xntq1a5fY5/TTT09qF3eP6dOnx8cff5xo77HHHnHmmWeWOHfTpk3jyiuvTLr2xBNPlDgmFT8fe/2za665Jpo0aVLimHPPPTd22WWXRPvDDz+MmTNnFuq3YsWKpMRO8+bNo1+/fiXOXbdu3bjmmmtSCT0i1i+8d+/ePdF+/vnni/x+F/Tee+8lJVbsXAQAAADAL4UcSsWTQ5FDKY0cStHkUJLJoQAAAABVSQFEJcvOzo6//OUvifby5cvj4YcfrvQ4Nlyk/uyzzyo9hoqS6kLWP//5z8SuNw0aNIiePXumfI/DDz88sTCbm5sb48ePL3ug5eyTTz6JU089NU499dT45ptvym3eggmOatWqxWmnnZbSuDPPPDMyM//3K6bgPOVlxIgRice//vWvy3SM9pFHHpl4PHXq1Fi+fHmhPqNGjUo8rlGjRpxzzjkbF2gpTjnllJT67bnnnonHX3/9ddLuQSXp0qVLdOjQodR+Xbt2TUo4zJo1q8h+BXcLili/6J9KouK3v/1t7LDDDon222+/nfJrKMratWvjnXfeSbRbt24d+++/f6njqlWrFmeccUbStaI+n+PGjUtKXPXu3Ttq1KhR6vzdunUr0/HpBRNdc+fOTXpNRXnmmWcSj2vXrl2m310AAAAAkM7kUCqeHIocSirkUAqTQ0kmhwIAAABUJQUQVeCYY46JNm3aJNpPPfVUzJ8/v1zmXrFiRYwYMSL69+8fvXv3jv322y86d+4cHTt2TDr+d4899kga98MPP5TL/atay5YtY7vttkup79ixYxOPO3XqVOyRzUVp1KhRNGjQINGeNm1a6kGWQfPmzWPGjBlF/pk0aVK888478eCDD8aRRx6ZWDz9+OOP47jjjiuXmPLy8mLSpEmJ9m677VbqEeE/22677ZKO0P30009L3ZWlLFatWhWTJ09OtLt161am8QUXkdetWxfTp09Pen7JkiUxY8aMRHvfffeNRo0abWS0xatWrVqpu0H9rHnz5onH+fn5RSYcilJw0b8kdevWTfpcFzy2uKCCuxpVq1Yt6Tj40vTo0SPxePXq1Zv0OZ02bVrSMckHH3xwymMPPvjgpOTSJ598UqjPp59+mtQueLx1acrS97e//W1svfXWiXbBxfkNzZs3L956661E+6ijjoq6deumfC8AAAAASHdyKBVHDkUOJVVyKIXJoSSTQwEAAACqUlZVB7AlyszMjAsvvDAuvPDCiFi/gDVo0KAyHfm5odzc3BgyZEjcf//9sWLFijKPX7JkyUbfO52UZfeaqVOnJh6/9957Sce5piIvLy/xePHixWUaWx6qV68eTZs2jaZNm8ZvfvObOOaYY+Ivf/lL5OTkxJIlS+Ivf/lLvPzyy6Ue2VuSefPmJe3eksoOOAXtsssuicX/FStWxLx582Lbbbfd6HgK+vzzz5N2vrnrrrvi7rvv3uj5NvwZfvHFF0nJhg0TXuWlfv36UbNmzZT61qpVK6m9cuXKlBIKTZs2TTme2rVrJ96Lgj/7gr766qvE41atWhWKqyQFEzoREV9++WV06tQp5fHFxRFRts9n3bp1Y4cddkjM8eWXXxbqU/CI5Ij1Ry2nqiy7F2VlZcWJJ54Y9913X0REvPPOO/H9998X+V15/vnnIzc3N9F2dDMAAAAAvzRyKBVHDkUOZWPJoawnh/I/cigAAABAVXICRBU55JBDkhaxnn/++ZgzZ85GzZWbmxsXX3xx3HbbbRu1cB8RSTuAbM7q1auXUr9Vq1bF6tWrE+38/PzIy8sr05+CitvlpTIdeOCB8cc//jHRnjNnTom7oKRi6dKlSe2tttqqTOML7sgSUb5JokWLFiW1161bt0k/ww13AtpwMT/VXZvKqizJlQ2PSP75+PHSlGVxveA9ipu/4OeirJ+JDftv+Bkriw0/Txt+3kpTsH9RcRS8lp2dHXXq1El57oYNG5YplhNOOCGystbXJObl5cWzzz5bqE9eXl688MILifZuu+1WKBkCAAAAAL8EcigVQw5FDkUOpTA5lKLJoQAAAADpTAFEFcnIyIi+ffsm2jk5OXH//fdv1FyPPfZYvP7664l29erV49hjj40777wzRo4cGR988EF8+umnMX369KTjfwtKdREw3WVnZ6fUb1MWDIuSLu/f7373u6T2yy+/vEnzbbigXdadkDbsv7HJpaKU989ww6OlN4x1U3aB+qUp+N5U5Wdiw7FlSVJsGEtRcRRMaqb6u+VnZTkOPmL9DlMFj7Z+4YUXknYpioh4++234/vvv0+07VwEAAAAwC+VHErFkEORQ9lYciipk0MpnRwKAAAAsKmyqjqALdkBBxwQXbp0iQkTJkRExMiRI+Pss88u0xHEa9eujYceeijR3nrrrWPIkCHRunXrEseV5yLq5mjDRb4jjjgi7rrrriqKpvxsv/32Ubt27cSxuzNmzIi1a9eWeSHxZ3Xr1k1qF3ecb3E27F+W3V9Ks+HP8Nprr42TTjqp3Obf1Nf+S1anTp1E8qQqPxMbjl21atVGx1JUHAV3Q1u1alWsW7cuMjNTqxvcMPGVilNOOSWRiF2wYEGMGTMmDj300MTzBXcjq1+/fhx55JFlvgcAAAAAbC7kUKqOHEpq5FD+Rw7lf+RQSieHAgAAAGwqJ0BUsX79+iUe5+XlxT333FOm8ePHj086OviSSy4pdeE+IuKnn34q0302VipHwRalohdK69evnzgqNaLwUcCbs4KLznl5eZt0ZHL9+vWT2j/++GOZxi9YsCCp3aBBg42OZUMbHo9b3j/DDeefN29euc6/OSv4uSjrZ2LD/ht+xspiw8/Thp+30hTsX1QcBT8D+fn58d1336U8d1n6/qx79+7Rtm3bRLvgYv3cuXPj3XffTbR79uxZ5t2aAAAAAGBzI4dSNDmUjSeHUjHzy6H8jxxK6eRQAAAAgE2lAKKKde/ePX71q18l2m+88UZMmzYt5fFffvllUnu//fZLadzkyZNTvsemKLi4VJadRebPn18R4SQpuEvUtGnT0uYI5k214a4pNWrU2Oi5mjZtmnTEbVk+mxERU6d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YWvzyMTXr9OnTGjdunE6fPi1PT081atRIN9xwg4KDg3X27FmtWLHCqgBk/PjxioyMVIsWLUzt38fHR7Vr11aNGjUUHh6ugIAApaam6vTp09q0aZPxGZuZmanPPvtMYWFh6t27t+n4MzMz9corr2j9+vWSpIiICN10002qUKGCkpOTdeDAAbtmkctPYmKinnnmGe3Zs0eSVL9+fUVGRiosLEwXLlzQ6tWrtX//fqP/Dz/8oMjISJszD1722Wef6fPPP7e6Ljw8XC1btlRERIQSExO1bds2bdy4URcvXtSAAQOsZlcCAAAAAABwNnIq/yGnkj9yKv8hp3Ll5VRmzZqlkSNHWl1XpkwZtWrVShUrVlRCQoI2btyobdu26cKFC0Uayw0MDFTdunVVvXp1hYWFqVSpUrp06ZKOHDmimJgYxcbGSpIuXbqkAQMGaNq0aWrYsGGu/Xh4eMjLy0uSrPI7Hh4e8vTMez5oW9c7OrbCio2NVd++fXXhwgXjutDQUDVt2lTXXHONAgMDlZycrLi4OO3bt087d+5UcnKyXbfh7Oecs4/h+fPn9eyzzxqfGbVr11bjxo1VunRpxcXFaevWrfLw8LDrmNjijM/FzMxM9evXT6tWrbK6/vL7YWhoqNX3jn379mngwIGslA0ANlAAAQAAcAWZMmWK1UB9cHCwhg0bpnvvvTdX319++UVvvfWWcSLsggUL1KxZM3Xr1q3Y4s3PyZMnNX36dHl4eKhfv356+umncw0Up6am2tx+9OjRSktL06233qphw4apcuXKVv+/dOmS3nrrLWMW9ZSUFH322Wf64IMPbO7Tz89PHTp0UIcOHdSkSRObS4lmZmZq6dKlGjp0qHEC9htvvKFbb71VpUqVMnX/nWnTpk1KTU01jm2fPn3k4+Nj1eeff/7RK6+8Yszcs3nzZk2cOFEvvPCCzf2+8cYbVgPCtWrV0pgxY9SgQQOrfqmpqYqOjtYXX3xhXDdy5EhFRUUV+9LWMTExeu+996wG6h966CENGDBA4eHheW6zY8cO/fjjj3k+lt27dzcG68+cOaMlS5borrvuKjCOzZs3a+fOnUa7S5cuBQ7EmlWcj8s777yj9PR0PfDAAxo8eHCuAdHz589r4MCBxuxA58+f17fffquBAwcW9u7lkpycrOeff96q+KF27dp644031Lx58zy3iY2N1W+//abZs2fb3K8zj2N8fLyGDBliVfzQuXNnvf766woKCrLqu2vXLr344ovav3+/xo4dazPenE6dOqWBAwca7/nBwcF69dVX1bFjxzyTcKtXr9bgwYONQeRhw4YpMjJSNWrUMH2btqxbt85YPl6SWrdurZEjR6ps2bJ59s/IyNCWLVs0e/Zs+fv7m7qNCRMmKC0tTddff71Gjhyp2rVrW/0/LS1N48aN0+TJkyVlvW+PHz8+3wIILy8vtWvXTp06ddJNN92kwMBAm31jYmL01ltvGYUDH3zwge6++25VqFDBVPwbNmyQxWKRr6+vhgwZoq5du+Z6T8jvM9AeU6dOVVpamq655hqNGjVKjRs3tvp/Zmamvv32W40aNcqYDSo6OlodO3a0+T61Y8cOq+XHPT09NXDgQD3xxBNGUuqybdu26aWXXtKhQ4ccukQ9AAAAAABAQcip/Iecim3kVP5DTuU/V0pO5ejRoxoxYoTVdX369FG/fv1yvYesWbNGL7/8sk6fPm3XWG5wcLC6deum+++/X40aNbJZ5GKxWPTLL7/o3XffVUJCglFY8uuvv+Y6qb1fv37q16+fJKlNmzbGhEcdO3bUqFGjXBpbYU2bNs1qMqi+ffvq2WeftfnemZycrFWrVmn69Ommn//OeM4V5zGcMWOGLBaLKlWqpJEjR+aZ03FU7sQZn4tTp07VihUrjHZYWJhGjRql22+/PVffX3/9VW+++aY2btxoVWQHAPiPY779AQAAwOUSEhL00UcfGW0fHx998cUXeQ7US9L999+vzz77zOpExLFjxyoxMdHZoZpyebaON954I89BNkn5zpyTlpamVq1a6fPPP881ICFJAQEBGjlypGrVqmVct3jxYiUlJdncZ8uWLTVmzBjdcsstNgebpKzZRdq0aaPvv//eOEE2Li5O8+bNs7lNcbo88PPiiy+qX79+uQbqJalFixb65ptvrE7w/eKLL2wuxfnPP/9YJYqqVKmiKVOm5BoQlrIet5deeknPP/+8VUw5Z5dxtoyMDL3++utWJ52/9tprGj58uM2BeilrdvS33norz8G+O+64w2rbGTNmmIolez8fHx899NBDprYrSHE/LmlpaXr44Yf1/vvv5zkbTOnSpfXxxx9b/e/nn382vX8zvvrqK6vER2RkpKZPn26z+EHKmsmoR48eNgsgnH0cJ02aZLVMeIcOHTRy5MhcxQ+SVLduXX377beqWLGiXYO4w4YNM5YeDg0N1ffff6+HH37Y5vvoTTfdpO+//954rJKTkx12cnr2mW2Cg4M1btw4m8UPUtbJ8zfeeKPeffddtW3b1tRtpKWlqV69evruu+9yFT9IWa+zV199Vbfccotx3YYNG6wSWznVrl1bn3zyidq1a5dv8YMkNWnSRNOmTTMKHtLS0vT999+bil367zNw/Pjx6tatW57Ji/w+A+2RlpamChUqaPr06bmKH6Ssz7TevXurS5cuxnXHjx+3KmLJ6cMPP7R6bx0yZIiefvrpXMUPknT99ddrypQpCg8Pd1hiAgAAAAAAoCDkVKyRU7GNnEoWcirWrpScyrhx46xWjX7mmWf04osv5vl+0bx5c+N5bs9Y7oMPPqihQ4eqcePG+a7w4eXlpY4dO2rSpEnGe+2+ffusThh3NHeK7XLRgZSVoxk4cGC+753+/v5q06aNvvzyy3xzLNk54zlXnMfQYrEoLCxMU6dOtTmhlSNzJ478XExOTlZ0dLTR9vHx0eTJk/MsfpCk9u3b69NPP5Wnpye5EwCwgQIIAACAK8Ts2bOtBqh69+6tqKiofLdp0aKF1VKs8fHxmjt3rtNitFdkZKQeffTRQm3r5+enESNG5DvQ4uPjY3X/U1JSrE6cLqqqVauqY8eORnvZsmUO23dRNWjQQE8++WS+fWrWrGnMniJlDfT8+OOPefb97rvvrNpvv/12gYNtzzzzjOrXr2+0//nnH+3Zs6eg0B1m4cKFVsuW33nnnfrf//5nevu8ZiPx8fFR165djfbq1at1+PDhfPdz8eJFLViwwGi3adMm32SBPYr7cQkPD9cbb7yRb5+QkBB16tTJaB8/ftxqtYaiSEpK0pQpU4x2cHCwPvroozwLCfJia4YZZx7HnK+rsLCwAo9heHi4Bg8enG+f7Pbv368lS5YY7VdeeUV16tQpcLvKlStbJW/mz5+vixcvmr5dWy4vCSxJNWrUKLCYoLBGjhypgICAfPs89thjVu1NmzY57PbDwsKslgG39zOgffv2atOmjcPiyc+bb76pcuXK5dvH7LE6cuSIVcKiUaNGBX6WR0RE6JVXXjEXLAAAAAAAgAOQU7FGTiV/5FTIqdhSknMqsbGxWrx4sdGuXr26nnvuuXy3yfk8d4bIyEi1bNnSaLvTe4EzYzt79qxx+frrr3fYfrNz9XNOKvoxfPHFF/MsSHA0R38u/vbbb7pw4YLRfuyxxwp8nG+66SZ17tzZzsgB4OpBAQQAAMAVIvvJrV5eXlYnXebnf//7n9XM0tn342qPPPJIobe98847TQ14Nm3a1Kq9f//+Qt9mXrLPPL5582aH7rsoevToYWo51Icfflj+/v5G+/fff8/VJzU1VX/99ZfRrlmzplq1alXgvr28vNS7d2+r64rz+Td//nyrdn5LUdvj4YcfNgbDMjMzC5yxaO7cuUpOTjbajloy3RWPy4MPPpjvbDSX5XzdHThwwNT+C7Jy5UpjlQMpa9nryzPwF5azj+OmTZusBrU7dOigkJCQAvd/5513qmLFigX2k7Jm58nMzJSUtfpDhw4dTG0nSffcc4+RmEpPT9f69etNb2tL9qKEgwcP5jtLXGFFRUWpXr16BfZr3LixVeLNmZ8B+/bts2tGQEe9FxSkYsWKplbWqFWrlsLCwoy2rWP1119/Gc83Ket+mFm++t5771Xp0qULDhgAAAAAAMAByKlYI6eSP3Iq5FRsKck5lb/++stqZvlu3brlucJJTjmf587gru8FkvNiyz5ZlCOLy7Jz9XPussIew4CAAD3wwAMOjcUWR38uZi/08PDwMP3e1aNHD1P9AOBqZLtEDQAAACWGxWLR1q1bjfYNN9yg8uXLm9q2UqVKatCggbH95s2blZGRYWog19maN29e6G1zDjbYknOGiPj4eFPbHT58WL/99pu2bt2qffv26cKFC0pMTDSWmb4s+0mgsbGxSk1NddjSm0VhaznNnIKCgtSsWTNj0Hf37t1KTk62GtjcuXOn1QCpmRNps/f19PRURkaGJGnjxo2mty2KzMxMxcTEGO3atWvruuuuc8i+IyIi1LZtWy1atEhS1mD8wIEDbT7uP/zwg3G5WrVqNpdstZcrHpfCvu4csaqAJK1du9aq3b59+yLv09nHMecs+mZfmx4eHrrttts0ffr0AvtmPy4NGza06z2odOnSCg0NVVxcnKSs42E2RlsaNmxoXL548aL69++vd999VxEREUXab3Zmn4tBQUFW98/sZ8CpU6f022+/adOmTdq7d6/Onz+vxMREpaWlWfXL/hmQkZGh06dPq3r16gXu38/PT40aNTIVS1HlLALJT+XKlQs8Vlu2bLFq33LLLab27ePjo2bNmhnvnQAAAAAAAM5CTiU3cir5I6dCTiU/JTWnUtj8RM7nuT127dqlhQsXaseOHTp48KAuXryoxMRE49hdlr198uRJu2+nMFwd2w033KDt27dLklasWKFx48apb9++KlWqlMNuw9nPOWcfw4YNGzr0eOTH0Z+L2XMn1157rapUqWJq//Xr11fp0qWtJoADAGShAAIAAOAKcOrUKaulms3Mep1d/fr1jcH6xMREnTp1yvTM4s5SqlSpIsVg9kTa7DORS7I6jnk5ceKEhg8fnuesPWZcvHhR5cqVK9S2jlK+fHmVKVPGdP969eoZg5jp6ek6dOiQ6tata/z/4MGDufqbFRQUpGuvvdbYh6NnELHl9OnTxkm8knTjjTc6dP89evQwBuvPnz+vhQsX5jkjybp167Rv3z6j/cgjj5g+EbkgrnhczL7ucg5OFvS6M2vv3r3GZX9/f6vnaWE5+zjmvK5OnTqm9282lh07dhiXV6xYYbUctxnZk5DZXzeF1a5dO1WuXFnHjh2TJC1fvlxt2rRRixYtdMsttygqKkr169cvUmLTnmKKgIAA434V9FyMi4vTBx98oNmzZ+casDfDbJKgatWqpmbacgSzyX3J+jPT1moWR44cMS6HhYXZtf/atWtTAAEAAAAAAJyOnEpu5FRsI6dCTqUgJTWnkn2m+oCAAFWtWtX0ttmf52bs27dP77zzTq6JrMxwVMGHLe4S26OPPqrZs2cbEy1NnDhRU6dO1W233aYWLVooKipKNWrUKNJz3lnPueI6hjVq1LB7/4XlyM/F9PR0HT9+3GjbkwuUsnIna9assWsbALgauL4EHQAAAEWWc2DA3sHgnMs3XrhwocgxFVVwcHCRtjc7+0POQaLsswvldODAAXXp0qXQA/WSlJKSUuhtHcXe50fZsmWt2jmfbzmfL2aWA7XV39mDmJflnCXDnhN0zWjevLlq1apltG0t2Zz9ej8/P3Xq1MlhMbjicXHG684e2RMwZcuWlZeXV5H36ezjmH3/np6ediXScr4285KUlGS1HHhmZqYsFotdf9mZndEtP76+vvr000+tXnfp6elasWKFRo8era5du6pJkybq2bOnJk6caHVCvVn2zACU/fmY33Px7Nmz6t69u2bNmlWo4gfJ/GdAUT8D7ZFzcD4/ZhIr2Z/nYWFhdsVSunRpu/oDAAAAAAAUBjmV3Mip2EZOhZyKGSUxp5I9zjJlyth1Yr2Z/MRl69evV5cuXQp1crzk3PcBd4rtuuuu04gRI6wmh0pISNCvv/6qN954Q/fee69atGih5557Tj/88EOhPnuc8ZwrzmMYEhJSqNsoDEceq5zvCaGhoXbFYm+uBQCuFqwAAQAAcAVISEiwattzMmNe/W3N6lycimvma7MsFov69++vM2fOGNdVrlxZHTt2VFRUlKpWraqyZcvK19fXamBqzpw5Gjx4sNF21KBkUdi7NGhBM1fkfL4UZf/F9dzLeTv2vmbM6N69u4YNGyYpa/Bv7969VktCx8bGavHixUb77rvvdugAVkl8XIoqe5yOekydfRyzv56yL4Nu775tcXQCzFHvYXXr1tUvv/yiL7/8UrNnz86VQEtJSdHatWu1du1affTRR7rjjjs0aNCgXEsJF6chQ4ZYzYhVtmxZdezYUU2bNtU111yj8uXLy8/Pz+ozYM2aNerVq5fRNnv83O0z0B7Zl4m3934UZdUPAAAAAAAAs8ipOB85lf+QUzGHnErxy/7cLOr7oC0JCQnq37+/1W1dd911euCBB9SoUSNVrlxZpUuXlq+vr9X7WHR0tCZMmGBXTPZyx9geeOABNWjQQJ9++qkWL15sNd4uZU0E9scff+iPP/7Q8OHD1b17d73wwgt2P18dpbiPobd3yTzV9fKqHpeROwEAxyiZnwoAAACwEhQUZNW2d+nTnP0DAwOLHNOVZtGiRdqzZ4/RvueeezRmzJgCBxwctQytIyUlJdnVP+d9yDmomfP5UpT9F9dzr6ivGTM6dOigsWPHGgPdM2bM0Jtvvmn8f86cOVYDl4888ohDb78kPi5Flf1xddRj6uzjmP31lH2lBnv3bUvOQe97771X48aNs+t2nCUsLEyvvPKKBgwYoA0bNmjdunXauHGjNm3aZJWEzszM1OLFi/XPP/9oypQpdi097ihbtmzR8uXLjXbTpk01ceLEXO8lObnjZ4CzZT8m9ib6cp58AAAAAAAA4AzkVJyPnMp/yKmYQ06l+GV/bhb1fdCW6dOnWxVC9e7dW4MGDSpwtYnieC9w19hq1qypsWPHKj4+XmvWrFFMTIw2bdqkbdu2WZ1In5ycrK+++kqrVq3S1KlTi3Vl6cvc9Ri6m5zvoeROAMAxPF0dAAAAAIou53KPZ8+etWv77AMTUt7LLhZ2edUrZQBj6dKlxuXAwEANHz7c1GwL9j4WxeHcuXNF6p/z+Zbz+ZLz+VSQ7P2La+nSnLMCnTp1yuG3ERQUpI4dOxrtefPmGQPmmZmZ+uGHH4z/1alTR1FRUQ69/ZL4uBRV9sf17NmzSk9PL/I+nX0cs+8/IyNDsbGxpvdt5rUcEhJiNSNOzpUW3IGPj4+aN2+ufv36afLkyVq3bp1mzZqlp556SmXKlDH6Xbx4UQMHDpTFYin2GP/880/jsoeHh0aNGlVg8YPknp8Bzla6dGnj8pkzZ3LNUJWfY8eOOSMkAAAAAAAAK+RUnI+cyn/IqZhDTqX4ZY8zNjbWrhVXzL4usr8XXHvttXr11VcLPDleKp73AneOTZKCg4PVrl07DRo0SDNmzFBMTIy++OILderUyer9dNeuXRo1alSxxJSTux9DdxEYGCg/Pz+jffz4cbu2t7c/AFwtKIAAAAC4AkRERFjN0rFz5067tt+xY4dxOTAwUBEREbn65JxF3OxM5adPn7YrFnd18OBB43Ljxo1Nz96ybds2Z4VUaKdOnbLrJOtdu3YZl729vVWtWjWr/1evXt2qbc/zLzExUYcPHzbaNWrUML1tUZQvX97qJN3Nmzc75Xa6d+9uXI6Pj9evv/4qSVq1apXV/e7atavDb7skPi5FVbt2beNySkqKdu/eXeR9Ovs45rzOnpizvzbzk/02du7c6RbLxufH09NTDRs21Msvv6zFixerbt26xv8OHjyodevWFXtM2T8DatSooSpVqpjazh0/A5ytfv36xuW0tDTTz1NJ2rp1qzNCAgAAAAAAsEJOxfnIqWQhp2IfcirFq2bNmsblS5cu6ciRI6a3NTvum/294Oabb5aXl5ep7YrjvcCdY8uLv7+/WrdurVGjRmnWrFlW76u//PKL3auMO0JJO4aulH118507d5qexC0xMVH79+93VlgAUKJRAAEAAHAF8PLyUsOGDY321q1bTQ+SnzhxQtu3bzfajRo1kqdn7q+JOWc+MTvTgCtOVHWG+Ph447LZmVtiY2O1Zs0aZ4VUJMuWLTPVLyEhweo+1KlTR/7+/lZ96tevbzVrxZIlS0zHsWTJEmVkZBjtyMhI09sWhYeHh5o2bWq09+zZY7Uct6PUqlVLzZo1M9ozZsyQJM2cOdO4LiAgQB06dHD4bZfEx6Wosh9rSUZypCicfRxvvPFGq7bZ12ZmZqbpvjfddJNxOS4urkS9LwcHB2vgwIFW19lzQr2jFOYzIC0tza7ny5Ui58xrCxYsMLXdkSNHnJY4BQAAAAAAyI6civORU8lCTsU+5FSKV2HzEzmf5/nJ/l6Q12o5edm9e7cOHDhgqq8kq1Ww7VlBujhic5a6detaFQKlpKRYFSMUl5J8DItb9txJfHy8/vrrL1PbLVq0yCEr3gPAlYgCCAAAgCtE27ZtjcsWi0VTpkwxtd0333xjNSjXrl27PPtlnwVEktauXVvgvlNSUjR79mxTcbi77LNoHDt2zNQ2kydPVmpqqrNCKpKpU6eamgV+1qxZVjOG3HHHHbn6+Pj4qFWrVkZ7//79pgZtMjIy9M0331hdZ+v55wz333+/VTs6Otopt9OjRw/j8rZt27Rs2TKrgfP7779fQUFBDr/dkvq4FMXNN9+ssmXLGu0ffvhBJ0+eLNI+nX0cb7zxRpUrV85oz5s3z2rA2Jbff//ddNK0ffv2Vu3o6Gir9313V7VqVat2WlpasceQ/TPA7HGfNWuW3cukXwluvfVWq+f0zJkzTb0Ox48f7/arkwAAAAAAgCsHORXnIqeShZyK/cipFJ9WrVrJx8fHaE+fPt3UidY5n+f5yf5ecPToUVPbfPbZZ6b65XUbFy9eLNR2zorNmdwtd1ISj2Fxylm4NWHChAJzdSkpKVft8QIAMyiAAAAAuEJ07tzZapDhm2++0YYNG/LdZu3atZo6darRDg4OVqdOnfLsW758eVWuXNloz507t8CTdIcPH37FnPxZq1Yt4/LmzZu1e/fufPv//fff+vrrr50dVqFt375dX331Vb59Dhw4oAkTJhhtHx8fPfTQQ3n27dmzp1X7nXfeKXBJ6M8//9xqpqybb75Z1113XUGhO0y7du2sljRevHhxgcckO7Mn6rZr107ly5c32i+//LLVAPIjjzxi+jbtVRIfl6Lw9/dXr169jHZCQoIGDBighIQEU9vbekydeRx9fHz04IMPGu3z589rxIgR+e773LlzGjlyZL59smvYsKFV4mbt2rUaM2aMXSebp6enO2z2tW3bttlVgJFz1rvsn0XFJftnwKlTp7R8+fJ8++/cuVPvv/++s8NySz4+PlZJykuXLum5557LN/H0zTff6JdffimO8AAAAAAAACSRU3E2cirkVHIip+J+ypQpo7vuustoHzhwoMCTrXM+zwuS/b1g6dKlOnv2bL79Z82apd9++830/iXrnMH27dtN5x+KIzZ7bNmyxa7+MTExVu1KlSo5MhxT3O0YurO6detardi+fft2DR061ObzNT09Xa+88or+/fff4goRAEocCiAAAACuEEFBQerfv7/RTktLU58+fWwOIsyfP199+/a1GjR86aWXFBAQYPM2OnbsaFw+d+6cXnjhBcXFxeXqFxsbq9dee00zZ860mjmkJMs+S09GRob69euX5/K+FotFU6dO1bPPPiuLxZLv8XQVX19fSdL777+vTz/9NM/ZXNatW6fHH3/c6sTxp59+WuHh4Xnus3nz5lbH6OjRo+rVq5d27tyZq29qaqrGjRunjz76yCqm1157rbB3qVA8PT01fPhwq6VxR48erTfffDP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X904Mw9CqVauUkZFRrOaFjIwMrVq16pp14XvsoQAAAODH7Nx4UK9OnK5dGw/d+MWloFb9qnrqhTHqO6Q9N4PDVK4Cp45+sUnpW4/4rEZEg6pqMKq77DHhPqsBACjfaIBAqWB0c/mUk5OjF1980fv8d7/7naKiooq93pUnUElSzZo1b/q9NWrUuOp5SU/DAgAAAAAAKE2GYWjL6n1KnpymFfO3yO32mJrHYrGo5+1tlDRhgLr2bymr1WpqHgDAtdq0aaPo6GhlZWXJ4XDoj3/8oyZNmnRLNyt5PB49//zzKiwslCRFRUUxRdtP2EMBAADADzl9PF1vvZSsr1LW+6VeeGSIHv7VnRo1YYCC7IF+qQn8mKwDp3R41joVZRf4ZH1LgFW172in+F7NZLHS6AMA+HE0QKDYGN1c/v3jH//QuXPnJEk9e/bU3XffXaL1zpw5c9Xz6tWr3/R7v//a06dPlygLAAAAAABAacjPLdTCmWuUPCVNh/eeNDuOIquE6a6xvTXy4f6qWa+q2XEAANdhsViUmJioKVOmyGKxaMmSJXr66af14osv3tReSEZGhp5//nnvBG2LxaJRo0b5OjYuYQ8FAAAAV8rPLdS0SQv06VuL5Sws8nk9m82q4Q/204Tf3K0qcRE+rwdcj9vp0vEFW3V27QGf1QitWUUNx/RQSHzxG88BAJUHDRAoNkY3l28bNmzQzJkzJUl2u11/+tOfSrxmXl7eVc/DwsJu+r3ff21+fn6J8wAAAAAAABTXt9+cVvLkVC2YvkZ5Ob450exWNGtbV0kTBuj2kV1lDwkyOw4A4CY9/vjjSklJUVZWlgzD0NKlS7Vu3TrdfffdGjBggFq0aHHV3kpGRob27NmjpUuXau7cucrNzfV+rUqVKnrsscfM+DYqHfZQAAAAcJnH49H8z1br7T9/rvSzWX6p2X1Aaz394hg1aHbzU8MAX8n59pwOz1wrR3rujV9cHFaLavZvpRoDWslqY8otAODm0ACBYmN0c/nldDo1ceJEGYYhSXrsscdUr169Eq9bUHD1zQBBQTe/GW+32696zsV7AAAAAADgb263R6sWb1PylDRtWLbb7DgKDArQgOGdlTQ+Qa06Nbyl624AgLIhIiJCb731lsaPH+/dC8nJydEnn3yiTz75RJJktVoVHByswsJCeTwe73sNw5DFYpFhGAoJCdFbb72liAhOfvU19lAAAABw2ZZV+/TqxOnav+OoX+rVb1pDz7x4j7oPbO2XesD1eFxunViyQ6dX7JUu/XxU2oKrRqrB6O4KrxPrk/UBABUXDRAoNkY3l19vvPGGDh8+LElq0KCBHn300VJZ9/LmzWW3cvH++6/9/loAAAAAAAC+knk+R3M/XqnZHyzX6WPpZsdRtZpVNPLh/rrrgT6KqRppdhwAQAl16NBB7733nn7961/r5MmT3oa2yzfYu93ua6YDWCwWb/NDzZo19a9//Uvt2rXze/bKiD0UAAAAHD90Rm+8OEvLv9zil3pRMeH6yW+Ha/iDfRUQYPNLTeB68k9l6tCMtSo4fcE3BSxSfM9mqj2orayB3MIKALh1/OmBEmF0c/mzb98+TZ482fv8hRdeuKWL7Nfz/ROIioqKbvq9TqfzumsBAAAAAACUtt1bDiv5vVQtnbNBTofL7Djq1Ke5Rk0YoF6D2rHZDQAVTKdOnfTFF19oypQpmjFjhs6fP3/d1xuGobi4ON177716+OGHFR4e7qeklRt7KAAAAJVbTla+pv57nma+u1SuIrfP6wUE2jTqJwP0yK/uUkRUqM/rATdiuD06tXKPTi7ZKcPtufEbiiEoOkwNRndTZMN4n6wPAKgcaIBAiTC6uXzxeDx67rnnvBfVR44cqW7dupXa+qGhV/8w5nA4bvq933/t99cCAAAAAAAoDY7CIqXO2aBZk1O1Z8sRs+MoNMyuIff0VOL4BDVoVtPsOAAAHwoPD9czzzyjxx9/XNu2bdOWLVt0+PBhZWdnKy8vT2FhYYqMjFSDBg3UoUMHtWvXToGBgWbHrjTYQwEAAKi8XC635ny4QpP/MUcX0nNv/IZS0HdoBz35/CjVacRN4CgbCs/n6NDMtco7ev2G/ZKI69xQdYd1lC2Yn3UBACVDAwRKjNHN5ce0adO0fft2SVJ0dLR+85vflOr637/g/v1/79fz/ddy8R4AAAAAAJSmU8fSNXvqMs39eKXfNrKvp37TGkoan6DBY3ooLCLE7DgAAD8KDAxU586d1blzZ7Oj4ArsoQAAAFROa1N36rXnZ+jwvpN+qdekdR0989I96tS7uV/qATdieAydXXdAxxdslcdHk08CwoPVILGrolvU8sn6AIDKhwYIlApGN5d9hYWF+u9//+t9/pvf/EYxMTGlWiM+/uqu9NOnT9/0e7//2urVq5dKJgAAAAAAUHkZhqENy3creUqaVi3aJo/HMDWP1WpRnyHtlTRhgDr1bu49SAQAAJiLPRQAAIDK58j+k3rt+Rlas3SnX+rFVovSY/83UkPu7SmbzeqXmsCNOLPydXjWOmV/c/M/n9yqKq3rqN6ILgoMs/usBgCg8qEBAqWG0c1lm9PpVH5+vvf5xIkTNXHixOu+5/IUj8tmz56tL774wvt8xIgR+stf/uJ93rBhw6tef/LkzXfHnzp16qrn318LAAAAAADgZuVm52v+Z6uV8v4yHfXh5t3Nio6L0N0P9NGIh/qpeu1Ys+MAAIDvYQ8FAACg8riQnqPJ//hCsz9YLrfb4/N6QfYA3fv4HRr37FCFhQf7vB5wMwzDUPqWIzo6d5PchUU+qWELDlS94Z0V064eB8EAAEodDRAodYxuLh/c7lsfWWYYxlXv83iu/kGwUaNGVz3fs2fPTa+9a9euq55z8R4AAAAAANyqg3tOKGVKqhbOWKuCfIfZcdSqc0MljU/QgLs7K8jOQSAAAJQX7KEAAABUPEVOl2ZNTtX7/5qr3OwCv9S8bWRXPT4xSTXqcCAGyo6i3EJ9O3uDMncd91mNyCbV1SCpm4KiQn1WAwBQudEAAaDUNG3aVJGRkcrOzpYkbdmy5abfe+VrbTabOnbsWOr5AAAAAABAxeMqcmnFgq1KnpyqLav3mx1HQcGBun1kVyVNSFDzdvXNjgMAAMoI9lAAAADMYRiGVizYqjf+NFPHD5/1S81WnRrqmZfuUZsujW78YsCPMncf15HP18uV65vDY6yBNtUZ1kFVuzZm6gMAwKdogAAqicjISO3bt++W3rNu3To9+OCD3ucjR47U3/72tx99fUBAgPr27at58+ZJujiSedu2bWrXrt1165w5c0Zbt271Pu/QoYNiYmJuKSsAAAAAAKhc0s9k6YuPVujzqct1/vQFs+OoRt04JT7SX3fe31tRMeFmxwEAALeAPRQAAICKaf+Oo5r0/Axt/nqvX+rF14rR4xOTdNvILrJarX6pCdwMV6FTx+Zt1vlNh31WI7xenBqM7q7g2Aif1QAA4DIaIACUqsGDB3sv3kvSJ598csOL95999tlVo6AHDx7ss3wAAAAAAKD8MgxDO9Z/o+QpaUqbu0muIrfZkdQtoZVG/WSAug9sI5uNjW0AAPDj2EMBAADwj/QzWXrnr59r3ierZBiGz+uFhNr1wDNDdN/jtys41O7zesCtyD54WodnrZPzQr5P1rfYrKp1extV79NcFhp/AAB+QgMEgFI1cOBANW3aVPv375ckzZkzR6NGjVKXLl1+8PWHDx/W5MmTvc+rVq2q0aNH+yUrAAAAAAAoHwrzHVqcvE7JU9J0YOcxs+MoPDJEw+7rpcRHElSnUbzZcQAAQDnBHgoAAIBvOQqc+ux/X2naq/OVn+fweT2LxaIh9/TQY39IVNXq0T6vB9wKt9Ol44u26ezq/T6rEVIjWg3H9FAo//0DAPyMBggApcpqteqXv/ylfvazn0m6eDLjE088oUmTJqlHjx5XvXb37t16/PHH5XB890PnU089peDgYL9mBgAAAAAAZdPxw2eV8n6avvxklXKyfHNC2a1o3Kq2EscnaFBSd4WEcZofAAC4NeyhAAAA+IZhGFoye4PefHGWzhzP8EvN9j2a6tmX7lGzdvX8Ug+4FblHz+vwzLUqPJ/jmwIWi2r0b6maA1rJGmDzTQ0AAK6DBggApS4hIUGPPvqo3n33XUlSdna2Hn74YXXq1Elt2rSR1WrVvn37tHr16qtGDd5999269957zYoNAAAAAADKAI/Ho7VLdyp5cqrWpu666tqBGWwBNvW/s6OSxieoXfcmslgspuYBAADlG3soAAAApWvXpkN6deJ07dxw0C/1atavqqf+OEr9hnXkOhHKHI/LrZOpu3Rq2W7JR9dV7bERajimu8LrxvlkfQAAbgYNEAB84pe//KUKCws1bdo0769t2rRJmzZt+sHXDx06VC+//LK/4gEAAAAAgDImOzNX8z5dpZT3l+nkkXNmx1FcfJSGP9RPd4/rq6qMcAcAAKWIPRQAAICSO3MiQ2+9nKzFs9b5pV5YRIge+dWdGvWTAQqyB/qlJnAr8k9f0OGZa5V/MtNnNar1aKrag9vJFsRtpwAAc/EnEQCfsFqteu6559SnTx+9+eab2rp16w++rmnTpvrJT36i4cOH+zcgAAAAAAAoE/ZtP6qUKalanLJejgKn2XHUrnsTjZowQP2GdVBAIJdPAQBA6WMPBQAAoPjycwv18esL9cmbi/1yLclqtWj4Q/30k98MV5W4CJ/XA26V4fHo9Nf7dGLxdhluj09qBEWFqsGobopsXN0n6wMAcKvYwQPwo7p166Z9+/aVaI1+/fqpX79++vbbb7Vr1y6dPXtWbrdb8fHxatKkiZo1a1ZKaQEAAAAAQHlR5HQpbd4mJb+Xqh0bDpodR8GhQRo0qruSxieocas6ZscBAADlAHsoAAAA/uXxeLRg+hq9/ecUnT+T5ZeaXfu30jMvjVHD5rX8Ug+4VYXpOTo8a51yfThRN7ZjA9W9q6MCgoN8VgMAgFtFAwQAv6hXr57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",
            "text/plain": [
              "<Figure size 3125x3125 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(layout='constrained', figsize=(31.25, 31.25))\n",
        "subfigs = fig.subfigures(3, 2, wspace=0.07)\n",
        "sns.set_style(\"white\")\n",
        "sns.set_context(\"paper\")\n",
        "#sns.color_palette(\"pastel\")\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "#sns.set(font_scale=1)\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "df_journal=pd.read_csv(\"../data/journal_data.csv\")\n",
        "#fig 2a \n",
        "fig_2a = subfigs[0,0].subplots(1, 1)\n",
        "\n",
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "\n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Journal','Code availability (Yes/No)','Code Policy']).size()}).reset_index()\n",
        "#replacing journal short names with full names\n",
        "labels = {'Bioinformatics':'Bioinformatics', 'Nucleic_Acids_Res':'Nucleic Acids Research', 'Nat_Biotechnol':'Nature Biotechnology', 'Genome_Med':'Genome Medicine', 'Nat_Methods':'Nature Methods', 'Genome_Biol':'Genome Biology','Nat_Genet':'Nature Genetics','BMC_Bioinformatics':'BMC Bioinformatics'}\n",
        "df_temp['Journal'] = df_temp['Journal'].replace(labels)\n",
        "#add percentage column\n",
        "percentages=[]\n",
        "for index, row in df_temp.iterrows():\n",
        "   value=(row['Counts']/df_temp.loc[(df_temp['Journal'] == row['Journal'])]['Counts'].sum())*100\n",
        "   percentages.append(value)\n",
        "df_temp['Percentage']=percentages\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "df_temp=df_temp.pivot(index='Journal',columns='Code availability (Yes/No)',values='Percentage')\n",
        "df_temp=df_temp.sort_values('no', ascending=True)\n",
        "order=list(df_temp.index)\n",
        "\n",
        "color=['#E69F00','#56B4E9']\n",
        "df_temp.plot(kind='barh', stacked=True, color=color,ax=fig_2a,legend=None)\n",
        "fig_2a.set_xlabel(\"Percentage of manuscripts (%)\\n(n=453)\",fontsize=fontsize)\n",
        "fig_2a.set_ylabel(\"Journal\",fontsize=fontsize)\n",
        "fig_2a.tick_params(axis='both', labelsize=fontsize)\n",
        "\n",
        "# fig 2b\n",
        "fig_2b= subfigs[0,1].subplots(1, 1)\n",
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Journal','Data availability (yes/no)','Data Policy']).size()}).reset_index()\n",
        "#f_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "labels = {'Bioinformatics':'Bioinformatics', 'Nucleic_Acids_Res':'Nucleic Acids Research', 'Nat_Biotechnol':'Nature Biotechnology', 'Genome_Med':'Genome Medicine', 'Nat_Methods':'Nature Methods', 'Genome_Biol':'Genome Biology','Nat_Genet':'Nature Genetics','BMC_Bioinformatics':'BMC Bioinformatics'}\n",
        "df_temp['Journal'] = df_temp['Journal'].replace(labels)\n",
        "#add percentage column\n",
        "percentages=[]\n",
        "for index, row in df_temp.iterrows():\n",
        "   value=(row['Counts']/df_temp.loc[(df_temp['Journal'] == row['Journal'])]['Counts'].sum())*100\n",
        "   percentages.append(value)\n",
        "df_temp['Percentage']=percentages\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "df_temp=df_temp.pivot(index='Journal',columns='Data availability (yes/no)',values='Percentage')\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp['Journal']=pd.Categorical(df_temp['Journal'],categories=order)\n",
        "df_temp=df_temp.sort_values('Journal')\n",
        "df_temp=df_temp.set_index('Journal')\n",
        "color=['#EA526F','#009E73']\n",
        "df_temp.plot(kind='barh', stacked=True, color=color, ax=fig_2b,legend=None)\n",
        "fig_2b.set_xlabel(\"Percentage of manuscripts (%)\\n(n=204) \",fontsize=fontsize)\n",
        "fig_2b.set_ylabel(\"Journal\",fontsize=fontsize)\n",
        "fig_2b.tick_params(axis='both', labelsize=fontsize)\n",
        "\n",
        "#fig 2c\n",
        "fig_2c=subfigs[1,0].subplots(1, 1)\n",
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "\n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Secondary analysis','Code availability (Yes/No)','Code Policy']).size()}).reset_index()\n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "#add percentage column \n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "df_temp_pivot=df_temp.pivot(index='Code Policy',columns='Code availability (Yes/No)',values='Counts')\n",
        "df_temp_pivot=df_temp_pivot.rename(columns={\"no\":\"Code Unavailable\",\"yes\":\"Code Available\"})\n",
        "df_temp_pivot = df_temp_pivot.reindex(columns=['Code Available', 'Code Unavailable'])\n",
        "chi2, p, dof, expected = chi2_contingency(df_temp_pivot)\n",
        "df_temp_pivot=df_temp.pivot(index='Code Policy',columns='Code availability (Yes/No)',values='Percentage')\n",
        "df_temp_pivot=df_temp_pivot.rename(columns={\"no\":\"Code Unavailable\",\"yes\":\"Code Available\"})\n",
        "df_temp_pivot = df_temp_pivot.reindex(columns=['Code Available', 'Code Unavailable'])\n",
        "color=['#56B4E9','#E69F00']\n",
        "df_temp_pivot.plot(kind='bar', stacked=True, color=color, rot=0, ax=fig_2c,legend=None)\n",
        "# statistical annotation\n",
        "y, h, col = 75, 10, 'k'\n",
        "x1, x2 = 0, 1  # columns  (first column: 0, see plt.xticks())\n",
        "fig_2c.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=5, c=col)\n",
        "fig_2c.text((x1+x2)*.5, y+h, r\"$p = {0:s}$\".format(as_si(p,1)), ha='center', va='bottom', color=col, fontsize=fontsize)\n",
        "fig_2c.set_ylabel(\"Percentage of manuscripts (%)\\n(n=204)\",fontsize=fontsize)\n",
        "fig_2c.set_ylim(0,100)\n",
        "fig_2c.set_xlabel(\"Journal policy on code sharing\",fontsize=fontsize)\n",
        "fig_2c.tick_params(axis='both', labelsize=fontsize)\n",
        "\n",
        "#fig 2d\n",
        "fig_2d=subfigs[1,1].subplots(1, 1)\n",
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Secondary analysis','Data availability (yes/no)','Data Policy']).size()}).reset_index()\n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "#add percentage column \n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "df_temp_pivot=df_temp.pivot(index='Data Policy',columns='Data availability (yes/no)',values='Counts')\n",
        "df_temp_pivot=df_temp_pivot.rename(columns={\"no\":\"Data unavailable\",\"yes\":\"Data available\"})\n",
        "df_temp_pivot = df_temp_pivot.reindex(columns=['Data available', 'Data unavailable'])\n",
        "chi2, p, dof, expected = chi2_contingency(df_temp_pivot)\n",
        "df_temp_pivot=df_temp.pivot(index='Data Policy',columns='Data availability (yes/no)',values='Percentage')\n",
        "df_temp_pivot=df_temp_pivot.rename(columns={\"no\":\"Data unavailable\",\"yes\":\"Data available\"})\n",
        "df_temp_pivot = df_temp_pivot.reindex(columns=['Data available', 'Data unavailable'])\n",
        "color=['#009E73','#EA526F']\n",
        "df_temp_pivot.plot(kind='bar', stacked=True, color=color, rot=0,ax=fig_2d,legend=None)\n",
        "# statistical annotation\n",
        "y, h, col = 90, 10, 'k'\n",
        "x1, x2 = 0, 1  # columns  (first column: 0, see plt.xticks())\n",
        "fig_2d.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=5, c=col)\n",
        "fig_2d.text((x1+x2)*.5, y+h, r\"$p = {0:s}$\".format(as_si(p,1)), ha='center', va='bottom', color=col, fontsize=fontsize)\n",
        "fig_2d.set_ylabel(\"Percentage of manuscripts (%)\\n(n=204)\",fontsize=fontsize)\n",
        "fig_2d.set_ylim(0,100)\n",
        "fig_2d.set_xlabel(\"Journal policy on data sharing\",fontsize=fontsize)\n",
        "fig_2d.tick_params(axis='both', labelsize=fontsize)\n",
        "\n",
        "#fig 2e\n",
        "fig_2e=subfigs[2,0].subplots(1, 1)\n",
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "#replace upon request with no in the Data availability column of the data\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "#create dataframe for counts data of code availability \n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Code availability (Yes/No)','PMC_Year','Secondary analysis','Code Policy']).size()}).reset_index()\n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "\n",
        "df_temp_mandatory_yes=df_temp[((df_temp['Code Policy']=='Mandatory') & (df_temp['Code availability (Yes/No)']=='yes') )]\n",
        "\n",
        "\n",
        "df_temp_encouraged_yes=df_temp[((df_temp['Code Policy']=='Encouraged') & (df_temp['Code availability (Yes/No)']=='yes') )]\n",
        "\n",
        "df_temp_mandatory_yes['Cumulative Percent Increase'] = 100*(\n",
        "df_temp_mandatory_yes['Counts'].cumsum() / \n",
        "df_temp[df_temp['Code Policy']==\"Mandatory\"]['Counts'].sum())\n",
        "df_temp_encouraged_yes['Cumulative Percent Increase'] = 100*(df_temp_encouraged_yes['Counts'].cumsum() / df_temp[df_temp['Code Policy']==\"Encouraged\"]['Counts'].sum())\n",
        "\n",
        "df_temp=pd.concat([df_temp_mandatory_yes,df_temp_encouraged_yes])\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "colors=['#391463','#CA78A6']\n",
        "ax=sns.lineplot(df_temp, x=\"PMC_Year\", y='Cumulative Percent Increase', hue=\"Code Policy\",ax=fig_2e,palette=colors,linewidth=10)\n",
        "ax.set_ylabel(\"Percentage of code available (%) \\n(n=204)\",fontsize=fontsize)\n",
        "ax.set_ylim(0,100)\n",
        "ax.set_xlabel(\"Year\",fontsize=fontsize)\n",
        "ax.tick_params(axis='both', labelsize=fontsize)\n",
        "ax.get_legend().set_visible(False)\n",
        "\n",
        "#fig 2f\n",
        "fig_2f=subfigs[2,1].subplots(1, 1)\n",
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "#replace upon request with no in the Data availability column of the data\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "#create dataframe for counts data of code availability \n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Data availability (yes/no)','PMC_Year','Secondary analysis','Data Policy']).size()}).reset_index()\n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "\n",
        "df_temp_encouraged_yes=df_temp[((df_temp['Data Policy']=='Encouraged') & (df_temp['Data availability (yes/no)']=='yes') )]\n",
        "df_temp_mandatory_yes=df_temp[((df_temp['Data Policy']=='Mandatory') & (df_temp['Data availability (yes/no)']=='yes') )]\n",
        "\n",
        "\n",
        "df_temp_mandatory_yes['Cumulative Percent Increase'] = 100*(\n",
        "df_temp_mandatory_yes['Counts'].cumsum() / \n",
        "df_temp[df_temp['Data Policy']==\"Mandatory\"]['Counts'].sum())\n",
        "df_temp_encouraged_yes['Cumulative Percent Increase'] = 100*(df_temp_encouraged_yes['Counts'].cumsum() / df_temp[df_temp['Data Policy']==\"Encouraged\"]['Counts'].sum())\n",
        "\n",
        "df_temp=pd.concat([df_temp_mandatory_yes,df_temp_encouraged_yes])\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "colors=['#391463','#CA78A6']\n",
        "ax=sns.lineplot(df_temp, x=\"PMC_Year\", y='Cumulative Percent Increase', hue=\"Data Policy\",ax=fig_2f,palette=colors,linewidth=10)\n",
        "ax.set_ylabel(\"Percentage of data available (%) \\n(n=204)\",fontsize=fontsize)\n",
        "ax.set_ylim(0,100)\n",
        "ax.set_xlabel(\"Year\",fontsize=fontsize)\n",
        "ax.tick_params(axis='both', labelsize=fontsize)\n",
        "ax.get_legend().set_visible(False)\n",
        "\n",
        "\n",
        "# Add labels to each subplot\n",
        "letter_annotation(fig_2a, -.25, 1, 'a',fontsize)\n",
        "letter_annotation(fig_2b, -.25, 1, 'b',fontsize)\n",
        "letter_annotation(fig_2c, -.25, 1, 'c',fontsize)\n",
        "letter_annotation(fig_2d, -.25, 1, 'd',fontsize)\n",
        "letter_annotation(fig_2e, -.25, 1, 'e',fontsize)\n",
        "letter_annotation(fig_2f, -.25, 1, 'f',fontsize)\n",
        "\n",
        "\n",
        "labels = [mpatches.Patch(color='#E69F00', label=\"Code unavailable\"),\n",
        "mpatches.Patch(color='#56B4E9', label=\"Code available\"),\n",
        "mpatches.Patch(color='#EA526F', label=\"Data unavailable\"),\n",
        "mpatches.Patch(color='#009E73', label=\"Data available\"),\n",
        "mpatches.Patch(color='#391463', label=\"Mandatory\"),\n",
        "mpatches.Patch(color='#CA78A6', label=\"Encouraged\"),\n",
        "] \n",
        "fig.legend(handles=labels, \n",
        "           loc=\"lower center\",ncol=len(labels),fontsize=fontsize,bbox_to_anchor=(0.5, -.03),frameon=False) \n",
        "\n",
        "sns.despine()\n",
        "fig.savefig('../Figures/fig3.png', format='png', dpi='figure',bbox_inches='tight')\n",
        "fig.savefig('../Figures/fig3.eps', format='eps', dpi='figure',bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "c452bc3a",
      "metadata": {},
      "source": [
        "# Supp Figs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Supplementary Figure 1"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "977cf276",
      "metadata": {},
      "source": [
        "## Supplementary Figure 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "id": "de79c725",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Analysis</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Upon Request</td>\n",
              "      <td>21</td>\n",
              "      <td>Primary Analysis</td>\n",
              "      <td>10.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>No</td>\n",
              "      <td>41</td>\n",
              "      <td>Primary Analysis</td>\n",
              "      <td>20.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Yes</td>\n",
              "      <td>142</td>\n",
              "      <td>Primary Analysis</td>\n",
              "      <td>69.61</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data availability (yes/no)  Counts          Analysis  Percentage\n",
              "1               Upon Request      21  Primary Analysis       10.29\n",
              "0                         No      41  Primary Analysis       20.10\n",
              "2                        Yes     142  Primary Analysis       69.61"
            ]
          },
          "execution_count": 102,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp=pd.DataFrame({'Counts': df.groupby(['Secondary analysis','Data availability (yes/no)']).size()}).reset_index()\n",
        "df_temp.loc[df_temp['Secondary analysis'] == 'hybrid', 'Secondary analysis'] = 'Hybrid'\n",
        "df_temp['Analysis']='Unknown'\n",
        "df_temp.loc[df_temp['Secondary analysis'] == 'yes', 'Analysis'] = 'Secondary Analysis'\n",
        "df_temp.loc[df_temp['Secondary analysis'] == 'Hybrid', 'Analysis'] = 'Both'\n",
        "df_temp.loc[df_temp['Secondary analysis'] == 'no', 'Analysis'] = 'Primary Analysis'\n",
        "#df_temp.loc[df_temp['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'yes'\n",
        "df_temp = df_temp.drop('Secondary analysis', axis=1)\n",
        "\n",
        "df_temp.loc[df_temp['Data availability (yes/no)'] == 'no', 'Data availability (yes/no)'] = 'No'\n",
        "df_temp.loc[df_temp['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'Upon Request'\n",
        "df_temp.loc[df_temp['Data availability (yes/no)'] == 'yes', 'Data availability (yes/no)'] = 'Yes'\n",
        "\n",
        "\n",
        "#dealing with both value for Analysis column\n",
        "df_temp.loc[2,'Counts'] += df_temp.loc[0,'Counts']\n",
        "df_temp.loc[4,'Counts'] += df_temp.loc[1,'Counts']\n",
        "df_temp = df_temp.drop(0)\n",
        "df_temp = df_temp.drop(1)\n",
        "\n",
        "\n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "id": "aec9b5fe",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_21447/524487939.py:7: FutureWarning: \n",
            "\n",
            "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
            "\n",
            "  sns.barplot(data=df_temp, x='Data availability (yes/no)',y='Percentage', ci=None,palette=color)\n",
            "/tmp/ipykernel_21447/524487939.py:7: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(data=df_temp, x='Data availability (yes/no)',y='Percentage', ci=None,palette=color)\n"
          ]
        },
        {
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            "text/plain": [
              "<Figure size 3125x3125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(31.25, 31.25))\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "color=['#CA78A6','#EA526F','#009E73']\n",
        "sns.barplot(data=df_temp, x='Data availability (yes/no)',y='Percentage', ci=None,palette=color)\n",
        "plt.xlabel(\"Data Availability\",fontsize=fontsize)\n",
        "plt.ylabel(\"Percentage of primary analyses manuscripts (%)\",fontsize=fontsize)\n",
        "plt.tick_params(axis='both', labelsize=fontsize)\n",
        "sns.despine()\n",
        "plt.savefig('../Figures/sup2.png', format='png', dpi='figure',bbox_inches='tight')\n",
        "fig.savefig('../Figures/sup2.eps', format='eps', dpi='figure',bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "ff4ad9a1",
      "metadata": {},
      "source": [
        "## Supplementary Figure 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "id": "c8d7e307",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data availability statement (yes/no)</th>\n",
              "      <th>Code availability statement (Yes/No)</th>\n",
              "      <th>Secondary analysis</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>36</td>\n",
              "      <td>17.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>0.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>91</td>\n",
              "      <td>44.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>75</td>\n",
              "      <td>36.76</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data availability statement (yes/no) Code availability statement (Yes/No)  \\\n",
              "0                                   no                                   no   \n",
              "2                                   no                                  yes   \n",
              "5                                  yes                                   no   \n",
              "8                                  yes                                  yes   \n",
              "\n",
              "  Secondary analysis  Counts  Percentage  \n",
              "0                 no      36       17.65  \n",
              "2                 no       2        0.98  \n",
              "5                 no      91       44.61  \n",
              "8                 no      75       36.76  "
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#create dataframe for counts data of code availability \n",
        "df_temp=pd.DataFrame({'Counts': df.groupby(['Data availability statement (yes/no)','Code availability statement (Yes/No)','Secondary analysis']).size()}).reset_index()\n",
        "#filter df_temp to only have primary studies \n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "\n",
        "df_temp.loc[5,'Counts'] += df_temp.loc[4,'Counts']\n",
        "df_temp.loc[8,'Counts'] += df_temp.loc[7,'Counts']\n",
        "df_temp = df_temp.drop(4)\n",
        "df_temp = df_temp.drop(7)\n",
