JLPT_Analysis_and_Forecasting.ipynb

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        "**Analysis of Japanese-Language Proficiency Test (JLPT) Trends and Performance (1984-2023)**\n",
        "\n",
        "**Author:** Heroshi Joe Abejuela\n",
        "\n",
        "**Overview:**\n",
        "\n",
        "The **Japanese-Language Proficiency Test (JLPT)** is a globally recognized assessment that evaluates the proficiency of non-native speakers in Japanese. It is a critical tool for individuals pursuing academic, professional, or personal goals in Japan, serving as a benchmark for language competence.\n",
        "\n",
        "This analysis aims to provide a comprehensive examination of JLPT trends and performance from **1984 to 2023**. The focus is on understanding the **growth in test participation**, **regional performance differences**, and **future participation forecasts**. By analyzing historical data, the report seeks to offer insights into the JLPT's **global reach**, the **impact of external factors** such as the COVID-19 pandemic, and **performance variations** by location and test level.\n",
        "\n",
        "Data for this analysis was gathered from the **official JLPT website**, covering several decades. Key findings include **trends in the number of applicants and examinees**, **variations in passing rates by location**, and **average scores across different JLPT levels**. This analysis not only identifies significant patterns and trends but also provides a **forecast for future participation**, contributing valuable insights for educational institutions and test-takers."
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        "# @title **Import libraries**\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import altair as alt\n",
        "import warnings\n",
        "import numpy as np\n",
        "warnings.filterwarnings('ignore')"
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              "   Year     Month Countries/Areas Number of Applicants Number of Examinees\n",
              "0  2023     Total              92              1481023             1265435\n",
              "1  2023      July              55               698062              602140\n",
              "2  2023  December              85               782961              663295\n",
              "3  2022     Total              89               932518              787954\n",
              "4  2022      July              53               426225              356505"
            ],
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              "      <th></th>\n",
              "      <th>Year</th>\n",
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              "      <td>2023</td>\n",
              "      <td>Total</td>\n",
              "      <td>92</td>\n",
              "      <td>1481023</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023</td>\n",
              "      <td>July</td>\n",
              "      <td>55</td>\n",
              "      <td>698062</td>\n",
              "      <td>602140</td>\n",
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              "      <td>2023</td>\n",
              "      <td>December</td>\n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 69,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 40,\n        \"samples\": [\n          \"2004\",\n          \"2007\",\n          \"2008\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Total\",\n          \"July\",\n          \"December\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Countries/Areas\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 44,\n        \"samples\": [\n          \"40\",\n          \"22\",\n          \"64\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Applicants\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 68,\n        \"samples\": [\n          \"419823\",\n          \"1027111\",\n          \"426225\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Examinees\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 68,\n        \"samples\": [\n          \"356244\",\n          \"887380\",\n          \"356505\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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      ],
      "source": [
        "# @title **Web Scraping Data from the official JLPT Website (1984-2023)**\n",
        "# URL of the JLPT statistics page\n",
        "url = \"https://www.jlpt.jp/e/statistics/archive.html\"\n",
        "\n",
        "# Request the webpage\n",
        "response = requests.get(url)\n",
        "soup = BeautifulSoup(response.content, \"html.parser\")\n",
        "\n",
        "# Initialize lists to store table data\n",
        "years, months, countries_areas, applicants, examinees = [], [], [], [], []\n",
        "\n",
        "# Locate the table and extract data\n",
        "rows = soup.find_all('tr')\n",
        "for row in rows[1:]:\n",
        "    cols = row.find_all('td')\n",
        "    if len(cols) == 5:  # Main rows with overall data\n",
        "        year = cols[0].text.strip()\n",
        "        years.append(year)\n",
        "        months.append(\"Total\")\n",
        "        countries_areas.append(cols[1].text.strip())\n",
        "        applicants.append(cols[2].text.strip().replace(',', ''))\n",
        "        examinees.append(cols[3].text.strip().replace(',', ''))\n",
        "    elif len(cols) == 4:  # Rows with monthly data\n",
        "        years.append(year)  # Reuse the previous year\n",
        "        months.append(cols[0].text.strip())\n",
        "        countries_areas.append(cols[1].text.strip())\n",
        "        applicants.append(cols[2].text.strip().replace(',', ''))\n",
        "        examinees.append(cols[3].text.strip().replace(',', ''))\n",
        "\n",
        "# Create DataFrame\n",
        "df = pd.DataFrame({\n",
        "    'Year': years,\n",
        "    'Month': months,\n",
        "    'Countries/Areas': countries_areas,\n",
        "    'Number of Applicants': applicants,\n",
        "    'Number of Examinees': examinees\n",
        "})\n",
        "\n",
        "# Separate the Year and Month\n",
        "# Identify rows where the Year is actually 'July' or 'December'\n",
        "df['Month'] = df['Year'].apply(lambda x: 'July' if x == 'July' else ('December' if x == 'December' else 'Total'))\n",
        "df['Year'] = df['Year'].apply(lambda x: None if x in ['July', 'December'] else x)\n",
        "\n",
        "# Forward-fill the Year column\n",
        "df['Year'] = df['Year'].ffill()\n",
        "\n",
        "#Save(Optional)\n",
        "#df.to_csv(\"1984_2023_jlpt_data.csv\", index=False)\n",
        "\n",
        "df.head()"
      ]
    },
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        "# @title **Number of JLPT Applicants and Examinees from 2009-2023**\n",
        "\n",
        "# Assuming df is your dataframe and the 'Year' column has been converted to integers\n",
        "df['Year'] = df['Year'].astype(int)\n",
        "\n",
        "# Convert 'Number of Applicants' and 'Number of Examinees' to numeric, handling commas\n",
        "df['Number of Applicants'] = pd.to_numeric(df['Number of Applicants'].str.replace(',', ''), errors='coerce')\n",
        "df['Number of Examinees'] = pd.to_numeric(df['Number of Examinees'].str.replace(',', ''), errors='coerce')\n",
        "\n",
        "# Filter the dataframe for relevant years and for the months of December and July\n",
        "df_filtered = df[(df['Year'] >= 2000) & (df['Year'] <= 2023) & (df['Month'].isin(['July', 'December']))]\n",
        "\n",
        "# Plot for July\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.bar(df_filtered[df_filtered['Month'] == 'July']['Year'] - 0.2,\n",
        "        df_filtered[df_filtered['Month'] == 'July']['Number of Applicants'],\n",
        "        width=0.4, label='Applicants (July)', color='blue')\n",
        "\n",
        "plt.bar(df_filtered[df_filtered['Month'] == 'July']['Year'] + 0.2,\n",
        "        df_filtered[df_filtered['Month'] == 'July']['Number of Examinees'],\n",
        "        width=0.4, label='Examinees (July)', color='green', alpha=0.7)\n",
        "\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of Applicants/Examinees')\n",
        "plt.title('Number of JLPT Applicants and Examinees (July, 2000-2023)')\n",
        "plt.xticks(df_filtered['Year'].unique(), rotation=45)\n",
        "plt.legend()\n",
        "plt.grid(False)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "# Plot for December\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.bar(df_filtered[df_filtered['Month'] == 'December']['Year'] - 0.2,\n",
        "        df_filtered[df_filtered['Month'] == 'December']['Number of Applicants'],\n",
        "        width=0.4, label='Applicants (December)', color='orange')\n",
        "\n",
        "plt.bar(df_filtered[df_filtered['Month'] == 'December']['Year'] + 0.2,\n",
        "        df_filtered[df_filtered['Month'] == 'December']['Number of Examinees'],\n",
        "        width=0.4, label='Examinees (December)', color='red', alpha=0.7)\n",
        "\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of Applicants/Examinees')\n",
