projectile_motion_pylint_test.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/reshnashrestha/23-Homework3G5/blob/main_documentation/projectile_motion_pylint_test.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdIls2zEzGjM",
        "outputId": "7158226c-6bb8-422d-eb89-f635fc547d9a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qI4-baNT0Kdy",
        "outputId": "f4325557-655a-4cf0-b056-d937db47fb5b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m72.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.1/510.1 kB\u001b[0m \u001b[31m15.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.3/45.3 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.6/62.6 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m275.1/275.1 kB\u001b[0m \u001b[31m25.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.2/91.2 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install black[jupyter] --quiet\n",
        "!pip install nbqa flake8 pylint --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XyqHzS3K0ab3",
        "outputId": "684409bf-53b7-4867-ca68-feacf0671c12"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:1:1: E402 module level import not at top of file\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:2:1: E402 module level import not at top of file\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:5:9: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:6:9: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:8:12: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:9:12: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:9:13: E262 inline comment should start with '# '\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:11:19: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:13:16: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:15:19: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:15:22: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:15:25: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:15:28: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:15:31: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:15:35: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:16:60: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:16:61: E262 inline comment should start with '# '\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:16:80: E501 line too long (92 > 79 characters)\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:18:7: E225 missing whitespace around operator\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:18:10: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:18:11: E262 inline comment should start with '# '\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:19:7: E225 missing whitespace around operator\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:19:10: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:19:11: E262 inline comment should start with '# '\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:21:28: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:23:3: E111 indentation is not a multiple of 4\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:23:36: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:24:3: E111 indentation is not a multiple of 4\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:24:36: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:26:3: E111 indentation is not a multiple of 4\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:26:4: E225 missing whitespace around operator\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:26:14: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:27:3: E111 indentation is not a multiple of 4\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:27:4: E225 missing whitespace around operator\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:27:14: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:29:3: E111 indentation is not a multiple of 4\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:29:8: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:31:3: E111 indentation is not a multiple of 4\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:31:14: E225 missing whitespace around operator\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:31:18: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:32:25: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:33:25: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:34:7: E225 missing whitespace around operator\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:34:49: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:35:7: E225 missing whitespace around operator\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:35:53: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:36:6: E225 missing whitespace around operator\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:36:10: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:38:3: E114 indentation is not a multiple of 4 (comment)\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:38:3: E265 block comment should start with '# '\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:39:3: E111 indentation is not a multiple of 4\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:40:3: E111 indentation is not a multiple of 4\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:41:3: E111 indentation is not a multiple of 4\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:41:8: E225 missing whitespace around operator\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:43:3: E114 indentation is not a multiple of 4 (comment)\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:44:3: E111 indentation is not a multiple of 4\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:45:3: E111 indentation is not a multiple of 4\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:47:1: E265 block comment should start with '# '\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:49:8: E225 missing whitespace around operator\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:49:18: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:49:25: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:49:33: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:49:42: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:49:51: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:49:58: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:50:22: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:50:26: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:51:29: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:52:55: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:52:80: E501 line too long (120 > 79 characters)\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:52:95: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:53:29: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:53:33: E231 missing whitespace after ','\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:53:74: E261 at least two spaces before inline comment\n",