        "#create percentage column\n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "df_temp\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "id": "d9da6054",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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JwbjIe4fT3O3P4oxWAIDPZ6XbzNPLrTyy0ErzE57kCgDAwyMSAwA4YwbTSX6xdye/3Lub4Wxa9RwAgFppJvnTucfSba3netGpeg4AQO3MtRp5armdJ5ZaaTfFYgAAZ4VIDADgjNgbDfLT3Vt542A7My/RAABOVLts5KViKc8Me+k96PAn3W7uLS5lszuX0mkXAAAnqt1IHl9q5+nlVubazarnAABceCIxAICK3Trez093bufd/l7VUwAAamcxrbw8Xc7jg3ZaH3On5KzVyt7SUu70FjJteoAJAHCSGkkeWWjlmeV2VnpeawEAVEUkBgBQgaIs8tbBvfx093bujY6rngMAUDuXy26+NFnM5UErn/SMsLLRyOHSUu7MLWTQap/qPgCAi2i918zTy+1cWWhVPQUA4MIRiQEAPETj2TS/3LubX+zdTX86rnoOAEDtPF3M54XxQlZGn+/6yOOFhWwuLGW/3TmhZQAAvG+h3cjTy+08tthKq+nabwCAh0EkBgDwEBxOhvnZzp28drCZSfEx9xwBAPCZNJO8MFvKc6O5zE1O9t897vWytbiYe525lA0PMAEATlKnmTy51M5Ty+10W15rAQCcJpEYAMAp2hwc5qe7t/P24U686AIAOFntspGXZ8t5ZthNZ3a6/12zTjtbS8vZ7M6LxQAATlgzydXFVp5eaWep06x6DgBALYnEAABOwY3+Xv7n3s3cGRxWPQUAoHY6aeSV6UqeGnTSfsiHtM7a7dxbXsqd7oJYDADgFFyea+YLq52s9sRiAAAnSSQGAHCC3jnayf/cu5mtYb/qKQAAtdMrm3lltpwnB520Kr7Be9ZqZWdpOXfn5jNreIAJAHDSNnrNfGG1nfW5VtVTAABqQSQGAHACrh3ey4/u3cy90XHVUwAAame+bOaV6UqeGLTTPGPvZBWtVnaXlnK7t5BZUywGAHDS1h/EYhtiMQCAz0UkBgDwGZVlmbcexGG740HVcwAAamcxrbwyWc5jZzAO+01Fs5m9paXcnlvMVCwGAHDiVrv3Y7HL82IxAIDPQiQGAPApFWWRNw6286N7N3MwGVU9BwCgdpbLdr4yWcqjg3YaVY/5lMpmM/tLi7k1t5hJ0wNMAICTttxp5AurnTyy4LUWAMCnIRIDAPiEZkWR1w428+N7t3I0HVc9BwCgdtbKdl6ZLOfKoHXu4rDfVDYaOXhwstio5QEmAMBJW+o08uxKO48utNJonPdXjwAAp08kBgDwe0yLWV7d28xPd2+nLw4DADhxG2Unr4yXcnlYw5iq0cjB0mJuzy1lKBYDADhxC+37sdjVxVaaYjEAgI8lEgMA+BiTYpZf7t3NT3duZzCbVD0HAKB2rpTdfHm8lI1hs+opp65M0l9azK2FpQya7arnAADUznyrkWdW2nl8SSwGAPDbiMQAAH7DeDbNz3fv5Od7dzKcTaueAwBQO1fLubw8WsjaqP5x2G/TX1zI7YWl9FudqqcAANTOXKuRp1faeVIsBgDwa0RiAAAPDKeT/Gz3dn6xdzfjYlb1HACA2nm8nMvLo8WsjDysS5LjhYXcXlzKkVgMAODE9ZofxmKtptefAAAiMQDgwhvNpvnJzq38Yu9OJkVR9RwAgNp5qpjPl0YLWRp7OPfbDObnc2dxOQdtsRgAwEnrNJNnVtp5aqktFgMALjSRGABwYU2LWX62eyc/2bnl5DAAgFPwWDGXr4wWxWGf0GB+PjeXll1DCQBwCnrNRr6w2s7jrqEEAC4okRgAcOEUZZFf7t3Nj+7dymA2qXoOAEDtXCq7+epoKeujZtVTzqWjxYXcXFjJsNWqegoAQO3Mtxt5brWdqwutNMRiAMAFIhIDAC6MsizzxsF2/vvejRxORlXPAQConeWyna9PVnJlIA773BqNHCwt5cb8YiZNsRgAwElb6jTy3Gonjyx4rQUAXAwiMQDgQnj78F5+uP1e9sbDqqcAANTOfNnM1yYreWzQjrMYTlbZaGR3eTm35hYza4rvAABO2mq3mefX2tmYE4sBAPUmEgMAau1Gfy8/3H4vW8N+1VMAAGqnk0a+Nl3Nk8ftNL3DdKqKZjP3lldye24hpWuRAABO3EavmS+udbLSE+YDAPUkEgMAamlzcJj/3H4vt44Pqp4CAFA7rTJ5pVjJs8fdtIqq11wss3YrW8sr2ezOi8UAAE7BI/OtPL/WzmJHLAYA1ItIDAColZ3RcX64/V7eOdqtegoAQO00yjIvFst5YTCXzqzqNRfbtNvJnaXl3OvOVz0FAKB2GkmuLrby/Go7c22xGABQDyIxAKAWDsbD/Nf2e3nr8F68uAEAOHnPFQt5abCQuWnVS/ioca+XO8sr2W13q54CAFA7jSRPLLXy3Gon3ZZTXAGA800kBgCca8fTcf7n3s38an8zhZc1AAAn7oliLq8MF7M48VDsLBvOz+XW4koO252qpwAA1E6rkTy13M6zK+20m14XAwDnk0gMADiXRrNpfrxzM7/YvZtpWVQ9BwCgdh4pe/nKcCmrYw/BzpP+wnxuLq1k0GxXPQUAoHY6zeSZ5XaeWm6nJRYDAM4ZkRgAcK4UZZGf7d7Jj+7dzLiYVT0HAKB21sp2vjZeyaVhs+opfEZlkqOlpdycX8qo1ap6DgBA7fSajTy/1s5ji600GmIxAOB8EIkBAOfG24f38h9b7+ZgMqp6CgBA7Symla+Nl/PooB2PueqhbDSy/yAWmzZFfwAAJ22508gL651szAnzAYCzTyQGAJx528N+/m3zem4PDqueAgBQO5008o3Jah4ftNP0LlEtFc1mdpeXc3NuMaWTLgAATtyV+WZeWOtkoSPMBwDOLpEYAHBmHU/H+c+td/P6wXbVUwAAaulLxVJe6M+lXVS9hIdh2m7nzspK7nXnq54CAFA7jSRPLbfz3Go77aYwHwA4e0RiAMCZMy1m+enO7fx451ampSeWAAAn7bFiLl8bLWZh7OHVRTScm8uN5ZX0W52qpwAA1E6nmTy32skTS600neIKAJwhIjEA4Ex5Y38r/7n9XvrTcdVTAABqZ6ls55vjlVwaugbnoiuTHC4v5cbCUiaNVtVzAABqZ7HdyAvrnVye91oLADgbRGIAwJlw5/gg/771bjaHR1VPAQConVaZfH22mqeOO2l6J4iPKJrN3FtZye3eQkonXQAAnLhLc828sN7JUscHNQCAaonEAIBKHU6G+Y+td3PtcKfqKQAAtfTFYjEvDebTnVa9hLNs2unk1spqdju9qqcAANROI8kTS608t9pJtyXMBwCqIRIDACoxKWb50b2b+dnu7cy8HAEAOHFXym6+MVzO0thDKD65wfx83ltayaDVrnoKAEDttBvJs6vtPL3cTtMprgDAQyYSAwAeqrIs89r+Vn64/V4Gs0nVcwAAame+bOYbk5VcHYh8+IwajewtL+XG/FJmDdciAQCctPl2Iy+sdfLIQqvqKQDABSISAwAempv9/fzb1vXsjAZVTwEAqJ1mklemK3nuuJumd3s4AUWrlbsrq9nszVc9BQCgltZ7zbyw3slKV5gPAJw+kRgAcOoOxsP829Y7eedot+opAAC19HQxn68MFtObVr2EOhr3erm1vJr9dqfqKQAAtfTYYisvrHXSbbmCEgA4PSIxAODUzIoiP9q5mZ/s3MrMSw4AgBO3XnbyB6PlrI6cPMDpO1pcyI2FlYxarkUCADhp7Uby3FonTy210miIxQCAkycSAwBOxXtHe/mXzbdzMBlVPQUAoHZ6ZTNfn67k8eN2PD7iYSobjeyurORmbzFF0//7AABO2nKnkS9tdLPa80EQAOBkicQAgBN1NBnlXzev57qrJQEATlyjLPNSsZwXjufSLqpew0U2bbdzd2Ul2935qqcAANTS4w+uoOy4ghIAOCEiMQDgRBRlkZ/t3sl/b9/ItPTEEgDgpD1ezuVrg6XMT6peAh8azc3lveWV9FudqqcAANROp5m8sNbJ40vtqqcAADUgEgMAPrfbxwf5wd23szseVD0FAKB25stm/nCylisD181wNpVJDpaX897CUmYN/z8FADhpq91mXt7oZKnrtRYA8NmJxACAz2wwneTft97JGwfbVU8BAKill2ZLeel4Li0HtXIOzNqt3F5Zy73uXNVTAABqp5HkqeV2nlttp910BSUA8OmJxACAT60sy/xy725+uP1exsWs6jkAALWzXnbyh6OVLI88/OH8OV5YyLuLKxm1WlVPAQConV6zkRfW27m66ApKAODTEYkBAJ/K5uAwP7h7PdujftVTAABqp5nkG9PVPN3vRB7GeVY2m9laWcntucWqpwAA1NJGr5mXNjpZ7LiCEgD4ZERiAMAnMppN859b7+ZX+5vx4gEA4OQ9Xs7l68dLmZtWvQROzrjXy7srq+m3OlVPAQConUaSZ1ba+cJKOy1XUAIAv4dIDAD4vV7b38x/bL2b4cwTSwCAkzZXNvOHk9U8MnA1H/VUJtlfXs5788spPLwEADhx861GXlzv5MqCnykAgI8nEgMAPtbO6Dg/uPt27gwOq54CAFBLL84W89LxfNpF1Uvg9E3b7dxaXctup1f1FACAWro838xL653Mt11BCQD8byIxAOB/mRSz/Nf2e/nF3t0UXioAAJy4lbKTb41WsjJyqhIXz/HiQq4vrmTSdNIFAMBJazWSZ1faeWalnWbDzxsAwIdEYgDAr3nvaC//fPdajqbjqqcAANROM8lXpyt59ribpndkuMCKZjNbq6u501uoegoAQC0tdRp5eaOb1Z5TxQCA+0RiAECSZDid5N+23skbB9tVTwEAqKWr5Vz+YLCUuUnVS+DsGPV6eW9lNf1Wp+opAAC100jy1HI7z6+202o6VQwALjqRGACQNw+286+b1zOcTaueAgBQO72ymW9OV3L1uF31FDiTyiR7qyu50VtK4eElAMCJm2818vKlTjbmXPcNABeZSAwALrCjySg/uPt23u3vVT0FAKCWnp8t5suD+bRnVS+Bs2/a6eTWymp2O72qpwAA1NJji628uNZJpyXMB4CLSCQGABfUL/fu5D+23s2kKKqeAgBQO8tlO98arWR11Kx6Cpw7R4uLeXdpOZOGky4AAE5at5m8tNHNowteawHARSMSA4ALZm80yD/dvZY7g8OqpwAA1E6jLPPVYjVf6HfT9I4LfGZFs5m7q2vZ7M1XPQUAoJauzDfzpY1uek4VA4ALQyQGABdEWZb5yc6t/Pe9G5n59g8AcOIul9380WAl85Oql0B9DOfm8s7yWoYtJ10AAJy0diN5Yb2TJ5baVU8BAB4CkRgAXAA7o+P8w+23sj3qVz0FAKB2GmWZr8/W8my/E5/Bh5NXNpvZXFnNnbmFqqcAANTSpblmXt7oZK7drHoKAHCKRGIAUGNFWeRH927lRzs3U/iWDwBw4jbKTv5osJLFiTwMTttwfj7Xl1YzcqoYAMCJazWSF9Y6eXLZqWIAUFciMQCoqe1hP/94563cGx1XPQUAoJa+Ol3Jc8fdNL2zAg9N0Wxmc3Utd3vzVU8BAKil9d79U8UWOk4VA4C6EYkBQM0UZZH/3r6Rn+zednoYAMApWCk7+fZoJcsjp4dBVY4XFnJ9aSWTplPFAABOWquRPL/WydNOFQOAWhGJAUCNbA4O8493rmV3PKh6CgBALb08W8qL/Tmnh8EZULRaub26mu2uU8UAAE7DWq+ZLztVDABqQyQGADVQlEX+a/tGfrJzK76xAwCcvMW08p3hWladHgZnztHiQt5ZXM206eElAMBJazWS51c7eXrFqWIAcN6JxADgnNsdHefvbr+Ze6PjqqcAANTSi7PFfOl4Pq2i6iXAx5m1Wrm1tp6dTq/qKQAAtbTea+aVS53MtYX5AHBeicQA4Bz76c6t/HD7vcx8OwcAOHHzZTPfGa9lfeghCJwXh0uLeWdhJTOnigEAnLh2I/nSRidXF50qBgDnkUgMAM6ho8ko/3Dnrdw6Pqh6CgBALT1fLOaVvtPD4Dyattu5tbae3Xa36ikAALX06EIrX1rvpNNqVD0FAPgURGIAcM68ebCdH9x9O+NiVvUUAIDa6T04PeyS08Pg3NtfXs6788spmh5eAgCctF6rkVcudbIx16p6CgDwCYnEAOCcGM2m+cHdt/PW4b2qpwAA1NIzxXy+eryYjhYfamPa6eS91fUctDtVTwEAqKWnltp5Yb2dZkOYDwBnnUgMAM6Bm/39/MOdt9KfjqueAgBQO5008u3xWh4Z+AQ81NXuynLem19O6eElAMCJW2w38srlbla6TmQGgLNMJAYAZ9isKPIfW+/m53t3qp4CAFBLT5bz+frxYrrTqpcAp23S7ebd1bUctZwqBgBw0hpJnltt59mVdhrCfAA4k0RiAHBGbQ/7+fvbb2Z3PKh6CgBA7bTLRv5ouprHjttVTwEeojLJ7upqbswtOlUMAOAUrPWaeeVSJ/Ntp4oBwFkjEgOAM6Ysy/x451b++96NFL5NAwCcuEfLXv7weDk9p4fBhTXu9XJ9dS2DplAUAOCktRrJi+udPLHktRYAnCUiMQA4Qw4nw/z97bdyZ3BY9RQAgFr62nQlz/W7cX4QUDYaubu2nru9+aqnAADU0pX5Zl7e6Kbb8hMYAJwFIjEAOCN+tbeZf9u6nklRVD0FAKB25stm/mS0ntWRhxPArztaXMj1pdXMGq5EAgA4ad1m8uVL3Vyeb1U9BQAuPJEYAFRsOJ3kn+5ey/Wj3aqnAADU0lPFfL5xvJj2rOolwFk17XTy7up6DtudqqcAANTSE0utvLjWSavpgzsAUBWRGABU6N2j3fzjnWsZzCZVTwEAqJ1mkm9O1/JUv131FOA8aDSytbKSW/NLVS8BAKil+XYjX7nUzWrPCa4AUAWRGABUoCiL/OfWe/np7u2qpwAA1NJy2c6fDFezOPYpdeDTGczP5+2V1UwarkQCADhpjSRfXOvkmRUf5gGAh00kBgAP2eFkmL+99WY2h0dVTwEAqKXni8W80p9Pq6h6CXBezdqt3FjbyF67W/UUAIBaujzXzJcvddNt+WAPADwsIjEAeIiuH+7kH+68lXExq3oKAEDttMrk29O1XD32iXTgZOysrOTG/FLKhoeXAAAnrddq5KuXOlmbc4IrADwMIjEAeAiKssi/b76bn+/dqXoKAEAtbZSdfGewmrlJ1UuAuhnN9fL2ynpGTQ8vAQBOWiPJc6vtfGG1U/UUAKg9kRgAnLKD8TDfv/VGtkf9qqcAANTSy7OlvNifS9M7HMApKZrN3Fpbz73uXNVTAABq6dJcM6+4fhIATpVIDABO0bXDe/mnO9dcLwkAcAp6ZTPfGa/l0rBZ9RTggjhYXs71hWXXTwIAnIJes5FXLney4fpJADgVIjEAOAWzosi/bl7Pq/ubVU8BAKilR8te/qi/nK4WH3jIJt1urq+u57jVrnoKAEDtNJJ8YbWdL6y00xDmA8CJEokBwAnbGw3y/duvZ2c0qHoKAEAtfW26kuf63XhcAFSlbDRyZ209m735qqcAANTSeq+Zr1zupuf6SQA4MSIxADhBb+xv5Qebb2dSFFVPAQConYWylT8erWV15CEBcDYcLS7k+tJqZg3X3gIAnLRuM3nlUjeX5l0/CQAnQSQGACdgWszyL5vX89r+VtVTAABq6aliPt84Xkzb9ZLAGTPrtPPO6kYO252qpwAA1NKzK+08v+r6SQD4vERiAPA57Y6O8/1bb2R37HpJAICT1kzyh5O1PHncrnoKwMdrNLK1sppb84tVLwEAqKW1XjNfudTNXFsoBgCflUgMAD6H1/Y38y93r2daul4SAOCkLZXt/OlwNYtjDwGA82EwP59ry2uZNl0/CQBw0joPrp+87PpJAPhMRGIA8BlMill+cPftvHGwXfUUAIBaeqKYyzf7S2lr8YFzZtZp5+21jfRbrp8EADgNTy+388W1dpqunwSAT0UkBgCf0s7oON+/9Xr2xsOqpwAA1NJXpyt5vt+Nt/uB86psNHJ7fSNb3bmqpwAA1NJqt5mvXu5kru0EVwD4pERiAPApvHWwnX+8c831kgAAp6BdNvKn4/VcGnqTH6iH/eXlvLOwnNIpFwAAJ67TTL56uZuNOddPAsAnIRIDgE+gLMv8x9a7+enu7aqnAADU0lrZzh8PVjM/EVIA9TKa6+WtlfVMmh5eAgCctEaSF9Y6eXqlXfUUADjzRGIA8HsMp5P87e03cvP4oOopAAC19Ewxn6/3F9NyWCtQU7N2K++uXcpBu1P1FACAWrq60MrLG520mj54BAAfRyQGAL/D9rCfv7n5Wo6m46qnAADU0jenq3mmL5oALoBGI3fX1nKnt1D1EgCAWlruNPK1K93Mt5tVTwGAM0kkBgAf4/X9rfzz3WuZ+VYJAHDiemUzfzJay/rIm/fAxXK4tJi3F1dTNpxyAQBw0jrN5CuXurk076pvAPhNIjEA+A1FWeTfNt/JL/buVj0FAKCWLpXdfOd4Jb1p1UsAqjHu9XJtdT2jpoeXAAAnrZHk+dV2nl11ajUAfJRIDAA+YjCd5Hu3Xs+dwWHVUwAAaumLxWJeOZpP07sRwAVXtFq5sb6R3Xa36ikAALX0yHwrX77USbvpBFcASERiAPCBzcFh/ubW6zmeTqqeAgBQO42yzLdm63mi3656CsCZUSbZXl3LrfnFqqcAANTSYqeRr1/uZqHTrHoKAFROJAYASV7du5t/2byewrdFAIATN18286fD9ayMfXob4Lc5XlzItaXVzBoeXgIAnLR2I3nlUjdXFlz1DcDFJhID4EKbFUX+ZfPt/Gp/q+opAAC19EjZy7f7y+nMql4CcLZNut28vbaeQdOJiwAAp+ELK+08v9apegYAVEYkBsCF1Z+M8r1bb2RzeFT1FACAWnpptpSXj+bi/DCAT6ZoNnNzfT07nbmqpwAA1NLluWa+crmbdtNPqgBcPCIxAC6k28cH+f6tNzKYTaqeAgBQO80k35ms5eqx03AAPoudlZW8t7Bc9QwAgFpaaDfytSvdLHVc9Q3AxSISA+DC+fnu7fz71rspfAsEADhxi2nlzwZrWRz7VDbA5zGYn8+15bVMmx5eAgCctFYj+fKlbh5daFU9BQAeGpEYABfGtJjln+++nTcOtqueAgBQS4+Xc/nD/lLas6qXANTDrNPO22sb6bc6VU8BAKilZ5bb+eJaO42GDzoBUH8iMQAuhOPpON+9+Vq2hv2qpwAA1NLLs6W8dDQXb6sDnKyy0cjN9Y3c685VPQUAoJYuzzXzlcvdtJt+ogWg3kRiANTevWE/3735Wo6m46qnAADU0rena3mi3656BkCtba2u5db8YtUzAABqaanTyDeudDPXdtU3APUlEgOg1t452snf3X4zk6KoegoAQO20y0b+fLSe9ZE30QEehoPlpVxfWEnpOiQAgBPXbSZfv9LLas/PuADUk0gMgNr66c6t/MfWu/GNDgDg5C2mlb84Xs/8pOolABfLcH4uby6vZ9b08BIA4KQ1k3z5UidXF52WDUD9iMQAqJ2iLPKDu2/nV/tbVU8BAKilK2U33+mvpDOregnAxTTtdPLW2qUMW62qpwAA1NJzq+08t9qpegYAnCiRGAC1Mp5N871br+fm8UHVUwAAaunZYiFfP1pI07sJAJUqWq28u34p+20PLwEATsPVhVa+fKmTpqu+AagJkRgAtXEwHuavb/4qe+Nh1VMAAGrpq9OVfLHfrXoGAA+UjUZur29kqztX9RQAgFpa7Tbz9SvddFtCMQDOP5EYALVw+/gg37v1eoazadVTAABqp1GW+ePpeq4et6ueAsBvsbO6mvfml6qeAQBQS/OtRr7+SDdLnWbVUwDgcxGJAXDuvb6/lX+6ey2Fb2kAACeuVzbz56P1rIx8ahrgLDtaXMi1pbWUrkMCADhx7UbylcvdXJ5vVT0FAD4zkRgA59p/br2bH+/cqnoGAEAtLZft/PnxWuYc1gpwLox6vby1tp5Jw8NLAICT1kjy4nonTy07ZRuA80kkBsC5NC1m+fvbb+Xto52qpwAA1NLVci7fOlpKu6h6CQCfxqzTzrW1SzlueXgJAHAanlxq5aX1ThpOcAXgnBGJAXDuHE/H+esbr2V71K96CgBALb1QLOWVw7l4uxvgfCqazdzYuJTddrfqKQAAtXRprpmvXu6m3fSTMwDnh0gMgHNle9jPd2++lv50XPUUAIBa+uZ0Nc/0O1XPAOBzKpPcXV/P3d5C1VMAAGppsdPIN650M99uVj0FAD4RkRgA58b1w5383e03My3deQQAcNKaSf5stJ7Lw1bVUwA4QXvLy3lncaXqGQAAtdRtJl+70staTygGwNknEgPgXPjJzq3859a78U0LAODkzZfN/PlwPUtj12QA1NHxwkKuLa9m1vDwEgDgpDWTfPlSJ1cX21VPAYDfSSQGwJlWlmX+dfN6frF3t+opAAC1tFF28if91XRnVS8B4DSNe71cW1nPqOXESACA0/DF1XaeXe1UPQMAPpZIDIAza1YU+bvbb+bto52qpwAA1NKT5Xy+ebSYltu8AS6EWbuV6+uXctTy8BIA4DQ8tdTOSxteawFwNonEADiTxrNpvnvztdweHFY9BQCgll6aLeXlo7m4YBLgYikbjdzYuJSdTq/qKQAAtfToQiuvXOqk2fATNwBni0gMgDPneDrOX914NTujQdVTAABq6euzlTx31K16BgAVKZPc2djIZne+6ikAALW03mvm61e6aTeFYgCcHSIxAM6UvdEgf3Xj1RxNx1VPAQCopW9P1vLEcbvqGQCcAVura7k1v1j1DACAWlruNPKNR3rptYRiAJwNIjEAzozNwWH++uZrGc6mVU8BAKidZpI/HW/kyqBZ9RQAzpDdlZW8u7Bc9QwAgFqabzXyB490s9DxszgA1ROJAXAmvHu0m+/feiPTsqh6CgBA7bTLRv5ivJ61oTelAfjfDpcW8/biasqGUy4AAE5ap5l840ovqz0/kwNQLZEYAJV7bX8z/3z37RS+JQEAnLhe2cz/Ga5naezBPwAfb7CwkDeX1lI0fb8AADhprUby1cvdXJ5vVT0FgAtMJAZApX5072Z+uP1e1TMAAGppMa38n+P1zE2qXgLAeTCa6+WNlY3Mmk65AAA4aY0kL2908vhSu+opAFxQIjEAKlGWZf5183p+sXe36ikAALW0Xnbyp8er6U6rXgLAeTLpdvPm2kbGTadcAACchi+utvPsaqfqGQBcQCIxAB66WVHk7++8mWuHO1VPAQCopUfKXr5ztJx2UfUSAM6jWaedt9YuZdByygUAwGl4aqmdlzaEYgA8XCIxAB6q8Wya7958LbcHh1VPAQCopSfL+fzh4WKaftoH4HMoWq28vXEpRy0PLwEATsOjC628cqmTZqNR9RQALgiRGAAPzfF0nL+68Wp2RoOqpwAA1NIXi8V85XA+3l4G4CSUzWbe3biUvXa36ikAALW03mvm61e6aTf9JA/A6ROJAfBQ7I0G+asbr+ZoOq56CgBALb0yW86LR72qZwBQM2WjkZsbG7nXmat6CgBALS13GvnGI730WkIxAE6XSAyAU7c5OMxf33wtw9m06ikAALX0zelqnum7DgyA01Emubu+nru9haqnAADU0nyrkT94pJuFTrPqKQDUmEgMgFP13tFevnfr9UzLouopAAC19Cfj9VwdtKqeAcAFcG91NTfml6qeAQBQS51m8geP9LLSFYoBcDpEYgCcmuuHO/n+7TdS+FYDAHDimkn+fLSRS0NvHgPw8OwtL+WdxdWqZwAA1FK7kXzjkV7Wen7WB+DkicQAOBVvHmznH+68JRADADgF7bKRvxxtZGXUqHoKABfQ0eJCri2tpWz4PgQAcNJajeTrV7rZmHNqOAAnSyQGwIn71d5m/vnutfgGAwBw8ubLZv5isJ7FiQfzAFRnMD+ft1bWMms45QIA4KQ1k3ztSjeX54ViAJwckRgAJ+rnu7fzr5vvVD0DAKCWlst2/uJ4Lb1p1UsAIBn3enljdSPTplAMAOCkNZJ89XI3jywIxQA4GSIxAE7Mj+/dzH9uv1f1DACAWtooO/nT/mo6s6qXAMCHpp1O3ly7lFHLw0sAgJPWSPLKpU6uLrarngJADYjEADgRP9x6Nz/auVX1DACAWrpUdvOnRytpF1UvAYD/TSgGAHB6Gkm+tNHJE0tCMQA+H5EYAJ/bv21ez89271Q9AwCgli4/CMRaAjEAzrBpu503Ny5n1BSKAQCchhfXO3l6WSgGwGcnEgPgc/nnO9fy6v5m1TMAAGrpStnNnwjEADgnpu123lq/nKETxQAATsUXV9t5drVT9QwAzimRGACfSVmW+Yc7b+WNg+2qpwAA1NIjZS9/fLQsEAPgXJm123lr/VIGLadcAACchmdX2vnimlAMgE9PJAbAp1aURf7u9pu5drhT9RQAgFq6Ws7lO4dLafqJHYBzaNZu5a2Nyxk0hWIAAKfh6eV2XlwXigHw6YjEAPhUZkWR7916Pe/296qeAgBQS48Vc/n2kUAMgPNt1mrl2sblHDtRDADgVDyx1MqX1jtpNBpVTwHgnBCJAfCJTYtZvnvztdw8Pqh6CgBALT1ezuVbThADoCaKVivXNi6l33LKBQDAabi60Morl4RiAHwyIjEAPpHxbJq/vvla7gwOq54CAFBLTxRz+SMniAFQM0IxAIDT9chCK1+51ElTKAbA7yESA+D3Gs2m+asbr2Zr2K96CgBALT1VzOebR4sCMQBqqWg28/alyzkSigEAnIrLc8187UpXKAbA7yQSA+B3Gkwn+b83fpmd0aDqKQAAtfR0MZ8/EIgBUHNFs5nrG5dz2BaKAQCchvVeM9+40k2rKRQD4LcTiQHwsY6n4/x/7/0ye+Nh1VMAAGrpmWI+f3C4GG/fAnAR3A/FLuWw3a16CgBALa09CMXaQjEAfotm1QMAOJsG04lADADgFH2hWBSIAXChNIsiX9i5l9XppOopAAC1tDcq8uOtcWaFc2IA+N9EYgD8L0OBGADAqXquWMjXD+cFYgBcOI2iyDM721mdjqueAgBQS0IxAD6OSAyAXzOcTvL/3fhldseDqqcAANTSF4vFfO1wQSAGwIXVKIo8c287a0IxAIBTsTsq8pOtcYpSKAbAh0RiAHxgNJvmr278KjsjgRgAwGl4oVjKV5wgBgBplGWevreddaEYAMCp2BGKAfAbRGIAJEnGs2n+6sar2R71q54CAFBLLxVL+crhnEAMAB5olGWeured9cmo6ikAALV0b1jkp0IxAB4QiQHwIBD7VbaGAjEAgNPw0mwpXz6cq3oGAJw5jbLMUzv3siEUAwA4FdvDIj/bFooBIBIDuPAmxSz/78avsjk8qnoKAEAtvTxbypePBGIA8HE+DMWGVU8BAKilrUGRn29PhGIAF5xIDOACmxaz/PWNX+WuQAwA4FS8PFvKlwRiAPD7lWWe2tnJpbFQDADgNGwOZvnFvUlKoRjAhSUSA7igpsUsf33ztdweHFY9BQCgll6cLQrEAODTKMs8ubuTdVdPAgCcirvHQjGAi0wkBnABzYoi3735Wm4dH1Q9BQCglp4rFvLlo/mqZwDA+fPg6snV6aTqJQAAtXTneJZf7gjFAC4ikRjABTMrivzNrddyUyAGAHAqnirm87XDhTSqHgIA51SjLPP0znaWZkIxAIDTcLs/y6s7XmsBXDQiMYALpCiLfO/W63mvv1/1FACAWnq8nMs3jxYFYgDwOTWLIl/YuZeF2bTqKQAAtXSrP8uvdsZVzwDgIRKJAVwQRVnk+7feyLv9vaqnAADU0iNlL986XErTbQ0AcCKas1me293O3GxW9RQAgFq6cSQUA7hIRGIAF0BRFvm722/m+tFu1VMAAGppo+zkj4+WBWIAcMJa01me391OTygGAHAqbhzN8vquqycBLgKRGEDNlWWZv7/9Vq4d7lQ9BQCgllbLdv6sv5pWUfUSAKin9nSaL+7dS6cUigEAnIZ3D6d5QygGUHsiMYAaux+IvZm3Du9VPQUAoJYW08qfH6+l7Zk1AJyq9mSSF3Z30ipU2QAAp+Gdw2ne3BOKAdSZSAygxn5w9+28KRADADgV82Uzf9lfT3da9RIAuBg643Fe2N9JqxSKAQCchusH01zfF4oB1JVIDKCmfrj1bl7d36x6BgBALfXKZv5ysJGeQAwAHqreaJQvHuymUZZVTwEAqKU396e5eeQND4A6EokB1NDPdm7nRzu3qp4BAFBL7bKRvxyuZ94HawGgEnODYV443BOKAQCckl/tTLJ5PKt6BgAnTCQGUDOv72/l37beqXoGAEAttcrkL4cbWRw3qp4CABfa/PFxnjvaq3oGAEAtlUl+vj3OzlAoBlAnIjGAGnnnaCf/dPda1TMAAGqpmeT/jDeyIhADgDNhqX+cL/T3q54BAFBLRZKfbI1zMCqqngLACRGJAdTE7eODfP/WGylctQAAcOIaZZk/H21kbejHaAA4S1YOj/JM/6DqGQAAtTQrkx9tjdKfCMUA6sC72wA1cG/Yz3dvvpaZQAwA4FT8yWQjlwRiAHAmrR0e5qnBUdUzAABqaVIkP9ocZzgVigGcd97hBjjnDsbD/NWNX2VcuBceAOA0fHuylkcHrapnAAC/w8b+fh4f9queAQBQS8NZmR9tjjOeOawA4DwTiQGcY8fTcf7vjVczmE2qngIAUEvfnK7mieN21TMAgE/gyt5eHh0dVz0DAKCW+tMyP94aZ1oIxQDOK5EYwDk1mk3zf997NYeTUdVTAABq6WuzlTzT71Q9AwD4FK7u7ubKeFj1DACAWjoYF/nJ1jhFKRQDOI9EYgDn0LSY5f/d+FV2x4OqpwAA1NLLs6U8f9StegYA8Bk8tnMvGxOhGADAadgdFfnZ9jilUAzg3BGJAZwzRVnkb26+ns3hUdVTAABq6bliIV86mqt6BgDwGTWSPLmzk9XppOopAAC1tDUo8uqO11oA541IDOAcKcsyf3/7rdw43q96CgBALV0t5/LVo4WqZwAAn1OjLPP0znbmi2nVUwAAaulWf5Y3doViAOeJSAzgHPmXzet56/Be1TMAAGppvezk20dLabotAQBqoVkUeW7nXjrFrOopAAC19M7hNNcPhGIA54VIDOCc+K/t9/LLvbtVzwAAqKX5spk/7a+mVVS9BAA4Se3pNF/c302r9E0eAOA0vLk3za0jp7cCnAciMYBz4Oe7t/M/925WPQMAoJbaZSN/MVhP1yEjAFBL3dEozx/spVE6LhQA4DS8ujPJ5rE3VgDOOpEYwBn35sF2/nXznapnAADUUqMs8xfj9SxOGlVPAQBO0fxgkGePD6qeAQBQS2WSn2+PszMUigGcZSIxgDPsZn8//3DnrapnAADU1h9P17M29KMxAFwEK4dHeXJwVPUMAIBaKpL8dGuco7FrvgHOKu+EA5xRu6PjfO/W6ylchQAAcCq+MV3N1eN21TMAgIfo0v5+Hh0Nqp4BAFBL0zL58dY4o5lnWwBnkUgM4Aw6no7z/278KuPCsbwAAKfhxdlivtDvVD0DAKjAo7s7WZ+Mqp4BAFBLw1mZH2+OMiuEYgBnjUgM4IyZFrP89Y3XcjQdVz0FAKCWnizn8+Wj+apnAAAVaSR5ancni7NJ1VMAAGrpcFLmZ9vjlG7LAThTRGIAZ0hZlvnb229me9SvegoAQC1dLrv5w8PFNKoeAgBUqlEU+cLuvfRmTnEHADgN28Mir+2K8gHOEpEYwBnyr5vX887RbtUzAABqaals54/7K2n6ECsAkKQ1neX5/Z20iqLqKQAAtXTjaJZ3DqZVzwDgAZEYwBnxs53b+cXe3apnAADUUq9s5i8Ga+k4LAQA+IjOeJwvHuym4SokAIBT8ebeJJvH3pABOAtEYgBnwPXDnfz71jtVzwAAqKVmkj8frmfODQcAwG8xNxzmuaO9qmcAANRSmeQX98bZHzm9FaBqIjGAim0ODvN3t9+Mz6sCAJyOPxutZ2XcqHoGAHCGLfWP8/TxYdUzAABqaVYmP9ka5XgiFAOokkgMoEKHk2G+e/P1TEsvigEATsMfTddyediqegYAcA6sHxzk6vC46hkAALU0LpKfbI0zmTk2AaAqIjGAioxm0/y/G7/KYObeIwCA0/Dl2XKe6rerngEAnCOP7O1mYzKsegYAQC31p2V+uj1OUQrFAKogEgOoQFEW+d6t17M39qYjAMBpeLZYyEtHvapnAADnTCPJkzs7WZ6Oq54CAFBLu6Miv7znAAWAKojEACrwj3eu5dbxQdUzAABq6dGyl68fLVQ9AwA4pxplmWd3dzI/m1Y9BQCglu4cz/LWnlAM4GETiQE8ZP+1/V7eONiuegYAQC2tlJ18+2g5TbcWAACfQ3M2y3N7O+mUs6qnAADU0tsH09w6EuUDPEwiMYCH6PX9rfzPvZtVzwAAqKVe2cyfH6+mXVS9BACog/Zkkuf3dtMo1ecAAKfh1Z1JdoaifICHRSQG8JDc7O/nn+5eq3oGAEAtNcoyfzZaT88HUAGAE9QbjfKF/n7VMwAAaqlM8tOtcY7GPvEH8DCIxAAegt3Rcb536/UUPnkKAHAqvjVbz+qoUfUMAKCGlo/6eXzYr3oGAEAtTcvkx1vjjGaeoQGcNpEYwCkbTCf5fzd+lXHhuFwAgNPwUrGUJ/rtqmcAADV2eW8v65NR1TMAAGppOCvz481RZoVQDOA0icQATlFRFvnerddzNB1XPQUAoJaulnN5+XCu6hkAQM01kjy5u5P5mbutAQBOw+GkzC93JlXPAKg1kRjAKfqXu9dzZ3BY9QwAgFpaTCvf6i/FJZMAwMPQLIo8t7eTVlFUPQUAoJbuHs9yfV8oBnBaRGIAp+SXe3fy6v5m1TMAAGqpVSZ/dryWthu9AYCHqD2Z5PnDvapnAADU1lv702wPvOEDcBpEYgCn4PbxQf51852qZwAA1NafjtezOHGGGADw8M0PBnn62MnxAACnoUzy8+1x+hOntwKcNJEYwAk7mozyvVuvpyjLqqcAANTS16YruTxsVT0DALjA1g8OcmU8rHoGAEAtTcvkJ1vjTAvP2gBOkkgM4ARNi1m+e/O1DGfTqqcAANTSM8V8nu93q54BAJDHdneyPJ1UPQMAoJaOp2V+tj1O6VAGgBMjEgM4Qf9w563cGx1XPQMAoJY2yk6+cbRY9QwAgCRJoyzzzN69dIpZ1VMAAGrp3rDIm3sOZgA4KSIxgBPyo3s3c+1wp+oZAAC11Cub+eP+apo+PAoAnCGt6Sxf3N9NwwkXAACn4p3Dae70hWIAJ0EkBnAC3jnayQ+336t6BgBALTXKMn82Wk/PIR0AwBnUHY3yXH+/6hkAALX16s4kB+Oi6hkA555IDOBz2h0d5+9uv1n1DACA2vrWdD2ro0bVMwAAPtbSUT+PD/tVzwAAqKVZmfxka5zxzOmtAJ+HSAzgcxjNpvnuzdcyKXx6AQDgNLw0W8oTx+2qZwAA/F6X9/ayPhlVPQMAoJZGszI/3R6ncM03wGcmEgP4jMqyzN/eeiMH3vwDADgVV8u5vHw0V/UMAIBPpJHkyd2dzM+mVU8BAKilvVGRX+1Mqp4BcG6JxAA+o//Yejc3jverngEAUEuLaeVb/aW4ZBIAOE+aRZHn9nbScuo8AMCpuNWf5b1DUT7AZyESA/gM3tjfyk93b1c9AwCgllpl8ueDtbRnVS8BAPj02pNJnj/cq3oGAEBtvb47yc7QG0cAn5ZIDOBT2hwc5p/uXqt6BgBAbf3ZeCMLY2eIAQDn1/xgkKePD6ueAQBQS2WSn22PM5g6vRXg0xCJAXwKx9Nx/ubW65mVZdVTAABq6euzlVwa+lEVADj/1g8OcmU8rHoGAEAtTYrkJ1vjzArP7AA+Ke+8A3xCs6LI39x8PcfTSdVTAABq6dliIc8ddaueAQBwYh7b3cnydFz1DACAWjqalPnFPc/tAD4pkRjAJ/Qvm29nc3hU9QwAgFpaK9v5+tFC1TMAAE5UoyzzzN5OOuWs6ikAALW0OZjl7X2hGMAnIRID+ARe39/Kr/a3qp4BAFBLrTL548Famm4HAABqqDWd5bmD/apnAADU1rX9aXaGonyA30ckBvB77IyO84O7b1c9AwCgtr49Wcu8D3wCADU2NxjkyWMn1AMAnIYyyc+3xxnNfAIR4HcRiQH8DpNilu/dfD3Tsqh6CgBALb04W8zVQbvqGQAAp27jYD9r03HVMwAAamlcJD/bHqcohWIAH0ckBvA7/OOdt7I/GVY9AwCgljbKTl7uz1c9AwDgoWgkeWp3J93CVUgAAKdhb1Tkrb1p1TMAziyRGMDH+Pnu7Vw73Kl6BgBALbXLRr5zvJqmD3cCABdIczbLcwd7aTjhAgDgVLxzOM3WsSgf4LcRiQH8FpuDw/z71rtVzwAAqK0/Ga9lzgc7AYALqDcc5qnBUdUzAABq6xf3xhlMi6pnAJw5IjGA3zCcTvL9W2+4sxwA4JS8PFvK5WGr6hkAAJVZPzjIxmRU9QwAgFqalslPt8ae9QH8BpEYwG/4+ztv5mg6rnoGAEAtXSm7eeloruoZAACVe2J3J73CVUgAAKfhcFLm9d1J1TMAzhSRGMBH/M/2jbzX3696BgBALfXKZr7dX0mj6iEAAGdAsyjy3P5uGk64AAA4FTeOZrnTn1Y9A+DMEIkBPHDreD//fe9G1TMAAGrrT8Zr6TosAwDgA93RKM8cH1Y9AwCgtl7dmeRoUlQ9A+BMEIkBJDmejvO3t96Mz20CAJyOr05Xsj70IygAwG9aPTzM5fGg6hkAALU0K5OfbY0zKzwFBPAOPXDhFWWR7996I4OZe8kBAE7D1XIuz/e7Vc8AADizHt/by/zMVUgAAKehPy3z6o7ngAAiMeDC++H2e7kzcKw/AMBpmC+b+aP+UhpVDwEAOMMaRZEv7O+m6YQLAIBTced4lhuHonzgYhOJARfaO0c7+cnO7apnAADU1p+M1tOZVb0CAODs64zHefb4oOoZAAC19fruJAfjouoZAJURiQEX1uFkmL+//VbVMwAAausPpitZHTlDDADgk1o+Osqjo0HVMwAAaqlI8rOtcSYzp7cCF5NIDLiQZkWR7918I+PCsRYAAKfhyXI+z/a7Vc8AADh3Ht3bzeJsUvUMAIBaGszK/GJnXPUMgEqIxIAL6V83r2d71K96BgBALS2mlT/oL1Y9AwDgXGqUZZ7d302rdBUSAMBp2B4Uub4vygcuHpEYcOG8ebCdV/c3q54BAFBLjbLMnwzX0nZgKwDAZ9YeT/Ls0X7VMwAAauut/Wl2h97AAi4WkRhwoRyMh/nnu9eqngEAUFt/OFvP8qhR9QwAgHNvqX+cq8PjqmcAANRSmeTn9yaZzMqqpwA8NCIx4MIoyiJ/d/vNTApH9QMAnIZnivk81W9XPQMAoDYe3d/L8tRVSAAAp2E0K/PqrtdawMUhEgMujP/evpHN4VHVMwAAammpbOfr/cWqZwAA1EtZ5um9nbR86BEA4FRsHs9y82ha9QyAh0IkBlwIt48P8uOdW1XPAACorT8erabl2SUAwIlrT6d5tr9f9QwAgNp6fXeS44k3toD6E4kBtTeeTfP3t9+MG8UBAE7H16YrWR41qp4BAFBbS/3jXBkPq54BAFBLszL5+b1JitLTRKDeRGJA7f3T3Ws5mo6rngEAUEtXym6e63erngEAUHtX93bTm82qngEAUEsH4yLX9l07CdSbSAyotdf3t3LtcKfqGQAAtdQqkz86XokzxAAATl+zKPLs4V7VMwAAauudg2l2hqJ8oL5EYkBtHYyH+ZfNt6ueAQBQW9+armXOBywBAB6aueEwjw/6Vc8AAKilMskv7k0ymbl2EqgnkRhQS0VZ5G9vv5FJUVQ9BQCglp4u5vPYcbvqGQAAF86Vg/0szSZVzwAAqKXRrMyrO15rAfUkEgNq6b+2b2Rr6FOVAACnYb5s5uvHi1XPAAC4mMoyz+ztplk44QIA4DRsDma5eeT4fKB+RGJA7dw63s9Pdm5VPQMAoLa+M1pLe1b1CgCAi6s9meTpwWHVMwAAauv13Un6EzcWAfUiEgNqZTSb5u9vvxWfowQAOB1fmi1lfeRHSQCAqq0eHmZ9Mqp6BgBALc3K5Ofb4xSlp45AfXhnH6iVf757Lf3puOoZAAC1tFa281J/ruoZAAA88MTebjqlI14BAE7D4aTMW3uunQTqQyQG1MZr+5u5drhT9QwAgFpqJvn2YDVNH54EADgzWrNZnj08qHoGAEBtvXM4zc5QlA/Ug0gMqIX98SD/cvd61TMAAGrrD6ZrWZw0qp4BAMBvWDg+zqOj46pnAADU1i+2JxnPfHISOP9EYsC5V5RF/u72m5mWRdVTAABq6Wo5l6f77apnAADwMR7d28v8zFVIAACnYVSU/z97fxqla3rehb3XO9S457173D2oJVltTZYtWZJtLGEsG0cyjrGYbU4CDofhQMKUxRRjAofYwUngMCzAwGHFkBOCQwbjADLBwpNkyULzLPXcu3t377nGd3yG80Hylnarhz3UXXc99/v7fdJbXbvqr7VqVb3Pc/+f64rPXZ7ljgFwy5TEgM778MWn4sJkN3cMAIAiLUUvvnn3cO4YAAC8iF7bxgNbG9FrTbgAAEjhwriJp7aV8oFuUxIDOu3saDM+cfls7hgAAMV66/R4LNe5UwAA8FKWp9O4d7yTOwYAQLEe2pjH7txmI6C7lMSAzprWVfzCM4+E5yMBANJ4RbMed0wGuWMAAHCdTmxtxdFqnjsGAECR6jbi0xdn0ZjeCnSUkhjQWb96/vHYrez/BgBI4VAM4vW767ljAABwA3oRcf/m5Ri0JlwAAKSwPW/jsU1rJ4FuUhIDOumJncvx0NbF3DEAAIr1rZPjMXC2CADQOYN5FS/b3codAwCgWI9vVbE1c+MM6B4lMaBzpnUVv3LusdwxAACK9Q3V0Tg67eWOAQDATTqysxun5pPcMQAAitRGxGcvWTsJdI+SGNA57z/3WIyqee4YAABFOtUuxyt3l3PHAADgFp2+ciWWmzp3DACAIu3M23jU2kmgY5TEgE55fPtyPLx9KXcMAIAi9SPiLeMjYYYYAED39ZsmXr69kTsGAECxntiqYmtq7STQHUpiQGdMqrk1kwAACb15fjzW5ipiAAClWB1P4vRkN3cMAIAitRHxmcvWTgLdoSQGdMb7zz8e49qaSQCAFO5uVuOe0TB3DAAA9thtm5ux1liFBACQwq61k0CHKIkBnfDY9qV4xJpJAIAkBm3EG8eHc8cAACCBXtvGy7Y2cscAACiWtZNAVyiJAQfepJrH+849njsGAECx3lQdixUPPAIAFGtlMo27rZ0EAEiijYjPXLJ2Ejj4lMSAA+995x+zZhIAIJE725W4d7SUOwYAAIndvrkZq3WdOwYAQJF2qzYe2fAUJnCwKYkBB9qj25fi0e3LuWMAABSpHxFvGh3JHQMAgH3Qa9t42fZG7hgAAMV6cruKTWsngQNMSQw4sMbVPN537rHcMQAAivXG+bFY9YAjAMDCWJ1M4q7pKHcMAIAitRHxWWsngQNMSQw4sN537rGY1E4tAQBSuKNdifusmQQAWDh3bG7GSmPtJABACrtVGw9bOwkcUEpiwIH0yNbFeGzHmkkAgBT6EfGm8eHo5Q4CAMC+6zVNPLC9mTsGAECxzmxXsWHtJHAAKYkBB864msf7zz+eOwYAQLG+qToWa3MVMQCARbU6Hscd03HuGAAARfr1tZN1Y+0kcLAoiQEHzq+ce9SaSQCARG5rl+P+XWsmAQAW3Z2bG7FSWzsJAJDCqGrjkU3nncDBoiQGHCgPb12Mx3eu5I4BAFCkfkS8eXzUmkkAAKLfNPGyHWsnAQBSObNdxcZEKR84OJTEgANjVM3i/eceyx0DAKBYb6iOxdo8dwoAAA6KtfE4bp9NcscAAChSGxGfvTy3dhI4MJTEgAPjfecei2mjTQ8AkMLxdhgPWDMJAMBz3LW5EUute3IAAClYOwkcJEpiwIHw6PYlayYBABJ68/SYNZMAAHyNfl3H/TtbuWMAABTrzHYVm9MmdwwAJTEgv1ldxa+efzx3DACAYr2mPhxHpipiAAA8v8O7ozg5n+aOAQBQpDYiPn95Fk1r7SSQl5IYkN2HLp6JUTXPHQMAoEiHYhAP7q7mjgEAwAF3enMjBq0JFwAAKWzP2zizbcU3kJeSGJDV+fF2fG7jXO4YAADFesvkWPQ9pAgAwEsYVFXcN9rJHQMAoFiPbs5jUinlA/koiQHZNG0Tv3zu0XBmCQCQxiubQ3Fi6rIPAIDrc2x7O46a+A8AkETdRnz+svdaQD5OC4BsPnXl2bg8HeeOAQBQpJW2H68dreWOAQBAx9y3eSV6rcc6AQBSuDhp4tzI2kkgDyUxIIvt+SQ+cvGp3DEAAIr15vmxGLrfBADADRrO53HvZDd3DACAYn3x8jyqRikf2H9KYkAW7zv3WFStndsAACnc267FHeNB7hgAAHTUya2tOFRbhQQAkMK0aePhDe+1gP2nJAbsu0e3L8WZ3c3cMQAAijRse/GNu4dyxwAAoMvaNu7fcv8OACCVp3fq2JwaqAHsLyUxYF/N6ip+9fzjuWMAABTrTfWxWLZmEgCAW7Q8ncZpaycBAJJoI+Lzl2fRtNZOAvtHSQzYVx+6eCZGlfGpAAApnGyX4vTuMHcMAAAKcWprK5YaTyAAAKSwPW/jye0qdwxggSiJAfvm/Hg7PrdxLncMAIBivWl6NHq5QwAAUIx+08T9u9u5YwAAFOuxzSomlbWTwP5QEgP2RdM28cvnHg0DUwEA0nhVcziOTFXEAADYW4d3d+OYzQAAAEnUbcTnL3uvBewPJTFgX3zqyrNxeTrOHQMAoEhL0YtXj1ZzxwAAoFD3bG1Er/X4JwBAChcnTZwbWfENpKckBiS3PZ/ERy4+lTsGAECx3jg/FkP3kQAASGRpNou7p6PcMQAAivXFy/OoGqV8IC0lMSC59517LKrWLm0AgBROtctxejTMHQMAgMKd2tqKpcaTCQAAKUybNh7esHYSSEtJDEjqka2LcWZ3M3cMAIBivXF6JHq5QwAAULx+08T9u9u5YwAAFOvpnTo2pwZvAOkoiQHJzOoqPnDhidwxAACK9ar6UByZqogBALA/Du/uxrFqljsGAECR2oj43OVZNK21k0AaSmJAMh+6eCZGlbGoAAApLEUvXj1eyx0DAIAFc8/WZvQcXAIAJLEzb+PJ7Sp3DKBQSmJAEufH2/HZjXO5YwAAFOuN82MxrHOnAABg0SzNZnH3dJQ7BgBAsR7brGJSWTsJ7D0lMSCJ959/PHcEAIBinWqX4/RomDsGAAAL6ratrVhuPLEAAJBC3UZ88YppYsDeUxID9twXNs/Hhclu7hgAAMV60+RI9HKHAABgYfWaJu7b3codAwCgWOfHdVyeKOUDe0tJDNhTs7qKD104kzsGAECxHqwPxeGZihgAAHkd3h3FsWqWOwYAQLG+eGUebdvmjgEUREkM2FMfvfR0jOt57hgAAEVail58/XgtdwwAAIiIiHu2NqPn4BIAIImdeRtP7ZgmBuwdJTFgz2xMx/HpjWdzxwAAKNYb58di6L4QAAAHxNJsFndPdnPHAAAo1qOb85jVSvnA3lASA/bMBy48Ho0nBwEAkritXY7To2HuGAAAcI3btrdjufEkAwBACvPmS0UxgL2gJAbsiSd3rsSZ3c3cMQAAivXGyZHo5Q4BAADP0WuauG93K3cMAIBiPb1Tx86syR0DKICSGHDLmraJXz3/eO4YAADFerA+FIdnKmIAABxMh3dHcbya5Y4BAFCkNiK+cMU0MeDWKYkBt+yTl5+Jrfk0dwwAgCKttP34+vFa7hgAAPCi7tnciF7b5o4BAFCkK9Mmzo2s+AZujZIYcEtG1Sw+dvnp3DEAAIr1TdXRGLr/AwDAATecz+PuySh3DACAYj10ZR51o5QP3DwlMeCWfOjCkzFv7MAGAEjhtnY57h4Nc8cAAIDrctv2Viw3nnAAAEhhUrfxxHaVOwbQYUpiwE07P96OL25dzB0DAKBYb5wciV7uEAAAcJ16TRP37WzljgEAUKwntqqYVAZ4ADdHSQy4ae8//3juCAAAxXqwPhSHZypiAAB0y+HRKI5Xs9wxAACKVLcRX9wwTQy4OUpiwE35wub5uDDZzR0DAKBIw7YXD47XcscAAICbcnprM3cEAIBinR/VcXlixTdw45TEgBs2q6v40IUzuWMAABTr9fWRWHKfBwCAjlqazeKO6Th3DACAYn3xyjzats0dA+gYJTHghn300tMxrue5YwAAFGmt7cfLRsu5YwAAwC25Y3sr+o2DSwCAFHbmbTy14ylT4MYoiQE3ZGM6jk9vPJs7BgBAsb5pfjT6ztIAAOi4QVXF3dNR7hgAAMV6dHMe89qNROD6KYkBN+QDFx6PxuhSAIAkTrZLced4mDsGAADsiVPbW7HUmnABAJDCvIl4ZNP2J+D6KYkB1+3JnStxZnczdwwAgGK9YXokerlDAADAHuk1Tdw72s0dAwCgWE/v1LEza3LHADpCSQy4Lk3bxAfOP5E7BgBAsU63q3Fi6hINAICyHNnejrWmyh0DAKBIbUR84YppYsD1cQIBXJfPbpyLzfkkdwwAgGK9fnwodwQAANhzvYi4d2c7dwwAgGJdmTZxYWTFN/DSlMSAlzSrq/jopadzxwAAKNarmsNxaG7RJAAAZVofjeJYZcIFAEAqD2/Mo23b3DGAA05JDHhJH798Nia1kfAAACn0I+LB8WruGAAAkNTp7c3cEQAAirVbtfH0jmliwItTEgNe1M58Gp+68kzuGAAAxXp9dTSW9fEBACjc8nQat83GuWMAABTr0c15VI1pYsALUxIDXtSHL56J2mhSAIAkVtt+PDBezh0DAAD2xV3bW9FzrxEAIIlZE/HktqdRgRemJAa8oEuT3Xho62LuGAAAxXpDdTQGTe4UAACwPwbzKu6e7OaOAQBQrCe2qpjWSvnA81MSA17Qr114MryFAABI43g7jNOjYe4YAACwr05tb8ew8aQEAEAKdfultZMAz0dJDHheT+1uxFOjzdwxAACK9YbZkejlDgEAAPus3zRxz3gndwwAgGKd3aljd66UD3wtJTHga7RtGx+88ETuGAAAxbqrXY1Tk0HuGAAAkMXxnZ1YrevcMQAAitRGxEMbpokBX0tJDPgaD21djMvTce4YAADF+obJodwRAAAgn7aNe0dbuVMAABTr4riJKxOlfOBaSmLANeqmiQ9fPJM7BgBAsV7RrMfhmUWTAAAstkO7ozhSzXLHAAAo1sMbVe4IwAGjJAZc4zMbz8aOmzMAAEn0I+LV4/XcMQAA4EC4Z8c0MQCAVDZnTZwfmSYGfIWSGHDVrK7iY5eezh0DAKBYr62PxIoH+AAAICIiVibTODWb5I4BAFCsRzbm0bZt7hjAAaEkBlz1ictnY9pokwMApLDS9uMVo5XcMQAA4EC5a2creg4uAQCS2K3aOLvr/Bf4EiUxICIiRtUsPnXl2dwxAACK9YbqaAya3CkAAOBgGc7mcddslDsGAECxHt2som6U8gElMeDLPnrp6ahap5YAACkcaYdxz2iYOwYAABxIt21tx6BxbxIAIIVp3caZnSp3DOAAUBIDYnM2js9vns8dAwCgWN84Pxq93CEAAOCA6td1nJ7s5o4BAFCsJ7aqmNemicGiUxID4j9cPBNN600BAEAKJ9qluH3s0gsAAF7MiZ2dGJomBgCQxLyJeGLbNDFYdE4qYMFdnOzGo9uXc8cAACjW62eHc0cAAIADr9c0pokBACR0ZruKSWVwCCwyJTFYcL924YncEQAAinWyXYrbJoPcMQAAoBOOb2/HUlvnjgEAUKS6jXh0c547BpCRkhgssLOjzXh6tJU7BgBAsV4/O5I7AgAAdEavbeP0yDQxAIBUntmtYzS34hsWlZIYLLCPXHwqdwQAgGKdapfj1MQlFwAA3IhjOzux1JgmBgCQQhsRj21VuWMAmTixgAV1drQZz4y3c8cAACjW62eHc0cAAIDO6bVtnJ6YJgYAkMqzponBwlISgwX1YVPEAACSua1djpOmiAEAwE05tr0Ty6aJAQAk0UbEo5umicEicmoBC+jp3c141hQxAIBkXj81RQwAAG5Wr23j9Ng0MQCAVM6NTBODRaQkBgvoI5dMEQMASOX2djlOTF1qAQDArTi6sxMrtWliAAApmCYGi8nJBSwYU8QAANJ6nSliAABwy740TWwndwwAgGKZJgaLR0kMFsyHL57JHQEAoFh3tCumiAEAwB45urtrmhgAQCKmicHicXoBC+Sp3Y04N/H0HQBAKq+fHsodAQAAytG2cdr9TACAZM6N6tg1TQwWhpIYLJCPXHwqdwQAgGLd1a7GMVPEAABgTx3d2Y2VxjQxAIAU2oh4zDQxWBhOMGBBmCIGAJDWayemiAEAwJ5r27hn5L4mAEAqponB4lASgwXxYVPEAACSubtZjWOzXu4YAABQpMM7O7FamyYGAJCCaWKwOJTEYAGc2dmI86aIAQAk89rpeu4IAABQrF5EnB5t544BAFAs08RgMSiJwQL4yCVTxAAAUjndrsbRmUsrAABI6fDubqzVJlwAAKTQRsSjpolB8ZxkQOFMEQMASOu100O5IwAAQPG+NE3MfU4AgFTOj+rYMU0MiqYkBoUzRQwAIJ1727U4Mu3ljgEAAAvh0O5urDUmXAAApGCaGJRPSQwK9uTOFVPEAAASes1kPXcEAABYGL2IOL27nTsGAECxLpgmBkVTEoOCmSIGAJDOfc1aHJ6ZIgYAAPvp8O4o1msTLgAAUjBNDMqmJAaFenLnSlyY7OaOAQBQrNdMTREDAIAc7hmZJgYAkMr5UR07M9PEoERKYlAoU8QAANK5v1mLQ6aIAQBAFuu7ozhUz3PHAAAo1qNbpolBiZTEoECmiAEApPVqU8QAACCr06Od3BEAAIplmhiUSUkMCvTRS0/njgAAUKzT7aopYgAAkNn67ijWahMuAABSecw0MSiOkhgU5pnRVpyfeIoOACAVU8QAAOBguMs2BQCAZM6P6hhXpolBSZTEoDCfuHw2dwQAgGKdapfj2NRlFAAAHARHdnZjqalzxwAAKFIbEU+YJgZFcboBBbk8HcWTuxu5YwAAFOs188O5IwAAAF/Wa9u4azLKHQMAoFjP7NYxq9vcMYA9oiQGBTFFDAAgncPtMG4bu4QCAICD5PjOTgxaa5AAAFKo24gz26aJQSmccEAhdubTeGT7Uu4YAADFem11OHq5QwAAANfoN03cOR3njgEAUKyndqqoG9PEoARKYlCIT15+JprWH2cAgBRW2n7cPR7mjgEAADyPkzs70XNvFAAgiXkT8fRunTsGsAeUxKAA07qKz2+ezx0DAKBYr6mPRN+ZEwAAHEiDqorb55PcMQAAivXkVmVgCRRASQwK8Jkrz0bVNrljAAAUadBG3DdZyh0DAAB4Ebft7OSOAABQrEndxrmRaWLQdUpi0HFVU8enN57NHQMAoFgPNodj6P4HAAAcaEuzWZyYT3PHAAAo1hNbVe4IwC1SEoOO+8LmhZjU/iADAKTQa9t4+WQ1dwwAAOA63LFrmhgAQCo78zYujj1NC12mJAYd1rZtfPLKM7ljAAAU6+XtoVjRxwcAgE5YnUziSDXLHQMAoFimiUG3KYlBhz2yfSm2jVAHAEjm6yZruSMAAAA34M6RaWIAAKlcmTaxOW1yxwBukpIYdNgnLp/NHQEAoFj3NKtxaN7LHQMAALgBh0bjWKtNuAAASOXxrXnuCMBNUhKDjnpqdyMuTUe5YwAAFOvrZ+u5IwAAADfhrslu7ggAAMW6MG5id26aGHSRkhh01MdNEQMASOa2djmOTV0uAQBAFx3Z2Y2lps4dAwCgWE9smdwKXeTUAzrowmQnzo62cscAACjWq+eHc0cAAABuUq9t425bGAAAknlmt45p3eaOAdwgJTHooE+YIgYAkMzhdhi3jV0qAQBAlx3b3olBaw0SAEAKbUQ8aZoYdI6TD+iYzdk4Htu+nDsGAECxXlsdjl7uEAAAwC3pN03cOR3njgEAUKynd6qoGtPEoEuUxKBjPnn5mfCnFgAgjZW2H3ePh7ljAAAAe+Dkzk70WndTAQBSqNqIp7ZNE4MuURKDDhlX8/ji1oXcMQAAivWa+nD0nSEBAEARBlUVt88muWMAABTrzHYdjVI+dIaSGHTIZzaejdofWQCAJAZtxH3j5dwxAACAPXTb7k7uCAAAxZo2bTy7W+eOAVwnJTHoiKZt4nMb53PHAAAo1tc3R2LY5E4BAADspaXZLE7Op7ljAAAU64yVk9AZSmLQEY9sXYpxPc8dAwCgSL22jQcmK7ljAAAACdxhmhgAQDLb8zY2JqaJQRcoiUFHfGbjXO4IAADFell7KFY88AYAAEVamUzikAdwAQCSObOjJAZdoCQGHXB+vB3nJ552AwBI5RWztdwRAACAhO4cj3JHAAAo1vlRHdO6zR0DeAlKYtABn77ybO4IAADFOt4O49i0lzsGAACQ0OHd3Rg2Te4YAABFaiPiqW2rGuCgUxKDA25czePRncu5YwAAFOvB6nDuCAAAQGK9to07ZuPcMQAAivX0ThVNa5oYHGRKYnDAfW7jnD+mAACJDNte3DUZ5o4BAADsgxO7u7kjAAAUa9ZEnBvVuWMAL0JJDA6wpm3is5vncscAACjWq5pDMbBxBgAAFsJwPo8T82nuGAAAxTqzrSQGB5mSGBxgj21fjlE1zx0DAKBYL5uu5o4AAADso9tHpokBAKSyNWtic+qpXDiolMTgAPv0lWdzRwAAKNbdzWqs6eMDAMBCWRuPY7U24QIAIJUz21XuCMALUBKDA+riZDfOTXZyxwAAKNar5uu5IwAAABncOTVNDAAglXOjOqZ1mzsG8DyUxOCA+vSVZ3JHAAAo1no7iJMTl0MAALCIju6Ootc6uAQASKGNiKd3TBODg8ipCBxAk2oej2xfyh0DAKBYX18dil7uEAAAQBb9uo47ZpPcMQAAivX0dh2NUj4cOEpicAB9bvN81P5oAgAk0WvbuGe6nDsGAACQ0cmRlZMAAKlMmzbOj+rcMYDnUBKDA6Zpm/jsxrncMQAAivWK9nAsuT8BAAALbXk6jSPVLHcMAIBindl2ExYOGiUxOGAe37kSu25OAAAk8/LZau4IAADAAXDHZJQ7AgBAsTZnTWxNm9wxgK+iJAYHzKevPJs7AgBAsU62S3Fk2ssdAwAAOAAO7Y5iqTXhAgAglTM7Ve4IwFdREoMD5NJkN54db+eOAQBQrK+fH84dAQAAOCB6bRt3TCa5YwAAFOvcbh2zus0dA/gyJTE4QD6zYYoYAEAqS9GLOyaD3DEAAIAD5PjuTu4IAADFaiLiadPE4MBQEoMDYlLN4+GtS7ljAAAU68H6cPQ9tAYAAHyVYVXFyblpYgAAqTy1U0fTujELB4GSGBwQX9i6EFXb5I4BAFCs+ycruSMAAAAH0O2jUe4IAADFmtZtXBg7B4eDQEkMDojPb5zPHQEAoFj3tmuxaqo5AADwPFbH41hrXDAAAKRy1spJOBCUxOAAeGa0FZtGmgMAJPN1s7XcEQAAgAPszrFpYgAAqVyeNDGpTBOD3JTE4AD4/KYpYgAAqRxuh3F84tIHAAB4YUd2d2PQOrgEAEihjYind+rcMWDhOSmBzGZ1FY9tX84dAwCgWF9fHYpe7hAAAMCB1m+auH1m2wMAQCrP7NbRtm3uGLDQlMQgs4e3L0blCTUAgCT6EXF6spQ7BgAA0AEnd3dzRwAAKNakbuPSxLk45KQkBpl9fuNC7ggAAMV6ZXM4hu47AAAA12FpNotj1Tx3DACAYp21chKyUhKDjC5OduPi1NNpAACp3D9dyR0BAADokNsm7tcCAKRyYVzHrLZyEnJREoOMPr95PncEAIBiHWmHcXTWyx0DAADokEOjcQxa44gBAFJoI+LsbpU7BiwsJTHIpGrqeHjrYu4YAADFelV9KHcEAACgY3pNE6dm09wxAACK9YyVk5CNkhhk8uj25Zg1/gACAKRy92QpdwQAAKCDTo5HuSMAABRrt2pjY+KcHHJQEoNMrJoEAEjndLMay+4zAAAAN2FlMonV2gUFAEAqT+96rwU5KIlBBpuzcTw73s4dAwCgWK+o1nJHAAAAOuz2qWliAACpnB/VUTVt7hiwcJTEIANTxAAA0hm2vTg1GeSOAQAAdNjRkZIYAEAqdRvxrGlisO+UxGCfNW0TX9y8mDsGAECxXtkcir6H0AAAgFswrKo4Uc1yxwAAKNbTO1XuCLBwlMRgnz2xcyXG9Tx3DACAYt03W84dAQAAKMDJ8W7uCAAAxdqet7E1a3LHgIWiJAb77AubF3JHAAAo1tF2KY7MXOYAAAC37tBoHIPWwSUAQCpnTRODfeX0BPbR7nwaZ3Y3cscAACjWq6r13BEAAIBC9No2bptNcscAACjWs7t1NG2bOwYsDCUx2Edf2LwQ/sQBAKRz93QpdwQAAKAgJ0aj3BEAAIpVtRHnRnXuGLAwlMRgH31hy6pJAIBU7m3XYsn9BAAAYA+tTKex1liDBACQytkdN3VhvyiJwT55ancjtufT3DEAAIr18tlq7ggAAECBbp+Mc0cAACjWlWkTo3mTOwYsBCUx2Cdf2DRFDAAglZW2Hyeng9wxAACAAh0d7eaOAABQtLO7ponBflASg30wq6t4fOdy7hgAAMV6RbMe/TZ3CgAAoESDqo4TtkQAACTzzE4dbesGL6SmJAb74NHty1H7owYAkMy9s5XcEQAAgIKdmoxyRwAAKNa0aePK1MpJSE1JDPbBw9sXc0cAACjWiXYpDs96uWMAAAAFWx+NY9g4uAQASOVZKychOSUxSGx3Po1nRlu5YwAAFOuV9aHcEQAAgML12jZum01yxwAAKNb5UR2N7VyQlJIYJPbI9qXwpwwAII1e28Zdk2HuGAAAwAI4MbZyEgAglaqNuDg2uRVSUhKDxB7eupQ7AgBAse5r12PJFHIAAGAfLE+nsV5XuWMAABTLyklIS0kMEtqYjuPidDd3DACAYj0wX8sdAQAAWCC3T0wTAwBI5eK4jqqxpwtSURKDhB7aupA7AgBAsVbafpycuKQBAAD2z5HRKHqtg0sAgBSaiDg3Mk0MUnGiAgk9vG3VJABAKl/XHIpe7hAAAMBCGdR1nKhmuWMAABTLyklIR0kMEjk33o7t+TR3DACAYt0zXc4dAQAAWEAnx7u5IwAAFGtj2sSkMrkVUlASg0Qe2rqYOwIAQLGOt8M4NDdHDAAA2H/r40kM2iZ3DACAIrVh5SSkoiQGCTRtE49aNQkAkMwD9aHcEQAAgAXVa9s4ZYsEAEAyz+5WuSNAkZTEIIGndjdjUvvDBQCQyp2zYe4IAADAAjs+HueOAABQrO15Gztzk1thrymJQQIPWzUJAJDMiXYp1q2aBAAAMlqbTGLQOLgEAEjl2V0rJ2GvKYnBHps3dTy+cyV3DACAYj1Qr+WOAAAALLq2jdusnAQASOackhjsOSUx2GOPb1+OqvUEGQBAKndOl3JHAAAAiGMTKycBAFIZ121sTBTFYC8picEee3jbqkkAgFROtkuxVlk1CQAA5GflJABAWs+OlMRgLymJwR4aV/N4erSVOwYAQLEeqNdzRwAAAPiSto1TVk4CACRzblRH07a5Y0AxlMRgDz26fckfKQCAhO6cWTUJAAAcHMetnAQASGbeRFyemNwKe0VJDPbQQ1tWTQIApHKqXY7Vee4UAAAAX7E6HsfQykkAgGSe3bVyEvaKkhjska3ZJM5PdnLHAAAollWTAADAQdOLiFPzSe4YAADFujCuo2ps84K9oCQGe+RhU8QAAJK6YzrMHQEAAOBrHJ8oiQEApFK3XyqKAbdOSQz2yKM7l3JHAAAo1qlmOVar3CkAAAC+1oqVkwAASZ0bKYnBXlASgz2wNZvE5ek4dwwAgGI9UK/ljgAAAPC8rJwEAEjr8riJ2spJuGVKYrAHHtu5nDsCAEDR7pwt5Y4AAADwgo5PPEQMAJBKExEXJya3wq1SEoM98Ni2khgAQCq3tcuxYtUkAABwgK2MJ7HUWoMEAJDKeSsn4ZYpicEt2p1P4/xkJ3cMAIBiPVCv544AAADwonoRcWo6zR0DAKBYF8d1NK2Vk3ArlMTgFj2+cyV3BACAot0xHeaOAAAA8JKOWTkJAJBM3UZctnISbomSGNyix3asmgQASOV2qyYBAICOWJlMYqmxBgkAIBUrJ+HWKInBLZhU83hmtJU7BgBAsR6orJoEAAC6oRcRp2aT3DEAAIp1YVxHa+Uk3DQlMbgFT+xeCX+CAADSsWoSAADokuMTJTEAgFTmTcSVqZWTcLOUxOAWPLZt1SQAQCp3tCuxbHo4AADQISuTSSxbOQkAkIyVk3DzlMTgJs3qKp4ebeaOAQBQrAfqtdwRAAAAbthtVk4CACRzYWySGNwsJTG4SU/ubkRt3zEAQDK3WzUJAAB00NHJOHcEAIBiTes2Nq2chJuiJAY36fEdqyYBAFK5s12J5Sp3CgAAgBu3MplaOQkAkJCVk3BzlMTgJtRNE0/ubOSOAQBQrJdZNQkAAHSYlZMAAOmcHyuJwc1QEoObcGZ3I6rWCEsAgFROTQe5IwAAANy0I9Np7ggAAMUaV23szJzXw41SEoObYNUkAEA6x9thrFa93DEAAABu2upkEgMPGgMAJGOaGNw4JTG4QU3bxBM7V3LHAAAo1n31eu4IAAAAt6Zt4/h8ljsFAECxzo+UxOBGKYnBDTo72opp4w8OAEAqd1RLuSMAAADcsmOzSe4IAADF2pm3MZqb3Ao3QkkMbtDjpogBACSzFL04MrVqEgAA6L71sZIYAEBKVk7CjVESgxvQtm08vnM5dwwAgGLd16yFihgAAFCCQV3HkcrKSQCAVC6MTBKDG6EkBjfg3Hg7RtU8dwwAgGKdrlZyRwAAANgzx2fT3BEAAIq1OWtiUrW5Y0BnKInBDbBqEgAgrRPTQe4IAAAAe+bwVEkMACClC1ZOwnVTEoMb8ISSGABAMne2KzE0HRwAACjI8nQay42DSwCAVC4qicF1UxKD67Q1m8TmfJI7BgBAse6tVnNHAAAA2HMn5qaJAQCkcmXSRN1YOQnXQ0kMrtOZ3Y3cEQAAinb7fCl3BAAAgD13zMpJAIBkmoi4MrWiAq6HkhhcJyUxAIB0DrWDWJvnTgEAALD3Vsfj6LWmWwAApGLlJFwfJTG4DnXTxNnRVu4YAADFur9Zyx0BAAAgiV7bxvH5LHcMAIBiXRqbJAbXQ0kMrsPZ0VZUrT8sAACp3FUt544AAACQzPH5JHcEAIBijes2dufO8+GlKInBdbBqEgAgnWHbi6NTlyYAAEC51sdKYgAAKZkmBi/NSQxchyd3r+SOAABQrHvbtei3uVMAAACkM6yqOFTNc8cAACjWpUmdOwIceEpi8BK2ZpPYmk9zxwAAKNbdtVWTAABA+U64zwwAkMyVSRN142lkeDFKYvASTBEDAEjr1HSYOwIAAEByhydWTgIApNJExOWJlZPwYpTE4CWc2d3IHQEAoFi3tUuxZAo4AACwAFam01hqXQABAKRi5SS8OCUxeBFVU8fZ0VbuGAAAxbqvXssdAQAAYN+cnM1yRwAAKNalsUli8GKUxOBFnB1tRd3aWwwAkMrts6XcEQAAAPbN0ZmVkwAAqYzrNnbnimLwQpTE4EVYNQkAkM5a249D817uGAAAAPtmdTyJngeTAQCSuWiaGLwgJTF4EUpiAADp3N+s544AAACwr/pNE8fqee4YAADFujSuc0eAA0tJDF7AxnQcW/Np7hgAAMW6q1rOHQEAAGDfHbNyEgAgmSvTJqrG5FZ4Pkpi8AJMEQMASKcfEcenLkcAAIDFc2isJAYAkEobEZcnVk7C83EqAy9ASQwAIJ27m9Xoe5gLAABYQEvzeazW1iABAKRyaeK9FjwfJTF4HlVTxzPjrdwxAACKdVe9kjsCAABANseqae4IAADFujQ2SQyej5IYPI+zo62oW6MtAABSOVkNc0cAAADI5vBUSQwAIJVJ3cbOXFEMnktJDJ7Hk1ZNAgAkM2x7cWjWyx0DAAAgmzUlMQCApC6OrZyE51ISg+dxRkkMACCZ0+1qqIgBAACLbFDXsV5XuWMAABTLykn4Wkpi8Bxbs0lszz3FBQCQyp31cu4IAAAA2R1zHxoAIJmNaRN10+aOAQeKkhg8x9nRVu4IAABFO1kNc0cAAADI7vBsljsCAECx2ojYnJkmBl9NSQye4+xoM3cEAIBirbT9WJ9ZNgkAALA6meSOAABQtMsTJTH4akpi8BxnxyaJAQCkck+7mjsCAADAgdBvmjhcz3PHAAAo1hUlMbiGkhh8lY3pOEaVi3IAgFTuqJdzRwAAADgwjs2tnAQASGVr1kTVtLljwIGhJAZf5ezYqkkAgJROzIa5IwAAABwYh6bT3BEAAIrVRsTG1DQx+HVKYvBVzo6smgQASGW9HcRqlTsFAADAwbE6nUavNd0CACAVKyfhK5TE4KsoiQEApHNPu5Y7AgAAwIHSa5o4WnuaBgAglSsmicFVSmLwZZeno5i4GAcASOb2ail3BAAAgAPn6NzKSQCAVLZnTVSNya0QoSQGVz1jihgAQFInZoPcEQAAAA6c9amSGABAKm1EbJgmBhGhJAZXWTUJAJDOkXYYy3XuFAAAAAfPynQafdMtAACSuTxREoMIJTG46pmxkhgAQCqnm9XcEQAAAA6kXtvGsXqWOwYAQLGuTDzBDBFKYhAREZcmuzGpq9wxAACKdUe1lDsCAADAgXVkriQGAJDKzryNeW1yKyiJQVg1CQCQ2rH5IHcEAACAA+vQdJo7AgBAsdqIuDK1chKUxCAizlo1CQCQzIlmKZZM8wYAAHhBS9NpDFoHlwAAqVyZeK8FSmIsvLZt4xmTxAAAkjndruaOAAAAcKD1IuJYZeUkAEAqV6aeZAYlMRbepekoZo0/CAAAqdxWLeWOAAAAcOAdmSuJAQCksjNvY1a3uWNAVkpiLLyzo83cEQAAinZs5rIDAADgpRyaTHNHAAAo2sbUykkWm9MaFt5ZqyYBAJK5vV2OgetuAACAl7Q0m8VSa+sFAEAqlyfea7HYlMRYaG3bxrPj7dwxAACKdXe9kjsCAABAZxyzchIAIJkrJomx4JTEWGgXJjsxa7SFAQBSua1ayh0BAACgM44oiQEAJLM7b2NWt7ljQDZKYiw0qyYBANI6MnPJAQAAcL3WpkpiAAApmSbGInNiw0J7ZqwkBgCQysl2KfoeygIAALhuS7NZDBoHlwAAqVyZ2DTG4lISY6GdG+/kjgAAUKw72pXcEQAAADrnaD3PHQEAoFibJomxwJTEWFgb03HMGi1hAIBUTlVLuSMAAAB0zuFKSQwAIJWdeRt1YwUGi0lJjIV1brKdOwIAQNGOVoPcEQAAADpnbTbLHQEAoFhtRGzOTBNjMSmJsbDOjZXEAABSWWn7serhdwAAgBu2Mp3mjgAAULQtKydZUEpiLKzzk53cEQAAinVHu5I7AgAAQCf1mybWmip3DACAYm2YJMaCUhJjIc3qKq5Mx7ljAAAU67Z6KXcEAACAzjo6N5oZACCVTZPEWFBKYiykC5PdaHOHAAAo2Il6mDsCAABAZ61Xs9wRAACKNW8iRnNFMRaPkhgL6dx4O3cEAICiHZ671AAAALhZa1MlMQCAlLasnGQBOblhIZ2f7OSOAABQrOPtMAaurwEAAG7acDaLQevCCgAglQ0rJ1lASmIsJCUxAIB07mxXc0cAAADotF5EHKmr3DEAAIq1qSTGAlISY+FszsYxcXENAJDMyXqYOwIAAEDnHZpbOQkAkMrOvI26aXPHgH2lJMbCOTc2RQwAIKVjcyUxAACAW3VoPs8dAQCgWG1EbM9ME2OxKImxcKyaBABIZ9j2Ys05BgAAwC1bmU5zRwAAKNqGkhgLRkmMhXNuvJ07AgBAse5sV3JHAAAAKEK/rmO1rnPHAAAo1uZUSYzFoiTGQqmaOi5PR7ljAAAU6/ZGSQwAAGCvHKmNagYASGVr2uaOAPtKSYyFcn6yE37NAwCkc7wa5I4AAABQjMPzWe4IAADFmjZtTCrTxFgcSmIslPPjndwRAACKdqRyiQEAALBXVmdKYgAAKW1YOckCcYLDQjk3URIDAEjlaLsUwzp3CgAAgHIsz2bRa+3HAABIZdPKSRaIkhgLxSQxAIB07myWc0cAAAAoS9vG0brKnQIAoFhbM5PEWBxKYiyMrdkkxvU8dwwAgGKdapZyRwAAACjOobmVkwAAqWzNmmhMbmVBKImxMM5bNQkAkNTR+SB3BAAAgOKsz6e5IwAAFKuNiK2ZkhiLQUmMhXFuvJ07AgBAsQZtxPq8lzsGAABAcVZnJokBAKS0ObVyksWgJMbCuDDZzR0BAKBYd7SroSIGAACw9wZVHSt1nTsGAECxtmZKYiwGJTEWQtu2cXk6yh0DAKBYtzfLuSMAAAAU60htmhgAQCo7SmIsCCUxFsLmbBJV6xc7AEAqx+th7ggAAADFOjSf544AAFCsUdVG3bS5Y0BySmIshItTqyYBAFI6XLm0AAAASGW1UhIDAEiljYiduZIY5XOSw0K4pCQGAJDMsO3FSpU7BQAAQLmWZ0piAAApbVs5yQJQEmMhXJqMckcAACjWqVjOHQEAAKBo/bqOpabOHQMAoFjbcyUxyqckxkK4NFUSAwBI5WSzlDsCAABA8Q7XRjgDAKSyM7NukvIpiVG8UTWLcW0UNwBAKsdrJTEAAIDU1islMQCAVHbmTbStohhlUxKjeBcnu7kjAAAU7XDtsgIAACC11WqWOwIAQLHqNmK3UhKjbE5zKJ5VkwAAaa3Pe7kjAAAAFG9lbpIYAEBKO7MmdwRISkmM4l2amiQGAJDKkXYYfQ9XAQAAJDeczaJnBRIAQDLbM++1KJuSGMWzbhIAIJ3b2pXcEQAAABZCLyIONaaJAQCksm2SGIVTEqNo86aOrfk0dwwAgGKdaIa5IwAAACyM9UpJDAAgle25khhlUxKjaJdMEQMASOpoPcgdAQAAYGGsV/PcEQAAijVvIiaVlZOUS0mMol2ajnJHAAAo2vq8lzsCAADAwliZK4kBAKS0Y5oYBVMSo2gXpyaJAQCkMmx7sVIriQEAAOyXZSUxAICktmZKYpRLSYyiXZqYJAYAkMptsZI7AgAAwELp13UsN3XuGAAAxdqZWTdJuZTEKFbTNnFlpiQGAJDKyWYpdwQAAICFc7iuckcAACjWtnWTFExJjGJtzCZRt1q+AACpHGsGuSMAAAAsnPXKykkAgFTGVRtVo2dAmZTEKNbFyW7uCAAARTs8VxIDAADYb6tKYgAASe3MTBOjTEpiFOvSVEkMACCl9aqXOwIAAMDCWZkpiQEApLQ1N0mMMimJUaxL01HuCAAAxTreDqPvOhkAAGDfDebz6LUuyAAAUjFJjFIpiVGsS9ZNAgAkc7Jdzh0BAABgIfUi4nBd5Y4BAFCsbSUxCqUkRpFG1SymTZ07BgBAsU40SmIAAAC5rNdWTgIApDKqTG2lTEpiFGljNs4dAQCgaEcqlxIAAAC5rFcmiQEApFK3EePKNDHK42SHIimJAQCkdUhJDAAAIJuV+Sx3BACAou3OTROjPE52KNLGdJI7AgBAsZaiF8s2ewMAAGSzNLNuEgAgpZGSGAVSEqNIJokBAKRze7uSOwIAAMBC6zdNrNSe3gEASGXXukkKpCRGkZTEAADSOdEs5Y4AAACw8A41pokBAKRikhglUhKjOFVTx041yx0DAKBYR5pB7ggAAAALb80kMQCAZHbnJolRHiUxirMxm+SOAABQtPVaSQwAACC3larKHQEAoFizJmJemyZGWZTEKI5VkwAAaa1VvdwRAAAAFt6ykhgAQFK7lZIYZVESozhKYgAA6fQjYtlGEwAAgOyG1Tx3BACAoo2snKQwSmIUR0kMACCd4+1S7ggAAABExKCqo9+YbgEAkMru3HstyqIkRnGUxAAA0jmqJAYAAHBgrLVWTgIApLJbmSRGWZTEKErbtrE5m+SOAQBQrKPNMHcEAAAAvmytVhIDAEhlZJIYhVESoyjb82nUrV/UAACpHGpdQgAAABwUq3WdOwIAQLHGVRuN/gEFccJDUayaBABIa70a5I4AAADAl62YJAYAkEwbEaNKSYxyKIlRFCUxAIC01upe7ggAAAB82dJcSQwAICUrJymJkhhFURIDAEhn2PZiySYTAACAA2OpUhIDAEhpd97kjgB7RkmMomzMJrkjAAAU63gs5Y4AAADAV+nXdQwaB5cAAKnsmiRGQZTEKIpJYgAA6RxrlcQAAAAOmvXGNDEAgFRGlZIY5VASoxiTah6T2sUwAEAqR5ph7ggAAAA8x6pJYgAAyVg3SUmUxCiGKWIAAGkdql0+AAAAHDSr1Tx3BACAYtVtxMQ0MQrhlIdibMwmuSMAABRtvXH5AAAAcNAs27ABAJDUqDJNjDI45aEYJokBAKS1WvdyRwAAAOA5lqs6dwQAgKLtzk0SowxKYhRja26SGABAKkvRi6FzBwAAgANnOLduEgAgpbF1kxRCSYxi7MxnuSMAABTrRLucOwIAAADPo980sdR6qgcAIJWJkhiFUBKjGNsmiQEAJHOsXcodAQAAgBewXje5IwAAFGtcea9FGZTEKMKsrmLaeFIKACCVI80gdwQAAABewEpd5Y4AAFCsSW2SGGVQEqMIO5VVkwAAKR2qXToAAAAcVKtKYgAAycybiKpRFKP7nPRQBKsmAQDSWlMSAwAAOLBMEgMASGtSKYnRfU56KML2fJo7AgBA0VarXu4IAAAAvICluZIYAEBKYyUxCqAkRhGUxAAA0llt+zFw/QsAAHBgDSslMQCAlMa1m+R0n5IYRVASAwBI53gs544AAADAi+g1TSw3de4YAADFsm6SEiiJUYSd+Sx3BACAYh1tBrkjAAAA8BLWlMQAAJJREqMESmIUYXs+yR0BAKBY6+0wdwQAAABegkliAADpjKsmdwS4ZUpidN6srmLq4hcAIJm1xmUDAADAQbdcO7gEAEhlUpskRvc57aHzdiqrJgEAUlpperkjAAAA8BJMEgMASGfeRFSNohjdpiRG51k1CQCQlpIYAADAwTeslcQAAFKaVEpidJuSGJ23PZ/mjgAAULTlWkkMAADgoFMSAwBIa6wkRscpidF5SmIAAOn0I2LY5E4BAADASxlUVe4IAABFG9dKYnSbkhidpyQGAJDOoWaYOwIAAADXoV/X0WsdXAIApGLdJF2nJEbn7cxnuSMAABTrSE9JDAAAoAt6EbHcGAUNAJCKkhhdpyRG523PJ7kjAAAU61CrJAYAANAVq62SGABAKuPKey26TUmMTpvVVUybOncMAIBirTUuGQAAALpi2f1yAIBkJrVJYnSbEx86baeyahIAICUlMQAAgO5YqpXEAABSmTcRVaMoRnc58aHTrJoEAEhrtXXJAAAA0BVLrZIYAEBKk0pJjO5y4kOnbc+nuSMAABRtpe7ljgAAAMB1WjZJDAAgKSsn6TIlMTptVM1zRwAAKNpyoyQGAADQFYNKSQwAICWTxOgyJTE6bVTNckcAAChWr21j6HwBAACgMwYmiQEAJDVrlMToLiUxOm1cmyQGAJDKegzDHDEAAIDuGCqJAQAkNbNukg5TEqPTrJsEAEjnSCzljgAAAMCNaNtYahTFAABSmSqJ0WFKYnSaSWIAAOkcage5IwAAAHCDVpXEAACSmXmrRYcpidFZbdvG2CQxAIBk1hslMQAAgK5ZbpvcEQAAimXdJF2mJEZnTeoq/PoFAEhnrXW5AAAA0DUrtfEWAACpzBotBbrLqQ+dNapmuSMAABRttXG5AAAA0DVLSmIAAMnUbUSlKEZHOfWhs8a1VZMAACmtNL3cEQAAALhBQyUxAICkrJykq5TE6KxRpSQGAJDSspIYAABA5yw1SmIAAClNlcToKCUxOsu6SQCAtJaq3AkAAAC4USaJAQCkNWtyJ4CboyRGZ1k3CQCQzno7CHPEAAAAuqdfKYkBAKRkkhhdpSRGZ1k3CQCQzuEY5o4AAADATei1bSy1imIAAKnMlMToKCUxOsskMQCAdNbbQe4IAAAA3KTlxg4kAIBUlMToKiUxOmtUzXJHAAAo1krrUgEAAKCrlloHlwAAqVg3SVc5+aGzrJsEAEhnxaUCAABAZw0b6yYBAFIxSYyucvJDJ9VNEzMXuQAAySw1vdwRAAAAuEnDxsElAEAqM1UFOkpJjE4a16aIAQCktBxKYgAAAF01bJvcEQAAijVr2mit96aDlMTopFE1yx0BAKBoJokBAAB016BREgMASKWNiLm3W3SQkhidNKpMEgMASGmpcakAAADQVSaJAQCkNatNEqN7nPzQSdZNAgCktdSaJAYAANBVg8ahJQBASlMlMTpISYxOMkkMACCtoYfOAQAAOqtf17kjAAAUTUmMLlISo5NMEgMASGugJAYAANBZg8ZFHQBASjOTW+kgJTE6aaIkBgCQTD8i+q5vAQAAOqunJAYAkFTl7RYdpCRGJ82MygYASGYtBrkjAAAAcAtMEgMASKsySYwOUhKjk2aNkhgAQCqrrZIYAABAp7VtDFpFMQCAVObeatFBSmJ00qypckcAACjWissEAACAzhu2plsAAKRikhhd5PSHTppaNwkAkMxq6zIBAACg65atnAQASKbyVosOcvpDJ5kkBgCQzrKSGAAAQOcNrZsEAEjGJDG6yOkPnVM3TdTGZAMAJGPdJAAAQPdZNwkAkI5JYnSR0x86Z2qKGABAUsuNywQAAICuG7Z17ggAAMWamyRGBzn9oXNmtQtbAICUltte7ggAAADcoqGDSwCAZJqIaExupWOUxOicmUliAABJLSmJAQAAdN6gsQMJACAlKyfpGiUxOmdqkhgAQFJDF7YAAACdN2jcSwcASMnKSbpGSYzOMUkMACCtpdZlAgAAQNcNHFoCACRlkhhd4/SHzpkqiQEAJDWoHSQAAAB0nXWTAABpVUr5dIySGJ0zs24SACCpYdPLHQEAAIBb1FcSAwBIyrpJukZJjM6ZNUpiAACpDNteqIgBAAB0n5IYAEBa1k3SNUpidM60tm4SACCV1RjkjgAAAMAe6NnKAQCQlHWTdI2SGJ1jkhgAQDprLhEAAACK0IuIoWliAADJVDpidIwTIDrHJDEAgHSWW5cIAAAApRi2Ti4BAFIxSYyucQJE55gkBgCQzjB6uSMAAACwRwZhkhgAQCpzb7XoGCUxOmfWmCQGAJDKQEkMAACgGH3DLQAAkjFJjK5REqNzprVJYgAAqQxbJTEAAIBS9MPBJQBAKpVJYnSMkhidY5IYAEA6JokBAACUo98qiQEApGKSGF2jJEan1E0TtYtaAIBkhq1LBAAAgFIoiQEApGOSGF3jBIhOmTdWTQIApOQCAQAAoByu8QAA0qkU8ukY1wd0St2q4gIApDR0iQAAAFCMvnvqAADJ2DZJ1zgBolOsmgQASGuQOwAAAAB7ZuCeOgBAMm1EtN5v0SFKYnSKSWIAAGn1Xc8CAAAUo5c7AABA4UwTo0uUxOgUk8QAANIatI4QAAAAStF3Tx0AICklMbpESYxOqZo6dwQAgKIpiQEAAJSjbzsHAEBS3m3RJUpidIpJYgAAaVk3CQAAUI6eazwAgKRqo8ToECUxOqX21BMAQFL9MEkMAACgFNZNAgCkpcFAlyiJ0SlKYgAAaQ2cHwAAABSj5546AEBSBonRJUpidIp1kwAAablAAAAAKEffLXUAgKSUxOgSZ0B0StV46gkAIKV+a90kAABAKXoevAYASKrxfosOURKjU6ybBABIq+/tFgAAQDH6Di0BAJIySYwuURKjU5TEAADScoEAAABQDpPEAADSUhKjS5wB0Sm1C1oAgKRMEgMAACiHkhgAQFrWTdIlSmJ0ikliAABp9VzPAgAAFENJDAAgLZPE6BIlMTpFSQwAIJ1e20YvdwgAAAD2TM89dQCApJTE6JJh7gBwI2q/YYF91jZNnHvk8bj45NMx3tyO/qAfh06eiNvuvydue9m9ueMB7KmBZ0gAAACK0nNPHTjgdrc24+xjD8X5p5+M0fZmtG0b60eOxsk7TsfLX/sNsXboSO6IAC/K2y26REmMTqnaOncEYEFMdnbjwz/znvjEe/59TLZ3n/dzTt1/T7zp+35zvOGd33lTX//Zhx6LZx96NJ794qPx7MOPxe7ljav//d7Xvzp+94//hZuND3BTlswRAwAAKIp1k8BB0zRNPPKpj8Yn3v/v4wsf+1CcffzhF/zcXq8XX//Gt8Y7fvt/Eq9769v2MSXA9Wu836JDlMTolNovWGAfPPvQY/Evf+xvxs5Xlbaez6Unn45/9/d+Kh76wEfit/yZ/1esHj70kl/7l3/qp+PhD34krjxzPsLvNOCAGZokBsAB9J//2J+Pj3/+U7f8dX743T8Uf+C3/d7n/W9v+09+y01/3V/8qZ+N4WBw0/8eAFJSEgMOmv/3D//WuHD2zHV9btu28fmP/lp8/qO/Ft/8m94ZP/SnfjRW11/6PjzAfqq93aJDlMTolLptckcACnfukcfjp/+rH49qOrv6seHKcjzwxtfHyXvujrZt4/JTz8QTH/9MVLMvfc7jH/tU/Msf/9vxO/7Kn4nB0ov/aX38Y5+OK2fPJf3/AHCzhq1JYgCUa21lNXcEAMii17bR9lzvAQfDzuaVr/nYHffcHy979TfE0ZOnYml5Ja6cfza+8PEPxcaFr9xL/8gv/lxsXb4Qf+y//XuxtLyyn5EBXpR1k3SJkhidYpIYkNJsNI6f/W//zjUFsVe8+Rvje/6L/ywOnTh+zeduX7oc//Zv/X/jiY9/JiIinvr05+OXfuqfxzv+4P/jhr/v6uFDcefXPXD1awHkMnRoAMABNOj3Y9C/8WmXdfOVB816vV58x1t+w3X9u16vF/0b+JvozycAB92gbaPyBws4YE7ddTp+wzvfHd/yPd8fJ26/82v+e1PX8f5/83/E//6T/0PMZ9OIiHjokx+J/+t//Lvx2/7wn97vuAAvSEmMLlESo1O++gYvwF772L/++dg6f/Hq63tf/+r4rT/yJ6L/PKtjjpw6Ge/+0T8dP/1f/Xg884VHIiLiE+/59/Gm7/vNcfzur72g/XVLqytxz2sfjLte9fK48+teHne96hVx4vSXPv+vf//v2+P/RwA3ZmDdJAAH0N/6Cz9+w//mlz78q/Ejf+vHrr7+xgdfF/fccfd1/dvf/wM/+IJrKQGgi/rh5BI4OE7eeTq+890/FN/ym//j5733/uv6g0G8/T/+nXHyzrvj7//oH4/2y2eEv/gz/yze8dt/bxy/7YXvwwPsp8Z7LTrEKRCdUlk3CST08X/981950evFb/6jv/9FL1IHS8P47j/6+6++bqo6PvjTP/ui3+MHf+Ivxu/5az8Sv+kP/FC85ju+7WpBDOAg6LmWBaAQ7/mVn7/m9bt+43dnSgIA+bnWAw6SP/f3/ll82zt/4EXvvX+11731bfHm73zn1dd1VcUnP/BLqeIB3DCTxOgSJTE6pbFuEkjk4pNPxc7ljauv73nNq+LkvS89aeCOl98fd73q5VdfP/TBj0Q9r1JEBNgH3msB0H1Xtjbjg5/4yNXXayur8Z1vfVvGRAAAwK8bDG580dU3/6Z3XvP6ic9/eq/iANwyJTG6REkMACLi/CNPXPP6nte86rr/7elXf+VzZ6NxPPGJz+xZLoD91Ov1ckcAgFv27z7wi1HVX3lw4zve8u2xvrqWMREAZOZSD+i420/fd83rrSuXMiUBgG5TEqNTWtMtgETGW9vXvD586sR1/9vnfu6ZT31+TzIB7DcrSAAowXNXTX7v278rUxIAOBhc6wFdNx3vXvN6MFzKlATga3mrRZcoidEptk0CqVTT+TWvB0vXf5E5WL72cy+feXpPMgEAADfm4Scfi4eeePTq67tuuyPe+Jo3ZEwEAADcqqcffeia1yduuyNTEgDoNiUxAIiIlcPr17ye7o6u+99Od659iunSU2f3JBPAfrNuEoCue8/73nvN63e+7R3+vgGw8HrmWwAd96H3/utrXj/4xrdmSgLwPLzVokOGuQMAwEFw6OTxa15ffOKp6/63z/3c7YuX9yISwP5zMQtAh1V1Hf/uV3/h6uterxfvett33/DX+dhnPxl/4YlH4+Ezj8XG1mYMBoM4fuRY3Hvn6fim17w+vvMtb4t77rx7L6MDAAAv4KFPfDge+sSHr75eO3Q4XvPm35AxEQB0l5IYndI6uQQSuefVr7rm9eMf+3TUVRWD4Yv/qaxms3jyk5+95mNNVUc1n8fwBlZWAhwEni4HoMt+7ZMficubG1dfv+HB195UmevjX/j013xsZ7QbT507Gx/85IfjH/2Lfxrf+S1vjz/xe/9QnDh2/BYSA8A+MVQT6KjpeBz/7G/+1Ws+9l2/4z+N1bX1F/gXAPtPh4EusW6STvELFkhl/fjRuOtVL7/6erSxGZ/6v3/pJf/dx//1e2Oyvfs1H5+Pp3uaD2A/9JwcANBh7/mVn7/m9bvefuNTxK5H3TTx8x/4pfjhH/3j8dlHvpDkewDAXuq5rQ501D//2z8W55964urrO+97eXz37/p9GRMBQLeZJAYAX/bmd78r/tV/9/euvv7ln/rpuO2B++Le1z74vJ//5Cc+G+//n/+P5/1v1WyWJCMAAPC1tna241c//qGrr1eXV+Idb33bDX2Ne+88HW//5m+NN73mDfHye18Wx48cjX5/EBvbm/H5Rx+K9/7aL8cvfuh9UTdNRERcvHIp/uxf/8vxD/7rv2H9JAAHnJYY0D3v/d/+p/jQz/+rq6+HS8vx+//8j8XS8krGVABfyzstukRJjE5p/YYFEnrw298aD7zpV+Lxj34qIiLmk2n8b3/xJ+Kbvu+743Xf+e1x4t67I9o2Lj/1THzmve+Lj7/nvdFUdfSHg+j3+1HN5le/1tKqC1Wge8wRA6Crfv6DvxSz+Vfej3/HW7491m9gBc3f+LN/Nd7y+jdGr/e1fw3vOHlb3HHytviNb/62+Ny73h0/8rd/PM5fuhARERvbW/Hf/IO/Hn//L/0Pt/5/AgASca0HdM1Hfunfxv/5j/4/13zsB//kX4z7H3xtpkQAL0KHgQ5REgOAL+v1evG9f/qPxL/40Z+IC489GRERdVXFR37m5+IjP/NzL/jvfvMf++H45f/xp79SEuv1YmV9bT8iA+wp6yYB6Kr3/Mp7r3n9vTe4avKt3/Cm6/q817ziwfibf+6/iT/0l/907Iy+tHb+Uw99Lj7w8f8Q3/ZNb7mh7wkAAHytz3/0g/FPf+IvRvvlCb4REb/1D/yJ+Nbv+f6MqQCgDP3cAeBGtGq4QGJrRw/H7/lrPxKve8fbotd/8bLE6pFD8X1/9o/G67/r7TEbT65+fGV9LXp9f2IBAGA/PP70k/G5R7949fVdt90Rb3rtG5J9v/vvvjd+7/f9jms+9vMf/OVk3w8AbpXHgYCueOxzn4p/+Jf/dFRfNSX4u3/X74vv+T0/nDEVwIvTYKBLTBIDgOdYXluNd/7JPxhv/m3vis/94gfiyU98JrbOX4rJ7m6sHj4cx++6PV71bW+O177j22P92NGYjsZRf9VF66n7TmdMDwAAi+U977t2itg73/aO510buZe+7zd+T/zDf/FPo22/dCv4I5/9RNLvBwC3wsEl0AVnH3s4/v6P/OcxHY+ufuw3vOvd8e4/+KcypgJ4aQr5dImSGJ1iBRKwn267/954+3/6OyPid77o51184qlrXt/1qlckTAUAAPy6pmni/37/L1zzsXe97cZWTd6ME8eOx+nb74qnzz8TEREXr1yKqqpiOHSrDYADSEsMOOAunD0Tf+fP/5HY3d68+rE3fcf3xA/+yR/NmAoAymMXFgDcovOPPH7N67seVBIDuslqbwC65j98+mNx4cqlq6/f8ODr4p47796X733i6LFrXm/ubO3L9wWAG+bZa+AA27h4Lv7On/vDsXX54tWPve6tb4vf/+d/LPp9R9kAsJf8ZaVTEm+LALgpD33gw1f/99LaarzyrW/MmAbg5qmIAdA1z101+b1v/659+96T2fSa18tLy/v2vQHgRrRaYsABtbN5Jf72n/0jcenZs1c/9qpvfHP8wf/6r8dguJQxGcAN8FaLDlESA4BbcPmpZ+Kpz3zh6utXv/1bYnltNWMiAABYDLvjUfzKRz549fXq8kq841vevi/fu2maePbi+auvh4NhHDl0eF++NwDcKA8EAQfReHcn/u5f+GNx7sxjVz/2wKu/If7IX/3bsbS8kjEZwI3REaNLlMTolJ5fscAB8wv/6H+OtvnSrbb+cBDf/P3/UeZEADfPwQEAXfLeD/5yTL9qmtd3vOU3xPra+r5870899LnYGe1eff2ql1k5D8AB5rY6cMDMppP4yR/9E/HkQ5+9+rF7XvFg/LEf/7uxuk/v6QFgESmJ0SlKYsBB8ov/+H+Jxz/2qauv3/LbvjdO3X9PxkQAt6b1VguADnnuqsl3vf279+17/5N/+c+vef3Wb3jTvn1vALhhnggCDpC6ruIf/9U/Ew9/6iNXP3bnfQ/Ef/HXfjLWjxzNmAzg5ugw0CXD3AEA4CB570/+01g7diRe+53fHsfvuuN5P+fK2XPxi//4n8Wj/+HjVz92+8vvj2/9Xd+/TykBAGCxPXXubHzqi1+ZOnDnqdvjm1/7jTf8dbZ2tmM6m8btJ2+77n/zj//3/1986FMfvfp6ZXklfuAd77rh7w0A+0VHDDgo2raN/+m//0vx6V/7lasfO3XXPfHH/7t/EEdOnMyYDAAWg5IYndJTwgUS293YjI//m/fGB/6Xn4lT952OO175QBw+dSIGg0HsbmzG+UefiHMPP37Nvzlxz13x7r/0p2K4vPySX//Mpz8f/+Iv/sRLft5Tn/58/I0f+OHn/W//z3/438fRO67/EAvgerWtowMAuuE9v3LtFLF3vu27oncTNw3OXToff/iv/Jfxzrd9V7zzbd8V3/Cq17zg13n63DPxk//rT8UvfOh913z8h37Lb7+hkhkA7D831oGD4fL5Z+I/vPfffM3HfvT3fu8NfZ2Td52Ov/JP/q+9jAZw87zVokOUxADgBVw6czYunTn7op/z8m9+Q7zzT/2hWD965Pq+aNtG2zTX96kv8HlKHEAqfrsA0AVt28a/ff+/v+Zj33sLqyZn83n87C/8XPzsL/xcHDt8NF71slfEfXfdE4cPHYpBvx8b21vxhcceji889nA07bXv0d/xLW+P/+zdP3TT3xsA9oNrPeDAeJ57223T3PDvqaau9iYPACwYJTE6xT5fILVXvvWNsX3hUpx75IkXLXPd89oH480/8K74um990z6mA0jMWy0AOuBjn/tkPHvx/NXXb3jwdXHPnXfvydfe3NmKD3/m4/Hhz3z8RT9v0O/H7/utPxi/7wd+901NMAMAAADK4K4AXaIkRqf4BQuk9rp3vC1e9463xWw8ifOPPRkbZ5+N0eZ21PMqltdW49idt8fdX/+KOHTi+E19/fu+4TXxX/7sP9nb0AB7xKBCALrguasmv/ft33XTX+uOk7fH737Xu+Njn/tkPHLm8ajr+kU//8ihw/Hd3/od8Tv/o++P++++96a/LwAALKJTd90Tf/fffTx3DIA9pcNAl/RaO6vokH915rNxdrSVOwYAQJHualbj27YP544BAFnM5vN4/Okn45kLz8bFjcsxGo+jaZs4vH44jh0+Eq+8/+XxwOn7TA4DoHM+d/udMRuYGQAAkMLpQ4N47anl3DHgurgqoFMGbsQCACTT9jw/AsDiWl5aigcfeGU8+MArc0cBgD3Vuq8OAJBM31stOqSfOwDciEHPjywAQCp1KIkBAACUprEECQAgmb5CPh2icUOnKIkBAKRTRZM7AgAAAHuscXAJAJCMSWJ0icYNnWLdJABAOnPrJgEAAIrShnWTAAApKYnRJUpidIpJYgAA6dStkhgAAEBR+u6pAwCk5N0WXeLnlU5REgMASGdu3SQAAEBRTBEDAEirb5QYHaJxQ6cMPfUEAJBM7VoWAACgKI2SGABAUhoMdImfVzrFJDEAgLQa5wcAAADlMNkCACApb7foEo0bOmXgqScAgKRab7cAAACK0YaLPACAlJTE6BIlMTrFJDEAgLRMEgMAACiHdZMAAGn1vd+iQzRu6JSBGi4AQFJKYgAAAOVoHVoCACQ18HaLDlESo1OGvUHuCAAARWt6be4IAAAA7BElMQCAtMy5oUuUxOiUgQtaAICkmtwBAAAA2DOtU0sAgKSsm6RLlMTolEHPjywAQEqNt1sAAADFaMKhJQBASjr5dIkjIDpFSQwAIK0mrJsEAAAoRePUEgAgKW+36BKNGzpl4DcsAEBStbdbAAAAxWisPwIASGrg7RYdoiRGpwxNEgMASMokMQAAgHK01k0CACTVV8qnQzRu6BTrJgEA0jJJDAAAoBytQ0sAgKQsQ6NLNG7oFCUxAIC06p5JYgAAAKWoTRIDAEhKSYwu0bihUwZ+wwIAJNXkDgAAAMCeaUwSAwBIyrpJukRJjE4xSQwAIC2TxAAAAMqhJAYAkI72Al3jZ5ZOGSqJAQAkVYWSGAAAQClaJTEAgGQsQqNrNG7olKX+IHcEAICimSQGAABQjtrBJQBAMkMtMTpGSYxO6fV6sdT3YwsAkIqSGAAAQDnacHAJAJDKUHWBjvEjS+es9Ie5IwAAFKvOHQAAAIA90/QcAwEApLJkkhgd4+qAzlkeWDkJAJBKFSaJAQAAlMK6SQCAdAYaN3SMH1k6Z9kkMQCAZOpockcAAABgj9Q9LTEAgFRMEqNrlMTonJWBkhgAQCpzk8QAAACK0bQOLgEAUhkq5NMxSmJ0znLfukkAgFSmvTp3BAAAAPbIvOcYCAAglaG3WnSMH1k6R0kMACCdiXWTAAAARWh7vWisQAIASGbovRYdoyRG51g3CQCQzsQkMQAAgCI0fUdAAAApLXm7Rcf4kaVzTBIDAEiniYjGw08AAACd1wzcSwcASMkkMbpGSYzOWe6bJAYAkFLlHAEAAKDzrJoEAEhrqHFDx/iRpXNWPP0EAJBU7RwBAACg82pbOQAAkjJJjK5REqNzTBIDAEhr3m9zRwAAAOAW1Q4tAQCSGnq7RccoidE5KwMlMQCAlOY9JTEAAICuq3uOgAAAUlpSyqdjXCHQOctGZAMAJKUkBgAA0H2Ve+kAAEkNNW7oGD+ydI6SGABAWrNekzsCAAAAt6gy2QIAIJlBL6LX836LblESo3OsmwQASGtmkhgAAEDnVX1HQAAAqVg1SRe5QqBzer1eLLm4BQBIZtY3SQwAAKDr6p776AAAqVg1SRf5saWTlvumiQEApDKJOncEAAAAbtHc+iMAgGSGJonRQUpidNLKYJA7AgBAsabWTQIAAHTe3CQxAIBkTBKji/zY0kkmiQEApDOJKncEAAAAbpGSGABAOkNTW+kgVwh00nLfJDEAgFQm0eSOAAAAwC1o+/1oHVwCACSzZN0kHaQkRietDEwSAwBIZRp17ggAAADcgqbv+AcAICXrJukiP7Z00qqSGABAMm2vF7WHoAAAADpLSQwAIK2lgZvodI+rBDppbbCUOwIAQNEq270BAAA6q1YSAwBIakVJjA5ylUAnrQ+Xc0cAACha1WtzRwAAAOAm1X2HlgAAKS1r29BBfmzppPWhSWIAAClVfSUxAACArqoHxkMDAKRkkhhdpCRGJ1k3CQCQ1twkMQAAgM6qe45/AABSWlYSo4NcJdBJ1k0CAKQ1N0kMAACgsyolMQCAZAa9iKH13nSQqwQ6aXUwDL9yAQDSmZkkBgAA0FlV3/EPAEAqywpidJSrBDqp1+vF2tDKSQCAVKa9JncEAAAAblLVc3AJAJCKVZN0lZIYnbU2UBIDAEjFJDEAAIDuqvuD3BEAAIq17K0WHaUkRmetmyQGAJDMJOrcEQAAALhJMyuQAACSWTFJjI5SEqOzTBIDAEhnt1fljgAAAMBNmobxFgAAqVg3SVcpidFZ68Pl3BEAAIq1rSQGAADQSU2/H41JYgAAySx7r0VHKYnRWdZNAgCkM482Gte5AAAAnVMPh7kjAAAUzbpJukpJjM6ybhIAIK2Z7SQAAACdUw9czAEApGTdJF2lJEZnWTcJAJDWbNDmjgAAAMANmg8c/QAApLSik09HuVKgs0wSAwBIa9pvckcAAADgBs37Ti0BAFIySYyuUhKjs9aHSmIAACmNe0piAAAAXTOzbhIAIJmlfkS/pyRGNymJ0VnLg2EMe36EAQBSGfXr3BEAAAC4QbOekhgAQCrLfQUxukvDhk5bM00MACCZUU9JDAAAoGtmA0c/AACpWDVJl7lSoNPWBkpiAACp7CqJAQAAdM7EJDEAgGRWlMToMCUxOm3dJDEAgGR2osodAQAAgBvQ9vtR9x39AACkYpIYXeZKgU5bHy7njgAAUKxJr4nG9S4AAEBn1ENTxAAAUlr2dosOUxKj06ybBABIa+6CFwAAoDOqvos4AICUlvuerKa7lMTotMNLJokBAKQ0G7S5IwAAAHCdqoGSGABASqtDJTG6S0mMTjs8XMkdAQCgaJNekzsCAAAA12muJAYAkNTqQEmM7lISo9OOLCmJAQCkNOmbJAYAANAVM+smAQCS6YVJYnSbkhiddnhpOfwKBgBIZ9yvc0cAAADgOs0Gjn0AAFJZGfSi39NQoLtcLdBp/V4/1ofLuWMAABRrZN0kAABAZ5gkBgCQjilidJ2SGJ1n5SQAQDq7vSp3BAAAAK7TpOfYBwAgldWBkhjd5mqBzlMSAwBIZyeUxAAAALqg7fWiMkkMACCZNZPE6DglMTpPSQwAIJ1Rr442dwgAAABeUjNUEAMASElJjK5TEqPzDg+VxAAAUpoPcycAAADgpVQDJTEAgJRWlcToOCUxOs8kMQCAtGZ9s8QAAAAOurlVkwAASa0OlMToNiUxOk9JDAAgramSGAAAwIFnkhgAQDq9MEmM7lMSo/MOLy2HX8UAAOlM+k3uCAAAALyEuZIYAEAyK4Ne9HuaCXSbkhid1+/1Y324nDsGAECxxj0lMQAAgINuqiQGAJCMKWKUQEmMIlg5CQCQzm6/yh0BAACAlzDrKYkBAKSyOlASo/uUxCiCkhgAQDqbPSUxAACAg25kkhgAQDJrJolRACUxiqAkBgCQzkbMckcAAADgRdSDQdQ9Rz4AAKkoiVECVwwU4fBQSQwAIJW6FzH3QDoAAMCBVS0Nc0cAACjaqpIYBVASowgmiQEApDUeNLkjAAAA8AJmAyUxAICUVgdKYnSfkhhFUBIDAEhrNFQSAwAAOKimw6XcEQAAitULk8Qog5IYRTi8tBx+JQMApLPbVxIDAAA4qKYDxz0AAKmsDHrR72kk0H2uGihCv9eP9eFy7hgAAMXa6lW5IwAAAPACxn3rJgEAUjFFjFIoiVEMKycBANLZ7M1zRwAAAOAFKIkBAKSzOlASowxKYhRDSQwAIJ3N3jza3CEAAAD4GvVwGE3fwSUAQCprJolRCCUxinFsaTV3BACAYjURMR/kTgEAAMBzzYemiAEApKQkRimUxCjGsWUlMQCAlEbDJncEAAAAnmOmJAYAkNShJdUayuAnmWKcWFnPHQEAoGjjgZIYAADAQTMdKIkBAKR0aMkkMcqgJEYxji2thl/NAADp7PTr3BEAAAB4julgkDsCAECxVvq9GPY1ESiDkhjFGPT7cXhpJXcMAIBibfWq3BEAAAB4jpFJYgAAyaybIkZBlMQoyvHl1dwRAACKtaEkBgAAcKC0ETHpmyQGAJCKVZOUREmMohxfXssdAQCgWNsxizZ3CAAAAK5qlobR9hxcAgCksr6kVkM5/DRTFCUxAIB02l4vpkM1MQAAgINiPlzKHQEAoGiHhgr5lENJjKIoiQEApDUeKIkBAAAcFLPhMHcEAICiWTdJSZTEKIqSGABAWqNBkzsCAAAAXzYdDHJHAAAo1qAXsTpUq6EcfpopytpwKVb6LooBAFLZ7le5IwAAAPBl475JYgAAqaxbNUlhlMQojmliAADpbPfr3BEAAAD4svFASQwAIJVDSyo1lMVPNMU5vqIkBgCQykbMc0cAAAAgIqLXi2nfMQ8AQCrrSyaJURZXDxTHJDEAgHR2elU0rosBAACyq5aG0fZcoAEApGKSGKXxE01xji+v5o4AAFC06bDNHQEAAGDhza2aBABI6tBQIZ+yKIlRHJPEAADSGg+UxAAAAHKbLimJAQCk0gvrJimPkhjFObq0Gn0jtgEAktkdNLkjAAAALLxpX0kMACCV1WFP74DiKIlRnF6vF8eWrJwEAEhlqzfPHQEAAGDhjYdKYgAAqVg1SYmUxCiSlZMAAOlcUhIDAADIbsckMQCAZNaX1Gkoj59qinR82SQxAIBUrvRn0eYOAQAAsMCq4TDqviMeAIBUDi2ZJEZ5XEFQJJPEAADSaSJispQ7BQAAwOKaLy/njgAAUDTrJimRkhhFOr6iJAYAkNLOsMkdAQAAYGGNh1ZNAgCkdMi6SQrkp5oimSQGAJDW1qDKHQEAAGBhKYkBAKSz1I9YGpgkRnmUxCjSUn8QR5ZWcscAACjW5f48dwQAAICFtTtYyh0BAKBYh00Ro1B+sinWqZX13BEAAIp1Kaa5IwAAACyktt+P8cAkMQCAVI4sq9JQJj/ZFOu2lUO5IwAAFGvca2I+yJ0CAABg8cyXTBEDAEjpyLJVk5RJSYxinVo1SQwAIKXRsMkdAQAAYOFMlpXEAABSOmLdJIXyk02xTpkkBgCQ1PZASQwAAGC/jQdKYgAAqfQjYn3JJDHKpCRGsQ4vrcTqYJg7BgBAsTYGVe4IAAAAC2c8VBIDAEjl0FIv+j0lMcqkJEbRTq5YOQkAkMql3jR3BADg/8/ef7ZJdp7nofa9YuXqqu6eDAxAZBCBQRRJJZO2ZVuH37+797a3vC1TlqxES5QoSgzIwCQAk6d7Ole9HyjJEgWCwKDXPN1PnecvuD7Uqqq1nmvdNwArZ6uqUkcAAMjWpFWjIV8+3WRt08pJAIDO3In9WKYOAQAAsEKOmjqOCkc7AABdURIjZz7dZG3DJDEAgM4siyJ2WjUxAACAx2WvaVNHAADI2qSxapJ8KYmRtY2+SWIAAF3aqhapIwAAAKyM3aZJHQEAIGtjk8TImE83WZu1/agKTV8AgK7cL49SRwAAAFgZD6s6dQQAgGwN6iLqUr+AfCmJkbWyKGPdykkAgM7cLvdTRwAAAFgZ25VJYgAAXZmYIkbmfMLJ3oaSGABAZ24WSmIAAACPw6IsY7eqUscAAMjWpDFFjLwpiZG9jd4odQQAgGztFYs4cEYBAADQuYPGFDEAgC6ZJEbufMLJnkliAADd2m4WqSMAAABkb69VEgMA6JKSGLnzCSd7G/1RGAoJANCd++VR6ggAAADZ26mVxAAAutKWEb1Ks4C8KYmRvaasYtr0U8cAAMjWveowdQQAAIDsbVd16ggAANkyRYxV4FPOStjoWzkJANCVW8V+6ggAAADZ2y5NEgMA6Mq4UZ8hfz7lrISN3ih1BACAbN2N/ViYwg0AANCZw6aJRenGCwCgK5PWfy3ypyTGSthUEgMA6MyyKGKnXqaOAQAAkK29xqpJAIAuWTfJKvApZyVYNwkA0K0HzVHqCAAAANnabdrUEQAAslUVEcPaJDHypyTGShjWbQyqJnUMAIBs3S4PU0cAAADI1raSGABAZ8ZNGUWhJEb+lMRYGZumiQEAdObjci91BAAAgCwtI+J+Zd0kAEBXJq2CGKtBSYyVcaY/Th0BACBbt4uDOHJ3AQAAcOwO2zaOCjdcAABdmbb+a7EafNJZGef6k9QRAACyttUsU0cAAADIzm5r1SQAQJfWeqozrAafdFbG2YFJYgAAXbpbH6aOAAAAkJ2tRkkMAKArTRkxalRnWA0+6ayMXlXHrO2njgEAkK2b5X7qCAAAANl5UDepIwAAZMuqSVaJTzsr5WzfNDEAgK58WOyljgAAAJCVRVnGTlWnjgEAkC2rJlklPu2slHODSeoIAADZ2isWsevsAgAA4Njs93qpIwAAZG3NJDFWiE87K0VJDACgW/fro9QRAAAAsvGwsWoSAKArRZgkxmrxaWelzNtBNKWPPQBAV27XB6kjAAAAZGOraVNHAADI1rApoi6L1DHgsdGWYaUURRFn++PUMQAAsvVxuZ86AgAAQDbuVyaJAQB0xapJVo1PPCtHSQwAoDu3Yi8WXrwCAAD4wg6bJo5sxgAA6IxVk6wan3hWzrnBJHUEAIBsLYsitppl6hgAAACn3m7PqkkAgC4pibFqfOJZOSaJAQB06259mDoCAADAqbfdKIkBAHSlLiLGjcoMq8UnnpXTr5tYa/qpYwAAZOtWdZA6AgAAwKn3oGpSRwAAyJYpYqwin3pW0tmBaWIAAF35sNhNHQEAAOBUW5ZlbNdKYgAAXZm26jKsHp96VtI5JTEAgM7sFIvYq1OnAAAAOL32WqsmAQC6ZJIYq8innpV0tj9JHQEAIGv366PUEQAAAE6tHSUxAIBOrZkkxgryqWclbfSGURc+/gAAXblTH6aOAAAAcGptNVZNAgB0ZVQX0VRF6hjw2GnJsJKKoogz/VHqGAAA2fqo3EsdAQAA4NR6UCuJAQB0ZWrVJCvKJ5+VdW5g5SQAQFduxl4svIgFAADwuR02TRwUVeoYAADZsmqSVeWTz8o62x+njgAAkK1lUcTDZpk6BgAAwKmz25oiBgDQpZlJYqwon3xWlkliAADdulsfpY4AAABw6jxs29QRAACyVRURo8YaDFaTkhgra1A3MW16qWMAAGTrVrWfOgIAAMCp86BWEgMA6MpaW0ZRKImxmpTEWGmmiQEAdOdGsZc6AgAAwKmyLIrYLuvUMQAAsrVm1SQrzKeflXZxOE0dAQAgWw+Lo9ivUqcAAAA4PfZ7bSxNtgAA6My8rybD6vLpZ6UpiQEAdOtee5Q6AgAAwKmx3fZSRwAAyFYZETOTxFhhPv2stEnTj3Hdpo4BAJCtj6r91BEAAABOjfuNkhgAQFfWemWUpraywpTEWHmmiQEAdOdqtZs6AgAAwKmwLIq4XzepYwAAZMuqSVadK4CVd3G4ljoCAEC2tuMo9urUKQAAAE6+vX4vliZbAAB0Zt2qSVacK4CVZ5IYAEC37jSHqSMAAACceFttP3UEAIBsVUXEVEmMFecKYOWNm15Mm17qGAAA2fqoPkgdAQAA4MS731g1CQDQlbW2jNLUVlackhiEaWIAAF26WuykjgAAAHCiLcoytiolMQCArsz76jHgKoCIuDhcSx0BACBbu8UiHjbL1DEAAABOrL1eL5YmWwAAdGber1JHgOSUxCBMEgMA6Nqd5ih1BAAAgBNrq9dLHQEAIFtVETFtFfJBSQwiYli3MWv7qWMAAGTro2o/dQQAAIAT617Tpo4AAJCtWa+M0tRWUBKDf3RhYJoYAEBXrpY7YeEkAADAv7aoqtiumtQxAACyNe+pxkCEkhj8EysnAQC6cxDLeNiqiQEAAPyi3b5VkwAAXZr3q9QR4ERQEoN/cHG4ljoCAEDWbtWHqSMAAACcOA8aJTEAgK7URcS0tWoSIpTE4J8M6ibm7SB1DACAbH1Y76eOAAAAcOLca9rUEQAAsjXrlVEUSmIQoSQG/4KVkwAA3bkeO2HhJAAAwP9xVNexU9WpYwAAZMuqSfg/lMTgn1ESAwDozlERsdVTEwMAAPhHOz2rJgEAujTvq8XAP3I1wD9zYaAkBgDQpVv1QeoIAAAAJ8ZWqyQGANCVpoyYNFZNwj9SEoN/pl83sd4bpI4BAJCt6+V+6ggAAAAnxt26TR0BACBbs14ZRaEkBv9ISQx+wcXBWuoIAADZ+qjcjYV7cgAAgDhomtirqtQxAACyNe/7rwX/nJIY/IKLQysnAQC6soiIB+0idQwAAIDkdvpWTQIAdGneU4mBf84VAb/gwnAahlsAAHTnZn2YOgIAAEByDxqrJgEAutKUEZNWJQb+OVcE/IJeVceZ/jh1DACAbF2vdlNHAAAASO6ekhgAQGc2rJqEf0VJDD7B5dEsdQQAgGx9XOzHkTsRAABghR20bRwUDi4BALqyMfAQGn6RqwI+wZPjWeoIAABZu98uUkcAAABI5mGvlzoCAEC2ijBJDD6Jkhh8gjP9cQyqJnUMAIBsfVwfpI4AAACQzP3WqkkAgK5M2zLaqkgdA04cJTH4JZ4craWOAACQrevlbuoIAAAASSwj4l6lJAYA0BWrJuGTuTLgl3hyNEsdAQAgW7eLg9g37RsAAFhB+/1eHJWOZwAAumLVJHwydyHwSzwxmoUBlAAA3bnVO0wdAQAA4LF70B+kjgAAkK2mjJi2TvrhkyiJwS/Rq+o4N5ikjgEAkK2rlZWTAADA6rnd9lJHAADI1ka/iqJQEoNPoiQGn8LKSQCA7lwtd2PhXh0AAFghh00TO2WdOgYAQLY2B2ow8Mu4OuBTKIkBAHRnERH3eovUMQAAAB6b7X4/dQQAgGwVEbHer1LHgBNLSQw+xWZ/FMO6SR0DACBb1+v91BEAAAAem3tWTQIAdGbaltFW1lfAL6MkBr/CE8O11BEAALL1QbmTOgIAAMBjsSzLuFu3qWMAAGRrw6pJ+FSuEPgVLo/nqSMAAGTrYXEU280ydQwAAIDOPez3YlmYbAEA0JVNqybhUymJwa/wxHAtSjfuAACdudkepo4AAADQufu9QeoIAADZasuIaU8FBj6NKwR+hbaq42x/nDoGAEC2PqisnAQAAPJ3p7FqEgCgKxsDU8TgV1ESg8/g8miWOgIAQLY+LvbjwP07AACQsf1eLw5KNz4AAF3Z6Ku/wK/iKoHP4EklMQCATt1uj1JHAAAA6MxWv586AgBAtoqI2Ogr5MOvoiQGn8FGfxTDukkdAwAgW9fqvdQRAAAAOnO77aWOAACQrWlbRlMVqWPAiackBp+RaWIAAN25UuzEwj08AACQoaO6ju3KS8gAAF3ZGKi+wGfhSoHP6LKSGABAZw6LZTxoF6ljAAAAHLvtgVWTAABd2hxYNQmfhZIYfEaXhmtRFsZbAAB05cPmIHUEAACAY3fPqkkAgM70yiKmreoLfBauFPiM2qqOc/1x6hgAANl6v9xJHQEAAOBYLYsi7lZKYgAAXVm3ahI+M1cLfA5PjeepIwAAZOtBcRg79TJ1DAAAgGOz2+/HorShAgCgK2esmoTPTEkMPocvTdZTRwAAyNrN3lHqCAAAAMfmfq+fOgIAQLaqImKjr/YCn5WrBT6HSdOPzd4odQwAgGxdqXZTRwAAADg2dxqrJgEAurLRr6IytRU+MyUx+JxMEwMA6M6HsROH7lIAAIAMHLRt7FXWHwEAdOXs0MNk+DxcMfA5fWmsJAYA0JVlUcSd3iJ1DAAAgC/sQd+qSQCArhQRsTlQyIfPQ0kMPqdZbxCz1s09AEBXrld7qSMAAAB8YXdbqyYBALqy3i+jtmoSPhclMXgEpokBAHTn/ephLFOHAAAA+AIWVRUP6jZ1DACAbJ0dmiIGn5eSGDyCL002UkcAAMjWQSzjQasmBgAAnF4PB7ZRAAB0pYiIM1ZNwuemJAaPYLM/irG3wAAAOvNRc5A6AgAAwCO721MSAwDoyqxXRltZNQmfl5IYPKIvTaycBADoytv1duoIAAAAj2RZlnGn6qWOAQCQrTNWTcIjURKDR/SlsZIYAEBXtuMotqycBAAATqHtfj8WpckWAABdOWvVJDwSJTF4ROcGkxhUTeoYAADZut7sp44AAADwud3tD1JHAADI1rQto18r5MOjUBKDR1QURTw9nqeOAQCQrXfqh6kjAAAAfC7LsozbjVWTAABdOTtQc4FH5eqBL+BLEysnAQC6sh1H8aBdpI4BAADwmW0P+rEsTLYAAOjK2aFVk/ColMTgC7g4nEZb+hECAOjK9fYgdQQAAIDP7I5VkwAAnRk1RQwbNRd4VK4e+ALKooynrJwEAOjMO6WVkwAAwOmwKMu4U1s1CQDQlbMDA1zgi1ASgy/oS2MrJwEAuvKwOIoHvWXqGAAAAL/Sw8HAqkkAgA5ZNQlfjJIYfEFPjNaiLlxKAABduVbvp44AAADwK1k1CQDQnUFdxKR1Lg9fhCsIvqC6rOLJ0Sx1DACAbL1TbaeOAAAA8Kl+vmqyTR0DACBbZ6yahC9MSQyOwZcmVk4CAHRlp1jE/dbKSQAA4OR6OLRqEgCgS1ZNwhenJAbH4PJoFqUHAAAAnbnW7qWOAAAA8Evd7lk1CQDQlV5ZxKyn3gJflKsIjkFb1XFpOE0dAwAgW++WD8MsMQAA4CRalGXctWoSAKAzZ4aqLXAcXElwTJ6dbKaOAACQrZ1iEQ96amIAAMDJs23VJABAp85ZNQnHQkkMjsmXJutRFy4pAICuXG2snAQAAE6eO1ZNAgB0pl8VMe8ricFx0GiBY9KUVVwez1LHAADIlpWTAADASbOoKqsmAQA6dH6kIAbHRUkMjtHzUysnAQC6slss4r6VkwAAwAmyPbBqEgCgS+etmoRjoyQGx+jJ0Sx6pR8pAICuWDkJAACcJHf6Vk0CAHRl3BQxbtVa4Li4muAYlUUZz0w2UscAAMiWlZMAAMBJsaiquFs1qWMAAGTLFDE4XkpicMysnAQA6M5esYj7vUXqGAAAALE1tGoSAKBL50dKYnCclMTgmJ0fTmNct6ljAABk62qznzoCAABA3OlZNQkA0JV5r4x+rdICx8kVBR14zjQxAIDOWDkJAACktqiquOtlYQCAzpyzahKOnZIYdEBJDACgO3vFIu71rZwEAADS2RqaIgYA0JUilMSgC0pi0IH13jDWjRoHAOjM1XovdQQAAGCF3W49/wUA6MrGoIymKlLHgOwoiUFHnpuYJgYA0JV3KisnAQCANI6qKu41Vk0CAHTlwqhOHQGypCQGHbFyEgCgOwexjNtWTgIAAAk8GA5TRwAAyFZVRGz2VVmgC64s6Mi46cWFwSR1DACAbL3b7KSOAAAArKCbfasmAQC6cnZYRVVaNQldUBKDDpkmBgDQnQ+Kh3FQpU4BAACskv1eL7arJnUMAIBsnR966AtdURKDDj0z2Yiy0HIGAOjCsijiw95h6hgAAMAKuTMwRQwAoCu9soh1qyahM64u6FCvquPyaJY6BgBAtt6st1NHAAAAVsSyKOJmqyQGANCVc6MqCkNYoDNKYtAxKycBALpzpziI7XaZOgYAALACdoaDOCwdqwAAdMWqSeiWuxno2FOjebSlHzMAgK580O6ljgAAAKyAm/1h6ggAANka1kVMeyos0CVXGHSsKst4ejxPHQMAIFtvlw9jYQI5AADQoaO6irt1mzoGAEC2zo8MXoGuKYnBY/D89EzqCAAA2dorFnGnt0gdAwAAyNj94SiWhbdTAAC6YtUkdE9JDB6Di8NpDOsmdQwAgGy90+ykjgAAAGTs4/4gdQQAgGyttWUMG/UV6JqrDB6DoijiBdPEAAA6c6V4GAdeNAMAADqw3+vFTlmnjgEAkK0LVk3CY6EkBo/JS2tnU0cAAMjWsijieu8gdQwAACBDd4bD1BEAALJVFRHnlcTgsVASg8dk2vbjwmCSOgYAQLbeqrdTRwAAADKzLIr4uOmnjgEAkK2zwyrqskgdA1aCkhg8RqaJAQB0525xGFvtMnUMAAAgI9vDQRyVjlIAALpyyRQxeGzc2cBj9MxkI9rSjxwAQFfeb/dSRwAAADJyu2/VJABAV0Z1EbO+83N4XJTE4DGqyjKen26mjgEAkK23q+1YmEwOAAAcg6O6jjtNL3UMAIBsXRzXqSPASlESg8fMykkAgO4cxDJu9RepYwAAABm4NzRFDACgK0VEXLBqEh4rJTF4zDb6o9jsjVLHAADI1jv1w9QRAACADNzsDVJHAADI1plhFW1lLQQ8TkpikMBLszOpIwAAZOtquRv7XkADAAC+gL1+L3Yq648AALpyyRQxeOyUxCCB5yabURcuPwCArlzvH6SOAAAAnGK3B7ZBAAB0pV8Vsd53Xg6Pm6sOEmirOp6ZrKeOAQCQrTeq7dQRAACAU2pZFHGr7aWOAQCQrYvjKorCqkl43JTEIJGX1s6mjgAAkK0HxWE86C1TxwAAAE6h7dEwjmyCAADoRBERF62ahCTc5UAi54fTmLX91DEAALL1QbuXOgIAAHAK3eoPU0cAAMjWer+Mfq2qAim48iAh08QAALrzVrEdCxPLAQCAz+GoqeNu3aaOAQCQrUvjOnUEWFlKYpDQC9MzUdq1DADQicNiGTf7R6ljAAAAp8i94Sh1BACAbLVlxOZATQVScfVBQv26iadG89QxAACy9WbzMHUEAADglFhGxEftIHUMAIBsnR/VhqhAQkpikJiVkwAA3fmw2IuH7TJ1DAAA4BTYGQ5jr6pSxwAAyNalsf9akJKSGCT2xGgtxnWbOgYAQLbebfdSRwAAAE6Bm1ZNAgB0ZtYrY9SoqEBKrkBIrCiKeNE0MQCAzrxZbsWROx8AAOBTHDZN3PEyLwBAZy6OTBGD1ByVwAnw4tqZsHkZAKAbR0XE9f5B6hgAAMAJdntkihgAQFfqIuLcUEkMUlMSgxNg3PTi0nAtdQwAgGz9tH6YOgIAAHBCLcsyPuoNUscAAMjW+VEVVWlsCqSmJAYnxJdn51JHAADI1v3iIO72F6ljAAAAJ9CD0TCOCsclAABduTiuU0cAQkkMToynxvMY123qGAAA2Xqr2UkdAQAAOIE+6g9TRwAAyNasV8a0VU2Bk8CVCCdEURSmiQEAdOj9cif2vLAGAAD8M7v9fmxXTeoYAADZemJcpY4A/AMlMThBXlo7G1VhFzMAQFeu9PdTRwAAAE6QW0NTxAAAutKrijg7VBKDk0JJDE6Qft3Ec9PN1DEAALL103IrFjr5AABARBzVVdxq+qljAABk69K4itKQFDgxlMTghHl1dj51BACAbO0Vi/i4f5Q6BgAAcALcG41j6dASAKATZUQ8Ma5TxwD+GSUxOGE2+qM4P5ikjgEAkK2fNdupIwAAAKkVRdzoDVKnAADI1tlhFW2lkA8niZIYnECvzk0TAwDoys1iP7baZeoYAABAQtvDQRyUVeoYAADZujwxRQxOGiUxOIGeHs9jVLepYwAAZOvd3l7qCAAAQEIfD8apIwAAZGutLWPaU0eBk8ZVCSdQWZTx5dm51DEAALL1VrkVh4YGAADASjpo27hXN6ljAABk68mJh69wEimJwQn18trZqAo7mgEAurCIiGu9g9QxAACABG6NRqkjAABkq1cWcXaoJAYnkZIYnFD9uolnJxupYwAAZOsnzVYsU4cAAAAeq0VZxsfNIHUMAIBsXZpUURqGAieSkhicYK/Mz6eOAACQre04ijv9ReoYAADAY3R/PIpF6dASAKALRURcGtepYwC/hJIYnGBn+uM42x+njgEAkK03m4epIwAAAI/RR71h6ggAANk6N6yiVynkw0mlJAYn3KumiQEAdOZquRu7TeoUAADA47A7GMROZbIFAEBXnpz4rwUnmZIYnHDPTNZjWDu5BADoyvu9vdQRAACAx+DjoSliAABdmbZlrPVUUOAkc4XCCVcWZby8di51DACAbP2s2oqFCegAAJC1w7qO200/dQwAgGw9Ma5SRwB+BSUxOAVenp2NsnByCQDQhYNYxoeDw9QxAACADt0dj1NHAADIVlNGnB8picFJpyQGp8CwbuOZ8XrqGAAA2fpZvZ06AgAA0JFlUcSH7SB1DACAbF0a14aewCmgJAanxKvz86kjAABk63ZxEPd6i9QxAACADjwYj+KwdBwCANCFIiKeGNepYwCfgbsiOCXODiZxpj9KHQMAIFs/bR+mjgAAAHTghueqAACdOTOsol+bIgangZIYnCKvzkwTAwDoytVyN7abZeoYAADAMdoeDmKnMtkCAKArl8dV6gjAZ6QkBqfIs9ONGNdt6hgAANl6s7+TOgIAAHCMPhyOU0cAAMjWWlvGrK8kBqeFkhicImVRxmvzC6ljAABk651iO/YMGQAAgCzs9vvxwEu3AACdeWrqYSqcJkpicMq8NDsbvVIbGwCgC8uiiHf6u6ljAAAAx+AjU8QAADozqos4O3RuDaeJkhicMk1ZxSvz86ljAABk62flVhy6UwIAgFPtoG3jTttLHQMAIFumiMHp4+gDTqFXZ+ejLly+AABdOCoiPhgcpI4BAAB8ATfHpogBAHSlVxVxfmSKGJw2WiZwCvXrJl5YO5M6BgBAtn5cPYhFkToFAADwKI7qOj5u+qljAABk68lJFWXhASqcNkpicEq9Pr8QfnYBALqxVyzi+uAwdQwAAOAR3B6PY+nQEgCgE3UR8cTYqkk4jZTE4JSatv14ZrKROgYAQLb+vt6KZeoQAADA57Ioy/iwN0gdAwAgW09M6qhLhXw4jZTE4BT7yvrF1BEAALK1VRzGzcEidQwAAOBzuDcZx1Hh6AMAoAtlRDw5MUUMTit3SnCKbfZHcWk4TR0DACBbP262UkcAAAA+o2VRxPXeMHUMAIBsnR9V0atMEYPTSkkMTrmvrl9KHQEAIFu3iv241zNNDAAAToMH41EclFXqGAAAWSoi4umpKWJwmimJwSl3abQWm71R6hgAANn6SfswdQQAAOAzuNH3nBQAoCtnBlUMGxUTOM1cwZCBr25cTB0BACBb18rd2G6WqWMAAACf4uFoGDuVyRYAAF0xRQxOPyUxyMCXxusxbXqpYwAAZOvN/k7qCAAAwKe4MRinjgAAkK15r4xpT70ETjtXMWSgKIp4ff1C6hgAANl6p9iOPS/KAQDAibTb78eDukkdAwAgW0+ZIgZZUBKDTLw4PRuDyoMQAIAuLIsi3unvpo4BAAB8go9GpogBAHRl0hSxOahSxwCOgZIYZKIqy3h1fj51DACAbP2s3IpDz0IAAOBEOWjbuNP0UscAAMiWKWKQDyUxyMiXZ+eiKV3WAABdOCoiPugfpI4BAAD8MzfHpogBAHRlUBVxbujNWciFNglkpFfV8dLa2dQxAACy9ePqQSyK1CkAAICIiMO6jo+bfuoYAADZenJaR1F4IAq5UBKDzLw+vxClH2oAgE7sFYu4PjhMHQMAAIiIO+NxLD0LBQDoRFNGXBqZIgY5URKDzIyaXjw32UgdAwAgW39fb8UydQgAAFhxi7KMD3uD1DEAALL15KSOqlTIh5woiUGGvrZxKfxcAwB0Y6s4jI8HR6ljAADASrs7mcRR4YgDAKALdRFxeVKnjgEcM3dQkKG1dhDPTzdTxwAAyNbfNqaJAQBAKouyjGu9YeoYAADZujytozZFDLKjJAaZ+vrGE6aJAQB05H5xEB+ZJgYAAEncnUziqHS8AQDQBVPEIF/uoiBT07ZvmhgAQIf+tnlgmhgAADxmpogBAHTLFDHIl5IYZOzrG09EWfgBBwDowoPiMD4cHqaOAQAAK+WOKWIAAJ0xRQzy5k4KMjZt+/HcZCN1DACAbP1tvWWaGAAAPCaLqorrfVPEAAC6YooY5E1JDDJnmhgAQHe2isO4MTBNDAAAHofb40kcFY41AAC60JSmiEHu3E1B5qZtP56fbqaOAQCQrb9tH5gmBgAAHVtUVdzoD1LHAADI1pMTU8Qgd0pisAK+tn7JNDEAgI5sx1FcN00MAAA6dcsUMQCAzpgiBqvBHRWsANPEAAC69bfNg1jo5AMAQCeOqipu9IapYwAAZMsUMVgNSmKwIr6+YZoYAEBXHhamiQEAQFduTyaxcGgJANAJU8RgdSiJwYqYNP14wTQxAIDO/G193zQxAAA4Zkd1FddNEQMA6MxlU8RgZSiJwQr5mmliAACd2SkWcW1wkDoGAABk5dZ4EkvPNAEAOtGUP181CawGJTFYIaaJAQB060f1A9PEAADgmBzVVdwwRQwAoDNPmSIGK0VJDFbM1zeeME0MAKAjO8Uirg4PU8cAAIAs3BxPTREDAOhIU0Y8YYoYrBQlMVgx46YXL07PpI4BAJCtH1X3TRMDAIAv6LCu48PeIHUMAIBsmSIGq0dJDFbQ1zYumSYGANCR3WIRVwYHqWMAAMCpdnMyMUUMAKAjpojBalISgxVkmhgAQLd+VD8wTQwAAB7RYV3HR60pYgAAXTFFDFaTkhisKNPEAAC6s1cs4oOhaWIAAPAoPjZFDACgM00Z8aQpYrCSlMRgRY2bXry0djZ1DACAbP1d9SCO3HEBAMDnctg08bEpYgAAnXlqWkdlihisJEcWsMK+un4xKm/kAQB0Yq9YxAeD/dQxAADgVLk5NkUMAKArbRnx5NgUMVhVSmKwwsZNL16ZnU8dAwAgWz+qTRMDAIDP6rBp4qO2nzoGAEC2vrTWmCIGK8xxBay4r65fjLasUscAAMjSQSzj/cFB6hgAAHAqfDQxRQwAoCuDuohLY+fCsMqUxGDF9esmvrJ+MXUMAIBs/ai6b5oYAAD8CodNEx+3g9QxAACy9exaHaVCPqw0RxVAvDY/H8O6SR0DACBLh8Uy3h3sp44BAAAn2oeTSeoIAADZmjRFnB/VqWMAiSmJAVGXVXx941LqGAAA2fpRfT/2PYMBAIBPtN/rxU1TxAAAOvPszMAQQEkM+AcvrZ2NadNLHQMAIEuLiPjZYDd1DAAAOJGuTdZSRwAAyNa8V8bmoEodAzgBlMSAiIgoizJ+ffPJ1DEAALL1RrkV280ydQwAADhRHg6Hca822QIAoCvPmSIG/AMlMeCfPDvdjM3eKHUMAIBs/WiwnToCAACcGMuIuDKepI4BAJCts4Mq1npqIcDP+TYA/oVvnjFNDACgK9eK3bjTW6SOAQAAJ8KDySR2yjp1DACALBUR8ezMfy3g/1ASA/6FJ0azuDScpo4BAJCtv+k9CEsnAQBYdcuyjCtDWw0AALpyYVTFqFEJAf4P3wjAv/KtM0+ljgAAkK07xUF8ODhMHQMAAJK6NZnGQVGljgEAkKWqiHh21qSOAZwwSmLAv7LZH8Vzk43UMQAAsvXXzf1YFKlTAABAGkd1Hdd7w9QxAACydXlSR6/yABL4l5TEgE/0zTOXoyr8cQAA6MJOsYj3hvupYwAAQBIfTaaxKD17BADoQltGPDWtU8cATiAlMeATjZtevDI7nzoGAEC2flQ9iAPbdQAAWDEHbRsf9QapYwAAZOuZtSZqhXzgEyiJAb/U1zcuRa90cgkA0IXDYhlvDHZTxwAAgMfq+nQtdQQAgGwN6yIujp3vAp9MSQz4pdqqjq9tXEodAwAgWz8rH8ROs0wdAwAAHoudwSDu1G3qGAAA2Xpu1kRZmCIGfDIlMeBTvTo/H5OmlzoGAECWlkURf9d/mDoGAAB0bhkRV8eT1DEAALI165VxdmiKGPDLKYkBn6osyvj1zSdTxwAAyNYH5U7c65kmBgBA3rbG49iumtQxAACy9dzMfy3g0ymJAb/Sc9PNONMfpY4BAJCtv+k9SB0BAAA6syzL+GA4Th0DACBbZwdVzHrqH8Cn8y0BfCbfOnM5dQQAgGzdKvbjo8FR6hgAANCJO5NJHJRWHwEAdKGIiOdmdeoYwCmgJAZ8JheHa/H0eJ46BgBAtv66vR+LInUKAAA4XkdVFdf6w9QxAACy9eSkjmGj+gH8ar4pgM/s22eeiqpwcgkA0IXtOIorw8PUMQAA4Fh9PJ3GUeEoAgCgC20Z8cyaKWLAZ+PODPjMpm0/Xp9fSB0DACBbf1Pdi0NbeAAAyMRB28ZH7SB1DACAbD07a6IuDfkAPhslMeBz+drGpRjVbeoYAABZOiyW8eZgN3UMAAA4Ftcn01jaTAAA0IlJU8TFkTdOgc9OSQz4XOqyim+duZw6BgBAtn5SPohdE+IBADjldgf9uNP0UscAAMjWi+ttFAr5wOegJAZ8bs9NN+P8YJI6BgBAlpZFET8ePEwdAwAAvpCr42nqCAAA2To/rGLWU/cAPh/fGsAj+c2zT4deOgBAN94tH8aD3jJ1DAAAeCRb41FsVU3qGAAAWaqKiOfn/msBn5+SGPBINvujeHHtTOoYAADZ+uveg1ATAwDgtFmWZVyxhQAAoDNfmtbRq4zzAD4/JTHgkX1z83K0ZZU6BgBAlm4W+3F9cJg6BgAAfC43J9PYqzwzBADowqAu4vK0Th0DOKWUxIBH1q+b+LWNJ1LHAADI1l839+PQ+RoAAKfEYdPE9f4wdQwAgGw9P2uiLEwRAx6NkhjwhbwyPxeztp86BgBAlvaKRfx0sJM6BgAAfCZX12axdGgJANCJjX4ZZ4feKAUenZIY8IWURRm/efbp1DEAALL1s2o7ttpl6hgAAPCptkbDuFu3qWMAAGSpiIgX5k3qGMAppyQGfGFPjGbx9HieOgYAQLZ+0H8QamIAAJxUy7KMD0bT1DEAALL15KSOUaPeAXwxvkWAY/HtM09FZZQ8AEAnbhb7cX14mDoGAAB8opuTaeyXVh8BAHShKSOeWatTxwAyoCQGHItp24/X5hdSxwAAyNZf1/fj0LkbAAAnzEHbxvX+MHUMAIBsPTdroi4N6wC+OCUx4Nh8feNSjOo2dQwAgCztFYv46WAndQwAAPgXrk3XYmnDAABAJyZNERdH3hwFjoeSGHBs6rKKb525nDoGAEC2flZtx1a7TB0DAAAiImJrNIy7XhoFAOjMi/MmCoV84JgoiQHH6rnpZpzrj1PHAADI1l/1H4SaGAAAqS3LMj4YTVPHAADI1vlhFbO+KWLA8VESA47db537UuizAwB041axH9eGh6ljAACw4m5Op7FfOrQEAOhCVUQ8N2tSxwAyoyQGHLvN/iheXDuTOgYAQLZ+0NyLQ+dxAAAkctC2cb03TB0DACBbT0/r6NfGcgDHS0kM6MQ3Ny9Hv6pTxwAAyNJBLOMng53UMQAAWFFXp2uxLBxaAgB0YVgX8dTUOStw/JTEgE706ya+deZy6hgAANl6o9qOB71l6hgAAKyYrdEo7tVt6hgAANl6ab2JUiEf6ICSGNCZF9fOxoXBJHUMAIBs/aD3INTEAAB4XJZlGe+PPO8DAOjKhVEV6/0qdQwgU0piQKd+59wzmu4AAB25VezHteFh6hgAAKyIm9NpHJQOLQEAutCUEc/PmtQxgIwpiQGdmvUG8dX1i6ljAABk6wfNvThwTgcAQMcO2jau94apYwAAZOv5WRNtZfgG0B0lMaBzX1u/FGtNP3UMAIAsHcQyfjrcTR0DAIDMXZ3OYmljAABAJ+a9Mi6O69QxgMwpiQGdq8oyfuf8l1LHAADI1hvlVjzoLVPHAAAgU1ujUdyrrT4CAOhCGREvrfuvBXRPSQx4LC4O1+KF6WbqGAAA2fqr3v1QEwMA4LgtyjLeH01SxwAAyNbTa3WMGtUNoHu+aYDH5ttnnop+ZUwqAEAXbhcHcW10mDoGAACZuTWdxkFZpY4BAJClUV3E01Pnp8DjoSQGPDb9uolvnbmcOgYAQLZ+UN+LA+d3AAAck4O2jeu9YeoYAADZenG9ibIoUscAVoSSGPBYvbh2Ni4MjKcHAOjCQSzjx8Od1DEAAMjElekslg4tAQA6cWFUxXrfG5/A46MkBjx2v3Pumag8XAIA6MRb5Xbc6S1SxwAA4JS7N5nE/bpJHQMAIEtNGfH8zH8t4PFSEgMeu1lvEF9dv5Q6BgBAtr7fvxcLnXwAAB7RUV3HB8Nx6hgAANl6ftZEW3mABzxeSmJAEl9dvxiztp86BgBAlrbjKH422k0dAwCAU+ra2iyOCscHAABdmPfKuDiuU8cAVpC7PCCJqizjt899KXUMAIBs/bjaige9ZeoYAACcMlujYdxueqljAABkqYyIl9etmQTSUBIDkrk4XIsXppupYwAAZOsve/dDTQwAgM9qUVXx/niaOgYAQLaeXqtj2KhpAGn49gGS+vaZp6JfGacKANCFO8VBvDs6SB0DAIBT4sbaWhwUVeoYAABZGtVFPD11LgqkoyQGJNWvm/j2madSxwAAyNYP63uxY4I9AAC/ws5gEB+3g9QxAACy9dJ6E2VRpI4BrDAlMSC5F9bOxMWhMfYAAF1YRMRfDR6kjgEAwAm2LMt4b7KWOgYAQLYujKqY901sBdJSEgNOhN8596WoNOcBADrxUbEXV0eHqWMAAHBC3ZxOY690aAkA0IWmjHh+ZtQ/kJ6SGHAirLWD+LWNJ1LHAADI1l9V92LfuR8AAL9gv9eLa/1R6hgAANl6ab2NtjIsA0hPSQw4Mb6yfjHO9sepYwAAZOmwWMbfjLZTxwAA4CQpinh/as0kAEBXzg6rODf05iZwMiiJASdGURTx3fPPWjsJANCRK8VOfDg4Sh0DAIAT4vZ0GtuV1UcAAF1oyoiX5v5rASeHkhhwosx6g/jG5pOpYwAAZOv77d048PIiAMDKO2jbuGLNJABAZ6yZBE4aJTHgxHl9fiHOWTsJANCJg1jGD4fWTgIArLJlRLy3Noulif4AAJ04Z80kcAIpiQEnTlEU8R1rJwEAOvN+uRMfWzsJALCy7lgzCQDQmbaMeNGaSeAEUhIDTqRZbxC/bu0kAEBnvt/cs3YSAGAFHbRtXBmY4g8A0BVrJoGTSkkMOLFem1+I84NJ6hgAAFnaKxbxo+HD1DEAAHiMlhHxwdSaSQCArpwfVnHWmknghFISA06sn6+dfCbqwlcVAEAX3i0fxs2+tZMAAKvi7nQaD2qrjwAAutArC2smgRNN8wI40dZaaycBALr0/fZeHHq5EQAge4dNEx9YMwkA0JkX15torJkETjAlMeDEe3V+3tpJAICO7BaL+DtrJwEAsraMiPfXrJkEAOiKNZPAaaAkBpx4RVHEd88/a+0kAEBH3i4fxq3+InUMAAA6cm8yiQd1mzoGAECWrJkETguNC+BUmLb9+OYZaycBALry/fZuHLpDBADIzs/XTJrSDwDQlZesmQROCUcAwKnx6vxCXPBACwCgEzvFIv5uZO0kAEBO/nHN5KJ0aAkA0IXzwyrOWDMJnBJKYsCp8h1rJwEAOvN2+TA+GhyljgEAwDG5s7ZmzSQAQEesmQROG00L4FSZtv341pnLqWMAAGTrz5u7sVenTgEAwBe13+vFlf4odQwAgGy9vGHNJHC6KIkBp84r8/NxcThNHQMAIEuHxTL+argVy9RBAAB4ZMuyjHem81gWDi0BALpwYVTF5sCaSeB0URIDTqXvnH/G2kkAgI7cKHbj/dFB6hgAADyiG2uz2K0cWgIAdKFXWTMJnE4aFsCpNGn68e2z1k4CAHTlB9Xd2G7NEwMAOG22h4P4qDdIHQMAIFsvrzdRlya2AqePkhhwan15dj6eGK6ljgEAkKVlUcRf9O/FwvMuAIBT46iu4t3xLHUMAIBsXRpbMwmcXkpiwKn23QvPRr+qU8cAAMjS3eIwfjLaTR0DAIDP6OraPA5Lj/0BALowqot4YWbNJHB6uVsETrVh3cZ3zz+bOgYAQLZ+Wm3Fnf4idQwAAH6Fe5NJ3Gl6qWMAAGSpiIhXN9uorJkETjElMeDUuzyexyuzc6ljAABk68/au3Foij4AwIl10Lbx3nCSOgYAQLaemzUxadUrgNPNtxiQhW+feSrWe4PUMQAAsrRbLOJvhtupYwAA8EmKIt5bm8WyMNUCAKALG/0ynprWqWMAfGFKYkAWqrKMf3fh+ag8DAMA6MT75U5cGx6mjgEAwC/4eLoW21WTOgYAQJaaMuLLG23qGADHQkkMyMZ6bxjfOnM5dQwAgGz97/pu7Dp/BAA4MXb7/bg2GKWOAQCQrS+vt9GrDKkA8qAkBmTl1fmFeHK0ljoGAECWjoqI7w/uxzJ1EAAAYlGW8e5kljoGAEC2nhhXcWZYpY4BcGyUxIDsfPf8czEwYh8AoBM3i/14e7SXOgYAwMq7PpvFXuXQEgCgC6OmiOdnzhuBvCiJAdkZ1E189/yzqWMAAGTrh/WDuN+aJwYAkMrWaBQ320HqGAAAWSoj4tWNNqrSmkkgL0piQJaeHM/i1dn51DEAALL15/17ceSOEgDgsTus63h3NE0dAwAgW8/Nmpi0HnwB+fHNBmTrW2cux3rPG5UAAF3YKg7j70c7qWMAAKyUZURcma3HUenRPgBAFzb6ZVye1qljAHTCnSSQraos499feD6qwihYAIAuvFlux8eDReoYAAAr4+7aNO7VTeoYAABZasqIL2+0qWMAdEZJDMjavDeMb595KnUMAIBs/Xl7J/a9XAkA0Ln9Xi8+6I9TxwAAyNYrG230KsMngHwpiQHZe2V+Pi6PZqljAABk6SCW8YPhVuoYAABZWxZFvLs2i6WJ+QAAnXhiXMXmoEodA6BTSmLASvju+WdjaBQ/AEAnrhW78f7wIHUMAIBsfbQ2i53S+FYAgC6MmyJemDtHBPKnJAashH7dxHfPP5s6BgBAtn7Q3Iutdpk6BgBAdraHg7jRH6aOAQCQpTIiXt1oozSxFVgBSmLAynhiNIvX5udTxwAAyNIiIv60fy+O3GUCABybw6aJd8az1DEAALL13LyJceuBFrAafNsBK+Wbm5djo+fNSwCALmwVh/HXo+3UMQAAsrAsinhvNo+j0mN8AIAubPTLuDyx0htYHe4ugZVSlWX87sXno/FwDQCgE++XO/H+8CB1DACAU+/D2Sy2qiZ1DACALPWqIl7ZaFPHAHistCSAlbPWDuI7559NHQMAIFt/Vd+N++0ydQwAgFNrazSKD03DBwDoRBERr2220VZF6igAj5WSGLCSnplsxKuz86ljAABkaVkU8af9u3FYpU4CAHD6HDZNvDuepo4BAJCt52dNzHqqEsDq8c0HrKxvn70cZ/vj1DEAALL0sDiKvxxtpY4BAHCqLIsi3pmtx1Hh0T0AQBfODqq4PK1TxwBIwp0msLLKoozfvfh89EojLgAAunCt2I13RgepYwAAnBrX5+vxsHJoCQDQhUFdxJc3mtQxAJJREgNW2rjpxb+98FzqGAAA2frr+l7c7S9SxwAAOPHuT8bxcdtPHQMAIEtlRLy+2UZdFqmjACSjJAasvMvjeXxt/WLqGAAA2frT9m4cGN4KAPBLHbRtvDucpo4BAJCtF9ebmLTqEcBq8y0IEBHf2HwyLnoQBwDQid1iEd8fPYhl6iAAACfQoizjnbV5LAtTLQAAunBhVMWlsZXeAEpiABFRFEX8+wvPx7C2hxwAoAsfFnvx5ngvdQwAgBPn2mweO5VDSwCALoybIl6aO/8DiFASA/gng7qJf3/h+fDOJgBAN35UPYjb/UXqGAAAJ8bdySRutf3UMQAAslQVEa9vtlGVTv8AIpTEAP6FC8Np/Prmk6ljAABk6096d2LPoAwAgNjv9eL94SR1DACAbH15o41hoxIB8I98IwL8gq9uXIqnxvPUMQAAsnQQy/iL4f1YeIETAFhhi6qKt6fzWBb+FAEAdOHJcR3nhlXqGAAnipIYwCf47vlnY9L0UscAAMjSzWI/fjreTR0DACCZK7N57FUOLQEAurDWlvH83Ch7gF+kJAbwCXpVHf/h4gtReZsTAKATPym34uPBInUMAIDH7vZ0Gne8nAgA0ImmjHhts4nSGR/Av6IkBvBLbPZH8Rtnn0odAwAgW3/W3IndJnUKAIDHZ7ffjyuDceoYAADZemWjjX6tBgHwSXw7AnyKL8/Ox3OTjdQxAACydFgs488H92LhxU4AYAUcVVW8PZ3F0lQLAIBOPD2tY3NgpTfAL6MkBvAr/Jvzz8Ss7aeOAQCQpdvFQfz9eDd1DACATi0j4sp8PQ5Kh5YAAF2Y98p4dq1OHQPgRFMSA/gV6rKK/3DxhagLX5kAAF14o9yKG4PD1DEAADpza20Wd+s2dQwAgCz1yiJe22yjMLEV4FNpPAB8BvPeML5z/pnUMQAAsvXn7d3YbpapYwAAHLudwSCuDkapYwAAZKmIiNc2m2grBTGAX0VJDOAzena6GV9dv5g6BgBAlhYR8SeDe3FoAxMAkJHDpom3JrPUMQAAsvXivIlZ3wMlgM9CSQzgc/j1zSfj8miWOgYAQJa2isP4/mgrzBMDAHKwKMt4e74eR6XH8AAAXXhiXMUTkzp1DIBTw90pwOdQFEX8uwvPxaztp44CAJClG8Vu/Hi8mzoGAMAXsoyIK/P12CkdWgIAdGHeK+OFeZM6BsCpoiQG8Dm1VR3/8eKL0ZZG1wIAdOGn1VZcHR6mjgEA8MhuzmZxp+mljgEAkKV+VcRrm22URZE6CsCpoiQG8AhmvUH8+wvPh7+eAADd+H59J+73LJ4EAE6frfEorvVHqWMAAGSpKiK+cqaNtnJKB/B5KYkBPKInx7P45pnLqWMAAGRpWRTxx707sWdDEwBwiuz3evH2aC11DACAbH15o41Jq+YA8Ch8ewJ8AV9ZvxjPTTZSxwAAyNJesYg/G96LhRdDAYBT4Kiu4s21eSytPQIA6MTT0zrODavUMQBOLSUxgC/o35x/JjZ7VggAAHThdnEQfz3eTh0DAOBTLYsi3pttxEHp0BIAoAubgzKemzWpYwCcakpiAF9QXVbxny69EIPKH1MAgC68V+7EW6P91DEAAH6p6/P1eFB7NgQA0IVRXcSrG23qGACnnpIYwDEYNb34Dxefj9I6AQCATvywvh83+0epYwAA/Ct3ptP4uO2njgEAkKWmjPjKmTbq0hkcwBelJAZwTM4Pp/HbZ7+UOgYAQLb+pL0T280ydQwAgH+yMxjE+8NJ6hgAAFkqIuLVjTaGjVoDwHHwbQpwjF6anY1XZudSxwAAyNJREfG/hnfjsEqdBAAg4rBp4q3JLHUMAIBsPTdrYmPgQRDAcVESAzhmv3H2qbg4nKaOAQCQpe04iu+PtsI8MQAgpUVZxtuz9TgqPWIHAOjC+WEVT03r1DEAsuIOFuCYlUUZv3vh+Zg0vdRRAACydKPYjR9PdlPHAABW1DIirszXY6dyaAkA0IVpW8aXN5rUMQCyoyQG0IF+3cR/uvRi1IWvWQCALvy03Iqrw8PUMQCAFXRzNos7Xg4EAOhEryzi9c02yqJIHQUgO9oLAB1Z7w3juxeeTR0DACBb36/vxL2exZMAwOPzYDyKa/1R6hgAAFkqI+L1M230awUxgC4oiQF06JnJRnx941LqGAAAWVoWRfyv3p3Ys+kJAHgM9nq9eGe0ljoGAEC2XlpvYq2nwgDQFd+wAB37xuaT8aXxeuoYAABZ2isW8afDe7HwgikA0KGjuoq31uaxtPYIAKATlyd1XBx7ExCgS0piAI/Bv73wbJztj1PHAADI0p3iIP56vJ06BgCQqWVRxHuzjTgoq9RRAACydHZQxfMzBTGArimJATwGdVnF7116MaZNL3UUAIAsvVfuxFuj/dQxAIAMXZ+vx4O6SR0DACBLa20Zr2w0UZjYCtA5JTGAx6RfN/F7l16KnrdOAQA68cP6fnw8WKSOAQBk5M7aND5u+6ljAABkaVAV8ZUzbVSlghjA46AkBvAYzXqD+I+XXozS2xAAAJ34k/Z23O8tU8cAADKwNRrF+4NJ6hgAAFlqyoivnm2jrZyZATwuSmIAj9mF4TS+e/7Z1DEAALK0iIj/2b8dO42iGADw6Hb7/Xh7vJY6BgBAloqIeH2zjVGjrgDwOPnWBUjguelmfGPjidQxAACydBDL+KPh3Tiw5RsAeAQHbRtvTuexNAkeAKATr2w0Me97cAPwuCmJASTy9c0n4sW1M6ljAABkaTuO4k9H92PhbBcA+ByO6ireWluPo9KjcwCALjyzVsf5UZ06BsBKcqcLkNDvnPtSXBpOU8cAAMjSrWI//nKyHRZPAgCfxaIs4535RuxVploAAHTh4qiKZ9aa1DEAVpaSGEBCZVHGf7j4Qqz3BqmjAABk6UqxE38/3kkdAwA46YoirqxvxHbl0BIAoAvrvTJeWvdfCyAlJTGAxNqqjt+79FIMa3+MAQC68LNqO94e76eOAQCcYNfn87hTt6ljAABkadQU8fqZNsqiSB0FYKUpiQGcAOOmF7936aWoC1/LAABd+JvqftwYHKWOAQCcQDfXZvFRa8o7AEAXemURXzvTRl0qiAGkpo0AcEJs9kfxuxefD3+RAQC68WfN7bjTW6SOAQCcIPcn47g6GKWOAQCQpaqI+MrZNvq1WgLASeDbGOAEuTyex2+efTp1DACALC2LIv64dye2m2XqKADACbAzHMa7w2nqGAAAWSoi4tWNNqatSgLASeEbGeCEeWV+Pl6bn08dAwAgS4fFMv5ocCf26tRJAICU9nu9eHO8FsvCTHcAgC68MG/izLBKHQOAf0ZJDOAE+vaZp+JL4/XUMQAAsrRTLOJPhnfjyB0xAKyko6aON9fmsSj9GQAA6MLlSR1PTryhB3DSuAsGOIGKooh/e+HZONsfp44CAJClu8Vh/MV4KxaGhwDASllUVbw134iD0lQLAIAunBmU8fxMQQzgJFISAzih6rKK/3TpxZg2vdRRAACydKPYjR+Od1LHAAAek2VRxHvzjdgpHVoCAHRhrS3j1Y02Ciu9AU4kJTGAE2xQN/Gfn3g5BlWTOgoAQJbeKbfjZ+O91DEAgMfg6nw97teesQAAdGFUF/GVM21UpYIYwEmlJAZwwk3bfvznJ16K1hoEAIBO/F31IK4MD1PHAAA69NF8FrfafuoYAABZ6ldFfO1sL9pKQQzgJFMSAzgFNvqj+L1LL0Zd+NoGAOjC95u7cau/SB0DAOjA3ekkrvdGqWMAAGSpKSO+draNfq0gBnDSaRsAnBLnh9P43YvPR2mPOwBAJ/64dzse9JapYwAAx2h7NIz3htPUMQAAslQVEV8724tRo3YAcBr4tgY4RS6P5/Gdc8+kjgEAkKVFRPxR707sNqmTAADHYa/fj7fGs9QxAACyVEbEV860MW1VDgBOC9/YAKfM82tn4jfPPpU6BgBAlvaKRfzR4G4cVKmTAABfxGHTxJvTeSxNZAcAOHZFRLy62cZ63wMUgNNESQzgFHp1fiG+vnEpdQwAgCxtFYfxJ6N7ceSOGQBOpaOmjjfmG3FY+jEHAOjCy+tNnB0qiAGcNu6SAU6pb2w+GV+enUsdAwAgS7eLg/jT8f1YGD4CAKfKUV3Fm7PN2C8dWgIAdOG5WR0Xx3XqGAA8AiUxgFPst84+Hc9ONlLHAADI0sfFfvzFeEtRDABOiUVVxVvrm7FbKYgBAHThqUkdT0+b1DEAeERKYgCnWFEU8W8vPBtPDNdSRwEAyNL1cjf+crIdy9RBAIBPtSjLeGd9I3ZKUy0AALpwcVTF83MFMYDTTEkM4JQrizL+46UX4lx/nDoKAECWrhQ78TeTHUUxADihlkUR761vxlbl0BIAoAtnBmW8vO6/FsBppyQGkIG6rOL3nngp1nuD1FEAALL0TrkdfzfZTR0DAPgFy6KIDzY2437t0BIAoAvzXhmvbbZRFEXqKAB8QUpiAJnoVXX85ydejknTSx0FACBLb5Rb8ZOxohgAnBTLiLiyvh536jZ1FACALE3bMr5ypo1SQQwgC0piABkZ1m38/554OQbWKwAAdOLH1Va8Nd5PHQMAiIjr6xtxu+mnjgEAkKVRXcRXz7RRlwpiALlQEgPIzLTtx39+4qVoyyp1FACALP2wuh/vjQ5SxwCAlXZjPo+PWwUxAIAu9Ksivna2jbZSEAPIiZIYQIY2+qP4T5dejMr4XwCATvxVfS+uDg9TxwCAlfTxbBYf9oapYwAAZKkpI752to1+rUoAkBvf7ACZujCcxu9efN6eeACAjvxFczc+HByljgEAK+X22lpc649SxwAAyFJVRHztTC9GjRoBQI58uwNk7Knxevy7C8+FmhgAQDf+tLkdt/qL1DEAYCXcnUzig8E4dQwAgCz9vCDWxrSnQgCQK9/wAJl7ZrIR/1ZRDACgE8uiiD/u3Y47PUUxAOjS/ck43htNU8cAAMhSVUR89Uwbs36VOgoAHVISA1gBz00347vnn1UUAwDowCIi/qh3O+63y9RRACBLW6NhvDNaSx0DACBLVRHxlTNtzBXEALKnJAawIp5fOxP/5vwzqWMAAGTpqIj4w/7t2FIUA4Bj9XA4jLfHs9QxAACyVEbE65ttrCuIAawEJTGAFfLi2tn4nXNfSh0DACBLh8Uy/mf/Tuw0qZMAQB52B/14czKLZWE2OgDAcSsi4vUzbWwMFMQAVoWSGMCKeXl2Ln773NOpYwAAZGmvWMQfDm7HXp06CQCcbnu9Xrw5nSuIAQB0oIifTxDbVBADWClKYgAr6Muz8/GbZ59KHQMAIEs7xSL+cHQn9j1nBYBHctC28cbaehwVHl8DABy3IiJe22zjzNCDC4BV4y4bYEW9Or8Q3z5zOXUMAIAsbcdR/NHoXhx63goAn8th08Qb8/U4Kj26BgA4bkVEvLrZxlkFMYCV5E4bYIW9vn4xvrn5ZOoYAABZul8cxP9SFAOAz+ywbeKN+UYcFH48AQCOWxERr2w0cU5BDGBlKYkBrLivblyKb2w8kToGAECWbhcH8ceje3Hg+SsAfKrDpok3ZhuxX/rRBAA4bkVEvLzexPlRnToKAAkpiQEQX998Ir6+cSl1DACALN0pDuKPR3cVxQDglzho2/jZXEEMAKArL683cXGsIAaw6pTEAIiIiG9sPhlfXb+YOgYAQJbuFofxP00UA4B/5aBt4435ehwoiAEAdOKluYIYAD+nJAbAP/nmmcvx+vxC6hgAAFm6XxzEH47uxr4zcACIiH+YIDbbiIPCjyMAQBdemjfxxERBDICfUxID4F/49tmn4rX5+dQxAACy9KA4VBQDgIjY7/XiZ7ONOCw9ogYA6MILCmIA/AJ34AD8K79x9ul4ZXYudQwAgCxtFYfxvfGd2POcFoAVtd/rxc/W1hXEAAA68vysicsKYgD8AnfhAHyi3zr3pXh57WzqGAAAWdqOo/jDkaIYAKtn7x8KYkcKYgAAnXhurY6nph44APCvuRMH4Jf6nfPPKIoBAHRkO47ie8M7sdukTgIAj8devxdvzBTEAAC68txaHU+vedAAwCcrlsvlMnUIAE62P/3o3fjbOzdSxwAAyNJgWcZ3d9ajf5A6CQB0Z7ffjzfX5nFUKIgBAHThhbkVkwB8OiUxAD6T73/8fvzg9rXUMQAAsjRYlvGdnfUYKIoBkKHdQT/emKzHoixSRwEAyE4RES+tN3FprCAGwKdTEgPgM/vrW1fjL25+kDoGAECW+ssyvrM7j+G+A3QA8rE7GMQbk7mCGABAB4qIeGWjifMjBTEAfjUlMQA+lx/duR5/8tF7qWMAAGSptyzjOzvzGB04SAfg9NsZDOKN6TyWhd81AIDjVkTEa5ttnB1WqaMAcEooiQHwuf3k7kfxRx++HX5AAACOX+8fJoqNTBQD4BTbGQ7jjclMQQwAoANlRLx+po3NgYIYAJ+dkhgAj+TN+zfjezfeioWfEQCAY9dEEd/dWY+xohgAp9DD4TDeVBADAOhEVUR85Uwb630FMQA+HyUxAB7ZOw9uxX+//qaiGABAB+plEd/dW4/JngN2AE6Ph6NhvDlWEAMA6EJdRHz1bC9mvTJ1FABOISUxAL6QD7buxn+79rM4XC5SRwEAyE69LOI7u+sxNVEMgFNgazSMtxXEAAA60ZQRXzvbi2mrIAbAo1ESA+ALu/bwXvy/V38aBwtFMQCA41YtmAGPzAAAnlBJREFUI767txFTE8UAOMG2RqN4e7ymIAYA0IFeWcTXzrYxVhAD4AtQEgPgWHy48yD+y5WfxP7iKHUUAIDsVMuI7+ytx9qeh8EAnDxb41G8NZ6ljgEAkKVeVcSvnW1j2HgmAMAXoyQGwLG5ubsd/8+VH8fu0WHqKAAA2Skj4nf21mN910NhAE6Oe5NJvDuapo4BAJClQV3E18+2Mag9CwDgi1MSA+BY3dl7GP/3lR/Hw8OD1FEAALJTLJfx7cN5nH9Yp44CAHFruhZXhuPUMQAAsjSqi/j6uV70Kuu8ATgeSmIAHLt7+zvxf3/w49g63E8dBQAgS792OIvL24piAKTz4XweN3rD1DEAALI0bor4+tletApiABwjJTEAOrF1sBf/1wd/H/cP9lJHAQDI0itHk3hhq5c6BgCrpiji6nweN9tB6iQAAFmatmV87UwbjYIYAMdMSQyAzjw83I//64O/j7v7u6mjAABk6bnFKF59MAiPjQF4HJZlGR+sb8Sduk0dBQAgS7NeGV8900ZdutMH4PgpiQHQqd3Dg/gvV38SH+9up44CAJClJxeD+PrWKEp39wB0aFFV8e58Ix7UTeooAABZ2uiX8fpmG5WCGAAdURIDoHMHi6P4b1d/Flce3ksdBQAgS+eWvfjW1iSqReokAOTosK7j7fWN2Cnr1FEAALJ0YVTFy+tNlIWCGADdURID4LFYLBfxhzfejjfu30wdBQAgS+vLJn5zey2ao9RJAMjJQdvGm7P12C+r1FEAALL09LSO52amtQLQPSUxAB6rP/vovfjhneupYwAAZGm8rON3Hs6if5g6CQA52Ov14s219Tgsy9RRAACyU0TE8/MmLk9MawXg8VASA+Cx++Hta/FnH7+fOgYAQJb6yzL+ze48RvtWVADw6HYGg3hzMo9F6fcEAOC4lRHxymYb54amtQLw+CiJAZDEm/dvxvduvBULP0MAAMeuXhbxb/bmsbZn8gsAn9/WeBRvj9ZiWSiIAQAct7qIeP1MG+t9BTEAHi8lMQCSubp9L37/2k/jYLFIHQUAIDtlRPzW3jw2dz10BuCzuzudxHvDaeoYAABZ6pVFfPVsG5PWS10APH5KYgAkdXN3O/7LlZ/EztFB6igAAFn65sEsLj2sU8cA4BT4eG0W1waj1DEAALI0rIv42tk2BrWCGABpKIkBkNz9/d34f678OO4f7KWOAgCQpa8cTuOZ7TZ1DABOqGVEfDhfjw97g9RRAACytNaW8ZUzbbSVdd4ApKMkBsCJsHN4EP/16k/i493t1FEAALL00mIcLz/op44BwAmzLIq4sr4etxu/EQAAXdjsl/HaZhtVqSAGQFpKYgCcGAeLo/hvV38WVx7eSx0FACBLzyyG8fqDYXgsDUBExKIs4/31zbhXN6mjAABk6cKoipfXmygLd+IApKckBsCJslgu4g9vvB1v3L+ZOgoAQJYuLfrxja1xlJ4GAKy0o6qKd9Y3YrtSEAMA6MLT0zqem/mvBcDJoSQGwIn0Zx+9Fz+8cz11DACALJ1ZtvHt7WnUR6mTAJDCYdPEW7ON2K2q1FEAALJTRMTz8yYuT+rUUQDgX1ASA+DE+uHta/FnH7+fOgYAQJamyyZ+++Fa9A5TJwHgcdrv9eLNtXkclApiAADHrYyIVzbbODf0XwuAk0dJDIAT7c37N+N7N96KhZ8rAIBj11uW8du785juF6mjAPAYbI+G8dZ4FsvC9z4AwHGri4jXz7Sx3lcQA+BkUhID4MS7un0vfv/aT+NgsUgdBQAgO2VEfHt/Hud2PMQGyNnt6TQ+GE5SxwAAyFKvLOKrZ9uYtGXqKADwSymJAXAq3N57GP/1yk9i63A/dRQAgCy9fjiNZ7fb1DEAOG5FEdfn8/ioHaROAgCQpUlTxFfO9KJfm9YKwMmmJAbAqbFzeBD/79Wfxke7W6mjAABk6ZnFMF7bGkbpSQFAFhZVFe/P1+NerQQMANCFzUEZr220UZUKYgCcfEpiAJwqR4tFfO/GW/HWg1upowAAZOnsshff3J5Ec5Q6CQBfxGHbxNtr67FT1amjAABk6fKkjudndRSFghgAp4OSGACn0v+++UH81a2rqWMAAGRptKzid3bmMThInQSAR7Hb78db03kclmXqKAAA2Ski4qX1Ji6NlfEBOF2UxAA4td68fzP+8MZbceSnDADg2NXLIn57bx7zPQUDgNPkwXgc74ymsTTRAgDg2DVlxGubbaz3q9RRAOBzUxID4FT7cOdB/P7Vn8XOkTEXAADHrVgu49cP53HpobejAU6Dm7NZXO2PUscAAMjSoC7iq2faGDVepgLgdFISA+DUe3CwG//1yk/jzv5O6igAAFn68tEkXtjqhZk0ACfTsiji6vp63Gr6qaMAAGRp3ivj9c02msqdMQCnl5IYAFnYPzqM/379jfhg+17qKAAAWXpyMYivb42i9BQB4EQ5qqp4b74RD+omdRQAgCxdGFXx8noTpXXeAJxySmIAZGO5XMaffPRu/N3dD1NHAQDI0vqyid/YXov2KHUSACIiDto23lpbj72qSh0FACBLz63V8fSaMj4AeVASAyA7f3fnRvzJR++GHzgAgOM3WJbxO7vzGO17gxogpZ3BIN6azuKoKFNHAQDITlVEvLLRxtmhMj4A+VASAyBLH2zdjf9+/Y3YXxhzAQBw3KplxG/tr8fGrmICQAp3p5N4bzhNHQMAIEu9soivnG1j2rrnBSAvSmIAZOvO3sP4r1d/Gg8O9lJHAQDI0q8dzuLydp06BsDKWEbER7N53OgPU0cBAMjSpCniK2d60a9NzwYgP0piAGRt5/Agfv/qT+PD3a3UUQAAsvTC0Si+vDUIj88BurUsy/hgvh53ml7qKAAAWTozKOPVjTaq0h0uAHlSEgMge0eLRfzhjbfizQe3UkcBAMjSxWU/vrE1jmqROglAng7rOt6dr8d21aSOAgCQpacmdTw3q6MoFMQAyJeSGAAr469uXon/fetK6hgAAFmaLpv4rYdr0T9MnQQgL3u9Xry1No+DskodBQAgO0VEvLTexKVxnToKAHROSQyAlfLug9vxvRtvxf7iKHUUAIDsNFHEb+7NY323TB0FIAv3J+N4dziNpYkWAADHrlcW8dqZNmY997AArAYlMQBWzt29nfj9az+Nu/u7qaMAAGTp9cNpPLvdpo4BcGotiyI+nM3jw94gdRQAgCzNemW8ttlGr1LGB2B1KIkBsJIOFkfxP66/Ge9u3UkdBQAgS08uBvG17VFUi9RJAE6Xo7qO92br8aBuUkcBAMjSE+MqXpg3UZrWCsCKURIDYKX94NbV+N83Pwg/hgAAx2+yrOO3dmYxOEidBOB02B304+3pLA6KKnUUAIDslBHx0noTF8d16igAkISSGAAr74Otu/EH19+IvcVR6igAANmplhG/cTCPMzsKDwCf5s50Eh8MJrE00QIA4Nj1qyJeP9PGtC1TRwGAZJTEACAi7u/vxu9f+2nc3ttJHQUAIEuvHE3i+a1eqD4A/EvLsoxrs1ncbAepowAAZGneK+O1zTbayh0pAKtNSQwA/sHh4ij+5423480Ht1JHAQDI0oVFP77xcBy1Aa4AERFx2DTx7mwe21WTOgoAQJYuT+p4flZHYVorACiJAcAv+uHta/HnH78ffiABAI7fcFnFb+3OYrzvAT2w2naGw3hrvBZHpZVHAADHrSoiXl5v4vyoTh0FAE4MJTEA+ATXHt6L/+/aG7F7dJg6CgBAdsqI+ObBLC489LAeWE2319big8E4dQwAgCwN6iK+stnGuFXGB4B/TkkMAH6JrYO9+G/XfhYf726njgIAkKUXjkbx8vYgSk8mgBWxKMu4Ml+PO00vdRQAgCxt9Mt4daONpjK9GgB+kZIYAHyKo8Ui/vijd+Kn9z5OHQUAIEtnlm188+E0WgNcgcwdtG28vbYeu1WVOgoAQJaentbx7FodRaEgBgCfREkMAD6Dv797I/7ko/di4WcTAODY9Zdl/ObePNb2PMgH8rQ1GsY7o1ksSt9zAADHrSoiXtlo4+xQGR8APo2SGAB8Rjce3o//7/ob8fDwIHUUAIDsFMtlfP1wFpcfNqmjABybZUTcnM3iWn+UOgoAQJZGdRGvn2lj1JSpowDAiackBgCfw8PD/fhvV38WH+5upY4CAJClZxbDeG1rGKWnFcApt6iqeH++HvfqNnUUAIAsnRmU8cpGG7VprQDwmSiJAcDntFgu4s8/fj/+9s6N1FEAALK0vmzi2w/XoneYOgnAo9nv9eLt6Tz2KiuPAACOWxERz67V8fSaSdQA8HkoiQHAI3pv63Z87/pbsbc4Sh0FACA7TRTxW3vzmO9aGQKcLvcn43h3OI1lYaIFAMBx61dFvLrZxqznXhEAPi8lMQD4ArYO9uK/X3vD+kkAgI68fjiNZ7bbULUATrplUcSHs3l82BukjgIAkKXNQRmvrLfRVO4QAeBRKIkBwBe0WC7i+zc/iL+5fT11FACALJ1b9uIb25NoDXAFTqiDto13ZvPYKevUUQAAslNExPOzJi5P/dcCgC9CSQwAjskHW3fjf9x4M3aPDlNHAQDITm9Zxrf2Z7Fh/SRwwtyfTOLd4cR6SQCADgyqIl7bbGNqvSQAfGFKYgBwjLYP9uIPrr8Z13cepI4CAJCllxbjeHGrH6WnGUBii7KMa7N53Gr7qaMAAGTp7KCKL280UZfK+ABwHJTEAOCYLZfL+MtbV+IHt66GH1kAgOO3vmziWztr0T9InQRYVXv9XrwzmcdeVaWOAgCQnTIiXpg38cTEekkAOE5KYgDQkavb9+IPrr8ZO0dOLwEAjlu1jPjm4SzOP3RoADxet6fTuDIYWy8JANCBYf3z9ZKT1npJADhuSmIA0KGdw4P4g+tvxNWH91NHAQDI0rOLUbyyPYhqkToJkLujuoors/W4W7epowAAZOn8sIqX1q2XBICuKIkBQMeWy2X84NbV+MtbV6yfBADowGRZx7d312K87yAB6MbOYBDvTNfioLBeEgDguFXFz9dLXhqbFA0AXVISA4DH5PrD+/EH19+M7cP91FEAALJTLJfx9cNZXH7YpI4C5KQo4uO1tbjWH6VOAgCQpdE/rJccWy8JAJ1TEgOAx2j38CD+x40344Pte6mjAABk6YnlIL62PYr6KHUS4LQ7bJp4f20eD2rlUwCALlwYVfHSvInKekkAeCyUxAAggb+5fS2+f/ODWPgZBgA4doNlGb+xN4+1PQcNwKPZGg3j3fFaHBUmWgAAHLeqiHhx3sRF6yUB4LFSEgOARD7ceRD//dobsWX9JABAJ14/nMYz222oigGf1bIo4sPZPD7sDVJHAQDI0rj5+XrJUaOMDwCPm5IYACS0d3QYf/zhO/HWg1upowAAZOncshff2J5Ea/0k8CsctG28M5vHTmmiBQBAF54c1/H8vI6y8CoPAKSgJAYAJ8Cb92/GH3/4TuwvnF4CABy33rKMb+3PYmPXm+rAJ7s/mcS7w0ksHVgCABy7XlXEKxtNrPer1FEAYKUpiQHACbF9sBffu/FWXH14P3UUAIAsvbQYx4tb/Sg9CQH+waIs49psHrfafuooAABZOjes4qV5E02ljA8AqSmJAcAJ87e3r8df3Hw/jvxEAwAcu/VlE9/aWYv+QeokQGp7/V68M5nHXmWiBQDAcauLiJfWmzg/ssobAE4KJTEAOIHu7D2MP7j+Ztzae5g6CgBAdqplxK8dzuLSQ4cVsJKKIm5Np3G1P7JeEgCgA/NeGa9stNGv/dcCgJNESQwATqjFchH/++aV+Jvb18KPNQDA8bu47MfXtsfRHqVOAjwuB20b763NYrtqUkcBAMhOGRHPzZq4PPVCDgCcREpiAHDC3Xh4P/7HjbfiwcFe6igAANmpl0X8+uFanDdVDLK2jIi7a9P4oD82PQwAoAOTpohXNtsYN2XqKADAL6EkBgCnwMHiKP7Xh+/Ez+7fTB0FACBLlxeDeP3hKBpTxSA7h20T709n8aBuU0cBAMhOERFPTet4Zq2OUhkfAE40JTEAOEXeeXAr/ujDd2L36DB1FACA7PSWZfz6wSzO7HjzHXJxdzqJD/qTWJQOLAEAjtugKuKVjSZm/Sp1FADgM1ASA4BT5uHhfvzhjbfig+17qaMAAGTpS4tRvLo9iHqROgnwqA7rOq7M5nHP9DAAgE5cGFXx4ryJWhkfAE4NJTEAOKX+/u6N+LOP3o/DpdNLAIDjNliW8c39WazvmioGp839yTjeH07iqHD9AgAct6aMeHm9jbND08MA4LRREgOAU+ze/k78j+tvxUe7W6mjAABk6fnFOF7e7kellw8n3lFdxdW1edxpeqmjAABkaaNfxpc32uhVpocBwGmkJAYAp9xiuYgf3LoWP7h9NRZ+1gEAjt0oqvjW7lqs7ZlKBCfV1mgU742mcVi6TgEAjltVRDw/a+KJSZ06CgDwBSiJAUAmPtp5EN+78Vbc3d9NHQUAIEsvHo3jpe1+lJ6kwImxqKq4tjaLW20/dRQAgCyttWW8stHEsFHGB4DTTkkMADJytFjEX966Ej+8c91UMQCADkyWdXxzdy2m+9arQGoPR8N4dzyNg6JKHQUAIDtVEfHsWhNPTqooCvc/AJADJTEAyNDN3e343o034/beTuooAABZeuVoEs9t90wVgwQWZRk3ZvP42PQwAIBOzHtlvLxuehgA5EZJDAAytVgu4ge3rsUPbl81VQwAoAOzZR2/vrsWY1PF4LHZGQzi3cla7JemhwEAHLeqiHh+1sQTkzp1FACgA0piAJC523sP43vX34qbe9upowAAZKdYLuO1o7V4ZrsNVTHozrIs48baWnzUG6aOAgCQpY3+z6eH9WvTwwAgV0piALACFstF/M3t6/FXt67EkZ9+AIBjt7Fs49d3pjE4SJ0E8rPb78e701nsmR4GAHDs6iLi+XkTl8amhwFA7pTEAGCF3Nl7GH944+34aHcrdRQAgOyUEfH64Vo8vd2YKgbHYFGW8fF0LW70TQ8DAOjCZr+Ml9bb6NfuYABgFSiJAcCKWS6X8aM7N+L7Nz+Iw+UidRwAgOzMlnX82u5aTPcdtMCjejgaxnujaeybHgYAcOyaMuLFeRPnR6aHAcAqURIDgBX14GA3/ujGO3Hl4b3UUQAAsvTiYhwvbvej0suHz+yoruPa2lrcbvqpowAAZOn8sIoX5k20lZdaAGDVKIkBwIr72b2P408/ejf2FkepowAAZKe/LOMbB2txZsc0JPg0y4i4N5nEleE4jooydRwAgOz0qyJeWm9ic+DeBABWlZIYABA7hwfxJx+9G289uJU6CgBAli4t+vGVnXH0DlMngZNnv9eLD6ZrsVU1qaMAAGSniIhL4yqemzVRl6aHAcAqUxIDAP7Je1u3448/fDe2D/dTRwEAyE75/2fvvqOjKte3j1/T0hslFEGKdBCQInY9YkNAQFQ8gih2f4oHea3YsJejHvSAgh09KiAqiIiIBbEhCIgoAqL0ACEkpLdp7x8h29nJJJkkk0wyfD9rscie7Jm5k0wmez/7eu5HUj9Xko7Os4tLM4DktVp1MCFB+yJj5LXwWwEAABBssXaLejSLUFIknVoBAAAhMQAAUEax26XVB3fr98zUUJcCAAAQlpp6HRpQmKC4YkIxOHIVxMRoZ2yCimwsdwQAABBsFkkdEuzqmGiXlTA+AAA4jJAYAADwK7UgR9+mblNGUUGoSwEAAAhL3T1x6poXJZsn1JUA9cdtt2lfYpLSHVGhLgUAACAsNYm0qltTh+IcdA8DAABmhMQAAECFvF6vfju0X2vSd8vp4eolAABAsEV7rRpYnKjmhXRTQvjLjo/Xrug4ua1csAQAAAi2CKvUpYlDrWPtoS4FAAA0UITEAABAlfJdxVp5YKf+ykkPdSkAAABhqZ0nWr3zYxXhDnUlQPA5IyK0OyFROfaIUJcCAAAQdiyS2sTZ1DnJIbuVpSUBAEDFCIkBAICApeRl6fsD25VZXBjqUgAAAMKOzSv1cyepbZ5dXNpBOPBaLEpPTNTeyBh5LbyqAQAAgi0xomRpyYQIOrUCAICqERIDAADV4vF69EvGPv2cniKXlyUoAQAAgq25N0L9C+MVW0yoBo1XQXS0dsUlqtDGUqoAAADB5rBKnRIdahvP0pIAACBwhMQAAECN5DgL9cOBndqZeyjUpQAAAISlXu54dc6LlJWRGzQiHptN+xOTlBYRFepSAAAAwtJRsSVLS0bYmFQCAACqh5AYAAColZ25GfrhwE7lOItCXQoAAEDYiZVNA4sS1bSQ5WPQ8GXHxWl3TLxcVl6vAAAAwRbnsKh70wglRXKsBQAAaoaQGAAAqDWXx62f01P0y6F98nBoAQAAEHQdPDHqlR+jCHeoKwHKc0ZEKCUhSVl2R6hLAQAACDs2S8nSkkfH22Sx0D0MAADUHCExAAAQNJlFBfr+wHal5GeHuhQAAICwY/NKvd2Jap/vYAlKNAgeq1VpCYlKjYyWlwuWAAAAQdcyxqauTRyKZGlJAAAQBITEAABA0P2VfVAr03Yq3+UMdSkAAABhJ1Y29StOVHIBy8wgNLySsuPjtSc6jqUlAQAA6kCs3aJuTR1qGmULdSkAACCMEBIDAAB1wulxa83B3frt0H5xsAEAABB8rbxR6lMQq1gnXQVQfwqjo7QnLkF5NpaWBAAACDabReqQYFf7BLusdGoFAABBRkgMAADUqfTCPH2Xul2phbmhLgUAACAsdfPEqWt+lOzuUFeCcOZyOLQvPkEZEVGhLgUAACAsNY+2qlsTh6LtdGoFAAB1g5AYAACoF1uz0rT64G7luYpDXQoAAEDYcciivs5Etc23i34DCCav1ar0+ATtjYqRl24WAAAAQRdrt6hLE4eaR7O0JAAAqFuExAAAQL1xedzakLFP6zP2yuX1hLocAACAsJPgdah/UbyaFNF9ALWXGxer3THxKrZywRIAACDYHFbpmESH2sTZWFoSAADUC0JiAACg3uW7irU6bZf+yD4Y6lIAAADCUltvtHoXxCrKGepK0BgVR0ZqT3yCcuwRoS4FAAAg7FgkHR1v1zGJdtmthMMAAED9ISQGAABC5mBhnn44sEP7C3JCXQoAAEDYsXi96ulJUKf8SNlo4ooAuO02pSYkKi0iOtSlAAAAhKXkaKu6JDkU46DzLwAAqH+ExAAAQMhtz0nXqrRdynYWhboUAACAsBPlteo4V4Ja5dtFnwL447VYdCg+XnujY+W2cMESAAAg2OIdFnVp4lDTKJbxBgAAoUNIDAAANAhuj0e/Ze7Xz+kpKva4Q10OAABA2GnmjdBxRfFKKCIqhr/lxURrT2yiCm1csAQAAAi2SKtFnZLsah1rk8XCcTgAAAgtQmIAAKBBKXQ5tSZ9jzZlpoqDFAAAgODr6IlRz/wYRZDLP6I5IyK0NyFRmfaIUJcCAAAQdqyS2iXY1SHBLruVcBgAAGgYCIkBAIAGKaMoXz8e2Kk9+VmhLgUAACDsWCX1diWoQ36ErIwMHVE8NpvS4hOUGhktL90sAAAAgq5VjE2dk+yKsrOMNwAAaFgIiQEAgAZtV+4h/Zi2U5nFhaEuBQAAIOzEyqbjihPUooClBsOdV1J2fLz2RMfJZeWCJQAAQLAlRljVtYlDiZEcawEAgIaJkBgAAGjwPF6PNmUe0Nr0PSp0u0JdDgAAQNhp4Y1U76I4JRTRWSoc5cVEKyUuQQVWe6hLAQAACDtRNos6J9nVKpZjLQAA0LAREgMAAI1Gsdultel7tDEzVR4OYQAAAIKurTdaPQtiFOskLBYOCqOitDcuXjn2iFCXAgAAEHZsFqlDgl3t4u2yWTl+BgAADR8hMQAA0OhkFRdoddpubc/NCHUpAAAAYekYT4y6F8QokiaujVJxZKT2x8XrkCMy1KUAAACEHYuk1rE2dUpyKNJGOAwAADQehMQAAECjlVaYq5/SdmtPflaoSwEAAAg7Fq9XXT3x6lIQJYc71NUgEK4Ih1Lj4nUwIjrUpQAAAISlFjE2dU60K8ZhDXUpAAAA1UZIDAAANHp787O0Om23DhTmhroUAACAsGPzSr08CeqQHyGbJ9TVwB+33aa0+AQdiIiW10I3CwAAgGBrFmVVpySHEiIIhwEAgMaLkBgAAAgbO3Mz9NPB3cooKgh1KQAAAGHHIYv6OBPUtsAhK6NJDYLHalV6fIL2R8bIYyUcBgAAEGxJkVZ1TrQrKcoW6lIAAABqjZAYAAAIK16vV3/lpGvNwd3KdhaFuhwAAICwE+O1qY8rXq3y7SKWFBpei0WZCfFKiYyV20o3CwAAgGCLd1jUKcmh5tGEwwAAQPggJAYAAMKSx+vR5qwDWpeeonyXM9TlAAAAhJ0Er0N9nPFKLiCkVG8sFmXHxmpPTJycVi5YAgAABFuM3aJjEu1qFWsPdSkAAABBR0gMAACENZfHrY2ZqVqfnqIijzvU5QAAAISd5t4IHVsUpyZFhMXqUm5sjFJiElRoIxwGAAAQbJG2knBY61ibrBb65QIAgPBESAwAABwRit0ubTi0T78e2ienxxPqcgAAAMJOa0+Uji2KVVwxF9WCqSA6Wilx8cqzOUJdCgAAQNhxWKWOCQ61jSccBgAAwh8hMQAAcEQpcDn1c3qKNmWlys1hEAAAQNC190SrR2GMop1cZKuNoshI7YtPVJadcBgAAECw2S1SuwS72sXbZbdy3AoAAI4MhMQAAMARKddZpLXpe/RHVpo4GAIAAAi+ru5YdSmIVgQrfleLMyJC++PjleGICnUpAAAAYccqqW28XR0S7IqwEQ4DAABHFkJiAADgiJZZVKA16bu1LScj1KUAAACEHaukHu54HVMQKTthsUq5HXYdiE/QgYjoUJcCAAAQdiySjoqzqWOCQ1F2wmEAAODIREgMAABA0sHCPK1L36MduYdCXQoAAEDYsXml7p54dSyIlIOwmIkrwqGDsfE6EBElr4ULlgAAAMFkkdQq1qaOCXbFOKyhLgcAACCkCIkBAAD4yCjK17r0Pdqek8EylAAAAEFmldTVHadOBVFH/DKUzogIpcXFKy2CZSUBAACCrbRzWIcEu6LthMMAAAAkQmIAAAB+ZRYV6OeMFP2ZfZCwGAAAQJBZvF518cSpc2G0Il2hrqZ+FUdG6kBsnNIJhwEAAASdVX+Hw6IIhwEAAJgQEgMAAKhEdnGh1mek6I/sg/Jw2AQAABBUFq9Xnbxx6lwYpWhneC+1WBQZqdS4eB1yRIa6FAAAgLBjs0ht4uxqn2BXpC28jysBAABqipAYAABAAHKdRVqfsVdbsg7IzeETAABA0HXwxKhrYbRiwywsVhgVpdS4eGXaI0JdCgAAQNixW6S28Xa1i7crgnAYAABApQiJAQAAVEO+q1i/ZOzVpswDcnk9oS4HAAAg7LTzRKtbUYziihv3Rb6C6GilxsYry+4IdSkAAABhx2GVjj4cDrNbG/dxIwAAQH0hJAYAAFADhS6nNhzap42Z++X0EBYDAAAItjaeKHUvilFCsTXUpVRLfkyM9sfEKYdwGAAAQNA5rFL7eLvaEg4DAACoNkJiAAAAtVDkdunXQ/v026H9Kva4Q10OAABA2GnljVLPohglFjXssFhebIz2xcQpz0Y4DAAAINgirRa1T7CrTZxNNsJhAAAANUJIDAAAIAiK3S5tzEzVr4f2qdDtCnU5AAAAYSfZG6FexXFqUthwwmJeSXlxsdobE6cCqz3U5QAAAISdKJtFHRLsOirOJquFcBgAAEBtEBIDAAAIIpfHrd8zU7Xh0D7lu5yhLgcAACDsNDscFmsWyrCYxaLs2Fjti45Toc0WujoAAADCVLS9JBzWOpZwGAAAQLAQEgMAAKgDbo9Hm7MOaMOhfcpxFoW6HAAAgLDTxOtQL2ecmhfYVF+XDb0Wi7Lj4rQvKlZFhMMAAACCLs5RsqxkqxibLITDAAAAgoqQGAAAQB3yer3anpuhDRn7dKAwN9TlAAAAhJ14r13HuuLUosAuax2NcnmtVmXFxmpvdKycVsJhAAAAwdYsyqp28XY1i+ZYCwAAoK4QEgMAAKgnqQU52pCxTztyM8QBGAAAQHBFe63q4Y5XmwKH7J7gPKbLbteh2DilRkXLbQnh8pYAAABhyCKpdaxN7eLtiovgWAsAAKCuERIDAACoZ9nFhfr10D5tyUqTyxukK5gAAACQJNm8UhdvnDoWRCnKVbPHKI6M1MHYOB10RMrLMkcAAABB5bBKbeLsOjrerkgbx1oAAAD1hZAYAABAiBS5XdqUmarfMvcr3+UMdTkAAABhp70nWp2LYpRQHNjFx4KYGKXGxCrLHlHHlQEAABx5YuwWtYu3q3WsTTYr4TAAAID6RkgMAAAgxDxej/7MTteGQ3uVUVQQ6nIAAADCTrI3Qt2ccWpeYFXZy5Fei0U5cbHaHx2rAqs9JPUBAACEs6RIq9rF25UcbZWFLq0AAAAhQ0gMAACgAUnJy9KGQ3u1Oy8r1KUAAACEnVjZ1NMZr9aFdlksNmXGxWlfZIxcVmuoSwMAAAgrFkktYmxqH29XQiTHWgAAAA0BITEAAIAG6FBRvjYc2qc/sw/KzeEaAABA0CRHxeq4pNZyWBO1O8etPBfHWgAAAMFis0ht4uxqF29TlJ1wGAAAQENCSAwAAKABK3A5tTFzv37PTFWh2xXqcgAAABoli6SO8U11bFIrtYpJMH3uYIFbu3NcSi/0hKY4AACAMBBps6hdvF1t4myyW1lSEgAAoCEiJAYAANAIuDxu/ZGdpl8z9ivLWRjqcgAAABqFSKtN3ZNaqFdSK8U5IivdN8/p0a4cl/bnueVmtAwAACAg8Q6L2ifY1SLGJquFcBgAAEBDRkgMAACgkdmdm6nfs1K1K/eQOJADAAAoLykiSsc2aaWuCcmyW23Vuq/T7VVKrkt7ct0qJC0GAABQjlVSixib2sbZlBRVvWMtAAAAhA4hMQAAgEYq11mkTZmp2pyVpgK3M9TlAAAAhNzRsYk6Nqm1jo5LqvVjebxepRV4tCvbpaxilqIEAACItlnUJs6mo+LsirDRNQwAAKCxISQGAADQyHm8Hm3PydDvmanaV5AT6nIAAADqVbTNoW6Jyeqe2EIJEVF18hy5xR7tyS1ZitLFSBoAADiCWCQ1i7KqbbxdzaKssrCkJAAAQKNFSAwAACCMHCrK1++ZqdqafVDFHneoywEAAKgzR8UkqEdiC3WMbyqrxVovz+n2eLU/3609OS7lOBlSAwAA4cthldrE2dUmzqZoe/0cawEAAKBuERIDAAAIQ06PW1uz0/R7ZqoyigpCXQ4AAEBQRFpt6pqYrB6JLZUUGR3SWrKLSrqLpea75WZ0DQAAhImkSKvaxtnUIsYmK13DAAAAwgohMQAAgDC3Pz9bv2emantuhtwc+gEAgEaoZVSceiS1VKf4ZrJZG1YnC5fHq315bqXkupRLdzEAANAI2SxS61ib2sbZFRfRsI61AAAAEDyExAAAAI4QhS6nNmcd0KasA8pxFoW6HAAAgEpFWG3qktBcPZJaqmlkTKjLCUhmkUd7clw6kO+WJ9TFAAAAVCHOYVHbOLtaxdpkt9I1DAAAINwREgMAADjCeL1e7c7L1O+ZqdqdlykOBgEAQEPSPDJWPZNaqnNCM9mttlCXUyNO99/dxfJcHG0BAICGwyKpZYxNbeNsSopqnMdaAAAAqBlCYgAAAEewHGehNmUe0JasNBW4naEuBwAAHKHsFqs6JzRTz6RWah4VG+pygiqj0K2UXLcO5LsJ5wMAgJCJtlnUJs6mo+LsirDRNQwAAOBIREgMAAAA8ng92p2XqS1ZadqVlykPh4gAAKAeNI2MLukaFt9cETZ7qMupU8Vur/bmupSS51YB3cUAAEA9sFmk5GibjoqzqSldwwAAAI54hMQAAABgUuhy6s+cg9qSlab0ovxQlwMAAMKMw2pVx7im6pHUUi2j40NdTkikF7iVkufWwXy3PKEuBgAAhJ2kSKuOirWpRYxNditdwwAAAFCCkBgAAAAqlF6Ypy1Zafoz56AK3a5QlwMAABopi6SjYhLUNSFZHeObym6lk4UkOd1epea7tS/Praxi4mIAAKDmomwWtY61qXWsTTEOa6jLAQAAQANESAwAAABV8ng92pWbqS3ZadrNcpQAACBASRFR6pqQrC4JzRXriAx1OQ1avtOjvXlu7c9zq9DNsRYAAKia73KSTSKtsljoGgYAAICKERIDAABAtRS4nNqanaY/stOUUVQQ6nIAAEADE2Wzq1N8M3VNTFZyVFyoy2mUMgpLuosdyHeLvBgAACgrMcKq1rE2tYplOUkAAAAEjpAYAAAAauxgYZ62ZB3Qn9kHVeRxh7ocAAAQIlaLRe1ik9Q1IVnt4pJktbDEUTC4PV4dKHBrX65bh4o8YhAPAIAjV6TPcpKxLCcJAACAGiAkBgAAgFrzeD3akXtIf2SVLEfJASYAAEeG5KhYdU1IVqf4ZoqyO0JdTlgrdHm1P8+lfXlu5bk42gIA4EhglZQcUxIMaxbFcpIAAACoHUJiAAAACKp8V7G2Zh/UH1lpOlTMcpQAAISbWHuEuiQ0V9eEZCVFRoe6nCNSdpFHe/NcSs13y+kJdTUAACDYWE4SAAAAdYGQGAAAAOpMemGe/spJ11856cpxFoW6HAAAUEN2i1Ud45uqa0KyjopJoItFA+HxenWwwKN9eS4dLGA5SgAAGrM4h0UtYmxqFWNTDMtJAgAAoA4QEgMAAEC9OFCQo205GforJ115ruJQlwMAAKpgkXRUTIK6JCSrY3xTOay2UJeEShS7vUrNd2t/nltZxbQXAwCgMYixW9QyxqaWsTbFEQwDAABAHSMkBgAAgHq3Pz9bf+Wka1tOhgrczlCXAwAADisNhh0T30wd45oqyu4IdUmogUKXVwfy3UrNJzAGAEBDE22zqGWsTS1ibEqIIBgGAACA+kNIDAAAACHj9Xq1Nz9bf+Uc1I7cQyp0u0JdEgAARxyCYeGt0OXRgXwPgTEAAEIo0na4Y1iMTYmRBMMAAAAQGoTEAAAA0CB4vB6l5P0dGCv2uENdEgAAYYtg2JGp0OVRar5HBwiMAQBQ5yKsUovDwbAmUSzbDQAAgNAjJAYAAIAGx+3xaHdepv7KSdfO3ENyebmICQBAbVkktY5JUCeCYRCBMQAA6oLDKiVH29Qq1qYmkVZZLJZQlwQAAAAYCIkBAACgQXN53NqVl6m/stO1K++Q3By+AgAQsNJg2DHxTdUxrpmiCYbBDwJjAADUnN0iNT8cDGsaZZWVYBgAAAAaKEJiAAAAaDScHrd25GRoR+4h7cnPlNPDRUwAAMoiGIbaIDAGAEDVHFapWZRNLWJsah5NMAwAAACNAyExAAAANEpuj0cp+VnamXtIO/MOKd/lDHVJAACEDMEw1IUCl0cHCIwBACBJirZblBxtU3K0VUksJQkAAIBGiJAYAAAAwsKBghwjMJZRVBDqcgAAqHMRVpvaxiaqfWwTHR2bpCiCYahDhS6vDha4dbDArYxCj4iMAQCOBIkRVjWPtio5xqY4hzXU5QAAAAC1QkgMAAAAYSe7uNAIjO3LzxYHvACAcJHgiFS72CZqH9dErWPiZbVwsRL1z+3xKqPQo7QCt9ILPCrycLQFAAgPVklNo6xqHm1TcoxNkTa6hQEAACB8EBIDAABAWCtyu7TrcGBsd16mnB76XgAAGg+LpJbR8WoXm6T2cU3UJDIm1CUB5WQXlQTGDha4leNkqBEA0Lg4rCoJhUXb1CzKKpuVYBgAAADCEyExAAAAHDHcHo/25mdrZ94h7cjNUL7LGeqSAAAoh2Uk0ZixLCUAoDGItluUHG1TcrRVSZFWWSwEwwAAABD+CIkBAADgiJVWmKuduSWBsYyiglCXAwA4grGMJMIRy1ICABqSxAirkqOtah5jU5yDYy0AAAAceQiJAQAAAJJynIXanZepPXlZ2pufrWKPO9QlAQDCWOkyku3jmqhdbBLLSOKIkF3k0cFCt9LyWZYSAFD3HFapaVTJEpLNom2KtNEtDAAAAEc2QmIAAABAGR6vRwcKcrUnP0t78rKUVpgrDpoBALUVZbOrTUyi2sUmsYwkjnily1KmF7p1qNAjFwdbAIBaskhKirSqWZRVTaNtSoigWxgAAADgi5AYAAAAUIUit0sp+Vnac7jTWK6rONQlAQAaAbvFqlbR8WoTm6g2MYlqHhUb6pKABsnj9Sq72KuMQrcyCj3KKvIQ0AcABCTWbinpFhZtVZNIq2xWuoUBAAAAFSEkBgAAAFRTVnGB9uRlaU9+lvbmZ8np8YS6JABAA2CR1DwqVm1iEtU2NlEto+Jls9LBAqgut8erQ0UeZRR6lFHoVi5LUwIADnNYpSalS0hGWRVl51gLAAAACBQhMQAAAKAWPF6PUgtyS7qM5WfpYGEenS8A4AiS4IhU29gktYlJ0FExiYq02UNdEhB2itxeHSp0K72wJDhW5OZoCwCOFBZJiYeXkGwWZVN8hEUWC93CAAAAgJogJAYAAAAEUaHLWbI0ZX6WUliaEgDCTpTNrjYxiUa3sDhHZKhLAo44ec6SsFh6oVuZhR65GN0EgLASY7eo6eFQWJMoq+wsIQkAAAAEBSExAAAAoA5lFhUoJT9L+wqytb8gR/kuZ6hLAgBUg91iVavoeLWNLQmGNYuKDXVJAHx4vV5lFXuVUehWRqFHWUUeuroCQCMTbbMoKcqqpEirmkZZFc0SkgAAAECdICQGAAAA1KPs4kLtK8jWvvyS0Fi2syjUJQEAfNgsFiVHxal1dLzaxCaqZVS8bFYuVAKNhdvj1aGikk5jmUUe5RQTGgOAhibWYVFSpFVNIq1KirQpyk6nMAAAAKA+EBIDAAAAQijPWaT9BTnaV5CjffnZOlRcEOqSAOCIEmWzq2V0vFod/pccFSurhVAYEC7cHq+yiksCY1lFJf+7GQ0FgHpjkRTnsKhJlE1JkSXdwiJshMIAAACAUCAkBgAAADQgRW6X0WVsX0G20ovy5eGQHQCCJsEReTgQlqBW0fFKiowOdUkA6pHX61WO06vMor+DY0WkxgAgaKySEg6HwZpEWpUYaZXdSigMAAAAaAgIiQEAAAANmNPjVmpBTkloLD9bBwpz5eYQHgACYpHULDK2JBQWU9IpLMYeEeqyADQwBS6PERrLLPIoz8mxFgAEymaR0SGsSaRVCZFWWS2EwgAAAICGiJAYAAAA0Ih4vB4dKMg1Oo0dKMhVkccd6rIAoEFwWK1qERVndAlrER0nh9UW6rIANDJO999LVGYWeZRd5JEn1EUBQAMRabUoIdKiJpEly0fGR1hkIRQGAAAANAqExAAAAIBGLru4UGmFuUorzNOBwlwdLMyTy8ulTADhL9YeoZbRccbykc0iY7hICSDoPF6vsou9yixyG0tUOjnUAnAEsFukhIiS7mAJESX/ouwcawEAAACNFSExAAAAIMx4vV4dKi74OzhWkKuM4nx5OPQH0IjF2B1KjopTcmSsmkfFKjkqTtF2R6jLAnCEKnR5lF3sVXaxRznFHmUXExwD0LhZJcUfDoIlRlqUEGFVjMMa6rIAAAAABBEhMQAAAOAI4PZ4lF5U0mksrTBPaYW5yiwuDHVZAOBXjN2h5pElQbDkqJJQWIw9ItRlAUClCI4BaCwskuIcFp9QmFWxDousdGQFAAAAwhohMQAAAOAIVex26WBRSaex0uBYrqs41GUBOMJE2xxGECz5cIcwAmEAwkXB4eBYTrFH2UUe5TgJjgGof9F2ixIjSpeMLAmH2awEwgAAAIAjDSExAAAAAIYCl9NYpjKjKF8ZRfnKdhaKkwYAwRBtc6h5VIySo+IOdwqLVawjMtRlAUC9ynd6lOP0KrvIY3Qdc3GwBSAILJJiHBbFOayK9+kU5rARCAMAAABASAwAAABAFVwetw4VFxihsZJ/BSpwO0NdGoAGymqxKNERpSaR0WoSEaNmkTFqHhWrOAJhAOBXvrMkMJbr9CrPWfJ/octLUB9AhaJsFsU5LIqLsJb877AqhiUjAQAAAFSCkBgAAACAGil0OZVRbA6OZRTly+VlDSXgSGGRlHA4DNY0MkZNIqLVJDJGSRFRslqsoS4PABo1t8erPFdJaCzP6VVuccn/BW6Gc4Ejid0iUxCsNBhmZ7lIAAAAANVESAwAAABA0Hi9XuU4i/4Ojh0OkWUVs2Ql0NglOCLV5HAQrDQQlhQRLZuVMBgA1Ce3x1sSGnP+3Xksz+lVIeExoFGzSIotEwSLc1gUZedYCwAAAEBwEBIDAAAAUOfcHo+xZOWh4pLQWLazUNnFRXQeAxqYOHtESQjs8FKRTQ93BrNbbaEuDQBQCZfHawqNlXYeK/Iw/As0JDaLFGO3KNphVYzdwlKRAAAAAOoNITEAAAAAIZXnLFKWs1BZxYVGeKz0fzenK0CdiLY5lOCIVEJElBIcUUpwRCoxIkpNImPkIAwGAGHF6S4JjuW7vCo4/K/kY4+cZPWBOlE2CFb6L9phVaSNIBgAAACA0CAkBgAAAKBB8nq9ynMVl4THnAXKLi4NkxUo21kkD6cyQIUskuIckSVBMEfU4TDY3x8TBAMASCXdx4zwmE+QLN/lVRHLVwKVslmkaLtFMfaSLmDRpWEwgmAAAAAAGihCYgAAAAAaHa/Xq1xXkd/uY3muYjk9tMVA+LNZLOUDYI4oJUREKt4RKavFGuoSAQCNmMfr03nM6VW+y2NsF7i8YlAZRwK7RYqylwTAou2Hu4I5SoJhUXaCYAAAAAAaF0JiAAAAAMJOsdulXFexcp1FynMVK+/wx7muYuU5S7ZdXoJkaNiibHbF2iPM/xwRxvKQsY7IUJcIADhCeb0lncZKO48VHu485vuPpSzR0NksUpTNoii7RZG+//t8bLcSBAMAAAAQPgiJAQAAADgiFbqcJeExV9HhENnfYbKS24tZ0hJ1wmaxKKZM+CvG7jAFwWLtEXQCAwA0am6POTRW6PaqyKVyYTKOtlAXbBYZga9I++H/D4e/SkNgBMAAAAAAHGkIiQEAAABABQpKg2SHg2MFbqcKXE4VuJ0qdLuMj4s97lCXigbAarEoymZXlM2uGNvfYa+y/6LsjlCXCgBAg+D1elXsKQmOFbq8Ki4Nk7m9KnJ5VeTxynm4KxmD2JAkh1WKsFoUYbPIYbMowipF2CyKsJq7gUXYCIABAAAAQFmExAAAAACgljxez+HAmMsnRFYSJCtyu1TodqnQ7VSRp/RjF13KGjiH1aoom0ORVrsR/Iq02Utus9kUZXMcvv3vbYfVFuqyAQAIWy5PSYjM6ZGcHu/hfzocIvPzsccrN4dbDZpVksNmkcMqOax/h74cVsvh7b8DYRGH97NaCH8BAAAAQE0REgMAAACAEHB63KYgWbHHLafxz+Pz8d/bxR63XF63it2Hb/d6CJuVYZHksNpkt1plt1hLPi7932reLhvyirT9HQhjqUcAABo/j9erYvfhgJmfIJnb65Xb9P/hj70ly2W6vZLHSxezsqySbFbJbrHIZi1ZStt++H/f2+0WyWa1yGaR7If/9w2C2VjuEQAAAADqFSExAAAAAGjEPF5PSWjMW3G4zH04TObxeuVRyf/ew/+Xvb30n+nz5fb9+/Gkko4OFllksUhWWWWxqGS79HOWko8tspi2jfuV2c9qsRq3lYa6SgNe5bYt5kCYzUq4CwAABFdpYOzvAFnJxx5vSQCt5HOSx+OV63CwrOR4Siodffcc/t9b+u/w5+WVsd/fnyu9j9d0H3klj3T4uEqHj7lKj6NKP/b5nMWi0iMjq6XM/of3Kc1pWXV4f4uMUJc5+PX3x3TzAgAAAIDGiZAYAAAAAAAAAAAAAAAAAIQxplgDAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYYyQGAAAAAAAAAAAAAAAAACEMUJiAAAAAAAAAAAAAAAAABDGCIkBAAAAAAAAAAAAAAAAQBgjJAYAAAAAAAAAAAAAAAAAYcwe6gIAAGgMunXrZnw8ceJE3XLLLX73W7Vqla644gpj+6233tIJJ5xQ5/X5M378eK1evVqSNGjQIP3vf/8L+nPUxfdl+vTpmjFjhrG9ZcuWIFXbuHg8Hm3btk2bNm1SZmamcnNz5XA4FBsbq9atW+voo49Whw4dZLPZQl0q0Cjdfvvt+vjjjyVJDzzwgMaNGxfiihoWl8ulYcOGaceOHbJYLJo/f7569+4d6rIAAABQDXv27NFff/2l9PR0ZWRkyOPxKDExUc2aNVPPnj111FFHhbrEWmH8IDw11jGkQMfIaqquvi/1MX7YGBQXF+v333/Xtm3blJ2drfz8fEVHRys+Pl5t2rRRhw4d1Lp161CXCTRKxcXFGjZsmHbt2iWr1aqFCxea3jMh7dy5U0OHDpXL5VJycrI+++wzxcbGhrosAGGKkBiAerFnzx6dddZZle7jcDgUGRmppKQkJScnq0OHDurevbsGDhyoXr16yWKx1FO1KMvpdOr0009XRkaGcdtxxx2nefPmhbAqIDylpKTof//7nxYsWKDMzMxK942OjlbPnj114okn6vTTT9dxxx1XLzUCjd2aNWuMgFj79u116aWXGp/LycnRsGHDlJqaKkmy2+2aP3++evbsWe3ncbvduuyyy/TLL78Yt73wwgs6++yza/kV1D273a5JkyZp8uTJ8nq9evTRRzV37lyOxwAAABq43bt366233tK3336r7du3V7pv8+bNddZZZ2nkyJEaMGBAPVWIqmzatEmjRo0y3Xb99dfrtttuC01BQBj7+eef9dZbb+nzzz+X0+msdN8mTZqob9++OvnkkzV48GAdffTR9VQl0Li98sor2rVrlyRp5MiRpoDYhg0b9M9//lNut1uS1KZNG3388cc1CkilpqZq2LBhysnJkSTFxcVp8eLFjSLg2b59e11yySWaM2eO0tLS9OKLL+qOO+4IdVkAwhQhMQANhtPplNPpVG5urvbs2aOff/7Z+Fzr1q01atQoXX755WrevHkIqzT74osvtGnTJklSfHy8JkyYENqC6siKFStMATFJWr9+vbZv366OHTuGqCpA+vDDD5WSkiKp5ARy9OjRIa6odt577z098cQTys/PD2j/goICrV27VmvXrtU777yjVatWVbhvQ36/asi1oW7s2bNHCxYsMLYvvPBCtW3btl6e2+v16rHHHjO2b775Ztntf58WxcfH65FHHtH1118vqaSj1pQpU/T+++/L4XBU67lmz55tCogNHz68UQTESp1//vl66aWXtHnzZq1fv14ff/yxRowYEeqyAAAA4EdmZqamT5+uefPmVRl0KHXw4EHNmzdP8+bNU//+/XXHHXeof//+dVwpquJ7rlRq0aJFmjx5sqxWawgqAkpMnz7d+HjQoEEhWz0hGIqLi/Xkk0/q3XffldfrDeg+hw4d0tdff62vv/5aa9eu1X//+98K9509e7YRVOnRo0eDGgtoyLWhbqxatcroGCgp6J0OK3PgwAG98sorkkomJE6cONH0+T59+mjChAl67bXXJJVMoH7mmWc0derUaj/X1KlTjde2JN15552NIiBW6qabbtIHH3yg4uJivfnmm/rnP/9JGBVAnSAkBiBkyi6R5vF4Kjwh27dvn2bOnKk333xTt9xyi6666qoG0cniiy++MAZu2rRpE7bBBn+DU5K0cOFCTZ48uZ6rAf62YMECU0v8xhwSe/311/XUU0+ZbrPb7erbt6+6du2qxMRESSUD/9u2bdPGjRuVl5cX8OM35Perhlwb6kZKSopp6YtBgwbVW0js008/1e+//y6p5PU2bNiwcvucccYZGjVqlBYuXChJ2rx5s1566aVyA1mV2bFjh2nAuFmzZrr33ntrV3w9s1gsuvbaa3X77bdLkmbMmKGhQ4eaQnUAAAAIvV27dum6667Tjh07TLdbLBb16tVLnTt3VtOmTRUREaGDBw9q//79Wrt2rQoKCox9161bp8suu4wlG0PM5XJp8eLF5W7fv3+/Vq5cqVNOOSUEVQElfM/jJ06c2GhDYi6XS5MnT9YXX3xhuj0uLk4DBgxQhw4dFB8fr8LCQmVkZGjLli36448/Ag7gSiXLgZZObL3wwgsbVBCrIdeGurF69WrT7299hsRefPFF43jj/PPP9zv+N2nSJH311VdGB9Q5c+bo/PPP16BBgwJ+no8++kjLly83tk866STTygGNQYsWLTRq1Ci99957cjqdeuGFF/Tkk0+GuiwAYYjRfQAhMWjQIP3vf/8z3eb1epWbm6usrCzt2bNH69ev148//qiVK1ca++Tn5+upp57SmjVr9Pzzz1e7oweqLyMjQytWrPD7uY8++kiTJk06ImYxMkjqX118X2655ZZ6PVFtCDZs2KB///vfptvGjx+vm266SU2bNvV7H7fbrZ9++klLly41ls0DUDmv12saFLviiisqDDzdc889+v7775WWliZJmjVrls455xxTS/zKnufee+9VYWGhcdsDDzxQ4e9zQzZ06FD95z//0d69e7Vz50599NFHuuiii0JdFgAAAA77448/dOWVV5o6wMfGxuqGG27QxRdfrGbNmvm9X2FhoX744Qe9/vrr+umnn+qrXFRhxYoVSk9P9/u5Dz/88IgIiZ1wwgmMw/lRV9+XsmP0R4LZs2ebAmKxsbG67bbbdPHFFysyMtLvffLz8/X1119ryZIl+uqrr+qrVKBR27dvn95//31j+5prrvG7X2RkpB5//HGNGzfOaCZx7733atGiRYqOjq7yeQ4ePKjHH3/c2I6JidGjjz5a+y8gBK6++mq99957kkq6iN54443q0KFDaIsCEHbC/6o+gEbDYrEoPj5ebdu21Yknnqgbb7xRs2fP1uLFi8vNZvnyyy/18MMPh6jSI8vixYtNs6R8g3n79u3Tjz/+GIqygLDyn//8x9RJ8e6779Z9991XaaDEZrPpxBNP1IMPPqgVK1YYnX4AVOzrr7/WX3/9JalkAOrCCy+scN/ExEQ9+OCDxrbT6dSUKVPkcrmqfJ53331Xa9asMbbPO+88DRkypOaFh5DNZjOFwmbPnh26YgAAAGCSn5+vSZMmmQJivXv31rJly3TDDTdUGBCTpKioKA0ePFhvv/22Zs6cqVatWtVHyahC2W7+vuNwX3zxhXJzc+u7JCCsFBQUaObMmca2w+HQG2+8oXHjxlUYEJNKQidDhw7VjBkz9Pnnn+uCCy6oj3KBRu2tt94yri316dNHPXr0qHDf/v37a/z48cb2rl279J///Ceg53n44YeVmZlpbN9+++31tmJBsHXs2NHooOZ2u/XWW2+FuCIA4YiQGIAGr0uXLnrhhRd01113mZaYfO+99zR37twQVnZkKF1qq9SkSZNM2xUtRQkgMIcOHTKWzJRKTgSru9xiXFycLrnkkiBXBoQf34GVc845x1jGtSJnn322hg8fbmxv3LhRr732WqX3SUlJ0TPPPGNsJyUlaerUqTWsuGG46KKLjGOwP/74w9TlFQAAAKHz6KOPatu2bcZ2nz59NHv2bDVv3rxajzN48GC9//776tu3b7BLRDUcOnRIX3/9tbF91FFHacyYMcZ2YWGhPv300xBUBoSP77//3hS2HD16dLXf+9q0aaNzzjkn2KUBYSU/P1/z5883ti+++OIq7zN58mS1a9fO2H777be1du3aSu+zbNkyffbZZ8b28ccfr7Fjx9ag4obDd5x/wYIFysnJCWE1AMIRITEAjcbVV1+tq666ynTbjBkzVFxcHKKKwt+WLVu0ceNGY7tfv3668sorTRfVP//8c2YxArWwefNmud1uY/vkk082BWIBBEdKSoop3BRoZ697773X1IFhxowZRjcyf+6//37l5+cb2/fdd1+lHRwag9atW+u4444ztj/44IPQFQMAAABJ0vbt2/Xhhx8a21FRUXrmmWcUFxdXo8dLTk7W22+/HazyUANlu/mPGDFCo0aNMu3DZE2gdnzHuqWScTgAwbds2TIj3GSz2QIKVkZHR+uxxx4zxsY9Ho/uueceFRUV+d0/MzNTDz30kLEdFRVlun9jNXjwYEVEREgqCdstXbo0xBUBCDf2UBcAANVx22236euvvzZmSaalpWn+/PkaN25cQPdPTU3V1q1btXv3buMANTExUa1bt1a/fv0UHx9fZ7U3xtrKdhEbOXKkIiIidP755xtd3AoKCrR06dKAZoLUF7fbrT///FPbtm3TgQMHlJ+fr+joaCUmJqpr167q3r27bDZbqMsspz5fAy6XS2vWrNGePXuUkZGhxMREtWvXTscff7zsdg4P6pPvsiCSajygX58a4vtVWXv37tWGDRuUnp6unJwcJSQkqGXLlho4cGCV3aMC5fV69csvv2jHjh06cOCAIiIi1LlzZw0aNMg4kfenoKBAa9as0bZt21RQUKCkpCQde+yxOvbYY2tdz+bNm7V161ZlZGSouLhYTZo00dFHH63+/ftXWlN1lL5/7N69WxkZGYqPj1eHDh00cODAoD1HXfj444+NZV2jo6N12mmnBXS/pk2b6oEHHjA6aRYXF2vKlCmaM2dOub8l8+fP1/fff29sDx48uMolKNxut3799Vft2LFDGRkZcrvdatq0qY455hj16dOnxn+vDh48qI0bN2rPnj3Kzc2V1+tVdHS0kpOT1a5dO3Xt2rVaP69zzjlHP//8s6SSZW7y8vIUGxtbo9oAAABQe2+88YZxfCuVTK5s3759rR6zOseHHo9Hv/76q7Zt26b09HRJUrNmzdSpUycde+yxslprNz998+bN2rRpk9LS0hQXF6dWrVrp+OOPD+r5Zl0di9dU2QDYiBEj1KlTJ3Xq1MmYqLJ27Vrt2rXL1Gkl1PLz87V161Zt375dhw4dUmFhoeLi4tS0aVP17t27QdVayuv1avv27dq2bZv27dunvLw8RUREKCEhQcccc4yOPfbYoJ7f5uTk6KefftL+/fuVm5ur5s2bq0ePHpUuvYa6Ufp+Vaqhj8PV92u1pjXWx3hUcXGxVq9erb179xrjUX369FHv3r0rvd/Bgwe1Zs0apaSkyO12Kzk5WYMGDVKbNm1qXc/69euN8XWLxaJmzZqpW7du6t69e9CCSrm5uVq9erXx82/SpIl69OihXr16Negw1KJFi4yPBwwYoKZNmwZ0v0GDBmns2LF65513JEk7duzQc889p7vuuqvcvo899pgOHjxobE+ePLnKY6GCggKtXbtW+/bt06FDhxQREaEmTZro2GOPVadOnQKq0Z8dO3Zo06ZNxvUom82m2NhYtWrVSh06dNAxxxwT8M8rLi5OJ598stFd9KOPPmIVEQBBxVVgAI2K3W7X+PHjTbMDFi1aVGFIzOPxaM2aNVqyZIm+//577dq1q8LHtlqtOvnkk3XjjTfq+OOPr3C/VatW6Yorrih3e0pKirp161bh/bZs2VLntQWTy+XSxx9/bGw7HA6df/75kqRRo0aZlvpcuHBhhSGxv/76S0OHDjW2r776ar8H9JV56qmn9Prrrxvbn376qY455hjTPtnZ2friiy+0bNky/fTTT5V2N4uPj9cll1yia665JuAlGHx/thMnTtQtt9xSra/Bn1C8Blwul2bNmqV333233KCIVLIs2tVXX61rrrkmoLBYXXxfpk+frhkzZhjbZX93PvzwQ02ZMqXc/VavXl3h72CbNm301VdfSZKGDh1qDK526NBBS5curdYJddn3gLvuuktXX311wPcvq+z3effu3TV+rLIa8vtVsGrz5XK59OGHH+rNN9/Un3/+6Xcfm82mk046SZMmTVKfPn0qfKxSd999tzFQX/o68ng8mjNnjmbPnu33e9GsWTPddtttuuiii0y3FxQU6IUXXtA777xj6jRVqkuXLnrwwQc1cODAKuvylZ2drVdeeUULFixQWlqa332io6M1YsQI3XzzzWrZsmWVjzl48GClpKRIki688EI9+eSTcrvdevnll/XOO+/4fZ7Y2Fhde+21uu666+RwOKp8XF/+XgulnnjiCY0ePbrKmqvy+eefGx/3799fUVFRAd93yJAhOu+884z29b/88otmz56ta665xtgnNTVVTz31lLGdmJhoOl4pKzU1VTNnztSSJUuUlZXld5+EhARdeumluu666wION65cuVIvvviifvrpJ9NFw7IcDof69eunsWPHGn/fK3PKKacYHxcUFOj777/XueeeG1BNAAAACK7i4mJ99NFHxrbVajUtS1iXsrOzNWvWLH3wwQfKzMz0u0+TJk00ZswYXX/99dUOYHz99df697//7bd7b1RUlIYMGaK77ror4IvN/tTVsXht/PHHH6YOR74Xq0eOHKn//Oc/xucWLlyof/3rX34fZ+7cuabl7mfNmqUzzzyzWrWMHj3aqKVly5b6+uuvy4X+UlJStGTJEn311Vf69ddfTR3QymrTpo0mTJigf/7znwGFRcqOF7z11ls64YQTqvU1+FNQUKCvv/5an332mX788UcdOnSown2joqJ0wQUX6IYbbtDRRx9d4+fMyMjQU089paVLl6qwsLDc5zt06KDbb789oA47dfV9GT9+vFavXi2pJJzxv//9z/R533ERXzNmzDCN3/kqHUdITU3V4MGD5XK5JJUsofboo49Wq76y44QffPBBrSbZlR2vCOY4XNlaSy1YsKDCLoD+vud18VoNVm2+6ms8Kjc3VzNmzNCHH37o9z27W7dueuihh9SvXz/T7SkpKfr3v/+tzz//3LSKQ6kzzzxTU6dOVevWrausy9eOHTs0ffp0ffXVV37H9ySpefPmuvLKKzV+/HhFR0dX+nh79uzRWWedZWyXjoNlZmbq3//+t5YsWaKCgoJy92vbtq1uv/32Csd0yj6ur8rGXr/88ku1bdu20pqrkpOTo1WrVhnbvmNKgShtFlH6WnjzzTc1ZMgQ09KwK1asMAXR+vXrV+n44m+//aYZM2bo+++/r3B1orZt2+q6667TxRdfHNC1EY/Ho3nz5umtt94yLf/tT3x8vE455RRdf/316tWrV5WP7RsSW7t2rTIyMmp17AMAvlhuEkCjM3LkSNMB2saNG/0eJEslYYLx48drzpw5lYYapJIDuu+++07jx4/Xc889F8ySG11tkvTdd9+ZTu7+8Y9/KCkpSVLJAXeHDh2Mz5V2lPGnU6dOphO0jz/+2BgYCITL5Sp3sF82ICaVnOhOmTJFy5cvr3L5y5ycHL3++usaOXJklWva16X6fg3k5ubqyiuv1PTp0/0GxKSSFs3/+c9/NHbs2LBd637s2LHGxzt27NCPP/5Yrfv7BiQjIyN14YUX1qqeVq1ambaXL1/uN0QTSg39/UoqGdS78MILdf/991cYEJNKZop/9913GjNmjGbNmlXt53E6nZo0aZIefvjhCr8X6enpuueee0yDbwcOHNCYMWP0yiuvVDiAtHXrVk2YMEErVqwIuJ5vv/1WZ599tl5++eUKB+SkkgHGefPmaejQofr2228DfvxSWVlZuuKKK/Tcc89V+Dx5eXl6/vnndfPNN1d6gSAUMjIy9PvvvxvbgwYNqvZjTJ061fg7KEnPP/+8tm/fbmw/+OCDpvfNKVOmqEWLFn4fa8GCBTr33HM1Z86cCi9KSX8PuA4bNky//fZblTU+88wzmjBhglavXl1pQEwqeS2vXr1a77//fpWPK5UMIPp+/d98801A9wMAAEDwbdiwwRQ4GTBgQLUvctfEzz//rHPPPVevvfZahQExSTp06JBeeuklDRkyJKDj2FJPPvmkbrjhhgqXdy8sLNTChQt14YUXavPmzdUtX1LdHYvXVtmQxsiRI42PR4wYYQppLVy4sMLj/WHDhpkmxFR3ecotW7aYwmoXXnih365wN910k5555hmtW7euyvO/lJQUPfbYYxo3bpxSU1OrVU8wzZs3T7feeqs+/fTTSkM3Uslrbf78+RoxYoS++OKLGj3fli1bNHr0aC1cuNBvQEwqGZeaOHGipk6dWuU5XGPUsmVLU0jlk08+qXLc1pfb7db8+fON7WB0YS8bVJo3b16FgZFQqe/Xak3U13hUamqqLrnkEr3xxhsVvmdv2bJFV1xxhenx16xZo1GjRmnp0qV+A2JSyRjsZZddpj179gRcz0svvaRhw4Zp8eLFFY7vSSXdy5599lmNHj26RkHE33//XaNGjdIHH3xQ4bWvPXv26NZbb9XMmTOr/fh17YcffjBdA6ruOFxsbKwee+wxY9vtdmvKlCnG72pubq4eeOAB4/ORkZF6/PHH/f69crvdeuSRR3TxxRdr+fLllf6+79mzR1OnTtX48ePLrf5RVn5+vq6++mo9+OCDVQbEpJJrUkuXLjWtQFAZ3xBw6Xg7AAQLncQANDqxsbHq2rWrcbHX6XRq/fr1Oumkkyq9n8PhUNeuXXXMMccoOTlZMTExKi4u1oEDB7R+/Xrt2LFDUkl75JkzZyopKUkTJkwo9zgWi8VoN+/xeEwn8DVtQx+s2oKpssEpqWSA6r///a9R18KFCyvsIjV69Ghjiaq0tDR9++23Ac9i/Oabb0wtg8t25vEnKSlJ3bp1U4cOHZSYmKjIyEjl5uZqx44dpi5jBw8e1A033KAFCxbUalZgMNTHa+Dee+/VmjVrJJXMZjrttNPUqlUr5ebmat26daZBwF9++UXXXHON3n777ZC3LC/LarUav2tlT/Ir+h30vX3UqFF69tlnjRP5OXPmVPn+USo9Pd3Ujei8885TkyZNqlV/WT179lRsbKzy8vIklQyeXHnllXrwwQd16qmn1uqxG/L7VTBr27JliyZMmGA6eY+NjdXAgQN1zDHHKCYmRtnZ2dqwYYN++eUXo75p06apuLi4whnY/jz66KNatmyZJOmoo47SySefrOTkZOXn52vVqlWmCxXTp0/XwIED1bdvX1177bX6448/JJX8zPv376/ExEQdPHhQK1as0P79+yWV/E2788479emnn1Y5O2zRokWaMmWKadCldEnN1q1bKyIiQmlpaVq9erV27twpqWQQ5YYbbtCrr76qk08+OaCv2eVyadKkScb7R8eOHTVo0CA1a9ZMeXl5WrNmjen9Y8WKFZo1a5bfvwk2m002m01er1cej8e43Wq1VtjRr7bL1EjSunXrTM9X1VIE/jRr1kz333+/brvtNklSUVGRpkyZonfffVeffPKJ0a1Qkk4//fQKA6Qvv/yynn32WdNt7dq1U//+/dWiRQtZrVbt379fP/74o/G6SEtL0/jx4zVv3jx17drV7+N+8MEHeuWVV0y3derUSX379lWLFi3kcDiUm5ur/fv3a8uWLQENYPmyWCw69thjjUGp0pnmAAAAqH+lx+alAumSXFtr167VNddcY7pYHRkZqVNOOUUdO3aUxWLRtm3b9N133xkXYEuPY998880qa3z++ef1xhtvmG5r3bq1Tj31VDVv3lxZWVlatWqV/vrrL+3fv1+33HKLBg8eXK2voa6OxWvL7Xabuvnb7XYNGzbM2G7durUGDRpkTHJLSUnR6tWr/XaRio+P19lnn63FixdLkr766isdOnQo4LGLsuOBgUyMa9Omjbp06aKjjz5asbGxslqtysrK0tatW7Vu3TrjnHXDhg266aabNGfOnJCPNcXGxqp79+7q2LGjkpKSFB0drfz8fO3evVtr1qwxxhfy8/N166236t13363W71lubq4mTpyoffv2SZI6d+6sQYMGKTExUenp6fr+++9NEwTnzp2rqKgov53zQ62icTiLxVLh+brv7WPHjjW6cufn5+ujjz6qcFWQspYvX24KFv7zn/+sdv1llf292bRpk66//nrdf//9tVpqTjKPddXke+VPsF6rwaytvsajiouLdf311xvjF7169VL//v0VHx9vjKeVvj6Ki4t1++23a+nSpcrIyND111+vvLw8ORwOHX/88erWrZuioqK0a9cuLV++3BgX3rdvn6ZMmVJpx7RSU6dONU0elqSuXbuqT58+at68ubxer/bs2aMffvjBCPdt27ZNl112mT788MMKJxKWdeDAAU2bNk0HDhyQ1WpV37591bt3b+Pr/u6770zvH88//7z69eunE0880fQ4NR17DcYSlr7HKTabLaDOWWWddNJJuvTSSzVv3jxJJSvWTJ8+Xbfddpueeuop4++0JN1yyy1+Gwu43W7dfPPNWr58uXGbxWJRr1691KtXLzVr1kzFxcXasWOHVq5caYzPr1u3TuPHj9f8+fMVExPjt76HHnpIK1euNH2dffv2Vbdu3dSkSRNZLBZlZ2dr165dxjKU1dG1a1dFRUUZIePVq1drxIgR1XoMAKgIITEAjVLv3r1NHUG2bdvmN+Rhs9l09tln68ILL9RJJ52k2NjYCh9zzZo1euCBB4zZis8884yGDBlSrsvPoEGDjOf2twxZoOqitmDJysoyfS1JSUk644wzTPuMHDlS06dPN04uFi5cqIkTJ/o9iRg6dKgee+wx44D2ww8/DDgk9uGHHxofR0dHV9g+uXnz5rr22ms1dOhQ9ejRo8KT2OLiYr3zzjt69tln5XQ6lZOTo4ceekivvvpqQPUEU32+BtavX6/i4mJZLBZNnDhRN9xwQ7n26j/++KPuuOMO44Tll19+0axZs6oVoKkPo0aN0qhRoyRV3RLfn7i4OI0cOVJz5syRVDJgmpaWpuTk5Crv+8EHH5hmyAZjcMrhcOjSSy81Lam6e/duXXPNNerQoYMGDx6sAQMG6Ljjjgt4edRSDfn9Kli15ebm6l//+pcxKBYZGamJEydq3Lhxfuv8/fffdddddxmBrZkzZ+qEE04IaImGAwcOaO7cuXI4HLrvvvs0ZsyYcu81b7/9th555BFj+/nnn1evXr20ZcsWtWrVSv/+97/LPVdRUZHuu+8+o2tiZmam3nzzTU2ePLnCWjZv3qz77rvPGJBr0aKF7r33Xp177rnlavJ6vVq6dKmmTp2qrKwsud1u3X777Vq8eHFAbcqXLl0qp9Oppk2b6rHHHvN7IWbJkiW68847jd+P1157TVdddVW5pWVKQ5Zll8mYPXt2UJbJqMivv/5q2q7pxZ3hw4dryZIl+vLLLyWVdFJ4/vnnjQErqeQ9xvc14Oubb74xLRPTsWNHTZ061e8xjMvl0rx58/TUU0+pqKhI+fn5mjRpkj766CO/F1RefPFF4+OmTZtq2rRp5QYIfe3fv19Lly41BmwD0bVrVyMktnPnTmVlZdXL0jsAAAAwK9tFq7addaqSk5OjO+64wxQQO+uss/TII4+oWbNmpn3T0tJ07733Gh2S8/Pzdfvtt2vBggUVnkuuX7/e1OnZbrfrzjvv1Pjx48ud3yxdulT33nuvdu3apXfffTfgr6Euj8Vrq2w3/1NPPbXc93XUqFGmTugLFy6s8BzqoosuMkJiTqdTixcv1vjx46uso2w3/wEDBphWEvDVuXNnXXjhhTrrrLMqnXiZnp6uZ599Vh988IGkkuW+Zs+ereuvv77KeoItPj5el112mS644AL17du3wqXESkN7jzzyiHJzc+V0OjVlyhQtXrw44PDEu+++q+LiYsXHx+uxxx7TeeedZ/q81+vVu+++qyeeeMI4j549e7bOPPPMSs/jQuHxxx/X448/Lsm8RN3NN99c4YRhXyeeeKI6d+5sdH2fO3duwCEx30BOfHy8hg8fXp3S/erbt6+OPfZYU4fAlStXatiwYTruuON0xhlnqF+/furdu3el41/+TJw4URMnTpRkXjZx1KhRevLJJwN+nLp4rQartvocj1q2bJmcTqdatWqlp59+ulw3Kn/jaa+//rq+++475eXl6fjjj9cTTzxR7j0qNTVV1113nbZs2SKpJHzzww8/VBpemzt3run12LdvX91///1+JyEWFhbq1Vdf1QsvvCCPx6O0tDTdfvvteuutt6r8mqWSpVydTqeOPfZYPfHEE+XGsJxOp6ZNm6bXXntNUsn3+fnnny/33tGmTRtj7LXscqO+19bqgu/vV7t27RQZGVmjx7nzzjv1zTffGIHb1157TYmJiXrvvfeMfXr37q2rr77a7/2fe+45U0Ds9NNP1z333KOOHTuW2zc7O1vTpk0zji3+/PNPPfLII3riiSfK7ZuSkqKFCxeaapg2bVqlfw83b96sjz76SPHx8ZV/0YdZrVZ17tzZ+F6WHdsEgNpguUkAjVLZMEdFrYa7du2qF154QWeffXaVJ3UDBw7Uu+++awQZnE6n3nnnneAU3Mhq++STT0xtd88///xyg2Bt27bVwIEDje09e/bop59+8vt4cXFxOvfcc43t5cuXV9kqWypZHqx03XVJOvfcc8sFDkrdcMMNuuOOO9SrV69KZ2BFREToqquu0tNPP23c9t1331W4lEFdqs/XQOnP8//9v/+niRMnlguISSWDNrNnzzbVUlXL8MbKd8lJp9MZ0HJrXq/XdALatWtXDRgwICj13HzzzercuXO523fs2KHXX39dN998s0455RQNHjxYt912m+bOnVutVui11ZDfr/773/8a3cscDodeeuklXX/99RXW2bNnT73zzjvGQLfH4zG6IlaldOD2ueee0z//+U+/7zWXX365adBy3bp1euedd5SQkKC3337b7yB+ZGSkHnvsMbVp08a4zXcWuT9TpkxRUVGRpJJBn/nz52vIkCF+a7JYLDr//PP1xhtvGMuOpKenBxSqlEq+7tjYWL399tsVztQfOnSoMdgolXTEK50p3BBs3brV+DgmJiagUGhFHnroIVMwatasWaa/aXfddZffAG9RUZHuueceI1zdq1cvzZ8/v8JOhna7XePGjdN///tfY3B327Ztpos2pXbv3m16T7j77rurvLDQqlUrTZgwQVOnTq10P19lZ2WWhi0BAABQv8ou9Rhod5KaeuONN0xdSwYPHqzp06eXCzJJJWN2L7zwgqkz9s6dOzV79uwKH//JJ580df598MEHdeWVV/o9vxkyZIhmzpwpm80W8BJxdXksHgxVdfOXSsbEfLuZLF26tMKlzk488UQdddRRFT5+RVasWKH09HRje/To0RXu++yzz2rChAlVduZv1qyZHn/8cdM4zDvvvFPh8m916aKLLtKDDz6oAQMGVBi6kUomyo0aNUqvvvqq0W3nzz//rNZSX8XFxbLb7Zo1a1a5gJhUcp4+btw4I3xVynd5tXDi+/P/448/tHbt2irvs3v3btOybCNHjlR0dHRQ6nnsscfKdQfyer36+eef9dxzz+nKK6/UwIEDdcEFF2jq1KlasmSJsrOzg/LcgajP12p11fd4VOl4mr/lCv2Np7366qv6/fff1bdvX73++ut+36NatmypadOmmWqu7P09LS3NFBQ6/fTT9fbbb1fYpT4qKkoTJ07U/fffb9y2atUqU9epyjidTvXo0UP/+9///E5ydDgcuvPOO3XKKacYt61bt65Gy1rWFd9xuIrCxoEoOxHT7Xabrus4HA49/vjjfjujbdq0ydRx/5JLLtHLL7/sNyAmSQkJCZo6daquvfZa47aFCxca486+fH+WVqtVzz//fJV/D7t376677rpLl112WaX7+fKt9c8//wzJ304A4YmQGIBGqWzavqKQWHUlJSWZOpv4BpRCrT5rC2Rwyt/tlQ06+Q4sOZ3OKgMQUklIwrdrU2WDU9V1/vnnq0uXLpJKBgFKZ7g2ZLV9DfTq1ct0kuNPp06dTEGPQANUjU3Xrl1Ngwvz5883DUj7891335lOtoPRRaxUXFycZs+ebQpe+pOSkqLFixdr6tSpOvvss3XllVcGPMBQn+rr/SozM1Pz5883tq+99tqAlg5NSEjQPffcY2yvWbPGNHhRmSFDhujss8+udJ9LL73UtO3xeDR58uRKBwsiIiJMy3ikpKRU2Ib822+/Nc34e+yxxwLqKtirVy9dfvnlxvbcuXNNreYrM2nSpCqXXRg7dqwpgLp+/fqAHrs++F7Qqm0XzuTkZNPrx9fJJ5+sMWPG+P3cggULjNCtzWbTM888E9DswX/84x8aMmSIsV3aBdGX77LMUt11kih78dH3+woAAID6U3YcrKIJdcFQXFzst3NuZUtVORyOciGMOXPmmMZ4Sm3ZskU///yzsX3iiSfqkksuqbSmQYMGVesia10ei9dWdna20alYKhnzPOuss8rtFxsbazoXzc/P17Jly/w+ptVqNTqwS9LGjRuNjjmV8e3mHxMTU2E3/5q4+eabjUDG/v37tWnTpqA9dl3p16+fTjvtNGO7umMb48aNq3KcZ8SIEabVFgINUDU2I0eONE3mK7tknz/vvfeeaawumONw3bt31+zZsysdH/B4PPrjjz80d+5cTZ48Waeeeqpuv/12v4GRUKvtazVQoRiPuvXWW6scT/O9ZuDxeGSxWPT4449X2vmxU6dOpt9P379DZb311lvGCikJCQl66qmnAuoqOXbsWNPSn9X5G/LEE09UuMxhqSuvvNK03VDG4bKzs5WTk2Nst2zZslaPd9ppp+miiy7y+7mbbrqpwtUCXnnlFeN11q5dOz3wwAMBdYOcNGmSWrduLank9eR7DFTKN1DdtGlTU1AxmHy/dy6Xy7T8LgDUBiExAI1S2Q4xpWuFB4PvQeWff/4Z1Meurfqo7a+//tKGDRuM7fbt26tfv35+9z3//PONWUCS9Nlnn5mWHvB14oknmg6WA5nF6LtPmzZtgr4Ume/385dffgnqY9eV2rwGxo0bV2mXtVJjxowx/VxLl4cLN76zGFNSUvTNN99Uur/vAFZMTEyF4cmaSk5O1ttvv62nn35aPXr0qHJ/r9erH3/8URMmTNBtt91mDFY0FPXxfrVs2TJj5rTVag14uQKpZIDBtxvUqlWrArpfIIOSvXv3Nv2uRUZGmgJgFenbt69pu6IOh77tzDt37hxQMK7UsGHDjI8zMjICCsdFRETo4osvrnK/hIQE0889FB0aK+IbuPPX7aC6Ro0apX/84x+m22JiYipcZlIy/9xOPfXUcl25KuP7c/v999+Vm5tb7rl91dUFl7JL3jI4BQAAEBplz6+qupBcG7/88otpUsIFF1xQ7rjQn1atWmno0KHGdlpammm8qVTZMYdAlkWUZJqYVJW6PBavrbLd/M8777wKl+XyDX5JVU/W9L0Y7hsA8ycjI8M0gfK8886r9lJ7lWnevLnpXKwxjsNVt+aavpbDcRwuLi7ONI722WefVbrKhNPpNJYolUqWPi2d7Bssffv21aeffqpbb701oBBLUVGRPv74Yw0fPlyvvvpqUGsJhvoY567v8ajIyMiAJo37BrGkklUO/K3YUJbvONzOnTv9Bpkl89c9fPjwgJbKLOX7dVe0CktZ/fv3D2hseMCAAab3+YYyDld20mswxuGmTJlS7ve0Z8+eFS5dXFBQYApSX3rppQEvFx0REWEKZfsbM/Y97kpPT6+z8THG4QDUlYr7lAJAA1Z2MCyQGZOpqan69NNPtX79em3dulWHDh1SXl5euYN/31ksHo9HBw4cqLAFbbA0pNrKDjCNGDGiwn3j4uJ01lln6ZNPPpFU8nNZtmyZ3/CMxWLR6NGjNX36dEklA2ubN29W9+7d/T72pk2bTBe5yw5uVWbdunX6/PPPtXnzZu3cuVO5ubnKz88v1ynKdzvUB9j18RrwnZlYmbi4OA0aNMgITW3ZskWFhYWm4Fg4OOecc5ScnGzMJp47d2650Eep1NRU0yy84cOH18lMbYvFohEjRmjEiBHaunWrvvnmG/3000/asGGDaYZSWYsXL9aBAwf0+uuv+11KNJga0vvV6tWrjY87dOhQrSUErVar2rZta8zADyRUY7PZKgzN+oqOjlZCQoKxBEzPnj0DWhKh7OzLipYy8B1UqmpWclnt27c3bW/atKnCGXelevXqFfDFgbZt22rjxo2SZJo1GGq+y7BUdMGluiZMmGB6XzjnnHPUtm1bv/sWFBTot99+M7Zr83PzeDzavHmz6TGOOeYYxcbGGsdHjz76qJo1a1atAdtAlP07UNHyNgAAAKhbZUNhdXlcVra7ir8uVxU599xzTd3Jf/75Zw0YMMC0j2+YwW63m7rhVKZ9+/bq1KlTlRfF6/pYvLYC7eYvSSeddJJatmxpjGGtWrVKe/fuNS0tWeroo4/W8ccfb5w3f/zxx7rjjjsqXLpu0aJFNermX1xcrBUrVujbb7/VH3/8ob179yovL0+FhYXlOgX5LpMV6nG4zZs3a+nSpfr999+1fft2ZWdnKy8vr9Kxw/379wf8+F26dKly6bFSJ554omJiYozf48YSoKuusWPH6t1335VUErhasGCBrr76ar/7fv7556ZxsOp0DqyOmJgY/d///Z9uuOEG/fTTT1q5cqXWrl2r3377rcL3VafTqaefflrZ2dn6f//v/9VJXb7q+rVaHfU9HlXT8bSyf2cCuZ/X61Vubq6aNGli2mfnzp2m0NPxxx8f0GOX8v26MzIytH///iq7rwX6HHFxcUpMTDTGHxvKOFzZ351gXFOIj4/X6NGjNXPmTOO2cePGVfg37eeffzb9Tavuz813icwtW7bI6/Wark35BhO9Xq8mTpyop59+ulZLa/pTdgyTcTgAwUJIDECjVPaA17cbTFmZmZl65pln9MEHH1S5nJw/FV2kD4aGVpvH49GiRYtMt1XVLWnkyJFGSEwqGdyq6D6jRo3SjBkzjEGiDz/8sMIlu3xnOFosloC68Kxbt04PPvhgQC30ywrWkqXVVV+vgRYtWlRrllOPHj2MkJjL5dKOHTsqDPQ1Vna7XZdeeqlmzJghSfrmm2+0b98+o520r/nz58vlchnbwWxxX5EuXbqoS5cuuuaaaySVDPCsX79eP/zwgz777DNjAKDU6tWr9eKLL2rSpEl1Uk9De7+SZGpxv23bNvXs2bNa9/cdoC77/fQnMTEx4IGNmJgY4zEDbateduDL34l/2dlp8+bNMy25WV2BfN1llxisjO/X0JA6cfrOzA9WkLJsZ8bKOjVu3brVNDg1bdo0PffcczV+7rI/N4fDocsvv1wvvfSSJOnQoUOaMGGCOnbsqDPPPFMDBw5Uv379qvV3wJ+ysy6Liopq9XgAAACombLjYMHubuVr+/btpu3qnHeV3Xfbtm3l9vENeXXs2LFakzp69OhRZUisro/Fa2Pbtm2mQFCbNm0qvZBttVp1wQUXGF2MvF6vFi5cqJtuusnv/hdddJEREktPT9fXX39t6o7iyzes1q5du4AuqH/wwQd65plnlJGRUeW+ZYVqHO7PP//UQw89ZJp0FqjqjGt069Yt4H2tVqu6detmBDIbSjegYOvSpYsGDRpkfO/nzZunq666yu+kYN9u/k2aNNF5551Xp7VZrVadcMIJxioWHo9H27dv19q1a/Xtt9/q66+/No0rSNJLL72kk046KeiTs0rV12s1UKEYj6rpeFqg41j+xuHKhsRKJ0KWuu2223T77bcH9PiSyoVlMzMzqwyJVWd5Rt/xx4YSICr7uxKscbiyy1xXNg5X9udW3aCp78/N5XIpNzfXtEx137591a9fP+N9e8OGDRoyZIj69++v0047TQMGDFDv3r0DCjlWhnE4AHWF5SYBNEplW9YmJCT43e/gwYMaO3as5s+fX6NQg1R3B14Nsbbvv//edLLXv3//Kme9nXrqqabuPatWrdK+ffv87tu2bVvTkpEff/yx3zbOTqdTH3/8sbFddqlKfz777DONHz++RgExKTQH2PX5GghkKQhfZdtA12VYMpTGjBljzDhyu92aN29euX3cbrdp5nPv3r3Vq1evequxVKtWrTRkyBA9/PDD+uabb3T77beXO8l+88036+TiQEN8v5JUbmkCt9tdrX++ApltV50Te99BzkDvV3ZgtOxAklT+a/Z6vXX+dVdn6ZpAOz7WN99BlYqWD6hLZX9uHo+nVj83f7/nt9xyi8455xzTbdu3b9frr7+um266SSeddJLOP/98Pfjgg1q5cmWNfpfLDvQFqysbAAAAqqfsReyy42TB5DseYLVaqzXxoFmzZqZzBH9jC763VXdSQyBLWNXHsXhNle0idsEFF1R5TlV2ycmPPvqown3LLhlZ0ZKTpd3+S1144YVV1vHEE0/onnvuqVFATCp/blEf1q5dq0suuaRGoRupeuMatRmHy8nJ8TseEA7Gjh1rfLxjxw79+OOP5fbZvn27aXm30aNHB7xMXLBYrVZ16tRJY8aM0fTp07V8+XK/3fVefPHFOnn++nytBioU41E1HU+ry3G46v4NKTv2Upfjjw3lfaPs72tDGIer7ms1kJ/btGnTTMtXe71erV27Vs8995zGjx+vgQMH6tJLL9Vzzz1X42tWZf9WhttKLwBCh05iABqlX3/91bTdqVMnv/vdc889ptlXzZo106hRo3T88cerXbt2atGihSIjI00HrqtWrdIVV1xhbNfVwXVDrG3hwoWm7aq6iEklMziGDx+uN954Q1LJidJHH32kG2+80e/+o0ePNgYAMjIytGLFinKzGL/++mvTgXxVLe5TUlJ05513mjo99evXT0OGDFHv3r3Vpk0bxcfHKyoqyjTj5O677y43IFef6vM1UN1ZK/W5dEUotWzZUmeffbaWLl0qSXr//fc1ceJEU6vqFStWmIKP9dFFrCqRkZG67rrr1KlTJ/3f//2fcXteXp5+/PHHCmcG11RDfL+SgttGvaEMpFQl2IHNxvJ111ZMTIwKCwslyfi/PtVF58+yHA6Hpk+friVLlui1114rN2tSKulUsG3bNs2ZM0cdOnTQ7bffXi5YVpmCggLTdqDLkAIAACC4unXrpk8//dTY3rhxo4YOHVonz+XbITgqKqpaE0OsVquio6ONMQV/3YZ9xxuqM0FFCmysoz6OxWv6ONXt5i+VdGPq1auXcby/Y8cOrVu3Tv379y+3b3R0tM4//3xj4ts333yjjIyMcmE83/CY1Wqtspv/kiVLNHv2bGPbZrPp7LPP1hlnnKHu3burVatWiomJKfd6GTx4sFJSUiTV/7lobm6uJk2aZHq9denSRSNGjFDfvn3Vpk0bNWnSRBEREaYJedOnTzc60FdHdcfhfPf3eDwqKCio9u9DY3DOOeeoRYsWRrB1zpw55Tpx+U7gtFgsDWIcrnnz5nriiSeUmJhojIFLJcsvZmZmKikpKWjPVd+v1UAdqeNRwV7CMVh/Qxqysu9dZceS6kN9/Nxat26tDz/8UG+99Zbefffdcsu8ulwurV+/XuvXr9fMmTN14oknasqUKdVaraXsGGY4/l0AEBqExAA0Onl5edq6daux7XA4dNxxx5Xbb8OGDVqxYoWxffzxx2vWrFmKi4ur9PHrIwzTEGvLzc3VF198Ybrt4Ycf1sMPP1zlfcue1C1YsKDCkNi5556rhx9+2Jh5+eGHH5YLtPgOTsXFxencc8+t9PlfeeUV0wHzPffcoyuvvLLKukMZfKrv10B1T8bKPk84n4CMGzfOCImlpaXpyy+/NLWx921xn5CQoGHDhtV7jRUZPHiwTjnlFH3//ffGbb/99ltQQ2IN8f2qVFRUlDEbrW/fvnrvvffq/DlDrexA83XXXVetNvdHqpYtWxoz3NPT0+v9+cv+3B588MFqt7oPhMVi0bBhwzRs2DBjVvbatWv1888/a/fu3aZ9d+zYoYkTJ2rixIm65ZZbAnr8st+76ixFCgAAgOAZOHCgaXvDhg119ly+EwMKCwvl9XoDDoqVhl38PVapmJgY42Judc8jAxnrqK9j8epauXJluQvKw4cPD+i+ZS9WL1iwwG9ITCqZeFkaEnM6nVq0aJEmTJhgfN7pdGrx4sXG9kknnaTWrVtX+vy+QZSYmBi9+uqrGjBgQJV1+wsJ1pc5c+YoLS3N2J4wYYLuvvvuKl/L9TUO57t/abgyHNntdl166aWaPn26JOmrr75SWlqasUpFUVGRaULvySefrHbt2oWkVn8mTZqk999/33jP8nq92rRpU1CXnKzv12qgjtTxqLKdm1555RWdfvrpIaqmcSi7XGYoxuHK/tyWLFlSYaOJ2oiOjtYNN9yg6667Tr/++qtWrVql9evXa926deW6mf34448aM2aMZsyYEfBr6ODBg6bt6ixFCgCVYblJAI3ORx99ZOoYdeyxx/pts/rVV18ZH1ssFj355JNVhhqk8gdedaEh1rZkyZJyMxNq2n53x44dxnrsZUVHR5tmt5bOYiyVnp6ub775xtgeNmxYlW10ly9fbnw8aNCggAJiUv38rCtS36+B6p6Mld2/oiVdw8GgQYPUpUsXY9s3FJaSkqJvv/3W2B45cmSDG6g78cQTTdtlT0BrqyG+X5XyXWIl2F93Q1V2WZkj5euuLd8li32XVa4vZWcV18fPrUOHDvrnP/+pp59+Wl988YVWrFihhx56SH369DHtN2PGDK1Zsyagxyz7vWvbtm3Q6gUAAEDg+vTpYxorWbNmTbnAUbD4jgd4PJ5qLS+Ynp5umljob2zB97bqLl0YyFhHKI7FA+Fv6cdAx+HKTtb89NNPK1xebsCAAerQoYOxXbaj/vLly6vVzX/Xrl2mTuPXXHNNQAGx4uLioHd2qQ7fscP27dvrzjvvDCjsGIpxuPj4+Gp17GtsxowZY3TAcjqdRohRKnktZ2ZmGtsNoYuYr+jo6HKT1YP9nlLfr9VAHanjUUfq110b8fHxpr/toRiHq++fm9VqVd++fXX99dfrxRdf1MqVK7Vo0SJNmjRJRx11lLFfUVGR7rjjjoD/Hvp+7xwOB5M1AQQNITEAjYrL5dLbb79tum3UqFF+992+fbvx8THHHBPwhczffvutxvUFqiHWFuxlFyt7PN8Bp9JZjKUWLVpkCgFWNThVUFBgGggNdBaG2+3Wpk2bAtq3LtT3ayA1NbVag62bN282Prbb7aYBxXA0duxY4+OVK1dq586dkqT33nvPFIJsaINTkpSYmGja9m01HwwN8f2qlO8MsD179igrK6vOnzPUkpOTTQMt/pYURHldu3Y1Ps7PzzeWtqgvxxxzjGlQNxQ/t1atWumf//yn5s+fX65z2Pz58wN6DN/3A4vFYvq+AgAAoP5ERkZqxIgRxrbH46mzzsodO3Y0bf/+++8B37fsvsccc0y5fXzP67Zv315h2Mkf37GLijSEY/Gy/HXzr42cnJxKH893bG3z5s2mn4tvWC0hIaHK5eh9zwmkwMfhNm3aJLfbHdC+dcG37pNPPlk2my2g+9V0bCOQ12Ypj8ejLVu2GNt10e2mIWnRooWpA/78+fONsTffiZstWrTQ4MGD672+qtTnOFx9vFYDdaSOR3Xu3Nm0faR83bXlO160Y8eOen/+UP/cLBaLunXrpptuukmfffaZTj31VONzmZmZWrZsWUCP4/t+0Llz54DfDwCgKoTEADQq06ZNM81Wa9GiRYUhIt80fqBdkJxOp7788suA67Hb/161tzoDHfVRW3Xs3LlT69atM7bbt2+vLVu2VOvf+vXrTcsGVDaLsV+/fqaBQd8BKd+PO3Xq5HcpUV/Z2dmm7bIn6hVZsWJFSJebDMVr4Ouvvw5ov9zcXK1atcrY7tatW5Xd3EKlpr+DZY0cOdJ4/Xq9Xs2dO1cul0sffPCBsc/xxx9f7gSzISgbdmnevLnf/Rry+1VNa/Nt5+/xeExdz8KVzWbToEGDjO0tW7aUW0awMfD9mUvll0sJtmOPPda0/ccff9Tp85XVtGlTde/e3dj+8ccfjWWXQ+Hmm29W06ZNje1AL2D4Xrho165dWHeZBAAAaOgmTJhgCj+9/vrr2rVrV60es7i4uNxtZZcxrM653ueff27a7tevX7l9+vbta3zscrn03XffBfTYO3fu1J9//lnlfg3tWFwqGTPz7eZ/8cUXV3sc7p133jE9pr/OZKVGjRplurBcuu/BgwdN3dOHDh2qyMjISmsvOw4X6DnBp59+GtB+dcV3bCPQscMtW7Zo27ZtNXq+P//8M+Bz9VWrVpnGKH1/JxqaYI3D+U7WTElJ0TfffKM//vjDtDLFJZdcUm7soCEoOw7XrFkzv/sFYxyurl6rNaktXMajqqtHjx6mjpTLly+v8zGsulD2d6muQ7u+43C7du0qt4JNXTvhhBNktf4dgQhmMLu6IiIidPfdd5tuC2Qczu12m66Flh3bBIDaICQGoNF488039dprr5luu+WWWxQREeF3f9/A0t69ewN6jvnz5ystLS3gmnyfo+wgSaD3q6vaqqNs1y/f2aiBio6O1nnnnWdsZ2dnVzpw6Bvu27JlizZu3KjffvvNdOG+qi5ikvl7KZUMLFTF4/HopZdeqnK/uhSK18Dbb79dbkmCip7H98StqlmkoVTT30F/j+PblXDBggVasmSJ6ftdV13ECgoKAvq5+OP1essNulc0mNiQ369qWtt5551nmrH50ksvVWvWeWM1fPhw42OPx6MZM2aEsJqaKfveXddd4AYMGGAaHPr111/r9Pn88f255ebm6vXXX6/3GkpZLBZTV0Cn01nlfbxer2nmpe/gMAAAAOpfp06dTOexBQUFuvPOO5WXl1ejxztw4IAuv/zycrf36dPHNBnp448/DmhZs9TUVH3yySfGdosWLcotfS7J1FFIUrkVBCryv//9L6D9pIZ1LC6VH4eraJWEygwYMMB0TL9y5coKl/Rq2bKlTjnlFGP7448/VnFxcblu/hdddFGVz1uTcbgDBw6YlhQMBd+69+zZE9B9Zs6cWePn83q9Ab+W33zzTdP2kTAON2jQIHXp0sXYnjt3rqmLmM1m05gxY2r8+JWpzaThtLQ0U5AtIiLCFEL1FYxxuLp6rda0tnAYj6ouq9WqoUOHGtu7du0K+oos9aG+x+GOP/5442OPx1PvnbwSExN12mmnGdurV6/Wjz/+WK81+Dr66KNN24GMw23dutV0jYZxOADBREgMQIP3119/6V//+pcef/xxU5Di0ksvrfRk0bfjT2pqqlasWFHp82zatElPP/10tWpr06aN8XF+fr4p2V+Z+qgtUF6vVx999JGxbbFYNHLkyBo9Vtn7VXbCNHLkyHKzGH1nPdpstoDqiIuLU+vWrY3tRYsWVTkz5fnnn9f69eurfOy6FIrXwMaNG6scCN22bZvpBN/hcOjiiy+u1fPWJd/fwR07dphm21WX7yzGQ4cO6eGHHza2mzZtqnPPPbfGj12Zn3/+WSNHjtTnn39e7bDYSy+9ZOrsk5SUpIEDB/rdtyG/X9W0tlatWplen9u3b9fdd98d0Il2Ka/Xq5UrVwZebAMwZMgQ02DqwoUL9dZbb1XrMYqKirR27dpglxawNm3amLoe1HVoKykpyXRBavXq1XX6fP6MHTvW1L1r1qxZ5UKeVcnJyfH7vdq9e3e1lhTOzMw0dV3w/R2syJYtW5SZmWlsB7qsDAAAAOrO/fffrw4dOhjbP//8s66++mqlp6dX63G+/PJLXXzxxfrll1/KfS4iIsI0aSo3N1cPPPBApV1IXC6X7rvvPlMYY+zYsX6XZevevbupw9gPP/xQaVcsSVqzZo3mzJlT6T6+6vJYvLp27dplOhdr27ZthefxlSk7fud2u7Vo0aIK9/ediJmZmanly5ebxu26dOniN8RXlu+5qKQqlzktLi7WnXfeWavxmmDwHdtYvnx5lUHH+fPn17r72TvvvGMKFPnzySefaPny5cZ2165dNWDAgFo9b13yPXes7e+D7zjcihUrTK/HM844Q61atarV41fk5Zdf1o033litpXOlklDHfffdZxpzOvXUUxUTE+N3f9/v1caNGwPuPlUfr9Wa1hYO41E1ccMNN5j+fj3yyCNV/m6XdfDgwXrvau+r7LhPXY/DnXTSSabvWSjG4SZOnGjavu2226q99OXu3bv9dszbvHmz3+6rFfnpp59M24GMw/mu9GK1Wk1LVgJAbRESA9BgeL1e5ebmau/evVq9erVefvllXXXVVRo2bJg+++wz077nnHOOHnjggUofr+xMxLvuuktr1qzxu+8nn3yiK6+8Uvn5+RWe2PlTdinEBx54QBs3bqyyXW991BaoH3/80dQdaODAgeVmNgTqhBNOMB3gfv/99+VacJdq0aKFaTbH4sWLTTNMTz/9dCUnJwf0vL7fz7179+qWW24xXcgulZOTo6lTp2rWrFmSVCffz0DV92ugtOPe008/rRdffNE0U7TUTz/9pKuuusq07ML1118f8M8hFHx/B51Op+655x799ddfNWr73blzZ51wwgnGtu8A5ujRoyvsWhgMW7Zs0cSJE3Xuuedq5syZVS4RsnfvXk2ZMkXTpk0z3f6vf/3L76C71LDfr2pamyRNnjxZ7du3N7aXLFmiyy+/vMoBp/379+vNN9/UiBEjdMsttwRca0NgsVj0+OOPm5YBeeyxx3TXXXdV2ep/8+bNmjZtms4888xy3TnrU3x8vDp16mRsz5kzR5988kmdLvvi+1r++eef673VfUxMjB555BEjHOd2u3XLLbfoySefrPQintfr1bp16/Too4/qH//4h98LPz/99JPOPPNM3Xvvvfrhhx/8vseXSklJ0f/93/+ZLtgFEoL9/vvvjY+jo6NNnQgAAAAQGrGxsXr++edNS2GtX79e5513nl5++eVKJxIUFRVp+fLluvzyy3XTTTdV2IVKkq666irTeM+XX36pf/3rX36PYw8ePKiJEyfqm2++MW5r3769rrjiigof/+677zZ1/r3//vsr7Ia+bNky3XjjjXK5XAGfp9flsXh1lZ1QOXLkSNMEmuoo24Fs4cKFFe571llnmV4n//nPf6rdzV8q6YbSrVs3Y3vp0qWaNm2a33OQHTt26Oqrr9bKlStltVqrXMqyLvl258rLy9MNN9ygffv2lduvqKhI//3vf3X//fdLqvnYYUREhJxOp2644Qa/gUSv16u5c+fqrrvuMt1+zz331Oj56ovv+M2GDRs0Y8aMCsd+qzJy5EjFxcVJKuk05HuOWlfd/KWS7/3y5ct14YUX6rLLLtP8+fOrnHT1yy+/aPz48fr666+N22w2m2699dYK7+P7vUpLS9NDDz0U0PKM9fFarWlt4TAeVROtWrUy/a4WFBToiiuu0KxZsyodx3K5XPr+++91zz336Mwzz9QPP/xQH+X61adPH9Pf2X//+99as2ZNtYJO1REbG6uTTjrJ2A7F196nTx9dddVVxvbBgwd18cUX69133610JYqioiJ98cUXmjRpks477zzTBO1Ss2fP1uDBg/Xcc89VuXTkTz/9pClTphjbFoul3Hi7P77fs/79+5vC7gBQWw1vQW8AR4TVq1erZ8+epts8Hk+VXXRiYmL0r3/9SxMmTKhyAKVXr14655xzjBPxQ4cOady4cRowYID69u2rqKgoHTx4UD/88IPRujk6OlqTJ0/WY489FtDX0adPH/Xo0UObNm2SVDKTcfTo0cbAh2+NvrNL6qO2QPkbnKopi8WiCy64wAhhlc5ivPbaa/3uP3r0aOPkumyoK9DBKUm69tpr9eGHHxpLKnzzzTcaPHiwzjjjDLVv315Op1M7d+7U999/bww4DBo0SK1btzZ1UatP9f0aOO6449SkSRN99tlnev755/Xuu+/qtNNOU8uWLZWbm6v169eXm0HUt29f3XjjjbX/YuvQ4MGDlZycbCxtuGzZMi1btkw2m800YHHUUUeZQogVGTdunGmWjlTyuq7LwSlfu3bt0nPPPafnnntOycnJ6tu3r1q0aKGkpCR5PB6lp6dr06ZN2rhxY7n3y+HDh+uyyy6r8LEb8vtVTWuTStqHz5w5U1dccYUxu3L9+vUaO3as2rdvr379+ik5OVkRERHKyclRamqqNm3apN27dxvfw/j4+IDqbEj69OmjJ598Unfeeacxi3XhwoVatGiRevbsqWOPPda4AJCTk6Pdu3fr999/D2hZmPoyZswYPf7445JKBj7/3//7f5KkyMhIU6fJhx56qEbLIJd1wQUX6D//+Y88Ho8KCgr07bff1vsyHmeffbbuvPNO/fvf/5bX65XX69Ubb7yht99+W3369FH37t2VlJQkl8ul7Oxsbd++XZs2bQpoGYDCwkK9//77ev/99xUbG6sePXqoU6dOSkpKksPhUFZWlrZs2aJ169aZLuD06tVLF154YZWP73th46yzziq3VAEAAABCo3v37po3b56uu+46Y8JRTk6Onn32WU2bNk3HHnusOnfurCZNmsjhcCg9PV379u3T2rVrVVBQYHos3wvIvuLi4vT000/r6quvNiZbfPHFF/ruu+90yimn6JhjjpFU0p38u+++M118jYmJ0TPPPFPp8eNxxx2nG264wVgyzeVy6ZFHHtFrr72mU089Vc2bN1dWVpZWr16trVu3SpLatWunwYMHa/bs2QF9n+ryWDxQZbv5SzVbarJUu3bt1L9/f61bt06S9Oeff2rDhg1+O4JFRERo+PDhxhKIvt1U7HZ7tc65/vWvf+nmm282tmfNmqWPPvpIp556qlq0aKHc3Fxt2rRJa9euNSZ/3XDDDVq0aFFAy1PWhTFjxuiNN97Q/v37JUm//fabzj33XJ122mlG56aUlBR9++23xs/8mGOO0T/+8Y8aLU86duxYffXVV9q1a5cmTpyoLl26aNCgQUpMTFR6erq+++67ct+LCRMmmEIVDdHFF1+suXPnGpMzp0+frunTp8vhcJgmLV5wwQWmDv3+xMbGatSoUeWW5Wzbtq1pYnFdWrdundatW6f77rtPxxxzjHr27KmmTZsqISFBhYWF2r9/v3799Vft3Lmz3H3vvfdeU2CyrBEjRmj69OnGe2bpkpp2u90UcB0wYIBeffVVY7s+Xqs1rU0Kj/Gomhg/frx27txpLHVcXFysadOmadasWerXr586deqkxMREFRUVKTs7W3/++ac2bdpUq+VNgyk5OVmDBw/WF198Iank78W4ceNksVgUFRVlGnv95JNPdNRRR9X6OUeMGGEExteuXauMjIx6Dzrdcccd2r17t/F15+Tk6KGHHtKzzz6rgQMHqn379oqPj1dBQYEyMzO1detWbdmypdIQWam0tDTNnDlTM2fOVHJysnr27Kl27doZ48wHDx7UL7/8Ui5kNn78eHXs2LHSx87NzTWFxGpzzQ4A/CEkBiBkAukQU6p169YaNWqUxo8fr2bNmgV8v8cff1y7du0yHYitXbvWb4eZ2NhYTZ8+XXZ79d4an376aV1zzTWmGZelF6BDXVtV8vLyTBd9o6KidP7559fqMUeNGmWExKSSk8SKQmJnnnmmkpKSygXEmjRpojPPPDPg52zVqpWmTZumW265xTiAz8vL05IlS/zu369fP82YMUNPPPFEwM9RF+r7NfD444/r4MGDWrt2rdLS0ipdvqFPnz569dVX67R7VjBERkbq2Wef1cSJE5WdnW3c7na7TSfhVf0+ljrrrLPUsmVL0+/zKaecUuPueoGIi4tTVFRUua5GaWlpxglsZRwOh6666irdeuutFQ7ol2rI71c1rU2SOnXqpA8//FC33nqrMTguSTt37vQ7kFdWQ3+dV2To0KFq1aqVJk+ebAweejwe/fbbb/rtt9+qvH+ov+7Sjm9lu4WWHYiprCtWdbRq1Uonn3yyvvvuO0nSp59+Wu8hMUm6+uqr1a5dO02ZMsV433I6nRX+TpUVyM8tLy9Pa9asqbDrX6m+fftq1qxZVf6+7t+/37RM80UXXVRlDQAAAKg/HTp00Hvvvafnn39e77//vnHh3uPxaMOGDdqwYUOVj3HKKafojjvuqPDzAwYM0OzZs/V///d/OnTokKSSiQpffvmlvvzyS7/3ad68uWbNmqXevXtX+fy33nqr8vPz9eabbxq37d271+9yhi1bttT06dOrvWRkfRyLV2bVqlWmYFC/fv3Url27Wj3mqFGjTOfBCxcurHDZyNGjR5cL5Egl3fybN28e8HOeffbZuummm/Tiiy8at+3bt0/z58/3u/9ll12mSZMmBaUTW03FxMTohRde0NVXX20Ea4qLiyt8/Xbs2FGvvvpqlUufViQuLk7Tp0/X9ddfr9TUVG3dutUIOPpz6aWX6u67767Rc9WnXr16GWFL3y7+TqfTtAxjoN2Jxo4dW+41OWbMmCrHt2qjadOmslqt5VYh2LZtm7Zt21bl/ZOSknTffffpggsuqHS/Fi1a6IknntDdd99tGudwuVymcY6yYyD18VqtaW2lGvt4VE3dd9996tSpk5544gnje1NQUKAffvghoE5Zof66p06dqu3bt+uvv/4ybvN6veXGXmuyQoc/Z599thITE5WVlSW3261ly5bV20TsUjabTdOnT9cLL7ygmTNnGtckc3NzTZ0BK1PRqh2+0tLStGLFiir3C/S9fvny5cb7aExMjIYMGVJ1oQBQDSw3CaDBsNvtiomJUZs2bXTcccdp1KhRuvvuu/XBBx9o+fLluvXWW6sVEJOkhIQEzZ07V1dccYWioqL87hMdHa3hw4fro48+qtHSSV26dNHixYt1zz336LTTTlPr1q0VHR1dZaez+qitKp999pkpSHPWWWcZbb5rqmPHjqaW1Vu3bq1wjfuIiAi/J9QXXHBBQAffvs444wzNnTtXxx9/fIX7tG3bVrfddpveeustJSYmVuvx60J9vwbi4uL01ltv6aabblKTJk387pOUlKTJkyfr3XffVUJCQq2er76ccMIJWrJkiSZPnqwTTzxRycnJFX4/q2K32zVs2DDTbXV98tqnTx+tXLlS//3vf3XJJZdUOZOoVLNmzTR+/Hh99NFHuu2220ydlyrSkN+valpbqZYtW+rdd9/Viy++qEGDBlUZeomJidFpp52mRx55pFxIqTHp37+/li1bpvvvv9+YUVqZ5s2ba/jw4XrxxRf1zDPP1EOFFbPZbPrvf/+rl19+WaNGjVLXrl0VHx8f9EC0L98lbj7//HO/SxPXh7PPPltfffWVJk2aZFq2pyJt2rTRJZdcotmzZ+v2228v9/nBgwfrwQcf1JlnnhnQe3f37t31yCOPaM6cOQHN4nz//feNzntdunTRySefXOV9AAAAUL+aNGmiBx98UEuWLNH48ePVvn37Ku+TnJysyy+/XB988IFef/119ejRo9L9+/Xrp88++0zXXHONaelCf7Vcf/31+uyzzwIKiJW65557NGvWLKMzWVml3bAWLlyo7t27B/y4voJ9LF4dZZeDrE0XsVLnn3++qZP6J598UmFAp1evXn47H9VkEsikSZM0bdq0SkNuffr00fPPP68HH3ywxktqBtOxxx6rDz74QGeeeWaF9SQnJ+v666/X+++/H9DrozLdu3fXwoULNXz48ArDIR06dND06dP18MMPN4jvUSCuuuoqLViwQFdeeaX69OljdK+uiU6dOqlXr17GtsPh0MUXXxysUv268sor9c033+jhhx/Weeedp+Tk5IDu16lTJ02ePFlLly6tMiBWaujQofrkk0904403asCAAWrWrFlAQaH6eK3WtLZSjXk8qjYuu+wyffnll7rmmmsCeu106NBB48eP1/z58zV27Nh6qLBiLVq00IIFC/Too4/qrLPOUtu2bRUTE1Nn7z3R0dG65JJLjO2KgsR1zWq16pZbbtHSpUt16aWXVnldyGKxqHv37rr++uv1ySef6Iwzzii3z4033qhbb71V/fv3r/L3xmaz6dRTT9Xs2bP18MMPBzSG7/u9GjVqVKO5TgOg8bB4q1rbDQDCRG5urtasWaOdO3eqoKBAzZo1U4sWLTRw4MCQL5nUkGtrjHbt2qV169YpLS1NVqtVycnJ6tChQ4UzKRuC+n4NOJ1OrVmzRnv27FFGRoYSEhLUoUMHHX/88XUa0GgMLr74YiPY2LJlS3311Vf1/j3JysrStm3btHPnTmVmZio/P18Oh0NxcXFKTk5Wt27d6rS7WWUay/tVXl6efv75Z+3fv1+ZmZlyuVyKiYlRcnKyOnbsqE6dOtV4ELMhS0tL0y+//KKDBw8qKytLFotFsbGxOuqoo9SpU6daz1Jv7Lxery644AJjBveUKVM0YcKE0BYlac+ePfrtt9+UkZGh7Oxs2Ww2xcfHq02bNurUqVO12vx7vV5t27ZNO3bs0L59+5SbmytJxuugR48e1Xo8t9utc845x+h48Nhjj9X5oD0AAACCY/fu3frzzz+VkZGhjIwMeb1eJSQkqHnz5urZs2etlpMq7VK2bds2ZWRkSCrp0NOpUyf17t271p2ANm3apN9//13p6emKjY1Vq1atdPzxxwf9Qmkwj8WPRB6PRxs3btTvv/+uQ4cOKSYmRi1atDCW3WqoUlNTtWbNGu3fv18ej0fJyclq27at+vXrF9AF/OrKzs7WTz/9pP379ysvL09NmzZVz5491bNnz6A/V2OSmZmp008/3ejKNHToUE2bNq3e60hNTdW2bdu0Z88e5eTkqLCwUNHR0YqLizPOo+t7mTzf2urztVpTR+p41F9//aXNmzfr0KFDys7OVmRkpBISEtS2bVt17tw54BBiuNq/f7/OPvtso9PgggULQv6+5/F4tHnzZv3555/KzMxUbm6uoqOjlZCQoPbt26tz586VBuHLKi4u1h9//KFdu3bpwIEDys/Pl91uV3x8vDp06KAePXpU6/F27NihIUOGyOv1ymazacmSJerQoUO1v04AqAwhMQAAgMM2btyo0aNHG9sTJ07ULbfcEsKKAATb0qVLNWnSJEklXQGWLVt2xIdjK7N48WLddtttkqR27drp008/5fsFAAAAAKi12bNn64knnjC233rrLZ1wwgkhrAhAsD388MN65513JEnDhw/Xs88+G+KKGrYHHnhA8+bNk1TSReypp54KcUUAwhHLTQIAABz21ltvGR87HA6NGTMmhNUAqAvnnXeesZxFSkqKFi9eHOKKGrZXX33V+HjixIkExAAAAAAAteZyufT2228b2127diUgBoShG2+8UdHR0ZJKJm7u3r07xBU1XGl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            "text/plain": [