        "plt.title('Number of JLPT Applicants and Examinees (December, 2000-2023)')\n",
        "plt.xticks(df_filtered['Year'].unique(), rotation=45)\n",
        "plt.legend()\n",
        "plt.grid(False)\n",
        "\n",
        "plt.show()"
      ],
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              "<Figure size 1200x600 with 1 Axes>"
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            "image/png": 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9aNAgw8XFxfzaMYzbz02OHDky7PKDGzduGIGBgUZAQIBx8+ZNc3uTJk2MYsWKpYq/cuWKIckYOHCgYRiG8ddff6X6XUjRv39/Q5L5NZTeMTMz/3sJCQkxPD09U/39Moz/+xuVcqtSpYrx999/pzlOgwYNjDJlylh3UgAyHDMHAGQ7xYoV0xtvvKGvvvpKp0+fzrBxu3TpYv63o6OjqlSpIsMw1LlzZ3O7t7e3SpUqpX/++SfV49u3b69cuXKZ77du3VoFCxbUr7/+KknauXOnDh8+rFdffVXnzp0zf8N55coV1atXT+vWrUs1LTi9K67/+uuvqlq1qsWlBzlz5tSbb76pf//9V/v27Uvfk5CBVq5cqfz58yt//vyqUKGCFi1apDfeeEMff/xxhh3ju+++k4ODg1q1amVua9eunX777bdUU6clKTg4WEFBQeb7RYoUUfPmzbVixYpUU8e7d+9ucb9nz56SZP55pseOHTt07Ngx9enTR97e3hZ9d17S4ebmZv53yjfh1atXl6Q0p/vf/bqoVauWzp07p4SEhAfm5O3trc2bN1s98+bOHG/evKlz586pRIkS8vb2TjPHN9980+Ica9WqpaSkJB0/flyStHr1aiUmJqpnz54WcX369LEqr+bNm2vVqlWpbs8//7w5xsHBQREREbp8+bIaNWqkzz//XIMGDUr1Le+d53jp0iWdPXtWtWrV0tWrV3XgwAGL2Jw5c1osHFeqVCl5e3urTJkyFlu3pfw7rb8ZdwsMDFStWrXM9/Pnz5/q782iRYtUq1Yt5c6d22KmREhIiJKSksyzmhYtWiQvLy/Vr1/fIi4oKEg5c+bUmjVrJMn8uly6dGmqleYf5Ny5c8qdO7dVj3kY7du3140bNyy2i124cKFu3bql119/PUOO0aNHD+3bt0+fffaZcuT4v/W8r127lubCka6urub+O/+b3tj0xGVm/mkZPXq0Vq9erbFjx6b6+yVJzz//vFatWqVFixbp7bfflpOT0z1n/aS8XgFkLXYrAJAtDR48WHPmzNHYsWMtrt1+FHdeJyrdngrp6uqaajqll5dXqmvBJalkyZIW900mk0qUKGG+rvjw4cOSpLCwsHvmEB8fb/FmOyAgIF25Hz9+PM29pFOmkh4/fjzTt3pMuTTDZDLJ3d1dZcqUSfMN5qOYO3euqlatqnPnzpl/JpUqVVJiYqIWLVqU6jKAu39G0u1V7a9evaozZ87I19f3nrHFixeXg4ODVXvTHz16VJIe+NyfP39ew4cP13fffae4uDiLvvj4+FTxd79WU14zFy5ckKen532PNW7cOIWFhalw4cIKCgpS48aN1b59exUrVuy+j7t27ZrGjBmjWbNm6b///rO43MHaHCWZiwR3P8/58+e36gNnoUKF0rU7RfHixTVs2DD1799fZcuW1YcffpgqZu/evRo8eLB+//33VIWWu8+xUKFCqdbs8PLyUuHChVO1SUqzWHW3u58z6fbzdudjDx8+rL///lv58+dPc4yU18/hw4cVHx+vAgUK3DeuTp06atWqlYYPH65Jkyapbt26atGihV599dV07aZgpHHZS0YrXbq0nn32Wc2bN89crJ03b56qV6+eagr7wxg/fry+/vprjRw5Uo0bN7boc3Nz040bN1I9JmX9kZSCUsp/0xubnrj4+HiLD+/Ozs7KkyePTfK/28KFCzV48GB17txZ3bp1SzPGx8dHPj4+km4Xw0ePHq369evr8OHDFn9Lpduvk8xc4wZA2igOAMiWihUrptdff11fffVVmtcD3+tNSFp7QKdwdHRMV5v0cG+IU2YFjB8/XhUrVkwz5u6t++71xu1JkC9fPptuKXj48GH99ddfktL+0D9v3ryHWiPgXmz5xvaVV17Rxo0b1b9/f1WsWFE5c+ZUcnKyGjZsmGo2ifRor8tXXnlFtWrV0k8//aSVK1dq/Pjx+vjjj/W///1PjRo1uufjevbsqVmzZqlPnz4KDg6Wl5eXeSu0jM7RVlauXCnp9taa586ds/gAc/HiRdWpU0eenp4aMWKEihcvLldXV23fvl3vvfdeqnO81/k9ynmn57HJycmqX7++BgwYkGZsyhaOycnJKlCgQJrX6ksyFxdMJpN++OEHbdq0SUuWLNGKFSvUqVMnTZgwQZs2bbrvdqJ58+ZNV9Ej5ThpPQf3+5t8p/bt26t37946efKkbty4oU2bNumzzz5L12PvJyIiQu+9957efvttDR48OFV/wYIFtWbNmlQfblNmrfn5+Znj7my/0+nTp5UnTx5zsSW9Y/bu3dtiW8I6deqkWuw0o/K/06pVq9S+fXs1adJE06dPT9V/L61bt9YHH3ygn3/+2bzGSYoLFy6kuW4BgMxFcQBAtjV48GDNnTs3zWnqKd883r1qcsq3lbaQMjMghWEYOnLkiMqXLy/p9jeXkuTp6ZnhH5qLFi2qgwcPpmpPmQpdtGjRDD3e42DevHlycnLSnDlzUn2oWr9+vaZOnaro6GiLb2Pv/hlJ0qFDh+Tu7p7qm9jDhw9bzNw4cuSIkpOT77uy+t1SfuZ79uy558/8woULioyM1PDhwzVkyJD75mqN+xUzChYsqHfeeUfvvPOO4uLiVLlyZY0aNeq+xYEffvhBYWFhFrt1XL9+PdXvWHqlvCYPHz5sMWvhzJkz6f7AaY3p06dr1apVGjVqlMaMGaO33nrLYvX0P/74Q+fOndP//vc/i9X3M3p3jUdVvHhxXb58+YF/Q4oXL67Vq1erZs2a6SoyVq9eXdWrV9eoUaM0f/58vfbaa/ruu+8sLre6W+nSpdP9/OTOnTvNSyvS+ze5bdu2Cg8P14IFC3Tt2jU5OTmpTZs26Xrsvfz888/q0qWLWrZsqWnTpqUZU7FiRX3zzTfav3+/AgMDze2bN28290vSU089pfz582vr1q2pxtiyZYtFQTi9Yw4YMMDisom7Z9RkZP53tr/00kuqUqWKvv/+e4tLFB4kZZZDWjOJjh07pgoVKqR7LAC2wZoDALKt4sWL6/XXX9eXX35pXlE5haenp/Lly5dqV4HPP//cZvl8++23FiuS//DDDzp9+rT5A1dQUJCKFy+uTz75RJcvX071+Lu3ebNG48aNtWXLFostsK5cuaKvvvpK/v7+Fm8Ks4t58+apVq1aatOmjVq3bm1x69+/vyRpwYIFFo+JioqyuD7+xIkT+vnnn9WgQYNUBYa732x/+umnknTfD9B3q1y5sgICAjR58uRUH6JTvkVNOe7d36pOnjw53cdJi4eHR6pjJiUlpXrjXqBAAfn5+aU59fhOjo6OqXL89NNP0/3N791CQkLk5OSkTz/91GLcRz3vtBw7dkz9+/dXq1at9P777+uTTz7RL7/8YrFNYFo/h8TERJv+zXgYr7zyiqKiorRixYpUfRcvXtStW7fMcUlJSRo5cmSquFu3bplfGxcuXEj1c035wPig10RwcLD27NnzwDjp9t/rAwcOWPyd27VrV7pXsM+XL58aNWqkuXPnat68eWrYsOEjfRO9bt06tW3bVrVr19a8efPk4JD2W+bmzZvLycnJ4nVgGIamT5+up556ymL7v1atWmnp0qUWW9hGRkbq0KFDevnll60eMzAwUCEhIebbneul2CL//fv3q0mTJvL399fSpUvvWVQ6e/ZsmrNAvvnmG0lKtZZHfHy8jh49muZWiQAyFzMHAGRrH3zwgebMmaODBw+m2ve5S5cuGjt2rLp06aIqVapo3bp1OnTokM1yyZMnj5577jl17NhRsbGxmjx5skqUKGHegtDBwUHffPONGjVqpGeeeUYdO3bUU089pf/++09r1qyRp6fnPbfge5CBAwdqwYIFatSokXr16qU8efJo9uzZOnbsmH788cd7vnF8WJGRkeZrVu/UokWLTFnbYPPmzTpy5EiqrfFSPPXUU6pcubLmzZtnsY992bJlFRoaql69esnFxcX8hvnObQBTHDt2TC+++KIaNmyoqKgozZ07V6+++qpV3345ODjoiy++ULNmzVSxYkV17NhRBQsW1IEDB7R3716tWLFCnp6eql27tsaNG6ebN2/qqaee0sqVKx/5G+ugoCCtXr1aEydOlJ+fnwICAlSqVCkVKlRIrVu3VoUKFZQzZ06tXr1af/31l8WMgLQ0bdpUc+bMkZeXlwIDAxUVFaXVq1c/9NaU+fPnV79+/TRmzBg1bdpUjRs31o4dO/Tbb79Z9aHv0KFDmjt3bqp2Hx8f1a9fX4ZhqFOnTnJzc9MXX3wh6fYWfz/++KN69+6tkJAQ+fn5qUaNGsqdO7fCwsLUq1cvmUwmzZkzJ0svg0hL//799csvv6hp06bq0KGDgoKCdOXKFe3evVs//PCD/v33X+XLl0916tTRW2+9pTFjxmjnzp1q0KCBnJycdPjwYS1atEhTpkxR69atNXv2bH3++ed66aWXVLx4cV26dElff/21PD09U12/frfmzZtr5MiRWrt2rRo0aHDf2E6dOmnixIkKDQ1V586dFRcXp+nTp+uZZ55J10Ka0u1LC1q3bi1JaRY9/v33XwUEBCgsLEwRERH3HOf48eN68cUXZTKZ1Lp1ay1atMiiv3z58uYZX4UKFVKfPn00fvx43bx5U88++6wWL16sP//8U/PmzbMoKr7//vtatGiRnn/+efXu3VuXL1/W+PHjVa5cOXXs2NEcZ82YmZX/pUuXFBoaqgsXLqh///5atmyZxZh3bhk5d+5cTZ8+XS1atFCxYsV06dIlrVixQqtWrVKzZs30wgsvWDx29erVMgxDzZs3v+95AcgEmbQrAgDYVFpbY6UICwszJFlsZWgYt7cl69y5s+Hl5WXkypXLeOWVV4y4uLh7bmV45syZVON6eHikOt7d2yambF+3YMECY9CgQUaBAgUMNzc3o0mTJsbx48dTPX7Hjh1Gy5Ytjbx58xouLi5G0aJFjVdeecWIjIx8YE73c/ToUaN169aGt7e34erqalStWtVYunRpqjilcyvD5ORkQ5LFlpEpWxne6zZnzhzDMG5vy9akSZN0524Y1m1l2LNnT0OScfTo0XvGDBs2zGLrwZTznjt3rlGyZEnDxcXFqFSpknnbwRQpz/2+ffuM1q1bG7ly5TJy585t9OjRw7h27ZpF7IO2Mkyxfv16o379+kauXLkMDw8Po3z58sann35q7j958qTx0ksvGd7e3oaXl5fx8ssvG6dOnUr3azWtrfEOHDhg1K5d23BzczNvDXjjxg2jf//+RoUKFcy5VKhQwfj888/v82zfduHCBaNjx45Gvnz5jJw5cxqhoaHGgQMHUj0H9/pdTeu5SUpKMoYPH24ULFjQcHNzM+rWrWvs2bMn1Zj3cr/XYsq2eVOmTDEkGT/++KPFY6Ojow1PT0+jcePG5rYNGzYY1atXN9zc3Aw/Pz9jwIAB5q0I78z77r8BKe71ur/7d+5eWxmm9di0tgC8dOmSMWjQIKNEiRKGs7OzkS9fPqNGjRrGJ598YiQmJlrEfvXVV0ZQUJDh5uZm5MqVyyhXrpwxYMAA49SpU4ZhGMb27duNdu3aGUWKFDFcXFyMAgUKGE2bNrXY8vN+ypcvb3Tu3NmibebMmYYkY/v27Rbtc+fONYoVK2Y4OzsbFStWNFasWJGurQxT3Lhxw8idO7fh5eWV6nfRMAxj9+7d6dqeL+W1eK/b3cdOSkoyRo8ebRQtWtRwdnY2nnnmGWPu3Llpjr1nzx6jQYMGhru7u+Ht7W289tprRkxMTKo4a8bMjPwf9Lf9zt/Hv/76y3j55ZfNrxkPDw+jcuXKxsSJEy22UUzRpk0b47nnnkvXuQGwLZNhPGYlbwDAEyEhIUFeXl4aPHhwmt/SPWlMJpO6d+/+wEXMhg0bpuHDh+vMmTMsoAU8wJw5c9S9e3dFR0ebdyOZOnWqevfurSNHjpjX3cgIt27dkp+fn5o1a6YZM2ak6v/88881YMAAHT161LyKPrJWTEyMAgIC9N133zFzAHgMsOYAAOChpOwEkB3XKwCQMV577TUVKVLEYo2Ov/76Sx4eHhm+EOrixYt15swZtW/fPs3+NWvWqFevXhQGHiOTJ09WuXLlKAwAjwnWHAAAWOXvv/82X6ueN29eNWnSJKtTAvCYcnBw0J49eyRJP/74o/744w/NmzdPXbp0sWql+/vZvHmz/v77b40cOVKVKlVSnTp10oy7+9p7ZL2xY8dmdQoA7kBxAABglf/9738aO3asqlSpokmTJsnT0zOrUwLwBOjXr58uXbqkzp07a9KkSRk27hdffKG5c+eqYsWK911oEABwf6w5AAAAAACAnWPNAQAAAAAA7BzFAQAAAAAA7BxrDmSi5ORknTp1Srly5ZLJZMrqdAAAAAAA2ZxhGLp06ZL8/Pzk4HDv+QEUBzLRqVOnVLhw4axOAwAAAABgZ06cOKFChQrds5/iQCbKlSuXpNs/FFb3BgAAAADYWkJCggoXLmz+PHovFAcyUcqlBJ6enhQHAAAAAACZ5kGXtrMgIQAAAAAAdo7iAAAAAAAAdo7iAAAAAAAAdo41Bx5DSUlJunnzZlanATvn5OQkR0fHrE4DAAAAQCagOPAYMQxDMTExunjxYlanAkiSvL295evr+8DFSwAAAAA82SgOPEZSCgMFChSQu7s7H8iQZQzD0NWrVxUXFydJKliwYBZnBAAAAMCWKA48JpKSksyFgbx582Z1OoDc3NwkSXFxcSpQoACXGAAAAADZGAsSPiZS1hhwd3fP4kyA/5PyemQNDAAAACB7ozjwmOFSAjxOeD0CAAAA9oHiAAAAAAAAdo7iAJ4Yw4YNU8WKFc33O3TooBYtWmRZPo/i3LlzKlCggP7999+HHuPu5+NBli9frooVKyo5OfmhjwkAAAAge6I48JgzmTL39rCioqLk6OioJk2aZNzJP8CUKVMUERGRaceTpLp166pPnz6PPM6oUaPUvHlz+fv7S5L+/fdfmUwm7dy585HHvpeGDRvKyclJ8+bNs9kxAAAAADyZKA4gQ8yYMUM9e/bUunXrdOrUqUw5ppeXl7y9vTPlWBnp6tWrmjFjhjp37pzpx+7QoYOmTp2a6ccFAAAA8HijOIBHdvnyZS1cuFDdunVTkyZNUn2b/8cff8hkMmnZsmUqX768XF1dVb16de3Zs8ccExERIW9vby1evFglS5aUq6urQkNDdeLEiXse9+7LCpKTkzVu3DiVKFFCLi4uKlKkiEaNGmXuf++99/T000/L3d1dxYoV04cffmixCn/KNP05c+bI399fXl5eatu2rS5dumQ+3tq1azVlyhSZTCaZTCb9+++/unDhgl577TXlz59fbm5uKlmypGbNmnXPvH/99Ve5uLioevXq94xJeT7utHjx4nsuELhu3To5OTkpJibGor1Pnz6qVauW+X6zZs20detWHT169J7HBgAAAGB/srQ44O/vb/6Qdeete/fukqTr16+re/fuyps3r3LmzKlWrVopNjbWYozo6Gg1adJE7u7uKlCggPr3769bt25ZxPzxxx+qXLmyXFxcVKJEiTSnok+bNk3+/v5ydXVVtWrVtGXLFov+9ORir77//nuVLl1apUqV0uuvv66ZM2fKMIxUcf3799eECRP0119/KX/+/GrWrJnFh/OrV69q1KhR+vbbb7VhwwZdvHhRbdu2TXcegwYN0tixY/Xhhx9q3759mj9/vnx8fMz9uXLlUkREhPbt26cpU6bo66+/1qRJkyzGOHr0qBYvXqylS5dq6dKlWrt2rcaOHSvp9mUMwcHB6tq1q06fPq3Tp0+rcOHC5uP99ttv2r9/v7744gvly5fvnnn++eefCgoKSvd5pUft2rVVrFgxzZkzx9x28+ZNzZs3T506dTK3FSlSRD4+Pvrzzz8z9PgAAAAAnmxZWhz466+/zB+yTp8+rVWrVkmSXn75ZUlS3759tWTJEi1atEhr167VqVOn1LJlS/Pjk5KS1KRJEyUmJmrjxo2aPXu2IiIiNGTIEHPMsWPH1KRJEz3//PPauXOn+vTpoy5dumjFihXmmIULFyo8PFxDhw7V9u3bVaFCBYWGhiouLs4c86Bc7NmMGTP0+uuvS7p9XXt8fLzWrl2bKm7o0KGqX7++ypUrp9mzZys2NlY//fSTuf/mzZv67LPPFBwcrKCgIM2ePVsbN25MVahJy6VLlzRlyhSNGzdOYWFhKl68uJ577jl16dLFHDN48GDVqFFD/v7+atasmfr166fvv//eYpzk5GRFRESobNmyqlWrlt544w1FRkZKun0Zg7Ozs9zd3eXr6ytfX185OjoqOjpalSpVUpUqVeTv76+QkBA1a9bsnrkeP35cfn5+Dzwna3Xu3NlixsKSJUt0/fp1vfLKKxZxfn5+On78eIYfHwAAAMCTK0uLA/nz5zd/yPL19dXSpUtVvHhx1alTR/Hx8ZoxY4YmTpyoF154QUFBQZo1a5Y2btyoTZs2SZJWrlypffv2ae7cuapYsaIaNWqkkSNHatq0aUpMTJQkTZ8+XQEBAZowYYLKlCmjHj16qHXr1hbfGE+cOFFdu3ZVx44dFRgYqOnTp8vd3V0zZ86UpHTlYq8OHjyoLVu2qF27dpKkHDlyqE2bNpoxY0aq2ODgYPO/8+TJo1KlSmn//v3mthw5cujZZ5813y9durS8vb0tYu5l//79unHjhurVq3fPmIULF6pmzZry9fVVzpw5NXjwYEVHR1vE+Pv7K1euXOb7BQsWtCgSpaVbt2767rvvVLFiRQ0YMEAbN268b/y1a9fk6ur6wHOyVocOHXTkyBHzazIiIkKvvPKKPDw8LOLc3Nx09erVDD8+AAAAgCfXY7PmQGJioubOnatOnTrJZDJp27ZtunnzpkJCQswxpUuXVpEiRRQVFSXp9gr55cqVs5g6HhoaqoSEBO3du9ccc+cYKTEpYyQmJmrbtm0WMQ4ODgoJCTHHpCeXtNy4cUMJCQkWt+xmxowZunXrlvz8/JQjRw7lyJFDX3zxhX788UfFx8dnWh5ubm737Y+KitJrr72mxo0ba+nSpdqxY4c++OADcxEphZOTk8V9k8n0wK3/GjVqpOPHj6tv3746deqU6tWrp379+t0zPl++fLpw4cJ9x3RwcEh1acadl2CkpUCBAmrWrJlmzZql2NhY/fbbbxaXFKQ4f/688ufPf9+xAAAAANiXx6Y4sHjxYl28eFEdOnSQJMXExMjZ2TnVomw+Pj7mRddiYmIsCgMp/Sl994tJSEjQtWvXdPbsWSUlJaUZc+cYD8olLWPGjJGXl5f5Vrhw4Qc/EU+QW7du6dtvv9WECRO0c+dO823Xrl3y8/PTggULLOLvnGVx4cIFHTp0SGXKlLEYb+vWreb7Bw8e1MWLFy1i7qVkyZJyc3MzXwJwt40bN6po0aL64IMPVKVKFZUsWfKhptY7OzsrKSkpVXv+/PkVFhamuXPnavLkyfrqq6/uOUalSpW0b9+++x4nf/78unTpkq5cuWJuS882h126dNHChQv11VdfqXjx4qpZs6ZF//Xr13X06FFVqlTpgWMBAAAAsB+PTXFgxowZatSokU2uxc4qgwYNUnx8vPl2v5X3n0RLly7VhQsX1LlzZ5UtW9bi1qpVq1SXFowYMUKRkZHas2ePOnTooHz58lnsNuDk5KSePXtq8+bN2rZtmzp06KDq1auratWqD8zF1dVV7733ngYMGKBvv/1WR48e1aZNm8w5lCxZUtHR0fruu+909OhRTZ061WK9g/Ty9/fX5s2b9e+//+rs2bNKTk7WkCFD9PPPP+vIkSPau3evli5det+CRmhoqPbu3Xvf2QPVqlWTu7u73n//fR09elTz589PcyHNtMb29PTURx99pI4dO6bq37Rpk1xcXCwu8QAAAACymslkuxvS57EoDhw/flyrV6+2WDzO19dXiYmJunjxokVsbGysfH19zTF37xiQcv9BMZ6ennJzc1O+fPnk6OiYZsydYzwol7S4uLjI09PT4mYtw8jcmzVmzJihkJAQeXl5pepr1aqVtm7dqr///tvcNnbsWPXu3VtBQUGKiYnRkiVL5OzsbO53d3fXe++9p1dffVU1a9ZUzpw5tXDhwnTn8+GHH+rdd9/VkCFDVKZMGbVp08a8XsCLL76ovn37qkePHqpYsaI2btyoDz/80LoTltSvXz85OjoqMDBQ+fPnV3R0tJydnTVo0CCVL19etWvXlqOjo7777rt7jlGuXDlVrlzZYjHElEsXcuTIIen2mgxz587Vr7/+qnLlymnBggUaNmzYA/NzcHBQhw4dlJSUpPbt26fqX7BggV577TW5u7tbeeYAAAAAsjXjMTB06FDD19fXuHnzprnt4sWLhpOTk/HDDz+Y2w4cOGBIMqKiogzDMIxff/3VcHBwMGJjY80xX375peHp6Wlcv37dMAzDGDBggFG2bFmL47Vr184IDQ01369atarRo0cP8/2kpCTjqaeeMsaMGZPuXNIjPj7ekGTEx8en6rt27Zqxb98+49q1a+ke70mxZs0aQ5Jx4cKFe8bMmjXL8PLyyrScstrSpUuNMmXKGElJSYZhGEZUVJQhyThz5swjj92pUyejWbNmqdrPnDlj5MmTx/jnn3/SPVZ2fl0CAADg8WHLr0Dt3f0+h94pR1YWJqTb35jOmjVLYWFh5m9NpdvbxnXu3Fnh4eHKkyePPD091bNnTwUHB6t69eqSpAYNGigwMFBvvPGGxo0bp5iYGA0ePFjdu3eXi4uLJOntt9/WZ599pgEDBqhTp076/fff9f3332vZsmXmY4WHhyssLExVqlRR1apVNXnyZF25csU8LTs9uQDWaNKkiQ4fPqz//vtPN27c0Pjx41WhQgXly5fvoceMj4/X7t27NX/+fP3yyy+p+v/99199/vnnCggIeJTUAQAAAGRDWV4cWL16taKjo9NcVX3SpElycHBQq1atdOPGDYWGhurzzz839zs6Omrp0qXq1q2bgoOD5eHhobCwMI0YMcIcExAQoGXLlqlv376aMmWKChUqpG+++UahoaHmmDZt2ujMmTMaMmSIYmJiVLFiRS1fvtxikcIH5QJYq0+fPtq5c6dq1KihihUr6ttvv32k8Zo3b64tW7bo7bffVv369VP1V6lSRVWqVHmkYwAAAADInkyGYe2V5nhYCQkJ8vLyUnx8fKr1B65fv65jx44pICBArq6uWZQhYInXJQAAADKDLRcOtPdPvPf7HHqnx2JBQgAAAAAAkHUoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDiDb6tChg1q0aJHVaaRL7dq1NX/+/Id+/B9//CGTyaSLFy+mK/7s2bMqUKCATp48+dDHBAAAAJB95MjqBHB/zRY0y9TjLWm3xKr4Dh06aPbs2anaQ0NDtXz58oxK66FMmTJFxhOwqekvv/yi2NhYtW3b1tzm7++vPn36qE+fPjY5Zr58+dS+fXsNHTpUM2bMsMkxAAAAADw5KA7gkTVs2FCzZs2yaHNxccmibP6Pl5dXVqeQLlOnTlXHjh3l4JC5E3k6duyooKAgjR8/Xnny5MnUYwMAAAB4vHBZAR6Zi4uLfH19LW65c+eWdHu6u7Ozs/78809z/Lhx41SgQAHFxsZKkpYvX67nnntO3t7eyps3r5o2baqjR4+a4//991+ZTCZ9//33qlWrltzc3PTss8/q0KFD+uuvv1SlShXlzJlTjRo10pkzZ8yPu/uygrp166pXr14aMGCA8uTJI19fXw0bNsziXC5evKguXboof/788vT01AsvvKBdu3ZZxPz888+qXLmyXF1dVaxYMQ0fPly3bt2SJBmGoWHDhqlIkSJycXGRn5+fevXqdc/n7syZM/r999/VrNm9Z4iknP/OnTst8jSZTPrjjz9SxV+5ckWenp764YcfLNoXL14sDw8PXbp0SZL0zDPPyM/PTz/99NM9jw0AAADAPlAcgE3VrVtXffr00RtvvKH4+Hjt2LFDH374ob755hv5+PhIuv1hNjw8XFu3blVkZKQcHBz00ksvKTk52WKsoUOHavDgwdq+fbty5MihV199VQMGDNCUKVP0559/6siRIxoyZMh985k9e7Y8PDy0efNmjRs3TiNGjNCqVavM/S+//LLi4uL022+/adu2bapcubLq1aun8+fPS5L+/PNPtW/fXr1799a+ffv05ZdfKiIiQqNGjZIk/fjjj5o0aZK+/PJLHT58WIsXL1a5cuXumc/69evl7u6uMmXKPNTzmxYPDw+1bds21WyOWbNmqXXr1sqVK5e5rWrVqhaFGwAAAAD2icsK8MiWLl2qnDlzWrS9//77ev/99yVJH330kVatWqU333xTe/bsUVhYmF588UVzbKtWrSweO3PmTOXPn1/79u1T2bJlze39+vVTaGioJKl3795q166dIiMjVbNmTUlS586dFRERcd9cy5cvr6FDh0qSSpYsqc8++0yRkZGqX7++1q9fry1btiguLs58WcQnn3yixYsX64cfftCbb76p4cOHa+DAgQoLC5MkFStWTCNHjtSAAQM0dOhQRUdHy9fXVyEhIXJyclKRIkVUtWrVe+Zz/Phx+fj4ZPglBV26dFGNGjV0+vRpFSxYUHFxcfr111+1evVqizg/Pz/t2LEjQ48NAAAA4MnDzAE8sueff147d+60uL399tvmfmdnZ82bN08//vijrl+/rkmTJlk8/vDhw2rXrp2KFSsmT09P+fv7S5Kio6Mt4sqXL2/+d8qsgzu/lffx8VFcXNx9c71zDEnmD86StGvXLl2+fFl58+ZVzpw5zbdjx46ZL3PYtWuXRowYYdHftWtXnT59WlevXtXLL7+sa9euqVixYuratat++ukn8yUHabl27ZpcXV3vm/PDqFq1qp555hnzYpFz585V0aJFVbt2bYs4Nzc3Xb16NcOPDwAAAODJwswBPDIPDw+VKFHivjEbN26UJJ0/f17nz5+Xh4eHua9Zs2YqWrSovv76a/n5+Sk5OVlly5ZVYmKixRhOTk7mf5tMpjTb7r4U4W53xt/9mMuXL6tgwYJpXsfv7e1tjhk+fLhatmyZKsbV1VWFCxfWwYMHtXr1aq1atUrvvPOOxo8fr7Vr16Y6tnR714ALFy7cN+eUWQV37rxw8+bN+z5Guj17YNq0aRo4cKBmzZqljh07mp+3FOfPn1f+/PkfOBYAAACA7I2ZA7C5o0ePqm/fvvr6669VrVo1hYWFmT+Qnzt3TgcPHtTgwYNVr149lSlT5oEflm2lcuXKiomJUY4cOVSiRAmLW758+cwxBw8eTNVfokQJ84d4Nzc3NWvWTFOnTtUff/yhqKgo7d69O81jVqpUSTExMfc955QP76dPnza33bk44b28/vrrOn78uKZOnap9+/aZL4W40549e1SpUqUHjgUAAAAge2PmAB7ZjRs3FBMTY9GWI0cO5cuXT0lJSXr99dcVGhqqjh07qmHDhipXrpwmTJig/v37K3fu3MqbN6+++uorFSxYUNHR0Ro4cGCWnEdISIiCg4PVokULjRs3Tk8//bROnTqlZcuW6aWXXlKVKlU0ZMgQNW3aVEWKFFHr1q3l4OCgXbt2ac+ePfroo48UERGhpKQkVatWTe7u7po7d67c3NxUtGjRNI9ZqVIl5cuXTxs2bFDTpk3TjHFzc1P16tU1duxYBQQEKC4uToMHD37g+eTOnVstW7ZU//791aBBAxUqVMii/+rVq9q2bZtGjx5t/ZMFAAAAIFuhOPCYW9JuSVan8EDLly9XwYIFLdpKlSqlAwcOaNSoUTp+/LiWLl0q6fY1/l999ZXatWunBg0aqEKFCvruu+/Uq1cvlS1bVqVKldLUqVNVt27dTD8Pk8mkX3/9VR988IE6duyoM2fOyNfXV7Vr1zavcRAaGqqlS5dqxIgR+vjjj+Xk5KTSpUurS5cukm5ffjB27FiFh4crKSlJ5cqV05IlS5Q3b940j+no6KiOHTtq3rx5FsWB5ORk5cjxf7+eM2fOVOfOnRUUFKRSpUpp3LhxatCgwQPPqXPnzpo/f746deqUqu/nn39WkSJFVKtWLaueJwAAAADZj8m480Jm2FRCQoK8vLwUHx8vT09Pi77r16/r2LFjCggIsMkCdXh8xcTE6JlnntH27dtVtGhRJSUlydPTU7Nnz1br1q0faew5c+aob9++OnXqlJydnS36qlevrl69eunVV1+95+N5XQIAACAz3LU0Voay90+89/sceidmDgBZzNfXVzNmzFB0dLQcHR317bffKikpSc8999xDj3n16lWdPn1aY8eO1VtvvZWqMHD27Fm1bNlS7dq1e9T0AQAAAGQDFAeAx0CLFi0k3d69IG/evJozZ458fX0ferxx48Zp1KhRql27tgYNGpSqP1++fBowYMBDjw8AAAAge6E4ADxGzp49myHjDBs2TMOGDcuQsQAAAABkf2xlCAAAAACAnaM48JhhfUg8Tng9AgAAAPaB4sBjwsnJSdLtheSAx0XK6zHl9QkAAAAge2LNgceEo6OjvL29FRcXJ0lyd3eXyZb7eQD3YRiGrl69qri4OHl7e8vR0TGrUwIAAABgQxQHHiMpq9OnFAiArObt7f1IuyYAAAAAeDJQHHiMmEwmFSxYUAUKFNDNmzezOh3YOScnJ2YMAAAAAHaC4sBjyNHRkQ9lAAAAAIBMQ3EAAAAAdseWSzux2Q+AJxG7FQAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOeyvDjw33//6fXXX1fevHnl5uamcuXKaevWreZ+wzA0ZMgQFSxYUG5ubgoJCdHhw4ctxjh//rxee+01eXp6ytvbW507d9bly5ctYv7++2/VqlVLrq6uKly4sMaNG5cql0WLFql06dJydXVVuXLl9Ouvv1r0pycXAAAAAACeNFlaHLhw4YJq1qwpJycn/fbbb9q3b58mTJig3Llzm2PGjRunqVOnavr06dq8ebM8PDwUGhqq69evm2Nee+017d27V6tWrdLSpUu1bt06vfnmm+b+hIQENWjQQEWLFtW2bds0fvx4DRs2TF999ZU5ZuPGjWrXrp06d+6sHTt2qEWLFmrRooX27NljVS4AAAAAADxpTIZhGFl18IEDB2rDhg36888/0+w3DEN+fn5699131a9fP0lSfHy8fHx8FBERobZt22r//v0KDAzUX3/9pSpVqkiSli9frsaNG+vkyZPy8/PTF198oQ8++EAxMTFydnY2H3vx4sU6cOCAJKlNmza6cuWKli5daj5+9erVVbFiRU2fPj1duTxIQkKCvLy8FB8fL09Pz4d/4gAAAPBITCbbjZ11766BJ5ctfyebzm9ms7GXtFtis7EzSno/h2bpzIFffvlFVapU0csvv6wCBQqoUqVK+vrrr839x44dU0xMjEJCQsxtXl5eqlatmqKioiRJUVFR8vb2NhcGJCkkJEQODg7avHmzOaZ27drmwoAkhYaG6uDBg7pw4YI55s7jpMSkHCc9udztxo0bSkhIsLgBAAAAAPC4ydLiwD///KMvvvhCJUuW1IoVK9StWzf16tVLs2fPliTFxMRIknx8fCwe5+PjY+6LiYlRgQIFLPpz5MihPHnyWMSkNcadx7hXzJ39D8rlbmPGjJGXl5f5Vrhw4Qc9JQAAAAAAZLosLQ4kJyercuXKGj16tCpVqqQ333xTXbt21fTp07MyrQwzaNAgxcfHm28nTpzI6pQAAAAAAEglS4sDBQsWVGBgoEVbmTJlFB0dLUny9fWVJMXGxlrExMbGmvt8fX0VFxdn0X/r1i2dP3/eIiatMe48xr1i7ux/UC53c3Fxkaenp8UNAAAAAIDHTZYWB2rWrKmDBw9atB06dEhFixaVJAUEBMjX11eRkZHm/oSEBG3evFnBwcGSpODgYF28eFHbtm0zx/z+++9KTk5WtWrVzDHr1q3TzZs3zTGrVq1SqVKlzDsjBAcHWxwnJSblOOnJBQAAAACAJ1GWFgf69u2rTZs2afTo0Tpy5Ijmz5+vr776St27d5ckmUwm9enTRx999JF++eUX7d69W+3bt5efn59atGgh6fZMg4YNG6pr167asmWLNmzYoB49eqht27by8/OTJL366qtydnZW586dtXfvXi1cuFBTpkxReHi4OZfevXtr+fLlmjBhgg4cOKBhw4Zp69at6tGjR7pzAQAAAADgSZQjKw/+7LPP6qefftKgQYM0YsQIBQQEaPLkyXrttdfMMQMGDNCVK1f05ptv6uLFi3ruuee0fPlyubq6mmPmzZunHj16qF69enJwcFCrVq00depUc7+Xl5dWrlyp7t27KygoSPny5dOQIUP05ptvmmNq1Kih+fPna/DgwXr//fdVsmRJLV68WGXLlrUqFwAAAAAAnjQmw2An1syS3v0lAQAAYFu23FOdd9eA9Wz5O9l0fjObjb2k3RKbjZ1R0vs5NEsvKwAAAAAAAFmP4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHYuR1YnAAAAAGQnzRY0s9nYS9otsdnYAOwbMwcAAAAAALBzVhcHrl27pqtXr5rvHz9+XJMnT9bKlSszNDEAAAAAAJA5rC4ONG/eXN9++60k6eLFi6pWrZomTJig5s2b64svvsjwBAEAAAAAgG1ZXRzYvn27atWqJUn64Ycf5OPjo+PHj+vbb7/V1KlTMzxBAAAAZC6TyXY3AMDjyeriwNWrV5UrVy5J0sqVK9WyZUs5ODioevXqOn78eIYnCAAAAAAAbMvq4kCJEiW0ePFinThxQitWrFCDBg0kSXFxcfL09MzwBAEAAAAAgG1ZXRwYMmSI+vXrJ39/f1WtWlXBwcGSbs8iqFSpUoYnCAAAAAAAbCuHtQ9o3bq1nnvuOZ0+fVoVKlQwt9erV08vvfRShiYHAAAAAABsz+qZA5Lk6+urXLlyadWqVbp27Zok6dlnn1Xp0qUzNDkAAAAAAGB7VhcHzp07p3r16unpp59W48aNdfr0aUlS586d9e6772Z4ggAAAAAAwLasLg707dtXTk5Oio6Olru7u7m9TZs2Wr58eYYmBwAAAAAAbM/qNQdWrlypFStWqFChQhbtJUuWZCtDAAAAAACeQFbPHLhy5YrFjIEU58+fl4uLS4YkBQAAAAAAMo/VxYFatWrp22+/Nd83mUxKTk7WuHHj9Pzzz2docgAAAAAAwPasvqxg3LhxqlevnrZu3arExEQNGDBAe/fu1fnz57VhwwZb5AgAAAAAAGzI6pkDZcuW1aFDh1SzZk01b95cV65cUcuWLbVjxw4VL17cFjkCAAAAAAAbsnrmgCR5eXlp8ODBGZ0LAAAAAADIAlbPHJCkP//8U6+//rpq1Kih//77T5I0Z84crV+/PkOTAwAAAAAAtmd1ceDHH39UaGio3NzctH37dt24cUOSFB8fr9GjR2d4ggAAAAAAwLasLg589NFHmj59ur7++ms5OTmZ22vWrKnt27dnaHIAAAAAAMD2rC4OHDx4ULVr107V7uXlpYsXL2ZETgAAAAAAIBNZXRzw9fXVkSNHUrWvX79exYoVy5CkAAAAAABA5rG6ONC1a1f17t1bmzdvlslk0qlTpzRv3jz169dP3bp1s0WOAAAAAADAhqzeynDgwIFKTk5WvXr1dPXqVdWuXVsuLi7q16+fevbsaYscAQAAAACADVldHDCZTPrggw/Uv39/HTlyRJcvX1ZgYKBy5sxpi/wAAAAAAICNWV0cSOHs7KzAwMCMzAUAAAAAAGQBq4sDV65c0dixYxUZGam4uDglJydb9P/zzz8ZlhwAAAAAALA9qxck7NKli2bMmKFatWqpR48e6t27t8XNGsOGDZPJZLK4lS5d2tx//fp1de/eXXnz5lXOnDnVqlUrxcbGWowRHR2tJk2ayN3dXQUKFFD//v1169Yti5g//vhDlStXlouLi0qUKKGIiIhUuUybNk3+/v5ydXVVtWrVtGXLFov+9OQCAAAAAMCTyOqZA7/99puWLVummjVrZkgCzzzzjFavXv1/CeX4v5T69u2rZcuWadGiRfLy8lKPHj3UsmVLbdiwQZKUlJSkJk2ayNfXVxs3btTp06fVvn17OTk5afTo0ZKkY8eOqUmTJnr77bc1b948RUZGqkuXLipYsKBCQ0MlSQsXLlR4eLimT5+uatWqafLkyQoNDdXBgwdVoECBdOUCAAAAAMCTyuqZA7lz51aePHkyLIEcOXLI19fXfMuXL58kKT4+XjNmzNDEiRP1wgsvKCgoSLNmzdLGjRu1adMmSdLKlSu1b98+zZ07VxUrVlSjRo00cuRITZs2TYmJiZKk6dOnKyAgQBMmTFCZMmXUo0cPtW7dWpMmTTLnMHHiRHXt2lUdO3ZUYGCgpk+fLnd3d82cOTPduQAAAAAA8KSyujgwcuRIDRkyRFevXs2QBA4fPiw/Pz8VK1ZMr732mqKjoyVJ27Zt082bNxUSEmKOLV26tIoUKaKoqChJUlRUlMqVKycfHx9zTGhoqBISErR3715zzJ1jpMSkjJGYmKht27ZZxDg4OCgkJMQck55c0nLjxg0lJCRY3AAAAAAAeNxYfVnBhAkTdPToUfn4+Mjf319OTk4W/du3b0/3WNWqVVNERIRKlSql06dPa/jw4apVq5b27NmjmJgYOTs7y9vb2+IxPj4+iomJkSTFxMRYFAZS+lP67heTkJCga9eu6cKFC0pKSkoz5sCBA+YxHpRLWsaMGaPhw4en78kAAAAAACCLWF0caNGiRYYdvFGjRuZ/ly9fXtWqVVPRokX1/fffy83NLcOOk1UGDRqk8PBw8/2EhAQVLlw4CzMCAAAAACA1q4sDQ4cOtUUekiRvb289/fTTOnLkiOrXr6/ExERdvHjR4hv72NhY+fr6SpJ8fX1T7SqQsoPAnTF37yoQGxsrT09Pubm5ydHRUY6OjmnG3DnGg3JJi4uLi1xcXKx7EgAAAAAAyGRWrzlgS5cvX9bRo0dVsGBBBQUFycnJSZGRkeb+gwcPKjo6WsHBwZKk4OBg7d69W3FxceaYVatWydPTU4GBgeaYO8dIiUkZw9nZWUFBQRYxycnJioyMNMekJxcAAAAAAJ5U6Zo5kCdPHh06dEj58uVT7ty5ZTKZ7hl7/vz5dB+8X79+atasmYoWLapTp05p6NChcnR0VLt27eTl5aXOnTsrPDxcefLkkaenp3r27Kng4GBVr15dktSgQQMFBgbqjTfe0Lhx4xQTE6PBgwere/fu5m/s3377bX322WcaMGCAOnXqpN9//13ff/+9li1bZs4jPDxcYWFhqlKliqpWrarJkyfrypUr6tixoySlKxcAAAAAAJ5U6SoOTJo0Sbly5ZIkTZ48OcMOfvLkSbVr107nzp1T/vz59dxzz2nTpk3Knz+/+bgODg5q1aqVbty4odDQUH3++efmxzs6Omrp0qXq1q2bgoOD5eHhobCwMI0YMcIcExAQoGXLlqlv376aMmWKChUqpG+++UahoaHmmDZt2ujMmTMaMmSIYmJiVLFiRS1fvtxikcIH5QIAAAAAwJPKZBiGkdVJ2IuEhAR5eXkpPj5enp6eWZ0OAABAmu4zSfSRPS7vPG15jk3nN7PZ2EvaLbHZ2EBW4nfSdtL7OdTqBQlTxMXFKS4uTsnJyRbt5cuXf9ghAQAAAABAFrC6OLBt2zaFhYVp//79unvSgclkUlJSUoYlBwAAAAAAbM/q4kCnTp309NNPa8aMGfLx8bnv4oQAAAAAAODxZ3Vx4J9//tGPP/6oEiVK2CIfAAAAAACQyRysfUC9evW0a9cuW+QCAAAAAACygNUzB7755huFhYVpz549Klu2rJycnCz6X3zxxQxLDgAAAAAA2J7VxYGoqCht2LBBv/32W6o+FiQEAAAAAODJY/VlBT179tTrr7+u06dPKzk52eJGYQAAAAAAgCeP1cWBc+fOqW/fvvLx8bFFPgAAAAAAIJNZXRxo2bKl1qxZY4tcAAAAAABAFrB6zYGnn35agwYN0vr161WuXLlUCxL26tUrw5IDAAAAAAC291C7FeTMmVNr167V2rVrLfpMJhPFAQAAAAAAnjBWFweOHTtmizwAAABgB5otaGazsZe0W2KzsQEgu7N6zQEAAAAAAJC9WD1zQJJOnjypX375RdHR0UpMTLTomzhxYoYkBgAA8DgymWw3tmHYbmwAAO7H6uJAZGSkXnzxRRUrVkwHDhxQ2bJl9e+//8owDFWuXNkWOQIAAAAAABuy+rKCQYMGqV+/ftq9e7dcXV31448/6sSJE6pTp45efvllW+QIAAAAAABsyOriwP79+9W+fXtJUo4cOXTt2jXlzJlTI0aM0Mcff5zhCQIAAAAAANuyujjg4eFhXmegYMGCOnr0qLnv7NmzGZcZAAAAAADIFFavOVC9enWtX79eZcqUUePGjfXuu+9q9+7d+t///qfq1avbIkcAAAAAAGBDVhcHJk6cqMuXL0uShg8frsuXL2vhwoUqWbIkOxUAAAAAAPAEsro4UKxYMfO/PTw8NH369AxNCAAAAAAAZC6r1xxYs2bNPfu+/PLLR0oGAAAAAABkPquLAw0bNlT//v118+ZNc9vZs2fVrFkzDRw4MEOTAwAAAAAAtvdQMwd++uknPfvss9q3b5+WLVumsmXLKiEhQTt37rRBigAAAAAAwJasLg7UqFFDO3fuVNmyZVW5cmW99NJL6tu3r/744w8VLVrUFjkCAAAAAAAbsro4IEmHDh3S1q1bVahQIeXIkUMHDx7U1atXMzo3AAAAAACQCawuDowdO1bBwcGqX7++9uzZoy1btmjHjh0qX768oqKibJEjAAAAAACwIauLA1OmTNHixYv16aefytXVVWXLltWWLVvUsmVL1a1b1wYpAgAAAAAAW8ph7QN2796tfPnyWbQ5OTlp/Pjxatq0aYYlBgAAAAAAMofVMwfuLgzcqUyZMo+UDAAAAAAAyHzpLg64u7vrzJkz5vtNmjTR6dOnzfdjY2NVsGDBjM0OAAAAAADYXLqLA9evX5dhGOb769at07Vr1yxi7uwHAAAAAABPhofayvBeTCZTRg4HAAAAAAAyQYYWBwAAAAAAwJMn3cUBk8lkMTPg7vsAAAAAAODJlO6tDA3D0NNPP20uCFy+fFmVKlWSg4ODuR8AAAAAADx50l0cmDVrli3zAAAAAAAAWSTdxYFatWqpWLFitswFAAAAAABkgXSvOVC+fHmVLVtW77//vrZs2WLLnAAAAAAA/5/JZLsbkCLdxYGzZ89qzJgxiouL04svvqiCBQuqa9euWrJkia5fv27LHAEAAAAAgA2luzjg6uqqZs2a6ZtvvtHp06f1448/Km/evHrvvfeUL18+tWjRQjNnztSZM2dsmS8AAAAAAMhg6S4O3MlkMqlGjRoaO3as9u3bpx07dqhWrVqKiIhQoUKFNG3atIzOEwAAAAAA2Ei6FyS8n5IlS+rdd9/Vu+++q3Pnzun8+fMZMSwAAAAAAMgEVs8cmD17tpYtW2a+P2DAAHl7e6tGjRo6fvy48ubNq5IlS2ZokgAAAAAAwHasLg6MHj1abm5ukqSoqChNmzZN48aNU758+dS3b98MTxAAAAAAANiW1ZcVnDhxQiVKlJAkLV68WK1atdKbb76pmjVrqm7duhmdHwAAAAAAsDGrZw7kzJlT586dkyStXLlS9evXl3R7N4Nr165lbHYAAAAAAMDmrJ45UL9+fXXp0kWVKlXSoUOH1LhxY0nS3r175e/vn9H5AQAAAAAAG7N65sC0adMUHBysM2fO6Mcff1TevHklSdu2bVO7du0yPEEAAAAAAGBbVs8cSEhI0NSpU+XgYFlXGDZsmE6cOJFhiQEAAAAAgMxh9cyBgIAAnT17NlX7+fPnFRAQkCFJAQAAAACAzGN1ccAwjDTbL1++LFdX14dOZOzYsTKZTOrTp4+57fr16+revbvy5s2rnDlzqlWrVoqNjbV4XHR0tJo0aSJ3d3cVKFBA/fv3161btyxi/vjjD1WuXFkuLi4qUaKEIiIiUh1/2rRp8vf3l6urq6pVq6YtW7ZY9KcnFwAAAAAAnkTpvqwgPDxckmQymTRkyBC5u7ub+5KSkrR582ZVrFjxoZL466+/9OWXX6p8+fIW7X379tWyZcu0aNEieXl5qUePHmrZsqU2bNhgPm6TJk3k6+urjRs36vTp02rfvr2cnJw0evRoSdKxY8fUpEkTvf3225o3b54iIyPVpUsXFSxYUKGhoZKkhQsXKjw8XNOnT1e1atU0efJkhYaG6uDBgypQoEC6cgEAAHhUzRY0s9nYS9otsdnYAIAnX7qLAzt27JB0e+bA7t275ezsbO5zdnZWhQoV1K9fP6sTuHz5sl577TV9/fXX+uijj8zt8fHxmjFjhubPn68XXnhBkjRr1iyVKVNGmzZtUvXq1bVy5Urt27dPq1evlo+PjypWrKiRI0fqvffe07Bhw+Ts7Kzp06crICBAEyZMkCSVKVNG69ev16RJk8zFgYkTJ6pr167q2LGjJGn69OlatmyZZs6cqYEDB6YrFwAAAAAAnlTpvqxgzZo1WrNmjcLCwvTbb7+Z769Zs0YrVqzQl19+qZIlS1qdQPfu3dWkSROFhIRYtG/btk03b960aC9durSKFCmiqKgoSVJUVJTKlSsnHx8fc0xoaKgSEhK0d+9ec8zdY4eGhprHSExM1LZt2yxiHBwcFBISYo5JTy5puXHjhhISEixuAAAAAAA8bqzerWDWrFkZdvDvvvtO27dv119//ZWqLyYmRs7OzvL29rZo9/HxUUxMjDnmzsJASn9K3/1iEhISdO3aNV24cEFJSUlpxhw4cCDduaRlzJgxGj58+D37AQAAAAB4HFhdHLhy5YrGjh2ryMhIxcXFKTk52aL/n3/+Sdc4J06cUO/evbVq1apHWsjwcTZo0CDzWg3S7W0gCxcunIUZAQAAAACQmtXFgS5dumjt2rV64403VLBgQZlMpoc68LZt2xQXF6fKlSub25KSkrRu3Tp99tlnWrFihRITE3Xx4kWLb+xjY2Pl6+srSfL19U21q0DKDgJ3xty9q0BsbKw8PT3l5uYmR0dHOTo6phlz5xgPyiUtLi4ucnFxSeczAgAAAABA1rC6OPDbb79p2bJlqlmz5iMduF69etq9e7dFW8eOHVW6dGm99957Kly4sJycnBQZGalWrVpJkg4ePKjo6GgFBwdLkoKDgzVq1CjFxcWZdxVYtWqVPD09FRgYaI759ddfLY6zatUq8xjOzs4KCgpSZGSkWrRoIUlKTk5WZGSkevToIUkKCgp6YC4AAAAAADyprC4O5M6dW3ny5HnkA+fKlUtly5a1aPPw8FDevHnN7Z07d1Z4eLjy5MkjT09P9ezZU8HBwebdARo0aKDAwEC98cYbGjdunGJiYjR48GB1797d/I3922+/rc8++0wDBgxQp06d9Pvvv+v777/XsmXLzMcNDw9XWFiYqlSpoqpVq2ry5Mm6cuWKefcCLy+vB+YCAAAAAMCTyuriwMiRIzVkyBDNnj1b7u7utsjJbNKkSXJwcFCrVq1048YNhYaG6vPPPzf3Ozo6aunSperWrZuCg4Pl4eGhsLAwjRgxwhwTEBCgZcuWqW/fvpoyZYoKFSqkb775xryNoSS1adNGZ86c0ZAhQxQTE6OKFStq+fLlFosUPigXAAAAAACeVCbDMAxrHlCpUiUdPXpUhmHI399fTk5OFv3bt2/P0ASzk4SEBHl5eSk+Pl6enp5ZnQ4AABnuIZciShfr3rHYji3Psen8ZjYbe0m7JemO5RwfzeNyjsg++Nv6aOz9dzK9n0OtnjmQcl0+AAAAAADIHqwuDgwdOtQWeQAAAACA1ezhW3UgMzhkdQIAAAAAACBrWT1zICkpSZMmTdL333+v6OhoJSYmWvSfP38+w5IDAAAAAAC2Z/XMgeHDh2vixIlq06aN4uPjFR4erpYtW8rBwUHDhg2zQYoAAAAAAMCWrC4OzJs3T19//bXeffdd5ciRQ+3atdM333yjIUOGaNOmTbbIEQAAAAAA2JDVxYGYmBiVK1dOkpQzZ07Fx8dLkpo2baply5ZlbHYAAAAAAMDmrC4OFCpUSKdPn5YkFS9eXCtXrpQk/fXXX3JxccnY7AAAAAAAgM1ZXRx46aWXFBkZKUnq2bOnPvzwQ5UsWVLt27dXp06dMjxBAAAAAABgW1bvVjB27Fjzv9u0aaOiRYtq48aNKlmypJo1a5ahyQEAAAAAANuzujhwt+rVq6t69eoZkQsAAAAAAMgCVl9WMGbMGM2cOTNV+8yZM/Xxxx9nSFIAAAAAACDzWF0c+PLLL1W6dOlU7c8884ymT5+eIUkBAAAAAIDM81BbGRYsWDBVe/78+c27GAAAAAAAgCeH1cWBwoULa8OGDanaN2zYID8/vwxJCgAAAAAAZB6rFyTs2rWr+vTpo5s3b+qFF16QJEVGRmrAgAF69913MzxBAAAAAABgW1YXB/r3769z587pnXfeUWJioiTJ1dVV7733ngYNGpThCQIAAAAAANuyujhgMpn08ccf68MPP9T+/fvl5uamkiVLysXFxRb5AQAAAAAAG7O6OJAiZ86cevbZZzMyFwAAAAAAkAXSVRxo2bKlIiIi5OnpqZYtW9439n//+1+GJAYAAAAAADJHuooDXl5eMplM5n8DAAAAAJ58zRY0s9nYS9otsdnYyHjpKg7MmjUrzX8DAAAAAIAnn0NWJwAAAAAAALJWumYOVKpUyXxZwYNs3779kRICAAAAAACZK13FgRYtWtg4DQAAAAAAkFXSVRwYOnSorfMAAAAAAABZJF3FgbRs3bpV+/fvlyQFBgYqKCgow5ICAAAAAACZx+riwMmTJ9WuXTtt2LBB3t7ekqSLFy+qRo0a+u6771SoUKGMzhEAAAAAANiQ1bsVdOnSRTdv3tT+/ft1/vx5nT9/Xvv371dycrK6dOliixwBAMgWTCbb3QAAAB6F1TMH1q5dq40bN6pUqVLmtlKlSunTTz9VrVq1MjQ5AAAAAABge1YXBwoXLqybN2+mak9KSpKfn1+GJAUAAHC3Zgua2WzsJe2W2GxsAACeBFZfVjB+/Hj17NlTW7duNbdt3bpVvXv31ieffJKhyQEAAAAAANuzeuZAhw4ddPXqVVWrVk05ctx++K1bt5QjRw516tRJnTp1MseeP38+4zIFAAAAAAA2YXVxYPLkyTZIAwAAAAAeL7a6nIlLmfA4sro4EBYWZos8AAAAAABAFrG6OCDdXnzwp59+0v79+yVJgYGBat68ufkyAwAAAAAA8OSw+tP83r179eKLLyomJsa8neHHH3+s/Pnza8mSJSpbtmyGJwkAAAAAAGzH6t0KunTpomeeeUYnT57U9u3btX37dp04cULly5fXm2++aYscAQAAAACADVk9c2Dnzp3aunWrcufObW7LnTu3Ro0apWeffTZDkwMAAAAAALZn9cyBp59+WrGxsana4+LiVKJEiQxJCgAAAAAAZB6riwNjxoxRr1699MMPP+jkyZM6efKkfvjhB/Xp00cff/yxEhISzDcAAAAAAPD4s/qygqZNm0qSXnnlFZlMJkmSYRiSpGbNmpnvm0wmJSUlZVSeAAAAAADARqwuDqxZs8YWeQAAAAAAgCxidXGgTp069+zbs2cPWxkCAAAAAPCEsXrNgbtdunRJX331lapWraoKFSpkRE4AAAAAACATPXRxYN26dQoLC1PBggX1ySef6IUXXtCmTZsyMjcAAAAAAJAJrLqsICYmRhEREZoxY4YSEhL0yiuv6MaNG1q8eLECAwNtlSMAAAAAALChdM8caNasmUqVKqW///5bkydP1qlTp/Tpp5/aMjcAAAAAAJAJ0j1z4LffflOvXr3UrVs3lSxZ0pY5AQDs0P/fHdcm/v+OuwAAALiHdM8cWL9+vS5duqSgoCBVq1ZNn332mc6ePWvL3AAAAAAAQCZId3GgevXq+vrrr3X69Gm99dZb+u677+Tn56fk5GStWrVKly5dsmWeAAAAAADARqzercDDw0OdOnXS+vXrtXv3br377rsaO3asChQooBdffNEWOQIAAAAAABt66K0MJalUqVIaN26cTp48qQULFlj9+C+++ELly5eXp6enPD09FRwcrN9++83cf/36dXXv3l158+ZVzpw51apVK8XGxlqMER0drSZNmsjd3V0FChRQ//79devWLYuYP/74Q5UrV5aLi4tKlCihiIiIVLlMmzZN/v7+cnV1VbVq1bRlyxaL/vTkAgAAAADAk+iRigMpHB0d1aJFC/3yyy9WPa5QoUIaO3astm3bpq1bt+qFF15Q8+bNtXfvXklS3759tWTJEi1atEhr167VqVOn1LJlS/Pjk5KS1KRJEyUmJmrjxo2aPXu2IiIiNGTIEHPMsWPH1KRJEz3//PPauXOn+vTpoy5dumjFihXmmIULFyo8PFxDhw7V9u3bVaFCBYWGhiouLs4c86BcgMeRyWS7GwAAAIDsI0OKAw+rWbNmaty4sUqWLKmnn35ao0aNUs6cObVp0ybFx8drxowZmjhxol544QUFBQVp1qxZ2rhxozZt2iRJWrlypfbt26e5c+eqYsWKatSokUaOHKlp06YpMTFRkjR9+nQFBARowoQJKlOmjHr06KHWrVtr0qRJ5jwmTpyorl27qmPHjgoMDNT06dPl7u6umTNnSlK6cgEAAAAA4EmVpcWBOyUlJem7777TlStXFBwcrG3btunmzZsKCQkxx5QuXVpFihRRVFSUJCkqKkrlypWTj4+POSY0NFQJCQnm2QdRUVEWY6TEpIyRmJiobdu2WcQ4ODgoJCTEHJOeXNJy48YNJSQkWNwAAAAAAHjc5MjqBHbv3q3g4GBdv35dOXPm1E8//aTAwEDt3LlTzs7O8vb2toj38fFRTEyMJCkmJsaiMJDSn9J3v5iEhARdu3ZNFy5cUFJSUpoxBw4cMI/xoFzSMmbMGA0fPjx9TwQAAI+g2YJmNht7SbslNhsbAAA8HtI1c6By5cq6cOGCJGnEiBG6evVqhiVQqlQp7dy5U5s3b1a3bt0UFhamffv2Zdj4WWnQoEGKj483306cOJHVKQEAAAAAkEq6igP79+/XlStXJEnDhw/X5cuXMywBZ2dnlShRQkFBQRozZowqVKigKVOmyNfXV4mJibp48aJFfGxsrHx9fSVJvr6+qXYMSLn/oBhPT0+5ubkpX758cnR0TDPmzjEelEtaXFxczDsxpNwAAAAAAHjcpOuygooVK6pjx4567rnnZBiGPvnkE+XMmTPN2Dt3CngYycnJunHjhoKCguTk5KTIyEi1atVKknTw4EFFR0crODhYkhQcHKxRo0YpLi5OBQoUkCStWrVKnp6eCgwMNMf8+uuvFsdYtWqVeQxnZ2cFBQUpMjJSLVq0MOcQGRmpHj16SFK6cgGQdWy1e4Jh2Gbch2HLHSIep/MEAABA1khXcSAiIkJDhw7V0qVLZTKZ9NtvvylHjtQPNZlMVhUHBg0apEaNGqlIkSK6dOmS5s+frz/++EMrVqyQl5eXOnfurPDwcOXJk0eenp7q2bOngoODVb16dUlSgwYNFBgYqDfeeEPjxo1TTEyMBg8erO7du8vFxUWS9Pbbb+uzzz7TgAED1KlTJ/3+++/6/vvvtWzZMnMe4eHhCgsLU5UqVVS1alVNnjxZV65cUceOHSUpXbkAAAAAAPCkSldxoFSpUvruu+8k3V7JPzIy0vxN/aOIi4tT+/btdfr0aXl5eal8+fJasWKF6tevL0maNGmSHBwc1KpVK924cUOhoaH6/PPPzY93dHTU0qVL1a1bNwUHB8vDw0NhYWEaMWKEOSYgIEDLli1T3759NWXKFBUqVEjffPONQkNDzTFt2rTRmTNnNGTIEMXExKhixYpavny5xSKFD8oFAAAAAIAnldW7FSQnJ2fYwWfMmHHffldXV02bNk3Tpk27Z0zRokVTXTZwt7p162rHjh33jenRo4f5MoKHzSU7YQozAAAAANiPh9rK8OjRo5o8ebL2798vSQoMDFTv3r1VvHjxDE0OAAAAAADYXrp2K7jTihUrFBgYqC1btqh8+fIqX768Nm/erGeeeUarVq2yRY4AAAAAAMCGrJ45MHDgQPXt21djx45N1f7ee++Z1wsAAAAAAABPBqtnDuzfv1+dO3dO1d6pUyft27cvQ5ICAAAAAACZx+riQP78+bVz585U7Tt37syQHQwAAAAAAEDmsvqygq5du+rNN9/UP//8oxo1akiSNmzYoI8//ljh4eEZniAAAAAAALAtq4sDH374oXLlyqUJEyZo0KBBkiQ/Pz8NGzZMvXr1yvAEAQAAAACAbVldHDCZTOrbt6/69u2rS5cuSZJy5cqV4YkBAAAAAIDMYXVx4E4UBQAAAAAAePJZvSAhAAAAAADIXh5p5gAAAE+CZgua2WzsJe2W2GxsAACAzMLMAQAAAAAA7JxVxYGbN2+qXr16Onz4sK3yAQAAAAAAmcyq4oCTk5P+/vtvW+UCAAAAAACygNWXFbz++uuaMWOGLXIBAAAAAABZwOoFCW/duqWZM2dq9erVCgoKkoeHh0X/xIkTMyw5AAAAAABge1YXB/bs2aPKlStLkg4dOmTRZzKZMiYrAAAAAACQaawuDqxZs8YWeQCZzpa1LMOw3diPC7aGAwAAALIPq4sDKY4cOaKjR4+qdu3acnNzk2EYzBwAgCeQrQo9FHkAAACeHFYXB86dO6dXXnlFa9askclk0uHDh1WsWDF17txZuXPn1oQJE2yRJwBkOmZHAAAAwF5YvVtB37595eTkpOjoaLm7u5vb27Rpo+XLl2docgAAAAAAwPasnjmwcuVKrVixQoUKFbJoL1mypI4fP55hiQEAAAAAgMxh9cyBK1euWMwYSHH+/Hm5uLhkSFIAAAAAACDzWF0cqFWrlr799lvzfZPJpOTkZI0bN07PP/98hiYHAAAAAABsz+rLCsaNG6d69epp69atSkxM1IABA7R3716dP39eGzZssEWOAAAAAADAhqwuDpQtW1aHDh3SZ599ply5cuny5ctq2bKlunfvroIFC9oiR2QzrAAPAAAAAI8Xq4sDkuTl5aUPPvggo3MBAAAAAABZ4KGKAxcuXNCMGTO0f/9+SVJgYKA6duyoPHnyZGhyAAAAAADA9qxekHDdunXy9/fX1KlTdeHCBV24cEFTp05VQECA1q1bZ4scAQAAAACADVk9c6B79+5q06aNvvjiCzk6OkqSkpKS9M4776h79+7avXt3hicJAAAAAABsx+qZA0eOHNG7775rLgxIkqOjo8LDw3XkyJEMTQ4AAAAAANie1cWBypUrm9cauNP+/ftVoUKFDEkKAAAAAABknnRdVvD333+b/92rVy/17t1bR44cUfXq1SVJmzZt0rRp0zR27FjbZAkAAAAAAGwmXcWBihUrymQyyTAMc9uAAQNSxb366qtq06ZNxmUHAAAAAABsLl3FgWPHjtk6DwAAAAAAkEXSVRwoWrSorfMAAAAAAABZxOqtDCXp1KlTWr9+veLi4pScnGzR16tXrwxJDAAAAAAAZA6riwMRERF666235OzsrLx588pkMpn7TCYTxQEAAAAAAJ4wVhcHPvzwQw0ZMkSDBg2Sg4PVOyECAAAAAIDHjNWf7q9evaq2bdtSGAAAAAAAIJuw+hN+586dtWjRIlvkAgAAAAAAsoDVlxWMGTNGTZs21fLly1WuXDk5OTlZ9E+cODHDkgOeVM0WNLPZ2EvaLbHZ2AAAAADs00MVB1asWKFSpUpJUqoFCQEAAAAAwJPF6uLAhAkTNHPmTHXo0MEG6QAAAAAAgMxm9ZoDLi4uqlmzpi1yAQAAAAAAWcDq4kDv3r316aef2iIXAAAAAACQBay+rGDLli36/ffftXTpUj3zzDOpFiT83//+l2HJAQAAAAAA27O6OODt7a2WLVvaIhcAAAAAAJAFrC4OzJo1yxZ5AAAAAACALGL1mgMAAAAAACB7sXrmQEBAgEwm0z37//nnn0dKCAAAAAAAZC6riwN9+vSxuH/z5k3t2LFDy5cvV//+/TMqLwAAAAAAkEkeaivDO2/9+vXTvHnzNGLECB08eNCqscaMGaNnn31WuXLlUoECBdSiRYtUY1y/fl3du3dX3rx5lTNnTrVq1UqxsbEWMdHR0WrSpInc3d1VoEAB9e/fX7du3bKI+eOPP1S5cmW5uLioRIkSioiISJXPtGnT5O/vL1dXV1WrVk1btmyxOhcAAAAAAJ40GbbmQKNGjfTjjz9a9Zi1a9eqe/fu2rRpk1atWqWbN2+qQYMGunLlijmmb9++WrJkiRYtWqS1a9fq1KlTFrslJCUlqUmTJkpMTNTGjRs1e/ZsRUREaMiQIeaYY8eOqUmTJnr++ee1c+dO9enTR126dNGKFSvMMQsXLlR4eLiGDh2q7du3q0KFCgoNDVVcXFy6cwEAAAAA4Elk9WUF9/LDDz8oT548Vj1m+fLlFvcjIiJUoEABbdu2TbVr11Z8fLxmzJih+fPn64UXXpB0e7eEMmXKaNOmTapevbpWrlypffv2afXq1fLx8VHFihU1cuRIvffeexo2bJicnZ01ffp0BQQEaMKECZKkMmXKaP369Zo0aZJCQ0MlSRMnTlTXrl3VsWNHSdL06dO1bNkyzZw5UwMHDkxXLgAAAAAAPImsnjlQqVIlVa5c2XyrVKmSChYsqPfff1/vv//+IyUTHx8vSeYiw7Zt23Tz5k2FhISYY0qXLq0iRYooKipKkhQVFaVy5crJx8fHHBMaGqqEhATt3bvXHHPnGCkxKWMkJiZq27ZtFjEODg4KCQkxx6Qnl7vduHFDCQkJFjcAAAAAAB43Vs8caNGihcV9BwcH5c+fX3Xr1lXp0qUfOpHk5GT16dNHNWvWVNmyZSVJMTExcnZ2lre3t0Wsj4+PYmJizDF3FgZS+lP67heTkJCga9eu6cKFC0pKSkoz5sCBA+nO5W5jxozR8OHD0/kMAAAAAACQNawuDgwdOtQWeah79+7as2eP1q9fb5Pxs8KgQYMUHh5uvp+QkKDChQtnYUYAAAAAAKSWYWsOPIoePXpo6dKlWrdunQoVKmRu9/X1VWJioi5evGjxjX1sbKx8fX3NMXfvKpCyg8CdMXfvKhAbGytPT0+5ubnJ0dFRjo6OacbcOcaDcrmbi4uLXFxcrHgmAAAAAADIfOlec8DBwcH8Ifpetxw5rKs1GIahHj166KefftLvv/+ugIAAi/6goCA5OTkpMjLS3Hbw4EFFR0crODhYkhQcHKzdu3db7CqwatUqeXp6KjAw0Bxz5xgpMSljODs7KygoyCImOTlZkZGR5pj05AIAAAAAwJMo3Z/mf/rpp3v2RUVFaerUqUpOTrbq4N27d9f8+fP1888/K1euXOZr9728vOTm5iYvLy917txZ4eHhypMnjzw9PdWzZ08FBwebdwdo0KCBAgMD9cYbb2jcuHGKiYnR4MGD1b17d/O39m+//bY+++wzDRgwQJ06ddLvv/+u77//XsuWLTPnEh4errCwMFWpUkVVq1bV5MmTdeXKFfPuBenJBQAAAACAJ1G6iwPNmzdP1Xbw4EENHDhQS5Ys0WuvvaYRI0ZYdfAvvvhCklS3bl2L9lmzZqlDhw6SpEmTJsnBwUGtWrXSjRs3FBoaqs8//9wc6+joqKVLl6pbt24KDg6Wh4eHwsLCLHIJCAjQsmXL1LdvX02ZMkWFChXSN998Y97GUJLatGmjM2fOaMiQIYqJiVHFihW1fPlyi0UKH5QLAAAAAABPoodac+DUqVMaOnSoZs+erdDQUO3cudO8w4A1DMN4YIyrq6umTZumadOm3TOmaNGi+vXXX+87Tt26dbVjx477xvTo0UM9evR4pFwAAAAAAHjSpHvNAUmKj4/Xe++9pxIlSmjv3r2KjIzUkiVLHqowAAAAAAAAHg/pnjkwbtw4ffzxx/L19dWCBQvSvMwAAAAAAAA8edJdHBg4cKDc3NxUokQJzZ49W7Nnz04z7n//+1+GJQcAAAAAAGwv3cWB9u3by2Qy2TIXAAAAAACQBdJdHIiIiLBhGgAAAAAAIKtYtSAhAAAAAADIfigOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg5ygOAAAAAABg57K0OLBu3To1a9ZMfn5+MplMWrx4sUW/YRgaMmSIChYsKDc3N4WEhOjw4cMWMefPn9drr70mT09PeXt7q3Pnzrp8+bJFzN9//61atWrJ1dVVhQsX1rhx41LlsmjRIpUuXVqurq4qV66cfv31V6tzAQAAAADgSZSlxYErV66oQoUKmjZtWpr948aN09SpUzV9+nRt3rxZHh4eCg0N1fXr180xr732mvbu3atVq1Zp6dKlWrdund58801zf0JCgho0aKCiRYtq27ZtGj9+vIYNG6avvvrKHLNx40a1a9dOnTt31o4dO9SiRQu1aNFCe/bssSoXAAAAAACeRDmy8uCNGjVSo0aN0uwzDEOTJ0/W4MGD1bx5c0nSt99+Kx8fHy1evFht27bV/v37tXz5cv3111+qUqWKJOnTTz9V48aN9cknn8jPz0/z5s1TYmKiZs6cKWdnZz3zzDPauXOnJk6caC4iTJkyRQ0bNlT//v0lSSNHjtSqVav02Wefafr06enKBQAAAACAJ9Vju+bAsWPHFBMTo5CQEHObl5eXqlWrpqioKElSVFSUvL29zYUBSQoJCZGDg4M2b95sjqldu7acnZ3NMaGhoTp48KAuXLhgjrnzOCkxKcdJTy5puXHjhhISEixuAAAAAAA8bh7b4kBMTIwkycfHx6Ldx8fH3BcTE6MCBQpY9OfIkUN58uSxiElrjDuPca+YO/sflEtaxowZIy8vL/OtcOHCDzhrAAAAAAAy32NbHMgOBg0apPj4ePPtxIkTWZ0SAAAAAACpPLbFAV9fX0lSbGysRXtsbKy5z9fXV3FxcRb9t27d0vnz5y1i0hrjzmPcK+bO/gflkhYXFxd5enpa3AAAAAAAeNw8tsWBgIAA+fr6KjIy0tyWkJCgzZs3Kzg4WJIUHBysixcvatu2beaY33//XcnJyapWrZo5Zt26dbp586Y5ZtWqVSpVqpRy585tjrnzOCkxKcdJTy4AAAAAADypsrQ4cPnyZe3cuVM7d+6UdHvhv507dyo6Olomk0l9+vTRRx99pF9++UW7d+9W+/bt5efnpxYtWkiSypQpo4YNG6pr167asmWLNmzYoB49eqht27by8/OTJL366qtydnZW586dtXfvXi1cuFBTpkxReHi4OY/evXtr+fLlmjBhgg4cOKBhw4Zp69at6tGjhySlKxcAAAAAAJ5UWbqV4datW/X888+b76d8YA8LC1NERIQGDBigK1eu6M0339TFixf13HPPafny5XJ1dTU/Zt68eerRo4fq1asnBwcHtWrVSlOnTjX3e3l5aeXKlerevbuCgoKUL18+DRkyxLyNoSTVqFFD8+fP1+DBg/X++++rZMmSWrx4scqWLWuOSU8uAAAAAAA8ibK0OFC3bl0ZhnHPfpPJpBEjRmjEiBH3jMmTJ4/mz59/3+OUL19ef/75531jXn75Zb388suPlAsAAAAAAE+ix3bNAQAAAAAAkDkoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDgAAAAAAYOcoDlhp2rRp8vf3l6urq6pVq6YtW7ZkdUoAAAAAADwSigNWWLhwocLDwzV06FBt375dFSpUUGhoqOLi4rI6NQAAAAAAHhrFAStMnDhRXbt2VceOHRUYGKjp06fL3d1dM2fOzOrUAAAAAAB4aDmyOoEnRWJiorZt26ZBgwaZ2xwcHBQSEqKoqKg0H3Pjxg3duHHDfD8+Pl6SlJCQYNtkH3M3r9602diPy3PLOT4azjFz2eo87eEcpcfnPDnHR8M5Zh7O8dE8LudoL/h/5KN5XM7THs7xflJyNAzjvnEm40ERkCSdOnVKTz31lDZu3Kjg4GBz+4ABA7R27Vpt3rw51WOGDRum4cOHZ2aaAAAAAACkcuLECRUqVOie/cwcsKFBgwYpPDzcfD85OVnnz59X3rx5ZTKZsjCzrJOQkKDChQvrxIkT8vT0zOp0bIJzzB7s4Rwl+zhPzjF74ByzB84xe7CHc5Ts4zw5R/tgGIYuXbokPz+/+8ZRHEinfPnyydHRUbGxsRbtsbGx8vX1TfMxLi4ucnFxsWjz9va2VYpPFE9Pz2z/y8k5Zg/2cI6SfZwn55g9cI7ZA+eYPdjDOUr2cZ6cY/bn5eX1wBgWJEwnZ2dnBQUFKTIy0tyWnJysyMhIi8sMAAAAAAB40jBzwArh4eEKCwtTlSpVVLVqVU2ePFlXrlxRx44dszo1AAAAAAAeGsUBK7Rp00ZnzpzRkCFDFBMTo4oVK2r58uXy8fHJ6tSeGC4uLho6dGiqyy2yE84xe7CHc5Ts4zw5x+yBc8weOMfswR7OUbKP8+QccSd2KwAAAAAAwM6x5gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gAAAAAAAHaO4gCQBdgkBE8Ke3itJiUlZXUKeETJyclZnYLNXb9+XZJ9nKuU/f/2ZPfzA54k/D7+H4oDeCxl11/Suz+EZOc3edn1Z2gvEhMTJUk3btyQlD1/njExMZIkR0fHbFsgOHz4sHbu3JnVadjU0aNH9dlnn+nMmTNZnYrN7Nu3T6VLl9auXbvk4JB937olJCTowoULiomJkclkypb/j7x165ak//ubmh3P8e7/X2TH/38ge0j5fz+v0f+TI6sTAA4ePKj58+fr+PHjqlKliurUqaNy5crJMAyZTKasTi/D7N+/X59++qlOnTqlMmXKqHXr1goKCsrqtDLUyZMnFRsbq6CgoGz1s7vTkSNH9P333+vgwYOqVauWQkJC5O/vn9VpZagDBw7o448/1qlTp+Tr66v+/furbNmyWZ1Whjp69KhKliyphg0b6tdffzUXCBwdHbM6tQyza9cuVapUSdOmTVPFihWzOh2b+Pvvv/XCCy8oLCxMZ8+eVf78+ZWcnJytPkDv3LlT9erV04ULF7Rq1SpVqFAh252jJO3du1fdunXT5cuXdfLkSc2dO1cNGjTI6rQyVMr7gDNnzsjPz09t27ZVcHBwVqeVoQ4ePKh58+YpOjpazz33nJ577jmVLl06W71mY2NjFR8fr6effjqrU7GZY8eOacWKFTp06JAaNWqkSpUqKV++fFmdVoY6dOiQvvjiC0VHR6tChQp64403FBAQkNVpZbns8VuKJ9aePXsUHByskydP6uLFi1q2bJnq1KmjFStWZKsPlwcOHFD16tV19epV5ciRQ9u2bVPNmjU1Z86crE4twxw8eFBVqlRRt27dtH79+qxOxyb27NmjGjVqaNeuXTp8+LC++uorffzxx7py5UpWp5Zhdu/erRo1asjFxUXFixdXbGysJk6cqJs3b2arynpcXJwKFSqkI0eOqGHDhpJuzyDILt/i7dq1SzVq1NCAAQPUrVu3rE7HJk6fPq2WLVsqLCxMEyZMUJkyZST932yX7GDXrl0KDg5Wnz591Lt3b02fPl23bt2Sg4NDtvp9PHDggOrUqaPq1aurf//+eumll9SjRw8lJCRIyh7f6u3du1c1a9aUYRjKnz+/YmNjVbt2bX3zzTfZ5v8h+/btU7Vq1bRv3z4dPnxY33zzjerXr6/IyMhs85rdv3+/qlatqg8//FB79+7N6nRsYvfu3Xruuef0yy+/aOnSperZs6dmzpyp5OTkbPEzlP7vvc6FCxeUnJys3377TQsWLJBhGNnmHB+aAWSRy5cvG6GhoUa/fv3Mbdu2bTNy585tuLi4GN9//71hGIaRlJSUVSlmmHfeecdo0aKF+X5sbKwxePBgw9HR0fj8888NwzCM5OTkrErvkZ0+fdqoW7euUbNmTaNRo0ZGgwYNjHXr1mV1WhkqOjraCAwMNAYOHGhumzZtmlGsWDHjv//+y8LMMs4///xjFC9e3Pjggw/MbcOGDTM6depkGMbt31nDePJ/J5OTk42oqCijTJkyxvz5842nn37aaNy4sbn/Sf957t+/38iRI4f5tZqcnGz8+OOPxuj/196dR1Vd538cf93LIgqyuiRCKCqBgIi5IGrqjPvSMSgZy6UkSyMRzZ+amcaBMUWd0caZLHRS08FO4zampjZ6SjRNHXDBJETNXUJEEUG4l/fvDw7fAbUGEPne7/2+Hud0Tlyu9n725S58vsudP19SUlIkMzNT5Qnrxtdffy3h4eEiUv4zOXnyZBk6dKh06dJF1q5dK0VFRSpP+HjS0tLE1tZW3n33XREROX/+vHh7e0tSUpLKk9Wt0tJSGTt2rIwdO1a5bc+ePRIRESF5eXly6dIlFaerG8XFxRIZGSmTJ09Wbrt69ar4+/uLvb29LFmyRES0/T7AZDLJ6NGj5ZVXXlFuS0tLk+joaLGxsZGvvvpKRLT9+nHlyhUJDw+