            "drive/MyDrive/Colab Notebooks/projectile_motion_pylint_test.ipynb:cell_4:53:80: E501 line too long (95 > 79 characters)\n"
          ]
        }
      ],
      "source": [
        "!nbqa flake8 /content/drive/MyDrive/'Colab Notebooks'/'projectile_motion_pylint_test.ipynb'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HO1W05Kc3B4r",
        "outputId": "2e64fba7-2189-43a8-ee39-5e8ff02918b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting ipytest\n",
            "  Downloading ipytest-0.13.3-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.10/dist-packages (from ipytest) (7.34.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from ipytest) (23.1)\n",
            "Requirement already satisfied: pytest>=5.4 in /usr/local/lib/python3.10/dist-packages (from ipytest) (7.4.2)\n",
            "Requirement already satisfied: iniconfig in /usr/local/lib/python3.10/dist-packages (from pytest>=5.4->ipytest) (2.0.0)\n",
            "Requirement already satisfied: pluggy<2.0,>=0.12 in /usr/local/lib/python3.10/dist-packages (from pytest>=5.4->ipytest) (1.3.0)\n",
            "Requirement already satisfied: exceptiongroup>=1.0.0rc8 in /usr/local/lib/python3.10/dist-packages (from pytest>=5.4->ipytest) (1.1.3)\n",
            "Requirement already satisfied: tomli>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from pytest>=5.4->ipytest) (2.0.1)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython->ipytest) (67.7.2)\n",
            "Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.10/dist-packages (from ipython->ipytest) (0.19.1)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython->ipytest) (4.4.2)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython->ipytest) (0.7.5)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipython->ipytest) (5.7.1)\n",
            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython->ipytest) (3.0.39)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython->ipytest) (2.16.1)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython->ipytest) (0.2.0)\n",
            "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython->ipytest) (0.1.6)\n",
            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython->ipytest) (4.8.0)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython->ipytest) (0.8.3)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython->ipytest) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->ipytest) (0.2.7)\n",
            "Installing collected packages: ipytest\n",
            "Successfully installed ipytest-0.13.3\n"
          ]
        }
      ],
      "source": [
        "!pip install ipytest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "VDsoq9UF3XVg"
      },
      "outputs": [],
      "source": [
        "import ipytest\n",
        "ipytest.autoconfig()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "8zH9zvpL0mYm",
        "outputId": "362e0851-5447-4ae9-e6b9-e8b4ae76eead"
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq4AAAIjCAYAAADC0ZkAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB21ElEQVR4nO3de1hU5do/8C+CoiAHQRHZ4owKpmKaQVIeKreWmh3UzCwrdbMrTDNsZ9G20ky3qF3qznzhzcy0NNNK++327qB5giQTfd2VnTxBmXlWQEVUWL8/RsaZYQ5rZtZ5fT/XxQXMaT3DMGvuda/7uZ8QQRAEEBERERFpXAO1B0BEREREJAYDVyIiIiLSBQauRERERKQLDFyJiIiISBcYuBIRERGRLjBwJSIiIiJdYOBKRERERLrAwJWIiIiIdIGBKxERERHpAgNXIvKL1WrFmDFjFN/u22+/jZCQEJSWltovu/XWW3HrrbcqPhY5jBkzBlarVe1hiDZt2jSEhIT4ddsTJ07IPCpg8+bNCAkJwebNm2XfFhEpj4ErkQHUBXV1X40bN0aHDh0wYcIEHD16VO3h+eUf//gH1q1bp8q2x4wZg5CQEERHR6Oqqqre9Xv37rX/jV999VW/H//w4cOYNm0adu/eLcFotUeJ127EiBEICQnBc889J9s26gLtuq+IiAi0adMGd911F5YuXYrq6mrZtk1E3jFwJTKQ6dOn45133sHrr7+Onj17Ij8/HzfddBPOnz8v2TZ+/vlnLF68WLLHc+Up+Hn44YdRVVUFi8Ui27YBICwsDOfPn8e//vWvetetWLECjRs3DvixDx8+jJdfftlt4Lp48WL8/PPPAT+20l544YV6wb3cgWtFRQX+9a9/wWq14r333oMgCPVuc/PNN6Oqqgo333xz0NvLz8/HO++8g4ULF+Kvf/0rTp06hb/85S/o0aMHfvvtt6Afn4j8x8CVyEAGDRqEhx56CH/961/x9ttvIycnBwcPHsTHH3/s8T7nzp3zaxvh4eFo2LBhsEP1W2hoKBo3biz69HSgwsPD0a9fP7z33nv1rlu5ciUGDx4sy3YbNmyI8PBwWR5bDmFhYUEF8YH48MMPUVNTg7feegu//fYbtm7dWu82DRo0QOPGjdGggfePNzEHc8OHD8dDDz2ErKwsvPTSS/jqq6/w7rvv4vvvv8d9993n8/5SHjASkQ0DVyID+/Of/wwAOHjwIADbqfCmTZti//79uOOOOxAVFYVRo0YBsAWwf/vb35CcnIzw8HBcc801ePXVV+tltdzVuJ45cwY5OTn2+6akpGD27Nmora11ul1tbS3++c9/4tprr0Xjxo3RokULDBw4ECUlJQCAkJAQnDt3DsuWLbOfpq3blrsaV3eqq6sxdepUpKSkIDw8HMnJyXj22Wf9Or374IMP4tNPP8WZM2fsl+3YsQN79+7Fgw8+6PY+Bw4cwH333Ye4uDhERETgxhtvxL///W/79Zs3b8YNN9wAABg7dqz9+b399tsA3Ne4in1NQkJCMGHCBKxbtw5dunRBeHg40tLS8Nlnn3l9noIgoHnz5nj66aftl9XW1iI2NhahoaFOz3/27NkICwvD2bNnAdSvcfX22tU5c+YMxowZg9jYWMTExGDs2LF+BXcrVqzAbbfdhr59+6JTp05YsWJFvdu4q3G99dZb0aVLF+zcuRM333wzIiIi8Pe//130dh2NGjUKf/3rX7F9+3asX79e1DY+/vhjDB48GElJSQgPD0f79u3xyiuvoKampt7jL1q0CO3atUOTJk3Qo0cPFBYWGqqWmyhYDFyJDGz//v0AgPj4ePtlly9fxoABA5CQkIBXX30V9957LwRBwN1334358+dj4MCBmDdvHq655hpMnjzZKahx5/z587jlllvw7rvv4pFHHsFrr72GXr164fnnn69336ysLHuAO3v2bOTm5qJx48b4+uuvAQDvvPMOwsPD0adPH7zzzjt455138Pjjj4t+vrW1tbj77rvx6quv4q677sLChQsxZMgQzJ8/H/fff7/oxxk2bBhCQkLw0Ucf2S9buXIlOnbsiOuvv77e7Y8ePYqePXvi888/xxNPPIGZM2fiwoULuPvuu7F27VoAQKdOnTB9+nQAwGOPPWZ/fp5Oafv7mhQVFeGJJ57AyJEjMWfOHFy4cAH33nsvTp486fF5hoSEoFevXk6Zy2+//Rbl5eUAgK+++sp+eWFhIbp3746mTZu6fSwxr92IESNQWVmJWbNmYcSIEXj77bfx8ssvexyfo8OHD2PTpk144IEHAAAPPPAAPvjgA1y8eFHU/U+ePIlBgwbhuuuuw4IFC9C3b19R93Pn4YcfBgB88cUXorbx9ttvo2nTpnj66afxz3/+E+np6XjppZeQm5vrdP/8/HxMmDABrVu3xpw5c9CnTx8MGTIEhw4dCnisRIYjEJHuLV26VAAgbNiwQTh+/Ljw22+/CatWrRLi4+OFJk2aCIcOHRIEQRBGjx4tABByc3Od7r9u3ToBgDBjxgyny4cPHy6EhIQI+/bts19msViE0aNH239/5ZVXhMjISOGXX35xum9ubq4QGhoq/Prrr4IgCMLGjRsFAMLEiRPrjb+2ttb+c2RkpNPjuz7HgwcP2i+75ZZbhFtuucX++zvvvCM0aNBAKCwsdLpvQUGBAED46quv6j2uo9GjRwuRkZH2596vXz9BEAShpqZGSExMFF5++WXh4MGDAgBh7ty59vvl5OQIAJy2W1lZKbRt21awWq1CTU2NIAiCsGPHDgGAsHTpUrfbtlgs9t/9eU0ACI0aNXK67L///a8AQFi4cKHX5zx37lwhNDRUqKioEARBEF577TXBYrEIPXr0EJ577jn784+NjRUmTZpkv9/UqVMF148QT69d3W3/8pe/OF0+dOhQIT4+3uv46rz66qtCkyZN7OP85ZdfBADC2rVrnW63adMmAYCwadMm+2W33HKLAEAoKCgQta268R4/ftzt9adPnxYACEOHDhW1jfPnz9e77PHHHxciIiKECxcuCIIgCNXV1UJ8fLxwww03CJcuXbLf7u233xYAOP2fE5kZM65EBtK/f3+0aNECycnJGDlyJJo2bYq1a9fiT3/6k9Ptxo0b5/T7f/7zH4SGhmLixIlOl//tb3+DIAj49NNPPW5zzZo16NOnD5o1a4YTJ07Yv/r374+amhp7Nu/DDz9ESEgIpk6dWu8xpKpbXbNmDTp16oSOHTs6jaWuZGLTpk2iH+vBBx/E5s2bceTIEWzcuBFHjhzxWCbwn//8Bz169EDv3r3tlzVt2hSPPfYYSktL8cMPP/j9XPx9Tfr374/27dvbf+/atSuio6Nx4MABr9vp06cPampqsG3bNgC2zGqfPn3Qp08fFBYWAgC+//57nDlzBn369PH7eTjKzs6ut+2TJ0+ioqLC531XrFiBwYMHIyoqCgCQmpqK9PR0t+UC7oSHh2Ps2LH+D9qNuqxzZWWlqG00adLE/nNlZSVOnDiBPn364Pz58/jpp58AACUlJTh58iQeffRRhIWF2W8/atQoNGvWTJJxExlBmO+bEJFeLFq0CB06dEBYWBhatmyJa665pt4klbCwMLRu3drpsrKyMiQlJdmDgjqdOnWyX+/J3r178e2336JFixZurz927BgAW9lCUlIS4uLi/H5eYu3duxc//vijz7GIUVcD/P7772P37t244YYbkJKS4rbGtqysDJmZmfUud/z7denSRfS26+7jz2vSpk2beo/RrFkznD592ut2rr/+ekRERKCwsBADBgxAYWEhXn75ZSQmJmLhwoW4cOGCPYB1DMwD4TrGuoDs9OnTiI6O9ni/H3/8Ef/3f/+HRx55BPv27bNffuutt2LRokWoqKjwen8A+NOf/oRGjRoFMfqr6up8XV8bT9vYs2cPXnjhBWzcuLFekF5XllH3eqakpDhdHxYWpqv+vkRyY+BKZCA9evRARkaG19uEh4f7nHHtj9raWtx222149tln3V7foUMHybYlZizXXnst5s2b5/b65ORk0Y8VHh6OYcOGYdmyZThw4ACmTZsm0SjlERoa6vZywU3LKEcNGzZEZmYmtm7din379uHIkSPo06cPWrZsiUuXLmH79u0oLCxEx44dPR4QyD3Gd999FwAwadIkTJo0qd71H374oc9sqmPWM1jff/89gPpBprttnDlzBrfccguio6Mxffp0tG/fHo0bN8auXbvw3HPP1ZvASETeMXAlIlgsFmzYsAGVlZVOWaS605jeeqe2b98eZ8+eRf/+/b1uo3379vj8889x6tQpr1nXYMoG2rdvj//+97/o16+fJOUHDz74IN566y00aNAAI0eO9Hg7i8Xitger69/PnzEF85r4q0+fPpg9ezY2bNiA5s2bo2PHjggJCUFaWhoKCwtRWFiIO++80+fjyNGqTBAErFy5En379sUTTzxR7/pXXnkFK1askKwMQIx33nkHADBgwACft928eTNOnjyJjz76yGkiXl2njzp1r+e+ffucJo5dvnwZpaWl6Nq1qxRDJ9I91rgSEe644w7U1NTg9ddfd7p8/vz5CAkJwaBBgzzed8SIESguLsbnn39e77ozZ87g8uXLAGDvXuBuFrljxi0yMtKpDZM/RowYgd9//93tAglVVVV+96zt27cvXnnlFbz++utITEz0eLs77rgD33zzDYqLi+2XnTt3Dm+88QasVis6d+4MwPbcAIh6fsG8Jv7q06cPqqursWDBAvTu3dsegNZ1CDh8+LCo+tZgXjtPvvrqK5SWlmLs2LEYPnx4va/7778fmzZtwuHDhyXdricrV67Em2++iZtuugn9+vXzefu6LLPj//jFixfxP//zP063y8jIQHx8PBYvXmx/zwC22l5f5R5EZsKMKxHhrrvuQt++fTFlyhSUlpaiW7du+OKLL/Dxxx8jJyfHadKPq8mTJ+P//b//hzvvvBNjxoxBeno6zp07h++++w4ffPABSktL0bx5c/Tt2xcPP/wwXnvtNezduxcDBw5EbW0tCgsL0bdvX0yYMAEAkJ6ejg0bNmDevHlISkpC27Zt3daPuvPwww9j9erVyM7OxqZNm9CrVy/U1NTgp59+wurVq/H555/7LKVw1KBBA7zwwgs+b5ebm4v33nsPgwYNwsSJExEXF4dly5bh4MGD+PDDD+2lGe3bt0dsbCwKCgoQFRWFyMhIZGZmom3btvUeM5jXxF833XQTwsLC8PPPP+Oxxx6zX37zzTcjPz8fAEQFrsG8dp6sWLECoaGhHhd+uPvuuzFlyhSsWrXKZ+s2f33wwQdo2rQpLl68iN9//x2ff/45vvrqK3Tr1g1r1qwR9Rg9e/ZEs2bNMHr0aEycOBEhISF455136pVHNGrUCNOmTcOTTz6JP//5zxgxYgRKS0vx9ttvo3379rIvvEGkG6r1MyAiydS1itqxY4fX2zm2e3JVWVkpTJo0SUhKShIaNmwopKamCnPnznVqVSUI9dth1d33+eefF1JSUoRGjRoJzZs3F3r27Cm8+uqrwsWLF+23u3z5sjB37lyhY8eOQqNGjYQWLVoIgwYNEnbu3Gm/zU8//STcfPPNQpMmTQQA9m2JaYclCIJw8eJFYfbs2UJaWpoQHh4uNGvWTEhPTxdefvlloby8POC/Tx137bAEQRD2798vDB8+XIiNjRUaN24s9OjRQ/jkk0/q3f/jjz8WOnfuLISFhTm1xnJthyUI4l8TAML48ePrbcvda+XJDTfcIAAQtm/fbr/s0KFDAgAhOTm53u3dtcPy9Np5ai/l7jV1dPHiRSE+Pl7o06eP17G3bdtW6N69uyAIntthpaWleX0Md8+t7qtx48ZC69athTvvvFN466237C2sHHnbxldffSXceOONQpMmTYSkpCTh2WefFT7//PN64xSEq+3IwsPDhR49eghfffWVkJ6eLgwcOFD0+ImMLEQQfFTFExE5SE5OxoABA/Dmm2+qPRQiw6utrUWLFi0wbNgwtyUwRGbDGlciEu3SpUs4efIkmjdvrvZQiAznwoUL9UoIli9fjlOnTnHJV6IrWONKRKJ8/vnnWLVqFaqqqkRNSiEi/3z99deYNGkS7rvvPsTHx2PXrl1YsmQJunTpgvvuu0/t4RFpAgNXIhIlLy8P+/btw8yZM3HbbbepPRwiw7FarUhOTsZrr71mbxv3yCOPIC8vT7LFE4j0jjWuRERERKQLrHElIiIiIl1g4EpEREREumD4Gtfa2locPnwYUVFRbOBMREREpEGCIKCyshJJSUn2RVvcMXzgevjwYSQnJ6s9DCIiIiLy4bfffkPr1q09Xm/4wDUqKgqA7Q8RHR2t8miIiIiIyFVFRQWSk5PtcZsnhg9c68oDoqOjGbgSERERaZivsk5VJ2dt3boVd911F5KSkhASEoJ169Y5XS8IAl566SW0atUKTZo0Qf/+/bF37151BktEREREqlI1cD137hy6deuGRYsWub1+zpw5eO2111BQUIDt27cjMjISAwYMwIULFxQeKRERERGpTdVSgUGDBmHQoEFurxMEAQsWLMALL7yAe+65B4BtzeaWLVti3bp1GDlypJJDJSIiIiKVabbG9eDBgzhy5Aj69+9vvywmJgaZmZkoLi72GLhWV1ejurra/ntFRYXsYyUiIiJtqampwaVLl9QeBl0RGhqKsLCwoFuTajZwPXLkCACgZcuWTpe3bNnSfp07s2bNwssvvyzr2IiIiEi7zp49i0OHDoGr2mtLREQEWrVqhUaNGgX8GJoNXAP1/PPP4+mnn7b/XtdegYiIiIyvpqYGhw4dQkREBFq0aMHFhzRAEARcvHgRx48fx8GDB5Gamup1kQFvNBu4JiYmAgCOHj2KVq1a2S8/evQorrvuOo/3Cw8PR3h4uNzDIyIiIg26dOkSBEFAixYt0KRJE7WHQ1c0adIEDRs2RFlZGS5evIjGjRsH9DiqdhXwpm3btkhMTMSXX35pv6yiogLbt2/HTTfdpOLIiIiISOuYadWeQLOsjlTNuJ49exb79u2z/37w4EHs3r0bcXFxaNOmDXJycjBjxgykpqaibdu2ePHFF5GUlIQhQ4aoN2giIiIiUoWqgWtJSQn69u1r/72uNnX06NF4++238eyzz+LcuXN47LHHcObMGfTu3RufffZZwOllIiIiItIvVUsFbr31VgiCUO/r7bffBmBL80+fPh1HjhzBhQsXsGHDBnTo0EHNIRMRERHJIj8/H23atEFkZCSGDRuG48ePqz0kzdFsjSsRERGRWXz00UeYPHkyFi5ciJKSElRWVmL48OGybnPatGno2LEjIiMj0axZM/Tv3x/bt293us2pU6cwatQoREdHIzY2FllZWTh79qzTbRYvXgyLxYLu3bvXu7/UGLgSERERqWzmzJmYMGEC7rnnHnTq1AnLli1DUVERioqKZNtmhw4d8Prrr+O7775DUVERrFYrbr/9dqdM76hRo7Bnzx6sX78en3zyCbZu3YrHHnvMfv2vv/6KOXPmYNWqVZgyZQrGjh0r23gBBq5EREREqjp9+jR27dqFwYMH2y9LSkpCly5dsGHDBtm2++CDD6J///5o164d0tLSMG/ePFRUVODbb78FAPz444/47LPP8OabbyIzMxO9e/fGwoULsWrVKhw+fBiAreNTbGwsunbtivT0dFRVVck2XoCBKxEREZGqDhw4AABISUlxujw1NdV+nTvZ2dlo2rSp1y+xLl68iDfeeAMxMTHo1q0bAKC4uBixsbHIyMiw365///5o0KCBvSSgS5cu6Nq1K2JiYpCWloYZM2aI3mYgNLsAAREREZEZnD9/HoAtUHVUXV2Ne+65x+P9pk+fjmeeeSaobX/yyScYOXIkzp8/j1atWmH9+vVo3rw5AODIkSNISEhwun1YWBji4uJw5MgR+2VLlizBnDlzEBERIfuiDwxciYiIiNwoKShBUV4Reuf2RkZ2hu87BCgiIgIAsHnzZsTGxtovf+qpp+zXuZOQkFAvsPRX3759sXv3bpw4cQKLFy/GiBEjsH37dr8fNz4+PqhxiMVSASIivSgoAKxW2/e6nx98sP5lnn52vC0R+VSUV4TysnIU5ck3QQoA2rVrBwCIjo5GSkqK/evChQv269yRolQgMjISKSkpuPHGG7FkyRKEhYVhyZIlAIDExEQcO3bM6faXL1/GqVOnkJiYGMQzDhwzrkREWlNQAOTlAbm5tt/rfs7LA8rKbN8B28+HDgE1Nc6XefrZ9bbutpGdrcxzJNKB3rm97RlXOTVr1gzp6ekoLCy096s/e/YsiouLMX36dI/3k6JUwFVtbS2qq6sBADfddBPOnDmDnTt3Ij09HQCwceNG1NbWIjMzU9LtihUiCIKgypYVUlFRgZiYGJSXlyM6Olrt4RARXeUrQLVYbJfX/Vx3nePte/YEtm2r/xjufna8rbttxMUBUVEMZknXLly4gIMHD6Jt27a6Wmlz7dq1GDduHJYvX462bdvihRdewLFjx7Bp0yZZtnfu3DnMnDkTd999N1q1aoUTJ05g0aJFWLlyJXbu3Im0tDQAwKBBg3D06FEUFBTg0qVLGDt2LDIyMrBy5Uq/t+nttREdrwkGV15eLgAQysvL1R4KEZFNfr4gWCyCEBcnCIDtZ4vl6s911+fnO/8sxxgctyFmPEQaV1VVJfzwww9CVVWV2kPxW35+vtC6dWuhSZMmwtChQ4Vjx47Jtq2qqiph6NChQlJSktCoUSOhVatWwt133y188803Trc7efKk8MADDwhNmzYVoqOjhbFjxwqVlZUBb9PTayM2XmPGlYhICY7Z1bpsp9YynGIywI5ZX2ZiSYP0mnE1AykyrgxciYjk4i5Y1WPw5yvo1sNzINNg4KpdUgSu7CpARCQlx9n8jpOpcnOvBq3Z2UBpqX4CPsfx1j0P4Opzc3zOREQyYuBKRBQsIwarntQ9j5kznbPHZWXAlCkMYIlIViwVICIKltWq3zIAKdQF7JWVwKlTtr9DaanaoyKTYqmAdrFUgIhILY5ZViNmVv3hLgvL8gEikgEXICAiEsvdJKW8PPMFqp5kZ1/9O9RloV0XO+DfiYiCwIwrEZFYnupXqT7Hv4/ril9ERAFi4EpE5A1LAgLjrhNBz54sHyCioDBwJSLyxjFbyGA1MHV/t23b2EKLiILCwJWIyJWnLCsFh+UDRF7l5+ejTZs2iIyMxLBhw3D8+HG1h6Q5DFyJiADPvViZZZUOyweIPProo48wefJkLFy4ECUlJaisrMTw4cNl3ea0adPQsWNHREZGolmzZujfvz+2b9/udBur1YqQkBCnrzyXA87FixfDYrGge/fu9e4vNfZxJSIC2ItVLY5/d/Z+JQnotY9reno6brvtNntQePjwYSQnJ2PLli3o3bu3LNtcuXIlEhIS0K5dO1RVVWH+/PlYs2YN9u3bhxYtWgCwBa5ZWVl49NFH7feLiopCZGQkAODXX39Fv379sHz5cvz+++946aWX8MMPP7jdHvu4EhEFgxOv1Of4d2fdK5nU6dOnsWvXLgwePNh+WVJSErp06YINGzbItt0HH3wQ/fv3R7t27ZCWloZ58+ahoqIC3377rdPtoqKikJiYaP+qC1oBW8AZGxuLrl27Ij09HVVVVbKNF2DgSkRmxpIA9Tn+3Vn3SiZ14MABAEBKSorT5ampqfbr3MnOzkbTpk29fol18eJFvPHGG4iJiUG3bt2crsvLy0N8fDy6d++OuXPn4vLly/brunTpgq5duyImJgZpaWmYMWOG6G0GggsQEJG5OC4i4FgSQOpzfD0cXyceTJDBnT9/HoAtUHVUXV2Ne+65x+P9pk+fjmeeeSaobX/yyScYOXIkzp8/j1atWmH9+vVo3ry5/fqJEyfi+uuvR1xcHLZt24bnn38ef/zxB+bNm2e/zZIlSzBnzhxERESgSZMmQY3HF9a4EpG5sKZSH/g6UYCkrHFV6vhp586dyMjIwI4dOxAbG2u//KmnnkJ8fDyWL18u27bPnTuHP/74AydOnMDixYuxceNGbN++HQkJCW5v/9Zbb+Hxxx/H2bNnER4e7te2WONKROQvtrfSB3YdIA1QqnqlXbt2AIDo6GikpKTYvy5cuGC/zh0pSgUiIyORkpKCG2+8EUuWLEFYWBiWLFni8faZmZm4fPkySlU6oGTgSkTG5zjph7Ws+uBu0QIihSl1nNusWTOkp6ejsLDQftnZs2dRXFyM2267zeP9pk+fjt27d3v98ldtbS2qq6s9Xr979240aNDAY0ZWbiwVICLj42ln/XI8Vwuw7pV80ms7rLVr12LcuHFYvnw52rZtixdeeAHHjh3Dpk2bZNneuXPnMHPmTNx9991o1aoVTpw4gUWLFmHlypXYuXMn0tLSUFxcjO3bt6Nv376IiopCcXExJk2ahEGDBmHZsmV+b1OKUgFOziIiY+IkLGPIzr4apNYdgNR1gSAykKFDh+Lo0aPIysrCyZMnMXDgQKxevVq27YWGhuKnn37CsmXLcOLECcTHx+OGG25AYWEh0tLSAADh4eFYtWoVpk2bhurqarRt2xaTJk3C008/Ldu4fGHGlYiMiVlW46k7GOnZ01ZCwMwruaHXjKsZcHIWEZEjTwsKkDGw7pXI9Bi4EpFxcEEBc+BqW0SmxcCViIyDWVZz4GpbRKbFwJWI9I2trsyNBytEpsLAlYj0jRk3c6s7WAFYMkBkAgxciUh/OAmLXPEAhsgUGLgSkf5wEha54oQtIlNg4EpE+sMsK7nihC0iU2DgSkT6wElYJBYPbIgMi4ErEekDs2gkFidskU7l5+ejTZs2iIyMxLBhw3D8+HG1h6Q5DFyJSB+YRSN/8WCHdOSjjz7C5MmTsXDhQpSUlKCyshLDhw9XbPvZ2dkICQnBggULnC4/deoURo0ahejoaMTGxiIrKwtnz551us3ixYthsVjQvXt3bN++XdZxMnAlIu1ieQAFgxO2SEdmzpyJCRMm4J577kGnTp2wbNkyFBUVoaioSPZtr127Fl9//TWSkpLqXTdq1Cjs2bMH69evxyeffIKtW7fiscces1//66+/Ys6cOVi1ahWmTJmCsWPHyjpWBq5EpF3MmFEwOGGLdOL06dPYtWsXBg8ebL8sKSkJXbp0wYYNG2Td9u+//44nn3wSK1asQMOGDZ2u+/HHH/HZZ5/hzTffRGZmJnr37o2FCxdi1apVOHz4MACgoqICsbGx6Nq1K9LT01FVVSXreBm4EpF2sTyApML/JdKwAwcOAABSUlKcLk9NTbVf5052djaaNm3q9cub2tpaPPzww5g8eTLS0tLqXV9cXIzY2FhkZGTYL+vfvz8aNGhgLwno0qULunbtipiYGKSlpWHGjBmin3cgGLgSkXa4ns5leQBJhRO2KAAFJQWwLrCioETe/5fz588DsAWqjkHnxx9/bL/OnenTp2P37t1ev7yZPXs2wsLCMHHiRLfXHzlyBAkJCU6XhYWFIS4uDkeOHLFftmTJEhw9ehQnT57EqFGjRD7rwITJ+uhERP5wXViASGr8HyM/5BXloay8DHlFecjOkO//JSIiAgCwefNmxMbG2i9/6qmn7Ne5k5CQUC+wFGvnzp345z//iV27diEkJCSgx3AUHx8f9GOIwYwrEWkHT+eS3Pg/Rn7I7Z0LS4wFub3l/X9p164dACA6OhopKSn2rwsXLtivcyeYUoHCwkIcO3YMbdq0QVhYGMLCwlBWVoa//e1vsFqtAIDExEQcO3bM6X6XL1/GqVOnkJiYGPwTD0CIIAiCKltWSEVFBWJiYlBeXo7o6Gi1h0NErgoKbNmv3FxmwEhZ/N8zpAsXLuDgwYNo27YtGjdurPZwRMvIyMC4ceOQlZUFADh79iwSEhKwfv169OrVy+19jh07hoqKCq+P61o3W+fkyZP4448/nC4bMGAAHn74YYwdOxbXXHMNfvzxR3Tu3BklJSVIT08HAHzxxRcYOHAgDh065LYLgTfeXhux8RoDVyJSl9VqO3VrsVytQSRSAv/3DEmvgevatWsxbtw4LF++HG3btsULL7yAY8eOYdOmTYqNwWq1IicnBzk5OfbLBg0ahKNHj6KgoACXLl3C2LFjkZGRgZUrV/r9+FIEriwVICJ18dSt9uwtANZZbd8dfzYa/u+RhgwdOhTTpk1DVlYWunXrhkuXLmH16tVqDwsrVqxAx44d0a9fP9xxxx3o3bs33njjDdXGw4wrEZFZ7C0A9uQBablAarbz78DVn/fkAefLgAiL7fK6n+uuc72948+pOjzlzpIBQ9FrxtUMmHElIn3iKkby8pQxrQtI91xpwu/4u+PPablXA1XHnz3d3vFnPWZruTgBkW6wHRYRKY8tiaThK2NaF6A6BqSOt/eUQU3Nds6cOv7sK+Pqaduebq+FDG1u7tWMKxFpGksFiEh5PDUbGNdT/eus/p3SVyJIDKb8QO0glv+XhsBSAe1iVwERGLgSaQSDgsA4BoKOwd+QUs9BotoBoDtiAlq1g1h2GTAEBq7axcBVBAauRBrBoEA8T8Gq2oGdHLT0XHlwZQgMXLWLk7OISD/Yekg8TxOlUrNtmVajBK2A83PyNBFMqUle2dm2g6rsbE4gJNIoZlyJSD7MYImnp9P+SvBWIqEEniHQLWZctYsZVyLSNrYZ8sw1i+iYYTRiZtVfnjKxSmVfeYaASJMYuBKRfPjh78xbT1XH4IycOQaxSpUQ1JUNACwZINIQBq5EJB/HmkHyXLsKMMsqltJ1sDxrQArKz89HmzZtEBkZiWHDhuH48eNqD0lzGLgSkXQ4oaU+x4DK6BOtlCBmMpeUeNaAFPLRRx9h8uTJWLhwIUpKSlBZWYnhw4crtv3s7GyEhIRgwYIFTpdbrVaEhIQ4feW5HMgtXrwYFosF3bt3x/bt22UdJydnEZF0OKGlPsdFApSaWGRGnNxGV+h1clZ6ejpuu+02e1B4+PBhJCcnY8uWLejdu7es2167di1efvllHD9+HJMnT0ZOTo79OqvViqysLDz66KP2y6KiohAZGQkA+PXXX9GvXz8sX74cv//+O1566SX88MMPbrfDyVlEpC3MTtU/Zc3aVWV4qoOVEs8okExOnz6NXbt2YfDgwfbLkpKS0KVLF2zYsEHWbf/+++948sknsWLFCjRs2NDtbaKiopCYmGj/qgtaAVvAGRsbi65duyI9PR1VVVWyjpeBKxFJx6w1rd4mXbEkQHlydSFgvSvJ5MCBAwCAlJQUp8tTU1Pt17mTnZ2Npk2bev3ypra2Fg8//DAmT56MtLQ0j7fLy8tDfHw8unfvjrlz5+Ly5cv267p06YKuXbsiJiYGaWlpmDFjhpinHLAwWR+diIyPvVrrT7pyPGVNykvNvnqgUFeqUXcgEUwJQW7u1f91MokCAHkAcgHIt387f/48AFug6qi6uhr33HOPx/tNnz4dzzzzTMDbnT17NsLCwjBx4kSPt5k4cSKuv/56xMXFYdu2bXj++efxxx9/YN68efbbLFmyBHPmzEFERASaNGkS8HjEYOBKRMFxzEKZJXB1rKesmyTk+Duzq9rh+Nq49sr1V3a286paZj5YM408AGVXvsv3WkdERAAANm/ejNjYWPvlTz31lP06dxISEpCQkBDQNnfu3Il//vOf2LVrF0JCQjze7umnn7b/3LVrVzRq1AiPP/44Zs2ahfDwcPt18fHxAY3DXywVIKLgmLGuleUA+iHHQgYsGTCRXACWK9/l065dOwBAdHQ0UlJS7F8XLlywX+dOMKUChYWFOHbsGNq0aYOwsDCEhYWhrKwMf/vb32C1Wj3eLzMzE5cvX0apShNw2VWAiEgMzlo3lmC6PbA8RtP02lUgIyMD48aNQ1ZWFgDg7NmzSEhIwPr169GrVy+39zl27BgqKiq8Pq5r3WydkydP4o8//nC6bMCAAXj44YcxduxYXHPNNW7vt2LFCjzyyCM4ceIEmjVr5utpOZGiqwBLBYjIf2b84HbMsjK7qn91pQMtetqCWH8OQlgyQDKYMmUKxo0bh+TkZLRt2xYvvPACMjMzPQatQHClAvHx8fVO7zds2BCJiYn2oLW4uBjbt29H3759ERUVheLiYkyaNAkPPfSQ30GrVFgqQET+M8upUk+LB5D+1ZUQHN8WeOsss7wPSBFDhw7FtGnTkJWVhW7duuHSpUtYvXq1qmMKDw/HqlWrcMsttyAtLQ0zZ87EpEmT8MYbb6g2JpYKEJH/zJJx5eIBxhdMCYhZ3gc6o9dSATOQolSAgSsRkSeu3QPI2HigYggMXLWLK2cRkTLMsmKQ60xzdgswl2C6DpjlPUKkMk0HrjU1NXjxxRfRtm1bNGnSBO3bt8crr7wCgyeJibTHLLV8ci0VSvoQzLKxZnmPEKlM04Hr7NmzkZ+fj9dffx0//vgjZs+ejTlz5mDhwoVqD43IXIzcq5UTsMgdf/8XjPweIdIQTde43nnnnWjZsiWWLFliv+zee+9FkyZN8O6774p6DNa4EpFXrGskX1jrrCuscdUuw9e49uzZE19++SV++eUXAMB///tfFBUVYdCgQR7vU11djYqKCqcvIgqAUWv2XGsXmWUlX/wpGzDq+4ZIIzQduObm5mLkyJHo2LEjGjZsiO7duyMnJwejRo3yeJ9Zs2YhJibG/pWcnKzgiIkMxKg1e1yulfzlz6Qto75viDRC04Hr6tWrsWLFCqxcuRK7du3CsmXL8Oqrr2LZsmUe7/P888+jvLzc/vXbb78pOGIiAzFqzR4zrOQvfyZtGfV9Q6QRmq5xTU5ORm5uLsaPH2+/bMaMGXj33Xfx008/iXoM1rgSEWsUSTLBLFhAimCNq3YZvsb1/PnzaNDAeYihoaGora1VaUREBmfU+jy2uSKpiM2+GvW9RLLKz89HmzZtEBkZiWHDhuH48eNqD0lzNB243nXXXZg5cyb+/e9/o7S0FGvXrsW8efMwdOhQtYdGZExGrc9jeQDJwdv/lVHfSySbjz76CJMnT8bChQtRUlKCyspKDB8+XNZtjhkzBiEhIU5fAwcOdLrNqVOnMGrUKERHRyM2NhZZWVk4e/as020WL14Mi8WC7t27Y/v27bKOGYKGVVRUCE899ZTQpk0boXHjxkK7du2EKVOmCNXV1aIfo7y8XAAglJeXyzhSIoPIzxcEi8X2Xc9+yReEtRbbdyK5uft/M8p7SYeqqqqEH374QaiqqlJ7KH65/vrrheeee87++++//y40aNBAKCwslG2bo0ePFgYOHCj88ccf9q9Tp0453WbgwIFCt27dhK+//looLCwUUlJShAceeMB+fVlZmZCSkiJs27ZNWLNmjdCpUyeP2/P22oiN1zRd4yoF1rgSmRB7s5KSfP2/FRTYMq+5uUA2a2Hlpsca19OnTyMuLg5bt25Fnz597Jd369YNQ4cOxbRp02TZ7pgxY3DmzBmsW7fO7fU//vgjOnfujB07diAjIwMA8Nlnn+GOO+7AoUOHkJSUhO+//x5jx47F5s2bcezYMfz5z3/GwYMH3T6e4WtciYgCwtIAUpKvdlksGyAfDhw4AABISUlxujw1NdV+nTvZ2dlo2rSp1y9fNm/ejISEBFxzzTUYN24cTp48ab+uuLgYsbGx9qAVAPr3748GDRrYSwK6dOmCrl27IiYmBmlpaZgxY4Zfz91fYbI+OhFpm1EyQa5dA+q+iJTg+P9Wl33dk3f1stzcq+8z0heFOpKcP38egC1QdVRdXY177rnH4/2mT5+OZ555JuDtDhw4EMOGDUPbtm2xf/9+/P3vf8egQYNQXFyM0NBQHDlyBAkJCU73CQsLQ1xcHI4cOWK/bMmSJZgzZw4iIiLQpEmTgMcjBgNXIjNzzATpOXB1nN3NgJXUlJbr3C4LsL239Pz+MjOF9i0REREAbNnP2NhY++VPPfWU/Tp3EhIS6gWW/hg5cqT952uvvRZdu3ZF+/btsXnzZvTr18+vx4qPjw94HP5gqQCRmRmlWTpLA0grHNtluZYNsEWW/ii0b2nXrh0AIDo6GikpKfavCxcu2K9zR4pSAddxNG/eHPv27QMAJCYm4tixY063uXz5Mk6dOoXExEQ/n6U0ODmLiPSJiwqQ1rlO2rJabWc4LBagtFTdsRmYHidnAUBGRgbGjRuHrKwsAMDZs2eRkJCA9evXo1evXm7vc+zYMVRUVHh9XNe6WW8OHTqENm3aYN26dbj77rvtk7NKSkqQnp4OAPjiiy8wcOBA++Qsf3ByFhH5zyhZHy4qQFrnmq0zyhkOksWUKVMwZcoUfPHFF9i7dy+ysrKQmZnpMWgFbKUCjhlad1+enD17FpMnT8bXX3+N0tJSfPnll7jnnnuQkpKCAQMGAAA6deqEgQMH4tFHH8U333yDr776ChMmTMDIkSP9DlqlwsCVyGyMMsOZ5QGkdXVlA4At+9oPtkwr613Jjbq2V1lZWejWrRsuXbqE1atXy7a90NBQfPvtt7j77rvRoUMHZGVlIT09HYWFhQgPD7ffbsWKFejYsSP69euHO+64A71798Ybb7wh27h8YakAkdnotZMASwNIr1xLBvT6HtQJvZYKmAFLBYjIf9nZ+sz6sDSA9Mr17IBRznoQqYCBKxHpA0sDSK9cOw0MrASS4ljrShQABq5EZqDXCVmO7YQcP/yJ9GpPHnDzKWBRlP7OehBpAANXIjPQ66lJlgeQ0bieOdDrQSWRShi4EpmBXtvwsDxAowoAWK98d/3d23VUr2zgpQn6PKgkUgkDVyIz0MuELNeVhlgeoDCxAWgegLIr3+Hyu7fr/Al4TWBPHnBnDdAiVH8HlUQqYeBKRNrB0gCFuQaLYgPQXACWK9/h8ru368QGvCbJ2qblAndbgK9e1/5BJZFGMHAlMiK91s2xNEAG/mROxQag2QBKr3yHy+/erhMb8PqTtdUx17IBx7MNROQWFyAgMiKuiW5idQFpXYBphS3os8AWQDr+nutyW61wfQ6Ov9cFsVoefwCy44EPTwH3xgEFJ9Ueja5xAQLt4gIEROSeXiZjMcskEbFZVNffXbOjWiE2a+v6XHWcjf0XgBNXvpNp5efno02bNoiMjMSwYcNw/PhxtYekOQxciYxIL5OxWNMaIG+1qa6BqrcgUI+8lR7ouKTgxZm2g83HB/BgzqQ++ugjTJ48GQsXLkRJSQkqKysxfPhwWbc5ZswYhISEOH0NHDjQ6TZWq7XebfJcumAsXrwYFosF3bt3x/bt22UdM0sFiEg9ewtsQWtaLjsH+MUK59P/rqfWzcoAJQXrrLaDuQiLrf6V/KbXUoH09HTcdttt9qDw8OHDSE5OxpYtW9C7d29ZtjlmzBgcPXoUS5cutV8WHh6OZs2a2X+3Wq3IysrCo48+ar8sKioKkZGRAIBff/0V/fr1w/Lly/H777/jpZdewg8//OB2eywVIKKr9Dghi+2uRHLNHvrKqpqVAUoKvu0J5ITavpNpnD59Grt27cLgwYPtlyUlJaFLly7YsGGDrNsODw9HYmKi/csxaK0TFRXldJu6oBWwBZyxsbHo2rUr0tPTUVVVJet4GbgSGYUeVsdiTasfvNWtMlD1TWxJgca8tQ04XmP7zveLaRw4cAAAkJKS4nR5amqq/Tp3srOz0bRpU69fvmzevBkJCQm45pprMG7cOJw8WX9yYF5eHuLj49G9e3fMnTsXly9ftl/XpUsXdO3aFTExMUhLS8OMGTPEPu2AsFSAyCgKCmxBa26udmtbeRrUC9fT/Vbo8lS3Ljj+rQFN/W0d38eJeXy/BEDSUgGF9quFhYW4+eabnTKZAFBdXY177rkHH3zwgdv7HTt2DBUVFV4f2zUYdrRq1SpERESgbdu22L9/P/7+97+jadOmKC4uRmhoKABg3rx5uP766xEXF4dt27bh+eefx9ixYzFv3jynxzp58iQiIiLQpEkTj9uTolSAgSsRKYc1rV5YwbpVNVih2QMEvl8CImngqlBrwZ07dyIjIwM7duxAbGys/fKnnnoK8fHxWL58uWzbdnTgwAG0b98eGzZsQL9+/dze5q233sLjjz+Os2fPIjw83K/HZ40rEWmf4+lO1rQ6YN2qNnirhVVRQQFwWx5whEGrqhRqLdiuXTsAQHR0NFJSUuxfFy5csF/njhSlAq7jaN68Ofbt2+fxNpmZmbh8+TJKVeoRzsCVSK/0MhmLLa88YN2qNniqhVV5ApdrzTrrXdWhUGvBZs2aIT09HYWFhfbLzp49i+LiYtx2220e7zd9+nTs3r3b65c/Dh06hJMnT6JVq1Yeb7N79240aNAACQkJfj22VMJU2SoRBc/xg02rNa2A7TRn3elOU3M99e94Wpq0IRtXDxysqL/srIIlBLm5V2srAecDQGZgDWnKlCkYN24ckpOT0bZtW7zwwgvIzMxEr169PN4nISEh4ADy7NmzePnll3HvvfciMTER+/fvx7PPPouUlBQMGDAAAFBcXIzt27ejb9++iIqKQnFxMSZNmoSHHnrIbfcBRQgGV15eLgAQysvL1R4KkbTy8wXBYrF9J43KFwTB4vAdV76T9mnstfslXxDWWmzfyauqqirhhx9+EKqqqtQeit/y8/OF1q1bC02aNBGGDh0qHDt2TLZtnT9/Xrj99tuFFi1aCA0bNhQsFovw6KOPCkeOHLHfZufOnUJmZqYQExMjNG7cWOjUqZPwj3/8Q7hw4UJA2/T22oiN1zg5i4ikxQklDqzQ7MQf8oNrtlzFiXN8f/mk1wUIzICTs4hIe0xd0+ptwhVrWPXL9bVTeBKXYz27qd9fRAxcifRFDxOy0nJtfSdNWdPKCVfmoPAkLsd6dlO/v4gYuBLpix5WxzJVyytfLa3ImBwPSBTIvjq2ZHJ8f7HTAJkQA1ciPVGop6BfTP3hyQwrKZB99dSSiWUDZEIMXIn0RKGegn4x9YcnM6ykYPbVtVSIZQNeGXzuuS7V1tYG/RjsKkBEwTHVLGcVZ5OTDjj+fwCS/68otPyo3tXU1GDv3r2IiIhAixYtEBISovaQTE8QBFy8eBHHjx9HTU0NUlNT0aCBc+5UbLzGwJWISDQrrra3KlV1JKR1Vkj+v1JQcHVRAsezLqY6eBTn7NmzOHToELOuGhMREYFWrVqhUaNG9a5j4HoFA1fSNU8fVGoy1Yekhvp3ks7InH11tM5qK9eJsNgmbhEAW+b10qVLag+DrggNDUVYWJjHDDgD1ysYuJKuafHUoKk+JK1ghpWCZ4Ws/0emOpgko+ICBERGoMUuAoaeEML2ViQHiTsPuE7SMlULOjI7ZlyJiOysYIaV5GVF0P9j3s7EMPtKOsWMKxGR35hhJblJkH31dibG1O3pyAwYuBJpjRaXdTXsIgOugQMXECC5SdD31Vs/Z0OX8hCxVIBIezghS0FWsDSA1MMuFUR1WCpApFeckCUjTr4iLXHMvgZQNuDr7Ixhz5SQmTHjSkQmYgUzrKRNVvj9v+nr7Ixhz5SQETHjSkSBM2ymhhlW0qoAJm35OjtjmDMlRFcx40pE9RkmU8MaQtIjKyQ9M8AWWaQDzLgS6YUWuwgYJlMT4KxtIlX5eWbA1z6ELbLIQJhxJVKbFrsIGAYzrqR3Iv6Hfe1DmHElHWDGlUgvtNhFQLfYl5WMRsRZA1/7EC4JSwbCjCsRGSgjYwW7BpCxOGZcgaDPIBjmvU5Gw4wrEYlnmBo4dg0go5FgpS1Hhnmvk1kxcCUiHU/GYmkAmYmIAzNfE7V0+14nsmGpAJHSCgqAvDxbPZq7tcbJD1awNIDMycOkLU72JJ1iqQCRVuXl2T5Y8lQ8VWeYBQZYGkBm5aFsQOxkT8PsA8hsGLgSKU0LXQR0W+fG0gAiGw8HbdnZtkyrr7M5ut0HkNkxcCVSmtgPFjnpts6NCwoQ2TgetIlcItaRbvcBZHYMXInMSLd9HVkaQFSfmwM6X5O0dLsPILPj5CwiIiJdczNRi5O0SGc4OYtIS3xlP+Sm24kYAZwCJTIdN2UDuT3F19Lrdv9AZsSMK5ES1M5+rLPaJmJEWGynB3XDCra7IvKHFX6/Z3S7fyAjYcaVSEvU7iSg24kYrGnVk4KSAlgXWFFQUhDQ7ySFAN4zut0/kBkxcCVSgtqdBHQzEYPtrrTGn+AzrygPZeVlyCuyTRLy93fHx2KQGyiXsoGCeMAa771MSTf7ByIGrkSkKWx3pTRfAaI/wWdu71xYYizI7W3L3Pn7u+Nj+RPkkid5QN4poOyUuAVPWOtKeiAYXHl5uQBAKC8vV3soRMr5JV8Q1lps33UlXxAEy5XvJIf8HfmCZb5FyN9h+xtb5lsETINgmW9x+7vr7X39LtXYfG3HcZxyjknf8gUhP04QLHGCkC/ib7HWIggrYPtOpDCx8RoDVyI55OcLgsUi7sNCDvwAoit8Bap6Dfocx+lv8G1OIg4KdXvAS0bAwPUKBq6kCotFEADbdzXo5gOIGVap+RuoGkEw2VnzsAi2j3yLusMg8oCB6xUMXEkVamdcdcMi8MM0OGYMVP3lLTtrmr9P/gOCYAm1fRdDNwe/ZBRi4zVOziKSg9pdBHSD7a785WvylOuEp+yMbJTmlCI7w7z/i45/A28TwgADT/rK2waU1di+i1nYY0+erbfrHk6UJG1h4EpkBLqYDezuw5LtrvzFQDU4rn8fX4GsYTj1khbRvYO9XUmjuHIWkRHoYuUbK7gKlv/q+qPm9s5FdkZ2vd9JWo5/XwAG/VsXwBa05oIHjaQVYuM1Bq5ERrC3wHZKLy1Xw03E+WHpi7ug1LrAirLyMlhiLCjNKVV3gCbj+rc35UGDLvYtZARc8pVISQUFgNXqfXUaOeli5RuWBbjyVa8K1D+VTcoxZBlBvX2Vj3pX1rqSxjDjSiQFqxUoK7PVkJWWqj0ajWCG1Rdm9PTFEGUE9fZVVngt4WHGlRTCjCuRkpwmPpANl2915Zph5cQqfXF8fXTbjaDevspHZw9dnM0hM2HGlUiPdJEFYcbVFetVjcM1O26M15bvWVKPYTKuv//+Ox566CHEx8ejSZMmuPbaa1FSUqL2sIjUpYu6M9a0+sqwkn55a6ulm+xrPT7Okuii7R4ZnaYzrqdPn0b37t3Rt29fjBs3Di1atMDevXvRvn17tG/fXtRjMONKhqTJjCuzNa6MkYUjf+n3dffxHtZF2z3SK0NkXGfPno3k5GQsXboUPXr0QNu2bXH77beLDlqJZMMuAm6wppUZVgLqv+6azMC63Yf5OEvCRQlIAzSdce3cuTMGDBiAQ4cOYcuWLfjTn/6EJ554Ao8++qjH+1RXV6O6utr+e0VFBZKTk5lxJWmxi4AbzLjqN9NGcnL8v8jtnauNbgQ+92F8P5OyDJFxPXDgAPLz85GamorPP/8c48aNw8SJE7Fs2TKP95k1axZiYmLsX8nJyQqOmExD6S4CuqgtM1dNq7ssGjOs5I7j/4VmesH63Id5OYOii/0RGZWmM66NGjVCRkYGtm3bZr9s4sSJ2LFjB4qLi93ehxlXMiRN1paZOyPD7CoFQj9L+Hp5f2tyf0R6Z4iMa6tWrdC5c2enyzp16oRff/3V433Cw8MRHR3t9EWke5qsLTNXTSvrV0kKrt0INJOBrcfxDIrL6lqa3B+RWWg6cO3Vqxd+/vlnp8t++eUXWCwWlUZEpBJNTsby0bjcYFwDDC4WQFJQvY2WqImmLgepmtwfkVloOnCdNGkSvv76a/zjH//Avn37sHLlSrzxxhsYP3682kMjIoPXtDLDSkrwthqXIvLybJO08rxt01wHqaRtmg5cb7jhBqxduxbvvfceunTpgldeeQULFizAqFGj1B4amZHaLbBU5XKq0ASYYSWlqZJ9FTXR1MtBKidqkcI0PTlLClyAgCSjVAssTS4uYIXtVKEFtg8w49PupBkyA21O/nMzYYsTtUgihpicRaQpSrXA0uRyrsY+Veguu8UMK6lJm6UpbiZkcqIWKYwZVyKt0WTG1di0md0iukr2MwAFBbY619xcINvT45u7BR7JixlXIr3ijF3ZceIV6Y3sE7dETdIy9oRM0gcGrkTkhrEnY3HiFemN7BO3/C6FctlHcJIWKYSlAkTkhhVGnozFiVekZ9oobbHCaR/BSVoUJJYKEAVLqfZXmsxUGGcyFidekdGovmiBbRRw2kdwkhYphBlXIk+Uan/FTIWstJGdIpKHNv6/OWmLgseMK1GwlGp/pXqmwlj1rJx4RWYi+f93QGeaHNpkafIMEhkJM65EpmeFkepZtZGBIlJH0PXbAZ1pcsi4rsvjGSQKCDOuRCSScepZAWZYydyCbpsV0JkmhzZZqp9BIqNj4EqkNM2dStNvb0ZOvCJyFvSBW3a2LdPqcRECH1IBDLnynUgGLBUgUhonY0mGZQFEnqnT9s0KI5UekXJYKkDkL6XaX6l+Ks04k7FYFkDkWdBlAwHtEx1KjzR3domMgBlXojpKtb9SnRV6zYhw4QAi8RzfLwD8f+8Eu0/k2SXyAzOuRP5Sqv2V6vQ7GUv29dqJDMSx3jug906w+8S0nkBEqO07kUSYcSUi3WDGlSgwrHclrRMbrzFwJSJNYpBKJA/l3ltcUYvEY6kAkVaoOkFBvxOxWBZAJA/l3ltXWu3tBSdpkWQYuBLJbc+VlWT2qBGAOSzFqDPsGEAkD7/fW8F2XNkz5co+cEpg9ydywMCVzMsU7a/0OxGLCwkQycP1veVuIQ8neXm27gJ5AR4ApwGIuPKdKEgMXMm8gt0Zi5WabWsFk6pGAKafVbF8fngSkSx8lg4E210gdSYwxGL7ThQkBq5kXqZpf6UPrGklUofP0oFgl4HV0QE0aR8DVzKvoHfGWqTfyVisaSVSh2PpgKxnPriSFknAr8D1xx9/xNSpU/HnP/8Z7du3R6tWrdC1a1eMHj0aK1euRHV1tVzjJNIPVXfO+piM5e7DkTWt+uRaKu7v76Qtsp75sE9UnQA9HlyTNogKXHft2oX+/fuje/fuKCoqQmZmJnJycvDKK6/goYcegiAImDJlCpKSkjB79mwGsGRuqnYR0MdkLJYFaFMgQadrqbi/vzPQ1RavZz6CfTHScq+spFUDrR9ck4YJIlitVmHRokXC6dOnvd5u27Ztwv333y/MnDlTzMMqory8XAAglJeXqz0UMotf8gVhrcX2ndzK35EvWOZbhPwd/BspJT9fECwW23d3vwuC7XfA9l3M72Ie19fvcmyTpFHvferuxfD/UQVBsFz5TnSV2HhNVOB68eJFvzbu7+3lxMCV7PjpRibib4Do7j5KBIiBbNPXc+FbXRqW+RYB0yBY5ltsF/APSzKSNHDVMwauZCdJtkBL9JG5YHZVfmKCOyNlKf3N4ur5uapJ1vfuL/mCsDZOEH6JE7S+DyNliI3XQgRBEPwtL9ixYwc2bdqEY8eOoba21um6efPmSVDAIB2xa9+SCRQU2ArrcnMN0knACttELAtsrWa0ybrAirLyMlhiLCjNKVV7OIbg+q9stdrqRi0WW6MMd7cxE29/n9xc8/5dglFQUoC8ojzk9s6VZgLlOqttLkAEbD1eNbwPI2WIjdf8bof1j3/8A5mZmVi6dClKSkrwf//3f/av3bt3BzNmInnJ0f5K1Q