              "<Figure size 3125x3125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Use the venn2 function\n",
        "plt.figure(figsize=(31.25, 31.25))\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "colors = ['#009E73', '#56B4E9','#CC79A7']\n",
        "venn=venn2_unweighted(subsets = (df_temp['Counts'][5], df_temp['Counts'][2], df_temp['Counts'][8]), set_labels = ('Data Availability Statement (Yes)', 'Code Availability Statement (Yes)'))\n",
        "venn.get_patch_by_id('10').set_facecolor(colors[0])\n",
        "venn.get_patch_by_id('01').set_facecolor(colors[1])\n",
        "venn.get_patch_by_id('11').set_facecolor(colors[2])\n",
        "for text in venn.set_labels:\n",
        "    text.set_fontsize(fontsize)\n",
        "for x in range(len(venn.subset_labels)):\n",
        "    if venn.subset_labels[x] is not None:\n",
        "        venn.subset_labels[x].set_fontsize(fontsize)\n",
        "\n",
        "plt.savefig('../Figures/sup3.png', format='png', dpi='figure',bbox_inches='tight')\n",
        "fig.savefig('../Figures/sup3.eps', format='eps', dpi='figure',bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "51148cc2",
      "metadata": {},
      "source": [
        "## Supplementary Figure 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "id": "203f7abd",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>PMC_Year</th>\n",
              "      <th>Secondary analysis</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percent Increase</th>\n",
              "      <th>Labels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>0.490196</td>\n",
              "      <td>Only Code Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2017</td>\n",
              "      <td>no</td>\n",
              "      <td>1</td>\n",
              "      <td>0.490196</td>\n",
              "      <td>Only Code Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2018</td>\n",
              "      <td>no</td>\n",
              "      <td>2</td>\n",
              "      <td>0.980392</td>\n",
              "      <td>Only Code Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2019</td>\n",
              "      <td>no</td>\n",
              "      <td>3</td>\n",
              "      <td>1.470588</td>\n",
              "      <td>Only Code Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>2020</td>\n",
              "      <td>no</td>\n",
              "      <td>6</td>\n",
              "      <td>2.941176</td>\n",
              "      <td>Only Code Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>2016</td>\n",
              "      <td>no</td>\n",
              "      <td>11</td>\n",
              "      <td>5.392157</td>\n",
              "      <td>Only Data Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>2017</td>\n",
              "      <td>no</td>\n",
              "      <td>19</td>\n",
              "      <td>9.313725</td>\n",
              "      <td>Only Data Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>2018</td>\n",
              "      <td>no</td>\n",
              "      <td>17</td>\n",
              "      <td>8.333333</td>\n",
              "      <td>Only Data Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>2019</td>\n",
              "      <td>no</td>\n",
              "      <td>12</td>\n",
              "      <td>5.882353</td>\n",
              "      <td>Only Data Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>2020</td>\n",
              "      <td>no</td>\n",
              "      <td>8</td>\n",
              "      <td>3.921569</td>\n",
              "      <td>Only Data Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>2021</td>\n",
              "      <td>no</td>\n",
              "      <td>14</td>\n",
              "      <td>6.862745</td>\n",
              "      <td>Only Data Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>no</td>\n",
              "      <td>8</td>\n",
              "      <td>3.921569</td>\n",
              "      <td>Data and Code Both Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2017</td>\n",
              "      <td>no</td>\n",
              "      <td>7</td>\n",
              "      <td>3.431373</td>\n",
              "      <td>Data and Code Both Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2018</td>\n",
              "      <td>no</td>\n",
              "      <td>4</td>\n",
              "      <td>1.960784</td>\n",
              "      <td>Data and Code Both Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2019</td>\n",
              "      <td>no</td>\n",
              "      <td>13</td>\n",
              "      <td>6.372549</td>\n",
              "      <td>Data and Code Both Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2020</td>\n",
              "      <td>no</td>\n",
              "      <td>13</td>\n",
              "      <td>6.372549</td>\n",
              "      <td>Data and Code Both Shared</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>2021</td>\n",
              "      <td>no</td>\n",
              "      <td>16</td>\n",
              "      <td>7.843137</td>\n",
              "      <td>Data and Code Both Shared</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Data availability (yes/no) Code availability (Yes/No)  PMC_Year  \\\n",
              "6                          no                        yes      2016   \n",
              "7                          no                        yes      2017   \n",
              "8                          no                        yes      2018   \n",
              "9                          no                        yes      2019   \n",
              "10                         no                        yes      2020   \n",
              "11                        yes                         no      2016   \n",
              "12                        yes                         no      2017   \n",
              "13                        yes                         no      2018   \n",
              "14                        yes                         no      2019   \n",
              "15                        yes                         no      2020   \n",
              "16                        yes                         no      2021   \n",
              "17                        yes                        yes      2016   \n",
              "18                        yes                        yes      2017   \n",
              "19                        yes                        yes      2018   \n",
              "20                        yes                        yes      2019   \n",
              "21                        yes                        yes      2020   \n",
              "22                        yes                        yes      2021   \n",
              "\n",
              "   Secondary analysis  Counts  Percent Increase                     Labels  \n",
              "6                  no       1          0.490196           Only Code Shared  \n",
              "7                  no       1          0.490196           Only Code Shared  \n",
              "8                  no       2          0.980392           Only Code Shared  \n",
              "9                  no       3          1.470588           Only Code Shared  \n",
              "10                 no       6          2.941176           Only Code Shared  \n",
              "11                 no      11          5.392157           Only Data Shared  \n",
              "12                 no      19          9.313725           Only Data Shared  \n",
              "13                 no      17          8.333333           Only Data Shared  \n",
              "14                 no      12          5.882353           Only Data Shared  \n",
              "15                 no       8          3.921569           Only Data Shared  \n",
              "16                 no      14          6.862745           Only Data Shared  \n",
              "17                 no       8          3.921569  Data and Code Both Shared  \n",
              "18                 no       7          3.431373  Data and Code Both Shared  \n",
              "19                 no       4          1.960784  Data and Code Both Shared  \n",
              "20                 no      13          6.372549  Data and Code Both Shared  \n",
              "21                 no      13          6.372549  Data and Code Both Shared  \n",
              "22                 no      16          7.843137  Data and Code Both Shared  "
            ]
          },
          "execution_count": 119,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "#replace upon request with no in the Data availability column of the data\n",
        "df_modified = df.copy()\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "#create dataframe for counts data of data availability and code availability, pmc_year, and analysis type \n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Data availability (yes/no)','Code availability (Yes/No)','PMC_Year','Secondary analysis']).size()}).reset_index()\n",
        "\n",
        "#filter df_temp to only have rows where data and code are available, only data shared, and only code shared \n",
        "df_temp=df_temp[~((df_temp['Data availability (yes/no)']=='no') & (df_temp['Code availability (Yes/No)']=='no'))]\n",
        "#filter df_temp to only have primary studies \n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "#create percentage column\n",
        "df_temp['Percent Increase'] = (df_temp['Counts']/204) * 100\n",
        "#df_temp['Cumulative Percent Increase']=df_temp['Percent Increase'].cumsum()\n",
        "#df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "#df_temp['Cumulative Percent Increase']=df_temp['Cumulative Percent Increase'].round(2)\n",
        "#Create labels column\n",
        "df_temp['Labels']='Unknown'\n",
        "df_temp.loc[(df_temp['Data availability (yes/no)'] == 'yes') & (df_temp['Code availability (Yes/No)']=='yes'), 'Labels'] = 'Data and Code Both Shared'\n",
        "df_temp.loc[(df_temp['Data availability (yes/no)'] == 'yes') & (df_temp['Code availability (Yes/No)']=='no'), 'Labels'] = 'Only Data Shared'\n",
        "df_temp.loc[(df_temp['Data availability (yes/no)'] == 'no') & (df_temp['Code availability (Yes/No)']=='yes'), 'Labels'] = 'Only Code Shared'\n",
        "\n",
        "df_temp\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "id": "95b01521",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Labels</th>\n",
              "      <th>Data and Code Both Shared</th>\n",
              "      <th>Only Code Shared</th>\n",
              "      <th>Only Data Shared</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PMC_Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>3.92</td>\n",
              "      <td>0.49</td>\n",
              "      <td>5.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>7.35</td>\n",
              "      <td>0.98</td>\n",
              "      <td>14.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>9.31</td>\n",
              "      <td>1.96</td>\n",
              "      <td>23.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>15.69</td>\n",
              "      <td>3.43</td>\n",
              "      <td>28.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>22.06</td>\n",
              "      <td>6.37</td>\n",
              "      <td>32.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>29.90</td>\n",
              "      <td>6.37</td>\n",
              "      <td>39.71</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Labels    Data and Code Both Shared  Only Code Shared  Only Data Shared\n",
              "PMC_Year                                                               \n",
              "2016                           3.92              0.49              5.39\n",
              "2017                           7.35              0.98             14.71\n",
              "2018                           9.31              1.96             23.04\n",
              "2019                          15.69              3.43             28.92\n",
              "2020                          22.06              6.37             32.84\n",
              "2021                          29.90              6.37             39.71"
            ]
          },
          "execution_count": 120,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_stacked = df_temp[['Labels', 'Percent Increase', 'PMC_Year']]\n",
        "df_stacked=df_stacked.pivot(index='PMC_Year',columns='Labels', values='Percent Increase')\n",
        "df_stacked=df_stacked.fillna(0)\n",
        "df_stacked['Data and Code Both Shared']=df_stacked['Data and Code Both Shared'].cumsum()\n",
        "df_stacked['Data and Code Both Shared']=df_stacked['Data and Code Both Shared'].round(2)\n",
        "df_stacked['Only Code Shared']=df_stacked['Only Code Shared'].cumsum()\n",
        "df_stacked['Only Code Shared']=df_stacked['Only Code Shared'].round(2)\n",
        "df_stacked['Only Data Shared']=df_stacked['Only Data Shared'].cumsum()\n",
        "df_stacked['Only Data Shared']=df_stacked['Only Data Shared'].round(2)\n",
        "df_stacked"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "id": "b3f84755",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 3125x3125 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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            "text/plain": [
              "<Figure size 3125x3125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(31.25, 31.25))\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "\n",
        "# create stacked bar chart for monthly temperatures\n",
        "df_stacked.plot(kind='bar', stacked=True,color=['#CC79A7','#56B4E9','#009E73'], rot=0,figsize=(31.25, 31.25))\n",
        "plt.ylim([0, 100])\n",
        "# labels for x & y axis\n",
        "plt.xlabel(\"Years\",fontsize=fontsize)\n",
        "plt.ylabel(\"Primary analysis manuscript  (%)\",fontsize=fontsize)\n",
        "plt.legend(loc='upper right', bbox_to_anchor=(1, 1),frameon=False,fontsize=fontsize)\n",
        "plt.tick_params(axis='both', labelsize=fontsize)\n",
        "sns.despine()\n",
        "plt.savefig('../Figures/sup4.png', format='png', dpi='figure',bbox_inches='tight')\n",
        "plt.savefig('../Figures/sup4.eps', format='eps', dpi='figure',bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "042aacf6",
      "metadata": {},
      "source": [
        "## Supplementary Figure 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "id": "7e134ae9",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>PMC_Year</th>\n",
              "      <th>Secondary analysis</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percent Increase</th>\n",
              "      <th>Cumulative Percent Increase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>no</td>\n",
              "      <td>19</td>\n",
              "      <td>9.313725</td>\n",
              "      <td>9.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>yes</td>\n",
              "      <td>2017</td>\n",
              "      <td>no</td>\n",
              "      <td>26</td>\n",
              "      <td>12.745098</td>\n",
              "      <td>22.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yes</td>\n",
              "      <td>2018</td>\n",
              "      <td>no</td>\n",
              "      <td>21</td>\n",
              "      <td>10.294118</td>\n",
              "      <td>32.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>yes</td>\n",
              "      <td>2019</td>\n",
              "      <td>no</td>\n",
              "      <td>25</td>\n",
              "      <td>12.254902</td>\n",
              "      <td>44.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>yes</td>\n",
              "      <td>2020</td>\n",
              "      <td>no</td>\n",
              "      <td>21</td>\n",
              "      <td>10.294118</td>\n",
              "      <td>54.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>yes</td>\n",
              "      <td>2021</td>\n",
              "      <td>no</td>\n",
              "      <td>30</td>\n",
              "      <td>14.705882</td>\n",
              "      <td>69.61</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data availability (yes/no)  PMC_Year Secondary analysis  Counts  \\\n",
              "0                        yes      2016                 no      19   \n",
              "1                        yes      2017                 no      26   \n",
              "2                        yes      2018                 no      21   \n",
              "3                        yes      2019                 no      25   \n",
              "4                        yes      2020                 no      21   \n",
              "5                        yes      2021                 no      30   \n",
              "\n",
              "   Percent Increase  Cumulative Percent Increase  \n",
              "0          9.313725                         9.31  \n",
              "1         12.745098                        22.06  \n",
              "2         10.294118                        32.35  \n",
              "3         12.254902                        44.61  \n",
              "4         10.294118                        54.90  \n",
              "5         14.705882                        69.61  "
            ]
          },
          "execution_count": 124,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#replace upon request with no in the Data availability column of the data\n",
        "df_modified = df.copy()\n",
        "df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "#create dataframe for counts data of code availability \n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Data availability (yes/no)','PMC_Year','Secondary analysis']).size()}).reset_index()\n",
        "\n",
        "#filter df_temp to only have rows where data and code are available, only data shared, and only code shared \n",
        "df_temp=df_temp[~((df_temp['Data availability (yes/no)']=='no'))]\n",
        "#filter df_temp to only have primary studies \n",
        "#df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "#create percentage column\n",
        "df_temp['Percent Increase'] = (df_temp['Counts']/204) * 100\n",
        "df_temp['Cumulative Percent Increase']=df_temp['Percent Increase'].cumsum()\n",
        "df_temp['Cumulative Percent Increase']=df_temp['Cumulative Percent Increase'].round(2)\n",
        "\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "id": "4c2ee3fa",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_21447/3111242735.py:7: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(data=df_temp, x='PMC_Year', y='Cumulative Percent Increase',palette=colors )\n",
            "/tmp/ipykernel_21447/3111242735.py:7: UserWarning: \n",
            "The palette list has fewer values (1) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
            "  sns.barplot(data=df_temp, x='PMC_Year', y='Cumulative Percent Increase',palette=colors )\n"
          ]
        },
        {
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            "text/plain": [
              "<Figure size 3125x3125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(31.25, 31.25))\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "colors = ['#009E73']\n",
        "sns.barplot(data=df_temp, x='PMC_Year', y='Cumulative Percent Increase',palette=colors )\n",
        "\n",
        "for index, row in df_temp.iterrows():\n",
        "    plt.annotate(f'{row[\"Cumulative Percent Increase\"]:.2f}%', xy=(index, row['Cumulative Percent Increase']),\n",
        "                 xytext=(0, 5), textcoords='offset points', ha='center', va='bottom',fontsize=fontsize)\n",
        "sns.despine()\n",
        "plt.xlabel(\"Year\",fontsize=fontsize)\n",
        "plt.ylabel(\"Cumulative data-sharing\\nprimary analysis manuscript growth (%)\",fontsize=fontsize)\n",
        "plt.ylim([0, 100])\n",
        "plt.tick_params(axis='both', labelsize=fontsize)\n",
        "sns.despine()\n",
        "plt.savefig('../Figures/sup5.png', format='png', dpi='figure')\n",
        "fig.savefig('../Figures/sup5.eps', format='eps', dpi='figure',bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "2f59fa9a",
      "metadata": {},
      "source": [
        "## Supplementary Figure 6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "id": "fb284a44",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>PMC_Year</th>\n",
              "      <th>Secondary analysis</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percent Increase</th>\n",
              "      <th>Cumulative Percent Increase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>no</td>\n",
              "      <td>9</td>\n",
              "      <td>1.986755</td>\n",
              "      <td>1.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>yes</td>\n",
              "      <td>2017</td>\n",
              "      <td>no</td>\n",
              "      <td>8</td>\n",
              "      <td>1.766004</td>\n",
              "      <td>3.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yes</td>\n",
              "      <td>2018</td>\n",
              "      <td>no</td>\n",
              "      <td>6</td>\n",
              "      <td>1.324503</td>\n",
              "      <td>5.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>yes</td>\n",
              "      <td>2019</td>\n",
              "      <td>no</td>\n",
              "      <td>16</td>\n",
              "      <td>3.532009</td>\n",
              "      <td>8.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>yes</td>\n",
              "      <td>2020</td>\n",
              "      <td>no</td>\n",
              "      <td>19</td>\n",
              "      <td>4.194260</td>\n",
              "      <td>12.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>yes</td>\n",
              "      <td>2021</td>\n",
              "      <td>no</td>\n",
              "      <td>16</td>\n",
              "      <td>3.532009</td>\n",
              "      <td>16.34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Code availability (Yes/No)  PMC_Year Secondary analysis  Counts  \\\n",
              "0                        yes      2016                 no       9   \n",
              "1                        yes      2017                 no       8   \n",
              "2                        yes      2018                 no       6   \n",
              "3                        yes      2019                 no      16   \n",
              "4                        yes      2020                 no      19   \n",
              "5                        yes      2021                 no      16   \n",
              "\n",
              "   Percent Increase  Cumulative Percent Increase  \n",
              "0          1.986755                         1.99  \n",
              "1          1.766004                         3.75  \n",
              "2          1.324503                         5.08  \n",
              "3          3.532009                         8.61  \n",
              "4          4.194260                        12.80  \n",
              "5          3.532009                        16.34  "
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#create dataframe for counts data of code availability \n",
        "df_modified = df.copy()\n",
        "#df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Code availability (Yes/No)','PMC_Year','Secondary analysis']).size()}).reset_index()\n",
        "\n",
        "#filter df_temp to only have rows where data and code are available, only data shared, and only code shared \n",
        "df_temp=df_temp[~((df_temp['Code availability (Yes/No)']=='no'))]\n",
        "#filter df_temp to only have primary studies \n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "#create percentage column\n",
        "df_temp['Percent Increase'] = (df_temp['Counts']/453) * 100\n",
        "df_temp['Cumulative Percent Increase']=df_temp['Percent Increase'].cumsum()\n",
        "df_temp['Cumulative Percent Increase']=df_temp['Cumulative Percent Increase'].round(2)\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "id": "4c15eab6",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_21447/3721156430.py:7: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(data=df_temp, x='PMC_Year', y='Cumulative Percent Increase', palette=colors)\n",
            "/tmp/ipykernel_21447/3721156430.py:7: UserWarning: \n",
            "The palette list has fewer values (1) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
            "  sns.barplot(data=df_temp, x='PMC_Year', y='Cumulative Percent Increase', palette=colors)\n"
          ]
        },
        {
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            "text/plain": [
              "<Figure size 3125x3125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(31.25, 31.25))\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "colors = ['#56B4E9']\n",
        "sns.barplot(data=df_temp, x='PMC_Year', y='Cumulative Percent Increase', palette=colors)\n",
        "\n",
        "for index, row in df_temp.iterrows():\n",
        "    plt.annotate(f'{row[\"Cumulative Percent Increase\"]:.2f}%', xy=(index, row['Cumulative Percent Increase']),\n",
        "                 xytext=(0, 5), textcoords='offset points', ha='center', va='bottom',fontsize=fontsize)\n",
        "sns.despine()\n",
        "plt.xlabel(\"Year\",fontsize=fontsize)\n",
        "plt.ylabel(\"Cumulative code-sharing\\nprimary analysis manuscript growth (%)\",fontsize=fontsize)\n",
        "plt.ylim([0, 100])\n",
        "plt.tick_params(axis='both', labelsize=fontsize)\n",
        "plt.savefig('../Figures/sup6.png', format='png', dpi='figure',bbox_inches='tight')\n",
        "fig.savefig('../Figures/sup6.eps', format='eps', dpi='figure',bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "5caeb85b",
      "metadata": {},
      "source": [
        "## Supplementary Figure 7"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "22a91271",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "152"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp['Counts'].sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "id": "64016013",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>PMC_Year</th>\n",
              "      <th>Secondary analysis</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percent Increase</th>\n",
              "      <th>Cumulative Percent Increase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>yes</td>\n",
              "      <td>2016</td>\n",
              "      <td>yes</td>\n",
              "      <td>24</td>\n",
              "      <td>5.298013</td>\n",
              "      <td>5.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>yes</td>\n",
              "      <td>2017</td>\n",
              "      <td>yes</td>\n",
              "      <td>20</td>\n",
              "      <td>4.415011</td>\n",
              "      <td>9.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yes</td>\n",
              "      <td>2018</td>\n",
              "      <td>yes</td>\n",
              "      <td>26</td>\n",
              "      <td>5.739514</td>\n",
              "      <td>15.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>yes</td>\n",
              "      <td>2019</td>\n",
              "      <td>yes</td>\n",
              "      <td>24</td>\n",
              "      <td>5.298013</td>\n",
              "      <td>20.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>yes</td>\n",
              "      <td>2020</td>\n",
              "      <td>yes</td>\n",
              "      <td>32</td>\n",
              "      <td>7.064018</td>\n",
              "      <td>27.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>yes</td>\n",
              "      <td>2021</td>\n",
              "      <td>yes</td>\n",
              "      <td>26</td>\n",
              "      <td>5.739514</td>\n",
              "      <td>33.55</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Code availability (Yes/No)  PMC_Year Secondary analysis  Counts  \\\n",
              "0                        yes      2016                yes      24   \n",
              "1                        yes      2017                yes      20   \n",
              "2                        yes      2018                yes      26   \n",
              "3                        yes      2019                yes      24   \n",
              "4                        yes      2020                yes      32   \n",
              "5                        yes      2021                yes      26   \n",
              "\n",
              "   Percent Increase  Cumulative Percent Increase  \n",
              "0          5.298013                         5.30  \n",
              "1          4.415011                         9.71  \n",
              "2          5.739514                        15.45  \n",
              "3          5.298013                        20.75  \n",
              "4          7.064018                        27.81  \n",
              "5          5.739514                        33.55  "
            ]
          },
          "execution_count": 131,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#create dataframe for counts data of code availability \n",
        "df_modified = df.copy()\n",
        "#df_modified.loc[df_modified['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'no'\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'\n",
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Code availability (Yes/No)','PMC_Year','Secondary analysis']).size()}).reset_index()\n",
        "\n",
        "#filter df_temp to only have rows where data and code are available, only data shared, and only code shared \n",
        "df_temp=df_temp[~((df_temp['Code availability (Yes/No)']=='no'))]\n",
        "#filter df_temp to only have primary studies \n",
        "df_temp=df_temp[(df_temp['Secondary analysis']=='yes')]\n",
        "#create percentage column\n",
        "df_temp['Percent Increase'] = (df_temp['Counts']/453) * 100\n",
        "df_temp['Cumulative Percent Increase']=df_temp['Percent Increase'].cumsum()\n",
        "df_temp['Cumulative Percent Increase']=df_temp['Cumulative Percent Increase'].round(2)\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "id": "eac03db7",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_21447/3373194000.py:7: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(data=df_temp, x='PMC_Year', y='Cumulative Percent Increase', palette=colors)\n",
            "/tmp/ipykernel_21447/3373194000.py:7: UserWarning: \n",
            "The palette list has fewer values (1) than needed (6) and will cycle, which may produce an uninterpretable plot.\n",
            "  sns.barplot(data=df_temp, x='PMC_Year', y='Cumulative Percent Increase', palette=colors)\n"
          ]
        },
        {
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            "text/plain": [
              "<Figure size 3125x3125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(31.25, 31.25))\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "colors = ['#56B4E9']\n",
        "sns.barplot(data=df_temp, x='PMC_Year', y='Cumulative Percent Increase', palette=colors)\n",
        "\n",
        "for index, row in df_temp.iterrows():\n",
        "    plt.annotate(f'{row[\"Cumulative Percent Increase\"]:.2f}%', xy=(index, row['Cumulative Percent Increase']),\n",
        "                 xytext=(0, 5), textcoords='offset points', ha='center', va='bottom',fontsize=fontsize)\n",
        "sns.despine()\n",
        "plt.xlabel(\"Year\",fontsize=fontsize)\n",
        "plt.ylabel(\"Cumulative code-sharing\\n secondary analysis manuscript growth (%)\",fontsize=fontsize)\n",
        "plt.ylim([0, 100])\n",
        "plt.tick_params(axis='both', labelsize=fontsize)\n",
        "plt.savefig('../Figures/sup7.png', format='png', dpi='figure',bbox_inches='tight')\n",
        "fig.savefig('../Figures/sup7.eps', format='eps', dpi='figure',bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "cbd0703b",
      "metadata": {},
      "source": [
        "## Supplementary Figure 8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "id": "fbd824a1",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data Repository</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Other</td>\n",
              "      <td>69</td>\n",
              "      <td>47.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>GEO</td>\n",
              "      <td>42</td>\n",
              "      <td>28.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SRA</td>\n",
              "      <td>26</td>\n",
              "      <td>17.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GSE</td>\n",
              "      <td>9</td>\n",
              "      <td>6.16</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data Repository  Counts  Percentage\n",
              "0           Other      69       47.26\n",
              "1             GEO      42       28.77\n",
              "2             SRA      26       17.81\n",
              "3             GSE       9        6.16"
            ]
          },
          "execution_count": 133,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#create dataframe of counts\n",
        "#df_temp=pd.DataFrame({'Counts': df['If Data availability = yes, where do they share data? (SRA/GEO/other)','Secondary analysis'].value_counts()}).reset_index()\n",
        "df_temp=pd.DataFrame({'Counts': df.groupby(['Secondary analysis','If Data availability = yes, where do they share data? (SRA/GEO/other)']).size()}).reset_index()\n",
        "#ensure data only contains primary analysis studies \n",
        "df_temp=df_temp[~(df_temp['Secondary analysis']=='yes')]\n",
        "\n",
        "#merge hybrid row into the Other category for no \n",
        "df_temp.loc[3,'Counts'] += df_temp.loc[0,'Counts']\n",
        "df_temp = df_temp.drop(0)\n",
        "\n",
        "#adding SRA,GSE to SRA and GSE Counts\n",
        "df_temp.loc[4,'Counts'] += df_temp.loc[5,'Counts']\n",
        "df_temp.loc[2,'Counts'] += df_temp.loc[5,'Counts']\n",
        "df_temp = df_temp.drop(5)\n",
        "\n",
        "\n",
        "#adding SRA,GEO to SRA and GEO Counts\n",
        "df_temp.loc[4,'Counts'] += df_temp.loc[6,'Counts']\n",
        "df_temp.loc[1,'Counts'] += df_temp.loc[6,'Counts']\n",
        "df_temp = df_temp.drop(6)\n",
        "\n",
        "#adding SRA,Other to SRA and Other Counts \n",
        "df_temp.loc[4,'Counts'] += df_temp.loc[7,'Counts']\n",
        "df_temp.loc[3,'Counts'] += df_temp.loc[7,'Counts']\n",
        "df_temp = df_temp.drop(7)\n",
        "\n",
        "#rename columns\n",
        "df_temp=df_temp.rename(columns={\"If Data availability = yes, where do they share data? (SRA/GEO/other)\":\"Data Repository\"})\n",
        "\n",
        "#add percentage column \n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "\n",
        "#sort by percentage \n",
        "df_temp=df_temp.sort_values('Percentage', ascending=False)\n",
        "#drop secondary analysis column \n",
        "df_temp=df_temp.drop('Secondary analysis', axis=1)\n",