XkJAQ6dq1q0RHR8vJkyfVHqtOXbhwQdq1ayezZ8+WkpISERGZNWuWtG3bVvPPqRWys7PFx8enynud6OhoiY2NFZHy5yQ945EDpJri4mJcunQJYWFhAMr3DHTq1Am/+93v0L17d4wePRqHDx+2isPQrl+/Dg8PD+XrZs2aISEhAQkJCYiJicGOHTtgMBg0u1p5+fJl2NjYICkpCbGxsbCxsUFiYiL279+v9mh1QkSwb98++Pn5YeLEicre5ejoaADle0uswZEjR9CjRw/ExsYqt925cwdHjx5Ft27dMGjQIOzatUvze4AMBgM6dOiA9u3bo3fv3li4cCF++uknREREYPz48fj0009x7949tcestW+//RZmsxk9e/ZEWVkZ+vbtiwULFmDFihWYP38+hgwZgu+//17tMR/bzZs3YWtbfnZknz59kJWVhZCQEHTr1g3jxo3DggULAGhzr3NBQQHmzJmD6dOnY/78+QAADw8PdOzYEfv27VN5urplMplw/vx5+Pr6KrelpqZi3759eO655xAUFIT4+HhNHxFSWlqKrKwsBAYGAihvbtGiBXr06IF+/fph+vTp2L59u6aPmCwrK8OlS5fg7e2t3NaxY0d8+OGHeOONN/Diiy/i0KFDmn5Pd+bMGTRu3Bhr1qzBW2+9hbS0NCxduhSnTp1Se7Q6YTabsXXrVoSGhmLy5MnKtoqLi0NJSQmysrJUnvDxmc1m7NmzB7///e/xzjvvKK8PDRs2xKlTp9CnTx+89tprOHjwoMqTqkjNlQnSt5ycHOnevbskJiYqq5Hnzp0TT09P2bhxowwaNEheeeUVMZlMml5NFynf++rt7a3skazoKSkpkYkTJ0pAQIBcu3ZNzREfW3p6uvLv27dvV44g+Pbbb5XbK/YYaHHPwfbt22XFihXK12VlZVJQUCAtW7aUL7/8UsXJ6s6tW7fkzJkzytdJSUni4OAgS5culRUrVsikSZPE3t5eTpw4oeKUdaO4uFhCQ0OVvVl79+4VV1dXMRgMSp+W9x588MEHYmNjI23atJHIyEjJzMwUk8kkP/zwg7z00kvSuXNnuXHjhtpjPpadO3eKg4ODrFmzRiIiIqr0rF27VgwGg6Smpqo44eOpfIRHxXNmamqqGAwG+ec//6nWWE9EbGysNG7cWP76179KTEyMNGzYUFJSUiQtLU3Wr18vBoNBNm3apPaYtVZSUiLDhw+X6OhouX37toiU76Ft0qSJ7N69W1599VXp0aOHFBYWqjzp44mJiZHu3btLXl5eldsvXrwokZGRMmTIEKVfi4qKiuTgwYPK13//+9+lU6dOEh0dXeV1UcvvWVevXi3Lli2rctuNGzfE1dVV9u3bp85QdezcuXNy6tQp5ev4+HhxcHCQ+fPny9y5cyUqKkp8fX3l3LlzKk6pHi4OkKri4uKkQ4cO8vLLL0tSUpI4OTlJTEyMiIgsWrRIAgMDxWQyqTxl7VT+Bfjw4cPSo0cPefvtt5U3sBXf/+abb8TT01PS0tLUGPOx/NYv+Tt27JBBgwbJwIEDlVMMpkyZIocOHaqv8erEo37+Kr/wP/vss7J161bl6zVr1mjusO1HNd6/f18mTJggu3fvVm6rWLxbu3ZtfY5XJyr/rFZsv3HjxsnmzZtFRGTUqFHi7u4uTz/9dJVTgLTkwe2YmJgowcHBDz23fPnll+Lh4aHJRZ7K29FsNssf/vAHad26tQQEBMjdu3fFZDIp9wkNDZU//elPao1aaxWH8j6orKxM7ty5I88//7yMGTNG7t27p8mF1gqVZ8/OzpaYmBgZPXq0dOrUSRYtWlTlvj169JCJEyfW94iPrXLj0qVLJSwsTHr16iXvvvuuODo6Kk0pKSnSqlUryc/PV2vUOvHFF19IaGioLFmyRO7cuVPle6tXrxZPT0+5ePGiStPVjQd/8V+9erWyQFBxikF8fLwcP35cjfHqVEVrUVGR+Pv7y+HDh5Xvbd26VdPbsqKtuLhYhgwZouwoEBHZv3+/NGvWrMr7Hz3R7rE9pDkXLlxAcnIyVq1ahZ07dwIA/vznPyMqKgr5+fnYuXMn3n//fSxfvhwA4OLigoYNG2rukND8/HwAgNFoVD4ipWvXrhg+fDgOHjyIxYsX48qVK8rhWv7+/nB0dNTUBYkqNz54AbeK7TV48GBMmTIFRqMR8+fPxwsvvICPPvoI9vb29T1urVQ02tjYKB89VaHyoZ+VD7F/77338Pbbb2vm0NDKjZU/yk9EYG9vjxUrVqB///7KNjYYDGjRokWVw0Yt3aN+Viu2T2BgINLT0zF69Gjs27cPO3bswMcff4zvvvsOUVFRao1cY7+2Hd977z2sXbsW/v7+AP77kWmenp5o2rQpGjVqVO+z1tajtqPRaERERARcXV1x/vx5ZGdnw8bGRrmPk5MT3NzcVJy6Zioa7ezsHnlhTIPBgMaNG6Nfv37YtGmT8jpiDa+Rvr6+WL58OZKTk2Fra4unnnoKQPkhwCaTCQ0aNNDUVcQrN1a8fkyZMgWTJk2Cr68vfvrpJ/zxj3/Exx9/DABo0KABnJ2d1Rq3Vq5evYqvvvoKmzZtwtGjRwEAI0eORFhYGJKTk7Fu3Trk5eUp9+/SpQsaNWqEgoICtUauscqNx44dAwDlFNCKx+i4ceMQGxuLtLQ0LFu2DFFRUYiPj1dOebJ0j9qOQPljr+K10mg0wmg0Kl/Pnj0bb775pmaee35tO5rNZjRo0ADbtm3D0KFDlW3q7u6O5s2bw93dXc2x1aPiwgTpyIkTJ8TDw0PCwsKkTZs24uTkJOPGjauySv7gKvP48eMlMjJS7t+/X9/j1trp06eldevW8v777yu3Vd4LNHfuXOnWrZsMHz5c0tPTJSsrS2bNmiU+Pj6aOa3gUY0P7r2qvLK+bds2cXNzE1dX1yqnHliy6jSKlK+m+/r6yubNm2XBggXi4OAgR48erc9Ra62m21FEZPbs2RISEmI1P6srV64Ug8Eg7dq1k2PHjolI+V6E7du3S1ZWVr3PWxuPavxfR1u98847Eh4eLrdu3XrC09WNRzVWPuXj888/l2eeeUacnZ1ly5Yt8s0338icOXPEy8tLM4eF1uTxWFZWJuHh4TJmzJhfPcrAUlXn5zU6OlqGDh0q58+fl9zcXJk3b560bNlS04/JB9/HPLjdJk6cKAMGDJB79+7Vy4yP68SJE+Lr6ytdu3aVJk2aSOfOnSUlJUX5/quvvirBwcESFxcnZ8+elV9++UVmzJghfn5+kpubq+Lk1feoxgdPIaz8GF21apXY2dmJi4uLZo4ErU6jSPkph02bNpUDBw5IQkKCODg4yJEjR1SYuOaq0/jge51Zs2ZJly5d5JdffqnPUS0GFwfoiSsoKJDu3bsrV+m9du2a7Ny5U9zd3aV///5y9uzZKvdPT0+XKVOmiIuLi6auAnvx4kXp2LGjtGvXToKCgiQ+Pl75XuU3Bp999pkMHjxYDAaDBAUFiY+Pj/znP/9RY+Qa+63GR72RNZvNEhcXJ40bN9bMtqxJo9lslp49e0pgYKA0atRIMy+WNWkUETlz5oxMnTpV3NzcNLPA81uNlX8ZmTlzpmYWdB5U3cYKP/74o8TFxYmbm5tmDnmt7vPq/v37Zdy4ceLk5CTt27eXDh06WOXzaoUJEyZIt27dlE8Q0YLqdq5bt0569+4t9vb2EhYWJk8//bRVbMvKC1oVv4wcOHBAYmJixNnZWTOPybNnz4qXl5fMmDFD8vPz5ejRozJu3DgZP368FBcXK/eLj4+XXr16icFgkGeffVaeeuopzWzH32p88DpYZWVlYjKZJDY2Vtzc3Kqcy27JatJYUFAgoaGh0qdPH03tBKlJo4jIzz//LP/3f/+nqdfIJ4GLA/TEFRUVSadOnWTDhg1Vbs/MzJQmTZrICy+8oLwxyM/Pl88//1xCQ0M1s/IqUv7isHDhQhkyZIjs3r1b5s2bJ/7+/r/6Rlak/DoEGRkZmtkLW53GB38hOXHihLRs2VIzLyQ1bSwtLZXw8HBNvZDUtPHUqVPKKro1NWr9I5lquh1PnDghU6dOleDgYM0s8NTmeTUrK0uuX78uN2/erO9xa6U2z6siIrdv35bs7Oz6HPWxVKez8t70kydPyqpVq2Tjxo3y888/qzFyjdV0W5rNZtm6dat0795dM4/J+/fvy7Rp02TkyJFVHnurVq0SDw+Ph44KyM3NlZ07d0pqaqpmPpKypo0iIj/88IMYDAbN7CCoaWN+fr74+PiIu7u71f6sHjlyRN566y0JCQnRTOOToo0TYkjTzGYzbty4gczMTOW20tJS+Pn54d///jfCw8Mxf/58zJkzBy4uLoiMjMSwYcPg6uqq3tA1ZDAYMHbsWDRv3hz9+/dHSEgIACAlJQUignnz5sHe3h6lpaWws7MDUH4dAi2pTqONjQ3KysqU6ykEBwfj9OnTmjmXsqaNtra2eP3119GrVy+0bdtW5emrp6aNgYGBGD16NKZOnYpmzZqpPH31VKfRwcEBZrMZNjY2Kk9bOzXdjsHBwRg7dixmzJihnM9t6ar7vGoymZTze9u0aaOZa34AtXteNZlMcHZ21szzKlC9Tjs7O+U1MigoCEFBQSpPXTM13ZZGoxHPP/88+vbti8aNG6s8ffWUlZXBy8sLAQEBsLe3h4jAYDAgPDwcTk5OKC0tVe5nNBrh4eGBQYMGqTx1zVS3sbIuXbogLy9PM+9ba9ro4uKCCRMmIDIyUrmGjaWraWPnzp1RVFSEOXPmoEWLFipNbSHUWJEg/VmyZIl4eXnJtm3blNsq9hIkJiZKt27dJDc3V9Mf//Kgq1evKnsOPvjgA+X2LVu2aPYTGB70W40V21fr2/TXGjdu3KjiVHVLz41btmzR9BXfK+N2tO7taE2NIr/euXnzZqt/jdRyY+XreFS8vl+7dk3atm1b5er1WjmF4FFq06i19zrVbdTK0RCPUt1GrRzdWl945ADVuWvXruHSpUu4desW+vXrBxsbG0RERODQoUNISkqCvb09BgwYoOxBb9KkCe7cuYOGDRtqao/PozqB8tXKiqu6v/HGGwCADRs2QERw+/ZtLFu2DJcvX4anp6ea41dLXTRa+jbldmQjGy0HG62jEdBHp54a8/LyMGDAAOVTIyofeXX79m3cunVL+TNz587F8uXLkZWVBXd3d828D2AjGy29sV6otSpB1un48ePi4+Mjfn5+4uLiIs8884ykpKRISUmJHDlyRIYNGyZdunRRrmpbUlIiM2bMkN69ez/0aQWW7MFOf39/+cc//qGc52o2m5VVyqtXr8rcuXPFYDCIm5ubZlYo2chGNloONrJRK40i+uhk43/3xmZmZkrTpk0lLy9PEhISpGHDhmy0IGy0jsb6wsUBqjM5OTni7+8vs2fPluzsbLly5YpERUWJn5+fxMfHS3FxsaSnp8vEiRPF1tZWQkJCJCwsTNzc3DR18cFf6wwICJB58+ZJTk6OiFQ9xGzMmDHi7OwsGRkZao1dI2xkIxstBxvZqJVGEX10svG/jSIiN27ckNDQUImKihJ7e3vN/LLFRjZqpbE+cXGA6kxGRoa0atXqoQfazJkzJTAwUBYvXixlZWVy9+5d+f777yUhIUFWrFihmc8urvBbncHBwZKUlCSFhYXK7StXrhRXV1dNnX/HRjZqBRvZqBV6aBTRRycbqzaePn1aDAaDNGzYUFM7e9jIRnoYFweozqSnp4uXl5d89913IiJy79495XuxsbHi4+OjmY9C+y3/q7N169ZVOq9fv17loihawEY2agUb2agVemgU0UcnG6s2Xrt2TWJiYuTHH39UZdbaYiMb6WEGERG1r3tA1qNr165wcnLC3r17AQD3799HgwYNAJR/1Evbtm2RkpKi5oh1orqdWv6oNDayUSvYyEat0EMjoI9ONlZ9T1dcXAwHBwfVZq0tNrKRqjKqPQBpV2FhIQoKCnDnzh3ltk8++QQZGRl4+eWXAQANGjSAyWQCADz33HMoLCxUZdbH8TidWnlDwEY2stFysJGNWmkE9NHJxv/9nk4Lv2yxkY2ANhrVxMUBqpXTp08jIiICvXv3RkBAANavXw8ACAgIwLJly7Bnzx689NJLKC0thdFY/mOWk5MDR0dHmEwmaOWAFT10spGNbLQcbGSjVhoBfXSykY1stBx6aFRdvZ/IQJqXkZEhHh4eMnXqVFm/fr1MmzZN7OzslAvtFBYWyr/+9S/x8vISf39/GTFihIwcOVIcHR3l5MmTKk9ffXroZCMb2Wg52MhGrTSK6KOTjWxko+XQQ6Ml4DUHqEby8vIwatQo+Pv7Y9myZcrtffv2RXBwMD766CPltoKCAiQmJiIvLw8ODg6YNGkS2rdvr8bYNaaHTjaykY2Wg41s1EojoI9ONrKRjZZDD42WwlbtAUhbSktLkZ+fjxdffBEAUFZWBqPRiNatWyMvLw8AIOWfgoHGjRtj4cKFVe6nFXroZCMb2Wg52MhGrTQC+uhkIxvZaDn00Ggp+H+LaqR58+ZYt24devXqBQAwm80AgJYtWyoPPoPBAKPRWOVCIQaDof6HfQx66GQjG7WCjWzUCj00AvroZCMbtYKN1tFoKbg4QDXWrl07AOWrcXZ2dgDKV+tycnKU+3z44YdYuXKlcqVQLT449dDJxnJstHxsLMdGy6eHRkAfnWwsx0bLx8ZyWm+0BDytgGrNaDRCRJQHXsXK3dy5c5GYmIi0tDTY2mr/R0wPnWxko1awkY1aoYdGQB+dbGSjVrDROhrVxCMH6LFUXM/S1tYW3t7eWLx4MZKSknD06FGEhISoPF3d0UMnG9moFWxko1booRHQRycb2agVbLSORrVwWYUeS8VqnZ2dHZKTk+Hs7IzU1FR06tRJ5cnqlh462Wgd2Ggd2Ggd9NAI6KOTjdaBjdZBD41q4ZEDVCcGDhwIADh48CA6d+6s8jRPjh462Wgd2Ggd2Ggd9NAI6KOTjdaBjdZBD431zSAVx2UQPabCwkI4OjqqPcYTp4dONloHNloHNloHPTQC+uhko3Vgo3XQQ2N94uIAERERERERkc7xtAIiIiIiIiIinePiABEREREREZHOcXGAiIiIiIiISOe4OEBERERERESkc1wcICIiIiIiItI5Lg4QERERERER6RwXB4iIiKjeiAj69euHgQMHPvS9v/3tb3B1dcXly5dVmIyIiEjfuDhARERE9cZgMOCzzz7D4cOH8cknnyi3nz9/HjNmzMBf/vIXeHl51el/s7S0tE7/PiIiImvExQEiIiKqV97e3li2bBmmT5+O8+fPQ0QQHR2NAQMGIDQ0FIMHD4aTkxOaN2+OMWPGIDc3V/mzX3/9NXr27AlXV1d4eHhg2LBhyM7OVr5/4cIFGAwGfPHFF+jduzccHBywfv16NTKJiIg0xSAiovYQREREpD8jRozA7du3ERERgYSEBGRkZCAwMBCvv/46xo4di6KiIsycORMmkwl79+4FAGzcuBEGgwEdOnTA3bt3MXfuXFy4cAHp6ekwGo24cOECWrdujVatWmHJkiUIDQ2Fg4MDWrRooXItERGRZePiABEREakiJycHgYGByMvLw8aNG3Hq1Cns378fu3btUu5z+fJleHt7IzMzE35+fg/9Hbm5uWjatClOnjyJoKAgZXFg6dKlmDJlSn3mEBERaRpPKyAiIiJVNGvWDG+++SYCAgIwYsQIHD9+HPv27YOTk5Pyj7+/PwAopw5kZWVh1KhR8PX1hbOzM1q1agUAuHjxYpW/u3PnzvXaQkREpHW2ag9ARERE+mVrawtb2/K3I3fv3sXw4cOxcOHCh+5XcVrA8OHD4ePjg+TkZHh6eqKsrAxBQUEoKSmpcn9HR8cnPzwREZEV4eIAERERWYROnTph48aNaNWqlbJgUNnNmzeRmZmJ5ORk9OrVCwCQmppa32MSERFZJZ5WQERERBYhJiYGeXl5GDVqFI4cOYLs7Gzs2rULr732GsxmM9zc3ODh4YFPP/0UZ8+exd69ezFt2jS1xyYiIrIKXBwgIiIii+Dp6YkDBw7AbDZjwIABCA4ORlxcHFxdXWE0GmE0GrFhwwYcO3YMQUFBmDp1KhYtWqT22ERERFaBn1ZAREREREREpHM8coCIiIiIiIhI57g4QERERERERKRzXBwgIiIiIiIi0jkuDhARERERERHpHBcHiIiIiIiIiHSOiwNEREREREREOsfFASIiIiIiIiKd4+IAERERERERkc5xcYCIiIiIiIhI57g4QERERERERKRzXBwgIiIiIiIi0jkuDhARERERERHp3P8DoOUSPXVoSuQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Over the past decade, the Japanese Language Proficiency Test (JLPT) has seen substantial growth, underscoring its rising importance globally. The number of participating countries/areas has expanded significantly, from just 4 in July 2009 to 85 by December 2023, reflecting the test’s growing international reach. The applicant numbers have also surged, with nearly 783,000 people applying in December 2023 alone, highlighting the increasing demand for Japanese language certification. A notable trend is the consistent preference for the December exam, which regularly sees higher participation compared to July, suggesting that candidates align their language goals with year-end opportunities. Despite a temporary dip due to the COVID-19 pandemic, which saw a reduced number of participating areas, the JLPT quickly bounced back, reaffirming its critical role in Japanese language education worldwide."
      ],
      "metadata": {
        "id": "ns5A_odsfmKA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Number of Participating Countries/Areas per Year in JLPT**\n",
        "# Filter rows where Month is 'Total'\n",
        "filtered_df = df[df['Month'] == 'Total']\n",
        "\n",
        "# Convert 'Countries/Areas' to numeric\n",
        "filtered_df['Countries/Areas'] = pd.to_numeric(filtered_df['Countries/Areas'].str.replace(',', ''), errors='coerce')\n",
        "\n",
        "# Prepare data for plotting\n",
        "yearly_data = filtered_df[['Year', 'Countries/Areas']]\n",
        "\n",
        "# Create the bar chart\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot bars\n",
        "bars = plt.bar(yearly_data['Year'], yearly_data['Countries/Areas'], color='steelblue')\n",
        "\n",
        "# Add text labels on top of the bars\n",
        "for bar in bars:\n",
        "    yval = bar.get_height()\n",
        "    plt.text(\n",
        "        bar.get_x() + bar.get_width() / 2,\n",
        "        yval + 0 * max(yearly_data['Countries/Areas']),\n",
        "        int(yval),\n",
        "        ha='center',\n",
        "        va='bottom'\n",
        "    )\n",
        "\n",
        "# Set labels and title\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of Countries/Areas')\n",
        "plt.title('Number of Participating Countries/Areas per Year in JLPT')\n",
        "\n",
        "# Set grid and layout\n",
        "plt.grid(False)\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 569
        },
        "id": "7e4kHaeynCDQ",
        "outputId": "ba280872-55ec-4628-cc5f-fbecdd937b4b",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Japanese Language Proficiency Test (JLPT) has witnessed a steady and significant expansion in the number of participating countries and areas since its inception. Starting with just 15 countries/areas in 1984, the JLPT's reach grew consistently, reflecting the increasing global interest in Japanese language learning.\n",
        "\n",
        "During the 1980s and early 1990s, the JLPT expanded gradually, reaching 22 countries by 1990 and 31 by 1996. This early growth period established a solid foundation for the test's global presence. The turn of the century marked a more rapid expansion, with the number of participating countries growing from 36 in 2000 to 54 by 2009.\n",
        "\n",
        "The 2010s were a decade of remarkable growth for the JLPT, as the number of participating countries climbed from 58 in 2010 to 81 in 2017. Despite a temporary decline in 2020, when the COVID-19 pandemic reduced participation to 29 countries, the JLPT quickly recovered, reaching 74 countries by 2021. The upward trajectory continued, culminating in a record 92 participating countries/areas in 2023."
      ],
      "metadata": {
        "id": "Bm5sSFXYf6EG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Number of JLPT Applicants and Examinees per Year**\n",
        "# Ensure the columns are in numeric format\n",
        "df['Number of Applicants'] = pd.to_numeric(df['Number of Applicants'])\n",
        "df['Number of Examinees'] = pd.to_numeric(df['Number of Examinees'])\n",
        "\n",
        "# Filter the data to include only 'Total' month\n",
        "total = df[df['Month'] == 'Total']\n",
        "\n",
        "# Melt the dataframe to have a single 'value' column for the two metrics\n",
        "melted = total.melt(id_vars=['Year'], value_vars=['Number of Applicants', 'Number of Examinees'],\n",
        "                    var_name='Metric', value_name='Value')\n",
        "\n",
        "# Create the plot\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot each metric\n",
        "for metric, color in zip(['Number of Applicants', 'Number of Examinees'], ['blue', 'red']):\n",
        "    subset = melted[melted['Metric'] == metric]\n",
        "    plt.plot(subset['Year'], subset['Value'], color=color, label=metric)\n",
        "\n",
        "# Add labels and title\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Number of Applicants / Examinees')\n",
        "plt.title('Number of JLPT Applicants and Examinees per Year')\n",
        "\n",
        "# Add legend\n",
        "plt.legend(title='Metric')\n",
        "\n",
        "# Show plot\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        },
        "id": "t3hqN8d7fpar",
        "outputId": "aea024d9-1732-4547-8e2e-7f9ced676a31",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Key Trends:**\n",
        "\n",
        "- **Steady Growth:** From 1984 to the early 2000s, applicant and examinee numbers steadily increased, surpassing 100,000 by the mid-1990s and 200,000 by 2000.\n",
        "- **Rapid Expansion:** The early 2000s saw accelerated growth, with applicants exceeding 900,000 by 2009, as the JLPT gained global traction.\n",
        "- **Pandemic Impact:** In 2020, COVID-19 caused a sharp decline, but participation rebounded quickly afterward.\n",
        "- **Record Numbers in 2023:** The JLPT hit an all-time high in 2023, with 1.48 million applicants and 1.27 million examinees, reflecting its growing global demand."
      ],
      "metadata": {
        "id": "od1sWUI2gFZe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **JLPT Examinees and Applicants Forecast**\n",
        "# Convert 'Year' column to datetime objects\n",
        "df['Year'] = pd.to_datetime(df['Year'], format='%Y')\n",
        "\n",
        "# Filter data for 'Total' month values\n",
        "df_filtered = df[df['Month'] == 'Total']\n",
        "\n",
        "# Convert 'Number of Applicants' and 'Number of Examinees' to numeric type\n",
        "df_filtered['Number of Applicants'] = pd.to_numeric(df_filtered['Number of Applicants'], errors='coerce')\n",
        "df_filtered['Number of Examinees'] = pd.to_numeric(df_filtered['Number of Examinees'], errors='coerce')\n",
        "\n",
        "# Drop rows with missing values\n",
        "df_filtered = df_filtered.dropna()\n",
        "\n",
        "# Prepare data for forecasting\n",
        "df_forecast = df_filtered[['Year', 'Number of Applicants', 'Number of Examinees']].copy()\n",
        "df_forecast = df_forecast.set_index('Year')\n",
        "\n",
        "# Prepare data for linear regression\n",
        "X = df_forecast.index.year.values.reshape(-1, 1)\n",
        "y_applicants = df_forecast['Number of Applicants']\n",
        "y_examinees = df_forecast['Number of Examinees']\n",
        "\n",
        "# Standardize features\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Create and fit the model for 'Number of Applicants'\n",
        "model_applicants = LinearRegression()\n",
        "model_applicants.fit(X_scaled, y_applicants)\n",
        "\n",
        "# Create and fit the model for 'Number of Examinees'\n",
        "model_examinees = LinearRegression()\n",
        "model_examinees.fit(X_scaled, y_examinees)\n",
        "\n",
        "# Forecast for the year 2024\n",
        "forecast_year = np.array([[2024]])\n",
        "forecast_year_scaled = scaler.transform(forecast_year)\n",
        "\n",
        "forecast_applicants = model_applicants.predict(forecast_year_scaled)\n",
        "forecast_examinees = model_examinees.predict(forecast_year_scaled)\n",
        "\n",
        "# Create a DataFrame for the forecasted values\n",
        "forecast_df = pd.DataFrame({\n",
        "    'Year': pd.to_datetime(['2024']),\n",
        "    'Number of Applicants': forecast_applicants,\n",
        "    'Number of Examinees': forecast_examinees\n",
        "})\n",
        "forecast_df = forecast_df.set_index('Year')\n",
        "\n",
        "# Concatenate the original and forecasted DataFrames\n",
        "combined_df = pd.concat([df_forecast, forecast_df])\n",
        "\n",
        "# Plot the results\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Plot historical data\n",
        "plt.plot(df_forecast.index, df_forecast['Number of Applicants'], label='Number of Applicants', color='blue', marker='o')\n",
        "plt.plot(df_forecast.index, df_forecast['Number of Examinees'], label='Number of Examinees', color='red', marker='o')\n",
        "\n",
        "# Plot forecasted data\n",
        "plt.plot(forecast_df.index, forecast_df['Number of Applicants'], 'o', color='blue', markersize=10, label='Forecast Applicants')\n",
        "plt.plot(forecast_df.index, forecast_df['Number of Examinees'], 'o', color='red', markersize=10, label='Forecast Examinees')\n",
        "\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Count')\n",
        "plt.title('JLPT Examinees and Applicants Forecast')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "riZXBqQ3qRe7",
        "outputId": "773078c2-b9b4-4341-d9ed-9a6beed16730",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **JLPT Examinees and Applicants Forecast for the year 2024**\n",
        "#Preprocess data\n",
        "forecast_df.reset_index(inplace=True)\n",
        "forecast_df['Year'] = forecast_df['Year'].replace('2024-01-01', 2024)\n",
        "\n",
        "# Plot forecast data\n",
        "ax = forecast_df.plot(kind='bar', x='Year', figsize=(10, 5), color=['blue', 'red'])\n",
        "\n",
        "# Add labels on top of bars\n",
        "for p in ax.patches:\n",
        "    height = p.get_height()\n",
        "    ax.annotate(f'{height:.0f}',\n",
        "                (p.get_x() + p.get_width() / 2., height),\n",
        "                ha='center', va='center',\n",
        "                xytext=(0, 3),  # 3 points vertical offset\n",
        "                textcoords='offset points')\n",
        "\n",
        "# Set title and labels\n",
        "plt.title('JLPT Examinees and Applicants Forecast')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Count')\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "dsyZXQTNqSeY",
        "outputId": "16552ce1-9581-4169-93bd-2c57cd2e0b8f",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Linear regression is a statistical technique used to predict the value of a continuous outcome based on various features. In this case, we used historical data on the number of JLPT applicants and examinees, along with the specific year, as features in the model. By analyzing these features, the linear regression model forecasts future trends. For 2024, the model predicts approximately 1.09 million applicants and around 937,870 examinees. This prediction reflects a strong and continuing interest in the JLPT, consistent with the growth trends observed in previous years."
      ],
      "metadata": {
        "id": "9hpZDOLdgmFa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Get July 2023 JLPT Data**\n",
        "# Define the data\n",
        "df_final = pd.read_csv(\"2023_examinees_data.csv\")\n",
        "df_final.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "xpN2Ta65uKyn",
        "outputId": "5c0f8245-d84f-4631-fdea-4a391f7b942d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Category      N1       N2      N3       N4      N5    Total  \\\n",
              "0               Applicants  47,043   57,199  63,957   41,566   4,111  213,876   \n",
              "1               Examinees※  41,484   52,189  60,570   39,237   3,664  197,144   \n",
              "2                Certified  13,349   18,267  25,683   17,993   2,419   77,711   \n",
              "3  Percentage Certified(%)   32.2%    35.0%   42.4%    45.9%   66.0%    39.4%   \n",
              "4               Applicants  94,349  109,149  92,816  127,184  60,688  484,186   \n",
              "\n",
              "   Location  \n",
              "0     Japan  \n",
              "1     Japan  \n",
              "2     Japan  \n",
              "3     Japan  \n",
              "4  Overseas  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-372f9730-0e8a-43dc-8bd5-35f9d43cd209\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>N1</th>\n",
              "      <th>N2</th>\n",
              "      <th>N3</th>\n",
              "      <th>N4</th>\n",
              "      <th>N5</th>\n",
              "      <th>Total</th>\n",
              "      <th>Location</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Applicants</td>\n",
              "      <td>47,043</td>\n",
              "      <td>57,199</td>\n",
              "      <td>63,957</td>\n",
              "      <td>41,566</td>\n",
              "      <td>4,111</td>\n",
              "      <td>213,876</td>\n",
              "      <td>Japan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Examinees※</td>\n",
              "      <td>41,484</td>\n",
              "      <td>52,189</td>\n",
              "      <td>60,570</td>\n",
              "      <td>39,237</td>\n",
              "      <td>3,664</td>\n",
              "      <td>197,144</td>\n",
              "      <td>Japan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Certified</td>\n",
              "      <td>13,349</td>\n",
              "      <td>18,267</td>\n",
              "      <td>25,683</td>\n",
              "      <td>17,993</td>\n",
              "      <td>2,419</td>\n",
              "      <td>77,711</td>\n",
              "      <td>Japan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Percentage Certified(%)</td>\n",
              "      <td>32.2%</td>\n",
              "      <td>35.0%</td>\n",
              "      <td>42.4%</td>\n",
              "      <td>45.9%</td>\n",
              "      <td>66.0%</td>\n",
              "      <td>39.4%</td>\n",
              "      <td>Japan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Applicants</td>\n",
              "      <td>94,349</td>\n",
              "      <td>109,149</td>\n",
              "      <td>92,816</td>\n",
              "      <td>127,184</td>\n",
              "      <td>60,688</td>\n",
              "      <td>484,186</td>\n",
              "      <td>Overseas</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_final",
              "summary": "{\n  \"name\": \"df_final\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Examinees\\u203b\",\n          \"Percentage Certified(%)\",\n          \"Applicants\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"N1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"40,975\",\n          \"120,407\",\n          \"47,043\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"N2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"60,870\",\n          \"144,712\",\n          \"57,199\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"N3\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"59,479\",\n          \"136,237\",\n          \"63,957\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"N4\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"63,184\",\n          \"148,410\",\n          \"41,566\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"N5\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"26,522\",\n          \"52,374\",\n          \"4,111\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"251,030\",\n          \"602,140\",\n          \"213,876\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Japan\",\n          \"Overseas\",\n          \"Japan & Overseas\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **JLPT 2023 July Passing Rate per Exam Level and Location**\n",
        "# Define a function to clean and convert the percentage strings to floats\n",
        "def clean_and_convert_percentage(series):\n",
        "    return series.str.replace('%', '').astype(float)\n",
        "\n",
        "# Filter the data for each location\n",
        "df_japan = df_final[df_final[\"Location\"] == \"Japan\"]\n",
        "df_overseas = df_final[df_final[\"Location\"] == \"Overseas\"]\n",
        "df_japan_overseas = df_final[df_final[\"Location\"] == \"Japan & Overseas\"]\n",
        "\n",
        "# Extract the passing rates directly\n",
        "passing_rate_japan = clean_and_convert_percentage(df_japan.iloc[3, 1:6])\n",
        "passing_rate_overseas = clean_and_convert_percentage(df_overseas.iloc[3, 1:6])\n",
        "passing_rate_japan_overseas = clean_and_convert_percentage(df_japan_overseas.iloc[3, 1:6])\n",
        "\n",
        "# Plot the passing rates\n",
        "labels = [\"N1\", \"N2\", \"N3\", \"N4\", \"N5\"]\n",
        "x = range(len(labels))\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "plt.plot(x, passing_rate_japan, marker='o', linestyle='-', color='b', linewidth=2, markersize=8, label=\"Japan\")\n",
        "plt.plot(x, passing_rate_overseas, marker='s', linestyle='--', color='r', linewidth=2, markersize=8, label=\"Overseas\")\n",
        "plt.plot(x, passing_rate_japan_overseas, marker='^', linestyle='-.', color='g', linewidth=2, markersize=8, label=\"Japan & Overseas\")\n",
        "\n",
        "# Customize the ticks and labels\n",
        "plt.xticks(x, labels, fontsize=12)\n",
        "plt.yticks(fontsize=12)\n",
        "plt.xlabel(\"Exam Level\", fontsize=14)\n",
        "plt.ylabel(\"Passing Rate (%)\", fontsize=14)\n",
        "\n",
        "# Adding title with customization\n",
        "plt.title(\"JLPT 2023 July Passing Rate per Exam Level and Location\", fontsize=16, pad=20)\n",
        "\n",
        "# Adding grid, legend, and background style\n",
        "plt.grid(True, linestyle='--', alpha=0.7)\n",
        "plt.legend(fontsize=12, title=\"Location\", title_fontsize='13', loc='upper left')\n",
        "\n",
        "# Add data labels\n",
        "for i, txt in enumerate(passing_rate_japan):\n",
        "    plt.annotate(f'{txt}%', (x[i], passing_rate_japan.iloc[i]), textcoords=\"offset points\", xytext=(0,10), ha='center', fontsize=10, color='blue')\n",
        "for i, txt in enumerate(passing_rate_overseas):\n",
        "    plt.annotate(f'{txt}%', (x[i], passing_rate_overseas.iloc[i]), textcoords=\"offset points\", xytext=(0,10), ha='center', fontsize=10, color='red')\n",
        "for i, txt in enumerate(passing_rate_japan_overseas):\n",
        "    plt.annotate(f'{txt}%', (x[i], passing_rate_japan_overseas.iloc[i]), textcoords=\"offset points\", xytext=(0,10), ha='center', fontsize=10, color='green')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        },
        "cellView": "form",
        "id": "vc8xe_p7aJub",
        "outputId": "82560bdf-7206-4137-cbc9-14130c52dcb7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The passing rates for the JLPT reveal notable differences between candidates in Japan and those overseas. In Japan, the rates are generally lower across all levels, with N1 being the most challenging at 32.2%, and N5 the least challenging at 66.0%. This trend indicates that advanced levels are significantly more difficult for domestic candidates.\n",
        "\n",
        "Conversely, overseas candidates generally experience higher passing rates. For instance, the rate for N1 is 35.0%, surpassing the Japanese rate, and N5 also shows a higher passing rate of 49.5%. This suggests that international candidates might be better prepared or face different challenges compared to those in Japan.\n",
        "\n",
        "When combining data from both regions, the overall passing rates fall between the Japanese and overseas figures. For example, the combined N1 passing rate is 34.0%, which aligns more closely with the overseas rate than with Japan's. This pattern underscores a broader, more balanced perspective on the exam's difficulty, reflecting a higher success rate globally while still acknowledging the advanced levels' inherent challenges."