4C+piIxTZXwREzIcpdS2JDdnsTyfW5O/59fE0OI/ecJlRK8UdLywUi4oC0OGh9H0Ya428qNyEhQVi6dGlgeWAVsFSAZLXWIggrYPtOLAuQgK/T3u5uQ+KxrCAwTu9tw5VdkRaIjdf8zrg2aNAAvXr1kj6CJtKjtFwgwmL7TmxzJQFmU+XlLRsLMCPriWOf5a0P9MShZqHY+kBPCbeg38VTSFl+B66TJk3CokWL5BgLkf6kZgNDSm3fZaf9HTvLAvzjLihyDaQYpMrL30CWgEdabkPyUzV4pOW24B/MXm41BXpYPIXU5/fkrNraWgwePBi//PILOnfujIYNGzpd/9FHH0k6wGBxchYZhxV6mIxFnomZVEXa4vqamXnSWx1JJ2rZJ2nFAUOiYKt3Nekf1uRkm5w1ceJEbNq0CR06dEB8fDxiYmKcvog0w3Dn+LQ3GUvWdc0NSEwZAGmLa0aWpQQSL89sL7eaCdsBOYNW8s7vjGtUVBRWrVqFwYMHyzUmSTHjamJMZ8mOra48c5eZY7ZO/5g1d3Dlj7H1gZ54pOU26VplkSnJlnGNi4tD+/btgxockSLkSGep2v5Ke1jTaiOmZRXAelUj8FUTa6oM7JV/8nb/u1rCSZnar+UndfmdcV26dCk+++wzLF26FBEREXKNSzLMuJKk7PVYFtukLNkUwDZJgfVeesAFAKiOqRY7kCXjagVr+c1Jtozra6+9hk8//RQtW7bEtddei+uvv97pi8jQFGt/lQetzbBlPetVrlk1tqyiOt4WOzCcK//kN89aaa95DXg/YT+b1RNaq+UnbfE74/ryyy97vX7q1KlBDUhqzLiSPmkv48p61qtMXddIopmxI0HA+wnFzmaRVomN1/wOXL0RBAEhISFSPZwkGLgSSUPytcp1gpOsSCpmOOAJeD+xtwDYk2c7m2Xvi629A3iSj2yB69y5czF58uR6l9fU1OChhx7Ce++95/9oZcTAlYiCYYZgg5Rh2AyshycS/MGuFax3NQ/Zalznzp2LJUuWOF1WU1ODkSNHYvfu3X4PlEgypprOKy+z1rOKWcmKKFC+esLqlocnEvwS0LlgvSu58jtw/fe//41nnnkGH3zwAQDg8uXLuO+++7Bnzx5s2rRJ8gESiSbHp4Ai7a+01/4l+A8cfWILK1KSYVppeTi6C7hdnn2/C3BRAnIVUI3rxo0bMWTIELz77rtYsmQJ9u3bh40bN6Jly5ZyjDEoLBUwETnOuykyYcAKrZ0OM0M9K2tXSWtYlnIFJ2qZkuyTs9atW4f77rsPnTp1wsaNG9G8efOABysnBq4UFLcTBqTGCQhqYJBAWuN44AQY6yDKr4NhTtQyJUkD12HDhrm9/Ouvv0ZKSopT0PrRRx8FMFz5MHAl8s4M2VV3mF0lLTPagVXw7fSs0NqZKZKWpJOzYmJi3H4NGDAA7du3d7qMiPTFDPWs7moHWbtKWqbL+lcvgwx+eWhO1CIbSfu4ahEzrkTemSHjarTsFZmPLv6HpR6kIqVapBWytcMi0gypUxCKdBAAtNZFIDsj275co1GIWZKVSE90kYEV+UYT3W5vT55tktYex7NB2tp/kvJEZVwHDhyIadOm4cYbb/R6u8rKSvzP//wPmjZtivHjx0s2yGAw42pgUh/dKzaT1QrWaslLF9kpoiDo+X9cdL2r24yrFdx/GpOkGdf77rsP9957Lzp37oznnnsOa9aswVdffYWdO3diw4YNeO211zBixAi0atUKu3btwl133SXZEyHySOo0WlquLWhNkzstp16tllkWFmCGlYzO8X9ck9lXL0TXu6Zm25IITmUCrHU1O9E1rtXV1VizZg3ef/99FBUVoby83PYAISHo3LkzBgwYgKysLHTq1EnWAfuLGVeiq4Kf2as97A5AZqfn7KsZauxJHNn7uJaXl6Oqqgrx8fFo2LBhwAOVGwNXoquM+CGh5w9tIilo7uDNjwEZ8WCaAiP75KyYmBgkJiZqOmglImd6n4jl7pQoywLI7Fxbu6leOuDH8tuiygY8TpzlRC0zYjssIlmpt9oLs6tE5qT6+0TqFLDHibNWcKKWcbAdFpEm5MG2Y1W+ub8RFxZgdpXIN9VbZwW4uofHyaMeJ85yopYZMeNK+iH1Ubwiza2ZcQ2U5ur2iHRK9QysSKx3NTfZJ2fpBQNXA9Ft31YKhF4+bIm0Ti8HgXo/2KbgyFYqMHr0aGzdujWowREFRLd9WykQLAsgkobjmXtFywb83JioyaNuJ2pxkpaZ+J1xHTJkCP7zn//AYrFg7NixGD16NP70pz/JNb6gMeNKZqD3TIVeMkJEeqfomYwgNuZxn+b2TJkVnKSlf7JlXNetW4fff/8d48aNw/vvvw+r1YpBgwbhgw8+wKVLl4IaNJG+qXfUr/eJWH50zyGiICg6cSuI0yYe92luz5RxkpaZBF3jumvXLixduhRvvvkmmjZtioceeghPPPEEUlNTpRpjUJhxJeVYodZRPzOuRBQIrdaS632fRv5TpB3WH3/8gfXr12P9+vUIDQ3FHXfcge+++w6dO3fG/Pnzg3loIh1S76hfTwsLuMvwBNg9h4iCpNVacj3t00hZfgeuly5dwocffog777wTFosFa9asQU5ODg4fPoxly5Zhw4YNWL16NaZPny7HeIkC43HlFSllw5Zp5Y7WG5YFEGmHahO3/OCxv6v7W4MTtYzN78C1VatWePTRR2GxWPDNN9+gpKQE2dnZTmndvn37IjY2VspxEgVnT56toH+P/qMl/3bi2qPVDA+R2cl2UBlkROy23tVjMkK9RV9IGX4HrvPnz8fhw4exaNEiXHfddW5vExsbi4MHDwY7NjI7KQ//DdT6Si8TsTy9fCwLINIm2Q4qg4yIc3vnwhJjQW5vh4F5TEZwopbRcQEC0i6tzhoAwBWxfNP0y0dEPkk2aVKO2ZeKrHxISuLKWVcwcNUxTU81t4J9A73T9MtHRD5p8eBTLwfu5D8GrlcwcCV5qJdxJSJSghYPPq0LrCgrL4MlxoLSnFIft+Z+Wk8UaYdFZF7KdBDQy0Qsrc5GJqLAabHjgNt6V484UcuIGLiSMSnS/kp+epmIxRZX+lZSUIIF1gUoKSjxeJlUtyF90sp7vF5/V6/7ek7UMiIGrmRMBml/5V92QT1scaUNgQaSRXlFKC8rR1FekcfLpLoNg1t9Cvo9LkPKtqCkAIe+nuBlX8/e2kbEwJWMSdL2V+o1tNbi6jFc+UobxASgYgJJAOid2xsxlhj0zu3t8TKpbhNIcMvAVn2u73G/41AZUrZ5RXmYcbIGh2pCDdHqkEQSDK68vFwAIJSXl6s9FPIlP18QLBbbd02xCIKAK9/JYhEEwPadlLEjf4cw3zJf2JG/w37ZfMt8YRqmCfMt8z3ezt393F2mJDFjcn1uYp4rKcvv/YAM+/f8HfmCZb5FyN/h6zHzBdv+W2ufLeRIbLymq8B11qxZAgDhqaeeEn0fBq46otmISJmdnvidsLo0e3xhEIEGqUYiJgB3/ZsY+e+hRfraD1gEJh+0T2y8ppt2WDt27MCIESMQHR2Nvn37YsGCBaLux3ZYOqLF3isK8q/NCxlBSUEJivKK0Du3NzKyMwAAC6wLUF5WjhhLDHJKczzezuxc/yaufzf+zZSjiV231wUJ2BZLDwzVDuvs2bMYNWoUFi9ejGbNmqk9HJKLyQsltTgRSystcIxA7KQod3WiGdkZyCnNYQDmwPVvIqaWlrWy8tBCx4HK/5sCnC+zfa+Hk7SMRBeB6/jx4zF48GD079/f522rq6tRUVHh9EWkB1qciKWFDyQ9YpCqPF+BLCCuwwH5z6+OAzIdDeedAkov2b6TsWk+cF21ahV27dqFWbNmibr9rFmzEBMTY/9KTk6WeYSkOoP0bNUitrnyjUGqNrn72/rKyjKQDYxfCxXIdDScnDETt562IDljpo9bqtclhqSh6RrX3377DRkZGVi/fj26du0KALj11ltx3XXXeaxxra6uRnV1tf33iooKJCcns8bVyNZZbX38IizAkNIgH0yZWiiut20crEnVL191suQ/q9UWl1ostmC2HtULYq2wraZlga18gLRC9JwkBSaKBWzt2rUCACE0NNT+BUAICQkRQkNDhcuXL/t8DHYVMIFf8gVhrcX2PWgWQYnZp5b5FgHTIFjmy7sdsfQ1Q1g9WmwvRdIR082AvNPCvsR7hxa2xtIqQ3QVqKysRFlZmdNlY8eORceOHfHcc8+hS5cuPh+DXQXIP+bMuPrMkpiQ2Bn/ZFzMpuvTlIJ4PNrkFBZXxWFm9km1h0MiGaKrQFRUFLp06eL0FRkZifj4eFFBK2mcJqesKzP7VGsTsVjLWp/YOlUyLjETvMg7NXbzuXGAtaHtOxmPpgNXMjhOWVcFl2ytz92kHE6mIjETvDihyzuPu3kZI9qo7jOBCIvtu0ecpKVXmi4VkAJLBTRM9SJ9c2JZQH0sAaBA8X/HO4+7eYV2RJ7LsqzgJC1tMUSpABmc6mk++Y+4C0oKYF1gRUGJdo7qzV4WIDa7SiQGM7DeeWyVpdCOKK8oD2XlZcgrck355sIWtJp0R6hjzLiSvnhd1s9fVsh9xM1lXNXFCVakNC4965kaZ3u0NhGWPGPGlYxpT56tZ+seKepi5T/iVnsZV03Of1MQJ1iR0sQsPWtWip/t2VuA7EN5KB3MoNVImHElfZE042p8Zqln9ZTVYraL1Ob6P8j/yatkn+YgenEaZdogkndi4zUGrkQGZpb5bzz9T3rB/9WrZD+wdkl0cKKWtrFUgEhhWpyIpfr8N4Xw9D/pBSdzXZWbC/w9rgDfVVrlqWdKzbZlWq+cneNELWNgxpWUYYLUHydiKYOnWslITJ+BVbCeiRO1tI0ZV9IW1RcbkL/1lZoTscw0CYuTXchITJ+BvTJja2vPXNn3YVpbsZACw4wrKUP1jKsVRq5hMuokLHfZVWZcycjMmoGVdR8mutaV1MSMK2lLoMWWewtsM0P3BnsYbuwaJqMuKuAuu8plV8nIzJqBlXUf5tJG0XOtK5eB1QNmXEnbRLczIT1jOysi98yagZUUuwvoAjOuZAxpubagNU1bqUQ1OwgYsZ7VU90qs6tkdqbJwDrs2CTfx7l0F/Bc62rsM3NGwYwrUQDU7CBgxHpWZlaJxDFsBtZhx2ZFqeH2ceQbM65EMlKzg4De61ndZYyYWSUSx7A9ix12bEru47TYf5u8Y8aVDIjL92mZYTNGRCoww9kKyZrSuFky3PPZM36OKI0ZV1KXqoWYebAV2KvVM1YaRqxlBQycMSJSgRn6GkvWBtyluwDg7eyZMT5HjIiBK8lD1QUHjFFgr/qaDUHyNImEZQFE0jHD5C3JSgfcTPblRC39YakAySOQcztuTuOoTc1G1aqv2RAklgQQKY/vO9IrlgqQugJZcMDNaRy1eW5ULb9A12xQg7ssD0sCiJSn+/edjxopOUuoOFFLH5hxJe1gxlW3mOUh0h5dTtzy0e9PznaAarY5JGZcSY9cmkRrgef6J+kYYRKW7rM8RAaky4lbPgpaJal39bCUuPuJWlwGVmuYcSVSmZ4WFNBlBofIpFzfr3z/XuHXUuJWcBlYZTDjSiah/6NhPS0ooMsMDpFJuXbwMNr7N+CzVX4tJc7uAlrDwJV0Trpee2oV5utpEhZLAoj0y2jv34BbBoooS7v6eQDYMq062EGbBANXkoZqhZrSHQ2r2UFAi7g0K5GxuL5/Nd3zVcRnipxnq/h5oF0MXEka/h76eiiO9182pDoa9ryCinT0NBHLaKcViciZpt/jIj5T5DxbxYla2sXAlaTh76GvBnu2KtFBQE+rYRnttCIROdP0e9zPz5SAkgJeEijuPw+4DKwWsKsAqUODPVuVoPfVsIjIuPTcdSCg7ix+dRcAbJnWPNhK07gDlxq7CpC2abBnqxK0OBFL03VuRKQYTZcO+BBQvavI7gKcqKUtDFxJR6SpL+Kyfs70/GFFRNJxLR3Q00FtQEkBkQkUTtTSFgaupCPS1BcpsRPS0yQsTde5EZFijNL3Ver9rxITd0k8Bq6kI9K0vlJiJ6TFSViesidscUVE7mjioDaAKFTq/W/9iVrsLqAmTs4ikoEWJ2EtsC5AeVk5YiwxyCnNUXs4RKQjqk3cCmDWlZz734KSAtzZYQJaR9eAy8BKi5OziFSkxUlYmsieEJEuqVY2EMCsq4D2vyJ7i+cV5WHG1hocqggFl4FVBwNX8p+/p24kW2yAxGBJABFJTbUDXwmyAKI+skT2Fs/tnYvP9lnwyS+vg90F1MFSAfKfv6du/O6Vpx8sCSAiM9JTz1dRH1km7S2uJSwVIPn4e+pGZK88Ocjd+kqLk7BYEkBEctNTxwFRH1kB9xbnRC2lMeNKGiTd6iTWBVaUlZfBEmNBaU6pBGNzpsWMKxGR3PSUcZVDQUkB8ory8N24SkSFnwInagWPGVfSMenWg5a79ZWak7D01ByciIzFtWZe9v2RRM1ZperxerUfOCBFm0YSj4EraZA0/VoBd/33jENPp+qIyNhk3x9JVJfl82FETiauS4okx8wEl4FVFgNX0qBsaG1HoMWVsFjLSkRaIfv+KIC2WAE9jMjuAkZOimgda1yJRAigBzYREekNuwuohjWuRBKS6GA/IKxlJSI90cs+y+2ZNHYX0DwGruSbP+fJVVpsQO62V2pOwmItKxHpiV72WVK1MywoKcChigmQalIxecfAlXzz590tsj5IaldneBpvp8FaViLSE73ss6Q6k8ZlYJXFwJV88+fd7fdiA9KcXpG77ZWauFQrEemJbK2yJJ4l6/VMmh9nD7kMrLI4OYtUZoXt9Io2mjeruaCA2Rt6E5ExSbYMtZKzZA28VLlWcXIW6YR0PVuloOYSrnqpCyMi8odkpQMyzpKtl8xVcaly8o4ZVyIHzLgSEclLi/s6qZK5W8seRLtmq3Hg9AjcbFkp1fBMQWy8xsCVdKVufejc3rls/ExEpEOSlQ5ISKqkxaGKMLSOtk3Uah19WboBmgBLBciQjNA9QC89DomI5KDFrgNStTw8cHoEDlWE4sDpEZKMi+pj4Eq6IlX3ADWXcGUtKxGZmdY7pQTz+XCzZSVaR19mmYCMGLhS4FRYbECq9aHVnISlxWwDEZEaAjoDJXPmwenzQaVFdcgzBq7kzJ8dgt+LDWhnSTw1l3DVeraBiEgpAZ2Bkjnz4PT5EOCiOnKv5mhmDFzJmT87BL/bheRBK0viKbGEK2tZiYi8C+gMlMyZB6fPhwDbYuUV5WFgShnu7DABWkjWGAm7CpAzWftBFcAWtObCDKuLaHHmLBGRlmmxVVYgCkoKcGeHCWgdXQOtLLCjdewqQIGRNRWZDdub1/djG+E0C2tZiYj8o7XJq4GW02ZnZKN19OvQ0gI7RsGMK2mSdYEVZeVlsMRYUJpTGvDjqLmgABER+UdrGVclV5k1O2ZcSdekanulRPcA1rISEUnDr8mrCvQ1rCunXf5i4N0FjHAGUUuYcSVDUyLjylpWIiLp+cy+KpkOXWe1dReIsABD/NuWVGcQjY4ZVyIo0z2AtaxERNLzWe+qZF/DtFxU1lowZWWu3wne5UN74rdJoVg+tKc8YzMZZlxJnL0Ftj52ablAqq8o0FzdA4iISHrGqXe1wtYKkt0FvGHGlaTlVxNm7fRrJSIifdLaYi2BJ3hzwe4C0mHganZii9v9asIs/k0qVdG6AjX6nIRFRKQitffBgZeeiW8FSb6xVMDsVO71IVXRuhJPg5OwiIjUY4R9cEFJAfKK8pDbOxfZGQxkHbFUgMRRsrjd3eYlanulxNPgJCwiIvV43Acrccqtzl5bW6ytSwoC2mReUR7KysuQV8RSukAx40pERES6Uzd5a1zlbISfOqJoW6xDpy1InlDq5yYLUFk9BXlFQHLMTGZcXTDjShQAtWuoiIhIHHu7LPRWtC0WIiw40Dg3gE3mISr8FGb2i2LQGgRmXIkcGKGGiojIDLTWLss3tor0hhlXUkgBbD3qlFnKTu5SJtaxEhHpg9baZfnm3F2AS8EGhhlXqs+vxQasULKxsspNEIiISIPUzL4GurQ4l4J1xowrBc6vxQaU7dmqchMEIiLSIJ/Lw0rtSncB7C1AXp4toZLnZ6MAqbrqmA0zrlSfXxlX8bR0dKm/2igiIvKk7MHn0Wz1/+L0iMdhWTlL/g1e6S6ACAsKjpQGkHFlvasrsfEaA1dSjJYaL3MSFhGRgShdRxZ0gscKJcvs9IClAlSfkk2a3cjOyEZpTqnqQSvASVhERIbiUEemSFvD1GxgSGkQZyXFl9mRMwauZhJoIY4K5I6x9TcblYiIPMrOtmVas7OVr3d1IP6z62qHAXYX8I+mA9dZs2bhhhtuQFRUFBISEjBkyBD8/PPPag9Lv3Q0s0lHMTYREWmImmfUAvns4jKw/tF04LplyxaMHz8eX3/9NdavX49Lly7h9ttvx7lz59Qemj45HJFqnVQxNlfCIiIyFzXPqAXy2cXuAv7R1eSs48ePIyEhAVu2bMHNN98s6j6cnCUVcTMgtTQBC+AkLCIis5O1i0zQk7TYXaCOISdnlZeXAwDi4uI83qa6uhoVFRVOXySFPNhmQHo/laG1Ux6chEVEZG6y1rx66Xsurt5V3GcrXaWbwLW2thY5OTno1asXunTp4vF2s2bNQkxMjP0rOTlZwVHqkEMTZe/EzYDU2ikPTsIiIjIRN9GirAmMtFwgwmL77kJcvavzZysnavmmm1KBcePG4dNPP0VRURFat27t8XbV1dWorq62/15RUYHk5GSWCnji0EQZQ0rVHg0REVHgNLQueCBLwWppoR6lGapUYMKECfjkk0+wadMmr0ErAISHhyM6Otrpi7zwcrQoN6laXnECFhERAfA5O0rJz4tA5kNr7aylFmk64yoIAp588kmsXbsWmzdvRmpqqt+PwclZ2iXVgTEnYBERkRj8vNAuQ2Rcx48fj3fffRcrV65EVFQUjhw5giNHjqCqqkrtoWmfyqtkiSFVyytOwCIiIjHU+rzw/ZFcANsysNr9zNYKTWdcQ0JC3F6+dOlSjBkzRtRjmDbjqqE6HyIiIkPz0RbL90eyFbbuAhbYVtQyH0NkXAVBcPslNmg1NZlXyeLMRyIi0jvJal69tMUCxHwks7uAWJrOuErBtBnXgIlrhmzmmY9ERKRxIqf0S1bzGvRCBM7M+BlriIwrqUFcM2R/Zj5KUW7LzgFERCSauCaq0tW8pmbbWkpKELQC7C7gDTOu5EL65eekKLflTFAiIhItkCaqCtHw0FTFjCvVJ2qVrGzYCsOlezdJUW7LzgFERCRaIE1UFSIyGUweMHA1Ex/F43KRYv/BpVuJiEhOkpSkiUgQ+U7msDWWNwxczUTFVbKIiIi0rCivCOVl5SjKKwr8QUQkiHwnc5znmrDDgDMGrmYicfG4HDgJi4iI1CBJSZokCSLn1lh5RXkoKy9DXhFrCwAGruSD0kd6khzxEhERAX61tZGkJE2SBJHzXBN2GHDGwNUoZFriVeyRnlSb5yQsIiKSTIAzoZQ6+yfmszM7IxulOaXIztDu2VIlsR2WUQTUc8p366uCkgLkFeUht3eu1zcNV5glIiLNCbD3lFItGPnZeRXbYZlNQD2nfC82IPZIT+YVZomIiPwXYFsbpc7+ef/sZHcBd5hxNTXpFxsgIiIyPUmWgLXCllyywFbzKv4sqB4x40oiSL/YgBjsHEBERIYmSd905+4CADsMAAxcjUnUClnqYecAIiLSE78TLgG0xao/Uat+cokdBlgqYEzrrLYjvQiLrS2HxpQUlKAorwi9c3tzJSwiIpJXgBO0HCkxWcvsE7VYKmBmgRzpiezXKkXbKy7fSkREigmwJZYjJSZrcZKzOMy4EgDAusCKsvIyWGIsKM0p9Xw7q7mPCImISGckyLiqq/5EaiNO0mLGlfwitm6GR4RERKQrAbbE8kT5Ccb1W1eaeZIWA1dT8N0LTmy/VrHvf3YOICIiI/JrgnGAk6Wdy/Lqdxcw8yQtlgrokd+nPaxw7QUnN6VWHSEiIlKSXxOMA5wsbcayPJYKGJnfheb1j9bkptSqI0RERErya4JxAJOlAZblecOMqx7pvtCciIhIYfzs1DRmXI1M4kJzV1K0vCIiItIUCdpiudLCfA6x7SyNgoGr3smwSpYM720iIiJ1yXD+XYmVIOuSSVu3up9obbYOAwxc9c7P9ZDFHJmJfW9r4UiTiIhIFBnOVioxn6MumdSuXf22WID5OgywxlXv9hbYgta0XCDV95tR7EIDYrBzABERkQh+flY7qivNXb68ADff7LwQgZGwxtUsUrNtLTZEvhGkPDJj5wAiIqKrPJ6J9PPsqKO6RPHNN2fD1tLSeEGrP5hxJSIiIpKAxzORQWRczYIZV1PyvUIWERERycPjmUg/z44GwizdBRi4Gor7wm0iIiK6Qsaej34tThAAbx0GzNJdgIGroYhfIUvs+5adA4iIyFB03PPRW4cBs3QXYOCqdX4dGYov3Bb7vlWiRx0REZFiFFpPVY7ET93QDxyon6jKzshGaU4psjOMXUPLwFXrAjgylLJXKzsHEBGRoci8+mQdt4mfIBcNYocBdhXQvgDWVpayVysRERH5r6SgBEV5Reid2/tqzes6q60tVoTFNlmL7NhVwCgcjwxFHqmZpc6FiIhIq9xO1ErLtQWtacF/PnurJDRyhwFmXPWER2pEREQEW9BaVmYr+ystdblOh2demXE1IgmP1ByxcwAREZmOjG2xXMk1Uevvfy/Ad99Z4dq/3chnXplx1aUC2Fpg+F6vWEyJrMeVPoiIiIzKW8pSYvJ9zlpha4tlgW2yln4x42po4hcaENOUgJ0DiIjIdBRqiwXI+Tkrvn+7UTDjqkvSZlyJiIhIQXsLgD15ttI/GZeB1ROx8RoDVyIiIiIlSTzZ2ghJKpYKmICR210QEREZUUlBCb5ceQOqaxMlm2ztqSzQiHECA1ct8XOGY15RHsrKy5BXpL/1lomIiMyoKK8IRWu6IP+F5yQrE/BUrmvEOIGBq5b4ubyrP+0u2PKKiIhIfXJM1MrOLkBpqRXZ2cZvi8UaVy2RsUiFLa+IiIhcGKE4FIAR2mKxxlWP6pZ37QdRS7vWEVNhwJZXRERELvw806ldtrZYW7fmKrWmgmqYcdUiP2cbKthDmYiIyDhUzriWLXkezS78L043fhyWrFlBP56e4wFmXPXMvrRrT9jS/94PnRTsoUxERGQcdWc6VSoTaHbhfxHd7DSaXfhfSR7PWzxglA4DzLhqmhV6r1khIiIi96TOuHpjXWBFWXkZLDEWlOaUyrqtQDDjagjmW8qNiIjILCxZsxA9/pSEQWsBPJ2pNUqHAWZcNa6gpAB5RXnI7Z2L7AzPpzJKCkpQlFeE3rm9kZGdoeAIiYiISBus0OuZWmZcDUJs8+CivCKUl5WjKK9IoZEREREZhJ8LAMkl+J7rV8/UauQpSY6Bq8aJTe2z3RUREVGANNIWK/gkVDZsmdZsrTwlyTFw1bjsjGxbEXVJttcjp4zsDOSU5rBMgIiIyF9qt+fZWwCss2Loi0clS0J5ekp67y7AGle1+Nk7Ts+92YiIiMgLP/u3B0Or3QVY46p1fubw1T4YJCIiIpnY+7fL/yGv9+4CzLiqpaAAeGUKcBeAv80EUvW8RjIRERFJKbhuQQUA8mCbrKWP+IIZV63LzgYWRQE3nwL2iMu6Bj/bkIiIiPQguIlaebC1xbLFF0bqMMDAVU1+Lu3KlldERETmEFy3IOcFjIzUYYCBq5pSs21F2KnbAJThUMUEr7P82PKKiIhIRhpKTQbXLehqWyzAWB0GWOOqCQU4VDEBM7bW4LN92prlR0REZBoma+GjpQ4DrHHVlWx88svr+Gyffmf5ERER6Z7aLXyu9HPFXucMqFxzXPTYYYAZVw3xs7UrERERGYmHfq4LrAtQXlaOGEsMckpz1BqdrJhx1SEjFU8TERGRnzz0cw18jksBXCd/a6iMNyDMuGrI8w+W4X9XN8PjI05j1kqL2sMhIiIiXbPC1hbLAttkLe2W8TLjqkMtt63FUzXz0XLbWrWHQkRERLrn3BYLUL+MN1gMXJUgMi/PdldEREQq0/i5dP8majm3xQJsc2hKS53n0uipLRZLBZRQl5dvEQp89TqXdyUiItIqrZ5Lv0KOiVpaaIvFUgEtyc0FWjQA7qwB9kxRezRERETkidrn0j20xKojx9lZPbXFYsZVKXvjgT2ncLp9CN6v/h9kZzDrSkRERC48tMSSmtZacDLjqjWpM/HrraF4vkzAlE/Z74qIiIjc8NASK3jOrbH02oKTgatistH9/tfxv19acOFfT0m++gUREREZQGq2LdMqYj6MfxO18mBrjWWLVNWuiAgUA1cFzRyajWYLv0XfHakoyitSezhERESkY0V5RSgvKxcZUzi3xtJrdwEGrgrKzga+eP0X9LfsY8srIiIiCop/E7Xqt8ZylVeUh7LyMuQVabd+gIGrwjKyM5BTmoOM7Ay1h0JERESeaLyfKyBdTFH3VHvWar+7ALsKEBEREblSu5/r3gJgT55tkpbM/d/VfqqAwboKLFq0CFarFY0bN0ZmZia++eYbtYdERERERqb27KU9eba2WHvkP22v9lP1h+YD1/fffx9PP/00pk6dil27dqFbt24YMGAAjh07pvbQiIiIyKjczV5Skp9tscR3GHBuiwWo/1T9oflSgczMTNxwww14/fXXAQC1tbVITk7Gk08+iVwRhwYsFSAiIiKjE78UrBW2tlgW2CZraYMhSgUuXryInTt3on///vbLGjRogP79+6O4uNjtfaqrq1FRUeH0paSf/tIaQkIIfvpLa0W3S0RERBJTe4KWj+VfHfXO7Y3GcY1xsfKij6xrTwChV747c3y6D374IMKmh+HBDx8McPDy0HTgeuLECdTU1KBly5ZOl7ds2RJHjhxxe59Zs2YhJibG/pWcnKzEUO2u+eR3hBy3fSciIiIdU3t5KT/qXDOyMxAeFY6qU1U++rpuA1Bz5bszx6e7es9q1Ag1WL1ndcDDl4OmA9dAPP/88ygvL7d//fbbb4pu/+c7/wShhe07ERER6Zjas5b8rHMV19fVeSECp2scnu6ItBEIDQnFiLQRAQ1dLpqucb148SIiIiLwwQcfYMiQIfbLR48ejTNnzuDjjz/2+RiscSUiIiLSNkPUuDZq1Ajp6en48ssv7ZfV1tbiyy+/xE033aTiyIiIiIhIaWFqD8CXp59+GqNHj0ZGRgZ69OiBBQsW4Ny5cxg7dqzaQyMiIiIiBWk+cL3//vtx/PhxvPTSSzhy5Aiuu+46fPbZZ/UmbBERERGRsWm6xlUKrHElIiIi0jZD1LgSEREREdVh4