        "\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "id": "9434e668",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_21447/1291845024.py:7: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(data=df_temp, x='Percentage',y='Data Repository',palette=colors)\n",
            "/tmp/ipykernel_21447/1291845024.py:7: UserWarning: \n",
            "The palette list has fewer values (1) than needed (4) and will cycle, which may produce an uninterpretable plot.\n",
            "  sns.barplot(data=df_temp, x='Percentage',y='Data Repository',palette=colors)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 3125x3125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(31.25, 31.25))\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "colors = [\"#009E73\"]\n",
        "sns.barplot(data=df_temp, x='Percentage',y='Data Repository',palette=colors)\n",
        "for index, row in df_temp.iterrows():\n",
        "    plt.annotate(f'{row[\"Percentage\"]}%', xy=(row['Percentage'],index),ha='left', va='top', fontsize=fontsize)\n",
        "plt.xlabel(\"Percentage of primary analysis (%)\", fontsize=fontsize)\n",
        "plt.ylabel(\"Data repository\", fontsize=fontsize)\n",
        "plt.tick_params(axis='both', labelsize=fontsize)\n",
        "sns.despine()\n",
        "plt.savefig('../Figures/sup8.png', format='png', dpi='figure',bbox_inches='tight')\n",
        "fig.savefig('../Figures/sup8.eps', format='eps', dpi='figure',bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "f5c11370",
      "metadata": {},
      "source": [
        "## Supplementary Figure 9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "id": "a253710c",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>yes</td>\n",
              "      <td>142</td>\n",
              "      <td>69.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>no</td>\n",
              "      <td>41</td>\n",
              "      <td>20.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>upon request</td>\n",
              "      <td>21</td>\n",
              "      <td>10.29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data availability (yes/no)  Counts  Percentage\n",
              "0                        yes     142       69.61\n",
              "1                         no      41       20.10\n",
              "2               upon request      21       10.29"
            ]
          },
          "execution_count": 135,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data_avail=pd.DataFrame({'Counts': df['Data availability (yes/no)'].value_counts()}).reset_index()\n",
        "#rename columns\n",
        "df_data_avail=df_data_avail.rename(columns={\"index\":\"Data availability (yes/no)\"})\n",
        "\n",
        "#add percentage column \n",
        "df_data_avail['Percentage'] = (df_data_avail['Counts']/df_data_avail['Counts'].sum()) * 100\n",
        "df_data_avail['Percentage']=df_data_avail['Percentage'].round(2)\n",
        "df_data_avail"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "id": "386ed796",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data availability statement (yes/no)</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>yes</td>\n",
              "      <td>302</td>\n",
              "      <td>67.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>no</td>\n",
              "      <td>144</td>\n",
              "      <td>32.29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Data availability statement (yes/no)  Counts  Percentage\n",
              "0                                  yes     302       67.71\n",
              "1                                   no     144       32.29"
            ]
          },
          "execution_count": 136,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_data_avail_statement=pd.DataFrame({'Counts': df['Data availability statement (yes/no)'].value_counts()}).reset_index()\n",
        "df_data_avail_statement=df_data_avail_statement.rename(columns={\"index\":\"Data availability statement (yes/no)\"})\n",
        "\n",
        "#add percentage column \n",
        "df_data_avail_statement['Percentage'] = (df_data_avail_statement['Counts']/df_data_avail_statement['Counts'].sum()) * 100\n",
        "df_data_avail_statement['Percentage']=df_data_avail_statement['Percentage'].round(2)\n",
        "df_data_avail_statement"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "id": "4a9e2dac",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data availability statement (yes/no)</th>\n",
              "      <th>Data availability (yes/no)</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Data availability\\nstatement not given\\n(32.29%)</td>\n",
              "      <td>Data unavailable\\n(20.1%)</td>\n",
              "      <td>28</td>\n",
              "      <td>13.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Data availability\\nstatement not given\\n(32.29%)</td>\n",
              "      <td>Upon request\\n(10.29%)</td>\n",
              "      <td>1</td>\n",
              "      <td>0.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Data availability\\nstatement not given\\n(32.29%)</td>\n",
              "      <td>Data available\\n(69.61%)</td>\n",
              "      <td>9</td>\n",
              "      <td>4.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Data availability\\nstatement given\\n(67.71%)</td>\n",
              "      <td>Data unavailable\\n(20.1%)</td>\n",
              "      <td>13</td>\n",
              "      <td>6.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Data availability\\nstatement given\\n(67.71%)</td>\n",
              "      <td>Upon request\\n(10.29%)</td>\n",
              "      <td>20</td>\n",
              "      <td>9.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Data availability\\nstatement given\\n(67.71%)</td>\n",
              "      <td>Data available\\n(69.61%)</td>\n",
              "      <td>133</td>\n",
              "      <td>65.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Data availability statement (yes/no)  \\\n",
              "0  Data availability\\nstatement not given\\n(32.29%)   \n",
              "1  Data availability\\nstatement not given\\n(32.29%)   \n",
              "2  Data availability\\nstatement not given\\n(32.29%)   \n",
              "3      Data availability\\nstatement given\\n(67.71%)   \n",
              "4      Data availability\\nstatement given\\n(67.71%)   \n",
              "5      Data availability\\nstatement given\\n(67.71%)   \n",
              "\n",
              "  Data availability (yes/no)  Counts  Percentage  \n",
              "0  Data unavailable\\n(20.1%)      28       13.73  \n",
              "1     Upon request\\n(10.29%)       1        0.49  \n",
              "2   Data available\\n(69.61%)       9        4.41  \n",
              "3  Data unavailable\\n(20.1%)      13        6.37  \n",
              "4     Upon request\\n(10.29%)      20        9.80  \n",
              "5   Data available\\n(69.61%)     133       65.20  "
            ]
          },
          "execution_count": 137,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp=pd.DataFrame({'Counts': df.groupby(['Data availability statement (yes/no)','Data availability (yes/no)']).size()}).reset_index()\n",
        "df_temp.loc[df_temp['Data availability statement (yes/no)'] == 'yes', 'Data availability statement (yes/no)'] = 'Data availability\\nstatement given\\n(' + str(df_data_avail_statement['Percentage'][0]) + '%)'\n",
        "df_temp.loc[df_temp['Data availability statement (yes/no)'] == 'no', 'Data availability statement (yes/no)'] = 'Data availability\\nstatement not given\\n(' + str(df_data_avail_statement['Percentage'][1]) +'%)'\n",
        "df_temp.loc[df_temp['Data availability (yes/no)'] == 'no', 'Data availability (yes/no)'] = 'Data unavailable\\n(' + str(df_data_avail['Percentage'][1]) + '%)'\n",
        "df_temp.loc[df_temp['Data availability (yes/no)'] == 'yes', 'Data availability (yes/no)'] = 'Data available\\n(' + str(df_data_avail['Percentage'][0]) + '%)'\n",
        "df_temp.loc[df_temp['Data availability (yes/no)'] == 'upon request', 'Data availability (yes/no)'] = 'Upon request\\n(' + str(df_data_avail['Percentage'][2]) + '%)'\n",
        "#add percentage column \n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 138,
      "id": "ee66cffe",
      "metadata": {},
      "outputs": [
        {
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4yBSHh8apJOlWjytJkiRJkqRHlCUASZIkSZIkPVDlSpvem2fo/ugNyuVVQiUjjAwRHqXN2LovYllSLixDCNT/4IvUv/y8pRJtOzNrS/zw1iX++up7XFyegxAYrdYZrTwcp5/Efk65sEw5u0Ds9SFJBuH/e/ncipKY5wCEVoN0apxkpHVvhQJJ+hzysmCl32M170KESKSWZpwa3cULkwc4NTrN8ZEpdtWHHorrckmSJEmSJG1/lgAkSZIkSZJ038UYKa7fpvuzt+m/8T4xLwjNBqH5cIRetfXK1TXiWofsxGGaf/Z10l0TWz2S9FvFGLm4MscPbl3ib66+x431FQIwXmsylFV31nVjjMT1LsX8IuX88sam/pSQpcDv+3lEYr+AsoAkIZ0cIxkfJTRqmzm5JP1eYox0ipzVvMta3icJgbIsmay3eHZiH89N7OP4yBTHhicZqni9JUmSJEmSpM1nCUCSJEmSJEn3Tezn9D+4RPdHb5BfnoEkIRluuZ1dmyb2c8rFZcJQk+affZ3KMyd2VnhaAspY8t7iLf7hxgX+zbUzLPU7ZCFhqt6ilmZbPd5vVkbKlVWKOwvE9hoQBlv/72VDf4zEfh9iJNSqJNMTpKPDkKWbNrYk3Q9ljLTzHqv9Lr0yJyEhBDg8NM4LE/t5cnwPx0emONQaI0u8TpMkSZIkSdK9sQQgSZIkSZKkTVcur9J78wO6P3qDcnmVUM0II0OEewmGSp8QYyQurhCLgtoXn6H+rS+RtBpbPZZ0z3pFzmtz1/g3187wg5sXyWNJM6swUWuShu1xHRrznHJhmfLOArHXhyQZhP/vpYBTlMS8DwSSkRbJ1DhJqwmJpR5JO1e/LFjtd1ntdwGIQCOtcHpsFy9OHuDE6DTHhieZrg9ZYpQkSZIkSdLnYglAkiRJkiRJmyLGSHH9Ft1/fpv+m2eIRUFoNgjNuqEmbaq43qVcWSXdv5vmn3+T7NDerR5Jui8Wumv88NYl/vXldzizfAeA8WqD4UptS65X43qHYm6Jcn4JYglpSshS4PedJRLzAooCkoR0coxkYpRQr23m2JK0bcQY6RQ5K/0u63mPJEkoY8lEtckTY7t5emIvx4YnOTI0wZ7GsPehJUmSJEmS9BtZApAkSZIkSdI9if2c/plLdH/0BvmVGxAgGR0iVCpbPZoeMrEoKReWCJWM+re/TO3lpzcCyNLDLcbIpdV5vjtzjr+6+h5z3TYJCdONFvX0Pl/XlpFypU0xO09cXQcYbP2/l5NdYiT2+xAjoVolmR4nGRvx+1nSI6mMkbW8x2reo1vkJARKSkYqdU6P7uKZiX13iwH7miOknqwlSZIkSZIkLAFIkiRJkiTp91Qur9J74wzdH79BudwmVCuEkRbBYJI2WYyRuNImdntUn3qcxr/4Ksn4yFaPJW2Jflnw5tx1/t31D/jujfP0ipx6VmGq1trUYGjMc8rFFcrbC8R+D0IyCP/fy1bqoiTmOQDJcItkaoxkqAWJm64l6ZPKGFkv+rT7PdaLPmkIFGVJq1LlxMg0z03s4/jIFEeGJjjQGiVLLFFJkiRJkiQ9aiwBSJIkSZIk6TOLMVJcu0X3p2/Rf/MDYlEQWg1Co064l2Co9BvEXp9ycYVkYpTmn3+D7MQRv9akDcu9Dj++/SH/vyvv8vbCDSIwWq0zWvk9r5NjJK51KOaXKBeWIZaQphsb+n/f77tIzAvIC0gTkokx0olRQqP2e348SXo0xRjpFDmreZf1vE8ASiL1tMLjI1M8M76XE6PTHBma4FBrjGqabfXIkiRJkiRJuo8sAUiSJEmSJOl3ir0+/Q8u0f3hG+RXb0AIJKPDg63Q0n0Qy0i5uAwxUv/6S9S/9iKhVt3qsaRt68rqAt+9cZ6/uvout9ZWSJLAVG2IRlb53X84LyiWVynvLBA7HSAMrt/v5WSBGIn9PsRIqFZJpsdJxkY2CgWSpM0QY6Rb5qz2e6zlPQIQgUqScnR4gmfG93JydBePDU9wuDVO/bPcJkiSJEmSJGlHsAQgSZIkSZKkXyvGSHH9Fr03P6D3+nvETpdQqRBGWoR7CYZKv0PZXie218iOHqT5H36DdM/UVo8k7RhFWfLWwg3+9voH/P3MWdaLnFqSMlVvkSWfCODHSOx0KeaXKecXoSwhSQhZBvdy2kZZEvs5EAnDLdKpcZKhFiSe4CFJD0KMkV5Z0M57rPa7hI1qQBISDg+N88zEXk6N7uKxoQmODI3TqngyiyRJkiRJ0k5kCUCSJEmSJEmfUi6v0nvvAr1/eovi9txg6/9Qk1A3IKT7K+YF5cISoVGn8Wdfo/rsKUJq4UT6fa32u/zTncv89ZV3eW3uOpHIcFplpFMQZxeJa+tAGAT/7+l7LRLzAooCQkIyOUo6MUZoeLshSdtFr8hp5z3aeY8yRj6qZh1ojfH0+B6eHNvD4aFxDrbGmKg1CfdSCJMkSZIkSdJ9ZwlAkiRJkiRJxH5OfuEqvdffpf/eRWJZEuo1QqtJcHuz7rMYI3FphdjPqb70FI0/fIVkuLXVY0kPlavXrvDdn/+Ef339PWboE5LAZKjSDOnv/sO/SYyDrf+xJFSqJLvGScaGB6UCSdK21984MaDd71HEkiQEirJkvNbk8ZEpnhzbzZHhCQ61xjnQGqWZVbd6ZEmSJEmSJG2wBCBJkiRJkvSIijFS3pqj99ZZuj97h9hegzQlGWkZ4NQDEztdyuVV0j1TNP/8m2SPHdjqkaSHRswL8vOX6fzkF+TnrgzeONTg/WbkHyor/H1llbVQUomByZhR4TOWvspyEP4HwlCTdGp8UNyxNCZJO14ZI2t5j7W8z3reJwmBSCQJgX3NEU6MTHN6dDeHhsY41Bpnb3OYLLmHQpkkSZIkSZJ+L5YAJEmSJEmSHjFle53+mYt0//ltims3gUGIM9RrhGCAUw9GLEvK+SVCllL/wy9R+9JzhIrlE2kzlIsrdH9xht5PfkG5tApZSjI6REg/HdJcp+Tn2Rr/Llvm59kaJTAUE8ZiSvIrhYBIzAsoCggJycQo6eQooVF/YJ+XJGnr9MvibjmgV+QkSUJRljSyCkeGxjk9tpvHh6c4NDTOwdYYk7WmP1tIkiRJkiTdR5YAJEmSJEmSHgGxKMk/vE7v9ffov32OmOeEapUw3CQkyVaPp0dIjJG4ukZc71A5fYzGn32NdHJsq8eSdrxYlOSXrtH76dv03rsAZUloNQiN+mcKYd4JOT/KVvlfs2WupD0CMF6mNGOAfjH4eNUKyfQ4yfiIJ8ZIkogx0i1z1vI+7bxHjIMTA4pYMl5tcGx4iifHdvPY8CQHW2McGhqjmVW3emxJkiRJkqSHgiUASZIkSZKkh1gxt0jv7bP0/vntwTboJJAMtwjVylaPpkdQ7PcpF5ZJxoZp/AffpPLEUTfESveoXGnTe+cc3R+/STm3BElCMtr6vUP6kci5pMv3WOJv0yVWkpJqrcbuqWmqIyOQ+D0rSfrtyhhZ3ygGrBc9EgIRCMDexggnx3ZxenTXxqkBo+xrjlJJ0t/1YSVJkiRJkvQJlgAkSZIkSZIeMrHbo3/mEt2fvUN+8RoAoVkfvBi41haIMVIuLENZUv/KC9S/8TKhUdvqsaQdK8ZIcfkG3dfeof/mB8SiGGz8bzXu6Xr+7kkdnS4hyyheOMGbxyf5q9UrvDZ3nQiMVxsMV2renkiSPrd+WbCW91nLe/SKnDRJKGKklqYcaY1zemw3J0amOdgaY39rlOl6iyR4apkkSZIkSdKvYwlAkiRJkiTpIRBjpLh6k94bZ+i98T6x1yNUMsLwECE1OKOtU651iCttsiP7aPz5N8n27drqkaQdq1zv0H/nPN0fv0lxaxbCxtb/yr2d7hL7OeXyKpQl6a5Jqq8+R/XJ4yStxt33ubq6yN/fOMf/cvlt5jprZEnCdGOIqpubJUn3IMZIt8zvlgPKGAkEiljSzCocGhrn8eEpjo9Msa85wr7mCHsbI9QzTzaTJEmSJEmPNksAkiRJkiRJO1i5tELvnXP0fvo2xZ0FCIFkuEWoVbd6ND3iYl5QLiwTahXqf/pVai88aSFF+j3EGClu3KH38/fovf4usdcn1KqE4da9b/1vrxPXO4Q0pfL049Reeor08D5C8pu/V/tlwc9nr/LXV97jH29dpCQyUqkzVr23UwgkSfqkMka6Rc560Wc979OPBSkJZSxJk4Q9jWGODk/ePTngo4KAt0eSJEmSJOlRYQlAkiRJkiRph4n9nP65y/Ree5f+mUsQITRqhJaBF229GCNxeZXY7VN9/hSNP/4KyejQVo8l7Tix26N/5hLdH79JfvUmBDal5BXzYrD1vyhIxkepvfIs1WdOkIx8/u/T2+srfPfGef7ny29zvb1EGganA9TS7J5mlCTpN4kxkseS9bzPWtGnW/RJQgIxUsbISLXOY0MTnBid5rGhiY1ywCi7G0Nknl4jSZIkSZIeIpYAJEmSJEmSdoCPNkH3f3GW7s/fIa53IE1JRoYImWEWbQ9xvUO50iadnqDx598kO3bQYor0ORW35+m98T7dn7492NJfqRBGWr91O//vEmMkrneJ7TVIEiqnHqP28tNkxw5tygkdRVnyi/kZ/uba+/z7mXP0y4JWpcZErUnidYAk6QEpYkmnyFnP+6wXfWKMhBAoYkktydjfHOX4yCSPj0yzvznC/uYoe5sjDFVqWz26JEmSJEnS52YJQJIkSZIkaRsrV9r03r9I75/forhxB0IgGWpCrWq4WttG7OeUiyuEWoX6H75C7eWnCRU3gUuf1d2t///8FvnlGYiQDDcJ9XsLJcaiHJzMkeckQy2qrzxL9bmTpOOjmzT5r1rorvH9mxf4nz58m4ur8wRgqt6imd3bCQaSJP2+Yoz0yoK1jXJAvyxIQ6DceJp8qtbi6PAEj49Oc7g1zr7mCPtbo0zWmoNTBiRJkiRJkrYhSwCSJEmSJEnbTLm6Rn7uMr03z5BfuEosS0KtShhqERKD/9o+YllSLiwDUHv5GerffIlkuLXFU0k7Q4yR4totem+eoff6e8Rub7D1f7h1z9v5Y6dLudKGEMiOHqT2pWepnDj8QMs5MUbeX7rFv712hn977QxreZ9mVmWy3iQ1UClJ2ibysmS96N89PSAhEMLgVIFWVuVga4zHhiZ4bHiS3Y0hdteH2d0YYrLWIr2HU3okSZIkSZLulSUASZIkSZKkbaBcaZOfv/Jx8D9GQpoShpuEzI3q2l5ijMTlNrHbIztxmOaffIV07/RWjyXtCOXqGr13z9P7p7cobs0OTngZbhFq97YpP5YlcaVN7PUJjTq1l56i+sJp0l2TmzT572+13+WHty7xP19+m3cXbhECTNSatDJPtZEkbU9ljHSKnE7Rp1P06RfF3VMBSkrqaYU9jWEOD43z2NAE+5oj7GmMsKs+xK7GELXUn+EkSZIkSdL9ZQlAkiRJkiRpi3wU/O++eYbC4L92iHK9Q1xpk05P0vizr5KdOGKIV/odYlGSX7pG77X36L9zjlgUhHqN0Gre8wkvsdsbbP2PkezgXmqvPkfl1GP3XCq4H2KMXFyZ42+vf8BfXXmXlbxHNUmZrg+RuU1ZkrRDlDHSK/NBSSDv0y3zwQkCBPJYkiUJ07UhDg6NcnR4koOtMXbVh9jdGJwiMJTVvP8sSZIkSZLumSUASZIkSZKkB+hu8P+NMxQXrxLLklDJCEMtQpZu9XjSbxT7OeXCMqFRo/5Hr1D7wlOEimUV6bcp5pfovX2W3j+/Rbm4AklCMjJ0z987MUbiyhqx2yVUK1RfeILqi0+Q7tu1Y0KFnbzPP925zP9y+R1+PncNgPFqg+GKwUhJ0s4VY6QfSzp5n06R0y1yIpEkBMqNp+VHq3UONMc4MjTOkaEJ9jQH5YBd9WEma01vByVJkiRJ0mdiCUCSJEmSJOk+K1fa9M9dpvfmBxvB/0iopAb/tSPEsqRcWIYItVefpf71l0iGmls9lrRtxV6f/rnLdH/6Nvn5qxBLQrNBaNbvOdQX+33KpTbEknT3JLVXn6fy5HGSZn2Tpt8aV1cX+c6Ns/zry+8w11kbbFBuDFFNvI2UJD1cirIcnCBQDEoC/bIgCwmRSBEjzazCvuYoh4fGOTo0wd7mCLsbw+yqD7GrMUTF20ZJkiRJkrTBEoAkSZIkSdJ98HHw/wz5xWtg8F87TIyRuLxK7PbJTh6h+SdfId0ztdVjSdtSjJHy5iy9Nz+g+/N3iOsdQpYSRoYI6b1d58cYie114nqXkCZUnj1J7QtPkR7e+9BtCu6XBT+fvcpfX3mPf7x9iTKWjFTqjFUbD93nKknSLytjpPuJgkCvyElCIBAoKMlCykStyd7GMPuboxwcGmO63mKq1mJy47Un6kiSJEmS9OiwBCBJkiRJkrRJypU2/bMbwf9L1yDGQQjU4L92mHKtQ1xtk+6eovGnXyV7/LBhIunXKNc69N+/SPeffkFx/RaEMDgpo1a9963/eU653IaiIJkYpfbqc1SfPkEy3Nqk6be32+srfPfGef6ny28z014iCQlT9RaNrLLVo0mS9MDFGOmXBd2yoFvk9MrBKQIJCSEMCgSRSCursqs+zP7mCAdaY+xrjjBZazJVbzFZazFZa1JNs63+dCRJkiRJ0iawBCBJkiRJknQPyuVV+ueu0HvzffJL16EsCZXM4L92pNjvUy6uEBp16t9+ldoLTxAqhoSkT4plSXF5hu7r79N/6wNiPydUq4ThJiFJ7u1jx0hc7xDbHUgC1SeOUX35abLHDhDSe/vYO1VRlvxifoZ/d/0D/n7mLN0ip5ZVmKq1yO7x71uSpIdJEUt6RUG3zOkWg5cYIQ2BuPH7WZIwUW2ytznC/ubI4DSB2tBGSWBQFhip1C0AS5IkSZK0A1gCkCRJkiRJ+px+ffC/QhhqGvzXjhTLknJhGYD6q89R+/oXSFrNLZ5K2l7KpVV675yl+09vUc4tQkhIRlqE6r1vpv94639JMtKi+qVnqD17imR85N4Hf4is9Dv85PZl/urKu7w5P0OMMFKtMVZtGFaUJOl3iDHSj+XgJIEip1vm9IqCNARCCHdPE2imVXY1htjXHOHgxmkCU7XBSQIflQU8TUCSJEmSpK1nCUCSJEmSJOkzGAT/L9N784zBfz00YozEpVVir0/l9FEaf/xl0t2TWz2WtG3EvCA/f5nuz9+lf+YSlJHQqBFa9x46jzESV9eInR4hCVSeOEb1C0+SHT3o7cpncK29yD/cuMC/vvION9aXCQSm6i2aWXWrR5Mkacf65dMEekVBSSQlEMPghJ4sSRit1Jmst9hdH2JPY5jdjWHGqg3Ga43B6+rgdT2797KkJEmSJEn69SwBSJIkSZIk/QZ3g/9vbAT/iYQsM/ivh0K5tk5cWSPdM0Xjz75GdvyQm7SlDcXteXq/+IDuz94mrq5Bmg62/mf3vvU2dnuUK22IkE6PU/3SM1SfPE4yMrQJkz96irLkncWb/N31D/jb62dZL/pUk5SpeotK4m21JEmb6aPTBHpFTr8s6G28FGVJGhJCgBgHZYIsSWhlVSZrLabrLfY2RtjdHGKi2vxUYWCs2qCVVf1ZRJIkSZKkz8kSgCRJkiRJ0oYYI+WdBfIPr9N76wPySzMY/NfDJvb6lEsrhEad+p98mdrzT/i1LTEI5/fPXKL7z2+RX56ByGDjf6N271v/y5K40ib2+oRqhcrzp6g9d5r00F4Db5toLe/xT7cv89dX3+Pns1cpiQxX6oxVGyT+PUuS9MDEGMlj+XFRoCjol4OXNARCCMQIZSwJIdDMKkzUmkzVWuxpDLOnMcxkvXX3RIGPSgMjlbr3nSRJkiRJ2mAJQJIkSZIkPdJit0d+eYb++Sv0371AubAMREKlYvBfD5VYlJSLywDUv/w8ta+9SNJqbvFU0taKRUlx9Sa9tz6g9/r7xF5vcP0/3CKkyb197Bih06VcXYMQyA7sofalZ6icOkpo1DbpM9BvcnNtme/fusi/vvwOV9qLBGCy1qJVqW71aJIk6ROKsrx7osCgNDA4ZSAQSAgQoIyRSKSWZIOyQL3FrvoQe5sjTFSbDFdqn3oZqdYZzmrU0szSgCRJkiTpoWUJQJIkSZIkPVIG2/7nyT+cof/uefJL14hFCUBo1AlNNwvq4RJjJC6tEnt9Kk8eo/HtL5PumtjqsaQtVdxZoPfuOXo/e5dyYQlCIBluEWr3HhCPeUG5vApFQWg1qH3hKarPniTZPentyxaIMfL+0i2+c/0s//baGVbyHpUkYao+RDWx6CdJ0k5Rxni3INArPi4NRCJJSPjoXlaMkSJG0pBQTzNGqnXGN04SmKy1mKo1Gas1GK4MigK/XCCoptmWfp6SJEmSJH1WlgAkSZIkSdJDL3a6n972v7gMBEIlI7QahIpP8uvhVK6tE1fXSPdO0/jTr5EdO2gIWY+ssr1G/8wluj99h+LaTYgQmjVCs3HP3xcxRmJ7nbjegSSh8vhhqi89ReXxw97GbCOdvM9PZ6/wN1ff5yd3LlPEklZWY6LWJPG6UZKkh8ZHRYB+WZDHcvC6HLzux4IAJIS79wHLGCljJEsSmmmF0WpjozjQZKrWZLLeYrRa/3RhIPv412lybydISZIkSZL0+7AEIEmSJEmSHjoxRsrb8+QfXqf37nmKS9eJ5ca2/2Z9sPHfsJ8eYrHXp1xaITQbNP74y1SfP03I3HitR0/s5+QXrtB7/X36718kFgWhWiUMNwmbENaK/T7lchvKkmR8lNoXn6H69OMk4yObML3upzudVf7x5kX+lyvvcGllHoDxWpOhrOp9BEmSHkFljBS/UhgoyTd+HUIgCYEAxDh4/0ikkqQMVaqMVOoMVWqMVGqMVOqMVusbb6vSzKo008rgdTZ43Ugrd/+/mqTe/5AkSZIkfW6WACRJkiRJ0kMhrnfJr8zQP3eF/nvnKRdXAAiVitv+9ciIRblx0gXUv/oita++SNKsb/FU0oMVY6S4epPe22fpvf7+YDt/mpIMtzbltiCWkbjaJnZ7hCyl8vQJqi8+QXZ4PyF1C+xOE2Pk3PIs/37mHP/rtfdY7HXIQsJUvUUt9b6DJEn6zcoYyT9RFihiJC9LiliSx5KiLAlwt0DwyT8XGdwPyZKELEkZyqq0surdQsFvKhI0NooDv1wqsEggSZIkSY8eSwCSJEmSJGlHijFS3pqj/+F1+u+ep/hwxm3/emTFGIlLK8ReTuXJ4zT++Muk0+NbPZb0QBVzi/TfvUD3Z29Tzi1CCCRDTahtzmb32OlSrqwBkO6dovqlZ6g+cYyk1bznj63toVfkvDZ3jX9z7X1+cPMSeSxpZhUmak3SYMFDkiRtro9OHyhiebc88CtFglgSIiQh4ZN3aWOMlPz6IkEzq9DYKAwMXldpptnG71Wppim1JKOaZtSSlGqSUUszqkn6G19XLBlIkiRJ0rZjCUCSJEmSJO0Ycb1LfnmG/vkr9N89T7m0AgRCJXPbvx5JMUbiSpvY6ZLu303jX3yV7OgBwxl6ZJRrHfIPLtH92Tvkl2cgQmjUBrcJmxH8L0riyiqxXxDqVaovPkH1uVOk+3b5ffaQW+iu8cNbl/jXV97hzNIdAMarDYYrNf/tJUnStvHJIkFRRvI4OJGgvPtSUvDxr2OMhBAGJxQQ+OW7NR+dUBA3PnYSAmkIJATq6aA40Egr1DZe19NsUDrIKjTTj08mqKcZ1SQjSxLSEEhDsvH/v+7Xg7f9yq9DSpqEu2/LQvIrv05C8L6ZJOlXxF6fmBdbPcbvFLKUUK1s9RiSpB3MEoAkSZIkSdq27m77vzTY9p9/eB02HsoIzcYg6OmTvXoExRiJ7XXiWod01wT1b79K5dRRQuqmaj38Yl6QX7xK740z9N89R8wLQqVCGG5tyvdAjJG43iG214FAdvQAtS8+TeXEEUKteu+fgHaUGCOXVuf57sw5/urqe8x110gITNZbNDOfqJckSQ+vwWkDnywUDAoH5SfefvfXn/x94qCcu1EyGDxq86uFg1+9QIhh4zXxozfdLSUEIIRAsvH6o4+f3S0IbLwOCZUkHbw9+fjtAR8/kqRHQlny6v/3p/zpzPYvAVDJmPp//z8tAkiSfm+WACRJkiRJ0rZSrncoLs/QP3eF/nvnKZfaQBwEPIcahMxt/3q0le11YnuNZHyUxh+9QuXpE4Qs3eqxpPsqxkhx/Tb9d87Rfe09YnsNkoRkpEWobM4TpbGfU660oShJRlpUX36a6jMnSKfGN+Xja+frlwVvzl3n72+c49/PnKdT9EmThKlai1rq/RNJkqT75aMiQCQS40evPz6x4OPfi58oDkTKTxQKJEmPhrn1Vb71k6v8j+d3xvX/5P/n/0XSrG/1GJKkHcpHpSVJkiRJ0paKnS75zB2Kazfpf/Ah+eWZT237T6bG3PYvAXG9Q7myRhhu0vjLP6T2/GlCxYf39HArF5bpvXee3s/epbg9ByGQtBqEqfFNuW2IZUlcXSN2e4QkofrEMapfeJLs6EHLNfoVlSTlpelDvDR9iP/rE1/l57NX+dvrH/CjWx+Sx5JqmjFVa5Ilfu1IkiRtpsHmf4C7RwtIkvRrdXq9j3+xnZ9XcG+zJGkT+CyhJEmSJEl6oMqVNsX12+RXb9I/9yHFzJ3BA94xEqoVktEht/1LnxA7PcqVVUK9RuNPv0bt5acItepWjyXdN3G9S//sh3R//i75xauD24d6jWSzgv8xEte7g9MEgHTv9GDr/+mjJCND9/zx9WhoZlW+tucYX9tzjMXuOv905zJ/c/V93py/ThkjrUqN8VqDNCRbPaokSZIkSZIk6SHkM+qSJEmSJOm+iTFSzi9RzNwmvzxD/9wVyjvzkCRQloNQp6F/6deKvT7l0gqhUqH+rVeovfKsR0ProRWLgvzSdXq/OEP/rbPEfkGopCTjI4R0c7aqx16fcqUNZUkyPETtm1+k+vTjJLsnPXFG92Ss1uBPDpziTw6c4tb6Cj+6dYm/uvou55fniMBIpcZotUHi15kkSZIkSZIkaZP4DLskSZIkSdo0sSgp78yTX79NfvEq+YWrlMurG8fuBkKjSjIxRkjdiiv9JrGfUy4tE5KU+ldfpPbl591OrodSjJHy5iy9d87R+/m7g4B+kpAMt0iqlc25jKIgLreJeU6oVKg+f5rqc6fIjuzbtHKB9Em7G8P8R0ee4S8PP83l1QX+8dZF/vrq+1xtLxKA8VqDoaxm8USSJEmSJEmSdE8sAUiSJEmSpN9b7OcUN+5QXL9N//xl8g9niOudQeg/BEKzTjI5TkgMukm/S8wLysVlCIHqy09T/9oXSMdHt3osadMVswv0z1yi99q7FLfmIURCs0kyNb4pwehYRmJ7jdjpQRLIjh2k9uKTVE4cJtRrm/AZSL9bCIEjwxMcGZ7gXx17gTNLt/n+jYv8zbX3udJeJAkwWWvRzKpbPaokSZIkSZIkaQeyBCBJkiRJkj6zuN4ln7lNfvUm+fnLFFdvEvsFEAlZRmjWCa3NCXFKj4pYlIPwf4xUnz1J/Rsvk+6a2OqxpE1VLizT++ASvdfeo7h+CwiEepVkcmxTimIxRmKnS2yvQ4R01wTVP3qa6uljJGPD9/4JSPcgCQlPjO3hibE9/Fcnv8hb8zf49zPn+M7MWe502mQhYareopb6lI0kSZIkSZIk6bPxEWVJkiRJkvQblcurFDO3yS/foH/uMsXN2Y3fKAnVKqHVGIT/Df1Ln1ssS8rFFShKKk8cpf4HXyLbN73VY0mbplxepX/2Mr3X3yO/PAMxEmofBf+TTbmM2OtTrrShjIShBvWvvkjlmROke6e9bdK2VElSXpw6wItTB/gfnvgKP5u9yt/NnOUfb14kjyXVJGWy3qKSpFs9qiRJkiRJkiRpG7MEIEmSJEmSgMEW5XJ+ieLaLfLLM/TPXaacW4SQDIKb9SrJ6DAhM5Qm3YtYRuLSCrGfkz1+iMYfvkJ6cI+BZT0UyvY6+bnLdN94n/zC1cHtRyUjGR8hpJtz+xGLkrjSJvZzQpZSfeYk1edOkh096G2UdpR6VuGre47y1T1HWe51+Kc7l/k3197ntdlrRKCRVZioNUnD5pRmJEmSJEmSJEkPD0sAkiRJkiQ9osr1DuXteYrb8+QXr5FfvDrYphwChECo1zZ1W7P0qIsxEpdXib0+2aG91P/oVbKjBwz/a8eL6136F67Qe/MM+QcfEsuSkKWbWhyLMRLb68T1DoRA9th+al94iuzEEZJmfVMuQ9pKI9U6395/km/vP8mdzio/unWJv77yHmeW7wCR4UqdsWqDxNsMSZIkSZIkSRKWACRJkiRJeugNgsdtijvzlLfnya/cIL92k3JhGQhAhCQhadRJpsYNJEubLMZIXF0jrndJ907R+ParZCeOWLDRjhZ7ffKLV+n94iz9d88T8wLShGS4RVLZnIedY4zQ7VGurgGQTI5R/8ZLVJ48TjoxuimXIW1H0/Uh/uXhp/mLQ09xtb3IP968yF9fe5+rqwuEEBirNhiu1LzPJkmSJEmSJEmPMEsAkiRJkiQ9RGJRUs4vUdyep7g9R3F5hvz6bWJ7DZIAJYQsgVqNZHyUkBpClu6Xu5vL19ZJpsZp/uW3qDxx3O877Vixn5N/eJ3e2+fov32W2OsPSmRDTZJqZVMvp1xpQ1EQmnVqX36e6tMnSA/sNvSsR0oIgUND4/yr4y/ynx17gbPLd/iHGxf4m2vvc6W9SAAmak1aWdXvDUmSJEmSJEl6xFgCkCRJkiRph4q9PsXsAuXteYobd8iv3KC4MUvs9QhJQiwjoZIR6lXC5DghMRwmPSjlWoe4ukYyOkTjP/421WdOEjZpO7r0IMWiIL98g/675+m9eYa43oUkDIL/I0ObdzllSVxpE3t9QppSfeIY1edPkx076PeOxKAQcHJ0FydHd/FfnniZtxdu8r0b5/nOzFkLAZIkSZIkSZL0CPLZE0mSJEmSdoCyvU55Z57izgL5tZsUV29S3FmAsrz7PqFWJTRqhJGW4S9pi8ROl3KlTWg2aPz5N6i9+CRhEzekSw9CLEuKa7fov3eB3hvvU66sQYBkqEmYGtu025i7p2WsdyFAdnAv1ZeeonLqCEmruSmXIT2MsiTl+cn9PD+5n//LE1/hrfkbfP/mBQsBkiRJkiRJkvQIsQQgSZIkSdI2EmMkLq1S3J6juD1PcfUm+dWblIvL8FGIKwRCrUoy0iJk/mgvbQex26NcXiXUqjT++MvUXn6G0Kht9VjSZxZjpLhxh/77F+m99i7l4srg9qbVINns4P96l9heByLJ+Ci1r71I9cnjpFPjm3IZ0qOkkqS8OHWAF6cO3C0E/ODmBb4zc+5uIWC81mTIQoAkSZIkSZIkPVRMCkiSJEmStEViUVDOLVLcWaC4OUdxZYb8+m3i2jokCZSRkKVQq5JMjhGSZKtHlvRLYq9PubRCqGTUv/kytVeeIxlyg7l2hhgj5e15emcu0nvtPcrZBQgQGg2SqfFNDf7T7VGurkGMJMMtal97kcqTx0n37/L2TdoknywE/PdPfIW3N04I+PsbFgIkSZIkSZIk6WFjCUCSJEmSpPsoxkhcXaNcWKZcWqFYWKa8NUdxa47yzgKx34ckDAL/1QqhViVsYvBS0v1xd/N/mlJ79XnqX32RZHRoq8eSPpNibpH+mUv0XnuX4ubcIPhfr21q8B82vk9W2xAhNGrUXnmO6pPHSA/tI6QG/6X7qZKkvDB1gBc+UQj4wa2LfGfmLFfbi4CFAEmSJEmSJEnaySwBSJIkSZJ0j2KMxJU25eIK5eJHQf9ZitvzlHOLxG5vsNkfoCwhTQeB/2adUBkyeCXtIHG9Q7m6RqhUqH/9JWpfeoZkdHirx5J+qxgj5Z15+mcv03vzDMXMbQiBUKuSTI5u6ib+2OtTrrQhRkKtSvXFp6g+/TjZkf2D020kPXCfLAT8n09/mXcWbvL9mxf4zsxZTwiQJEmSJEmSpB3KEoAkSZIkSZ/Bp4L+C8sUiyuUN2cp7nwi6B/C4OWTQf96jTDUIiQGqqSdKsZIXOsQ2+uEZp36t79M7aUnSVrNrR5N+o1ijBQ3Zumf/ZD+m+9T3F4YbPyvVUkmxzY3+N/PB8H/oiRUUqrPnKT6zAmyowcI1cqmXY6ke1dJUp6f3M/zk/vvFgJ+cPMCf2chQJIkSZIkSZJ2FEsAkiRJkiRtiGVJXFmjXPoo6L9MeXOO4vYc5fzSL230j5AmHwf9h1sGpaSHTIyRuLpGXO+SjLSo//k3qD13mtCobfVo0q8Vi5Li+i36H3xI7xdnKOeXAQiN2kbwf/Nup2JeEFfaxDwnpCnV00epPHuSyrGDhLrfI9JO8MlCwH/3iULAd2bOcrW9CFgIkCRJkiRJkqTtyhKAJEmSJOmRcjfov7hMubhCsbBEeWue4tYc5cJG0D8kQIQYCVkKFYP+0qMkxkhcbhO7XZLJMRp/9jWqT59wo7m2pZgXFFdv0D9zid4vPqBcbg82/jfqJFNjm3q7FYuCuLJG7PcJSUL2+CGqz50me/wwSbO+aZcj6cH7dYWAf7x1kb+7biFAkiRJkiRJkrYjSwCSJEmSpIdCLAriWofYXqdsrxPX1gf/v9KmXFwZvCyvDsKLvY2gfwDK0qC/JGCjJLS0Ssxz0j1T1L/5RSqnjw6uI6RtJPZz8ssz9N+7QO/ts8R2ZxD8bzY2P/hfloMTMbp9CJA9tp/q809QOXGYZLi1aZcjafv4ZCHgvz316qcKAVfaCwSChQBJkiRJkiRJ2mKWACRJkiRJ21aMEbq9Qah/I9hffhTy/1Swv0251oGigBAGL4MPMHhJ00GIN0sJjRphxKC/pI/FoqRcWoGiIDu0j/o3XyI7fpiQJls9mnRX7PbIL12j9+55+u9eGJxcQyAZahDuR/C/vU7sdAFID+6h+sITVE8+RjI2vGmXI2n7+9QJARuFgB984oSAAIzVGgxlNe9fS5IkSZIkSdIDZAlAkiRJkvTAxbwYbOpf63wc8P9oa//CMuXy6iDcv7pG7PUHQf5kI4z7UbA/SQjpRrA/S0lGWoOwv+EjSZ9RzItB+L+MZI8fov71l8ge2+/1iLaNcr1DfuEq/XfO03//AjEflN2SoSZhqLm5wf8YB7fH6xvB/92TVL/1RSqnjpJOjm3a5UjaubIk5bnJ/Tz3qULAJf7u+gdcaS8AgdFqnZFKncTbUkmSJEmSJEm6rywBSJIkSZI+l1gU0MuJ/T6xn0Nv8Dr2c+j3ib18ENzvb7zPepey0yUurVIurVCurhF/3db+MgJxsLE/zQbh/nqNMNSEEAzlSto0sZ8Pwv9A9anj1L76BdL9u7ye0bZQrrTJL1yl9/ZZ8nOXiUU5CP4Pt0iqlU29rBgjca1DXFsHIJkap/a1F6meOkqya8LvCUm/0acLAa/w3uItfnL7Mt+ZOcvV9gIArazGeLVBmniyjiRJkiRJkiRtNksAkiRtgtjrDzYy7gAhSwmbHByRJG1fMS8+DuN/MrD/UUh/49efDOzHTo/Y6Q5eur2Nlz6x1xv8+aKEooRYfhzi3wgJBiCy8Z8YP/rVr2ztD27tl7QFYq9PubQKSaD6hSepv/o86e7JrR5LolxapX/uMr1ffEBx6RqxjIQsIQy1SCqb+xBujHFwG7+6DkSSsWFqX/oilSeOke6d9rZZ0ueWJSnPTOzjmYl9/Ncnv8TFlTl+OnuFv7t+lnPLswDU0oyJWpNKkm7xtJIkSZIkSZL0cLAEIEnSPYq9PrP/+f8D+vlWj/K7xQghkJ0++vHWZUnSQywOMvh3A/vJIKX/ycB+3Ajpx433DQGSQEgSSAJ84nUIARr1j3/P7fySdojY6VKurBEqKbWvPE/tS8+SToxu9Vh6xBVzi/TPXab/iw/Ir9wAIGQZYXSYJNvckOzHG/87QCQZblH72ouD4P+B3YPbdknaBCEEjo1McWxkiv/d0ReYWVvitdlrfGfmLG/MzRCJpEnCRLVJI3NJhSRJkiRJkiT9viwBSJJ0jz7asLxjxEho1gfbmCVJD7+NUL+BfUmPonKtQ2yvEWpV6n/wMrWXnyYZGdrqsfSIijFS3p4fbPx/8wzFzG0gEKoVkvGRTf8ZLZaRuLZOXO8ADDb+v/wSlVOPke7fTUgN/ku6//Y1R9l3aJT/8NCTLHTXeH3uOt+7cZ4f3/6QO51VAjBea9LKqv68IkmSJEmSJEmfgyUASZI203Z/snJj23OoZJYAJEmS9FCKMRLbg+BzaDVp/IuvUn3xSZJmfatH0yMo5gXFtVv0z1+m//ZZijuLECDUqiSTY5u+gT+W5eDrv9MDIsnUOLUvP0/l5BHSvdNu/Je0pcZrTb6173G+te9x1vIev5if4Ye3LvHdG+e50l4gxMBorc5wpU6y3R9jkyRJkiRJkqQtZglAkiRJkiRJO16MkbiyRux0ScaHqf/xl6k9e5JQq271aHrElGsd8kvX6Z+5SP/9C8S1LgChUdsI/m9usDUWJbG9Ruz2IATS3ZNUnztN5cRhkt2TbtaWtC01syqv7DrCK7uO8H978mu8t3iLn9y+zN/PnOVqewEIDGVVxmoN0mCBSZIkSZIkSZJ+mSUASZIkSZIk7VgxRuLSKrHfJ52eoP4X36Ty5HFCxYe99GDEGCnnl8gvXqP39lmKS9eJZQkhkLQahKmxTQ/ix7wgrq4R+zkESA/sofrcKSqPHxoUDQz+S9pBKknKsxP7eHZiH//NyS9xfnmWn965wt/OnOXCyhzESCOrMF5rUkk82VKSJEmSJEmSwBKAJEmSJEmSdqBYlpSLK1AUpPt3U//my1ROPkZI3Ras+y8WBcX12/TPXaH/9lmK23MQAiHLCCMtkmzzH3aN/XwQ/C8KCIHsyH6qz56k8vhhkrHhTb88SdoKIQQeH53m8dFp/rNjLzCztszPZ6/ynZmz/GJ+hghkScJErUk9rWz1uJIkSZIkSZK0ZSwBSJIkSZIkaceIeU65tAplSfbYAerffJns2EE3n+u+i+td8g+v0ztzif5754ntDoRIqNcH2/eTzS+gxH6fcmUNypKQJGTHD1F95gTZ8UMkw61NvzxJ2k5CCOxvjbK/NcpfHH6K+e4ar81e43s3zvOTO5fpl6sEAhO1Js2s4n0BSZIkSZIkSY8USwCSJEmSJEna1mKMxE6XuLoGSUL16RPUXnmW9OAeA3+6b2KMlAvL5Bev0X/nHPmFq8SihBBIhhqEqbH78vUXuz3K1TUoIyFLqZ4+SuXpx8mOHSJp1jf98iRpp5ioNfmj/Sf4o/0nWMt7vDF3nX+8dYnv37zAbLtNAEardYYrdRLvH0iSJEmSJEl6yFkCkCRJkiRJ0rYUy0hcWSX2ckKrQf2PXqX2/GmSseGtHk0PqViUFDO36Z+/Qv+tDyhuzQMQKilhuEVS2fyHU2OM0O1RttcgQqhWqD57ksqTx6kcO0ioVTf9MiVpp2tmVb68+zG+vPsx/senvs67Czf5ye3LfGfmLNfai8QIzUqFsWqDSpJu9biSJEmSJEmStOksAUiSJEmSJGlbif2ccnkVypL0wG7qX36ByumjhGplq0fTQyh2uuQfXqf3wSX6714grq4DkVCvkUyOEpJk8y/zo9Mt2usAhHqN6heeovrEMbIj+/1al6TPoZKkPDe5n+cm9/N/OvUKF1bmeH3uGv9w4wJvL9wkEklCYLzapJlVPEVIkiRJkiRJ0kPBEoAkSZIkSZK2XIyRuN4lttcIaUr1mZPUvvQM6cE9hvW06cqFZfoXr9F/5xz5havEogAgaTUIU2P35Wsuxkhc6xDXO4PLGmpSffU5KqePkR3aS8jcVC1J9yqEwPGRKY6PTPG/few5Frpr/GJ+hh/eusQPb11itrsKEYardUarddKw+UUvSZIkSZIkSXoQLAFIkiRJkiRpy8SyJK60ib0+YahJ/Y9epfbCaZLR4a0eTQ+RWJQUN+7Qv3CF/ltnKW7cASBkGWGoSVK5Pw+TxqIktteJ3R4QScZHqL38NJVTj5Hu301IDZ9K0v00Xmvyjb3H+cbe4/TLgjOLt/nZ3FW+N3OeS6vzwOAkgfFag3rqKSySJEmSJEmSdg5LAJIkSZIkSXrgYj+nXFqFWJIe2EP9Ky9QOX2UcJ/C2Hr0lOsdiss36J/9kP475ylX20Ag1Kskk2OE5P4E8GO/T1xdIxYlAOm+XVSfOUF27CDpnqn7drmSpN+ukqQ8PbGXpyf28l88/jI31pZ5Y+463791gZ/duUqvXAVgtFpnuFIn8SQiSZIkSZIkSduYz6pKkiRJkiTpgYgxEte7xPYaIU2pPn+K2stPkx7cQzBop3sUi5Li1iz5pev037tAfnkGYgQgtBokU+P35essxkjsdIntDhAJWUp24gjVJ4+THT1IMjq06ZcpSbp3e5sj7G2O8KcHT7Oe93l74Qb/fOcK37t5nqvtBQAaaZXxWoNKkm7xtJIkSZIkSZL0aZYAJEmSJEmSdF/FsiQut4n9nGSoQe2Pv0ztudOGo3XPypU2+Yczg23/Zy4R22sAhGqVZHSIkN2fhz9jWRLb68ROD4gkwy2qrzxD5cQRssP7CLXqfblcSdL90cgqvDx9iJenD/Hfn/4yl1bneX32Gt+7eYG3FmaIEZIQGKs2aGVVy4uSJEmSJEmStpwlAEmSJEmSJN0XsZ9TLq1AjGQH91L7yvNUTh0lVHxISr+fmBcU127Rv3SN/rvnKW7cGfxGCCTNBuE+bfuHwddzXF0jFgUA6d5pqk+fIDt+kHTvNCFJ7svlSpIerBACR4cnOTo8yX/82LMsdtf5xcIMP771IT+4dZEr7UUgMpTVGKs2SL3+lyRJkiRJkrQFfMZVkiRJkiRJmybGSFzvENvrhDSl+sJpai8/Q3pgt1tz9bnFGCkXlikuz9A/c5H+uSt3t++Heo1kfJSQ3p/wZYwROl3K9joAIU3JHj9E5cnjVI4eJBkbvi+XK0naXsZqDb6+5xhf33OM/3v5Dc4s3ea12Wt898Z5LqzMAZAlKePVBo2sssXTSpIkSZIkSXpUWAKQJEmSJEnSPYtlSVxuE3t9kpEW9T/+MtXnT5OMDG31aNphYrdHfvUm+YWr9N49Tzm7ACFAkpC0GoSh5v3b9l+WxPY6sdMFIBlqUfviM1ROHiE7sp9Qq96Xy5Uk7QxZkvLU+F6eGt/Lf/74S9xaX+GNuev84OZF/mn2Mnc6qwCMVOuMVOokFiAlSZIkSZIk3SeWACRJkiRJkvR7i/0+5dIqRMgO7aX2leepnHyMUPFhJ302MUbK2/Pkl2fov3eB/OI1YlEADLb9T44Rkvuz7R8g9nPi6hoxLyBAumeKytMnqBw/SLp31307aUCStPPtbgzzJwdO8ScHTtHJ+7y9cIOf3rnK926e52p7EWKkmmaMVRvU08xTkSRJkiRJkiRtGp+NlSRJkiRJ0ucSYySudYhr64Q0pfrCE9S++DTp/t2G2/SZlGsdissz9M9dof/++UGRJARClhGGmiT3sUQSY4ROj7K9DkRCmpIdO0jlqeNkRw+Qjo/et8uWJD286lmFl6YP8dL0If67069yeXWBtxdu8MNbl/j53DXudHIikaGsxmi1QXYfC26SJEmSJEmSHn6WACRJkiRJkvSZxLIkLq8S+znJ8BD1P/kq1edOkowMbfVo2uZiUVLcuEN+6dpg2//VmxAjAKHZIJkav68FkliWg+LKemdQNmg2qL70JNVTj5Ed3k9o1O7bZUuSHj0hBI4MT3BkeIL/8NCTdIuc9xdv8cb8dX5w8yLnlmcH70dgrFanldVILFJKkiRJkiRJ+hwsAUiSJEmSJOk3+nhr+hpEyA7vpfbl56mcfIxwH7e1a+crl1bJL8/Q/+AS/Q8uEdc6ECBUqySjw4Qsva+XH3t94to6MS8gRtLdk1S+8gKV44dI9+0ipG5gliQ9GLU047nJ/Tw3uZ//4+MvM9dp8/bCDX42e5V/vHWJq+2FjferMFatU08rWzyxJEmSJEmSpO3OZ2olSZIkSZL0K2JeUC6vQlEQGnXqX3mByrMnSfdO39eN7dq54nqX/Pot8g+v03//IsXNwZZjQiBpNQgPatt/pzu42CwlO3aQyuljZMcOkoyP+LUrSdoWJustvrH3ON/Ye5z/8amv8+HqAm/P3+Afb13i9blr9MpVIjBcqTFSqZMlFtckSZIkSZIkfZolAEmSJEmSJAGDrf+xvU5c70AIVE49RvXFJ6k8ftit//oVsdenuH6L/uUb5GcukV+7ufEbkVCvkYyP3tdt+zFG6PUp2+tQloNt/7smqHzpGbKjB8kO7iFU3aQsSdrekpBwdHiSo8OT/MXhp+jkfd5bvMUb89f5wc2LXFiZAyCEwFi1wVBWtdQmSZIkSZIkyRKAJEmSJEnSoy52e5Qr7UGIenqc6h+8TPWpx0lGh7d6NG0jMS8obtwhv3KD/plLFJdniEUBREK1SjI6TMjS+ztDURDbHWJ3Y9t/o071yeNUTj1GdmQ/yZhfs5Kkna2eVXhh6gAvTB3gvzzxRe50Vnl7/gY/vXOVH96+xJX2wuD90gpj1Qa11Kf6JEmSJEmSpEeRjwxKkiRJkiQ9gmJREldWif2cUK1Se/kZqs+fIj24x+2yAgZfI8WtOYqrN+h/8CH5xWvEXh9CJFQqhKEmyX0+ISLGCJ0u5VoHYgQg3b+bypPHqDx2gHTfrvtePJAkaStN14f4g32P8wf7HqeMJRdX5nlrfoYf3LrIm/Mz5GUJwFClxmilTprcv1N4JEmSJEmSJG0flgAkSZIkSZIeETFG4lqHuNYBIDt6gNpLT1E59RihVt3i6bTVYoyUd+bJr94kP3uZ/vkrxPUOhEDIUkKzQRhp3feSSMxzYnud2MuBSDLUovqFJ6k8fpjs0F6S4dZ9vXxJkrarJCQcH5ni+MgU/9GRZ1jLe7y3eIvXZq/xj7cucml1gQAkITBWbdDKqpY7JUmSJEmSpIeUJQBJkiRJkqSHXOz3KZfbUEaS0SFq336F6tMnSCfHtno0baEYI+X8EsXVm/TPX6F/7jJxpQ0hQJKQNBuEqfH7H/ovI3G9MygcACFJSI/so/LEMSpH9pPsniS41ViSpF/RzKp8YeogX5g6yH9z6hVura/w9sIN/un2ZX5y5zJX2osQI42swki1Tj2tbPXIkiRJkiRJkjaJJQBJkiRJkqSHUCxL4soasdcjZBnVZ09SfeEJsiP7CamB6kdVubRKfvUG+cVr9M9colxcHoT+Q3hwof8Yob+x7b8oIEIyMULthdNkxw+RHdxLaNTu6wySJD2MdjeG2d0Y5g/3naAoSy6szPGL+Rl+eOsSby/coFeuEoFGWmG0WqeW+jShJEmSJEmStFP56J4kSZIkSdJDIsZI7HSJq2sApAf2UPviM1ROHyVp1rd4Om2FcnWN4tpN8kvX6Z25RHlnARKAQGjUSSbHCcn9Df3DRill7RPb/qsVsscPUzl9lOzIPpLJsftePpAk6VGSJgknRqc5MTrNf/LYs3TyPmeX7/D2wg1+cvsy7yzcJI8lMUZalRojlRpVSwGSJEmSJEnSjuGjeZIkSZIkSTtczHPK5TYUBWGoSf0bL1N95gTJ7kmD1Y+YuN4lv3aT/MPr9M9corg5e/f3QqNGMjlKSO7/SRAxRuj2KNvrECNESHdPUnn1OSpHD5Ae2EOo+NCkJEkPSj2r8MzEPp6Z2Me/OvYia3mPD5bu8Pb8DX58+0PeW7pFjJFyoxQwWq1TSdKtHluSJEmSJEnSb+AzbZIkSZIkSTtQjJG4ukbsdAlJQvWJo1RfeJLs2EHD1Y+Qsr1Gcf02+dWb5Gcvk1+/NQjdA6FeIxkfIaQPJsAX+31iu0Ps54PLb9SoPnOSyskjg23/o8MPZA5JkvS7NbMqz0/u5/nJ/fwfHv8Cq/0uZ5Zu89bCDX5860M+WL4DMVICwxsnBWSWAiRJkiRJkqRtw2eEJUmSJEmSdpDY6VKurkGMpLsmqX77VapPHCMZGdrq0XSfxRgpF5cHof/LM+TnLlPcWYAARAjVKsnoMCF7QKH/PCeudYjdHoRAyFKyw/vITj1Gdmgv6d7pB1ZAkCRJ92aoUuMLUwf5wtRB/ovHX2a51+H9pVv8Ym6GH9/5kPPLc4QQiDEyVKkxUqmTPYDThSRJkiRJkiT9epYAJEmSJEmStrlYFMTlNjEvCPUqtVeeo/rcSdL9uwkhbPV4uk9iUVLemSe/fpv80jXyC1col1Zh4998sOl/lJA+mABeLIpB6L/TGxQPQiA7uIfs1GNUDu8j3beLUK08kFkkSdL9NVKt88Xpw3xx+jD/9alXWOyu897iLd6cv86Pb1/mw9X5u6WA4UqdkWqNNFgKkCRJkiRJkh4USwCSJEmSJEnbUCxL4uraxpb1hOz4QWovPUXlxBGD1g+p2M8pbtyhuH6b/vnL5B/OENc7g9B/CIRmnWRynJA8mOJHLMtB6H+9Owj9E0j3TVM59dhg4/+B3YR67YHMIkmSttZYrcGru4/w6u4j/Henv8xcp817i7d4Y6MUcK29CAzuMoxU6wxVLAVIkiRJkiRJ95MlAEmSJEmSpG3i4+B/HwKk+6apPn+ayumjpOOjWz2eNlm51qGYuU1+9Sb5+csUV28RiwKAkKWERp3QGn9gpz3EsiSudwfFAwIQSacnqLz0FNlj+0kP7CFpNR7ILJIkaXubrLf46p6jfHXPUf6HJ77Knc4q7y7c5PW56/zk9odcby9tdAgDo5VBKSDxBCtJkiRJkiRp01gCkCRJkiRJ2kKf3vg/2LRefeEJKieOkE6ObfV42iQxRuJym/z6LYrLN+ifv0xxaw4iECOhViEMNUkqD+7huhgjsdMltjtAhBhJJkapPneS7LGDZAd3k4wMPbB5JEnSzjVdH+Ibe4/zjb3HifFr3Fxf4b3FW7w+d40f377M1fYiAQghMFKpM1SpelKAJEmSJEmSdA8sAUiSJEmSJD1gnwr+A+n+3VRfOG3w/yESy5JydpFi5jb9D6+Tn7tCubAEGxtwQ71GMjZMSNMHN1OM0O1RrnWgKIFIMtyi8uJpKscOkR7aQzI28sBOHpAkSQ+nEAJ7myPsbY7wrX2PE2NkZm2Zdxdv8sbcdX42e5Vr7SUSBn3IoUqN4UqNSvLg7hdJkiRJkiRJO50lAEmSJEmSpAfA4P/DLeYFxc1Zipnb5Beu0r94jdhe2wj9B0KzRjI5Rkge3MbbGCP0+sS1DrEoBicONBtUn3qc7PFDZAf3kkyNGfqXJEn3VQiB/a1R9rdG+fb+k8QYme22ObN4m3cWb/LTO1c4vzI4Iakk0kgrDFdq1NPM+ymSJEmSJEnSb2AJQJIkSZIk6T75OPjfByDdv8vg/0OiXGlT3JobbPo/d4Xi6g1iL4cAIU0IjTphavyBB9div09sd4j9jVmqFbLHD1M5eYTs4B6SXZOE9MEVESRJkn5ZCIHp+hDTe4b46p6j/LenXmW13+Xs0h3eW7rFT+9c4d3FW9zp5ABkScpIpUYzq5JYCpAkSZIkSZIASwCSJEmSJEmb6leC/wd2U33+NJUThw3+71Cx16e4PUdxY5b88gz5h9cp55cgSaAsCdXKIPQ/8mC31d7d9L/eJeZ9IBCylOzwfrJTR8gO7SXdM03I0gc2kyRJ0u9jqFLjhakDvDB1gP/9sRfpFTkXV+b5YOk2P5+7xutz17jaXiQBCIGRSo2hSo00WG6UJEmSJEnSo8kSgCRJkiRJ0j2KRUlsrxG7PQiBdL/B/50qliXl/BLFzVmK67fpX7hKceMOlOXgHUIgNGokk2OE5MGGzmKM0OlSrncH88RIqFXJjh4gO36IbP9u0n3ThGrlgc4lSZK02appxqmxXZwa28VfHH6KMpZcay/xwdId3py/zs9mr3KtvURCoKRkKKsxUq1TSSw/SpIkSZIk6dFgCUCSJEmSJOn38CvB/7sb/4+QToxu9Xj6jMr2GsWNWYobd8gvXSO/coO41oGNjf6hViUZbhEqD/5htFiWgy3/610gQowkwy2qTz9OdvQg2f5dJLsmCKlhN0mS9HBLQsKhoXEODY3zR/tPEGNkrrvGB0u3eWfhJj+dvcK55VlihJKSRlphuFKnnj7Yk5okSZIkSZKkB8USgCRJkiRJ0mcUi5K4ukbs/VLw/+QR0nGD/9td7OcUt+Yobs6SX5khv3Sdcn4JCBBLQqVCqFcJU+NbEhaLeUFc7xA7vY0SQiSZHKP6zEmyI/tI9+8imRg1yCZJkh55IQSm6i2m6o/x5d2P8d/wCqv9LueWZ3lv8SY/u3OVdxZvcruTE4AsSRiu1GllVRLvS0mSJEmSJOkhYAlAkiRJkiTpt/hU8J9AenAP1edPGfzf5mKMlPNLFDfnKK7fIr94lWLmDrEoBu8QAqFeIxkfJaTJlsxHPx+E/nv9Qeg/BNJ901SOHyY7tHcQ+h9uPfDZJEmSdqKhSo3nJ/fz/OR+/tWxF+mXBRdX5vhg6Q6vzV3jtdlrXG0vknzi/YcqNSqJpypJkiRJkiRp57EEIEmSJEmS9EtiURBX1+8G/7NDe6g8f5rKicMG/7epcq1DcfMOxY075BevkV+5SWyvDcL1kcGG/6EmIUu3Zst/jNDtDUL/RQlAqGSkB/dSefwQ6YE9ZPumCfXaA59NkiTpYVRJUk6O7uLk6C7+/NCTxBi51l7ig+XbvDV/g9fnrnF5dYEIRKCapAxVap4WIEmSJEmSpB3BEoAkSZIkSXrkfRTQLtvrECMA2cG9VJ4/ZfB/G4r9nOLOPMWNWfKrN8gvXaecXQTiIPBfSQn1GmFqfEsC/wCxLImdLnGtuzFXJLSaVE4fIzt2kHTfLtI9U4TMzbOSJEkPQgiBg0NjHBwa4w/3nQBgpd/hwvIc55ZneWP+Om/N37h7WkBJpJlVGa7UqCXZlt2vlCRJkiRJkn4dSwCSJEmSJOmRFIuC2F4ndvsAhGaN6nOnqJw8QnZkP8lwa4snFEDs9ihuzw9eZm6TX56huDUHZbnxHoHQqJGMjxDSZOvmLAriepfY6W68IZKMj1J54jiVx/aT7ttFMjVGSLZuRkmSJH3acKXOc5P7eW5yP//JY88SY+Tm+grnl2f5YOk2r81d54Ol2/TKAhgUCYYrNYayGpn36yRJkiRJkrSFLAFIkiRJkqRHwt1t/6vrwGDbf7p/N9Unj5MdPUC6b5qQupV9K5Xtdco7g8B/fu0m+eUblHOLd09nAAj12qCgkaVbt+U/Ruj1B6H/fh9CgBBId09SOf4U6eG9ZPt3EUaG3BgrSZK0g4QQ2NscYW9zhK/uOcp/BfSKnA9XFzi/PMu7izd5fe46V9uLBAJljNTSlKFKjWZWJfG+nyRJkiRJkh4QSwCSJEmSJOmh9fG2/x4AodWg+sIpKiceIzuyz23/WyTGSFxpU9yep7w9T37lBvnVm5QLS4NAfQSSQKhXSUaHCdnWljNinm9s+e9BAGIkNOpkR/aRHj1Atn/3YNN/s76lc0qSJGnzVdOME6PTnBid5k8PngZgqbfO+eU5zi3f4c25Gd5evMHV9gIJCWWMtCpVhis1qsnWFVclSZIkSZL0cLMEIEmSJEmSHhp3t/231z9a9k92cDeVJ4+TPXaAdO80IU22dshHTIyRcnGZ8tY8+a1Ziis3KK7dolxpQ5JAGQlZArUaycTYlv/7xLIkdnrEThfKcvDGJBls+T92kPTAbtLdUySTWz+rJEmStsZotcGLUwd4ceoA/+nR54kxMrO2zPmVWd5fvM3rc9c4tzxLvywASELCcKXKUFYjTbwPKUmSJEmSpHtnCUCSJEmSJO1od7f993pAIDQbVF84TeXEEbIj+0mGmls94iMjFgXl3BLFnXmKm3fIP7xBMXObuN6BkAw26FcyQq06CNFvcQAqxgi9PrHTJfb6G6WEkmRshMrjh8iO7CfdM0W6e5JQq27prJIkSdq+Qgjsb42yvzXK1/ccA6Bb5Fxamef8yixvzc/w5twM19aWSAiUlNTSCsOVGs204mkBkiRJkiRJ+twsAUiSJEmSpB0lxgidHuXa+iBUHgLpAbf9P2ixn1POLlDcnqe4cYf88gzFjdlBGSMJgw3/1SqhXiW0xrdFsCnmxSDw3+luvAFCrUJ6aC/ZYwfI9u8i3TNFGG5ti3klSZK0c9XSjFNjuzg1tov/4OATACx01zi/PMvZ5Tu8OTfD24s3me20B8WAWNLIqrQqVRpphcT7o5IkSZIkSfotLAFIkiRJkqRtL+YFcW2d2O1DgNBqUH3hCSonj5Ad2UfSctv//RJjJK6tU84uUswuUly/TX5lhuLWHBQlBDbC9FVCo0YY2R4B+lhGYrdLXO9CWQ7eGALp7kmyo6fJDuwh3TNFMjVuaUSSJEkPxHityUvTh3hp+hD/6tiLlLHkenuZD1fnubAyx1vzM7y/dJvZzioJyd0TA4ayKs2sajFAkiRJkiRJd1kCkCRJkiRJ285g23+Xcq0DMQKB7OAeKk8eIzt6gHSP2/7vh9jpUswuUs4tDjb8X7tJcXOWcqUNIQEiAKFeIxluQZZuj8B/jNDPievdwUkEIYFYkowObZwQsZ90z9Rgy3+tutXjSpIkSQAkIeHg0BgHh8b46p6jwOC+7c31lbvFgLfnb/De4i2uthdIQkIRS2pJxlBlUAxIgz8XSZIkSZIkPYosAUiSJEmSpG0h9nPiWofY6wMQhhpUX3ySyokjZEf2uu1/E8V+Pgj6zy1S3JmnuH6bYuY25eIqHwX9iZFQrRBqVZLJMUKyfcJFMS+InS6x0wPixkkEFdIDu8mOHSTbNz0oimyTUwkkSZKkzyqEwN7mCHubI7yy6wgcGxQDZrttLq3Mc3FljrcXbvLe4k2urS6ShIRIJEsSWlmNVlYl20b33SVJkiRJknR/WAKQJEmSJEkPXIwRev3B5va8DwRIko+3/T92gHT3lNv+71EsCsqFZcrZRYrZBYrrt8lnblPOL0FRQJJAGQmVFKpVkvFhQppu9dh3xRjho8B/t/fxb4RAumuC7PkDZAf2kO6ZIpke31azS5IkSZslhMB0fYjp+hAvTx/iP2VwX3mht86HK/NcWp3nnYWbvLtwk5m1JYBBMSCktCpVWlmVSuJ9ZUmSJEmSpIeJJQBJkiRJknTfxRih06Nc70BRAoPN7dnR/WTHD5Pt3026b5pQrWzxpDtTLEvi0irF3CLl3CL5zG2K67cp78wTezmkCZQlpOlgs/9Ia/D/22hLfowR+vkg8L9xGgRASFOSPVNkh/eS7dtFMj1BOj1OqFW3cFpJkiRpa4UQmKg1mag1eWHqAP+bI88AsNzrcGl1ng9X5nl38SZvL9zkWnsRGJz5FUJgKBsUA6qpTxVLkiRJkiTtVD6yI0mSJEmSNl0sy8GW//XOxhsiyVCT6pPHyY4dJN2/m3TXBCFzG+XnEWMkrq5Rzi5QzC1R3JyluH6L4ubsYFN+kkCMEAKhViW0GoTRbFuF/WEj8N/tEbu9QeA/JBBLQq1Kum+a7NA+0n3TpNMTJFPjfp1IkiRJn9FItc6zE/t4dmIff3H4KQDa/S4fri5waXWeM4u3eWvhBpdXFyhiSSAQibQqNVpZhVqy/X5+kCRJkiRJ0q+yBCBJkiRJku5ZLIqN0H8XQhiE/idGqDz9OJUj+0n37yKZHDNM8hnFsiSurFEuLFMuLFHcnie/dpPi5hyxvTYIzRMBBmH/WpUw3NqWf7+xLD8O/PfzQVGhLAmtBtnhfaRH9pPuniTdNUEyPkpIk60eWZIkSXqotCo1nhzfw5Pje/gPDj4BQCfvc7m9wKWVeT5Yus0v5m9waXWeXpGThISSkmqS0cqqNLMKWWIxV5IkSZIkaTuxBCBJkiRJkj632M+Jax1irzcI/YdAumeKysuHSQ/uIdu/mzCyPUPp20lc71IsLFEurFDOL1LcmqO4OUs5t/jxZn+AMhKqFUKtQpgcJyTb8+81FiWx2yV2elCUkAQoS5KRIbITR8gO7d0I/E8SRof8+pAkSZK2SD2rcHJ0FydHd/EnB04B0C8LZtaWuNpe4srqAu8t3uLs0h1urC1v/KlASUkjrdLKqjSyCon36SVJkiRJkraEJQBJkiRJkvRbxRih1yeudwab3EMgpOkg7H/8ENnBPaT7dpE061s96rYU84JycZlycYVyfonizjzFjVnKO/OUq2uDdwqDsDxpOgj712vbdrP/R2JeEDvdQVkhRiAAkWRilMrjh8kO7iXZPUE6PUEYam7rz0WSJEkSVJKUw0MTHB6agN2P3X17u98dFAPaC1xcmef9xVucW55ltr1KQkIkkoRAc+PUgFqSef9fkiRJkiTpPrMEIEmSJEmSPiXGOAh3r3UG4e4IoVEjO3aI7PFDZPt3k+6dJlR8WOEjMUbiSptyYZlyYZliboni5h2KW3OUC8tQFIOt/jECDIL+1QrJ+CghTbZ4+t/ubgmk2x+c/PCREEh3TZAe3Et2YDfprgmSXRMkDcsgkiRJ0sOkValxamwXp8Z23X1bjJG57hpX24tcWV3g7PId3l+8zeX2Ap0iJwmBsiypptndckAlSbfws5AkSZIkSXq4+Gy9JEmSJEmPuFiWgy3/6z0IEcpIMtyi8sxJsmMHyPbvJpme2PZh9QchdnuDbf4fhf1vzVLcnKOcXSB2uhASYKM4UUmhUiEZbkGWbvtNmDFG6OfEXn+w3b8sB8WFoiQ0aqR7J0n37ybbt4tkeoJ0epxQq2712JIkSZK2QAiBqXqLqXqL5yf33317XhbMrC1ztb3I5dUF3l+6zQdLt7mxtgIMStEl0EgrNLMKzaxKss1/VpIkSZIkSdqOLAFIkiRJkvQIiXlB7HaJnd7GVvoAsSSZGqf6zCmyx/aR7ttFMjG67UPr90vs55RLK5RLq5TzS5SzCxQ37lDcnqdcWQMiIQTiRkj+o63+Yai5I/7OYoxQFIPN/t3e4JSCkEAsCZUKya4J0n27yPZNk0yNk06OEUaHdsTnJkmSJGlrZUnKoaFxDg2N8+Xdj919+1re42p7kavtRS6tzPPOwk3Or8xytb1AEhJiHPyc1do4NaCWZP4MIkmSJEmS9FtYApAkSZIk6SF0d6t7tzcIen8kBNKpcdInjpEd2EM6PU6ya4Kk1dy6YR+wWBTElbVB0H95lXJpleLOAuXsPOXcEuXqGhQlpMmgKBHZCPpnJOPDhDTd6k/hM4sbYX96PWI/hyQMlm8mCenkGOmJw2QH9pBMjpJMjZOM7azPT5IkSdLO0MyqnBzdxcnRXXffFmNkrrt2txxwbvkO7y3e4sPVBdbzPlmSUMZIINDIKjTSCvUsIw2eUidJkiRJkmQJQJIkSZKkHS7GCBth/9jrD4LeZSRUKqR7pkgP7yXbO00yPUE6PU6oVbd65PsqxkhcXRts8l9eJS6tUswvUt6ep5hfolxahX4OSQIB+GijfyUbbMIfHYY02VFbJ2NZQrdP7H30NZDcPekhGR8mPbKP9MBu0qkJ0slRkskxQsWHhSRJkiRtnRACU/UWU/UWz0/uv/v2oiy53VllZm2ZmbUlLq3Oc3bpDh+uznOtvUrC4Ge1kkgtHZQDGlmFSthZP8dJkiRJkiTdC5/tlSRJkiRpB4lFOdjs3+0S8+LjwH+zQXZ4H+nhfaS7J0mnJ0gmRx/Kre4xRuj0PrXJv1xcprg9Tzm7QLm4Mvg7CoEQAjGWQBiE3isVkqEmZOmODIfEGKHX/7jwwUefQyQZapId2T8I+09PkEyNkU6OEeq1rRxZkiRJkj6XNEnY2xxhb3OEFzlw9+0xRpZ6HWbWl5hZW+Zae4mzy3e4sDzHjfVliliShISyjGRJcvf0gFqakezAn/8kSZIkSZJ+G0sAkiRJkiRtUzHPiZ3Bhv/BVncgRpKxEbKj/3/27ju+qaqNA/jv3jTpHrR0QEFAOthDSgUcKEMZggVUXtk4UUFEUJGpyFQElSUqyHAgyB6igixZpezRllEZrbaU7jZpkyb3/SPtNWmbNOmgrfy+n7cv99yce85zM9oKz3NOPSjqB0Dh5w2Fnw8ET7camdRuiaTLl5P8pYxs6NOzjCv5p6TDkJoOSZNn7CgIxudGAgSlAlA6QHByhODmCkGsuc+HZDAYk/0LvgAY79VggODsBEVdPzjU84fo7wNF7VrGgg8X5//Ue4CIiIiIiMiUIAjwcnSGl6MzmnkFmD2Wp89HoiazYPeATFzLTMHlzGTczEnD7dwsKAQRkmTcPaBw5wBnhRIOolhFd0NEREREREREVD4sAiAiIiIiIiKqYpIkAbp8SLl5kPJ0/y7uLopQ+NaCon4dOBSu7u5bC6Krc5XGW15Svh5SthqGbDWk7BwYsjXGFf1TM/79ylYDBgMgioAEQDJAcFAASiUEpQMEbycINTxZQ37dCxP99fqC+5UASYLg6gJFXV8o/H2hCPCBWLuWcWV/D1cm+xMREREREZlwVDiggZs3Grh5m503SAak5KmRkGPcPeBGThouZyTjr6xU/K3OAGD8T3C9JEGlcICzQgkXByVUYs3cPY6IiIiIiIiI7h0sAiAiIiIiIiK6SyRJAvL1xoRvnQ6SLh8QROPq7o4qKOrUhsN9daGoUxuinzcUtWtBUCmrOmybSZIEaHXG5P4sNQzZOcZk/4xsGFLSYUjPMq7ur84F8vWAKJis5C8BCgUEBwcISgeInu6AQqzxSReSJAF6g/E11xa+5sYVLCWDAYJKCdHby5jkH1AbCm9PiF4eEL09ITg71vj7JyIiIiIiqkqiIMLXyQ2+Tm5o4xNo9li2Lg//qDPxtyYTCTkZuJKZjKuZKYhXZ0Br0EMhCDAU7MrnpFDCSeEAJ4UDlCwQICIiIiIiIqJqgEUARERERERERBXIUqK/IACSwQA4OECs5QGFb10oAv2h8PeBws8bYi1PCIrqu7K9JEmQ1Bpjcn+OBlJWDgw5ahhSM40r96dnwpCZAylXa1zBv/BeJAMgAYKDA6B0gOCggOjh9p9I8DclGQyANh+SrmBVf0kyFjgYJEAUIHq5Q1HPH4q6vsakf28P42vu7lqtX3ciIiIiIqL/KjelI4I9fRHs6Wt2XmfQ47YmG3+rjbsHxOdkIC47BTez05Gcmw2tpIcDREiQYCjYQcBYIKCEo8IB4n/ov3WJiIiIiIiIqPpiEQARERERERGRnYyJ/vmQtPkmif7Cv6u7OzhA9PaE6FsLigAfKLy9IHq5Q/Byh+jhBkGhqOpbMCPl6yHlaP5duT9bDUNWjjG5PzUThowsSJk5kHQ6Y2J7YUKD3gCIIgQHBaBUQFA6QHB2AkThP5XgX+jf111nTPgv3M0AMO7m4OJsfL0DfAsKOzyMX14eNWpHByIiIiIionuZUlQg0NUTga6exR4rLBC4nZuNJE0W/lFnIi4rBTdy0vG3OgO3c3UQYfzvRL0kQSkq5AIBJ4UDFCKLwImIiIiIiIioYrAIgIiIiIiIiKgElhL9jau7GyAoHSDW8oToV8u4mn9Bor/o5QHBw61KV3eXJAlSbh4kda7xS5MrHxuy1TBkZMGQmf3vqv65eYBe/+/9SZLxS6EwJvg7OEBwcYLg4AZB/O8l95uSd3LQmbzuEAABBa+7EqKPJxT+PhADfAtW9Dd+CS7O/8niByIiIiIiIjKyViBgkAxIzdPgdm4WEtVZSNRk4UZ2Gq5npyFenY4EdQ4AQBQEGCQJAgQ4Ofy7i4CD8N/aMY+IiIiIiIiIKheLAIiIiIiIiOieZZbor9VByreU6O9dkOjvWWWJ/pIuvyCRXwODaVK/WgMpI8eY2J+VAylHDSlHA0mnByABpqsMGgzGPwuT+xUKCAoRgoer8fgeSDaQJMn4POjyjc+pVmdsFz5PBgOgUED0dIMi0A+KOr4Qa3sZ3we13CF6uFdpgQcRERERERFVT6IgoraTK2o7uaKZV4DZY5IkITs/D0ka4w4Ct3OzcSsnHXFZKbiVnYEkTRb0kgRFQYGABAmOBbsHOCkcoBIdIN4D/81ORERERERERLZjEQARERERERH9J8nJ3vl6