      ],
      "metadata": {
        "id": "Lk2OfroghMch"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **JLPT Passers by Location (Japan and Overseas) (Levels N1-N5) 2023 July**\n",
        "# Filter the dataset for certified passers\n",
        "df_passers = df_final[df_final[\"Category\"] == \"Certified\"]\n",
        "\n",
        "# Reshape the data to a long format\n",
        "df_passers = df_passers.melt(\n",
        "    id_vars=['Category', 'Location'],\n",
        "    value_vars=['N1', 'N2', 'N3', 'N4', 'N5'],\n",
        "    var_name='Level',\n",
        "    value_name='Passers'\n",
        ")\n",
        "\n",
        "# Convert the 'Passers' column to integers\n",
        "df_passers['Passers'] = df_passers['Passers'].str.replace(',', '').astype(int)\n",
        "\n",
        "# Define the levels and the width of each bar\n",
        "levels = ['N1', 'N2', 'N3', 'N4', 'N5']\n",
        "bar_width = 0.35\n",
        "index = np.arange(len(levels))\n",
        "\n",
        "# Create the figure and the bar plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Separate the data by location\n",
        "japan_passers = df_passers[df_passers['Location'] == 'Japan']\n",
        "overseas_passers = df_passers[df_passers['Location'] == 'Overseas']\n",
        "\n",
        "# Plot the bars for Japan and Overseas side by side\n",
        "plt.bar(index, japan_passers['Passers'], bar_width, label='Japan', color='#1f77b4', alpha=0.9)\n",
        "plt.bar(index + bar_width, overseas_passers['Passers'], bar_width, label='Overseas', color='#ff7f0e', alpha=0.9)\n",
        "\n",
        "# Add labels, title, and ticks\n",
        "plt.xlabel('Level')\n",
        "plt.ylabel('Number of Passers')\n",
        "plt.title('Number of Certified Passers by Exam Level and Location (2023 July)')\n",
        "plt.xticks(index + bar_width / 2, levels)\n",
        "plt.legend(title='Location')\n",
        "\n",
        "# Display the plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "QiNtV4Hi2wqf",
        "outputId": "d76c1f4a-9d33-444f-c95f-470092f52826",
        "cellView": "form"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **N1 Level:**\n",
        "  - Japan: 13,349 passers\n",
        "  - Overseas: 27,626 passers\n",
        "  - Combined Total: 40,975 passers\n",
        "  - Overseas candidates have a higher number of passers compared to Japan.\n",
        "\n",
        "- **N2 Level:**\n",
        "  - Japan: 18,267 passers\n",
        "  - Overseas: 42,603 passers\n",
        "  - Combined Total: 60,870 passers\n",
        "  - The majority of passers are from overseas, indicating a greater international success rate.\n",
        "\n",
        "- **N3 Level:**\n",
        "  - Japan: 25,683 passers\n",
        "  - Overseas: 33,796 passers\n",
        "  - Combined Total: 59,479 passers\n",
        "  - Again, overseas candidates surpass Japanese passers.\n",
        "\n",
        "- **N4 Level:**\n",
        "  - Japan: 17,993 passers\n",
        "  - Overseas: 45,191 passers\n",
        "  - Combined Total: 63,184 passers\n",
        "  - The overseas count is significantly higher, showing strong international participation.\n",
        "\n",
        "- **N5 Level:**\n",
        "  - Japan: 2,419 passers\n",
        "  - Overseas: 24,103 passers\n",
        "  - Combined Total: 26,522 passers\n",
        "  - Overseas passers make up the vast majority of N5 candidates.\n",
        "\n",
        "- **Summary:**\n",
        " - Overseas JLPT pass rates exceed those in Japan at all levels. Overseas passers outnumber Japanese passers for **N1** (27,626 vs. 13,349), **N2** (42,603 vs. 18,267), **N3** (33,796 vs. 25,683), **N4** (45,191 vs. 17,993), and **N5** (24,103 vs. 2,419). This indicates stronger performance and higher international success rates across all JLPT levels."
      ],
      "metadata": {
        "id": "xCrgNvHthrBT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **JLPT Examinees by Location (Japan and Overseas) (Levels N1-N5) 2023 July**\n",
        "# Filter the dataset for certified passers\n",
        "df_examinees = df_final[df_final[\"Category\"] == \"Applicants\"]\n",
        "\n",
        "# Reshape the data to a long format\n",
        "df_examinees = df_examinees.melt(\n",
        "    id_vars=['Category', 'Location'],\n",
        "    value_vars=['N1', 'N2', 'N3', 'N4', 'N5'],\n",
        "    var_name='Level',\n",
        "    value_name='Passers'\n",
        ")\n",
        "\n",
        "# Convert the 'Passers' column to integers\n",
        "df_examinees['Passers'] = df_examinees['Passers'].str.replace(',', '').astype(int)\n",
        "\n",
        "# Define the levels and the width of each bar\n",
        "levels = ['N1', 'N2', 'N3', 'N4', 'N5']\n",
        "bar_width = 0.35\n",
        "index = np.arange(len(levels))\n",
        "\n",
        "# Create the figure and the bar plot\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Separate the data by location\n",
        "japan_passers = df_examinees[df_examinees['Location'] == 'Japan']\n",
        "overseas_passers = df_examinees[df_examinees['Location'] == 'Overseas']\n",
        "\n",
        "# Plot the bars for Japan and Overseas side by side\n",
        "plt.bar(index, japan_passers['Passers'], bar_width, label='Japan', color='#1f77b4', alpha=0.9)\n",
        "plt.bar(index + bar_width, overseas_passers['Passers'], bar_width, label='Overseas', color='#ff7f0e', alpha=0.9)\n",
        "\n",
        "# Add labels, title, and ticks\n",
        "plt.xlabel('Level')\n",
        "plt.ylabel('Number of Examinees')\n",
        "plt.title('Number of Examinees by Exam Level and Location (2023 July)')\n",
        "plt.xticks(index + bar_width / 2, levels)\n",
        "plt.legend(title='Location')\n",
        "\n",
        "# Display the plot\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "cellView": "form",
        "id": "Vz1d-c6IPPie",
        "outputId": "93b8dc0b-a678-4549-88fe-41189bc05f01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **N1 Level:**\n",
        "  - **Japan:** 47,043 applicants\n",
        "  - **Overseas:** 94,349 applicants\n",
        "  - **Total:** 141,392 applicants\n",
        "  - The number of applicants is significantly higher overseas, indicating a strong international interest in the N1 level.\n",
        "\n",
        "- **N2 Level:**\n",
        "  - **Japan:** 57,199 applicants\n",
        "  - **Overseas:** 109,149 applicants\n",
        "  - **Total:** 166,348 applicants\n",
        "  - Overseas applicants outnumber those in Japan, suggesting greater global engagement with the N2 level.\n",
        "\n",
        "- **N3 Level:**\n",
        "  - **Japan:** 63,957 applicants\n",
        "  - **Overseas:** 92,816 applicants\n",
        "  - **Total:** 156,773 applicants\n",
        "  - The pattern of more overseas applicants continues for the N3 level.\n",
        "\n",
        "- **N4 Level:**\n",
        "  - **Japan:** 41,566 applicants\n",
        "  - **Overseas:** 127,184 applicants\n",
        "  - **Total:** 168,750 applicants\n",
        "  - Overseas applicants again significantly exceed those in Japan, showing a robust international demand.\n",
        "\n",
        "- **N5 Level:**\n",
        "  - **Japan:** 4,111 applicants\n",
        "  - **Overseas:** 60,688 applicants\n",
        "  - **Total:** 64,799 applicants\n",
        "  - The majority of N5 applicants are from overseas, highlighting the global reach of this entry-level exam.\n",
        "\n",
        "- **Summary:**\n",
        " -  The data shows a clear global interest across all levels. Overseas applicants significantly outnumber those in Japan at every level: N1 (94,349 vs. 47,043), N2 (109,149 vs. 57,199), N3 (92,816 vs. 63,957), N4 (127,184 vs. 41,566), and N5 (60,688 vs. 4,111). This indicates strong international demand for the JLPT."
      ],
      "metadata": {
        "id": "-TcgbJgqiI07"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Average Scores and Standard Deviations JULY 2023**\n",
        "\n",
        "df_new = pd.read_csv('2023_avg_and_std_scores.csv')\n",
        "df_new\n",
        "\n",
        "# Melt the DataFrame to long format for easier plotting\n",
        "df_melted = df_new.melt(id_vars=['Scoring sections', 'Location'],\n",
        "                        value_vars=['N1_Average', 'N1_Standard_Deviation',\n",
        "                                    'N2_Average', 'N2_Standard_Deviation',\n",
        "                                    'N3_Average', 'N3_Standard_Deviation',\n",
        "                                    'N4_Average', 'N4_Standard_Deviation',\n",
        "                                    'N5_Average', 'N5_Standard_Deviation'],\n",
        "                        var_name='Score_Type', value_name='Score')\n",
        "\n",
        "df_melted.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2ydbaETCwduh",
        "outputId": "35485d8d-1ac5-42c0-a8f7-03315c698a87"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Scoring sections  Location  Score_Type  Score\n",
              "0  Language Knowledge (Vocabulary/Grammar)     Japan  N1_Average   27.7\n",
              "1  Language Knowledge (Vocabulary/Grammar)  Overseas  N1_Average   30.8\n",
              "2  Language Knowledge (Vocabulary/Grammar)     Total  N1_Average   29.7\n",
              "3                                  Reading     Japan  N1_Average   28.6\n",
              "4                                  Reading  Overseas  N1_Average   29.6"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d70c9e02-25bd-4eec-b584-ea1474221a44\" class=\"colab-df-container\">\n",
              "    <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>Scoring sections</th>\n",
              "      <th>Location</th>\n",
              "      <th>Score_Type</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Language Knowledge (Vocabulary/Grammar)</td>\n",
              "      <td>Japan</td>\n",
              "      <td>N1_Average</td>\n",
              "      <td>27.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Language Knowledge (Vocabulary/Grammar)</td>\n",
              "      <td>Overseas</td>\n",
              "      <td>N1_Average</td>\n",
              "      <td>30.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Language Knowledge (Vocabulary/Grammar)</td>\n",
              "      <td>Total</td>\n",
              "      <td>N1_Average</td>\n",
              "      <td>29.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Reading</td>\n",
              "      <td>Japan</td>\n",
              "      <td>N1_Average</td>\n",
              "      <td>28.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Reading</td>\n",
              "      <td>Overseas</td>\n",
              "      <td>N1_Average</td>\n",
              "      <td>29.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d70c9e02-25bd-4eec-b584-ea1474221a44')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d70c9e02-25bd-4eec-b584-ea1474221a44 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d70c9e02-25bd-4eec-b584-ea1474221a44');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-35f0afe9-b7bd-4202-a552-5772a88d58c3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-35f0afe9-b7bd-4202-a552-5772a88d58c3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-35f0afe9-b7bd-4202-a552-5772a88d58c3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_melted",
              "summary": "{\n  \"name\": \"df_melted\",\n  \"rows\": 140,\n  \"fields\": [\n    {\n      \"column\": \"Scoring sections\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Reading\",\n          \"Total Score\",\n          \"Language Knowledge (Vocabulary/Grammar) & Reading\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Japan\",\n          \"Overseas\",\n          \"Total\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score_Type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"N5_Average\",\n          \"N1_Standard_Deviation\",\n          \"N3_Standard_Deviation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25.864954602453977,\n        \"min\": 8.3,\n        \"max\": 95.8,\n        \"num_unique_values\": 82,\n        \"samples\": [\n          33.6,\n          27.7,\n          31.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title **Total average scores per section by location**\n",
        "# Filter and replace values in the dataframe\n",
        "df_filtered = df_melted[df_melted['Score_Type'].str.contains('Average')]\n",
        "df_filtered = df_filtered.replace({\n",
        "    'Scoring sections': {\n",
        "        'Language Knowledge (Vocabulary/Grammar) & Reading': 'Lang. Knowledge & Reading',\n",
        "        'Language Knowledge (Vocabulary/Grammar)': 'Lang. Knowledge'\n",
        "    }\n",
        "})\n",
        "\n",
        "# Set up the grid for subplots\n",
        "g = sns.catplot(\n",
        "    data=df_filtered,\n",
        "    x='Location',\n",
        "    y='Score',\n",
        "    hue='Location',\n",
        "    col='Scoring sections',\n",
        "    kind='bar',\n",
        "    height=5,\n",
        "    aspect=0.7\n",
        ")\n",
        "\n",
        "# Adjust the subplot titles and layout\n",
        "g.set_titles(\"{col_name}\", size=12, rotation=20, ha='center')\n",
        "g.fig.subplots_adjust(top=0.85, wspace=0.2)\n",
        "\n",
        "# Set the overall figure size if needed\n",
        "g.fig.set_size_inches(15, 6)\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "i6ZiuSkCV06k",
        "outputId": "2a4e6643-c593-47f1-a544-f9aa97a6abc4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **N1 Level**:\n",
        "  - Examinees who took the exam overseas scored higher in Language Knowledge (30.8 vs. 27.7) and Reading (29.6 vs. 28.6).\n",
        "  - Examinees in Japan outperformed in Listening (34.7 vs. 31.2).\n",
        "  - Total scores were nearly equivalent between the two groups.\n",
        "\n",
        "- **N2 Level**:\n",
        "  - Overseas examinees scored higher in Language Knowledge (29.3 vs. 24.2) and Reading (28.8 vs. 24.6).\n",
        "  - Examinees in Japan excelled in Listening (36.5 vs. 33.6).\n",
        "  - Overall, overseas examinees had a higher total score (91.8 vs. 85.3).\n",
        "\n",
        "- **N3 Level**:\n",
        "  - Overseas examinees outperformed in Language Knowledge (32.1 vs. 28.6) and Reading (30.3 vs. 26.8).\n",
        "  - Examinees in Japan led in Listening (37.6 vs. 33.4).\n",
        "  - Overseas examinees had a higher total score.\n",
        "\n",
        "- **N4 Level**:\n",
        "  - Examinees in Japan performed better in Listening (33.8 vs. 27.6).\n",
        "  - Overseas examinees scored higher in combined Language Knowledge and Reading (58.9 vs. 55.3).\n",
        "  - Overall total scores were slightly higher for examinees in Japan.\n",
        "\n",
        "- **N5 Level**:\n",
        "  - Examinees in Japan outperformed in Listening (33.1 vs. 28.8) and achieved higher total scores (91.9 vs. 82.7).\n",
        "  - Overseas examinees did better in Language Knowledge and Reading (58.9 vs. 53.9).\n",
        "\n",
        "- **Summary**:\n",
        " - Examinees who took the exam in Japan consistently performed better in Listening, while those overseas often excelled in Language Knowledge and Reading, particularly at the N1-N3 levels. These insights can inform preparation strategies and highlight areas for improvement."
      ],
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      "source": [
        "### **Conclusion**\n",
        "\n",
        "This analysis provides a detailed examination of the Japanese-Language Proficiency Test (JLPT) from 1984 to 2023, focusing on participation trends, regional performance, and forecasts. **Key findings include a steady increase** in both applicants and examinees over the years, with a significant surge in participation up to 2009. A temporary decline in 2020, due to the COVID-19 pandemic, was followed by a swift recovery, demonstrating the JLPT’s **resilience and enduring global appeal**.\n",
        "\n",
        "The analysis of participation trends reveals that the JLPT has expanded its reach substantially, with a growing number of **participating countries and regions each year**. Forecasting using **linear regression** suggests that the number of examinees and applicants will continue to rise in 2024, indicating **sustained global interest** in Japanese language proficiency.\n",
        "\n",
        "The July 2023 data shows that **passing rates and average scores** differ between Japan and overseas locations. Specifically, **examinees who took the test overseas outperformed in Language Knowledge and Reading**, while those who took the exam in Japan excelled in Listening. These differences may reflect **regional variations in test preparation and educational resources**.\n",
        "\n",
        "The assessment of average scores and standard deviations highlights performance patterns across different JLPT levels. These insights reveal **regional strengths and areas for improvement**, offering valuable information for both educators and test-takers. Overall, this analysis underscores the JLPT’s **expanding international footprint** and provides actionable insights into performance trends, contributing to more informed educational strategies and future research."
      ],
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}