EpEREREusDAlYiIiIh0gYErEREREekCA1ciIiIi0gUGrkRERESkCwxciYiIiEgXGLgSERERkS4wcCUiIiIiXWDgSkRERES6wMCViIiIiHSBgSsRERER6QIDVyIiIiLShTC1ByA3QRAAABUVFSqPhIiIiIjcqYvT6uI2TwwfuFZWVgIAkpOTVR4JEREREXlTWVmJmJgYj9eHCL5CW52rra3F4cOHERUVhZCQENm3V1FRgeTkZPz222+Ijo6WfXukLXz9zYuvvbnx9TcvvvbSEAQBlZWVSEpKQoMGnitZDZ9xbdCgAVq3bq34dqOjo/kPbGJ8/c2Lr7258fU3L772wfOWaa3DyVlEREREpAsMXImIiIhIFxi4Siw8PBxTp05FeHi42kMhFfD1Ny++9ubG19+8+Nory/CTs4iIiIjIGJhxJSIiIiJdYOBKRERERLrAwJWIiIiIdIGBKxERERHpAgNXiS1atAhWqxWNGzdGZmYmvvnmG7WHRDKbNm0aQkJCnL46duyo9rBIJlu3bsVdd92FpKQkhISEYN26dU7XC4KAl156Ca1atUKTJk3Qv39/7N27V53BkqR8vfZjxoypty8YOHCgOoMlSc2aNQs33HADoqKikJCQgCFDhuDnn392us2FCxcwfvx4xMfHo2nTprj33ntx9OhRlUZsXAxcJfT+++/j6aefxtSpU7Fr1y5069YNAwYMwLFjx9QeGsksLS0Nf/zxh/2rqKhI7SGRTM6dO4du3bph0aJFbq+fM2cOXnvtNRQUFGD79u2IjIzEgAEDcOHCBYVHSlLz9doDwMCBA532Be+9956CIyS5bNmyBePHj8fXX3+N9evX49KlS7j99ttx7tw5+20mTZqEf/3rX1izZg22bNmCw4cPY9iwYSqO2qAEkkyPHj2E8ePH23+vqakRkpKShFmzZqk4KpLb1KlThW7duqk9DFIBAGHt2rX232tra4XExERh7ty59svOnDkjhIeHC++9954KIyS5uL72giAIo0ePFu655x5VxkPKOnbsmABA2LJliyAItvd5w4YNhTVr1thv8+OPPwoAhOLiYrWGaUjMuErk4sWL2LlzJ/r372+/rEGDBujfvz+Ki4tVHBkpYe/evUhKSkK7du0watQo/Prrr2oPiVRw8OBBHDlyxGk/EBMTg8zMTO4HTGLz5s1ISEjANddcg3HjxuHkyZNqD4lkUF5eDgCIi4sDAOzcuROXLl1yeu937NgRbdq04XtfYgxcJXLixAnU1NSgZcuWTpe3bNkSR44cUWlUpITMzEy8/fbb+Oyzz5Cfn4+DBw+iT58+qKysVHtopLC69zr3A+Y0cOBALF++HF9++SVmz56NLVu2YNCgQaipqVF7aCSh2tpa5OTkoFevXujSpQsA23u/UaNGiI2Ndbot3/vSC1N7AER6N2jQIPvPXbt2RWZmJiwWC1avXo2srCwVR0ZESho5cqT952uvvRZdu3ZF+/btsXnzZvTr10/FkZGUxo8fj++//55zGVTCjKtEmjdvjtDQ0HozCI8ePYrExESVRkVqiI2NRYcOHbBv3z61h0IKq3uvcz9AANCuXTs0b96c+wIDmTBhAj755BNs2rQJrVu3tl+emJiIixcv4syZM06353tfegxcJdKoUSOkp6fjyy+/tF9WW1uLL7/8EjfddJOKIyOlnT17Fvv370erVq3UHgoprG3btkhMTHTaD1RUVGD79u3cD5jQoUOHcPLkSe4LDEAQBEyYMAFr167Fxo0b0bZtW6fr09PT0bBhQ6f3/s8//4xff/2V732JsVRAQk8//TRGjx6NjIwM9OjRAwsWLMC5c+cwduxYtYdGMnrmmWdw1113wWKx4PDhw5g6dSpCQ0PxwAMPqD00ksHZs2edMmgHDx7E7t27ERcXhzZt2iAnJwczZsxAamoq2rZtixdffBFJSUkYMmSIeoMmSXh77ePi4vDyyy/j3nvvRWJiIvbv349nn30WKSkpGDBggIqjJimMHz8eK1euxMcff4yoqCh73WpMTAyaNGmCmJgYZGVl4emnn0ZcXByio6Px5JNP4qabbsKNN96o8ugNRu22BkazcOFCoU2bNkKjRo2EHj16CF9//bXaQyKZ3X///UKrVq2ERo0aCX/605+E+++/X9i3b5/awyKZbNq0SQBQ72v06NGCINhaYr344otCy5YthfDwcKFfv37Czz//rO6gSRLeXvvz588Lt99+u9CiRQuhYcOGgsViER599FHhyJEjag+bJODudQcgLF261H6bqqoq4YknnhCaNWsmRERECEOHDhX++OMP9QZtUCGCIAjKh8tERERERP5hjSsRERER6QIDVyIiIiLSBQauRERERKQLDFyJiIiISBcYuBIRERGRLjBwJSIiIiJdYOBKRERERLrAwJWIiIiIdIGBKxGRBi1ZsgS33367/fcxY8ZIvmzsxYsXYbVaUVJSIunjEhHJhYErEZHGXLhwAS+++CKmTp0q63YaNWqEZ555Bs8995ys2yEikgoDVyIijfnggw8QHR2NXr16yb6tUaNGoaioCHv27JF9W0REwWLgSkQkk+PHjyMxMRH/+Mc/7Jdt27YNjRo1wpdffunxfqtWrcJdd93l9bF37NiBFi1aYPbs2QCAadOm4brrrsNbb72FNm3aoGnTpnjiiSdQU1ODOXPmIDExEQkJCZg5c6bT4zRr1gy9evXCqlWrgnimRETKCFN7AERERtWiRQu89dZbGDJkCG6//XZcc801ePjhhzFhwgT069fP4/2Kiorw8MMPe7x+48aNGDZsGObMmYPHHnvMfvn+/fvx6aef4rPPPsP+/fsxfPhwHDhwAB06dMCWLVuwbds2/OUvf0H//v2RmZlpv1+PHj1QWFgozZMmIpIRA1ciIhndcccdePTRRzFq1ChkZGQgMjISs2bN8nj7M2fOoLy8HElJSW6vX7t2LR555BG8+eabuP/++52uq62txVtvvYWoqCh07twZffv2xc8//4z//Oc/aNCgAa655hrMnj0bmzZtcgpck5KSUFZWJs0TJiKSEQNXIiKZvfrqq+jSpQvWrFmDnTt3Ijw83ONtq6qqAACNGzeud9327dvxySef4IMPPnDbYcBqtSIqKsr+e8uWLREaGooGDRo4XXbs2DGn+zVp0gTnz5/392kRESmONa5ERDLbv38/Dh8+jNraWpSWlnq9bXx8PEJCQnD69Ol617Vv3x4dO3bEW2+9hUuXLtW7vmHDhk6/h4SEuL2strbW6bJTp06hRYsWIp8NEZF6GLgSEcno4sWLeOihh3D//ffjlVdewV//+td6GU9HjRo1QufOnfHDDz/Uu6558+bYuHEj9u3bhxEjRrgNXgPx/fffo3v37pI8FhGRnBi4EhHJaMqUKSgvL8drr72G5557Dh06dMBf/vIXr/cZMGAAioqK3F6XkJCAjRs34qeffsIDDzyAy5cvBz3GwsJCp8UOiIi0ioErEZFMNm/ejAULFuCdd95BdHQ0GjRogHfeeQeFhYXIz8/3eL+srCz85z//QXl5udvrExMTsXHjRnz33XcYNWoUampqAh5jcXExysvLMXz48IAfg4hIKSGCIAhqD4KIiJzdd999uP766/H888/Lup37778f3bp1w9///ndZt0NEJAVmXImINGju3Llo2rSprNu4ePEirr32WkyaNEnW7RARSYUZVyIiIiLSBWZciYiIiEgXGLgSERERkS4wcCUiIiIiXWDgSkRERES6wMCViIiIiHSBgSsRERER6QIDVyIiIiLSBQauRERERKQLDFyJiIiISBf+P7g5W9T/go20AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\u001b[33m\u001b[33mno tests ran\u001b[0m\u001b[33m in 0.00s\u001b[0m\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "%%ipytest\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "g = 9.81 # m / s^2\n",
        "dt = 0.5 # time step in seconds\n",
        "\n",
        "x_start = 0 # starting x position\n",
        "y_start = 0 #starting y position\n",
        "\n",
        "const = 4*(10**-5) # Air Drag B_2 constant (1 / meter)\n",
        "\n",
        "v_initial = 700 # meters per second\n",
        "\n",
        "angle_degree = [30,35,40,45,50,55] # launch angle in degree\n",
        "angle = [math.radians(degrees) for degrees in angle_degree] #convert launch angle to radians\n",
        "\n",
        "x_list=[] #list to store x postions\n",
        "y_list=[] #list to store y postions\n",
        "\n",
        "for j in range(len(angle)): # loop over launc angles\n",
        "\n",
        "  vx = v_initial*math.cos(angle[j]) # intial x velocity\n",
        "  vy = v_initial*math.sin(angle[j]) # initial y velocity\n",
        "\n",
        "  x=[x_start] # list to store x postions\n",
        "  y=[y_start] # list to store y postions\n",
        "\n",
        "  i = 0 # index for while loop\n",
        "\n",
        "  while y[-1]>=0: # loop ta calculate x and y positions\n",
        "    x.append(x[i]+vx*dt) # calculate and save x\n",
        "    y.append(y[i]+vy*dt) # calculate and save y\n",
        "    vx=vx-const*dt*vx*math.sqrt((vx**2)+(vy**2)) # update x velocity\n",
        "    vy=vy-dt*(g+const*vy*math.sqrt((vx**2)+(vy**2))) # update y velocity\n",
        "    i=i+1 # update index\n",
        "\n",
        "  #interpolate between last two points to determine final position\n",
        "  r = -y[-2]/y[-1]\n",
        "  x[-1] = (x[-2]+r*x[-1])/(r+1)\n",
        "  y[-1]=0.0\n",
        "\n",
        "  # saving x and y positions\n",
        "  x_list.append(x)\n",
        "  y_list.append(y)\n",
        "\n",
        "#plotting\n",
        "\n",
        "colours=[\"purple\",\"blue\",\"green\",\"yellow\",\"orange\",\"red\"] # list of colours\n",
        "plt.figure(figsize=(8,6)) # set plot size\n",
        "for i in range(len(x_list)): # plotting trajectory for each launch angle\n",
        "    plt.scatter(list(map(lambda z: z/1000, x_list[i])),list(map(lambda z: z/1000, y_list[i])), # converting from m to km\n",
        "                c=colours[i],s=1,label='θ = {}°'.format(angle_degree[i])) # colours and legends\n",
        "plt.xlabel('x (km)')\n",
        "plt.ylabel('y (km)')\n",
        "plt.legend()\n",
        "plt.title(\"Projectile Motion with Air Drag\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyM8uFnaCX45liAN9uTm/I9D",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.11.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}