SPl6QP/vnxBFQIIx0V+lhOjtAdHXG4qA2lAUrOouenlAcHettIRvSa83rtafU7BSvyYXhsLjzGwYMnNgyMyGIbsgqT9PC+gNgFhQpGC8ScAgAQoRgkIBOBQk9bs6Q1AoIIj3ZrK6ZJLkD53O+LoLoryaP0QRopsLFAG1IfrWgqJ2LYie7hA93CB6uhlfdwdFVd8GERERERER/UcIggB3pRPclU4I8qhd7PE8fT5ua7KRlJuFJE0W/lZnIi4rFTdz0vCPOhO5+nwoBBGABINk/GsBR4WD8Ut0gEqhKHiciIiIiIiIiO4VLAIgIiIiIiKiGkUySMZE/vx8SHqD8c98vTEhvjDRGzAmzCtECI4qiO4uEN3dIHi5Q6zlYb6ifxkT/SVJMu4ekKeFlKuFpC34M08HKTcP0Gph0OQZE/hzNDDkqCFp8oxJ/rla4wr0ej0AARAFCDDeAiQDIAhyUj8UIgSlAwRnR0AU74nV+ksjSZJJkn8+JJ3u3x0cJAmQAMHZEaK3pzHB37cWFLWMuzeInm4QPdwgqJRVfRtEREREREREAIwJ/fXdvFDfzavYY3qDASl5Obidm42UPDXu5ObgH3UWbuWk4W91JpJys3A7VwcBgCgIxvUCYICDqICjWFgooIBSvDd2ACQiIiIiIiK6V7AIgIiIiIiIiKpcsVX78/WQ9AWr9hckdwsoKACABDg4QHRzhsLLA4Knm3Hlfk83iC7OxlXwXZwhuhYcW0n2lvR6SHlaGPLygFytMaG/MKm/8Fid++9q/OpcSLm5MGjyAE2esQjBYEzah2jyD+mFyfwSjEn8oggoROMOBIVJ/U4qQFRAEPkP8EVJkmR8D5gm+UsAjIseApJk3MHBywNibS8ofL0hentC9HCF6OkOwcMNgrMjkxuIiIiIiIioxlOIIvyc3eHn7F7i45IkIUuXh5S8HNzJzUFKnhrJudmIz8nArZx0JGqykJybA51BD4UgQhCMhQWCIEBVsJOAo0IBR4UDdxMgIiIiIiIiqkFYBEBEREREREQVSipYiR0GgzER3iBBKkjwh74gyd8gGZPmC1duN0gmq/a7QvR0M67a7uUB0d0FgouTMZnfUQlBqQQUDv/uBqDLB3Q6SAUrw0tpmZCSUyFp84HcPBhy84C8ghX7C1fuL7oavwTjavyCAMl4E8abKYxTNEnkL0zqd3OGICrk68g2klS4k0PBe0GXDyk/37iDQ2GygcEAKBQQPd2gqOtrXMnfp9a/K/l7uhkLPEQmJxAREREREdG9TRAEeKic4KFyQiN3nxL76Ax6pOapzQoF/s7JxC11OhJyMnA7NxvJuTnG/zQv+LsavSRBKRqLA7ibABEREREREVH1wyIAIiIiIiKie5jFhH1Dwcr8BgmQDMYV7wtX6wdgzNiWB5H/lAyGf9tCQXK80gGCQ8HK9+6uEF2cIDoWJPIrCpZ2L4xBb4Ck1RpX3k/NALTX5eR+ef6ChP3COUwJ8j0VfImiMQ5RLCg6KPiTq/FXCuMK/vmQdAUFGvnmuznIBR8OCoguzlD4eBl3cfD2hOjlDsHVGaJbQRGIuysEB0VV3xIRERERERFRjacUFfB3doe/ld0EsvPz5AKBO7nGYoFbOelIUGfgb3WmyW4CAgABEiQYJAkOogiV6AClqIBKoYBKVMBBEFksQERERERERFTJWARARER0D9Jdu8W/gCciuteYJOoXfkkG6d/zhQRBTpY3/puuIH9JMCbVCwX/2Gv8nwCI+De5XhRNvv5N1JcMeiBHAylHA9yRoJdjgclK+yZJ+sK/5wRHFQQnx3/78WdYlZAMhoJV+00S/A0G8wR/STIWV7i5QvTz/jfB38MNopsLBDcX484Ori5M8CciIiIiIiKqJgRBgLvSCe5K67sJpOWpcScvB2l5GqRpNUjLUyNRk4UkTRaSNNlIyVMjLU+NfIMBioK/W5IKdhVQiKKxUMDky0FUQOTf8xARERERERGVCYsAiIiI7kWFyXpERPTfZ5JYL8jJ9iZJ+oqCY4UxIVsQRRTs/W6W7P9vMYDJcWEhAH+m1GiSJAF6w7+J/YV/FuykIKCgAEAwFmSIHm4QvdyNyf21PIzJ/e6ucpK/4OJkfB8RERERERER0X+GUlTAz9kdfhZ2EyiUp89HulaDtDwN0rXGrzStpqBQIAtJudm4k5sj7ywgCoJcCKCXDBAg/FssoFAUHDuwWICIiIiIiIioCBYBEBER3YOU99eDoODqu0RERP9FUuFuD/l6SHo9kK8H9IZ/V+4XTYoBJQkwSIBChODiZEzs93KHwtsLope7ceV+N2djkr+rC+CkYtEHEREREREREVnkqHCAv7M7/EspFsg36JGuzZULBQoLB5Jzc5CoyUSSJhvJudlIy9NAa8iHAAHGpQoAAyQYIEElGosEHAQFlAU7DTiIIkQI/PsLIiIiIiIi+s9jEQARERERERFRNScVJPVDX5jYbzD+qdfDbLcGwLiqvyhAUDpAcHWBWMsTgrsLRE93CB5uEF2cILg4Q3RxhODsZFy539kJgpJ/RUBEREREREREd4eDqEBtJ1fUdnK12s8gGZCpzUOaVm1WNHAnNweJmiyk5OXIuw6k5qmhM+hhkCQoCne5lAqKBiQJCkGEQ0GxgFIU4SAqoBSMf3KnASIiIiIiIqpp+C/8RERERERERHeZJEnGZP3CpH694d+V+yUJEEQU/Du1caV+SIBCYVyt39MdopuLMaHfyw2iq4sxid/VGYKzo7GPizOgUnLVOyIiIiIiIiKq0URBhJejM7wcnUvta5AMUOfrkKXLM/nKRZYuD5m6PKTk5eBObg5S89RI02qQodUgXadBvmRcZEEE5L9LMUjGwgEHk2KBwp0GlKICCkFk4QARERERERFVKRYBEBEREREREdlJkiRjsr7eAOgNkAx6Y7K+Xg9JbwAMhoJkfgEQBAgoSOiXAEgG4yCiCMFRBcHVGaK7C0R3N4gerhA83SA4O8kr9htX63eE4OQEQSFW3U0TEREREREREVVjoiDCTekIN6Uj6th4jd5gQHZ+XpHCAeNXhk6DO7lqpOTmIFWrQXqeGhm6XKTmqZFvMEAUBLkQQJIAqWDHAUEQ4CCIUAgCFKJYcCyaHxfuVEBERERERERURiwCICIiIiIionuSvBq/oWBFfoNBbsuJ/ICcyA8BgAEw7iMvAaJgXJ3fUWVM2nd2guBi/BJdnQFXF4hOKghOjhAclRAcVYCjIwQnVUHbEYKDogqfASIiIiIiIiKie5tCFOGpcoanqvSdBgrpDHqzXQaydHnI1mmhztdCrdchS5uLdG0uMnXGL+PjecjO1yIjX4d8yVBQLGAsXCgsBWAhAREREREREdmDRQBERERERERUbRlX3Idx9XyDJK+wL5kcw2B8TDI5BgCIBf9XkL8vyYPCfDV+pQMEZ0eIzq4QnB0huDhDdHWB4OZkXH3fSWVM9HdyNP5ZmMDvpAJUSv5jKxERERERERHRPUQpKuDt6AJvRxe7rzVIBuTq86HO1xmLBvJ10Oj/PS4sJMjW5SFdq0FGQTFBdr7WWEig0yIjXwO9ZIBekiAWFAKY/u2UJEkwFPwJAVBAlHctUAgCRKFIu4THBYB/50VERERERFTNsQiAiIjoXmQwGFc0JiIiKoUkSYUHBcnzEgCT48LzkEwS9s3P/9suTN6XCpLwC7Lz5Z9Jhdn6JtcaJBQsi2ZM2HdQQFAqIagcIKiUEFQFifiOSmPbUQU4qSCqlIBSaVxpX+kAwcHB+GfBFxwLEvsdVeVbjV+r+7e4gIiIiIiIiIiIyAoBgDMAZ0EJH6USUNo/hiRJBYUDBQUEBcfqgmKCPIMeefp8aA16aPJ1UOuNhQWagj4afT40eh1y9fkFXzqoDXpoDfkwSJL8VVKBQeFdSJCMf01Y8KcoQC4kECEWFBMIcjGBAAGiABiP/j0nmJwTSzjHQgQiIrKbVLCAFBER0T2ARQBERET3IO3Zy1UdAhER3Q3yXuImx/IDUgl/FiEVucT8QE7gF2D8s+Bf6IwPFP4DnWCS6C+Ixn8RLEjqF0TR5LqC7ctFweyccRzBGItBAnR6SDo9oC7700JERERERERERFTTOQBwL/iqCAZI0AqAFhK0QuEXTI7/PZcnSNAJEvIECVpIUAsSNKLxz1zRAI1JH50gQQ8gv+BPnQDoIUEvAHoByEfBzgUApIK/BpQgyX81KVj401gq8O/fbUoABEkAhIICBeHfv/OUUOLffhIR0X+QRjAYf1ZIEhdGJCKi/zwWARAREd2DFIF+xhWRiYjo3mCWiG+anP9v0r5QWp/CZPyCNlfhIiIiIiIiIiIi+u8QYUwgcSnvQGXIuJcgIR8FxQEA9IIkH+cXFgxAKvL4vwUEpm29ybnCMQ3lvSciIqo59Plo+M/Nqo6CiIjormD2HxER0T1I9PGCqCrDHrNEREREREREREREREREFUgAoCrPACUVHXDpfyKie5JBp4Mup6qjICIiujvEqg6AiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhswyIAIiIiIiIiIiIiIiIiIiIiIiIiIiIiIqIagkUAREREREREREREREREREREREREREREREQ1BIsAiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhqCBYBEBERERERERERERERERERERERERERERHVECwCICIiIiIiIiIiIiIiIiIiIiIiIiIiIiKqIVgEQERERERERERERERERERERERERERERERUQ7AIgIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiohmARABERERERERERERERERERERERERERERFRDcEiACIiIiIiIiIiIiIiIiIiIiIiIiIiIiKiGoJFAERERERERERERERERERERERERERERERENQSLAIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIaggWARARERERERERERERERERERERERERERER1RAsAiAiIiIiIiIiIiIiIiIiIiIiIiIiIiIiqiFYBEBEREREREREREREREREREREREREREREVEOwCICIbLJo0SKEhobKX9YMHTpU7jd06NC7FGFxx48fN4v5+PHjFT5HZT0vpmMuWrSoIkMmIiIiIiIiIiIiIiIiIiIiIiIiIqIazKGqA7iXxMfHo2vXrlb7KJVKODo6wsvLC76+vmjYsCGaNGmCsLAwNG/eHIIg3KVoiYiIiIiIiIiIiIiIiIiIiIiIiIiIiKi6YRFANaPT6aDT6ZCdnY34+HicPn1afqxOnTqIiIjAkCFDULt27SqM0tyePXsQHR0NAHB3d8eIESOqNiCie0h8fDw2b94st/v164d69epVYURERERERERERERERERERERERERERFSZWARQxRQKhVnbYDBAkqQS+/7zzz9YtmwZVq9ejTFjxmDkyJHVYmeAPXv2yEnIgYGBLAIguosSEhKwePFiuR0eHs4iACIiIiIiIiIiIiIiIiIiIiIiIiKi/zAWAVSh8PBwrF271uycJEnIzs5GRkYG4uPjcebMGRw7dgxHjx6V+6jVasybNw9RUVH4/PPPoVQq73bodA8aM2YMxowZU9VhVDuV9bzExsZW+JhERERERERERERERERERERERERERFTziVUdAJkTBAHu7u6oV68eOnTogFGjRmHVqlXYsWMHunXrZtZ37969mDFjRhVFSkRERERERERERERERERERERERERERER3G4sAaojg4GAsWbIE7733HgRBkM+vX78e69atq8LIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiOhuYRFADfPCCy9g5MiRZucWL14MrVZbRRERERERERERERERERERERERERERERER0d3iUNUBkP3Gjx+P/fv3Iy4uDgCQnJyMDRs2YPDgwTZdn5SUhCtXruDWrVvIysoCAHh6eqJOnTpo27Yt3N3dKy32mhZbVlYWYmNjcf36dWRkZECn08Hd3R21a9dG69atERAQcFfjsUdqaiouX76MmzdvIiMjAwaDAR4eHvDz80Pbtm3h7e1d1SEWo9VqceXKFcTFxSElJQVqtRqurq7w8vJCs2bNEBQUZLYTRnnFx8fjwoULSExMhF6vh7+/P9q1a4c6depU2BxEREREREREREREREREREREREREREQViUUANZCDgwOGDh2KDz/8UD63bds2i0UABoMBUVFR2LVrFw4fPoybN29aHFsURXTq1AmjRo1C+/btLfY7fvw4hg0bVux8QkICQkNDLV4XGxtb6bGV17Vr17Bz507s378f0dHRMBgMFvuGhITghRdewNNPPw1RtLyxxttvv42dO3cCAJRKJf788094eXnZHFNaWhoeeeQR6HQ6AEDv3r2xYMGCYv3Onz+PXbt24dChQ7hy5YrVMdu2bYuXX34ZXbt2tSmGRYsWYfHixXK76GtZVnfu3MGvv/6KPXv24NSpU8jNzbXYt3bt2hg0aBCGDx8ONze3Ms8ZHR2NuXPn4vjx45AkyewxQRAQHh6OyZMnW30vF6qs58V07tGjR2PMmDFmj3fp0gUJCQnFrivpc1lozpw56N+/P3bt2oVx48bJ5z/55BP07dvXrviGDh2KyMhIAECtWrVw8OBBqFQqu8YgIiIiIiIiIiIiIiIiIiIiIiIioqphT/6jac5geHg41q5dW+nxlaRo/vKaNWvw4IMPVugclZUXWtFYBFBDPf3005g1axby8/MBABcvXoRGo4Gzs3OxvrGxsRg6dKhN4xoMBvz55584fPgwRo0ahbfeeqsiw64RsQ0cOFDehaA0ly9fxsSJE7F79258+umnFhPT+/fvLxcB6HQ67NixA0OGDLE5ph07dsgFAAAwYMCAYn0yMzPxzDPP2Dzm6dOn8frrr2PAgAH44IMPqiyBe9q0adi7d69Nfe/cuYMvvvgC27Ztw9KlS9G4cWO759uxYwcmTZqEvLy8Eh+XJAnHjx/HgAEDMHPmTERERNg9R3XXvXt3+Pr6Ijk5GQCwbt06u4oArl27Jv8wB4B+/fqxAICIiIiIiIiIiIiIiIiIiIiIiOwSHx9f6iK2SqUSjo6O8PLygq+vLxo2bIgmTZogLCwMzZs3hyAIdylaIqLqhUUANZSrqytCQkJw6dIlAMbE8jNnzqBjx45Wr1MqlQgJCcH9998PX19fuLi4QKvV4vbt2zhz5gyuX78OwJgIvWzZMnh5eWHEiBHFxhEEAQqFAoAxOd90NfXC8/aqqNgqUsOGDREUFITAwEC4urpCkiSkpqYiJiYG586dk+97//79ePfdd7F06dISx+nUqRMCAgKQmJgIANi0aZNdRQCbN2+Wj+vUqVPq6yyKIoKCgtC4cWP4+/vD1dUVer0ed+7cwYULFxATEyP33bhxI1xcXDBlyhSb46ksvr6+CA0NRYMGDeDu7g4HBwdkZmYiLi4OJ06ckBP3r1+/jpdffhmbN2+Gp6enzeNfunQJCxYsgFarhSiKCAsLQ7NmzeDk5IRbt27h4MGDcgGITqfD+++/D1dXV3Tv3r1S7rc8FAoFFAoFJEky261CFEWLv9gW7lahVCrx3HPPYcmSJQCAkydP4sqVKwgODrZp7nXr1snHgiDgf//7X1lvg4iIiIiIiIiIiIiIiIiIiIiIyCKdTgedTofs7GzEx8fj9OnT8mN16tRBREQEhgwZgtq1a1dhlOb27NmD6OhoAIC7u3ul5zkS0b2JRQA1WMuWLeUiAACIi4srMTlcoVCgW7du6NevHzp27AhXV1eLY0ZFRWHatGm4du0aAGD+/Pno0aMHAgICzPqFh4fLc0+cOFFOUg8MDMQff/xh8z1URmzl1bp1a/To0QOdO3eGn5+fxX63bt3CrFmzsG/fPgDA3r17sWvXLvTq1atYX1EUERERgS+//BKAceeG2NhYhIaGlhpPbGwsLl68KLcjIiLkZG5TgiCgY8eOGDBgAB555BF4eXlZHXP69OnyL0Rr165Fnz590Lp161LjqWiBgYEYO3YsnnjiCQQFBVnsl52djeXLl+Prr7+GJElISEjAggUL8OGHH9o8V2EBQHBwMObPn48mTZqYPZ6Tk4O5c+di/fr1AIwFLlOnTkW7du3g7e1dthusJL///juA4lvbrFq1yqatbQYOHIjly5fLu4msW7cOU6dOLfW6vLw8bN26VW537NgRDRo0sDd8IiIiIiIiIiIiIiIiIiIiIiKiYoouQlx0kWJT//zzD5YtW4bVq1djzJgxGDlyZLXYGWDPnj1mOZUsAiCiylA8k5hqDF9fX7N2RkZGif1CQkKwZMkSdOvWzWqSPQCEhYXhhx9+kBPrdTodvv/++4oJuIbEtmLFCjz77LNWCwAAoH79+li6dCkef/xx+dzq1ast9u/fv79Z23R1f2s2bdpkdZxC7u7uWLVqFfr06WO1AAAAQkNDsWbNGjRv3lw+Zy32yjR58mS8/vrrVgsAAMDNzQ3jx4/HhAkT5HNbt261+L4viVarRWBgIFavXl2sAAAw7rDx0Ucf4dlnn5XPpaWlWdzhoSbz9/c320pr69at0Gg0pV63a9cus+ecuwAQEREREREREREREREREREREVFFKFyc2PQrOjoaUVFR2Lt3L1avXo1x48YVWyxZrVZj3rx5eOONN6DT6aooeqKaZ8yYMYiNjZW/qGZhEUAN5u7ubta2JxnaGi8vL7OVxffv318h41aE6habKIp444035PaZM2eQlpZWYt8GDRogLCxMbm/btk1ehd2S/Px8bN++XW63b98e9913XzmjNlKpVHjllVfk9sGDBy1WTFYnw4cPh4eHBwBAo9Hg2LFjdl0/adIk+Pj4WO0zceJEsyKbLVu22JQgX9MMHjxYPs7KysKOHTtKvWbdunXysa+vr1khARERERERERERERERERERERERUUUSBAHu7u6oV68eOnTogFGjRmHVqlXYsWMHunXrZtZ37969mDFjRhVFSkR0d7EIoAYrunJ+Tk5OhY0dEhIiH1+9erVCxy6v6hZbaGioWfvcuXMW+5qu4p+SkoIDBw5YHXv//v1ISUkp8fqKYPpcZmVl4dq1axU6fmVQKpW4//775fbZs2dtvjYwMNCmpHU3NzcMGDBAbmdlZeHo0aP2BVoDPPjgg2Y7MJgm+JckJiYGZ86ckdvPPPMMHBwcKis8IiIiIiIiIiIiIiIiIiIiIiKiEgUHB2PJkiV47733IAiCfH79+vWl5kEREf0XMHuzBiua/O7m5lbqNUlJSfjll19w5swZXLlyBWlpacjJySm2BY7pivAGgwG3b99Go0aNKibwGhJbTk4Ofv/9dxw/fhyxsbFITk5GdnY2tFqt1RXzExMTLT7Ws2dPzJw5E2q1GgCwadMmq0npmzZtko9dXFzQo0cPm2JPT0/H7t27ERUVhcuXLyM1NVWO3ZrExESzpPC7Sa/X4+jRo9i/fz9iYmIQHx+P7OxsaDSaYs+3Xq+Xj5OSkmye47HHHjP7hc+arl274ssvv5TbZ8+eRZcuXWyeq6YYNGiQXP164cIFXLx4Ec2bNy+xr+kvxwqFAgMHDrwrMRIREREREREREREREREREREREZXkhRdeQHJyMlauXCmfW7x4Mfr37w+VSlWFkRERVS4WAdRgWVlZZm1PT0+LfdPT0zF//nxs3LgRBoPB7rkyMzPtvsZW1S22/Px8rF69GkuWLCnTLgMZGRkWHytM5C9M7j9w4ABSU1Ph7e1drG9qaioOHjwot3v27AkXFxerc2s0GixZsgSrVq0qVjxhi8p8na3Zt28fZs6cifj4eLuvtfZ8F9WkSROb+4aEhEAURfk9WRN2SSiLp59+Gp9++qn8Xv/xxx8xc+bMYv1ycnKwfft2uf3oo4+iTp06dy1OIiIiIiIiIiIiIiIiIiIiIiKikowfPx779+9HXFwcACA5ORkbNmzA4MGDbbo+KSkJV65cwa1bt+S8TE9PT9SpUwdt27aFu7t7pcVek2O7G/Lz8xEVFYVbt24hNTUV7u7uaNiwIcLCwspc5JGVlYXY2Fhcv34dGRkZ0Ol0cHd3R+3atdG6dWsEBARU8F1UnNTUVFy+fBk3b95ERkYGDAYDPDw84Ofnh7Zt25aYi1rVtFotrly5gri4OKSkpECtVsPV1RVeXl5o1qwZgoKCbF7c2Rbx8fG4cOECEhMTodfr4e/vj3bt2t3VfEdJkhATE4MrV64gNTUVWq0WtWrVQv369fHAAw9UWIESiwBqsNu3b5u1PTw8Sux3584dDBs2rFxJzHl5eWW+1prqFlt+fj7Gjx+P3bt3l3mM0lbbHzBggFwEoNPpsG3bNowYMaJYv23btpkl8vfv39/quGq1Gi+//DKioqLsD7pAZb3O1qxevRqzZ88u8/X2xOzj42NzXycnJ7i5ucmFEfYUG9Qkbm5uePrpp/HDDz8AAHbu3ImJEycW21lkx44dyM7OltvPP//8XY2TiIiIiIiIiIiIiIiIiIiIiIioJA4ODhg6dCg+/PBD+dy2bdssFgEYDAZERUVh165dOHz4MG7evGlxbFEU0alTJ4waNQrt27e32O/48eMYNmxYsfMJCQkIDQ21eF1sbGylx1YeEydOxObNmwEAgYGB+OOPP2y6btOmTXj//ffl9t69e1GvXr0S+3bp0gUJCQkAgH79+mHu3LnQ6/X46quv8P333yM5ObnYNa6urnjppZfw8ssvQ6lUlhrPtWvXsHPnTuzfvx/R0dFWF6wOCQnBCy+8gKeffhqiKFrs9/bbb2Pnzp0AAKVSiT///BNeXl6lxlIoLS0NjzzyiJwn2rt3byxYsKBYv/Pnz2PXrl04dOgQrly5YnXMtm3b4uWXX0bXrl1timHRokVYvHix3C76fiyrO3fu4Ndff8WePXtw6tQp5ObmWuxbu3ZtDBo0CMOHDy+Wt2iP6OhozJ07F8ePH4ckSWaPCYKA8PBwTJ482ernsbwyMzPx9ddfY/PmzSW+bwHA2dkZffv2xRtvvAF/f/9yzccigBrs/PnzZu3GjRuX2G/SpElmSfY+Pj6IiIhA+/btcd9998HPzw+Ojo5mlSVFfyAV/UBUlOoW27fffmtWAKBSqdC7d288/PDDCAoKgp+fH1xcXODo6GhWeWT6TaG0eMLCwtCgQQPcuHEDALB58+YSiwAKf3ACkCvXrPn444/NCgDc3NzQt29fdOzYEY0aNYKfnx+cnJzg6Ogo94mPjzf7Zl9Zr7MlZ8+exZw5c8zOPfzww+jatStatGiBgIAAuLm5wcnJyeyH6dChQxEZGWn3fKXtpFCUs7OzXASgVqvtnq+mGDRokFwEoFarsXXr1mK/AP/000/ycWBgIB555JG7GiMREREREREREREREREREREREZElTz/9NGbNmoX8/HwAwMWLF6HRaODs7Fysb2xsLIYOHWrTuAaDAX/++ScOHz6MUaNG4a233qrIsGtUbHdLRkYGXn/9dasLIufk5ODzzz/HmTNnsGTJklILAQYOHCjvpFCay5cvY+LEidi9ezc+/fRTi4np/fv3l4sAdDodduzYgSFDhtg0B2BcmNd0oegBAwYU65OZmYlnnnnG5jFPnz6N119/HQMGDMAHH3xQYSvO22vatGnYu3evTX3v3LmDL774Atu2bcPSpUst5kJbs2PHDkyaNMniotKSJOH48eMYMGAAZs6ciYiICLvnKM2hQ4cwfvz4Uhec1mg0+Omnn7Bz50589tln5crFZBFADZWTk2NW0aNUKtGmTZti/c6dO4cDBw7I7fbt2+PLL78stVrmbiQ8V7fYtFotli9fLrd9fX2xevXqUr+h5OTk2D1Xv3798NlnnwEAYmJiEB0djaZNm8qPX7p0CTExMWb9rbl9+zbWr18vt4OCgrBy5cpSq4SqOrF98eLFcuGBKIr4/PPP8cQTT5R6XVmec8D++9VoNPKxvQUENUlwcDDCw8Plwop169aZFQGcO3cOFy9elNvPPfec1QpHIiIiIiIiIiIiIiIiIiIiIiKiu8nV1RUhISG4dOkSAGNS9pkzZ9CxY0er1ymVSoSEhOD++++Hr68vXFxcoNVqcfv2bZw5cwbXr18HYEwiXrZsGby8vEpc9FcQBCgUCgDG5HzTBXkLz9uromKrSfLz8zF27Fi5AKBRo0YIDw+Hj48PcnJyEBUVZZbLduDAAXz55ZcYM2aMzXM0bNgQQUFBCAwMhKurKyRJQmpqKmJiYnDu3Dn5tdu/fz/effddLF26tMRxOnXqhICAACQmJgIw7n5gTxGA6ULRderUKfW9KooigoKC0LhxY/j7+8PV1RV6vR537tzBhQsXzHJON27cCBcXF0yZMsXmeCqLr68vQkND0aBBA7i7u8PBwQGZmZmIi4vDiRMn5MT969ev4+WXX8bmzZvh6elp8/iXLl3CggULoNVqIYoiwsLC0KxZMzg5OeHWrVs4ePCgXACi0+nw/vvvw9XVFd27d6+we9y2bRvef/99uQgJAPz9/REWFoY6depApVIhOTkZkZGR8gLi2dnZePXVV/HNN9+gU6dOZZqXRQA11NatW83eLC1atICTk1OxfqZbrwiCgLlz59q0XcadO3cqJlArqltsJ0+eNKv0mjBhgk0VRSkpKXbPFRERgS+++ELeUmbjxo1m32w3bdokH4uiWGrV0YEDB6DX6+X2Bx98YNM2IWWJvaJoNBocO3ZMbkdERNhUAACUPe7U1FSb++bl5SE7O1tu2/NDpSYaPHiwXARw+fJlnDx5Eu3atQNgLAoopFQq7aosJCIiIiIiIiIiIiIiIiIiIiIiuhtatmwpFwEAQFxcXImJ1QqFAt26dUO/fv3QsWNHuLq6WhwzKioK06ZNw7Vr1wAA8+fPR48ePRAQEGDWLzw8XJ574sSJcoJ3YGCgWa5kaSojtppk9+7d0Ol08Pb2xqxZs9ClS5difXbt2oV3331XXkV/xYoVGDlypNX809atW6NHjx7o3Lkz/Pz8LPa7desWZs2ahX379gEA9u7di127dqFXr17F+hbmdn755ZcAjLtPxMbGIjQ0tNT7jI2NNStmiIiIKHFhXkEQ0LFjRwwYMACPPPIIvLy8rI45ffp0nD59GgCwdu1a9OnTB61bty41nooWGBiIsWPH4oknnkBQUJDFftnZ2Vi+fDm+/vprSJKEhIQELFiwAB9++KHNcxUWAAQHB2P+/Plo0qSJ2eM5OTmYO3euvNC2wWDA1KlT0a5dO3h7e5ftBk3ExMRgypQpck63n58fJk+ejCeeeKLYaypJEnbv3o3p06cjIyMDer0eEyZMwI4dO8oUC5dyroHy8/Px3XffmZ2zlCT+119/ycf3338/6tWrZ9McFy5cKHN8tqpuscXFxZm1bd1ioyzx1KlTx6xyx3RbF61Wi+3bt8uPFVaLWWP6XLq4uCAsLMymOM6fP29P2BUqISEBWq1Wbtv6fCcnJ8uVc/aKjo62uW9sbKxcpAGgTFvM1CTdunUz++WmMPE/KysLu3btks937doVtWvXvuvxERERERERERERERERERERERERWePr62vWzsjIKLFfSEgIlixZgm7dullNsgeAsLAw/PDDD3IOn06nw/fff18xAdew2O4GnU4HV1dXfPfddyUWAABAr169MHr0aLmt0Wjw66+/Wh13xYoVePbZZ60WAABA/fr1sXTpUjz++OPyudWrV1vs379/f7O26er+1pguFF3SOIXc3d2xatUq9OnTx2oBAACEhoZizZo1aN68uXzOWuyVafLkyXj99detFgAAgJubG8aPH48JEybI57Zu3Wrxs1sSrVaLwMBArF69ulgBAGDcJeSjjz7Cs88+K59LS0uzuMODvd5//315N4PAwEBs2LABPXr0sFjU0bNnT3z77bfywu8pKSlYu3ZtmeZmEUANtHDhQrlyCzBWjVj6BmC6sr2Hh4dN4+t0Ouzdu9fmeBwc/t1QwnQ1+tLcjdjsYRoPYHtMv/zyS5nmGzBggHyclpYmV47t27cP6enpJfazxDR2Nzc3CIJgUwy7d++2MdqKl5mZada2daX98sR84MABs22WrClafVkV1XC2MP38ATArXLB3nIEDB8rtX3/9FWlpadi6dSs0Go18/vnnny9boERERERERERERERERERERERERJXI3d3drG1PIrE1Xl5eGDZsmNzev39/hYxbEapzbGU1duzYUhftHTRoEJRKpdw+c+ZMhc0viiLeeOMNs7HT0tJK7NugQQOzRZu3bdsmrwhvSX5+vtlC0e3bt8d9991XzqiNVCoVXnnlFbl98OBBm3Mmq9Lw4cPlnF2NRoNjx47Zdf2kSZPg4+Njtc/EiRPNCoW2bNlilhtZFocOHTLbfWTWrFk27cTRvHlzDBkyRG6vW7euTK8TiwBqmNWrV2PFihVm58aMGQOVSlVif9NKsL///tumOTZs2IDk5GSbYzKdo2hit63XVVZs9ihaNZeQkFDqNbGxsWUuSujWrZtZ4nthZZdpJZinpye6detW6limsaelpdn0jenAgQNm28ncbWV5vtVqNVatWlXmOePj423aWiknJwcbN26U2+7u7iVuC1UdFH0ey/OL63PPPSf/YpSXl4fNmzfjp59+kh9v1KgROnToUObxiYiIiIiIiIiIiIiIiIiIiIiIKkvRXKqcnJwKGzskJEQ+vnr1aoWOXV7VOTZ7qVQqPPPMM6X28/DwMLtv04W1K0JoaKhZ+9y5cxb7mi7inZKSggMHDlgde//+/UhJSSnx+opg+rxkZWVV+HNTGZRKJe6//365ffbsWZuvDQwMRNeuXUvt5+bmZrYod1ZWFo4ePWpfoEVs2bJFPg4KCrIrz7R3797ycWpqKq5cuWL3/A6ld6Hq4Nq1a/j888+LbVkycOBAPPfccxavCwoKwm+//QYASEpKwoEDB9C5c2eL/aOjo/HJJ5/YFVtgYKB8rFarce3atVKrsO5WbPYouu3I+vXr8e6771rsn5GRgQkTJti1+4EplUqF3r1744cffgBgrAiKiYnBoUOH5D5PPfWUxQIPS7HrdDps2rQJgwcPttg/ISEBU6dOLVPcFaVBgwZQqVTQarUAgJ9//hnPPPNMiVugAIAkSfjggw8QHx9frnlnz56Ntm3bwtvb22KfefPm4fbt23I7IiICzs7O5Zq3sgQGBkIQBLkK7Pz58+jRo0eZxvLz80O3bt3k3S2WLl1qtsvE//73v/IHTEREREREREREREREREREREREVAmKJr+7ubmVek1SUhJ++eUXnDlzBleuXEFaWhpycnKg0+nM+pmu0m0wGHD79m00atSoYgKvgbFVlubNmxcr5rCkXr168kLIpnlu1uTk5OD333/H8ePHERsbi+TkZGRnZ0Or1VpdiT0xMdHiYz179sTMmTOhVqsBGBeEtpaUXrhgNAC4uLjYnO+Xnp6O3bt3IyoqCpcvX0ZqaqocuzWJiYnF8mPvFr1ej6NHj2L//v2IiYlBfHw8srOzodFoij3fprm4SUlJNs/x2GOPQRAEm/p27doVX375pdw+e/YsunTpYvNcRZ04cUI+Nt0RwhYNGjQwa0dHR5sVcNiCRQDVjCRJyMnJQWZmJuLj43HmzBkcPXoUR48eLfaG7969O6ZNm2Z1vG7dumHp0qVy+7333sPixYtLfLPt3LkTH374IdRqNVxcXORvSKVp06aNWXvatGmYNGkSmjRpAoVCUaWx2aNdu3bw9vZGamoqAODbb79FYGAgBg0aVOwbxPnz5zFp0iRcvny5XPH0799fLgLIz8/H2LFjzbaCsbXCq3PnzlAqlfIP948//hi+vr544oknivU9fPgwpkyZgqSkpEp7Lm3h5OSEhx9+WF6Z/+zZs5g8eTKmT58OJycns763b9/GRx99JBeNlDVulUqF+Ph4DB8+HPPnzy9WradWqzFv3jyz1e9r1aqF119/3e657hZ3d3c0btwYV69eBQD8+OOPaNasGTp37mzTL7FFDR48WC4CMP3FyNHREf369auYoImIiIiIiIiIiIiIiIiIiIiIiCpY0URwT09Pi33T09Mxf/58bNy4EQaDwe65MjMz7b7GVtU5tsrm5+dnc1/ThX1L2/0gPz8fq1evxpIlS8q0U0JGRobFxwoT+QuT+w8cOIDU1NQSFypOTU3FwYMH5XbPnj3h4uJidW6NRoMlS5Zg1apVxQpAbFFV74d9+/Zh5syZZVr42drzXVSTJk1s7hsSEgJRFOXPVXl2SUhJSTErVvjpp5+wYcOGMo+Xnp5u9zUsAqhCkZGRaNasmdk5g8FgtZoIMH7DePPNNzFixIhSq1eaN2+O7t274/fffwcApKWlYfDgwWjXrh1at24NJycn3LlzB0eOHJE/aM7Ozhg3bhxmzZpl0320atUKTZs2RXR0NAAgKioK/fv3hyiKcHR0NIvx9OnTdzU2e6hUKrz22mvy2AaDATNmzMCaNWvQqVMn1KpVC5mZmThz5gzOnz8vXzdp0iRMmTKlTHO2bNkSISEhuHz5MgDg+vXr8mMhISFo0aKFTeP4+vpi8ODBWLVqFQAgNzcXY8aMQbNmzRAWFgZ3d3ekpaXhxIkT8pYhCoUCEydOLLWQpDK98cYbOHjwoFz4sGnTJuzbtw+dO3dG3bp1kZubi6tXr+Lo0aPyD6++ffsiMTERkZGRds/39ttvY8GCBbh8+TIiIiIQFhaG5s2bw9HREfHx8Th48KDZDzxRFPHRRx9Z3TWgOnjuuecwe/ZsAMZfZt5++20AxsR900KcDz/8EH379rU6Vvv27c3ek4V69uxp9ZdiIiIiIiIiIiIiIiIiIiIiIiKiqnT79m2ztoeHR4n97ty5g2HDhpUrATgvL6/M11pTnWO7G0pLiDdl6+rv+fn5GD9+PHbv3l3WsEpdbX/AgAFyEYBOp8O2bdswYsSIYv22bdtmlshf2kLRarUaL7/8MqKiouwPukBVvB9Wr14t5zSWhT0x+/j42NzXyckJbm5ucp6oPcUGRaWlpZm1JUky283AXrbuZmGKRQBVzJ4XvE6dOoiIiMDQoUPtetPOnj0bN2/eRGxsrHzu5MmTOHnyZLG+rq6uWLRoERwc7HtrfPLJJ3jxxRfNqloMBgM0Gk2Vx2aPYcOG4eLFi9iyZYt87vr162bJ+YUEQcC4cePw7LPPlrkIADB+E587d26x8wMGDLBrnPHjxyM2NhZHjx6Vz126dAmXLl0q1lepVGLmzJl2bz9S0Vq0aIHp06dj+vTpcmVVWlqa2fNvqmvXrpg1axZefPHFMs3XrFkzzJ49G5MmTYJWq0VkZKTFYgIHBwfMmjUL3bt3L9Ncd9OQIUNw8uRJ/Prrr2bni/4gNN1lwppBgwbhgw8+MDv3v//9r1wxEhERERERERERERERERERERERVSbTxX0BoHHjxiX2mzRpklmSvY+PDyIiItC+fXvcd9998PPzg6OjI1Qqldzn+PHjGDZsmNwubbHnsqrOsdVU3377rVkBgEqlQu/evfHwww8jKCgIfn5+cHFxKbbodWhoqHxc2nMaFhaGBg0a4MaNGwCAzZs3l1gEsHnzZvm4YcOGpeZwfvzxx2YFAG5ubujbty86duyIRo0awc/PD05OTnB0dJT7xMfHo2vXrjbHXtHOnj2LOXPmmJ17+OGH0bVrV7Ro0QIBAQFwc3ODk5MTRFGU+wwdOrRMi0PbUzgCGBcjLywCUKvVds9XqKJ3WCjL68QigGrGwcEBKpUKtWrVgq+vLxo2bIgmTZqgffv2aN68uc2VS6Y8PDywbt06LFy4EOvXr0dubm6xPs7OzujatSveeust1K9fH8ePH7drjuDgYOzYsQObN2/GoUOHcPXqVaSnpyM3N9fqG/NuxGavefPmoVWrVli2bBmSk5OLPS6KIsLDw/Haa6+hQ4cO5Z6vb9+++PTTT82qu5RKZakrthelUqnwzTff4Msvv8Tq1atL/AajVCrxyCOP4M0330TTpk3LtM1KRXvuuedQv359zJs3T95Noqjg4GAMHToUzz77rNk3/bLo06cPGjVqhHnz5uHEiRPF3p+CIKB9+/aYPHmyXdvEVCWFQoEvvvgCBw4cwK5du3Dp0iX8888/0Gg0Nif+m+rTpw9mzJghF2aEhoaibdu2FR02ERERERERERERERERERERERFRhcjJycGVK1fktlKpRJs2bYr1O3fuHA4cOCC327dvjy+//BJubm5Wxy9PsrCtqnNsNZVWq8Xy5cvltq+vL1avXm2xQKRQTk6O3XP169cPn332GQAgJiYG0dHRaNq0qfz4pUuXEBMTY9bfmtu3b2P9+vVyOygoCCtXroS/v7/V66r6/bB48WI5L1MURXz++ed44oknSr2uLM85YP/9mi5ubm8BgSlnZ2ez9ssvv4wJEyaUebyyYBHAXVSvXj2zFe/vJhcXF0yePBljx45FVFQUbty4AY1GAx8fH/j5+SEsLAyurq5y/wcffNDuWD08PDB8+HAMHz682sVmr8GDB+O5557D2bNnERsbi6ysLLi7u8PPzw+tWrUq9k20PPH4+PjgwoUL5Q0ZgLGIZPTo0XjppZdw6tQpXLt2DTk5OfDy8oKfnx8eeOABeHl5yf3teU+OGTMGY8aMsanv2rVr7Yq7Y8eO2LJlCy5fvoxz584hNTUVKpUKvr6+CA4ORkhISJnGt/ReadGiBdauXYtbt27h/PnzuH37NvR6Pfz8/NCuXTvUrVvX5tgr63kpy3uqc+fO6Ny5s93XFXXy5Em5AAAAnn/++XKPSUREREREREREREREREREREREVFm2bt1qtmBqixYt4OTkVKzfH3/8IR8LgoC5c+eWmmQPAHfu3KmYQK2ojrGZLlxtz0rlVZ2IXujkyZPIysqS2xMmTCi1AAAAUlJS7J4rIiICX3zxhZx7t3HjRkyZMkV+fNOmTfKxKIqIiIiwOt6BAweg1+vl9gcffFBqAQBQttgrikajwbFjx+R2RESETQUAQNnjTk1NtblvXl4esrOz5banp2eZ5gSAWrVqmbXT0tLKPFZZsQjgHuPm5obHHnusqsMoUXWLTalUIiwsrNTtVqojJycndOrUCZ06darqUOwSEhJSLOG/MtWvXx/169e/a/PVFOvWrZOPXVxc0KdPnyqMhoiIiIiIiIiIiIiIiIiIiIiIyLL8/Hx89913ZucsJVj/9ddf8vH999+PevXq2TRHRS30a011jM10tXPTFdRLc/v27coIx25xcXFm7UceecSm68rynNapUwedOnXCn3/+CQDYsWMH3nvvPSiVSmi1Wmzfvl3u26lTJwQEBFgdz/T94OLiYnMu6/nz5+2OvaIkJCRAq9XKbVuf7+TkZCQmJpZpzujoaJv7xsbGmi2QbEtBiCW+vr7w8PBAZmYmAODixYtlHqusxLs+IxERVVs3btzA/v375Xa/fv1sqiYlIiIiIiIiIiIiIiIiIiIiIiKqCgsXLsS1a9fktp+fH/r3719iX9NV4T08PGwaX6fTYe/evTbH4+Dw7/rcpiu5l+ZuxGYv0zjS09ORk5Nj03UnTpyorJDsYvqcArY/r7/88kuZ5hswYIB8nJaWhn379gEA9u3bh/T09BL7WWIau5ubm9muDNbs3r3bxmgrXmFCfCFbV9ovT8wHDhyweZcK0902AKB169ZlnlehUCA8PFxux8bG4tatW2UeryxYBEBERLKFCxfKlW6iKGLIkCFVHBEREREREREREREREREREREREVHJVq9ejRUrVpidGzNmDFQqVYn9XV1d5eO///7bpjk2bNiA5ORkm2MynaNoUrSt11VWbPYKCgqSjyVJQlRUVKnXxMTE4NSpU5UWkz1Mn1PAuFJ9aWJjY8tcWNGtWzezxPdNmzYBADZv3iyf8/T0RLdu3UodyzT2tLQ0m3ZiOHDgQJWsSF+oLM+3Wq3GqlWryjxnfHx8seT+kuTk5GDjxo1y293dHR07dizzvADw1FNPyccGgwGLFy8u13j2YhEAEREhKSkJs2bNMqtgfOqpp3D//fdXYVRERERERERERERERERERERERETFXbt2DW+++SZmz55ttgr4wIED8dxzz1m8zjSpPSkpCQcOHLA6T3R0ND755BO7YgsMDJSP1Wq12S4F1tyN2OzVpk0bs/aaNWus9tdoNJg0aVIlRmQf0+cUANavX2+1f0ZGBiZMmGDXDg6mVCoVevfuLbcPHTqEmJgYHDp0SD731FNPWSxSMWUau06nkwsKLElISMDUqVPLEHXFadCggdm9/fzzz/KixCWRJAkffPAB4uPjyzXv7NmzkZqaarXPvHnzcPv2bbkdEREBZ2fncs3bo0cPBAcHy+0tW7aU+hkpKi8vDydPnizT/CwCICK6R02bNg1t27ZFy5Yt8eijj5r98PHw8MD48eOrMDoiIiIiIiIiIiIiIiIiIiIiIrrXSZKE7Oxs/P3334iMjMRXX32FkSNHonfv3vj111/N+nbv3h3Tpk2zOl7RFdjfe+89i6vb79y5E8OHD4darYaLi4vNMRdNnJ82bRouXrxYamL53YjNXvXq1UP79u3l9p9//olPPvkE+fn5xfpeuXIFQ4cOxcWLF6FUKistJnu0a9cO3t7ecvvbb7/F999/b1Y4Uuj8+fMYMmQILl++XK7ntH///vJxfn4+xo4da/Z8mT5uTefOnc2ex48//hi//fZbiX0PHz6MIUOGICkpqVLfD6VxcnLCww8/LLfPnj2LyZMnIzc3t1jf27dv480338TWrVsBoMxxq1QqxMfHY/jw4YiNjS32uFqtxvTp0/HTTz/J52rVqoXXX3+9TPOZEgQBs2fPhqOjo3xu1qxZeO+993Dr1i2r18bExGDhwoV4/PHHi+1mYiuHMl1FREQ1nlarhVqtLnZepVJh/vz5CAgIqIKoiIiIiIiIiIiIiIiIiIiIiIjoXhQZGYlmzZqZnTMYDCUmbJtycXHBm2++iREjRkAQBKt9mzdvju7du+P3338HAKSlpWHw4MFo164dWrduDScnJ9y5cwdHjhyRVyd3dnbGuHHjMGvWLJvuo1WrVmjatCmio6MBAFFRUejfvz9EUYSjo6NZjKdPn76rsZXFW2+9hSFDhsivwzfffINffvkFjzzyCHx8fJCVlYULFy7gzJkzMBgM8PX1xeDBg/HZZ59VWky2UqlUeO211+Tnx2AwYMaMGVizZg06deqEWrVqITMzE2fOnMH58+fl6yZNmoQpU6aUac6WLVsiJCQEly9fBgBcv35dfiwkJAQtWrSwaZzC53HVqlUAgNzcXIwZMwbNmjVDWFgY3N3dkZaWhhMnTuDKlSsAAIVCgYkTJ5ZaDFOZ3njjDRw8eFAufNi0aRP27duHzp07o27dusjNzcXVq1dx9OhR6HQ6AEDfvn2RmJiIyMhIu+d7++23sWDBAly+fBkREREICwtD8+bN4ejoiPj4eBw8eBCZmZlyf1EU8dFHH5kVh5RHq1atMHfuXLz77rvy/WzZsgXbtm1Ds2bN0KJFC3h5eQEAsrKycOvWLVy6dAl37twp99wsAiAiIiiVSvj5+eHBBx/ESy+9hMaNG1d1SEREREREREREREREREREREREdI8pbbV8U3Xq1EFERASGDh0KHx8fm6+bPXs2bt68abZq+MmTJ3Hy5MlifV1dXbFo0SI4ONiXbvvJJ5/gxRdfRFJSknzOYDBAo9FUeWz2CgsLw4QJE/DJJ5/I5xISErBu3bpiff39/bFs2bISV2SvKsOGDcPFixexZcsW+dz169fNkvMLCYKAcePG4dlnny1zEQBgXO1/7ty5xc4PGDDArnHGjx+P2NhYHD16VD536dIlXLp0qVhfpVKJmTNnIiwszP6AK1CLFi0wffp0TJ8+HQaDAYCxoMX0+TfVtWtXzJo1Cy+++GKZ5mvWrBlmz56NSZMmQavVIjIy0mIxgYODA2bNmoXu3buXaS5LevXqhYCAAIwbNw6JiYkAjJ/3Cxcu4MKFC6Ver1KpyjSvWKariIioxps7dy5iY2MRGxuLCxcu4I8//sCcOXNYAEBERERERERERERERERERERERNWCg4MDXFxcEBgYiDZt2iAiIgITJ07Exo0bsW/fPrz11lt2FQAAgIeHB9atW4dhw4bBycmpxD7Ozs546qmnsHXrVjz00EN2xx0cHIwdO3Zg0qRJeOSRR1CnTh04OzuXulPB3YitLF566SUsXrwY9913X4mPOzo64umnn8aWLVvQvHnzuxKTPebNm4dp06bB19e3xMdFUUSHDh2watUqvPrqq+Wer2/fvlAqlWbnlEol+vbta9c4KpUK33zzDcaMGQMPD48S+yiVSnTp0gUbNmxAREREWUOuUM899xxWrlyJpk2bWuwTHByMGTNmYPHixWVOgi/Up08f/PjjjwgPDy/xMyYIAsLDw7Fx48ZKe44eeOAB/Pbbb5g6dSqCgoJK7V+7dm089dRTWLp0KebPn1+mOQWptH1SiIiIyCqDOhcpw943Nkr5Rb3KFVRXKsNbQlQpS+lMREREREREREREREREREREVDMYtDroIs8bG2I1Xh+5IGXTZ80ciC4lJ3nT3ZOdnY2oqCjcuHEDGo0GPj4+8PPzQ1hYGFxdXRlbEZIkySvRp6amwtXVFQEBAQgPD7eYpF6d6HQ6nD17FrGxscjKyoK7uzv8/PzQqlUr+Pv7V3V4VuXm5uLUqVO4du0acnJy4OXlBT8/PzzwwAPw8vKq6vAsunz5Ms6dO4fU1FSoVCr4+voiODgYISEhlTLfrVu3cP78edy+fRt6vR5+fn5o164d6tatWynzWZKcnIyzZ8/izp07yMjIgCAIcHV1Rd26ddG4cWOLBTX2YBEAERFRObEIgIiIiIiIiIiIiIiIiIiIiKhqsQiAiIjuJdX4Jx0REREREREREREREREREREREREREREREZliEQARERERERERERERERERERERERERERERUQ3BIgAiIiIiIiIiIiIiIiIiIiIiIiIiIiIiohqCRQBEREV06dIFoaGhCA0NxcSJE6s6HCIiIiIiIiIiIiIiIiIiIiIiIiIiIhmLAIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIagiHqg4AADIzM7F69Wq53a1bNzRt2rQKI/pXdY6NKs+mTZuQkJAAAAgMDET//v2rOKLqbdGiRfJxeHg4HnzwwSqMhoiIiIiIiIiIiIiIiIiIiIiIiIiI6L+r2hQBLF68WG4HBgZWm0T76hwbVZ7NmzcjMjISgDGpnUUA1pl+RkaPHs0iACIiIiIiIiIiIiIiIiIiIiIiIiIiokpSLYoAiIiqkz/++KOqQyAiIiIiIiIiIiIiIiIiIiIiIiIiIiqRWNUBEBERERERERERERERERERERERERERERGRbVgEQERERERERERERERERERERERERERERERUQ7AIgIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiohnAo7wD5+fmIjY3FlStXkJaWBrVaDUdHR3h4eKBu3boIDg6Gv79/RcRqE61WiytXriAuLg4pKSlQq9VwdXWFl5cXmjVrhqCgIAiCcNfisRTjmTNnEB8fj9TUVAiCAB8fH4SGhqJJkyYVFl9qaipOnTqFxMRE5OTkwNfXF+3bt0f9+vWtXhcXF4ezZ88iOTkZSqUSderUQadOneDh4VGueDIzM3Hq1Cncvn0baWlpcHZ2ho+PD9q0aYPAwMByjW0qISEB586dQ2JiIgwGA3x8fNCuXbtS77u6qqz7SU1NxcmTJ3H79m1kZWXB3d0d/v7+eOCBB+Dt7V1B0d8dFy5cQGxsLFJSUuDm5oaAgACEhYWV+z1bHWk0Gpw8eRL//PMP0tLSoFKpUKtWLbRo0QKNGzeusHlSUlJw6tQp/PPPP8jLy4O3tzdat26NoKCgCpuDiIiIiIiIiIiIiIiIiIiIiIiIiIjsJ0iSJJXlwuzsbCxbtgybNm1Camqq1b4BAQHo0qULxowZY5ZcPHToUERGRto17+jRozFmzBizc3fu3MGvv/6KPXv24NSpU8jNzbV4fe3atTFo0CAMHz4cbm5uFvtVVGymrl+/jkWLFuGPP/6AWq22GN/w4cMxdOhQODs7W50vPj4eXbt2ldtz5sxB//79cePGDXz66af4448/oNPpzK4RBAFdunTB9OnTixVnnDhxAp988gnOnj1bbC6lUokhQ4bgrbfegpOTk9W4ijp69CiWLl2KkydPQq/Xl9gnJCQEr732Gnr27FlqEcSmTZvw/vvvy+29e/eiXr16iI2Nxbx583DkyBGU9LYOCwvD5MmT0axZM5vGtUVgYCD++OMPu64pbd6Kuh9Ljhw5gkWLFuH06dMljiuKIh544AGMHTsW4eHhFseZOHEiNm/ebNfc/fr1w9y5c+26xpodO3Zg4cKFiI+PL/aYo6MjnnzySbz//vvw9vbGokWLsHjxYvnx2NhYi+N26dIFCQkJFmNet24dpk+fLre//PJLPP7443bF3r9/f1y8eBEA4O/vj/3790MULW/OcuHCBSxevBiHDx+GVqstsU+9evXw8ssv45lnnoGDg/Uar+PHj2PYsGFye82aNXjwwQfx999/Y86cOdi3b1+x7x8AEBoaivfffx8dO3a05TbvGQZ1LlKGFXyOq7jQrFQGAwBAGd4SokpZxcEQERERERERERERERERERERVQyDVgdd5Hljw0oeTpUryNnyWTMHoot9uXj3iiNHjmDkyJEAgFatWmHDhg2lXpOdnS0vIJ2eng6dTgcPDw8EBASgTZs2FbowblJSEs6dO4ekpCRoNBr4+/ujUaNGaNmyZYXNUV3ExcUhKioKaWlp8PT0RJs2bdCkSZMyjXX69Gn8+eefAICWLVviscces+m6/Px89O7dG9evX4cgCNiwYcN/8rkmsleZdgL466+/8MILL+Dvv/+2qX9iYiJ++OEHPP3005Wywvi0adOwd+9em/reuXMHX3zxBbZt24alS5dW6MrZ1ixfvhxffPEF8vPzS43v008/xebNm/HVV1/Zvdr78ePH8frrryM7O7vExyVJwt69e3Hx4kX89NNPCAgIAACsWrUK8+bNg6EgObQonU6Hb7/9FrGxsVi+fDlUKlWpsWg0Grz33nv49ddfS+17+fJljBs3Djt37sT8+fNLLYAoasuWLZg2bRry8vIs9omKisKgQYOwZMkSPPTQQ3aNf7dVxv3k5+dj6tSp2LRpk9V+BoMBUVFRGDp0KP73v/9h2rRpUCgUdt9DZdLr9Xj//fexdetWi33y8vKwbds2REZGYvny5RU6f+/evTFnzhy54Gjz5s12FQHExsbKBQCAsdDAUgGAXq/H7Nmz8f3335dYtGEqPj4e06dPx9atW7FkyRK7v98ePnwY48aNQ0ZGhtXYX3zxRcyePRsRERF2jU9ERERERERERERERERERERERGSNTqfDRx99JLcnTJhgse/58+fx22+/4ciRI7h06ZLF/EfAWEwwfPhw9O7du9SFii05d+4cPv/8cxw5cqTEuRo0aIChQ4diyJAhZZ7DHjk5Obh06RLOnTuHc+fO4fz58/Lit0D5Fjn++++/MW3aNBw6dKjYY23btsWsWbPsyr/VarV49913cfPmTSiVSqu5f0U5ODhg7NixGDduHCRJwsyZM7Fu3bq78hwTVWd2FwFotVqMGjXKrADAxcUF7du3R8OGDeHu7g6dToeMjAzExcXh0qVLFhPSRVGUk4tNV4gXRdHih7O0D62vry9CQ0PRoEEDuLu7w8HBAZmZmYiLi8OJEyfkpOrr16/j5ZdfxubNm+Hp6VmpsU2fPh3r1q0zOxcSEoJWrVqhdu3akCQJ8fHxOHLkCNLS0gAYq6eef/55bNq0CX5+flbvudCNGzcwc+ZM5OTkQKlUIjw8HKGhoVCpVLh58yb2798v70CQmJiId955B2vXrsWWLVswZ84cAIC7uzs6deqEBg0aQJIkREdHm/3AOnLkCJYvX251xwPA+MNl+PDhOH/+vHzOwcEBrVu3RtOmTeHp6QmNRoMrV64gMjJSfl327NmDUaNGYeXKlTYnnh89ehQffPAB8vPz4ezsjA4dOqBRo0ZwdHTErVu3cODAAWRlZQEwFiaMHz8eu3btKpYgbek1B2AxlspIjq+o+zElSRLGjh2LPXv2mJ1v2LAhOnToAG9vb6SmpuLYsWO4fv26/Pi6deuQmZmJhQsXFhvT0vMlCILFhHZrK93bozDR3VT9+vXRqVMn+Pj4IDMzEydPnkR0dDQSExMxZswYdOnSpULmBoyfk27dumHHjh0AgD/++ANpaWmoVauWTdcX3UGhX79+JfbT6/V44403sG/fPvmcIAho3rw5mjdvDh8fH2i1Wly/fh1Hjx5FTk4OAODUqVMYOnQoNmzYABcXF5tiunz5MhYsWAC1Wg2lUomwsDCEhobCxcUFiYmJOHDgAFJSUuS4pk2bhpYtW961QioiIiIiIiIiIiIiIiIiIiIiIvrv++GHHxAXFwcA6NixIx588MES+40cORJHjhyxedxz585h/Pjx+Pnnn/Hxxx/bnJdZaMWKFViwYIHVhaALczj37duHhQsXlpibWhG+/fZbbNq0CVevXrVa+FBWiYmJGDJkiFlBganTp09j0KBB+O677xAcHGzTmCtWrMDNmzcBAEOGDLE776xnz55Yvnw5YmJicObMGWzfvh19+/a1awyi/xq7iwB27dplliQ8YMAATJo0CW5ubiX21+l0iIqKwvr16+HgYD7d6tWrARhXr+7atat8ftasWejfv7/NMQUGBmLs2LF44oknEBQUZLFfdnY2li9fjq+//hqSJCEhIQELFizAhx9+WKxvRcW2bt06swKA1q1bY+rUqSVuRZKbm4tvvvkGS5YsgcFgQHJyMiZMmIA1a9bYNNeKFSug0+nQvn17zJkzp9guAklJSXj55ZcRGxsLAIiMjMTPP/+MWbNmAQCef/55TJgwodhrefLkSbz66qty4vmKFSswcuRIi685AEydOtWsAKBfv34YN24c/P39i/VNTk7GRx99JO8YcOzYMSxbtgyjR4+26b4//PBD5Ofno2/fvnj//feLJcOnpaVh3LhxOHr0qNxevXo1xo0bZ9YvIiJCXtl86NChiIyMBACEh4dj7dq1NsVSESrqfkytWbPGrADA3d0dM2bMQK9evYr13b59O6ZNmyYXjOzatQvh4eF4/vnnzfrNnj0bs2fPBgCEhobK5994441Si0TK448//jDb3snR0RHTp0/HgAEDivU9fPgw3nvvPdy8eRM//PBDhcYxYMAAuQhAp9Nhx44dGDp0aKnX5efnY9u2bXK7Xbt2aNiwYYl9P/vsM7MCgEcffRSTJk1Co0aNivUtLNYovM+rV6/io48+kgt8SjNv3jzodDo8/PDDmDFjBgIDA80eV6vVmDZtGrZv3w7AuNPCsmXLMH/+fJvGJyIiIiIiIiIiIiIiIiIiIiIisiYnJwfLly+X26+88orFvqmpqcXO1a1bF23atIGfnx9cXFxw584dREZGmuW8Hj16FCNGjMD3339v86Kv69evx8cff2x2rmnTpmjXrh2cnZ1x7do1HDx4UC4QOHz4MMaOHYtvvvmmWN5sRThx4gQuX75c4eMWmjx5slwAIIoiunTpguDgYNy8eRO//fYbdDod0tPTMWHCBGzcuLHUe/znn3/k17V27do254aaEgQBL730krwzxOLFi9GrV69KeX6Jagq7l+U2rZxq2LAhZs6caTUZXKlUomPHjli4cCFatGhRtihLMXnyZLz++utWCwAAwM3NDePHjzfbHmbr1q3IyMiolLiSk5PNEnAfffRRfPfddyUWAACAk5MTRo8ejalTp8rnjh8/Lid7l0an06F169ZYuXJlsQIAAPD398fChQvNVmOfOnUq1Go1hg8fjg8++KDE17Jdu3Z455135LZGo8Fvv/1mMY49e/Zg586dcnvs2LGYO3duiQUAgHH3hi+++MIsIX3lypXIzMy0fsMFdDodnnvuOXzyySclroZfq1YtfPHFF2aPmSZhVzcVfT/Z2dn47LPP5LZSqcRXX31VYgEAAPTp0wfLli0z2+Xg008/lVeZr2pFk84XLlxYYgEAADz00ENYuXIlnJ2dodVqKzSODh06oG7dunK76Or+lpiuqA/AYlFRdHQ0vv76a7n97LPP4quvviqxAAAAPDw8MH36dLz00kvyuS1btpj9AmuNTqfDo48+iuXLlxcrAACMO77MmTPH7Pvsb7/9Bo1GY9P4RERERERERERERERERERERERE1nz//fdyblWTJk3QqVOnUq+pXbs2XnnlFezevVtegf/999/H2LFj8dFHH2H37t34/PPPzVblv3btGqZPn25TTDExMZgxY4bcVqlUmD9/PrZs2YKpU6diwoQJWLZsGXbv3o2QkBC539GjR7Fo0SJbb73cXFxc0L59e7i4uJRrnFOnTuHPP/8EYCwAWLRoEZYsWYK33noLCxYswKpVq+Do6AjA+NxYyyUtNHfuXDnPrKSFqm3Vq1cvOWfvxo0b2Lp1a5nGIfqvsLsIwDR5tVmzZmYJ5TXF8OHD4eHhAcCY0H7s2LFKmWfNmjXIzc0FYEzQnTdvHlQqVanXDRo0CK1atZLbP/74o03zCYKAWbNmWZ2jcePGCAsLk9sGgwF16tQxK4woSd++feHs7Cy3z5w5Y7HvN998Ix+3a9cOr732mg3RGwsSCn8A5eTkyCuOl8bX1xdTpkyx2sfDwwP9+vWT23///TeSkpJsGv9uq+j72bhxo7yqPwCMGDECDzzwgNXxO3TogEGDBsntrKwsm5PcK1NkZCSuXbsmt3v27Gm2U0dJQkJCrFaElpUoivLOEQBw8eJFeZcNazZt2iQfu7i4oGfPniX2K9yxBADuu+8+TJs2DYIglDr+2LFjUadOHQDGz/dPP/1U6jWAcUeF2bNnW62MVCqVZu+LvLw8REdH2zQ+ERERERERERERERERERERERGRJTqdDmvXrpXbAwcOtNrf29sbEydOxL59+zB+/HiLi6sKgoAePXrg22+/NcuB/PXXX3Hu3LlS4/rss8+g0+nk9kcffYQ+ffoU61e/fn2sXr0aPj4+8rnVq1cjOTm51Dns5ejoiFatWmHw4MGYO3cuduzYgZMnT+K7776zeXcDS3bs2CEf9+nTB926dTN7PCwsDMOHD5fbpS3IfPToUezevRsA0KZNG7OcO3spFAqzBYNXrVpV5rGI/gvszuA3rRKKiYmBwWCo0IDuBqVSifvvv19unz17tlLm2bJli3z81FNPlbiquyW9e/eWj0+cOGHTNe3atUNwcHCp/Vq3bm3W7t+/f6nFCc7OzmZValevXi2x3/Xr13H69Gm5PXToUJsSlwHjD+UOHTrI7ePHj9t03YABA+TKMmvat29v1o6Li7Np/Lutou9n79698rFCocCwYcNsiuOFF14wK/IxHaeq7N+/36xtmpBuzcCBAytl25/+/fubvb9NE/xLkpqaigMHDsjtJ598Eq6ursX6Fd1tY+DAgTYVEAHGSlPTX7xs/Rw98cQT8PX1LbVf0fedaVEGERERERERERERERERERERERFRWfzyyy+4ffs2AGOSe0mJ9qa+/vprjBw50ua8qubNm5slrwPGQgBroqOjsW/fPrndvn17q0ns3t7eePvtt+W2RqPBypUrbYrPHgsXLsSGDRswbdo09OvXD8HBwRW2oLfpAtFPP/10iX1MnwNr+bf5+fmYNWsWAOOiu1OnTrU5n9SSAQMGyGNcvnwZR48eLdd4RDWZ3Z/6li1bysdxcXGYPHkyMjIyKjSostLr9fjzzz8xc+ZMDBkyBI899hjCwsLQvHlzNGvWzOzL9BtVZawIf+PGDfkHElA8cbY0DRo0kI9TU1ORmJhY6jWmK/xbExAQYNZu166d3ddlZWWV2KdowYK9992wYUP52NYVxm2dIzAw0KydmZlpc1x3U0Xej16vx/nz5+V2y5Yt4efnZ9P4devWRfPmzeX22bNnq7zox7Ty0snJyeb3ro+PD5o0aVLh8dSvX9/s9dq+fTvy8/Mt9t+2bZtZVWj//v1L7Hf69GmzfuX5HMXGxso7ClhT1vedpe8FREREREREREREREREREREREREttq4caN8/NBDD8Hd3d1q/7IsCmu6ODOAUncCKFzBvpAti9b26dMHHh4ecru0QoPqJiEhQT5u2rRpiX3uv/9+eVeF1NRUqNXqEvt99913uHLlCgDgmWeeQYsWLcodX506ddCmTRu5bfq+IbrX2P1d8JlnnsFXX30lJ35u2rQJO3fuxMMPP4yHHnoIbdu2RWhoKBQKRYUHa82+ffswc+ZMxMfH231tZRQxXLx40aw9fvx4TJgwwebriybtpqenF0veL8rW5G7TLW0AwN/f3+7rcnJySuxT9L4fffRRm8YuZJpknp6ebtM1ZYkfgMUfPFWtIu8nKSnJ7LylH8qWNGvWTC4iyMnJQVJSEurUqWPXGBXp1q1b8nHjxo3t+j4TEhKCCxcuVHhMAwYMQGRkJAAgJSUF+/fvL7YFUqHNmzfLx/fdd5/FxPuin6Pnn3/erphMv3/k5+cjOzu71F+KbX3fme4GA1TfzxEREREREREREREREREREREREdUMiYmJcg4WADzxxBOVMo/p4syAMd/Lmj/++EM+ViqV6Nq1a6lzODo64rHHHsO2bdsAGJPqo6Oj7c7dqyrZ2dnysaenZ4l9BEGAh4cHNBoNAONCskXzyu7cuYPFixfL44wbN67CYuzevTtOnz4NANizZw9ycnLg6upaYeMT1RR27wTg7e2NRYsWmSWU5uXlYe/evZgxYwb69euHsLAwvPTSS1i9erXZaviVZfXq1Rg1alSZCgAAY/wVLS0tzaxtMBig1+tt/iq64rotq20XTQq3pOh2Kk5OTmW6riRF79uee9br9WbJy7auMF7W+7ZldfSqUJH3U3R3gNq1a9sVi6+vr1m7qnf9ML0fLy8vu661t7+tnnzySbNfIDZt2lRiv0uXLiEmJkZu9+vXz+Jnqryfo7v5/aO6fo6IiIiIiIiIiIiIiIiIiIiIiKhm2Lt3r1nOU6dOnSplnqKLH1vbTSAzMxOXL1+W282aNYOjo6NN85iuVA8AJ06csD3IKqZUKuVjnU5nsZ/pY6bXFPr000/lvLWxY8fC29u7wmJ86KGH5GONRoPDhw9X2NhENYndRQAA0LFjR+zYsQMDBw4ssXpGrVbj0KFDmD17Nh5//HFMnjy5WFJrRTl79izmzJljdu7hhx/G9OnTsWHDBhw6dAinT59GdHQ0YmNj5a/w8PBKiaeQrQnstiqa1FtdVeR9M7m4/Eyr8oDiq7iXpmh/SztA3C1arVY+LukXB2tUKlVFhwPAmDzfs2dPuX3w4EGkpqYW62daHCCKIvr162dxzHv1+wcREREREREREREREREREREREd17Dh06JB83bNgQ/v7+lTJPbGysWTsgIMBi32vXrpm17VnJv3nz5lbHqs5q1aolHyckJJTYR61WyznBDg4O8PDwMHv8zJkz2Lx5MwAgNDQU//vf/yo0xtDQULNFgQ8ePFih4xPVFJbLmEoREBCAGTNmYNKkSYiMjERUVBROnz6Nc+fOITc3V+6Xn5+Pn3/+GYcPH8aPP/6IOnXqVEjghRYvXiwni4uiiM8//9ymrWAqO5m56Or6X3/9NR599NFKnbM6KHrf586ds7n6jSqem5ubWVutVtt1fdH+Vb1ljpubG9LT0wHY/xmuzM98//798fPPPwMwVjhu27YNI0aMkB/X6XTYsWOH3O7YsaPV74VFP0e7du1C48aNKzZoIiIiIiIiIiIiIiIiIiIiIiKiauDkyZPycYsWLSptnm3btpm1O3ToYLFvXFycWbtu3bo2z1M0N6zoWNVZaGgo/v77bwDAsWPHSsxbO3bsmJy327hxY7MdFQwGA2bMmCE/PnXqVCgUigqNURAEtGjRAn/++ScAIDIyskLHJ6opyrQTgCknJyc8+uijePvtt7F27VpERUVhzZo1GDx4sFnC8D///IP333+/vNOZ0Wg0OHbsmNyOiIiwqQAAAFJSUio0lqJMq6EAVNpOCNXNvXrf1VXRCrs7d+7YdX1ycrJZ29PTs9wxlYfp+6vwFw1bWapKrAjt2rVDw4YN5XZhFWOhffv2mX0W+vfvb3U8fo6IiIiIiIiIiIiIiIiIiIiIiOhecOPGDWRmZsrt0NDQSpnn+vXr2L59u9xWKBRW802TkpLM2vbsTuDr62uW+F50rOrsoYceko9XrVoFjUZj9rher8fy5ctL7A8AGzZswMWLFwEATz31FNq3b18pcYaEhMjHN27cQEZGRqXMQ1SdlbsIoCilUokHH3wQ06ZNw65du8y2Szl69Cji4+MrbK6EhARotVq5/cgjj9h0XXJyMhITEyssjpIEBQWZtQu/qf3XFb3vCxcuVFEkBBh/8XBxcZHb0dHRdl1/6dIl+djV1bXStlmyVbNmzeTjhIQEm4t5JEnC+fPnKyssAOaJ/TExMWbP3aZNm+RjDw8PdO/e3epY9+r3DyIiIiIiIiIiIiIiIiIiIiIiurdcuXLFrN2gQYMKn8NgMGDKlCnQ6XTyuYiICNSrV8/iNTk5OWZtNzc3m+cTRRHOzs5yW61W2xFt1erbt6+8APjNmzfxyiuvIDo6GlqtFteuXcOYMWNw5swZAICDgwMGDhwoX5uRkYGFCxcCAFxcXPDuu+9WWpz333+/Wfvy5cuVNhdRdVXhRQCmAgIC8Morr5idi4mJKdZPqVSatfV6vU3jm1Z/AbavUr57926b+gFlj61p06bw8vKS2/v27YPBYLB53pqqY8eOZu29e/dWUSTlY7o9ja2veXWkUCjQqlUruX3+/Hncvn3bpmv/+ecfs+Tz1q1bQxRL/pZxt56vBx54wKy9a9cum66LjIwstqtBRYuIiDCr3ixM/L9z5w4OHTokn+/VqxccHR2tjvXggw+aPdd79uyp4GiJiIiIiIiIiIiIiIiIiIiIiIiqXtGFpU0Xnq4oixYtwokTJ+S2t7c3JkyYYPWaoivgq1Qqu+Y0zRErWlBQnXl6euKdd96R25GRkYiIiEDLli3Rq1cvs5zQl156CQ0bNpTbn332GdLS0gAAr732WqUuOuzn52fWTkhIqLS5iKqrSi0CAFCsUsp05f5ChVVDhYom91tS9DpbPsRqtRqrVq2yafzyxCaKInr16iW3b968ic2bN9s8b00VGhqK4OBgub19+3bExcVVYURlY/q62/qaV1ddu3aVj/V6PdasWWPTdatWrTIrXOnWrZvFvnfr+erVq5dZYc7XX39d6i9IkiTh888/r7SYCvn7+5ttbbR9+3ZotVps27YN+fn58vkBAwaUOpanp6fZziaRkZE4duxYxQZMRERERERERERERERERERERERUxYouauvt7V2h4//6669YtmyZ3BYEAbNmzSp1ntzcXLO2vUUApv3z8vLsuraqPf/883j99dchCILFPgMHDsRbb70lt2NiYvDTTz8BABo2bIgRI0aY9c/NzcXXX3+NZ555BmFhYWjdujWefPJJzJw5E//884/dMdauXdusnZSUZPcYRDWd3UUA586ds6t/VFSUWbuk7VPc3NzMVvE/f/68TWM3aNDA7Bvlzz//bHW1fUmS8MEHHxSrHLOmrLEBwKuvvmqWsPzRRx/h9OnTNl8PGFcRr0nblAiCgNdff11u63Q6jB49Gnfu3LFrnJiYGKSmplZ0eDYLDAyUj69fv46srKwqi6W8+vfvb5akv2rVKpw6dcrqNZGRkfjuu+/ktru7O/r162exv+nzZc9nxF7e3t54+umn5XZSUhLefvvtEouLAONnfu7cuTh58mSlxWSqf//+8nF6ejr27dtnVvwTHBxstjODNaNHjzZrjx8/HtevX7crnlu3buHWrVt2XUNERERERERERERERERERERERHS3qNVqs7aTk1OFjR0VFYV33nkHkiTJ50aPHo0uXbqUeq3pSv6AMRfSHqY5bUXHqgnGjh2LH374AU899RTq1KkDZ2dn+Pv7o3v37lixYgVmzJhhViTw0UcfQa/XAwAmTZpkltt769Yt9O3bF/Pnz8f58+eRlZWF3NxcXL9+HWvXrkWvXr1w4MABu+Ir+j4p+j4iuhfYXQTw1ltvoW/fvli7di0SExMt9jMYDPjpp5+wcuVK+VydOnXQokWLEvu3adNGPv7tt9/www8/yNuCWOLk5ISHH35Ybp89exaTJ08uVoEFGKvF3nzzTWzduhUA4OLiYnXs8sYGGLelee+99+S2RqPBsGHD8OWXXyI7O9vidfn5+Th8+DAmTZqExx9/HEeOHLE51uqgZ8+eePLJJ+X2tWvX0K9fP+zYsUP+Jl+S7OxsbN++HS+++CKefvrpKq3MMn3NdTodJk2ahGvXrlktMqmu3NzcMHbsWLmt0+nw6quv4pdffimx/86dOzFq1Ciz1evHjx9v9TNj+nydO3cOixcvLlahWVEmTJhgVsW3f/9+PP3009i6dSuSk5NhMBiQnp6OP/74A0OHDsWqVasgCIJZjJWla9eu8PLyktsLFiwwK+IxLRIoTatWrTBy5Ei5fefOHTzzzDP44YcfrFaG5uXlYc+ePRg7diyefPJJxMbG2ncTREREREREREREREREREREREREd0nRBWBNF14uj5iYGLz22mtmuVbPP/98scVZLSmaL2fvav6m/e3JV61OHnjgAXz66afYv38/zpw5g4MHD2Lx4sVmebsAsG3bNnnB8McffxydO3eWH9NoNHjppZdw48YNAMaFxF999VW89dZbaNu2LQBjAv/o0aPtWjC76M4MNW23BaKK4FCWi2JjYzFz5kzMmjUL9913H5o1awZ/f3+4u7tDq9Xi77//RmRkZLEk7smTJ0MUS647eO655+RKHr1ejw8//BAffvghVCoVHBz+DfPVV1/FqFGj5PYbb7yBgwcPygnLmzZtwr59+9C5c2fUrVsXubm5uHr1Ko4ePSpXYvXt2xeJiYmIjIy06X7LGhsADB06FDdu3MDatWsBGH9gLVy4EF9++SXatm2Lxo0bw9PTE3l5ecjMzMTVq1cRHR1do6uSBEHA3LlzkZSUhDNnzgAwFmGMHz8eM2fORPv27REYGAhXV1eo1WqkpqYiNjYWV69etbtarrJ06dIFvr6+SE5OBmAs/vjtt9+gUCjMqvLq1q2LnTt3VlWYNhs2bBiOHz+OvXv3AgAyMzPx1ltv4fPPP0eHDh1Qq1YtpKWl4dixY/jrr7/Mru3Vqxeef/55q+M/88wzWLdunVwksWjRIixatAhKpdLsl7I+ffpgxowZ5bqXWrVqYfHixXjxxReRk5MDAIiLi8O7775r8ZqRI0fCxcVFfj8qFIpyxWCJSqXCU089Je+iYLpyv4ODA/r27WvXeO+88w5u3bqFPXv2AACysrLw4Ycf4tNPP0VYWBgaNGgAd3d3aDQapKen48qVK4iNjeXQzuP6AAA2zUlEQVQvNEREREREREREREREREREREREVCMUTeauiBzCmzdv4qWXXkJmZqZ8rlevXpg2bZrNYxRN3C/MVbOFwWAwW8y6phYB2CI7OxuffPIJAONrOWnSJLPHV6xYIefRtW3bFt9++y2cnZ0BAK+99hpmzpyJtWvXQqvVYubMmVizZo1N8xYtHqmJuy0QlVeZigAKSZKEGzduyBU6liiVSkyfPh3du3e32Kdbt24YNmxYsQ+wVqs1+7AW/QbfokULTJ8+HdOnT5cTkNPS0rBly5YS5+natStmzZqFF1980WrMFRFboSlTpqBx48aYM2eOnJyr0Whw5MgRm1b5L/pDriZwcXHB2rVrMXPmTPz000/y+bS0NPz222+lXi8IQoVV9JWFo6MjPv30U4wePdrsFwG9Xm9WoKHRaKoiPLsJgoAvvvgCU6ZMwebNm+Xzf/31V7Gkf1MDBw7E9OnTSx2/efPmePfdd/Hxxx+b7Zag0+nMPhdFf/CWVdu2bbF69WpMnDgRV69etdhPoVBg7NixeOWVV+RfNADj7giVpX///nIRgKlHH33UbAcDWygUCixatAhLlizBsmXL5J00srOzsX//fpvGqMrPERERERERERERERERERERERERkTVFE+RNk+fLIikpCSNGjJAXAAaARx55BB9//LHFRaxL4u/vb9ZOTEy0+drk5GR5YWsACAgIsPnammbp0qW4ffs2AOCFF17AfffdZ/a4af7ohx9+KBcAFHrnnXewe/duJCcn4/jx47h+/ToaNmxY6rxFczddXV3LeAdENZft39EKfPzxxxgxYgSCg4MhCILVvi4uLoiIiMDOnTvx7LPPljr25MmT8cMPP2DgwIFo2rQpPD09bUpgfe6557By5Uo0bdrUYp/g4GDMmDEDixcvLlNSfVljK/T8889j7969ePHFF+Hr61tq/4YNG2Lo0KHYsGEDBg0aZHe81YFKpcKMGTOwZcsW9O7du9RqNoVCgdatW2Ps2LHYu3cvgoKC7lKkJXvwwQexa9cujBs3Dh06dICvry+cnJyqNKbycHBwwNy5c7Fy5Uq0bdvW4udXFEW0a9cOa9aswYwZM2xeNX/kyJHYvHkzhg8fjlatWsHLy6tSE9BbtmyJLVu2YO7cuXjsscdQp04dqFQqeHt7o1mzZnj55Zexc+dOvPrqqxAEAWlpafK1lVkE0Lx5c4SGhhY7P2DAgDKNJ4oixowZg927d2PgwIHw9PS02l8QBDRp0gSvvPIKdu7caba1EhERERERERERERERERERERERUXVSNNk+JSWlzGOlpqZi5MiRSEhIkM+FhYVh8eLFduey3X///Wbtv//+2+Zr//nnH7N2o0aN7Jq7poiLi5MX1w4ICMCrr75q9vhff/0lFwg0atSoxLw6R0dHdOnSRW6fOHHCprmLvk/8/Pzsip3ov0CQJEkq68WZmZm4fPkybt26hbS0NGg0Gjg5OcHLywtBQUFo0qTJXd9i4/Llyzh37hxSU1OhUqng6+uL4OBghISE3NU4SnPt2jXExMQgLS0NmZmZcHR0hIeHB+rVq4egoCCbCgVqmvz8fFy4cAHXr19Heno61Go1XFxc4OXlhYYNGyIoKKhSk7PJXEpKCqKionDnzh1kZmbCw8MDvr6+CAsLg7e3d1WHV+EiIiIQHR0NAHj44YexYsWKKo6obAwGA2JiYnD16lWkp6cjOzsbzs7O8PDwQIMGDRAUFAQvL6+qDvOeY1DnImXY+8ZGKQVyVa5gtw5leEuIKu4UQURERERERERERERERERERP8NBq0OusjzxoYdK77fdQUpmz5r5kB0qbmL0laUPXv24I033pDbn3/+OXr06GH3ONnZ2Rg2bBguXrwon2vevDnWrFlTprzEjIwMhIeHy+3WrVtj/fr1Nl37/fffY8aMGXJ70qRJGD58uN0xlEWXLl3kIojAwED88ccflTbXiy++iD///BMAsHDhQvTq1cvs8f3798uFAd27d8fixYtLHGf16tWYPXu2POa7775b6tzr16/H1KlT5fb333+PsLCwMt0HUU3lUJ6LPTw8EBYWVq0+OCEhIdUu4b8kjRs3RuPGjas6jLvKwcEBbdq0QZs2bao6FALg4+ODJ598sqrDuCtu3ryJ2NhYud2iRYsqjKZ8RFFEs2bN0KxZs6oOhYiIiIiIiIiIiIiIiIiIiIiIqFyKrg5/48YNu8fIzc3Fq6++alYAEBwcjBUrVpR5YWJPT08EBwfjypUrAIBLly4hLy/PpoWxT58+bdZu3759mWKozvbs2SMXAISHhxcrAACMC40Xsva8OTs7y8dZWVk2zf/XX3/Jx4Ig1Ii8YaKKVo3L3YiIKsZnn30GQ8Hq5wDw+OOPV2E0REREREREREREREREREREREREBAD169eHl5eX3L58+bJd1+t0Orz55puIioqSzzVo0AArV65ErVq1yhVbly5dzObZu3dvqdfk5eXhwIEDcrtu3br/uQVf8/LyMGfOHACAQqHAlClTSuynUqnk45SUFIvjJScny8e2FFkAMFsU+L777oOHh4dN1xH9l7AIgIhqnJiYGMTFxZXaT5IkLFy4EDt37pTPNW3alLtREBERERERERERERERERERERERVRNhYWHy8fn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            "text/plain": [
              "<Figure size 3125x3125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(31.25, 31.25))\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "category_labels = [\n",
        "    'Data availability\\nstatement not given\\n(' + str(df_data_avail_statement['Percentage'][1]) +'%)',\n",
        "    'Data availability\\nstatement given\\n(' + str(df_data_avail_statement['Percentage'][0]) + '%)',\n",
        "    'Data unavailable\\n(' + str(df_data_avail['Percentage'][1]) + '%)',\n",
        "    'Data available\\n(' + str(df_data_avail['Percentage'][0]) + '%)',\n",
        "    'Upon request\\n(' + str(df_data_avail['Percentage'][2]) + '%)'\n",
        "]\n",
        "colors = [\"#EA526F\",\"#009E73\",\"#EA526F\",\"#009E73\",\"#CA78A6\"]\n",
        "#colors = [\"#fe2626\",\"#21a0fc\",\"#eef91a\",\"#40ee40\",\"#b09ed7\"]\n",
        "color_dict = {}\n",
        "for i, label in enumerate(category_labels):\n",
        "    color_dict[label] = colors[i]\n",
        "\n",
        "sankey(\n",
        "    left=df_temp['Data availability statement (yes/no)'], right=df_temp['Data availability (yes/no)'],  aspect=20,  rightWeight=df_temp['Counts'].values.astype(float), leftWeight=df_temp['Counts'].values.astype(float),\n",
        "    fontsize=fontsize,colorDict=color_dict\n",
        ")\n",
        "plt.savefig('../Figures/sup9.png', format='png', dpi='figure')\n",
        "fig.savefig('../Figures/sup9.eps', format='eps', dpi='figure',bbox_inches='tight')\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "ca64eb63",
      "metadata": {},
      "source": [
        "## Supplementary Figure 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "id": "59bfb580",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Format</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Other</td>\n",
              "      <td>418</td>\n",
              "      <td>92.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RMD</td>\n",
              "      <td>23</td>\n",
              "      <td>5.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Notebook</td>\n",
              "      <td>12</td>\n",
              "      <td>2.65</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Format  Counts  Percentage\n",
              "0     Other     418       92.27\n",
              "1       RMD      23        5.08\n",
              "2  Notebook      12        2.65"
            ]
          },
          "execution_count": 139,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_modified = df.copy()\n",
        "df_modified.loc[df_modified['If yes, notebook/Rmd'] == 'no ', 'If yes, notebook/Rmd'] = 'no'\n",
        "df_modified.loc[df_modified['If yes, notebook/Rmd'] == 'uncertain', 'If yes, notebook/Rmd'] = 'no'\n",
        "df_modified.loc[df_modified['If yes, notebook/Rmd'] == 'yes', 'If yes, notebook/Rmd'] = 'Rmd'\n",
        "df_modified.loc[df_modified['If yes, notebook/Rmd'] == 'notebook', 'If yes, notebook/Rmd'] = 'Notebook'\n",
        "df_modified=df_modified.fillna('no')\n",
        "df_temp=pd.DataFrame({'Counts': df_modified['If yes, notebook/Rmd'].value_counts()}).reset_index()\n",
        "df_temp=df_temp.rename(columns={\"index\":\"Format\"})\n",
        "df_temp.loc[df_temp['Format']=='no', 'Format'] = 'Other'\n",
        "df_temp.loc[df_temp['Format']=='Rmd', 'Format'] = 'RMD'\n",
        "#add percentage column \n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "#df_temp=df_temp[~((df_temp['Format']=='no'))]\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "id": "eac32ecb",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_21447/2994411204.py:8: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(data=df_temp, x='Format',y='Percentage', palette=colors)\n",
            "/tmp/ipykernel_21447/2994411204.py:8: UserWarning: \n",
            "The palette list has fewer values (1) than needed (3) and will cycle, which may produce an uninterpretable plot.\n",
            "  sns.barplot(data=df_temp, x='Format',y='Percentage', palette=colors)\n"
          ]
        },
        {
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dyy+/HJMmTUrVhx56aJxzzjnRpk2btLAg1deCBQvSll9v3br1BgsLfvHFF/HGG29ERERubm6cdNJJG+S8G7Ply5fHZZddFkVFRZXWZ3Z2dsaB3po1K35ro3bt2tG+ffto27ZtNGvWLHJzc2PJkiXx/fffx+jRo+OXX35JtX3llVdizpw5cf/990etWrVW2+8xxxwTd955Z8yfPz/ef//9GDVqVHTp0qXC4wMAAIANQVgQAAAAqJbGjx8fp5xySsydOze1r169etG7d+84+uijo1mzZmUet3Tp0nj//ffj/vvvj48//nhDDRfWmzfffLOqh1DtrQx6RURsscUW8Z///Cdq1KhRhSNiY3Lrrbemto8++mizCmbg7rvvjvHjx0dERIsWLda4xG8mrr322jjyyCPXuZ+SWrduHfvss09069Ytdtttt8jJySmzXX5+fjz88MNx0003pWYxHjVqVNx3331x1llnrfYc9erVi+OPPz4GDhwYERG33XZbPPTQQ5X6PAAAAKCy+FotAAAAUO0sWbIk+vTpkxYU3GWXXeK1116L3r17lxsUjIioU6dOdOvWLR566KG48847Y9NNN90QQwaq0DfffJPa7tq1q6AgGRs/fnyMHDkyVR933HFVOJqNw6RJk1LBuLp168Zf/vKXKh5R+e66667o27dvdOnSpdygYERETk5OnHbaaXHNNdek7R88eHAUFhau8TxHH310albEjz/+OL766qt1GzgAAACsJ8KCAAAAQLVzzTXXpC0p2qFDhxg0aFA0b968Qv1069Ytnnrqqdh1110re4hANVIyWLy6MDGUNmTIkNT27rvvHttss00Vjqb6Ky4ujssuuyzy8/MjIuKcc86J1q1bV/GoKs8RRxwR2223XaqeO3dujB07do3HbbHFFrHXXnul6sGDB6+X8QEAAMC6EhYEAAAAqpXJkyfHM888k6rr1KkTN954Y9SvX3+t+mvRooXlACHhlixZktquWbNmFY6EjUleXl689NJLqfrAAw+swtFsHB577LEYPXp0RERst912cdppp1XxiCpf586d0+rp06dndFzJn5/XXnstFi1aVKnjAgAAgMrgf84AAACAauWBBx6I4uLiVH366afHVltttU59rm7pwdKKioriyy+/jEmTJsWcOXMiYsVMZe3atYudd945srPX7buX48aNi2+++SZmzZoV9evXj0033TQ6d+4cDRo0WKd+SyosLIwvv/wypkyZEnPnzo3CwsJo2rRpbL311tGhQ4cNtkTrsmXL4ptvvokJEybEL7/8EsuWLYs6depEo0aNonXr1rHddttF06ZN1+kc06ZNiy+++CJmzJgRRUVF0axZs+jUqVNsscUWa9VfcXFxTJ48OSZNmhTTp0+PxYsXR05OTjRs2DC23nrr2HnnnSv087Qmy5Yti9GjR8f06dNjzpw5Ubt27dh3332jbdu2lXaOlebMmROffvppTJ8+PZYtWxZNmzaNXXfddZ1mUisqKooxY8bEpEmTYu7cudG4ceNo1apVdOnSJerUqVOJo98wpk+fHmPGjIm5c+fG4sWLo1GjRrHpppvGHnvssdaB5bLMnTs3Pv3005g1a1bMnz8/GjduHH/84x8z+j2Qn58fo0aNip9++inmzp0bDRo0iA4dOsQuu+yy2uNmz54do0ePjmnTpkVhYWG0aNEiunTpstazwuXn58d3332X+l25ZMmSqFevXjRu3Dh23HHH2GabbVLLslZXb7zxRlrQ9P/+7/8qpd+vv/46vvvuu5g5c2bUrl079Zlo3LhxpfRfVWbOnBk33XRTRERkZWXFlVdeGbVq1ariUVW+0p/1oqKijI7bf//944orroiioqJYunRpDBs2LI466qj1MUQAAABYa8KCAAAAQLWRn58fzz33XKrOzs6OY489doOce8GCBTFw4MB4+umnY/78+WW2adKkSRx77LFx5plnVjg49Pbbb8cNN9wQEydOXOWxOnXqxIEHHhj/+Mc/1ik8N3PmzLjzzjvj5Zdfjl9++aXMNg0bNozjjjsuevXqFY0aNVrrc63OrFmzon///vHSSy+tcWalNm3aRPfu3aNPnz5Ru3btjM/x7bffxn/+8594//3308KlK+2xxx5xySWXxI477rjGvvLy8uLtt9+OYcOGxYcffhjz5s0rt22dOnXi0EMPjd69e2ccSGzfvn1q+7zzzovzzz8/fvnll7jpppvKfY1KhgW7desW06ZNi4iIHj16xPXXX1/meT766KPo2bNnqh4yZEjsueee8dNPP8V1110Xb731VixfvrzM8fXr1y9tCc01KSoqiocffjjuuuuumDVr1iqP169fP3r06BF/+ctfIjc3N/r27RtDhw6NiIjWrVvHm2++mfG5yvLMM89Ev379ynzs9ttvj9tvv32V/Stfj7K8/PLLMXDgwPj222/LfLxWrVqx9957x4UXXhg77LDDGsdX3nsxYcKEuOGGG+K9996LgoKCtGN22223VN9lveeLFi2K22+/PZ555pkyP9/t27ePK6+8Mnbfffe0/dOmTYsbbrghXn/9Zr4ZeQAAKE1JREFU9SgsLFzluP322y8uv/zyaNWq1Rqf1+zZs2PYsGHxxhtvxKeffhpLly4tt23z5s3jxBNPjFNOOWW1vy9PPvnkGDVq1Cr7+/XrV+57vPJztK5ef/311HabNm0yDk6W9/M8bNiwuP3222P8+PGrHJOdnR2HH354/PWvf43mzZuv89irwtVXXx0LFy6MiIhjjz02OnbsWMUjWj9++umntDrT3/XNmjWL9u3bxzfffBMRK36+hAUBAACobixDDAAAAFQbX3zxRVr4pFOnThkFWNbVmDFj4oADDoj77ruv3KBgRMS8efPirrvuigMPPDC++uqrjPu//vrro3fv3mUGBSMili5dGs8++2z06NEjxo0bV9HhR0TE0KFD44ADDohHH3203KBgxIpQ5D333BOHHHJIhZ5DpsaMGROHHHJIPP744xktwThlypS47777YsGCBRmf49lnn41jjjkm3nvvvTKDghERo0ePjhNPPDHee++9Nfb3+OOPx4UXXhivvPLKaoOCESveqyeffDIOO+yweOONNzIec0nffPNNHH744Rm/RuvivffeiyOOOCJee+21MoOCESuCl2eccUY8++yzGfWZl5cXZ5xxRlxzzTVlBgUjIhYtWhQPPvhgHHPMMRkv4VkVFi9eHH/+85/joosuKjcoGBGxfPnyePvtt+PII4+MO+64Y63O9dxzz8VRRx0VI0aMWCUouCYzZ86MY445Jh544IFyP9/ffvtt9OzZM959993UvtGjR8cRRxwRr776aplBwYiIt956K0444YSYOnXqGsfxr3/9K6666qp4//33VxsUjFgRLLztttviqKOOKvd3X1UqLCyMDz74IFV36dJlrfsqLi6Oa665Ji644IIyg4IRKwK2Q4cOjRNOOGGVMNrG4LXXXkuFK5s1axYXX3xxFY9o/Vi4cGHaZ2jTTTfNKCC8UslA8kcffRT5+fmVOj4AAABYV2YWBAAAAKqN0aNHp9UdOnRY7+f85JNP4owzzoi8vLzUvtq1a8fee+8dbdu2jaysrJg0aVKMHDkyddN/1qxZcfLJJ8fgwYPXOMZbb701HnjggbR9rVq1in322SeaN28ev/zyS3z00UcxceLEmDFjRpx//vnRrVu3Cj2Hu+++O7U05EpbbrlldOzYMTbZZJPIzs6OGTNmxIcffhgzZsxIew6PP/54bLfddhU6X3nmzp0bZ511VlqYqVGjRtG5c+fYcssto169erF06dKYP39+TJgwIb755ps1Bo5K++CDD+KKK66IgoKCqFu3bnTt2jXatm0btWvXjh9//DFGjBiRmvkqLy8vLr744nj55ZcznrGxXr16sf3220fbtm2jcePGUbdu3ViyZEn8+OOPMXr06Jg7d25ERCxZsiQuvPDCeOSRRyr0czpv3rw455xzUgG67bbbLjp16hRNmjSJ+fPnx5dffllpS7eOHz8+/ve//8WSJUuiVq1asccee0T79u0jNzc3ZsyYESNGjEgttV1YWBj/+te/Ypdddol27dqV22dxcXGcd9558f7776ft32677aJz587RqFGjmDt3bnz44YcxZcqUmDBhQlx00UWx+eabV8pzWik7OzttOe2SQbisrKwylwsv/brm5+fHqaeeGl988UXa/h122CE6deoUDRo0iFmzZsXIkSNTn5uioqK49dZbY8mSJfHXv/414/F++umnMWDAgFi+fHnUrFkzOnbsGNtvv33Uq1cvZs2alRZaKy0/Pz/OPPPMmDRpUkRE7LTTTtGxY8do0KBBzJ49O0aMGBEzZ85Mtf3rX/8ar776asydOzfOPPPMWLx4cdSqVSs6d+4c7du3jzp16sQPP/wQb731VmoJ3unTp0e/fv3iwQcfzPg5tWjRItq3bx9bbbVVNGjQIGrWrBkLFiyISZMmxccffxzLli2LiBWB4F69esXQoUPLnM205HtZ8n3Mzs4u97NQGZ+R8ePHp4WU17SM8+r0798/9do1b9489tprr2jdunUUFBTE+PHj4/33308FRH/44Yfo27dvDB48uNov07zSwoUL46qrrkrVffv2XS8z0w4bNixefvnlmDhxYsybNy9q164djRs3jm233TY6d+4cBx10UGyyySaVft6V8vPzo1+/fmlfGjj//PPTftesScmfoyVLlsSXX34ZnTp1qsxhAgAAwDoRFgQAAACqjdKz6u28887r9XwLFy6Mv/3tb2lBwe7du8fVV18dzZo1S2s7a9asuOSSS2LEiBEREamw0NChQ6NevXpl9v/ZZ5/FwIEDU3XNmjXj73//e5x88smrhJleffXVuOSSS+KHH36IRx55JOPn8M4778T//ve/VN22bdu4/PLLy1xStqCgIB5//PH4z3/+E8uWLYslS5ZEnz594rnnnoucnJyMz1meRx55JC1kcdZZZ8U555xT7vLCS5cujffffz8effTRMsNdZbnyyiujoKAgDjvssOjXr98qIcB58+bFRRddlApfzZs3LwYPHhwXXXRRuX02aNAgTjjhhDj00ENj1113jZo1y/4vs8LCwnjhhRfi6quvjkWLFsXy5cujX79+8eKLL2Yc+nnssceisLAwNttss7juuuuia9euq7SprJmo/vOf/8Ty5ctjn332iauuumqVJVaXLFkS//rXv+KFF16IiIhly5bFnXfeGTfeeGO5fT700EMxcuTIVN24ceO4/vrrY7/99lul7YsvvhiXXXZZjBkzJsaOHVspz2mlI444Io444ohUXXKp53PPPTejJWpvvPHGtKBgy5Yt4z//+c8qn52ioqIYMmRI/Pe//00Fvu69997o3Llz7LvvvhmNt3///lFYWBhdunSJa665Jrbaaqu0x4uLi8udbXDlrJCbbrpp/Pe//11lBrxly5bFpZdeGs8//3xERMyfPz/uv//+GDlyZCxevDg6d+4c11133SpLqc6cOTN69eqVmlFx1KhR8f7778dvfvObcp9H69ato0+fPnHAAQfENttsU267RYsWxV133RX33HNPFBcXx7Rp0+J///tfXHnllau0HTx4cERETJ06Nbp3757af+2118aRRx5Z7jnWVemZVdc2NP3zzz/HHXfcETVq1Ii//OUv0bNnz1V+n3777bfRu3fvVEj4o48+ig8++GC1r/Xtt9++1rNYZqJ169ZpyzCvzg033JCaRfQ3v/lNHHbYYetlTG+//XZanZeXF/Pnz48pU6bE66+/HjfeeGMcffTR8de//rXcv3crKj8/P6ZOnRoffvhhDB48OKZMmZJ6rGfPnnH00UdXqL+Sv4siQlgQAACAascyxAAAAEC1UXoJ4PU5g1BExAMPPBDTpk1L1d26dYv+/fuvEhSMWDGL1oABA2KfffZJ7fv+++9j0KBB5fZ//fXXR1FRUaq+4oor4pRTTikzGHfggQfGnXfeGTVq1Mg4LLZs2bL45z//mVqKd6eddoonn3yyzKBgxIqw4kknnRS33XZbKtw2adKkVMhoXZWcHW2vvfaKiy66qNygYEREnTp1olu3bnHPPfeU+ZqXZfny5XHsscfGf//73zJnC2zSpEncdtttaY+t6fkdddRRccUVV0SnTp3KDQpGRNSoUSOOOOKIuPfee1MzTU2YMCEtPLcmhYWF0bhx43jooYfKDApGRKUENyNWvFa/+93v4q677lolKBgRkZubG9ddd11a6Ou1115LC8+WtHTp0ujfv3+qrlWrVtx3331lBgUjIv74xz/GHXfcEdnZ2dVuKc4ffvghbRa9Ro0axeDBg8v87GRnZ8epp54a1157bWpfcXFxXHvtteUug11aYWFhdOzYMe67775VgoIRK2bKq1WrVpnHLl++PBo2bBgPPfRQmUvl1q5dO6699tq09/jee++Nr7/+Onbddde4//77VwkKRqwIR958881pv4/W9Fm55JJL4pxzzlltUDAion79+nHxxRenzb743HPPrXaJ9A2t9HLBZb0vmVi+fHkUFxfH9ddfH3/+85/L/Py2b98+LdQdseL1WJ3i4uIoLCxcr38y8fHHH8eTTz4ZESt+1q644orMXpj1ID8/Px555JE4+uij4/vvv1+rPk444YTYcccdU3922WWXOOigg+LKK69MBQU333zzuPHGG+OSSy6pcP9bbbVVWnh8dcubAwAAQFUQFgQAAACqjdJBkvr166+3c+Xn58fjjz+edq6rr756tcsN1qpVK6699trIzc1N7Xv00Udj+fLlq7T99ttvY8yYMam6a9euccwxx6x2TF26dIkTTjgh4+cwdOjQ1GxPNWrUiBtvvDEaNGiwxuN+//vfx4EHHpiqH3300YzPuTqzZ89Oba+vWSFbtGgRl1566WrbNGzYMHr06JGqf/rpp9QyrZVh9913j9/+9repuvRsWGvyl7/8pczwXmWrXbt2/Pvf/15tALJWrVpx4oknpuply5bFN998U2bbV155Je0zesopp6zxfd5rr73W6+xwa+uhhx5KC/JedNFF0bZt29Uec8QRR6QtEf79999n/N5nZWXFNddcs9ZB0AsvvLDMwN9KOTk5aa9zUVFRZGVlxb///e/VnrNdu3axxx57pOqSv7MqwymnnBINGzaMiBWzxH344YeV2v+6KBkUr127djRp0mSt+9p///3XONtex44d05ao/eyzz9b6fBtKfn5+XHbZZalQbO/evdc6VLk67du3j3PPPTfuv//+eOedd+KLL76Izz//PIYPHx7/+9//4ne/+11a+0mTJkWvXr1i3rx5FT7XmgKTO++8c/z73/+OQw89dK2eS05OTlpYveTPGQAAAFQHwoIAAABAtbF48eK0umQor7J9/vnnaeG2Qw89NJo3b77G4zbddNM4+OCDU/WsWbPSljJdqfTyjieffHJG4+rZs2dG7SIinn322dT2PvvsE1tvvXXGxx5yyCGp7a+//joWLVqU8bHlKbksZHmBs3V11FFHrXa2wpU6d+6cVk+aNKlSx1FyydLPP/884+Nyc3PX2xKepR1wwAHRokWLNbYr/VpNnDixzHYlg3FZWVkZB1tPOumkjNptSMOHD09tN2rUKI466qiMjjvjjDPK7Wd19txzz2jXrl3mAyyhdu3aGQUuO3TokFbvsccea5wBMCJi1113TW1///33ZYaf11atWrXSfi9V5LOyvv3888+p7UxnNi1Ppj/jJT9ra3qtzz///Pj222/X258333xzjeMdMGBATJ48OSJWLHHfq1evjJ5npho3bhyPPfZYPP/883HBBRfE3nvvHS1btozatWtHnTp1YvPNN49DDjkk7rnnnrj77rujUaNGqWO///77uOaaayp1PBErlqfu2bNnHH/88annXlElf54qM6gOAAAAlaH8rxUDAAAAbGClw4FLlixZb+cqPYNW9+7dMz72gAMOiKeeeiqtr06dOqW1KRmKqVmzZtpMdKuz1VZbRbt27coNbK2Ul5cXX331VaouOTtYpudZqaioKMaNG1fhPkrbZZddYuzYsRERMXLkyLj55pvjrLPOirp1665TvyWVDraVp/TMfQsWLMjouHHjxsWrr74aX3/9dUyePDkWLFgQixcvTpuFLiLS6hkzZmTUd8SKQFdlvh6rs7av1cKFC8tsVzIUu9VWW8Xmm2+eUf877rhjNGnSZK1mAVsfZs+eHVOnTk3V++yzT8Yz/u2xxx7RtGnTmDt3bkRkPhNfWcsHZ2rHHXfM6Gdm0003TatL/07K5Lji4uJYtGjRGmfZKywsjA8++CDefvvtGDduXEydOjUWLVoUeXl5qyzNXHL2tuoUnCr590smAeTy1KhRIzp27JhR25KftUxf66ry7bffxn333Zeqr7zyykpbIn2l+vXrx+67755R23333TfuuOOOOOWUU6KgoCAiIl566aXo3bt3Wnh7TZ544onUdlFRUSxatCimTJkSH3zwQTz66KMxffr0iFjx2T7uuONiyJAhsf3221fgWUXUqVMntb0+/x0DAAAAa0NYEAAAAKg2Ss4aFBGVMttdeUrPGLTjjjtmfGzptmXNWlcy7Ne2bdsKhVF22GGHNYYFv/vuu7RZqW6++ea45ZZbMj5HafPnz1/rY1f605/+FE8//XRqXAMHDoyHHnoofv/730fXrl2jY8eOsfXWW0dWVtZan6Nly5YZtSsdrlpTYGPChAlx5ZVXxqhRoyo8pkyDiBFRodkf11Wmr1UmId2CgoL46aefUnX79u0rNJbtttsuPvroowods76U/uzvsMMOFTp+xx13jJEjR6b6Ki4uXuPP9Lq872v7M7/JJpus1XFLlixZbYDtrbfeimuuuSYtcJmp0kvNV6X8/PzUdq1atda6n4YNG6aFw1anoq91VSkqKopLL7009bu8R48eseeee1bxqFaEdY866qh4/PHHI2JF4PKVV16pUFiwpOzs7GjYsGF06NAhOnToECeffHJcdtll8eKLL0bEip/XPn36xHPPPZfxexwRaaHKkj9nAAAAUB1YhhgAAACoNkqHJkouE1nZSga8srOzo2nTphkf26xZs7RwUFlhsZL7KtL3yv7XpPQsbUVFRVFYWFihPyVVRjBz2223jX//+99pQYlFixbFiy++GJdeemkcfPDB0bVr1zj33HPjiSeeWKvgUKaz8pUOb5We7aykTz75JI455pi1CgpGRCxbtizjtg0bNlyrc6yNynytSv+Mlw72rknjxo0r1H59Kv1zl8lSzeW1LywsXGX59LKsy/u+tu/j+visDB48OP5fe/ceFNV9hnH8UQQJF28F40gFBUVQpJE2k6TeoiE201hrnXgtRotNNPGWtpEQDUISk5iLw5hqQ5sUMdJkFKXGjLVEqfUyRjKgoihKrGjVBBVQEBCQsP3D8XSX666wsJLvZyYz+1t/55zX3T3LDHl83/nz599VUFCy7V6xN/PvqZaMXq4btm2KLa91e9q4caPRSbRHjx6Kiopq54r+76mnnrJYf/nll612bjc3N73zzjsWXXbPnTun1NRUm85jHhBsSddKAAAAAADsgbAgAAAAAABwGHW7ld0ZaWsP5gEfV1dXm7rdde7c2SKI01BYyLw7my1hEsm6kI8t3eysUXfM7t2aOHGitm3bpgkTJjQ4svL69evavXu3YmJiNHr0aL399tu6efNmq1z7bpSVlWnJkiUW79egQYP0hz/8QR9//LHS09N1+PBh5eTk6PTp08Z/CxcuvKvrdelybw76qBumsrUTW2uPL22JuverrWOh697P1oQF79X33Vx2drbeeusti+dGjhyp2NhYpaSkaP/+/Tpy5Ihyc3Mt7pWWjGC2J/P3sbKysh0rcSyVlZUWXWqjoqJsDrzbU0hIiMX3j3nH09bg5ORU7/s9LS3NpnOYf55s/fkPAAAAAIC93fu/pQIAAAAAAB2GeTcfSUZnI3twd3c3HldWVlo1SvSO2tpai4Cb+bnucHNz040bNyQ1PwK3LmvCc3UDTnFxcZoxY4ZN17GXgIAArV69Wjdu3FBGRoYyMzN19OhR5eTkWITOKisrlZiYqIMHDyo5OVmenp5tXuunn36qq1evGus5c+YoOjq62c+Cre/pvc7Dw8NibU1Azpw9R4rbqu79amtYte5739D93xGtXbvW6ITXuXNnrVmzRuPHj2/2OFs/K23l/vvv1/HjxyVJxcXF7VxNfWvXrtWf/vQnu53fx8dHu3btqvd8dXW1xWc8JiZGMTExTZ6rbofEbdu2afv27cZ60qRJevPNN1tY8W2dO3dWjx49jO/tul12W8ODDz4oZ2dn4+fV6dOnbTq+sLDQeGztGHEAAAAAANoKYUEAAAAAAOAwQkND5erqanTlyczMVEFBgfr06dPq1zIfC1pbW6vi4mKrxv9KUlFRkUU4oqERo926dTPCgrYGUYqKiprdU3esqz0CEy3l6emp8PBwhYeHS7odDszIyNDOnTu1Y8cOY1TjqVOntGrVKr3xxhttXuOePXuMx35+foqKirIqNGoeBvk+cHd3V9euXY0xsrZ282rt7l8tUXeEsnlY1Brm+52cnL4XYcGbN2/q0KFDxnrSpElWBQUl677P2oOPj4/xuKqqSsXFxQ7VQc9kMtUbF9+arD333dRQt/bW6lx7h3nnPnuM+e3SpYtFINGWsHN1dbXFz2PzzxkAAAAAAI6AMcQAAAAAAMBhdO3aVRMnTjTWtbW12rx5s12uNWDAAIv1yZMnrT627l5/f/96ewICAozH+fn5RsjKGqdOnWp2j7+/v0WozZ4jm1uLq6urxowZo1WrViklJcUiZPX555+3yyjQ/Px84/FPf/pTOTk5WXVcTk6OvUpyWMHBwcbj3Nxc1dTUWHVceXm5/vOf/9irLJvVvfdzc3NtOt58/4ABA2waYX6vunTpkhHulaRRo0ZZddzVq1dVUFBgr7JaJDAw0GJ9/vz5dqoEtrh27ZoRxJdkdcjfVuYdMRv6BwGNOXfunMU/Jhg8eHCr1gUAAAAAQEvRWRAAAAAAADiUOXPmKCUlxfif7YmJiZo0aZJ8fX3v+pzV1dVycXGxeC4sLMxinZ6ebnUApu7oxuHDh9fb86Mf/Uj79u2TJNXU1OjAgQN67LHHmj33+fPndebMmWb39erVS0FBQUZw6dChQyorK6s3LtZRBQUFadq0aUpMTJR0u7NXfn6+RSCtLZiHTup2nGvM6dOndfbsWXuV5LDCwsJ09OhRSbdft3379mncuHHNHpeWlmZ1sLAteHl5qV+/frpw4YIk6cCBAw1+RzQkKyvLolNeQ/d+R1RaWmqxtvZe+ec//2n1NZydnS3W9uyqJ0khISEW67y8PId6PxctWqRFixa1+XW7detm89jdjIwMPf3008b6V7/6lVatWtXapUmS8XP1jqCgoFa/Rn5+vsUoZlu6G9d97ep+zgAAAAAAaG90FgQAAAAAAA4lICBAkyZNMtY3b95UVFSURZcfW1y5ckURERH1ng8NDZWXl5ex/vzzz60aLXv58mXt2LHDWPfu3VuhoaH19t0ZvXtHcnKyVfVu3LjRqn2SNGHCBONxWVmZEby7V/Tr189ifevWrTavwby74cWLF6065oMPPrBXOQ7tl7/8pcV67dq1zY4XraqqcsjXyzy4W1JSoq1bt1p13Pr16y3Wjz/+eKvW5ajqjlq+dOlSs8dUVFQoKSnprq9RN6DY2gIDAy3GuR8/ftyu10PL3bp1Sx9++KHFcyNHjmz169TtaDxixAirjzXvOuvm5qZhw4a1Wl0AAAAAALQGwoIAAAAAAMDhxMTEqH///sb6yJEjioyMtOjoZY309HQ99dRTys7OrvdnLi4umj59urEuKyvTihUrmuxmVVNTo1deecWi49DMmTPrdcSSbnc7Mu9SdfDgQaWmpjZZb2Zmpj799NMm95ibOXOmevXqZawTEhLqdT1szo0bN1otJHPs2DGb9mdmZlqs+/bt2yp12GLgwIHG4z179jQbGE1JSdHOnTvtXZZDCgoK0iOPPGKsT5w4obi4uEYDgzU1NVq6dKn++9//tlWJVvv1r3+tzp3//6vR+Ph4nTt3rsljtm/fbnF/9e/fX6NHj7ZXiQ7Fz8/PovPili1bmgyKmkwmxcXFWR3AlSQPDw+LjoX2Du917tzZIgT21Vdf2fV6sHT58mVdv37d6v21tbVasWKFvv76a+M5b29vPfnkk40ec+LECZs7VO7du9citO/k5KRf/OIXVh+fkZFhPH7ooYes6lgKAAAAAEBbIiwIAAAAAAAcjru7u9asWWPR9eno0aP62c9+pr/85S8qLi5u9Niqqirt2bNHERERev7553X58uVG9/7mN7+Rj4+PsU5PT9fixYsbDCUWFhZq4cKFFiMQ/fz8LEYv1hUdHW0RSIqJiVFycrIxYtncF198ofnz56umpsbqcIGbm5tef/11derUSdLtsZ2LFi3SqlWrmgxWmkwmHT58WCtXrtSjjz6q7du3W3W95kyZMkUzZ85USkpKk+9RdXW11q1bZ9GhMSwszKLTY1sx7wxXXl6uefPm6dtvv623r6qqSu+//75iYmIk3X7tv4/i4uLk6upqrDdt2qTp06dr165dunbtmmpra1VUVKQdO3Zo8uTJSktLk4uLi4YOHdqOVdfn6+urWbNmGeuSkhLNnj1bhw4dqre3trZWGzdu1LJlyyyeX7ZsmXHvdXSurq4WHdyys7O1fPlyVVZW1tt75coVLV68WJ999pkk2+6VBx54wHj8xRdf6JNPPtG1a9fuvvBmmHeAPX/+vFUdE9E6jh8/rnHjxuntt9/WyZMnm9ybm5urOXPm1Avc//73v2/y87Vu3TpNmDBBGzdu1NWrV5u8RlFRkVavXq0FCxZYdLmdPn26AgMDrfgb3T6H+Rjiuh2GAQAAAABwBF3auwAAAAAAAICGBAUFadOmTXrmmWeMzmQ3btzQ6tWrFR8fr5CQEA0cOFA9e/aUs7OzioqK9O233yorK0s3b960OJd5YM+ch4eH3n33XUVGRhqhl927d+vAgQMaMWKE/P39JUlnz57VgQMHVFVVZRzr5uam9957r97oTHMPPPCA5s2bZ4xhramp0euvv66//vWvGjlypLy8vFRSUqKvvvrK6Jbk6+urcePGWT2+Mzw8XFFRUXrnnXdkMplkMpm0fv16JScnKzQ0VEFBQerRo4dqampUWlqq/Px85ebmqqSkxKrz2yorK0tZWVmKjY2Vv7+/goKC5O3tLXd3d1VWVurChQvKyMiwCAB16dJFL7/8sl3qac7UqVO1fv16FRQUSLo9QnL8+PEaNWqU0XXw0qVL2r9/v/Ga+fv769FHH73nxj63hv79++u9997T7373OyNQk52drYULFzZ6zEsvvaScnBydOHFC0u1OXY7gxRdf1JEjR4yOmAUFBZo9e7aGDh2qsLAweXp6qrCwUPv3768XIP3tb3+rMWPGtEfZ7WbBggXat2+fampqJEmpqanas2ePxowZo759+6qyslJnzpzRl19+aXw2Jk6cqIKCAqu79k2dOlV79+6VdDv8/Oqrr+rVV1+Vi4uLunT5/6+y582bp/nz57f47zRu3Dh5eHiorKxMkrRr1y7NmTOnxeeFdcrLy5WYmKjExER5e3tryJAh6tevnzw9PWUymVRUVKTs7Gzl5eXVO3bu3LmaPHlys9c4e/asVq5cqTfffFN+fn4KDg6Wt7e3PD09devWLV2/fl15eXk6fvy48dm+Y+TIkYqOjrb677N7926j42bXrl31xBNPWH0sAAAAAABthbAgAAAAAABwWP3799fmzZu1Zs0abdmyxQig1NbW6tixY1aNvR0xYoSWLl3a6J//+Mc/VlJSkp577jkjwFZZWan09HSlp6c3eIyXl5cSEhI0bNiwZq//wgsvqKKiQhs2bDCe++abb7R58+Z6e++//3798Y9/tHmUcGRkpHx9ffXyyy+rtLRUknTr1i0juNcce4xJ/O677/T1119bjIxsiLu7u+Lj4xUaGtrqNVjDzc1N69atU2RkpBEGrK6ubvT9HzBggD766KNmR0p3ZI8//rg++OADxcTENNiF8Q5XV1fFxsZq8uTJWrJkifG8h4dHW5TZLBcXFyUlJWnx4sU6cOCA8fyJEyeMYGNdnTp10qJFi7RgwYK2KtNhhISEKDY2VrGxsUYg6tq1a9q2bVuD+x977DG98cYbmjt3rtXXCA8P19NPP62PP/7Y4vnq6mpVV1cba/POby3h6uqqJ598Ups2bZIk7dy5k7BgO7l69aoRFG2Kq6uroqOjNWPGDJvOX1tbq/z8fOXn5ze718nJSbNnz9YLL7xg08/HtLQ04/H48eMd5rsOAAAAAABzjCEGAAAAAAAOrWfPnoqLi9M//vEPzZo1S35+fs0e4+3trYiICG3dulWJiYkKDg5ucv/w4cOVlpamuXPnWow+bqiWZ599VmlpaVYFBe9YtmyZEhISjE6Fdbm4uGjChAnatm2bgoKCrD6vufDwcP3rX//SkiVLLEYrN8bHx0dTpkxRUlKSXnzxxbu6Zl1//vOfNW3aNPn6+ja7t0ePHoqIiFBaWlq7d2gLCQnR1q1bNXbs2EbHynp7e+vZZ5/Vli1brHp9O7pRo0Zpx44dWrFihR5++GH17t1bzs7O8vb2VmhoqJYsWaK0tDSj85d5J0lHCtC4u7vro48+Unx8vAYPHtzovi5dumjMmDFKTU39XgYF75g6dWqz36mDBg3Sa6+9prVr195VEHn58uX65JNPNG3aNAUHB6t79+5ydnZuSdlNMh8lf/To0WYDzmgdgwcP1syZMzVw4MBGu/+au/MdnJaWZnVQ8LnnntPcuXM1ZMgQi86UjenZs6ciIiL02Wef6aWXXlLXrl2tuo4kXbx4UQcPHjTW5p8rAAAAAAAcSSeTyWRq7yIAAAAAAABsceHCBZ05c0bFxcUqLi6WyWRSt27d5OXlpSFDhqhv3753fe47XQvPnj2r4uJiSVKvXr0UEBC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            "text/plain": [
              "<Figure size 3125x3125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(31.25, 31.25))\n",
        "sns.set_style(\"white\")\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "colors = ['#56B4E9']\n",
        "sns.barplot(data=df_temp, x='Format',y='Percentage', palette=colors)\n",
        "plt.xlabel(\"Code sharing format (n=453)\",fontsize=fontsize)\n",
        "plt.ylabel(\"Percentage (%)\",fontsize=fontsize)\n",
        "plt.tick_params(axis='both', labelsize=fontsize)\n",
        "plt.ylim(0,100)\n",
        "sns.despine()\n",
        "plt.savefig('../Figures/sup10.png', format='png', dpi='figure',bbox_inches='tight')\n",
        "fig.savefig('../Figures/sup10.eps', format='eps', dpi='figure',bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "a3c06758",
      "metadata": {},
      "source": [
        "## Supplementary Figure 11"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "id": "a9d74c78",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Journal</th>\n",
              "      <th>Code Policy</th>\n",
              "      <th>Data Policy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMC Bioinformatics</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Encouraged</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Genome Biology</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Genome Medicine</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Encouraged</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Nature Biotechnology</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Nature Genetics</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Nature Methods</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Nucleic Acids Research</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Journal Code Policy Data Policy\n",
              "0          Bioinformatics   Mandatory   Mandatory\n",
              "1      BMC Bioinformatics  Encouraged  Encouraged\n",
              "2          Genome Biology   Mandatory   Mandatory\n",
              "3         Genome Medicine  Encouraged  Encouraged\n",
              "4    Nature Biotechnology   Mandatory   Mandatory\n",
              "5         Nature Genetics   Mandatory   Mandatory\n",
              "6          Nature Methods   Mandatory   Mandatory\n",
              "7  Nucleic Acids Research  Encouraged   Mandatory"
            ]
          },
          "execution_count": 141,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_journal=pd.read_csv(\"../data/journal_data.csv\")\n",
        "df_journal"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "id": "e161f6b9",
      "metadata": {},
      "outputs": [],
      "source": [
        "df_modified = df.copy()\n",
        "df_modified['Code Policy']='Unknown'\n",
        "df_modified['Data Policy']='Unknown'\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Genet'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Genetics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Med'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Medicine', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Genome_Biol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Genome Biology', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Biotechnol'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Biotechnology', 'Data Policy'].item()\n",
        "\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nat_Methods'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nature Methods', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Nucleic_Acids_Res'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Nucleic Acids Research', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'BMC_Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'BMC Bioinformatics', 'Data Policy'].item()\n",
        "\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Code Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Code Policy'].item()\n",
        "df_modified.loc[(df_modified['Journal'] == 'Bioinformatics'), 'Data Policy'] = df_journal.loc[df_journal['Journal'] == 'Bioinformatics', 'Data Policy'].item()\n",
        "df_modified.loc[df_modified['Secondary analysis'] == 'Hybrid', 'Secondary analysis'] = 'no'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "id": "efc9c664",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>Code Policy</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>yes</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>63</td>\n",
              "      <td>13.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>no</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>108</td>\n",
              "      <td>23.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>no</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>119</td>\n",
              "      <td>26.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>yes</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>163</td>\n",
              "      <td>35.98</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Code availability (Yes/No) Code Policy  Counts  Percentage\n",
              "2                        yes  Encouraged      63       13.91\n",
              "0                         no  Encouraged     108       23.84\n",
              "1                         no   Mandatory     119       26.27\n",
              "3                        yes   Mandatory     163       35.98"
            ]
          },
          "execution_count": 143,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp=pd.DataFrame({'Counts': df_modified.groupby(['Code availability (Yes/No)','Code Policy']).size()}).reset_index()\n",
        "#add percentage column \n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 144,
      "id": "d197a848",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Code availability (Yes/No)</th>\n",
              "      <th>Code available</th>\n",
              "      <th>Code unavailable</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Code Policy</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Encouraged</th>\n",
              "      <td>63</td>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mandatory</th>\n",
              "      <td>163</td>\n",
              "      <td>119</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Code availability (Yes/No)  Code available  Code unavailable\n",
              "Code Policy                                                 \n",
              "Encouraged                              63               108\n",
              "Mandatory                              163               119"
            ]
          },
          "execution_count": 144,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp_pivot=df_temp.pivot(index='Code Policy',columns='Code availability (Yes/No)',values='Counts')\n",
        "df_temp_pivot=df_temp_pivot.rename(columns={\"no\":\"Code unavailable\",\"yes\":\"Code available\"})\n",
        "df_temp_pivot = df_temp_pivot.reindex(columns=['Code available', 'Code unavailable'])\n",
        "df_temp_pivot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "id": "84d648d3",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Chi-squared statistic: 17.876254622464177\n",
            "P-value: 2.357442656284854e-05\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Perform chi-squared test\n",
        "chi2, p, dof, expected = chi2_contingency(df_temp_pivot)\n",
        "\n",
        "# Display the test results\n",
        "print(\"Chi-squared statistic:\", chi2)\n",
        "print(\"P-value:\", p)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5b86f7c7",
      "metadata": {},
      "source": [
        "### OR for How likely were articles to share their code if the journal only encouraged sharing code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "id": "7ed28903",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.42669200298951576 1.8770835865385955e-05\n"
          ]
        }
      ],
      "source": [
        "#stat=odds_ratio([[63,108],[163,119]])\n",
        "stat=odds_ratio([df_temp_pivot.values[0],df_temp_pivot.values[1]])\n",
        "res = fisher_exact(df_temp_pivot.values, alternative='two-sided')\n",
        "print(stat.statistic,res.pvalue)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "91ee8b84",
      "metadata": {},
      "source": [
        "### OR for How likely were articles to share their code if the journal mandated code sharing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "id": "0f0a1cba",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.3436108316859654 1.8770835865385955e-05\n"
          ]
        }
      ],
      "source": [
        "#stat=odds_ratio([[63,108],[163,119]])\n",
        "stat=odds_ratio([df_temp_pivot.values[1],df_temp_pivot.values[0]])\n",
        "res = fisher_exact(df_temp_pivot.values, alternative='two-sided')\n",
        "print(stat.statistic,res.pvalue)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "id": "35a18f15",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_21447/2940657198.py:7: FutureWarning: \n",
            "\n",
            "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
            "\n",
            "  sns.barplot(data=df_temp, x='Code Policy', y='Percentage',hue='Code availability (Yes/No)', ci=None,palette=color)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 3125x3125 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(31.25, 31.25))\n",
        "fontsize=28\n",
        "plt.rcParams.update({'font.size': fontsize})\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "color=['#56B4E9','#E69F00']\n",
        "sns.barplot(data=df_temp, x='Code Policy', y='Percentage',hue='Code availability (Yes/No)', ci=None,palette=color)\n",
        "plt.legend(labels=['Code available','Code unavailable'])\n",
        "# statistical annotation\n",
        "y, h, col = df_temp['Percentage'].max()+10, 10, 'k'\n",
        "x1, x2 = 0, 1  # columns  (first column: 0, see plt.xticks())\n",
        "plt.plot([x1, x1, x2, x2], [y, y+h, y+h, y], lw=1.5, c=col)\n",
        "plt.text((x1+x2)*.5, y+h, r\"$p = {0:s}$\".format(as_si(p,2)), ha='center', va='bottom', color=col, fontsize=fontsize)\n",
        "\n",
        "sns.despine()\n",
        "plt.ylabel(\"Percentage of code shared in all studies\\n(n=453)\",fontsize=fontsize)\n",
        "plt.ylim(0,100)\n",
        "plt.xlabel(\"Journal policy on code sharing\",fontsize=fontsize)\n",
        "plt.tick_params(axis='both', labelsize=fontsize)\n",
        "\n",
        "plt.savefig('../Figures/sup11.png', format='png', dpi='figure')\n",
        "fig.savefig('../Figures/sup11.eps', format='eps', dpi='figure',bbox_inches='tight')\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "f37265c3",
      "metadata": {},
      "source": [
        "## Supplementary Table 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "f02c4b4f",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Source</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10xGenomics</td>\n",
              "      <td>1</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Downloadable File</td>\n",
              "      <td>2</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>GitLab</td>\n",
              "      <td>2</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CRAN</td>\n",
              "      <td>2</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bioconductor</td>\n",
              "      <td>2</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Sourceforge</td>\n",
              "      <td>3</td>\n",
              "      <td>1.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Bitbucket</td>\n",
              "      <td>6</td>\n",
              "      <td>2.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Supplementary</td>\n",
              "      <td>6</td>\n",
              "      <td>2.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Zenodo</td>\n",
              "      <td>14</td>\n",
              "      <td>5.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Website</td>\n",
              "      <td>16</td>\n",
              "      <td>6.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>GitHub</td>\n",
              "      <td>180</td>\n",
              "      <td>76.92</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Source  Counts  Percentage\n",
              "0         10xGenomics       1        0.43\n",
              "1   Downloadable File       2        0.85\n",
              "2              GitLab       2        0.85\n",
              "3                CRAN       2        0.85\n",
              "4        Bioconductor       2        0.85\n",
              "5         Sourceforge       3        1.28\n",
              "6           Bitbucket       6        2.56\n",
              "7       Supplementary       6        2.56\n",
              "8              Zenodo      14        5.98\n",
              "9             Website      16        6.84\n",
              "10             GitHub     180       76.92"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#replace upon request with no in the Data availability column of the data\n",
        "df_modified = df.copy()\n",
        "#create dataframe of counts\n",
        "df_temp=pd.DataFrame({'Counts': df_modified['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'].value_counts()}).reset_index()\n",
        "#rename columns\n",
        "df_temp=df_temp.rename(columns={\"index\":\"Source\"})\n",
        "#adding Github,Zenodo to Github and Zenodo Counts \n",
        "df_temp.loc[df_temp['Source']=='GitHub','Counts'] += int(df_temp.loc[df_temp['Source']=='GitHub, Zenodo','Counts'])\n",
        "df_temp.loc[df_temp['Source']=='Zenodo','Counts'] += int(df_temp.loc[df_temp['Source']=='GitHub, Zenodo','Counts'])\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='GitHub, Zenodo'].index[0])\n",
        "\n",
        "#adding Github,Supplementary to Github and Supplementary Counts\n",
        "df_temp.loc[df_temp['Source']=='GitHub','Counts'] += int(df_temp.loc[df_temp['Source']=='GitHub, Supplementary','Counts'])\n",
        "df_temp.loc[df_temp['Source']=='Supplementary','Counts'] += int(df_temp.loc[df_temp['Source']=='GitHub, Supplementary','Counts'])\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='GitHub, Supplementary'].index[0])\n",
        "\n",
        "#adding Website,Github to Github and Website Counts\n",
        "df_temp.loc[df_temp['Source']=='GitHub','Counts'] += int(df_temp.loc[df_temp['Source']=='Website, Github','Counts'])\n",
        "df_temp.loc[df_temp['Source']=='Website','Counts'] += int(df_temp.loc[df_temp['Source']=='Website, Github','Counts'])\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='Website, Github'].index[0])\n",
        "\n",
        "#drop NA row\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='NA'].index[0])\n",
        "\n",
        "\n",
        "#add percentage column \n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "\n",
        "#sort data by percentage\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "df_temp"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "7e5ce4f2",
      "metadata": {},
      "source": [
        "### Figure for Supplementary Table 1 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "id": "749098cc",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Source</th>\n",
              "      <th>Counts</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Sourceforge</td>\n",
              "      <td>3</td>\n",
              "      <td>1.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bitbucket</td>\n",
              "      <td>6</td>\n",
              "      <td>2.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Supplementary</td>\n",
              "      <td>6</td>\n",
              "      <td>2.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Other</td>\n",
              "      <td>9</td>\n",
              "      <td>3.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Zenodo</td>\n",
              "      <td>14</td>\n",
              "      <td>5.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Website</td>\n",
              "      <td>16</td>\n",
              "      <td>6.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>GitHub</td>\n",
              "      <td>180</td>\n",
              "      <td>76.92</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Source  Counts  Percentage\n",
              "0    Sourceforge       3        1.28\n",
              "1      Bitbucket       6        2.56\n",
              "2  Supplementary       6        2.56\n",
              "3          Other       9        3.85\n",
              "4         Zenodo      14        5.98\n",
              "5        Website      16        6.84\n",
              "6         GitHub     180       76.92"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#replace upon request with no in the Data availability column of the data\n",
        "df_modified = df.copy()\n",
        "df_modified.loc[df_modified['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Downloadable File', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Other'\n",
        "df_modified.loc[df_modified['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'GitLab', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Other'\n",
        "df_modified.loc[df_modified['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'CRAN', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Other'\n",
        "df_modified.loc[df_modified['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == 'Bioconductor', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Other'\n",
        "df_modified.loc[df_modified['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] == '10xGenomics', 'If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'] = 'Other'\n",
        "\n",
        "#create dataframe of counts\n",
        "df_temp=pd.DataFrame({'Counts': df_modified['If Code availability = yes, where do they share code (supplementary/ GitHub/ other)'].value_counts()}).reset_index()\n",
        "#rename columns\n",
        "df_temp=df_temp.rename(columns={\"index\":\"Source\"})\n",
        "#adding Github,Zenodo to Github and Zenodo Counts \n",
        "df_temp.loc[df_temp['Source']=='GitHub','Counts'] += int(df_temp.loc[df_temp['Source']=='GitHub, Zenodo','Counts'])\n",
        "df_temp.loc[df_temp['Source']=='Zenodo','Counts'] += int(df_temp.loc[df_temp['Source']=='GitHub, Zenodo','Counts'])\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='GitHub, Zenodo'].index[0])\n",
        "\n",
        "#adding Github,Supplementary to Github and Supplementary Counts\n",
        "df_temp.loc[df_temp['Source']=='GitHub','Counts'] += int(df_temp.loc[df_temp['Source']=='GitHub, Supplementary','Counts'])\n",
        "df_temp.loc[df_temp['Source']=='Supplementary','Counts'] += int(df_temp.loc[df_temp['Source']=='GitHub, Supplementary','Counts'])\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='GitHub, Supplementary'].index[0])\n",
        "\n",
        "#adding Website,Github to Github and Website Counts\n",
        "df_temp.loc[df_temp['Source']=='GitHub','Counts'] += int(df_temp.loc[df_temp['Source']=='Website, Github','Counts'])\n",
        "df_temp.loc[df_temp['Source']=='Website','Counts'] += int(df_temp.loc[df_temp['Source']=='Website, Github','Counts'])\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='Website, Github'].index[0])\n",
        "\n",
        "#drop NA row\n",
        "df_temp = df_temp.drop(df_temp.loc[df_temp['Source']=='NA'].index[0])\n",
        "\n",
        "\n",
        "#add percentage column \n",
        "df_temp['Percentage'] = (df_temp['Counts']/df_temp['Counts'].sum()) * 100\n",
        "df_temp['Percentage']=df_temp['Percentage'].round(2)\n",
        "\n",
        "#sort data by percentage\n",
        "df_temp=df_temp.sort_values('Percentage', ascending=True)\n",
        "\n",
        "df_temp=df_temp.reset_index()\n",
        "df_temp=df_temp.drop('index', axis=1)\n",
        "df_temp"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "0907f250",
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_27034/3148919613.py:6: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(data=df_temp, x='Percentage',y='Source', palette=colors)\n",
            "/tmp/ipykernel_27034/3148919613.py:6: UserWarning: \n",
            "The palette list has fewer values (1) than needed (7) and will cycle, which may produce an uninterpretable plot.\n",
            "  sns.barplot(data=df_temp, x='Percentage',y='Source', palette=colors)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_style(\"white\")\n",
        "sns.set_context(\"paper\",font_scale=1)\n",
        "#sns.set_palette(\"pastel\")\n",
        "rcParams['font.family'] = 'sans-serif'\n",
        "colors = [\"#E69F00\"]\n",
        "sns.barplot(data=df_temp, x='Percentage',y='Source', palette=colors)\n",
        "plt.xlim(0,100)\n",
        "for index, row in df_temp.iterrows():\n",
        "    plt.annotate(f'{row[\"Percentage\"]}%', xy=(row['Percentage'],index),ha='left', va='center')\n",
        "sns.despine()\n",
        "plt.show()\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "id": "77936599",
      "metadata": {},
      "source": [
        "## Supplementary Table 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "175cdf0c",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Journal</th>\n",
              "      <th>Code Policy</th>\n",
              "      <th>Data Policy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bioinformatics</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BMC Bioinformatics</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Encouraged</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Genome Biology</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Genome Medicine</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Encouraged</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Nature Biotechnology</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Nature Genetics</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Nature Methods</td>\n",
              "      <td>Mandatory</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Nucleic Acids Research</td>\n",
              "      <td>Encouraged</td>\n",
              "      <td>Mandatory</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Journal Code Policy Data Policy\n",
              "0          Bioinformatics   Mandatory   Mandatory\n",
              "1      BMC Bioinformatics  Encouraged  Encouraged\n",
              "2          Genome Biology   Mandatory   Mandatory\n",
              "3         Genome Medicine  Encouraged  Encouraged\n",
              "4    Nature Biotechnology   Mandatory   Mandatory\n",
              "5         Nature Genetics   Mandatory   Mandatory\n",
              "6          Nature Methods   Mandatory   Mandatory\n",
              "7  Nucleic Acids Research  Encouraged   Mandatory"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_journal=pd.read_csv(\"../data/journal_data.csv\")\n",
        "df_journal"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}