1-0Intro to Timeseries20230316.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97f2f23d-794b-45cd-9543-4e3c0faf9b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary packages\n",
    "from matplotlib.axes._axes import _log as matplotlib_axes_logger\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import earthpy as et\n",
    "\n",
    "# Handle date time conversions between pandas and matplotlib\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "# Dealing with error thrown by one of the plots\n",
    "matplotlib_axes_logger.setLevel('ERROR')\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "# Adjust font size and style of all plots in notebook with seaborn\n",
    "sns.set(font_scale=1.5, style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4e4d05c9-0aed-4da9-9cfa-e07def4d8dbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Download the data\n",
    "data = et.data.get_data('colorado-flood')\n",
    "\n",
    "# Set working directory\n",
    "os.chdir(os.path.join(et.io.HOME, 'earth-analytics', \"data\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "640eea8b-cee7-4f8d-b5c7-ba330f191226",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>DATE</th>\n",
       "      <th>HPCP</th>\n",
       "      <th>Measurement Flag</th>\n",
       "      <th>Quality Flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>20030101 01:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>g</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>20030201 01:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>g</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>20030202 19:00</td>\n",
       "      <td>0.20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>20030202 22:00</td>\n",
       "      <td>0.10</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>20030203 02:00</td>\n",
       "      <td>0.10</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1835</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>20131222 01:00</td>\n",
       "      <td>999.99</td>\n",
       "      <td>[</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1836</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>20131223 00:00</td>\n",
       "      <td>999.99</td>\n",
       "      <td>]</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1837</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>20131223 02:00</td>\n",
       "      <td>0.10</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1838</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>20131229 01:00</td>\n",
       "      <td>999.99</td>\n",
       "      <td>[</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1839</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>20131231 00:00</td>\n",
       "      <td>999.99</td>\n",
       "      <td>]</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1840 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          STATION     STATION_NAME  ELEVATION  LATITUDE  LONGITUDE  \\\n",
       "0     COOP:050843  BOULDER 2 CO US     1650.5  40.03389 -105.28111   \n",
       "1     COOP:050843  BOULDER 2 CO US     1650.5  40.03389 -105.28111   \n",
       "2     COOP:050843  BOULDER 2 CO US     1650.5  40.03389 -105.28111   \n",
       "3     COOP:050843  BOULDER 2 CO US     1650.5  40.03389 -105.28111   \n",
       "4     COOP:050843  BOULDER 2 CO US     1650.5  40.03389 -105.28111   \n",
       "...           ...              ...        ...       ...        ...   \n",
       "1835  COOP:050843  BOULDER 2 CO US     1650.5  40.03380 -105.28110   \n",
       "1836  COOP:050843  BOULDER 2 CO US     1650.5  40.03380 -105.28110   \n",
       "1837  COOP:050843  BOULDER 2 CO US     1650.5  40.03380 -105.28110   \n",
       "1838  COOP:050843  BOULDER 2 CO US     1650.5  40.03380 -105.28110   \n",
       "1839  COOP:050843  BOULDER 2 CO US     1650.5  40.03380 -105.28110   \n",
       "\n",
       "                DATE    HPCP Measurement Flag Quality Flag  \n",
       "0     20030101 01:00    0.00                g               \n",
       "1     20030201 01:00    0.00                g               \n",
       "2     20030202 19:00    0.20                                \n",
       "3     20030202 22:00    0.10                                \n",
       "4     20030203 02:00    0.10                                \n",
       "...              ...     ...              ...          ...  \n",
       "1835  20131222 01:00  999.99                [               \n",
       "1836  20131223 00:00  999.99                ]               \n",
       "1837  20131223 02:00    0.10                                \n",
       "1838  20131229 01:00  999.99                [               \n",
       "1839  20131231 00:00  999.99                ]               \n",
       "\n",
       "[1840 rows x 9 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define relative path to the data\n",
    "file_path = os.path.join(\"colorado-flood\",\n",
    "                         \"precipitation\",\n",
    "                         \"805325-precip-daily-2003-2013.csv\")\n",
    "\n",
    "# Import the file as a pandas dataframe\n",
    "boulder_precip_2003_2013 = pd.read_csv(file_path)\n",
    "boulder_precip_2003_2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "81a0b37a-d859-4ed3-b9b9-13740ad83d84",
   "metadata": {},
   "outputs": [
    {
     "data": {
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iFdTpm5AAs3203WAwoK6ujjt4A9YBF2fPns1dybHHvi6ipKQEkydPhlqthkqlQn19PZRKJRYuXIjFixdDq9UGJP9KpRJ33XUXli1bBkCczt6Bdscdd6ChoQELFy7Enj17sGfPHqjVamg0GtTX1/OGZLB/zL9Dhw749NNPMWPGDBQXF2POnDlQKBSIjY2FTqfjAh1XwyG4c9999+HQoUOYP38+FixYAI1GwwtAZsyYgZtuusmrZU6cOBGbN2/G0aNHMXToULRq1YobAmLHjh0AgClTpmDr1q24cOECXnzxRbz00kuIiYlBbW0tGIbBxIkTYTAYnF79EpqGOwqFAkuWLMHUqVPx999/43//+x+effZZREVFcVf45HI5nn32WdHG9SJEbBQwERJgbGdamUyGqKgotGnTBomJiUhLS8PQoUMxfPhw7uDjzBVXXIH169fjww8/REZGBurq6hAdHY1hw4ZhypQp6Nu3LxYvXhzQMowZM4YLmKTa2dvevffei+uvvx5ff/01/vrrLxQUFKC2thYxMTFISkpC//79MWLECAwZMsRh3vT0dPzyyy9Ys2YNfv/9d1y4cAH19fVo06YNkpKSMHLkSIwdO9brPKlUKqxevRqrVq3C5s2bcfHiRcTGxuKKK67Aww8/7FOwMHDgQCxfvhyrV6/GmTNnUF5ezgvIAetVt2+//RYfffQRtm/fjtLSUigUCgwaNAgTJ07Erbfe6vSlwd6k4Un79u3x3Xff4ZtvvsGvv/6K8+fPQ6fTISEhAYMGDcLDDz/ssp8eIVIgY+gZUkKIB5999hneeOMNJCQk4Pfff5ds/yWpeuCBB3DgwAGvXq5LCJEW6sNECHHLbDZzryq5++67KVgihDRLFDARQlyyWCxYvHgx8vPzodFouJfyEkJIc0N9mAghDrZs2YI333wTVVVV3OPms2bNcvtKEkIIacooYCKEONBqtSgsLIRKpUK3bt0wadIk3H///aHOFiGEhAx1+iaEEEII8YD6MBFCCCGEeEC35ERw9OhRMAzDvYOKEEIIIdJnNBohk8nQv39/j9PSFSYRMAzj80sphSzbYDAEbPlEGqidmw9q6+aB2jk8eHP8pitMImCvLAXiDdtarRaZmZno0aNHQN8XRkKL2rn5oLZuHqidw8PJkycFT0tXmAghhBBCPKCAiRBCCCHEAwqYCCGEEEI8oICJEEIIIcQDCpgIIYQQQjyggIkQQgghxAMKmAghhBBCPPBpHKbLly9j7969OHXqFE6ePInMzEzo9XoMGjQIX375pdt5jUYjPv/8c/z444/Iz8+HSqVCr1698MADD+Dmm292O++ZM2ewYsUKHDx4EDU1NWjXrh2GDx+OmTNnun2Luj9pEkIIIYT4FDD9/PPPeP31172eT6/X4+GHH8bhw4ehUCjQo0cP6HQ6HDhwAAcOHMAjjzyC//3vf07n3bZtG+bOnQuj0YjWrVujZ8+eyMnJwZdffoktW7bgm2++QVJSkqhpEkIIIYQAPt6Si4mJwTXXXINHH30UH374IWbOnClovrfeeguHDx9Gp06dsHnzZvz444/47bffsHTpUqjVanzyySfYsWOHw3wlJSV46qmnYDQaMXPmTOzZswfff/899uzZg+uvvx6XL1/G7NmznQ5v7muahBBCCCEsnwKmu+66C6tWrcLcuXNx0003oXXr1h7nKSsrw7fffgsAWLhwIbp168b9NnLkSEybNg0A8OGHHzrMu3LlSuh0OgwcOBBPPPEElErrhbHY2Fi88847iI2NxalTp7Bz507R0iSEEEIIYQWt0/eOHTtgNBqRnJyMIUOGOPx+zz33AABOnz6N/Px83m9bt24FAEyYMMFhvpYtW2LMmDEAgF9//VW0NAkhhBBCWEELmI4dOwYAGDBggNPf27dvj06dOvGmBYDi4mKUlJQAAAYOHOh03quvvhoAcPz4cVHSJIQQQgix5VOnb1/k5uYCADp37uxyms6dO6OgoAA5OTkO86lUKnTo0MHpfGxn74sXL8JoNEKlUvmVpi8YhoFWq/VrGc7odDowDIMfdmejRUwURgzoKHoa3iour8fW/QVIbKPBzYMcO9r7as+xIlTVGfDva7pAbzTjpz9zUVNvxKD0dujb3fNtX2csFgY/7c1Dq9gIXN8vweV0v2bkQ6WQY9TATqiuM+CXfXkYkNoWKZ3jfCyNd2pq67H7ZA12Z57E1WntcXVaO/7v9Qb88lc++qW0Rq8u8YKXm1tci79OXsLowUlo3TJS7GwHVXWdAZv35uFMbgUi1Ur069kaFgYYe20XyOUy3rT5JXX483gxRg3shHbxUV6ndb6wGvtPl2LMkCS0atFYbzq9CT/9mYfULnG4sofndbJBb8K6HedxLr8KE0f2QN8eraHT6azL+uf/zvx24CIKy7S4eVAnmMwMfj9UAIVchluGdkabOOHlqarT49d9+bi6Vzv0TGopeL5gO5dfha37L0KtkuOWoV3QpmUkNu/NRa/keK+2/e92XcCWjHw8MaEvrujm+snp3w8V4PDZy+jSIRZjr0uGJtL1odBgNGPTH7kwmS0Ye20yYjQqQXnZsi8HZ7KroIouR4/OwJFzl7F4/Sn06NQCaV3icdv1yVApaWQfT2rqDVj6/Wno9Cb89+4+ou/HGIaBTCbzPCGCGDBVV1cDsN5Cc4X9raamhvuuqqqK+81VoeLi4gAAFosFdXV1iI+P9ytNXxiNRmRmZvq1DFdKq034ZnshAKCFvBLRkYqApCPUDxkVOHbBGhzGyqsQF+3/atRgtOCj74oAAFFMNcprjdi4rxIAcPBMMWaPcx3suJN/WY81v13+J68ViFA57qDKaoxY/XPJP9NU4Y/TNTj4dz22ZOThmbuCE6BmFeqw82QNgBr8daoEz97NT3frkSrsO1uHzX/l4vkJwvP0/qZiVNWbkZ1Xgruv8y3olIo/Ttfg9+ON2+mpCxUAgChUIalNBG/aj36+hMvVJpw5fwn339jG67Te2ViEWp0FuQWluGNo44H3r8xabDtq3a+8dF8nj8vZf64Ovx6uAgAs/PwIbx72hM5eVb0JK3+6BAAoulQGvdGCswUNAICKigqMvipOcDk2H6jEoex6bN2fj6fvTBQ8X7C9tq4QBpP1oZ1T2aW4sqsG24/VAMgRVM8AUF5rwrrfrfX2yqrDLuer1ZmxYlMxAODwuTJUV5Xj+vQWLpd75qIOG/4oBwDo66swODXGY17qGsxY9UvxP58zcfd1rfHGmgIAwMnzFTh5vgLmhkr0SdYIKltztuVwFY5m1QEAPv3hCMYOEn7CKJRarRY0XdACJr1eDwDc1R9n2Ew3NDT4NJ/t9P6k6QuVSoUePXr4tQxndDodCg6d4/5O7trdqzPMQNh64gQAa8CU1LkrOrXzvAPxpKbeAMAaMCV2SgIu1wOwBkyMTIG0tDSflmtQlQOwBkw9eqQ4PTvMKaoBYA2YOid3RfXh0wCABgPjc7reKtVdBGDdKZvMcEj3+/2HAQBGk3d5qvpnJ11YYQ5aWQLlRGE2AMcTm8SOnZFmdzXh8j/lzi01+FTu2n/mv1zLb4uMC2cBWAMmIcs9nJcFoIr7Oy0tDTqdDrm5uUhOTkZUlOO2XHi5HoD1wB+piQGjNwOw7p9iYuO8Ks8Xu/cDAHR6i6Tb32Aq4D5frjaBUcaCbWuh+c4trgVbb+7mK6nQAihu/EIV6zaNy/oisNtmfOu2SEtL9piXy1U6Lg2lOvKf5RfwpomNa4u0NNd3P4jVhozD3Of+vTsjLU1YAC1Udna24GmDFjBFRFjPAI1Go8tpDAYDACAysvGSmzfz2U7vT5q+kMlk0GgCf7YQGRUVlHTcUSgar3BFiZQfo6VxmZERkVCrG9vMn7qNiKjjPkdpoqDROJ5JREY2rj9RkVGQyxuvQgWrru3PcOzTldvUuS95Ctb6GUiuTnwiIiLcls2fcstkct787BO6QpertMuz7Tyutp3ISHPj/AolFPLG4VKUSqVX5QnFuiwGb+sZAKKi+Pt5V/NF6fjDz3iqU7W68ZiiVqkE5SfK5vxboVA4nUelFras5k6haFyH28TFiF5nQm/HAUHs9N2ihfWSJ3ubzBn2N3ZaoPGWWXV1tdNxloDG23ZyuRwxMY1XO3xNkxBCCCHEVtACpuTkZABAXl6ey2nYR/vZaW0/G41GFBcXO5nL2tkbADp16sQ7C/U1TUIIIYQQW0ELmPr16wcAOHLkiNPfS0pKUFBQwJsWABITE9GunfWJoUOHDjmdl/3edj5/0pQaxuUfoWGbBVdX/bxeps1iGDABKbOrrDIuPgeVTeYk0MSS1BzrxX5baI51IJTQXZH3uyzvt02p7bOJOIIWMI0cORIqlQq5ubnIyMhw+J0dkbt3797o0qUL77fRo0cDANatW+cwX3V1NbZs2QIA3ACWYqRJCCGEEAkR3t0oIIIWMLVp0wYTJ04EADz//PO4cOEC99uOHTuwcuVKAMDjjz/uMO/UqVMRGRmJgwcP4oMPPoDZbO0UWVtbi3nz5qG2tha9e/fGiBEjREuTEEIIIYTl01NyxcXFuP3227m/2SfNjhw5gsGDB3PfT5s2DY888gj395NPPonTp0/j6NGj+Pe//42ePXtCq9Vy/YimTJmCUaNGOaSXkJCAN954A/PmzcPSpUuxdu1adOjQATk5OdBqtWjTpg3ef/99p73dfU2TEEIIIYTlU8BkNpu5J9NsmUwm3vf2YxtFRkbiiy++wOrVq/HTTz8hNzcXKpUKgwYNwqRJk7hbb86MGTMGSUlJWL58OQ4dOoSsrCy0a9cO48ePx8yZM12+ANifNAkhhBBCAB8Dpk6dOuHcuXOeJ3RCrVZj+vTpmD59utfzpqenY/HixUFNkxBCCCGEXmQTDhinH0MnAPmxfRaIYRyfmhMlDQGPyYn11J+3+E/VSKKVJUdI+zUFtuVkGEhi/QwHQuvG2/0J40P9M/TUa5NEARMhhBBCJC/ED8lRwEQIIYQQ4gkFTIQQQgghHlDARAghhBDiAQVMYSAQryLxBxOYXt/8z/YdXwPIvsN5KDABqNLmQqyHAqTCvjRNrXyBIriWvKxOf/cJUthnE3FQwEQIIYQQyXMyNnVQUcBECCGEEOIBBUyEEEIIIR5QwEQIIYQQ4gEFTMRrgeig7NjR1Xl6/ixY2n0vJZ05SZBE+4UgD0zYrMMh5mPdeFOnQqcVNB21ZdihgIlITrCfCpLaQUhq+ZG6Jldf9CqUgPK+RqkNpCO0vb4pYCKEEKFC/W4GIrqQPXlF61LYoYCJEEIIIcQDCpgIIYQQQjyggIkQQgghxAMKmMKAlJ+SEatTKmP3KhT+Yn1Pg/faEwHLCVUnW6m1qxS5apumVnXunhClTuCuCX1YxNs65D8VLDANCbxuiYiPAiZCCCGESB69GoUQQgghROIoYCKEEEII8YACJkIIIYQQDyhgCjPBHgXbaR4C3IuRgXidJnnzulgOr8O570n5xeHVMNRTVLgmVlX2bU+rgjCivrbEdnqXfwibiZqv6aCAiRBCCCGSF+rB0SlgIoQQQgjxgAImQgghhBAPKGAihBBCCPGAAqZwI4EehIEYedxhOQHoNCmBqnNN0pmTBkl0fg5BHmjU6MDypk596PPtOgFqy7BDAVMYaHbbVZALLNJbWERFB0bhpPDkaKDQehB61AbSIQvxUN8UMBFCiFChfkyHiC5kx2Bal8IOBUyEEEIIIR5QwEQIIYQQ4gEFTGFGarfTxRqR2n4xgjpNClou4/Sz23l8Ts13ge6HI7X1xhe+lMH/1dPdihl8Xq8nzagDjq/7okBUkRTeHtBUSGkVpoApHEhohQmGoHfileBrDKSSj3AgpR2qGHhPoYJpcuULNe8DK3pKkVhRwESInabYF7MplkkI/zv0ytz+KXkhfqooHFAVSRuvfULcVhQwEUIIIYR4QAETIYQQQogHFDARQgghhHhAAVOYEeupNP/yYPNZrGXaLUmsNEJfW8JIoFklTwrrfqhXKClUgVT5WjVe1anQJ22FvD6K2jLsUMAUBpryqx+cCfZBgb9zk0hdSyUfJOj4Q2GEMCNNlNfPyFEbSEao++dTwEQIIUKFeo9NREevRiFCUcBECCGEEOIBBUyEEEIIIR5QwBRmpHA/nQnE0NgOyxGpH4eQzpeECBWCdUgy/eqkzsdq8qZ6RW0JatawQwETafak2KleejmSrqYWUDSt0kiQt6/jC0wuiA9kIR6WnQImQggRijrqNjnU6ZsIRQETIYQQQogHFDARQgghhHhAARPxGn8UbnHu8Nsvhd8txfc0fBkEMBR9FgLdDacp9MPwpY78r1fG7Z/B5nV/rSbWv8sdX4saiCri73eaTxsEgpSqjwKmMCClFSYYgr2DEfQagyCTSj7CQZOrKvsYrckVMLS8rs5AjbxO7Rp2lKFItLKyEqtWrcLOnTtRUFAAo9GIVq1aoX///njggQdw9dVXO52vvr4eK1aswNatW1FUVASNRoMrr7wSU6ZMweDBg92mmZGRgVWrVuH48ePQarVITEzEmDFjMH36dGg0mkAUk4SpptgXsymWSQj/O/SGtub8PnkI8VNFhPhLSqtw0K8w5ebmYuzYsVi+fDmys7PRunVr9OjRA3V1ddiyZQsmTZqE1atXO8xXUVGBO++8Ex9//DEKCwvRvXt3REREYNeuXXjooYfw9ddfu0zzyy+/xOTJk7Fr1y5ERESge/fuKCwsxLJly3DXXXehqqoqcAUmhDQdEtp5E3HQU3JEqKAHTP/3f/+Hy5cvIzk5GT/99BO2b9+OH374Afv27cOUKVPAMAzeeust5Obm8uZ7/vnnkZOTg/T0dGzfvh0bN27Erl27sGDBAjAMg4ULFyIzM9MhvVOnTuG1114DACxYsAC7du3Cxo0bsX37dqSnp+P8+fOYP39+MIpOCCGEkDAV1ICprq4O+/fvBwA8+eST6NGjB/dbREQEnnrqKXTp0gUmkwl//vkn99uZM2ewY8cOyOVyvPfee2jfvj0A6yBWEydOxLhx42A2m7F06VKHNJcuXQqLxYJx48Zh4sSJ3MBX7du3x7vvvgu5XI5t27bh7NmzgSw6IYQQQsJYUAMmg8HA3ZPv3Lmzw+8ymQxJSUkAAJPJxH2/detWAMCQIUPQpUsXh/kmTpwIANi9eze0Wi33fX19Pf744w8AwIQJExzmS05OxpAhQwAAW7Zs8alMwSa1Jy7Eyo59ucTqiM3wPkur7mxJOW9SIYk6CvVTcqFNXtJ8XT+8ezWKsIkZF59dTkTCQlADplatWqFDhw4AgKNHjzr8rtVquSs9ffr04b4/duwYALjsDN63b1+o1Wro9XrebbnMzEwYDAao1Wr07dvX6bwDBgwAABw/ftz7ApGACOV+RDoBqVTyEQaaWFXZHpSbWNEkwdttnNpAOkLdATzofZjmzZsHmUyGN998E+vXr8fly5eh0+lw4sQJzJgxA2VlZbjtttu4QAYA15/J2VUpAFCpVEhISAAA5OTkcN+znxMTE6FSqZzOyy7Tdj5CCHGKOuo2OdTpmwgV9GEFbrvtNsTGxmLZsmV44YUXeL+1bdsWL730Eu655x7e99XV1QCAli1bulwu+1tNTY1P87HT+ophGN7tQLHodDre3w0NDdBqQzIaBMdsNnOfrfnxv9wNDQ3cZ71eD6PRwP3NAD6nodfrG9PQ6aCNcJ92Q0MDLBYL93cg2tQZo8HI+1ur1UGlbDyfsdjUuS95CtT6GUwmo8np93q93m3Z/Ck3w1h489t2FRCyXJPRvl213DZtv22zbNdHi9kMs6Wx7c0ms1flCcW6LAZv6xkAGhr0vL9dzWdbv2xa7tIwGBr3RSajUVB+GnS2bWhxOo/RIGxZzZ3Z3LgOe9rWfcEwjOCX+obkyJuXl4fy8nLI5XIkJCQgJiYG+fn5uHz5MjZu3IgBAwYgJSWFm5496Lm6SgQAarUagOOBV+h8tgdWXxiNRqdP6Ynt/IULqC13XZ5gqKur4z7n5ubCWKv2e5mVdY07yIsXL6KspvFvi8Xic90WFDQelP7OzsblaMdVPq+0se1zcnKgtTmQBaNNAaCsrJ7399mzZ6FUNG7EdfWNv/uSJ5PJFLSyBEpFRZXT7y9evAgNU+b0N3/WHQDQGwy8+W3zIGS55XZ5tp3H/klgVmF54wG6rq4OemPjTaHq6mqvytMQgnVZDJWVVdxnofnOv8zfh7ua71Klgfd3ZWWV2zQuXWrc310uK0NmptHltKzSqsZpGhoanC7/UsklZGbWO3xP+Opt9n35+RcRYbosehpsHOBJ0AOml19+GWvWrEGfPn2wcuVKdO3aFYB1pVq8eDE+/fRT3Hvvvfjxxx/RsWNHANYn6HQ6HYxG1ysqexYQGRnJfRcRYb2cIGQ+dlpfqVQq3lN/YtHpdLhwqfEJvm5duyGpfYzo6Xgj+uARANbANDk5GT06ub6CJ1RJhRbAJQBAUlIS5JfrAViv+sllMqSlpfm03DqmFEA5AKBHjx5oGxflOFFUJQDrRti1a1doTp8FYF0vfE3XW/nVOQAqub979erFu8IUs18LQO9DngoAAEqlMmhlCZT9OecA1Dl8n5SUhLRebe2+tZZbLpf7WG7r/BFqNW/+fefPcnkQstzDeVm8PKelpUGn0yE3NxfJycmIinJcH9WF1QBKAQAxMTGQN5jAro8tWrTwqjyRu2sAGAXnN3QKeH/Fx8cBsB4oheZbpmncjt3Np7lUC7Z+ASAuLs5tGhdrLwKoAgC0ad0GaWme9/PRJXUASgAAEZER6NWrF+zL2L59B6SlOe9mQhpFZzTu+zp3TkJazzaiLj87O1vwtEENmM6ePYtvvvkGKpUKH3zwARcQAdZA56mnnsKZM2ewb98+LF++HAsWLABg3UnodDq3t83Y31q0aMF9J+R2m5DbdkLIZLKAjRhu2+kwMioy5COTKxQK7nNkpDj5idI1ljJCHQG1yuYs0I+6VdsEwlGRUU6XExHReIk3IjIScnljoBKsular+Gc4UVFRUKsa61luU+e+5CmQ62ewqJTOd1dqtdpt2fwpt0wm581vmwchy1XaXd22nScqytX62HilRC5XQC5vvCWhVCq9Kk8o1mUxKL2sZwCIiODf4nQ1X2Qk/wTaU52qbLZNlUolKD9RkY1XyBVyhdN51AKX1dwpFI3rcGSE+Mc/obfjgCB3+j58+DAYhkGXLl14wZKta6+9FoB1wElWcnIyAOutPGeMRiOKiop409p+LioqcnmVKT8/32E+QghxijrqNjnU6ZsIFdSAyfZepCe2He369esHwBpwOXPixAkYjUZERETwLq2mpaVBpVLBYDDgxIkTTudll8mmQQghhBBiL6gBE9tfKS8vD4WFhU6n2bt3L29aABg9ejQAYP/+/U6vMq1duxYAMGzYMERHR3Pfx8TE4LrrrgMArFu3zmG+3NxcZGRkAADGjBnjdXkIIYQQ0jwENWC69tpr0bp1axiNRjzxxBO8sY8aGhrw5ptvYt++fQCAcePGcb+lp6dj+PDhMJvNmDNnDkpLrR32GIbB2rVrsWnTJsjlcsyYMcMhzZkzZ0Imk2HTpk1Yu3YtN2hZaWkp5s6dC4vFglGjRv3TKS8MSGAUNd4otiIN9Gi/GLFG+oYPgwCGoooDnaYEVhu/+VIG/1dP+xXT3+X5x+vySGYgVukKRBXZv2GAmsF3Uqq7oHb61mg0ePvtt/H444/j5MmTuOWWW5CYmIjo6Gjk5+dz45Lcf//9GDVqFG/e1157Dffeey9Onz6NkSNHokePHqisrERxcTFkMhmee+45pKenO6TZt29fPPPMM1i0aBFefPFFLFu2DPHx8cjOzobBYEDXrl3xyiuvBKX8hBDiLQkdL5o9UduCGjbsBH1YgWuuuQY//vgjVq9ejb/++gtFRUUoKSlBXFwcrrnmGkyYMAE33nijw3ytWrXCd999h08++QRbtmxBdnY2NBoNhg0bhqlTp3LvhHNm8uTJSE1NxWeffYYTJ06gvLwciYmJGDNmDKZPn867jSdJzWzDCvbrSRi7P6TQF1PsGpBCmQLFXV3536E3tDXn97YQ6ndJSBBdpQsvvFU4xKtzSAauTEpKwvz5872eLyYmBnPmzMGcOXO8nnfo0KEYOnSo1/MRQgiH4o8mh56SI0IF/V1yhBBCCCHhhgImQgghhBAPKGAKM5K4m844/ejnIhm7v23/8D0V/tN2kqg9p6ScN6mQRB2F+BFK++2ENPJ1/fBmNqFp2E7HMC5WG2rKsEMBUxhobttV0MsrwQOSJIKDMNHUqop3sA1hPpoqb7ctagPpCHW3LwqYCCFEqFDvsYnoqNM3EYoCJkIIIYQQDyhgIoQQQgjxgAKmMCOFvi28PIjX69tlGv4kEfraIqKRQmOGIA9MIJ6yaIJ8rZqg7FKdJUJtGXYoYAoHzWzDCnZMaHtAkkA8aiWVfISFplVZTas00kMDfYevUA9cTwETIYRIFR2sCZEMCpgIIUQoerKpyaGn5IhQFDARQgghhHhAARPxmu1dArHu77tbjF9p8Eb69j8vgRLofhJN4c6OL2Xwv17tn0bwd3n+8Tr55tQBx8eiBqKKGLv9TjNqBdFJaRWmgIkQQiRMSgeM5k7UtqB2DTsUMIWB5rddBbfE9jtBKXQtELsGpFCmQHF3EPO/f0oIak7MV/WE+rEiKfKySqXyuqTmynYVloV4T0YBEyGECEXxR5NDnb6JUBQwEUIIIYR4QAETIYQQQogHFDCFGUl0ABWzjwW7HLuC8f/0PQ3eKN4S7osg3ZxJhxReCxSaV6PYfJZCHUiUr9u3N1UqtP75r3ZinKdBTRl2KGAKC81rywr6MYH3CLA06loq+QgHTa2m7B9JJ+LyOrCiNpAOejUKIYSECeqo2+RQp28iFAVMhBBCCCEeUMBECCGEEOIBBUxhRgp9W3gdqcV6NYrDGyjESYM3b+irzjUJtKvUSaKGQvzeHEnUgUT5ugkF5NUoDn84SYQaM+xQwERIGKDuDoSQ5i7UA9dTwBQGmtvFh+A/JCf9M3ip5ksSmljlSHn4i6bA2/0ptQZhUcBECCFC0aW+JoeekiNCUcBECCGEEOIBBUxhRgqXh4Nyi5Bx+jEoywlJv94wX35Q+FAI/9dVh6cRQsvb9JvT/XwJdfq23+80p2YQm5TqjgImQgiRMAkdL5o9UQ/e1LBhhwImIj1B3pHYDz0gha4FYp9VSaFMgeKuk7T//VOCX3Oivhol1I8VNQFSusLRHNmuwrIQ78koYCKEEKEo/mhyqNM3EYoCJkIIIYQQDyhgIoQQQgjxgAKmcCOx++livarFfjn8Vwv4ngb/FSsSqzwbEs6aZEiiinj9i4KTI16fJmnUgiT5WjfeNKPwJ235r3ZyOh81ZdihgCkMNLftKtgHBV6fb4lUtkSyERak0mYkPHgf6NIKJhn0ahRCCCHO0BUlQqSDAiZCCBGKnmxqcugpOSIUBUyEEEIIIR5QwBRmpHCJnt8JVaRlBigNUQcBJCEliU77oVifaB0WxNe68arTt8CJ+ZMxzuejtgw7FDAREgbo6j0hpLkL9X6QAqZw0MzORIJ+Fi3Fx7bdDbNA+MKocoRcobCdhq4oiY/eX0x8RQETIYQIFepTXCI66vRNhKKAiRBCCCHEAwqYwowULg8zCG6vb3/K7MusoajiQHdolsBqExL+Vyvj9k/Jk8IOI0h8LWmgq4hhEH7rjYRIaRWmgIkQQnwQvIfkJHTEIOKhZg07FDCFgWa3XQW5wPbvfZJC1wKxq0AKZQoUdwGF//1TxK05IWfLorZ9yDroSJiXFSylKxzNke0qLAvx+kwBEyGEEEKIBxQwEUKIUHTBpsmhp+SIUBQwEUIIIYR4oAxl4rt378b69etx7NgxVFVVoWXLlkhKSsLgwYMxa9YsKJX87BmNRnz++ef48ccfkZ+fD5VKhV69euGBBx7AzTff7DatM2fOYMWKFTh48CBqamrQrl07DB8+HDNnzkSrVq0CWcwmh//aEnFu8Ds8JCfKUmH3tJ10OyNIN2fSIYnms80DwyAYlwn4r/eRQiVIVFBejSJ0Opv9DlxkjZoy7ITkCpPJZMKTTz6J6dOn47fffoNSqUSvXr0QFRWFU6dO4eOPP4Zer+fNo9fr8dBDD+Gtt95CdnY2OnfujLi4OBw4cACzZs3C22+/7TK9bdu2YcKECfj111/BMAx69uyJiooKfPnll7jttttw8eLFQBeZEL/Q1XtCCAmtkFxheumll/Djjz+iT58+WLBgAXr37s39ptPp8Ndff0GtVvPmeeutt3D48GF06tQJn3zyCbp16wYA+P333zF79mx88sknuOqqqzBixAjefCUlJXjqqadgNBoxc+ZMPP7441AqlaitrcWcOXPwxx9/YPbs2diwYUPIe+ATq2A/Ri3Fk3b7PEkwi5IhxfZzRVBW6YpSQHlfpdQGxCroV5gyMjKwfv16dOzYEatXr+YFSwAQFRWFkSNHQqVScd+VlZXh22+/BQAsXLiQC5YAYOTIkZg2bRoA4MMPP3RIb+XKldDpdBg4cCCeeOIJ7jZfbGws3nnnHcTGxuLUqVPYuXOn6GUlhDQxdE7V5NB5MhEq6AHTqlWrAABTpkxBTEyMoHl27NgBo9GI5ORkDBkyxOH3e+65BwBw+vRp5Ofn837bunUrAGDChAkO87Vs2RJjxowBAPz666/CC0EIIYSQZiWot+T0ej327t0LABg6dCiys7Oxdu1anD9/Hmq1GmlpabjrrrvQsWNH3nzHjh0DAAwYMMDpctu3b49OnTqhoKAAx44dQ+fOnQEAxcXFKCkpAQAMHDjQ6bxXX3011q9fj+PHj4tRxICT2iV6sbJjXy6HW1IM49MtU8bFZ8mRdOakQRLrfgDeCuRV8hKogmATuu37eivfq07fAtPgTcUwTtfdZtiUYS+oAdPZs2dhNBoBAIcPH8aCBQu4vwFg586dWLlyJV5//XX8+9//5r7Pzc0FAC4QcqZz584oKChATk6Ow3wqlQodOnRwOl9SUhIA4OLFizAajbxbgd5gGAZardaned3R6XS8vxsa9AFJxxtms5n7rNeLk5+GhgabZRp46wUAaLVanwImg97QmIauAVqt4ypv+4BBQ0MDLBYLL91gsC+vTqeFUmbi/vY3T4FaP4PJZDY5/d5gcL8O+lNuhrHw5jeaGvOg1WqhVLi/SG+yb1etFnq9dV2337ZZDfrGbcFisfDa3mw2e1WeUKzLYjDZ1bOQbd/+QSFX5bWtXzYtd3VjsGlDk9H9tFwaDbZtyDhta4PBEFZtEipmc+M6LNbxxpY3J+NBDZguX77MfWY7e7/wwgvo1asXiouL8d577+HXX3/FM888g27dunH9m6qrqwFYb6G5wv5WU1PDfVdVVcX95qpC4uLiAFh3LHV1dYiPj/epbEajEZmZmT7N64ntyUlubg5MdREBSUco2xU2Ly8PCn2J38u8VNkY2BQWFqKshn+gyczM9ClgKiyq5z5fuHABdRWOAXF+YePOLDcvD1qbnVug2tReeUUt7+9z57IQHang/q6rq/MrTyaTKWhlCRR2e7ZXUFiETJXz3ywWi1/l1hsMvPkrKyu5z2fPnoVC7n6dLK/g5+tMZiY3D3tCZy+vpPFgW6/VQm9s3AHU1tV6VZ6GEKzLYqisrOI+C93284v4gZCr8uZf5gdWVVVVbuumpKRx26yoqBBUjxfLGtPQG/Q4d+6ck+WWIDOTAiZP6usb9+F5eblgtOIf/+wfMnMlqAGTbcEjIyPxySefcIFOly5d8O677yI3NxeZmZn4+OOPsXjxYgCNZw7urv6wBeZfqRA+n+30vlCpVOjRo4fP87ui0+mQebFxA01O7oqeSa4Dx2DQ7K0DYA1wunTpgrRu/o9jpblUC6AUANCxYyKg1gJo3FGlpaX5FDCV6YsBWA9y3bp1Q1J7x35zDYrLAMoBWMujOfc32PKlpaV5naYv/r58HkA193dKSgpaRDeumzH7tQD0PuSpAACgVCqDVpZA2X3uNADHA0zHxESkpSXYfWstt1wu97Hc1vkj/ukqwMq4cBaAdT/Wq1cvj1eYDudlAWgMdtN69YLBoEdubi6Sk5MRFRXlMI85ogJAGQBAE6WBTG4CYD2BiI2J9ao8kbtruHml3f4FvL/i4+PA1rPQbV+vLANbb+x8zsg0VQAaT97j4+Pc1k1WWS7YbbNVq1ZIS0v1mBfFxcY0ItQRSE1NBVDEm6Z9+/ZIS+vicVnNXXRG476vS5dk9OoSJ+rys7OzBU8b1IApIqIxMrzjjjscrhjJ5XJMnjwZTz/9NP78809YLBbI5XJuPvvbFrYMBusBLjIy0iE9IfPZ589bMpkMGo3G5/mFioyMCEo67igUjVc+IiLEyU9kZGMbqdURUKn4babRaHwKmNQRjUFHZFSk07xGRDSuM5EREZDLGw+Cwapr+6A+KioKGk3j+ii3qXNf8hSs9TOQlArnuyu1h3XQn3LLZHLe/LaD6Wo0Go8Bk9KuXTUaDRT/zGNtY8e8RUY2nljK5XLe+qhQKLwqTyjWZTHY17OQbd9+/+2qvJGR/NtjSqXSbd2obdpQqXI/bWMajSfucrnMaWCsVqvDqk1CRWGzjQXi+OfNcSWoT8nZBkjdu3d3Og07ZEB9fT13Cb5FixYAGm/NOcP+xk5rm151dbXLDqNsGnK5XPBTe6EkhY6CDBP4nq/2nSt97ezK+NDrOySdecN8+VLlfyfpMB8Qqwn0Ehc+unZgl+/dQp1+JD6Q0ioc1IDJdvwkV7fJbM8S2A6LycnJAKz9ZVxhhxNgp7X9bDQaUVxc7HQ+dpTvTp06+dzhmxDS/ARrRy6JpwOJlYhNQc0afoIaMLVv354bMsDV60jY7yMiIrgO2f369QMAHDlyxOk8JSUlKCgo4E0LAImJiWjXrh0A4NChQ07nZb+3nY+EVrD3I/bvfWqK49g1xTJx3Bx5/B+UUNyaE7Jui3ogpVEZHXhbvxTXhJbtKiwL8Z4s6ANX/utf/wIA/PTTT7xHR1kbNmwAYB03ib2PzY78nZubi4yMDId52FHAe/fujS5d+J3oRo8eDQBYt26dw3zV1dXYsmULAHADWBJCCCGE2At6wDR16lTExsaioKAACxYs4J5MYxgGX3zxBXbu3AmZTIbp06dz87Rp0wYTJ04EADz//PO4cOEC99uOHTuwcuVKAMDjjz/uNL3IyEgcPHgQH3zwATeGUG1tLebNm4fa2lr07t3b4R10hBDigC7YNDl0EY4IFfSX77Zq1QqLFy/GjBkzsHbtWvzyyy9ITk7GpUuXcPnyZchkMjz55JMYPHgwb74nn3wSp0+fxtGjR/Hvf/8bPXv2hFar5fouTZkyBaNGjXJILyEhAW+88QbmzZuHpUuXYu3atejQoQNycnKg1WrRpk0bvP/+++Hz4l2JXR8O2ItyRetryzj55FXSQRHofioSW2184ksVNbVO316Xpwl0lAn4AxEBSID3XAzTJJohZKRUd0G/wgQA11xzDTZt2oTx48cjOjoaZ8+ehclkwogRI/DFF19g6tSpDvNERkbiiy++wLx589C9e3fk5uaisrISgwYNwuLFi/H000+7TG/MmDFYt24dd3suKysL8fHxmDRpEn788UeH23jEvUDvYAgJD8FZaWnTkA5x24JaNtwE/QoTKzk5Ga+//rpX86jVakyfPp13u06o9PR0biBMQtyR4rVGf/MkxTIFg9Q6fQdduFw5J8QFXqfvEK/OIbnCRLzT7M5Dgny5iXH5R+g4vHw4NNkIC+FUN4JWbd7tnHAqXdNEbUBYIbvCRAghYYcu2IQEwzAwGo28lwk7sBjRpkXjIc32NVm8ycwG3nRRasbltACgkpu56SOU7qd1lkZLjRx6fQMvTQBQysyCltXcxUTKuLqzmA0u60wul0OlUgW0PzIFTIQQQiSpRZQCRYWF0Om03BPOrijNZky+qQP3d05OjtPpGBN/uii13OW0ANApzshNHxXBuJ2WZTZZuHmUCjmKiy7y0gSAmCi9oGU1dzf01mBoinVAa5O2HDk5lS6nZV8d1K5dO8Ev1PUGBUxhRmpXh8XKj/1lb4fFMr4NKcnQ7Y0mI2BPZHqXicaPQRvp2zZ5CdRBkLSPU2Hc0NbQ6xsQHx+PqKgoKBQKl1cQtA1GlFY2vicuOaGF0+kaDCaoyxtf4hwTpUKbOMd3vbGq6vSoqrUOf9NCo0Krlq6ndZaGWilH+9YaKErqeNPEt4hAy2jf31/aXJSU10NnsAbLHVprEKl2DFsYhoHZbIZOp0N1dTVyc3PRqVMn0d87RwETIWGA7gSR5mZQSgw6tmuB5ORk3st4XTEzCiiUjVehbF/EbouRmaBQNg6arFKrXU4LAGoDoFAygqZ1loZSJUdkZCQUSgNvGrU6QtCymjulygSFxVqXERGRiIxwvS7ExMSgVatWyMvLQ1lZGTp37ixqXqjTdzhoZldGgl5aCZ7BO7x8OET5CAfhtXl4zqxU1sFQUsoZdE+IQqSmBRQKhbgLD68VhnhJoVCgVatWqK+vd/o2EX9QwEQIIRLVXA/tSoW1749cQS9EJ96LiPinzxMFTIQQEiJ0bzR4glTX1KRNT6CelKOAKcxI4XK9bedpsXJjvxyPncCFLteX12n4mJY/An2XIPRrjf/o1SjN89UogRaUGqJmaBIoYCKEEB8E7xhIR9smiZo17FDARIgdKV6ip1ej+IZejRLm+SfERqjXZgqYwkBzOxEJ9l0E3i1GqVS2tO4ESVz41I6Q9Ys3TfgULWxQlRJfUcBECCFChfoUl4iOmpQIRQNXhhkpXAEJyMtqPSzH93J7f/UoJJ2+A5yqBFabkGhqnb69JoUdhp98G+Pfu+UHUrBa4IEHHsCBAwfwn//8B7NmzXI53f79+/Hggw8CAM6dO8d9v2TJEnz44Ye8aWUyGaKjo9G1a1cMHz4cDz74IGJjY10uOysrCxs2bMD+/ftx6dIl1NfXIzo6Gt26dcOQIUNwxx13OAwmOWLECBQWFvK+U6lUiIuLQ3p6OobfdCsGDB4muB4CiQImQgjxQbBetdMEYp4mozk0RUxMDFJSUgAAFosFhYWFOHnyJE6ePInvv/8eX331FRISEnjzGAwGvPrqq1i3bh0YhoFcLkfnzp3RuXNnVFVV4dixYzhy5AhWrFiB2bNn45FHHnFINzk5Ga1atQIAaLVa5OXlYdeuXdi1axdu/tc4/Hfec4EvvAcUMBFiR4qX6KnTt2+o03eY558EXe/evfHll1/yvtu5cyfmzZuHgoICvPTSS1i+fDn3m8ViwYwZM/Dnn38iKioKjz/+OO6++27ExcVx01RUVGDz5s1YsWIFjh496jTdRx99FOPHj+f+1ul0ePvtt/HVV19h26+bcNXAwbj3rnHiFtZL1IeJSFBwz+MCcovRTw7jUoUkF+EhnK7ACMkqv893GBWONFnDhw/H448/DgDYs2cPqqqquN+WL1+OP//8E2q1GqtXr8YjjzzCC5YAoFWrVnjwwQfx888/4/rrrxeUZlRUFJ5//nl0694TALBr+xZRyuIPCpgIIYQQ4tbgwYMBWK8o5efnAwDq6urw2WefAQAee+wx9OvXz+0yWrZsiXvvvVdwmnK5HH2vvAoAUFiQ70OuxUUBEyGECEV3uJocalJhLBaLw3d79uxBTU0NFAqFV4GQNxjGMd1QoT5MxHuM7UdxbhnYL8fxNotv6dguJ1iddH0i4axJhSTaLwRjJIXNOhwCDMNAbzADAHQGI/RGM/ebrsHo9J1iDQYTb7oGgxkNetcvadXbTK/3MC23TH1jXhiGcUhTrQy/axUHDhwAAK5DNwAcPnwYANCjRw+uw7aYLBYLTh639nnq2Kmzh6kDjwImQsIAnQUTwscwDJ7+8E9k5laEOite65rYAi9NGxKQZX/44YcOwwP4a9euXVi6dCkA4LrrruP6KJWUlAAAkpKSRE0PaOz0feFCNgDghhGjRU/DWxQwhYHmdlIZ/JG+bT5L5FKPWC8fbg7CafsQdIUojMpDpCchIcHhsX9bdXV1yMrKcvn7mTNnuNtr7LACly9fBgAkJibipZde4qatr68HAGg0Gr/zvXz5cqxfvx6ANVjKzc2FTqcDAIwafSuuu2Gk32n4iwImQgiRqHAKBoNNJpPhjf9cx92Sq2sw4FK5jvu9e2ILp7fktHojisq03N8tNGq0i49ymU5lbQPKa/QAgJbRarSNcz0tS6c3ovCfNNRKOTq2jUZOcS33u1opd5o3Mdx5552CB650pq6uDkeOHAHQOHBleno6RowYgQcffBAtWrTgpo2OjgZgHTfJX7m5ucjNzQUAKJVKxMXFYdCgQRg+6lZcPfQGv5cvBgqYCCFEKLo3KikymQyREdbDmMliQYRKwf0WGaF0GpRYGIY/nVrBLcOZCL0SESprv6UID9OyGJs01Co5ItVKXppSNmjQIIdxmFxp3749AKCgoMDvdF9//XXeOEyswtI6aAX0GwuG8Ot51sxJodOn7W0rsbJjvxyHW1I+puNLH93QvBolvJcfDL6Uoam9GsXr5CWwv/BbgIsQiMXbj+3WBFrBqQEDBgAA/v77b1RUhF9fMm9RwEQIIT4I3kGwqR5um7em0KrDhg1DbGwszGYzvvnmm1BnJ+AoYCLEjhTvutCrUXxDr0YJ8/wTSYuJicHDDz8MAPj4449x7Ngxt9NXV1f7F1iFeHWmgIlITvDvIoh/i1FsEs2WRIRR7Qh6SM5mfQxgVggRw2OPPYZrrrkGBoMBDz/8MFauXInq6mreNFVVVfj6669x66234o8//ghRTv1Hnb4JIUQoumDT5FCT+kehUGD58uVYsGABNmzYgLfeegvvvvsuOnfujNjYWFRXV6OgoABmsxkqlQpXX311qLPsMwqYwowUzjhDcRXG1yQZH3p9h6TTdxh2bA06HwrR1Dp9e52+VC+ZBoIID4YQ36jVarz66quYNGkSNmzYgAMHDqC4uBgXL15ETEwMrrzySlxzzTUYP348OnbsGOrs+owCJkII8UGwnlhtTjGP5Em4LYQOBTB48GCcO3fO4ftZs2a5Hb9JiF69euGFF17wap4dO3b4lWYwUR8mQuxI8RI9dfr2DXX6DvP8EyIhFDCFgeZ3hhncAvNfjSINDuNShSYbYSGctg8hWQ2n8jQH1B6ERQETIYRIFR2tCZEMCpgIIUQousPV5FCTEqEoYAo3Ejjh5D14JtIZsKdXofieju2YNhKoPFfoSoJHkqiiED8hKoVXIzU1VKNEKAqYCAkDdBZMCCGhRQETIYQQQogHFDARyQn2XQf7N4tLAT0lJ1w41Y23t9TCqWxNF7UCsaKAiRBChKJ7o8ETpDiFmrTpCVRfPwqYwowkOi4zth2pRVqkh799fu0B4/yzN3kJhkCnKYG1xm++rPtN7dUoXicfpp3ETWbAZLbAYjYGPK3wrCHijl6vBwAoleK+zIQCJkII8UHQYpFmeEQ3WWQ4X6xDg7YGZrM51NkhYcRsNqOiogLR0dGiB0z0LjlC7EjxEj29GsU39GqU8M3/gaw6JLSOQGxUDuLi4hAVFQWFQgGZizLp9QaYTQbu74aGBqfT6vVG3nRGA4OGBtfXDgwGPTe9p2mdpWFi5GhoaOClaV2uDA0NHhfV7JmMephN1qBZ39AAWBzDFoZhYDabodPpUF1dDYvFgoSEBNHzQgFTGGhuJ5hBLy/v1SjSqG37fEgjV9IUpnedXKKxlqxKqoz4dtdlvPZIAiorK1FWVuZ2er3BjOp6Pfe3RRflNGAyGM2oqmucrlqtRG2l2uVy63VG1DdYbw3WqJWocTOtszQUcjkaaiNQVq3jTVNXpUJlpMrjspq7qlo9DP8ETIa6SKiUrgNWhUIBjUaDdu3aQa323E7eooCJEEIkqrnHTjU6M9p3SESEWgGj0QiLxeJy2qPnSrH6t5Pc30v+NxxKhePB9UxOOVb/doz7+9q+iZj0r64ul7v5zwv4ee9F67RXJmLSGNfTss7mVmD1b0cBAO3iozD3/gF4e8OfvGluu74b/nWN52U1d+9/ewTn8ioBAM8+NBCdO7RwOp1cLodKpXJ5BVIMFDCFGSnsQAPyGL6Hkb39H+c7sPP4K9DtKoHVxn8+FEL0Tt+h5m12pLDD8BMDQCaTebxiYJEpUVZj4v6OjIx0GjDJ5CredDqjDJGRkS6XazDLuekbPEzrLA212gK1KoKXJgAYLQpBy2ruanUMV3cKpTqkdUadvgkhxAfB6/Md/kFPKEg9VqR2DT8UMBFiR4rdZKnTt2+o03eY5580e7arcCBvtwlBAVM4aGYnIsEf6dtmXCmp1LW0hv+RuPCpHSHrV/iUJjw1w7uaRCSS6MO0e/duTJ8+HQDQsWNH7Nixw+l09fX1WLFiBbZu3YqioiJoNBpceeWVmDJlCgYPHuw2jYyMDKxatQrHjx+HVqtFYmIixowZg+nTp0Oj0YheJkII8RsdrAmRjJBfYaqvr8dLL73kcbqKigrceeed+Pjjj1FYWIju3bsjIiICu3btwkMPPYSvv/7a5bxffvklJk+ejF27diEiIgLdu3dHYWEhli1bhrvuugtVVVXiFYgQ0nTRHa4mh+5aEqFCHjC99957KCoqwsiRI91O9/zzzyMnJwfp6enYvn07Nm7ciF27dmHBggVgGAYLFy5EZmamw3ynTp3Ca6+9BgBYsGABdu3ahY0bN2L79u1IT0/H+fPnMX/+/ICULSAkcMbJf92IOBnytBif07F9jQtdWw9rkmg920wEaX3ibW/SqIWgErzd+vg+JW+aUXhWGJd/2XxNwkxIA6Zjx47h66+/xsiRIzFq1CiX0505cwY7duyAXC7He++9h/bt2wOwdgCbOHEixo0bB7PZjKVLlzrMu3TpUlgsFowbNw4TJ07kOo21b98e7777LuRyObZt24azZ88GppCEiIBOggkhzV2orwaGLGAyGo2YP38+IiMj8eKLL7qdduvWrQCAIUOGoEuXLg6/T5w4EYC1L5RWq+W+r6+vxx9//AEAmDBhgsN8ycnJGDJkCABgy5YtvhWEEEIIIU1eyAKm5cuXIysrC0888QQ6dOjgdtpjx44BAK6++mqnv/ft2xdqtRp6vZ53Wy4zMxMGgwFqtRp9+/Z1Ou+AAQMAAMePH/ehFMHR/C7DB7e8UrxTZ58lCWZRMqTYfq4IymozvwUXcF5WKbUBYYXkKbnz589j+fLlSE9PxwMPPOBx+tzcXABA586dnf6uUqmQkJCAvLw85OTkcEFQTk4OACAxMREqlfN39rDLZKf1FcMwvKtbYtHp+O8fatA3BCQdb9i+nkCv14uSnwZ941soDQYDjEYj73edTgdYjPazeWQw2L6M03leDfrG90rp9Q288gWrrp2V1zZpi80b233JU6DWz2AymUxOvzcYDG7L5k+5GcbCm99kbsyDVqeDHM7zxE1v365aLTeP/bbN0tusjxaLhdeHx2y2eFWeUKzLYrBta6Hbvm29AYBWq3P63jHbfQ0AmM0mt3Vj24Ymk/tpG9OwbUPGaVsbjMawapNQMZsb12GdrgFarbjv32MYRvD4TkEPmBiGwQsvvACTyYSXX34ZCoXC4zzV1dUAgJYtW7qchv2tpqbGp/nYaX1lNBqddjoXW15+PpSG0oCn406DzSu2LxYUIBrlfi8z/3LjDqa4uBjl1fwD0dlz5xCp8v6CaPGlOu5zTm4OTHURDtMUFDbutPLzL0Krbdy5BaNNAaCysob3d3Z2NspjGjfPuvp6v/JkNJmCVpZAsd22bRVfKkZmZq3T3ywWi1/l1usNvPkrKiq5z1lZWYhSu18nyyuqeH+fs5mHPRG0d7Ggcf1raNDDYGw8YNTX13tVHtsDdTi1f2VlFfdZ6LZvux0DwNmzZ6FUOB4ILxbxA6bKyiq3dXO5rPHYUFXlflpWfnFjGkajEVlZfzsu9/JlZGbqHb4nfPU2+76cnAvQVYn/Ul2hL+oNesC0Zs0aHDlyBA888AD69OkjaB72zMHVVSKgscC2B3Nv5rM/O/GWSqVCjx49/FqGMzqdDsdzTnN/d+7cGWndW4uejjcid1QDsJ51derUCWlp7fxepkxTBeAyACAhIQEWZT2AxmAnNSUVmkjvV9eLtRcBVAGw9llLSYpzmKbKdAlABQCgc+ckRF3IAcqtV6bS0tK8TtMXJwqzADQGBD169EC7+Cju75j9WgB6H/JUAABQKpVBK0ugtDhxAoDjmXpChwSkpXWy+9ZabplM5mO5rfOr1Wre/PvOnwVg3YGn9ExBjMb92e7hvCzw1+MUyGFCbm4ukpOTERUV5TCPVnYZ+OckJDIyAhaYAFivMEZHR3tVnqhdjduqtNu/gPdXfHwc2HoWuu1XmUvAbscAkJqaCrXK8YRcrywDUMb9HRcX57ZuThZlA7AG5C3jWgqqR6OqnEtDpVKhZ88eAIp507Rt0xZpad08Lqu5i85o3Pd17doNyQmxoi4/Oztb8LRBDZhKSkrw7rvvon379pg9e7bg+SIiIqDT6RxuW9hib73YvpgvIsJ6NUHIfOy0vpLJZEEZADMyIiLkA23K5Y1nexEi5ScysvFAqFaroVQaeL9rNFHQRHp/Kdb2zCEyMtJpXtU2bR8REQmFTfmCVdcqJX9TjIqK4qVteyXWlzzJg7R+BpKrq9Fqtdpl2fzdLuVyOW9+23aK0kRBo3F/Zqq0O1mL0mggZ6z7I/s2Ztnui+RyOWQ266N9fjzm38/1JlSUNvUsdNuPiOC3hUajcRowRUbwX96qVCrd1o1tGyoV7qdtzEtjkCyTyZwGxiq1KqzaJFQUNi9Qjopyvg/3hzevWwlqp+9XXnkFdXV1eOGFFxATEyN4vhYtWgBwf9uM/Y2dFhB2u03IbTsSXEHvxBuAcaX8ZZ8PaeRKmsKqbgSsX1JZB5sqrztxU3OQfwT1CtOZM2cAAC+//DJefvll3m/srbTi4mJce+21AIAlS5bgqquuQnJyMkpKSpCXl+d0uUajEUVFRQCst11Y7OeioiIYjUant+by8/Md5iOEECmg2IkQ6QjJU3JlZWUuf7NYLNzv7K20fv36Yf/+/Th8+LDTeU6cOAGj0YiIiAje/eW0tDSoVCoYDAacOHGCe3rOFrvMfv36+VqcoJLCDjQQL6u1X479WaAIA30LPlMMRRUHOk0JrDZ+86UM/q+fYV5zUthh+Enw6NoSKqqEshL2pNSuQb0lt2PHDpw7d87pv9dffx2A9eW77HfsC3VHjx4NANi/f7/Tq0xr164FAAwbNgzR0dHc9zExMbjuuusAAOvWrXOYLzc3FxkZGQCAMWPGiFhSQkhTJ2RHLsrtNSkdMcKI1GuNmjX8hPxdckKkp6dj+PDhMJvNmDNnDkpLrY/VMwyDtWvXYtOmTZDL5ZgxY4bDvDNnzoRMJsOmTZuwdu1abgdWWlqKuXPnwmKxYNSoUejVq1dQy0SkS4qvIfE3T1IsUzD4/yqFMK+5UL9LghA/2a7C3nTQDoSQ3JLzxWuvvYZ7770Xp0+fxsiRI9GjRw9UVlaiuLgYMpkMzz33HNLT0x3m69u3L5555hksWrQIL774IpYtW4b4+HhkZ2fDYDCga9eueOWVV0JQIkIIIYSEi7AJmFq1aoXvvvsOn3zyCbZs2YLs7GxoNBoMGzYMU6dO5d4J58zkyZORmpqKzz77DCdOnEB5eTkSExMxZswYTJ8+nXcbT4qa26Xb4D8kZ9MnK8hpCyXVfElCGG0gQnIaPqUJT96uLtQehCWZgGn8+PEYP36822liYmIwZ84czJkzx+vlDx06FEOHDvU1e4QQEvZ36IgjumtJhAqLPkykkRTOdvhnaAHKkcNTc74uR/wn+gJCynmTCinUUQjG7OI96CmFOggywUW232cIrCxv6lTwtHbridP5mmNjhjkKmAgJA3QSTAhp7kK9H6SAiRBCCCHEAwqYiPQE+VK1L4NbBprjQJ7ElXCqG2FjNwU+H0Q4elUNYVHARAghEkUHa0KkgwKmcCOx/ad4r0bx8LJZHxPiXzwS2AnUp5T8480LQX05iEpstfGJ1y9NRdN7NYr3L46VVv59IrTztoTayn6okqbQDKEipbqjgIkQQgKE3owSQgGoNzGDMmrW8EMBEyF2Qv0khjP0ahTf0KtRwjz/pNnjrcIhXp0pYAoDze1MJPgjfdt8lsjpvP2ZrDRyJU0SaTJBhK1fYVSgMCSVbZyEHwqYCCFEoujQToh0UMAUZqTQsdH2DE2s3Ngvx2MncKHLZZx/9iYvwRCQ0YZt5/F+FsnxqdxNrdO3t9lpAldTBA+uLaGi2g9VIoX9driSUrtSwEQIIT4QNKaSGAdKKR0xwkggak3MpqBmDT8UMBFiR4rdZKnTt2+o03eY5580e1JahSlgIoQQQgjxgAKmcNDMLt0G/VJ1APpk+Y1ejSJcE7u30cSK41Gwn1prht3AiEgoYCKEEImigzUh0kEBU5iRwg6UcfmHWAt1/NvXcjfFp1OaXonCh9ev2hGjz7f/iwhrvj7dKpXxlhjuP/bfSyN/RDgKmAgJAxLq90gIIc0SBUyEEEIIIR5QwBQGmt2F2xB2ApXIVXxPdyiJjXCqG2FjNzUvwX/Iw8vJpbJTICFHARMhhEgWHawJkQoKmIjXeK8bEWmH7ullsz6f5fFejSJsGVJ/NYovp+RN4bDryzog9qtRQn21wev0m8DVEcFlllBZea+PYqh7tz8k1KwUMBFCiE+CdHtNSgeM5k7UgJnaNexQwESIHSk+kUavRvENvRolzPNPmj0prcIUMBFCCCGEeEABE5GcEL4ZJQSpO2d/6V8auZKmcLpl5e1gl+FUNl8Ff3tvBpVKAoICpjAjjY3dtkOjSEt0GNlbrM7kzj8LnSd4hKfqS/6ksNb4y5dVQvRO3/4uzk9el0cS+4vgkFJJpTrqeDiSUtVRwEQIIT4Qsh8X50ApoSNGGAnEgVbMRVKrhh8KmAixI6E+hhzq9O0b6vQd5vknzZ6UVmEKmAghhBBCPKCAKQxI6R5uMAS/vOL3yfKXQ5+u0GQjLITVsICC+nyHUXnEEMJXIQmavpk1B3GNAqYwI7ltN0gZ8nmgb196fYeAN+XzqfOz97M0CWJ3+g52Rfqd/yZwtA/Dgb6b7wYXAFJqVwqYiNcCsgLTFRUSZgR16BZhRZbSASOcBKTaRHxSk56cCz8UMBESBqjTt2+o03eY5580e1JahSlgIoQQQgjxgAKmsNC8Lt0Gu9Or7ZVxqda0VPMlCWFUOcLGbgp4NiTFWXEDWQfeLruZNQdxgwImQgiRLDpcEyIVFDCFGwnsP/lXZMR6hYn7Xt++p2MzZIDAZfAerAvS6b53qXifJwmsNn6jV6P4sD42gctVwrd938oaiG3cdpkMmkQzhIyU6o4CJkIICRB6MUoIBSQQEn2RJIxQwESIHQk9lMGhp+R8Q0/JhXn+SbMnpVWYAiZCCCGEEA8oYAoDze4qcChHU5ZIZdv3q5BItiQpnOomWINdhhNnVRLYvoNe9hik+3DkHxQwhRlpbLziv3vN47vTRHg1iuBXLLiYP6Do1Sge+dLxX/RO38F+75m/I+BLYn/hpzB8NQovKwzT/N4PKCIptSsFTIQQ4gNBF4vE2NtL6IARTgLzBifxliqlQIAIQwETIXYk1MeQQ52+fUOdvsM8/6TZk9IqTAETIYQQQogHFDCFg2Z26Tb4xfV+cMtAs8+FNHIlTU3t1oZU1sHgCW2/MEKEooApzEhhWw/Eu9ccAgSRnhLz5Qm4UDw0581B0pc8SWG98ZcURvoONr8ffmgC0YHgcb5FeDBELA77yPBvhpCR0ipMARMhhPggWCMENL8rTuIIyIFWxGVK44ln4g1lMBNjGAZHjx7Fjh07cPjwYVy4cAF1dXWIjY1F7969cfvtt2Ps2LGQuejlVV9fjxUrVmDr1q0oKiqCRqPBlVdeiSlTpmDw4MFu087IyMCqVatw/PhxaLVaJCYmYsyYMZg+fTo0Gk0gikvClIT6GHKo07dvqNN3mOefNHtSWoWDGjBlZGRg8uTJ3N9JSUno2LEjCgsLsXfvXuzduxc///wzlixZArVazZu3oqIC9913H3JycqBWq9GjRw9UVFRg165d2L17N+bPn4/777/fabpffvklFi5cCIZh0KFDByQkJCA7OxvLli3Dtm3bsGbNGsTFxQWw5IQQQggJZ0G9JccwDDp16oTnn38ef/31F7Zv347vv/8e+/fvxxtvvAG1Wo1du3bhgw8+cJj3+eefR05ODtLT07F9+3Zs3LgRu3btwoIFC8AwDBYuXIjMzEyH+U6dOoXXXnsNALBgwQLs2rULGzduxPbt25Geno7z589j/vz5AS87IYQQQsJXUAOmvn37YsuWLXjwwQfRunVr3m+33347Hn/8cQDAhg0bYLFYuN/OnDmDHTt2QC6X47333kP79u0BADKZDBMnTsS4ceNgNpuxdOlShzSXLl0Ki8WCcePGYeLEidztvvbt2+Pdd9+FXC7Htm3bcPbs2UAV22/N7U53sG/t+zIaeMD5O8JzsxI+tSNssEvbP8RMW5r15PzVKAFMz9vppVltJASCGjDFxMRApVK5/H3YsGEAgKqqKlRUVHDfb926FQAwZMgQdOnSxWG+iRMnAgB2794NrVbLfV9fX48//vgDADBhwgSH+ZKTkzFkyBAAwJYtW7wtTkhIYeO1H/Zf/IU6+dnndLwfMiAg5fOUplevRvHhFSFez9E0iP9qFH+X5x+vO4CHOsMiEL6++1bWwHSq578+KvxbIXSktApL6im5hoYG7nNkZCT3+dixYwCAq6++2ul8ffv2hVqthl6v592Wy8zMhMFggFqtRt++fZ3OO2DAAADA8ePH/c0+IaQZEXSgFePNKBI6YISTQFxRo6Zo3oLa6duTn3/+GQDQq1cvxMTEcN/n5uYCADp37ux0PpVKhYSEBOTl5SEnJ4cLgnJycgAAiYmJLq9ssctkp/UVwzC8q1ti0el0vL8Nen1A0vEGY3O7VG8wiJKfBn1jsGw0GmAymXi/63Q6aFXe764MRqNNGs7rzmAwNH7W63nl02q1UCgCf17hUN6GBmi1jZun7S1qnVYHi0nhXQIBWj+DyWw2O/3eYDC6LJtMBr/Kbb9d27ZTg04HbYT7+Z2txzIzw312xmDQc58tjIV34LdYLF6Vx2Izr1ardfkEcigZjI7tajI31pvQbV9vsx2z86nkjss26PW8v00ms9s6NZmMNp9Ngupfr2/MC8MwTtvaaBS2rObOdt/XoOPvF8XAMIzg7UIyAdOpU6fw7bffAgCmT5/O+626uhoA0LJlS5fzs7/V1NT4NB87ra+MRqPTTudiu1hQgBhZecDTccdgaNyBFBUWIVNd5fcyLxY1BkwlJSWorOYfaP7+OxstNF4GCQBKS2q5z/n5+VAZSh2mKSqu5z4XFBZCa7Nzyzx7Fgp54A8y9uvfhQsXUF/RGOTX19Vxn8+eOwu10rsgzmQyBWX9DKQ6mzqwVVJSgsxM5wcei8XiV7n1BgNv/qqqKu7z39nZuBztfhdaWVnJ+/vv7L/RUmOdhz0RtFdY2FgWg8EAs7kxWNDpdF6Vp8F2Xc7MlGTAZDQ7BkO8eha47RcX1fP+zsrKQnSk43wFBfx1pbq6ym2dlpc35qWmpkZQ/RcUNNa72WzC+fPnnSy3DJmZJofvCV99fWO7XrhwAXUVrrv1+Mr+qXxXJBEwlZWVYdasWTCZTLjppptw66238n7X/3NG4K7/E1tg29t63syntzvr8JZKpUKPHj38WoYzOp0OB/8+xf2d1KkT0tLaiZ6ON9RbygFYN/TEjolIS0vwe5l6ZRmAMgBAu3btYZLXA2jcUHr27IFWLSKdz+zG3+W5AKzBSOfOnZHWvbXDNMXaQgDWA1vHjh2hyc8HYD1DTOvVKyhXmA7mZAJoDO66de2Kzh1iub+j92sBWNfRXqm9EKEWGjwWAACUSiXS0tJEym1oxBw6CqDB4ft27dshLS3Z7ltrueVyuY/lts4foVbz5t9z7gzY9bJnjx5oExfldil7/86E7Xrco0dPRKsZ5ObmIjk5GVFRjvOXG4oBWPtwqtVqmCwmANaz7KioKK/KE7m7BoD1BCctLU2SAZP1ClMh77uWLeMAWAMbodv+JV3jdgwAKSkpaBHteCCsNpeArV82LXd1eiDnHABrsN6iRQtB9V/LlAKwntgqFEp0794dQAlvmtat2yAtrafHZTV30RmN+75u3bohqX2M+xm8lJ2dLXjakAdMtbW1eOSRR1BUVIT09HQsWrTIYZqIiAjodDoYbW6v2GNvq9j2fYqIsF4vFzIfO62vZDJZUAbAVEeoQz7QpkzWGECo1eLkx7b+1WoVFAr+qhkVFQWNxv3ByRmVsjFYjlBHOM2r7dlFhDoCMnlj+aI0GiiDEDAplfzyRkZF8fIqVzQGSFFRUYiM8HLTDdL6GUhyufN2UKtcr4MMA7/Kbb9dK2zawb6NnLFv16jIKERFWK+oRLmYX22zLchkMl6QI5PJvSqP3GbeqCgN5EG4WuothZNbckolf30Xsu2rVfzgyDqf437dfl+vUCjc1qlS1diGSqVSUP3b7lNkMv5xiaVSCVtWc2e73UdGRYpeZ96cRIS003d9fT2mTZuGM2fOoGfPnvj00095fZdYLVq0AOD+thn7GzstIOx2m5DbdoQQ4iBYr0ahnsaSIWZHcmrX8BOygEmn0+HRRx/FsWPHkJycjFWrViE+Pt7ptMnJyQCAvLw8p78bjUYUFRXxprX9XFRU5PIqU35+vsN8pHmT3jk4vRrFV/RqlDDPP2n2pLQKhyRg0uv1mDFjBg4ePIiOHTti9erVaNu2rcvp+/XrBwA4fPiw099PnDgBo9GIiIgI3v3ltLQ0qFQqGAwGnDhxwum87DLZNAghhBBC7AU9YDIajZg1axb27duH9u3b4/PPP0dCgvtOw6NHjwYA7N+/3+lVprVr1wKwDnwZHR3NfR8TE4PrrrsOALBu3TqH+XJzc5GRkQEAGDNmjG8FCoLmduk2pCN9S2SkFftL/9LIlTSF0/YhbOymMCqQCJzd5grsSN/eDv4ZmHyQ8BPUgMlsNmPevHnYvXs32rZti88//xxJSUke50tPT8fw4cNhNpsxZ84clJZaHw1nGAZr167Fpk2bIJfLMWPGDId5Z86cCZlMhk2bNmHt2rXcxllaWoq5c+fCYrFg1KhR6NWrl7iFDRAp7EsZu1FsRVmm/atA7AMGH9Px5S0TvHmCVN9ejfTty/J9mEdqfCq3yCN9B5vDduHHAsJ1HRDahlIqn/0+RAr77XAlpboL6lNyv/76K/eaE7Vajeeee87ltPPnz0fv3r25v1977TXce++9OH36NEaOHIkePXqgsrISxcXFkMlkeO6555Cenu6wnL59++KZZ57BokWL8OKLL2LZsmWIj49HdnY2DAYDunbtildeeUX8whJCmjQh+3FxOglL6IgRRgJxoBVzkdSq4SeoAZPtiMqFhYUoLCx0OW1tbS3v71atWuG7777DJ598gi1btiA7OxsajQbDhg3D1KlTuXfCOTN58mSkpqbis88+w4kTJ1BeXo7ExESMGTMG06dP593GI0RCfQw51OnbN9TpO8zzT5o9Ka3CQQ2Yxo8fj/Hjx/s8f0xMDObMmYM5c+Z4Pe/QoUMxdOhQn9MmhBBCSPMlqZfvEkIIIYRIEQVMRHKC38lP/E7sYpNotiQijGonSINdhpUQPhUraPrAZIOEIQqYwowUNl7+Dic4OfL5KTn+mAHC5nHzV6B486izLx2JpbDe+M2HQoj9lFzQh7xw+MLboz3j/HMYEfyUnBj7CLF4v9shLkhptaWAiRBCfBCQA63TdIKSTJMTkDHVRFxksNYfIh4KmAixI6GHMjj0lJxv6Cm5MM8/afaktApTwEQIIYQQ4gEFTGGg+V25DW6BfejmFHB+j/DcjITT9iFssMuAZ0NSnBU3oLervO4G1swahLhEAVO4kcC2G4hXh3h6FYoY/RGELiMUr0YJdLtKYLXxm0+d3cXu9B3iR7qa5atRfHqpkTfLFx9j1+ubgi7fSanqKGAihJAAEWNnL6HjRXgJSJ9vao3mjAImQuxIqI8hhzp9+4Y6fYd5/kmzJ6VVmAImQgghhBAPKGAihBBCCPGAAqYwI4l76LYdScXq9G23eIdy+jyKr/PPLhfN8FMOVZ9vh07wvN/8X344clUGd2WT2kjfwjr/up7G6/Sl1GPWBWd14svLBASPCO7tvsXPEeYZaey1w5aUVmEKmAghxAdCduT+Ph3FCE1IyLIkdOAJhoA8/SbqSN/iLYsEBwVMhNiRUB9DDnX69g11+g7z/JNmT0qrMAVMhBBCCCEeUMBECCGEEOIBBUxhoLl1GQx6aSU4GrJDJ/CQ5CI8NLVRlJtYcXwTyDejePtqlMBkg4QhCpjCjBR2pn6+rEHIQkV7l5ovwSb/ibTgVLg36fiSIwmsNv7z82kl4fOE4jlJ5xzy79dTcuG5FgT2xSgB2qfaP50bnlUvCVI45rEoYCKEEB8E68qvhI4XYUXqVx6b252DpoACJkLsSOihDA49JecbekouzPNPmj0prcIUMBFCCCGEeEABUzhoZldug30p3d9RtANCpD5cRFqEDXYZ+HxIibPyBrIOvO8G1swahLhEAVO4kcC2K+R1I94v0/2CfN5p8fIqbBm+vJYhqHyoCykWw1s+deD3u6O4/atRglyTDq/I8fYRL/FfYxRsguvcxwIGoi+R/TLDtOolQUrrLQVMhBDiiyBdLZLSAaO5EzVgpnYNOxQwEWJHQn0MOdTp2zfU6TvM80+aPSmtwhQwEUIIIYR4QAETIYQQQogHFDCFAf5YvRK48R2AV4k4DGgsUl8BIeMc23fyDcXYyI4jm7vuNNpcR/p2tUq4W1V86vQt+C9fli1kCa6n8bo8YdABylkO/R2h3/2E3j2C6tP2ZvdgDD1p5zspVR0FTIQQ4gNhoY9/e3tGcEpCl9V8BOJAS32+mzcKmAixI6E+hhzq9O0b6vQd5vknzZ6UVmEKmAghhBBCPKCAiRBCCCHEAwqYiOQEvZOfBEdD9rJfarMmiQchhKJXozhy+m6UACYX8BlIU0UBU5iRws40EO9e8xQg+PxmFB8eL+OXLzgV7k0q/j/51Xz41HzuVprQvhnFrwWE65NaAX4zSkCaVHJPNocxKa22FDARQogPhAQg9GqU0JF6vYVrANucUcBEiB0JPZTBoafkfENPyYV5/kmzJ6VVmAImQgghhBAPKGAihBBCCPGAAqYwwBtmP3TZ4Ni/SkSkpdp8Ypz0tfW/17erPgP2ndhDUsce+jP42xFdCuuNv1wWO5ivRvGzIv17MYoPbR8G/WScvxrF9rO4vb4dHjDxYtsTzG6nHQbNIFlSqjsKmAghxAfB2ZGL+IyVhA48wRCIp9NEbfNm1h5NAQVMhNiRUB9DDnX69g11+g7z/JNmT0qrMAVMhBBCCCEeUMBECCGEEOIBBUzhRhI94MR/lYj94MoOyxVhFF+XfYbtOrEHYiRzTxxHNnfX9dj/5YcjV31S3HeS9iEdNw81+Nsvxt/BLr1/rYf0W97pm1F8eNJFeEm927b8fciC8XEZxEpKVUcBEyGEBIi/B0pGxCesJHTcCY4AFFjMjuTNrj2aAAqYCLEjoT6GHOr07Rvq9B3m+SfNnpRWYQqYCCGEEEI8oICJEEIIIcQDZagzEEwZGRlYtWoVjh8/Dq1Wi8TERIwZMwbTp0+HRqMJdfZcam73ugNVXld9QZgAdGL3l0h93psFqbSZEMLyGkYFEoGzfl6BrIFwWl+ItDSbK0xffvklJk+ejF27diEiIgLdu3dHYWEhli1bhrvuugtVVVWhzqIgUtjWA/GqFscAQZynxPx9NUaw6tubfPr/uo/w5P8Tb4LncvHZ8c9Ac3x60tsFhH/LCy2Bz0UNROdwhv85/FshdKS0CjeLgOnUqVN47bXXAAALFizArl27sHHjRmzfvh3p6ek4f/485s+fH+JcEkLCiaAhAsRJSIylNLtH2wNRWjGrsLm1R1PQLAKmpUuXwmKxYNy4cZg4cSJk/3S7b9++Pd59913I5XJs27YNZ8+eDXFOiRRI6KEM0TTFMglBT8mFef5JsyelVbjJB0z19fX4448/AAATJkxw+D05ORlDhgwBAGzZsiWoeSOEEEJIeGjyAVNmZiYMBgPUajX69u3rdJoBAwYAAI4fPx7MrBFCCCFNlt5oxsnzZU3m9mOTf0ouJycHAJCYmAiVSuV0ms6dO/Om9QXDMNBqtT7P74y2wYTvd2bjTH7jcn/+8wIOni4Gw9h0i2asnaTZdZL55292AgbsvffGaep0RtTpjEhqFwMAMFsYmM0WKJVyRKoVAGSQyf65ISEDZP/8DQD1DUYuP7/tz8Xp86X//GWdwPYSqszug8zmFoftdKWVOu7z3uOF0DWYeHXx6aYT0EQqYTRZoNObEatRoabegPoGEzq00kDuIvTPKarlPq/bfhY7DuZBpZT/M70MchlQcLmem2bb/lwUlDb+vWTtYaiUgT+vOJdXxft7zZZMtIxR/9MGMuRfquF+W7rhKNQq7/JUWavH218dECOrQcUwQE29ATFRKhSU1Dqd5q/jhSgsrYFcBm5dZRlNFq/LbbE0fr5UUc+bPzO3kvv87prD6NQu2uVy9AYLMk6X8L777MeTiFDJUFdXh99OHodSqXCYr7iscXuvqtXzfiuv1uHtrw663DYvV+pgsjBoGxcJACgsbayzd74+hKgIx/TEUFtvhIVhEBXReEiR2W/z/P9xXSNMZpsK/0dWXgX3md32PSm02Y4B4N2vD6FtfKTDdJfKdby/z+VVuF1HsvKruc+HMkvw9lcH4amHVElFYxoGoxlf/3rGYZpf/spFvU7v8D3LYrFut2zZzWYLIiOUUMgb12+hcYjgJxGdvabGyZdC0q2pN+B0jnV7iYlSoWdSS1gsDIwmC6KjVDBbLFDIrccbV/tvAMgrbtz3NegaoNWKG7YwDMOti57ImKYS+rmwcuVKvPXWW7jyyiuxbt06p9Ps3r2bG1rg6NGjXqdx8uRJGAwGf7PqIPOiDmv/KBd9uYQQQki4eXJ8AqIjxQ/61Wo1+vTp43G6Jn+FSa+3RvCuri4B1sqyndYXKpUKPXr08Hl+Z7r3MCNCk4eCS5cRqWmBSLXKerZhd1YJWM/WGs84wYuYG89GZf/8BtTpTCir0qFDaw3kchkUchnMZgZ6o9k67z9XsBqvWvGvaMVoVNazBbOl8WzDJvZ28hW3HPtp2A86gwkWC4MW0WpYLAyio1TomhiLszZXX8xmBnU6I1rGqFGvM6KyVo9O/1wlc8VosiAmSgWlQgajyQKj2WK9isAwsDDWPNXpjFDI5dBEKmFhGJRVNaB9qyjI5cLOPPxlNBqhravC1enJyC6qh8XSWP8MY70yWF7TgLZxUVAohOeJYYCyKh3axkcFLvMBVlmjR1xsBGQyoFVsBFq3jMTRrDKolHKYLdarGmwdAYDln+e4L1frkNjG9RUgdxgGKK9uQJs4xysUZVUNaBGtglrlecedU1TDXXVp3TISKqUcRqMRlRWViG8V73K/pFTIEatRoapOD4YBkhNiodWZUFKpc71t/rOOtNCooYlq3LWXVGjRqkVkQK+UsleEW8SoeVe22fzZ4vJtcxXcbGFQXt2Amnrrth0fG4Hy6gbEt4jwKt/1OiMuVzUgobUGEWrX7aNtMMFossBiYRDfIsLjciP/WVat1gilXAaZgP2CxWyCtrYKak1LKBRK6I1mlFbqEPPP1ZV2cVEel1N4uR6d2kZDrVJA22CC0tW27+RrmbMv4V0namdXXlzObveDwWiBUiFDrEYFk5mBXGY9ZumNZjQYzJDBup4bnVxhtFdR3YABvdqiT/fWwjMvUHZ2tuBpm3zAFBFh3RiMRqPLadirQ+y0vpDJZKIPfqkBcOt13ZCZqUdaWpqkB9cMpH6piaHOQsBptVpkZmYirVtbDLiiS6izI3kDencMdRZ8xrV1Ws9mu003B9TO4UHo7TigGXT6btmyJQCgurra5TTsb+y0hBBCCCG2mnzAlJycDAAoKipyeZUpPz+fNy0hhBBCiK0mHzClpaVBpVLBYDDgxIkTTqc5fPgwAKBfv35BzBkhhBBCwkWTD5hiYmJw3XXXAYDTp+Ryc3ORkZEBABgzZkxQ80YIIYSQ8NDkAyYAmDlzJmQyGTZt2oS1a9dyT22UlpZi7ty5sFgsGDVqFHr16hXinBJCCCFEippFwNS3b18888wzAIAXX3wRw4cPxx133IGRI0fi9OnT6Nq1K1555ZUQ55IQQgghUtXkhxVgTZ48Gampqfjss89w4sQJlJeXIzExEWPGjMH06dMRHe3beC2EEEIIafqaTcAEAEOHDsXQoUNDnQ1CCCGEhJlmcUuOEEIIIcQfFDARQgghhHhAARMhhBBCiAcUMBFCCCGEeEABEyGEEEKIBzKGHcWR+OzIkSNgGAZqtVr0ZTMMA6PRCJVK5dVblUl4oXZuPqitmwdq5/BgMBggk8lw1VVXeZy2WQ0rECiB3BhkMllAAjEiLdTOzQe1dfNA7RweZDKZ4GM4XWEihBBCCPGA+jARQgghhHhAARMhhBBCiAcUMBFCCCGEeEABEyGEEEKIBxQwEUIIIYR4QAETIYQQQogHFDARQgghhHhAARMhhBBCiAcUMBFCCCGEeEABEyGEEEKIBxQwEUIIIYR4QAETIYQQQogHFDARQgghhHig9GZihmFw9OhR7NixA4cPH8aFCxdQV1eH2NhY9O7dG7fffjvGjh0LmUzmchn19fVYsWIFtm7diqKiImg0Glx55ZWYMmUKBg8e7Db9jIwMrFq1CsePH4dWq0ViYiLGjBmD6dOnQ6PROEx/6dIlbNiwASdPnsSFCxdQUVEBvV6P+Ph49O3bFxMmTMANN9wgapoAsH//fhw/fhynTp3CyZMnUVRUBAD44osvPJYRAM6cOYMVK1bg4MGDqKmpQbt27TB8+HDMnDkTrVq1cjrPiRMncOTIEZw8eRInT55EXl4eAOD111/H+PHjPaZpy76dz58/j9raWu63qKgoDBw4EFOnTnVZnry8PCxatAiHDh1CbW0tGIYBAPTq1QubNm1ym76QOr948SKWLl2KvXv3oqKiAkaj0e0y27Rpg71794JhGGzYsAHr169HdnY2AKBHjx649tprkZOT41WdA8D58+excuVKZGRk4PLly4iNjUXfvn3x0EMP4ZprrnE6T0NDA/7880+cPHmSW0eqq6sBAOfOnXNbDme+/vprLFiwAAAwaNAgfPnll4LnZdv622+/xeHDh1FSUsKrywkTJmDBggUut+n9+/fjwQcfdJvGLbfcgvfee8/pb57a2tX+YsCAAThy5AhOnTqFyspKqNVqdO3aFTfddBMeeughaDQah2W3a9cOPXv2RLt27XD27FlkZmZCr9cLqjNfty8ptvWOHTtw6NAhZGZmoqGhAQCgUCjQpUsXzJ49G6NHj3a5DLY9fvzxR1y6dIlbbnR0NJKTkzFo0CA89thjaNmyJTfP5cuXsXfvXq78ruq9vLwcy5Ytw86dO1FaWooWLVqgZ8+eSEtLQ2lpqdt637p1K7766iucPXsWRqMRSUlJSE9PR9u2bXHmzBnB9W42m5GRkYFdu3bh6NGjyM3NRUNDA+Li4tCnTx9MnDgRN954o9t6dlaOgQMH4tFHH0VaWprTeQoKCrBv3z5uPcnKyoLRaMQdd9yBRYsWuU3PE1f7vLvvvht33XWX021b7PXWXjDjADHJGPZIJsC+ffswefJk7u+kpCS0aNEChYWFqKqqAgDceOONWLJkCdRqtcP8FRUVuO+++5CTkwO1Wo0ePXqgoqICly5dgkwmw/z583H//fc7TfvLL7/EwoULwTAMOnTogFatWiE7OxsGgwHdu3fHmjVrEBcXx5tn+/btePzxxwEA8fHxaNeuHRiGQUFBAbRaLQBg4sSJ3A5IjDQB4Oqrr+YCDFtCAqZt27Zh7ty5MBqNaN26NTp06ICcnBxotVq0bdsW33zzDZKSkhzmGzduHM6ePevwvS8Bk307K5VKmEwmp9O++OKLDm1WUVGBW2+9FRUVFYLnYQmp85ycHEyZMgVarRYtW7ZEp06dcPr0aQCAXC5Hz549ER0dzVtuXFwcPvroI8yZMwdbtmwBYN1pAOB2IgC8qvNt27bhf//7H/R6PWJjY5GcnIyysjIUFxcDAObOnYtHH33UYb7MzEzcfvvtTsvv7c6opKQEt9xyC+rq6gB4fxC1b2tn3G3TttuYTCZDZGQkTCYTF3QlJSVh3LhxmDVrlsO8ntr6o48+wowZM5zuL1ixsbFISkpCdXU1ioqKwDAMunbtinHjxuGDDz7gLfvcuXMwm80O+RBSZ75uX+HQ1nK5HBaLhft7ypQpePrppx2ms91/O5uX3U/89NNPSElJ4aZZvXo1Xn/9dYfl2eY/Ly8P9913H8rKyqDRaNC1a1dcunQJ5eXlTstiW+9vvPEGPvvsMwBA586dERUVhb///ptXJlvu6n39+vV44YUXuLJ17twZ0dHRyMvL4+p94sSJePnll50GGu7KoVKp8MEHH2DkyJEO8y1cuBBffPGFw/f+BkwWi8XtPu/WW2/FO++841AWMddbe8GOA0TFeGHv3r3MiBEjmM8//5wpKyvj/bZx40bmiiuuYFJSUpg333zT6fyPPfYYk5KSwtxxxx3MpUuXGIZhGIvFwnz77bdMSkoKk5aWxpw5c8ZhvpMnTzK9evViUlNTmW+//ZaxWCwMwzDMpUuXmDvuuINJSUlh/vOf/zjMd+7cOea7777j0mI1NDQwH3/8MZOSksKkpKQwv/zyi2hpMgzDTJw4kXn66aeZr776ijl27BgzcOBAJiUlhcnIyHA6PevSpUvMlVdeyaSkpDDvv/8+YzQaGYZhmJqaGmbq1KlMSkoKM378eC4vth5//HFmzpw5zGeffcYcPHiQGTt2LJOSksJ89913btN0xradp0yZwmsz23Z21WZsO/fv359ZsGABs23bNua9995zOw/DCKvzxx57jLn22muZlJQU5tlnn2UaGhoYhmG4ZaekpDDDhg1jdDqdw/JXrVrFpKSkMIMGDWKOHDnCLbtPnz7cvKtXrxZU5/n5+Uzfvn2ZlJQU5uWXX+bywTAM88svvzDp6elMSkoK89dffznkIysri7n77ruZBQsWMBs3bmS2bt3Kpe+txx57jElLS2MeffRRJiUlhZk0aZJX87NtPXbsWGbmzJkO60/v3r3dbtMTJ05kUlJSmPT0dNG3abadbfcXBw4c4OoqNTWVOXHiBLfM06dPM8OGDeP9brvsTz/9lOnfvz+TkpLC3H333cz7778vuM583b6k2Nb33Xcfk5KSwowYMYI5f/48wzDW/Tfb1ikpKczvv//uNH329+HDhzP79+93aOtvvvmGqaio4M23fv16ZvLkycw777zDbNu2zaHeLRYLM27cOCYlJYWZOnUqU1NTwzAMwxiNRmb06NHc+rVt2zaHet+2bRuTkpLCXHHFFcz27du5NH///XduPzV16lTB9b5u3Tpm7NixzLp167h8sHlZuXIlk5qayqSkpDBff/21w7zuysGWuV+/fkxJSYnDvB999BEzffp0ZsmSJcyuXbuY5557jklJSWGefvppt/n1xNk+j2EY5siRI8ygQYOYlJQU5osvvnCYT8z11l6w4wAxeRUw1dbWMgaDweXvy5Yt4xrHbDbzfjt9+jSTkpLC9OrVi8nNzXWY98knn3RZ4BkzZjApKSnMU0895fBbTk4O06tXLyYlJYXJzMz0pjjMtGnTmJSUFOaJJ54IaJrsiukpYHr11VeZlJQU5v7773f4raqqihkwYIDLnZk9dgXyJWBi29lVm7HtzAYFtm3map4vv/ySSUlJYYYOHep3O6ekpDA33XQTb11kv7/xxhud7gQMBgMzePBgJiUlhdmwYQP3PVvnN998M5c/NlB1V+evv/46k5KSwtxyyy2MyWRyyO+iRYuYlJQUZuLEiS7rmXXu3DmfdkY///wzk5KSwrz66qvM4sWLfTqIutqmbXdAnrbplJQU5vrrr3dYhhjbdGpqKm89YuuVPQmxX/YPP/zA5cnTst9++22f6oxhfN++Qt3WxcXF3Ha7b98+3u/sdp2SksLcfvvtvN9s27p///5McXEx73d3bW2P3Rew+f/tt9+YlJQUZsCAAUxVVZXD9Pfffz+TkpLCvP766w71fttttzEpKSnMBx984DDfX3/9xQVTtoG2O5WVlU5PSFkvvPACk5KSwtx2220Ov3lTDk/Y9dyfgMnVPo+1fv16h32eK76ut/akGAd4w6tO3zExMVCpVC5/HzZsGACgqqrK4XbM1q1bAQBDhgxBly5dHOadOHEiAGD37t3c7TLAeq/zjz/+AGDtT2EvOTkZQ4YMAQDusqNQ3bt3BwDuPn4w0nSHrSNnabZs2RJjxowBAPz666+ipekM286u2oxtZ/a2i22beWrndu3aOcwDeFfngPVStbN1ke1/YV9HBw4cQGVlJTQaDcaOHct9z+b30UcfhUajQXl5OQ4ePAjAfZ0fPnwYADBq1CgoFAqHfPzrX/8CABw9ehQFBQUOv/ururoaCxcuRIcOHTB79myfl+Npm05NTQXgfpsGrLdj7PmzTSckJAAAOnbsyFuP9Ho9L1/2y2bXL3fLZtejM2fOOBZYgsRs6z179sBoNDpsT0Djdg1Y6yY/P5/727atJ06ciA4dOvDmddXWQrDb1pgxY3h9n1hsO9pvg7m5udxtUjZ9W0OHDkWXLl1gMBiwf/9+QXmJi4tz2weXrSPb25L+liNQXO3zWLfddpvDPi/QpBgHeEPUp+RsA4/IyEjeb8eOHQNg7d/jTN++faFWq6HX65GZmcl9n5mZCYPBALVajb59+zqdd8CAAQCA48ePC84r808HSAC44ooreL8FKk13iouLUVJSAgAYOHCg02nYuhMrTU9ctZltO6tUKl6beWrn6Ohoj+387rvvIjU1Fc888wxv3quuuor77KqOLly4AAA4cuQInn76afzwww8wGAxcvtj1DODX+dChQ9GnTx9eGWzLYV/nbAfI9u3bO82H7QHFdnliWbRoEcrKyjB//nyH/lpisu0A7mqbBoC6ujq8+OKLmDx5Mh555BG88sorqKurc9vWCoUC9913H0aMGOGQLnvQsj94sZ1mCwsLHdY9wNqvjJ3P07ZbWFjosfxSIGZbs23G1oEt+xNH2/a1/Tx06FAUFRXh3XffxbRp0/DII49g27ZtTtuD9cwzzyA1NRUPPPCAw2/stuVqn8F+f+nSJV5fSjZPSUlJLrdDtpxidFQGGusoKirK4TdvysHudwLJ2T7PllqtdrrP89eSJUuQmprqdLuWWhzgLVEDpp9//hmA9UmomJgY3m+5ubkArJ3ynFGpVNxZpW30zn5OTEx0eSbMLtNZ1G9Pq9Xi9OnTmDt3Lo4dO4bk5GQ89NBDvGnETlMItn5UKpXD2RuL7Xh88eJFj0+FiZkn+zazbefExEQAjfXgqZ3lcrnHdpbLna+WtuuUs07YgPXMBLAGxD/88AOefvppjBkzhttQbfNlX+fO2tRVncfGxgKAyx2fbcdkNogTy759+/D9999jxIgRGDVqlKjLtsfufNxt04A1gFy7di327duHPXv24KuvvsL06dO5tnTW1vbLs8U+NGH/8MS4ceOQkpKCwsJC7qpWVlYWSkpK8NVXX2H9+vUArAGrp23XVYdiKRG7rd1tn+x2zT5pZNtmtutwfn4+xo4di+XLl+OPP/7Anj17sHr1am778GafaDAYuMDV1T4jISGBa0v2CqOnsrDY38QKjtk6sg84vS2H2PsEZ7ypH7GOY55IJQ7wlWgB06lTp/Dtt98CAKZPn+7wO3tG7uxSJYv9raamxqf52Gmdufrqq5Gamor+/ftj/Pjx+P333zFt2jSsW7eOO/iJnaY32KcMW7Zs6fKSMNv732KxcE9sBJKzerBvZ/s2E6Od4+Pj0b59e4dl2AZS9k9CjBw5EosXL8ZPP/3EfTdnzhwkJSWhsLAQf/31l0O+7OvcWb5c1Tl7Zvb77787ffLK9rKw7fL81dDQgBdffBEajQYvvviiaMt1hb2V4W6bvuGGG7B69Wrs3r0bJ0+exI4dO/D0009Do9FwZ+RsXdvOFx0djfbt26Nt27YOy9bpdLz/s9RqNdasWYP777+fO3i+9NJLGDZsGF555RV069YNAP/WnD22ne2XLTWBaGtX26ftdt2zZ08AzrdPwHrFq02bNli9ejVOnjyJ3bt3864cHTlyxCHdli1bon379oiPj+d9X1dXxz3N5mqfIZPJ0KJFCwDgbWve7GvE2F9u374dO3fuhEwmw7Rp03i/eVsOMfcJrvi6L/ZXTEyMy+061HGAv0QJmMrKyjBr1iyYTCbcdNNNuPXWWx2mYXdu7vpLsJcNbS8NezOf7dmHvX79+uGqq65Cjx49EBUVBb1ej23btmHfvn1+5dVdmt7wJk0x0/UmT87a2b7NxGjnxYsXY8+ePXj22WddLsN++UuXLsXo0aO5x2YBa5C8bt06JCYmcpfybeezz6uzfLmq83vuuQcKhQJ///03/u///o83zw8//MB7RFjMA/PixYuRn5+PJ554gjsTCwS2viwWi8dt+uGHH8bQoUPRoUMHqNVqdOzYEVOmTOEe9Qas4xjZz5eYmIg9e/Zg7dq1Dstmr1Y4u5JaXl6O0tJSbmyvtm3bcldl2bNLZ32qWGybuhoqQyoC0dbOtk/77To5ORkAfzswGAzcZ4vFguXLl2Po0KFQq9Xo0KEDXnjhBe6AxfY1sfXss89iz549WLx4sdP8AHB628j+N9uhAoK5nz5//jzXReChhx7idQ+wX76Qctjf/gwEX/fF/nr44YddbtehjgP85XfAVFtbi0ceeQRFRUVIT093OWZEREQEAOc7QBa7Udr2lfBmPnZaZ1auXIlvvvkGP//8Mw4cOIBnn30WxcXFmD17tkMnPLHS9IY3aYqZrtA8uWpn+zYLZDvbcjWdbR1FRkaiVatWvKsjzuqQXZazfLmq8169euG5556DTCbD+vXrMXjwYIwbNw5DhgzB008/jbS0NPTq1QsAROtjdObMGXz++efo3bu3074gYqmtreUGCOzYsaPP23T//v25g6jtLQghbc3uGO13kBcuXMCECROwfft2LoiYMmUKdu/ejXXr1nF9S9j8O8O2qbugKtQC1db2de9su3a2HdgGATfeeCMXVNlq06YNAKC0tNTlGGyu8gPwtzV77G+2V5mDtZ8uLi7GtGnTUFtbixtuuAH/+9//HKbxthz2/QEDwdd9sRTzFIpjsjN+BUz19fWYNm0azpw5g549e+LTTz912S+BvRTp7nIZ+xs7LSDsMpuQy3W21Go1Jk+ejP/+979gGMZhFOJApOmJbZqMi7FE2dsacrncbf8PsbDtUFpa6rKd7dsskO1sezne9haPs2XbLrN///7cd5cvX3aaJsMwTvPlrs4nTZqEr776CiNHjoRGo8H58+cRGxuLmTNn4ssvv+Q2YPZA4q/nn38eFosFCxYscPpknhjYbZo9u3v44Yf92qbZA5zt5XUhbc3uLO0717733nuorq7G3XffzQVTbD6uvPJK3H333QCsV01s+5HZYtN11nFXKgLV1rZt5mr/7Ww7sO22wD5dbM/2zF7ok6ExMTHcOuJqfWAYhlt/bOvCm32Nr/vLy5cvY/LkySgqKsKgQYOwZMkSp1c5vC2Hbd0Giq/7YinmKRTHZGd8PsXS6XR49NFHuY7Tq1atcrg/bSs5ORklJSUuz/yMRiP3ChHbsxf2c1FREYxGo9OVlX381dlZjzs33ngj3nnnHeTl5aG2tpbbKQQyTVfY5RiNRhQXF3OdqW1dvHgRANCpUye3lybFwrbZRx99hKKiIod2dtZmntrZYrH43M62/RDy8/OdPhnDtotarebq0HZZbB3apsnWubM29VTnV199tdMnPgwGAzcv29/JX2fOnIFCocBjjz3m8Bv7CO7Ro0dx7bXXAgA2bNjg1a0c221arVbDYDC4vTomZJt2dqAT0taxsbGoqalxONAdOnQIADB48GBs3LiRtzzA2p9q5cqVAICTJ086fYCCbedWrVoFpS+JLwLV1snJyThy5AjOnz/vcv/tbDvo3r07ysrKADi/LWI0GnkPQLg66bPHbqcFBQXIz893uNUFWK/wsFcWbK8edO3aFYD7q4lsWRITE7m3AQhVXl6Ohx56CLm5uejfvz8+/vhjl1cvvC0Hm/dAYttPSP2IdRzzRIpxgDd8usKk1+sxY8YMHDx4EB07dsTq1auddvCy1a9fPwCN49fYO3HiBIxGIyIiInjv20lLS4NKpYLBYOD1g7DFLpNNQyjbKxa2nwOZpiuJiYlcR1X2oGCP/V6sND1hh1soKipy2s7O2sxTO9fX1/vczuwwEIDnOurTpw93kP7777+539nHUwF+nWdkZODkyZMA+FekfK1zdrybuLg4p49w+8psNqOsrMzhH3sQNRqN3HfOOqO7Yr9NC9npCNmm2TzYPtUopK1dqa+vB2A9CLhaj1i27W6LzW+nTp28SjvYAtHWbJtt27bN6f67pKSEuzpku87bfrY96WCx+wKWqyd93eXJ0zbdoUMH3m3UK6+8EoD1aparp1XZtmZvjwtVVVWFhx9+GOfPn0d6ejo++eQTj7fWvSmHN/XjKzY/J0+edHqb0GAwON3nBSNPUooDvOF1wGQ0GjFr1izs27cP7du3x+effy7ozIYdUHD//v1Oo0u2g9iwYcN4K2ZMTAyuu+46AMC6desc5svNzUVGRgYAcIMMCsUOopWYmMh76iqQabrD1pGzNKurq7knr8RM0xWj0cgLUF577TWHdnbWZp7aubS01GEewLs6B4CNGzc63M82GAz4/vvvATTWkclkwqpVqwBY+6zodDrek3RsfpcvXw6tVotWrVpxYzz5WucGgwFLliwBANx3331uO4F649y5cy7//ec//wFgfT8X+53QgMDZNi3kCqantv7888+5z7aDIgppa/Z9fEVFRbxls4Ec+wJn+/XIdrwdZztW2/Wod+/eHkoYOoFq6xtuuAEymQwNDQ2Ij4932H+zT8r17t2bN7Cg7Qt5t2/f7nBrxLaDb9euXV2Oi+QMu+wtW7Y4veXCriP222DXrl25d9Y562C8b98+5OXlQaVSCXrpOauurg5TpkzBuXPnkJKSgk8//dThSWoxyxEogwcPRlxcHLRaLW+fx/rxxx8d9nmBJsU4wBteBUxmsxnz5s3D7t270bZtW3z++ecux8Oxl56ejuHDh8NsNmPOnDncgZNhGKxduxabNm2CXC7HjBkzHOadOXMmZDIZNm3ahLVr13KXe0tLSzF37lxYLBaMGjXK4SzijTfewO7dux2i6/r6enzyySdYsWIFADiMw+RPmv6YOnUqIiMjcfDgQXzwwQfcWWNtbS3mzZuH2tpa9O7d2+mAYGJi2/nIkSPcwf7NN98U1Gbu2hmwXub21M4//PADBg8ezHU2tq3z4cOHo23btsjLy8P//d//Qa/X4+2338a6devw3HPPIT8/H+3atcPdd9+N4uJi/Pe//8WxY8egVCpx7733cmVhg8GpU6dCrVZz44NMnz4dSqVSUJ1v2LDBYaPPy8vD9OnTcfbsWfTo0cPpLRUp8Xebbtu2rdO2fuedd7gTkvj4eNxzzz28eWfOnAnA+kThTTfd5HT7atOmDffyUHbZt912GwBrPctkMl6H/uPHj/MGO921a5fbbdebg3pTYDab8dprr3H1oVareScdO3bs4G5nsi9UZqWnp+OGG24AYL0lOHfuXNTW1vL2BSxn6/wbb7yBESNGYM6cOQ6/jRo1CqmpqaitrcX//vc/buwts9mMDz74AAcPHkRUVBSmTJniMC8bPH7yySfYsWMH9/2FCxe4l+jed999gvu16HQ6TJ8+HadPn0a3bt2wevVqt11NxCpHIKhUKu7l37b7PMB6tf6tt94CYG0vMR+AWL16NUaMGMHtb22FIg4Qk4wRerMZwObNmzFv3jwA1qdn3O1w5s+f73AGV1FRgXvvvRe5ubncW4orKytRXFwMmUyG559/3uUTIatXr8aiRYvAMAwSEhIQHx/PvaW4a9euWLNmDVq1asWbh33DuEqlQufOnREbGwutVoucnBxuRzFp0iS88MILTsc+8iVNAHjllVewefNm7m+2U3FMTAxvxXQ2XP+WLVswb948mEwmtG7dGh06dEBOTg60Wi3atGmDNWvWOB1S/pNPPuF2doD1LMlkMkGj0fCucPzwww8erwjatnNCQgIqKiqg1+udvo1+2rRpePLJJ3nzV1RU4O677+Yu7SsUClgsFm4Fj4qK4voCsCMFs2zfbB4VFYWuXbs61HlOTg6mTZsGrVaLli1bwmKxcDsnmUyG7t27w2KxICcnBwzDICIiAq+++ir+/e9/44knnuBGg7Z/czcAr+qcXb/atGmDDh06oK6ujgu8UlNTsXLlSpfjAd1xxx3cvXqLxcL1pbG90nnVVVdh2bJlrhvKxpIlS/Dhhx96/QZ727aWy+XcdsAG67bfRUVFYfPmzbz156qrruJuk7Hrh16v5x7/jo2NxVdffeV0JzZ16lT8+eefAOB0+1q6dClmzJjB21+wbzVnxcTEoHPnzrzvW7dujTvuuAOffvopb9v9+++/YTQaIZfLERsbC6PRCK1WC6VSyesrZb9OAv5tX1Jsa7aPGmBtV4vFwnXabteuHZYvX+50/33nnXdyZZHJZFAqlbyga9KkSZg/fz5vvuLiYtx8880wGAxQKpVQq9UO9W6xWGAymaDVaqHRaNC1a1dcunSJG1w0OjoaKpXKab3rdDou7507d4ZGo8Hff/8Ns9kMhUKB2NhYwfW+fPlyvPvuuwCAbt26OYz3Zmvx4sUOXVFycnJw//33o7y83KEcKpUK7733Hm666SaHZR0+fJg7iQCsj9Q3NDRArVZzA4kCwIsvvuh0iA9XLBaL233emDFj8N577zkdLNjX9ZZdPzt27MgLYlnBjgPE5FVYaXulprCw0O3oqfYj9ALWTpbfffcdPvnkE2zZsgXZ2dnQaDQYNmwYpk6d6vBuI1uTJ09GamoqPvvsM5w4cQLl5eVITEzEmDFjMH36dKf3l5988kns2rULR44cQWlpKfLy8qBWq5GUlIT+/fvjrrvucto5z580AesVLGdPcQkZPG3MmDFISkrC8uXLcejQIWRlZaFdu3YYP348Zs6cidatWzudr6GhwWmaWq2W904eIX0dbNuZvTUCWM8C7McUsr3VwmrVqhWWLVvGvb/IPk2dTsctx378j8mTJ+P777/HuXPnYDKZkJ2d7VDnrVq1wqZNm7B06VLs3bsX5eXliIyMRGRkJORyOXcZvmfPnhg6dCgmTZrEjQK7ePFirFu3DuvXr8f58+cBWPs7XXfddbhw4YJXdT5p0iRs3boVWVlZOHfuHDQaDQYMGIBbb70VEyZMcHtbq7q62ml72X4XjMFJ7cfXsWf7XV1dnUNbPvnkk8jIyEBGRgZqamqg0+m4AfpGjx6Np556yuUTOP369cOff/6JiIgI6HQ6p9uXs/3F9ddfj169euHMmTPIzMxEVlYW1Go1evXqhRtvvBEPPfQQWrVqheuuu4637bZr1w6FhYWwWCy8WyYmk4lX787GpPFn+5JiW9t+tt+mS0tLXe6/f/rpJ7z//vvYtGkTampqYDQaoVQqkZqaipkzZzodjdxsNnPpmUwmbvwr+3qfMmUKDAYDdu7ciaysLLRo0QLdu3fH+fPnuaCcZV/v8+fPx9atW5GZmYnS0lJ0794dly5dQk1NjUPdu6t323rxNBq3s/F+unbtih9//BHLli3jlWP06NF47LHHXN4Gtq8L2/zY5snbMYbkcrnLfd6ECRNw9913uxwoOVDrbbDjADF5dYWJEEIIIaQ5EvVdcoQQQgghTREFTIQQQgghHlDARAghhBDiAQVMhBBCCCEeUMBECCGEEOIBBUyEEEIIIR5QwEQIIYQQ4gEFTIQQQgghHlDARAghhBDiAQVMhBBCCCEeUMBECCGEEOKBVy/fJYSQUGLfhG5LJpNBo9EgJiYGiYmJSEtLw+DBgzFixAjujfaenD9/HrfccgsAIDIyEnv37kVMTAxvmtTUVJ/z/frrr2P8+PEoKCjAyJEjvZ6PEBJ6FDARQsJSmzZtuM8NDQ0oLS1FSUkJjh49ijVr1iAuLg6zZ8/Gvffe63FZGzZs4C1r8+bNuOeee1ymZ0ur1UKr1bqdJjIy0uG7mJgYp997mo8QEhoUMBFCwtLevXt5f5vNZmRnZ+Ovv/7CV199hYKCArz00ks4dOgQ3n77bchkMqfLMRqN2LRpEwDggQcewJdffokNGzY4BEz26bFsr3q5msaZ559/nq4eERJGqA8TIaRJUCgUSE1NxcMPP4zNmzfj1ltvBQBs3rwZK1ascDnfzp07UV5eju7du2Pu3LnQaDQ4efIksrKygpV1QkgYoICJENLkREVFYdGiRejduzcAYMWKFaiqqnI6LXs77vbbb4dGo8Ho0aN53xNCCEABEyGkiVKr1Xj00UcBAHV1ddi+fbvDNCUlJfjzzz8hl8tx2223AbAGTgDw448/wmAwBC2/hBBpo4CJENJkXX/99VAoFACAgwcPOvz+/fffw2w2Y/DgwejQoQMAYPDgwejYsSMqKyvx+++/BzW/hBDpok7fhJAmKzo6GklJScjNzUV+fj7vN4Zh8N133wFovKoEWIcpGDt2LD7++GN89913+Ne//hWQvC1cuBDvvPOO22m86UROCAksCpgIIU1ay5YtAQDV1dW87/fv34+LFy9Co9Hg5ptv5v12++234+OPP8bevXtRXFyMhIQE0fNVV1eHuro60ZdLCAkMCpgIIc0Se3Xp5ptvhkaj4f3WtWtX9O/fH0ePHsX333+Pxx9/XPT0aVBKQsIL9WEihDRp7JWluLg47rva2lps27YNAP92nC32+40bN4JhmEBmkRASBugKEyGkyaqvr8fFixcBAJ07d+a+/+mnn9DQ0AAAmDx5sttlXLx4ERkZGRg6dGjA8kkIkT66wkQIabL++OMPmM1mAMCgQYO4770dY4nGZCKE0BUmQkiTZDAYsHz5cgBAbGwsRo0aBQA4e/YsTp8+DcAaCHXr1s3lMv7880/897//xW+//Yaamhq0aNEi8BknhEgSXWEihDQ5DQ0NePbZZ3HmzBkAwPTp07lgh71a1L17d/Tp0wfR0dEu/w0fPhyxsbHQ6/X46aefQlYeQkjoUcBECGkSLBYLsrKysGrVKtx6663YvHkzAGDcuHF45JFHAFivOrGBz5gxYzwuU61WY8SIEQAan6ojhDRPdEuOEBKWrr32Wu6zwWBAXV0dLBYL9118fDxmz56Ne+65h/vut99+494pJyRgYqfbtGkTTp8+jbNnz6JXr16i5F/IwJX/+te/8MILL4iSHiHEPxQwEULCUllZGQDryNxRUVFo06YNEhMTkZaWhqFDh2L48OFQq9W8eWxvx6WkpAhK57rrrkNsbCxqa2uxYcMG0QIYIQNX0sCWhEiHjKEBRgghhBBC3KI+TIQQQgghHlDARAghhBDiAQVMhBBCCCEeUMBECCGEEOIBBUyEEEIIIR5QwEQIIYQQ4gEFTIQQQgghHlDARAghhBDiAQVMhBBCCCEeUMBECCGEEOIBBUyEEEIIIR5QwEQIIYQQ4gEFTIQQQgghHvw/V6gjayS3z80AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boulder_precip_2003_2013.plot(x=\"DATE\",\n",
    "                              y=\"HPCP\",\n",
    "                              title=\"Daily Precipitation \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d8e3f10e-df9b-430e-a891-5e931832b94b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1840.000000\n",
       "mean       51.192587\n",
       "std       220.208147\n",
       "min         0.000000\n",
       "25%         0.100000\n",
       "50%         0.100000\n",
       "75%         0.100000\n",
       "max       999.990000\n",
       "Name: HPCP, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the range of values in the data - specifically the HPCP column\n",
    "boulder_precip_2003_2013[\"HPCP\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3045d88c-8f9d-4c44-8d08-1fe1c4f69460",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION              object\n",
       "STATION_NAME         object\n",
       "ELEVATION           float64\n",
       "LATITUDE            float64\n",
       "LONGITUDE           float64\n",
       "DATE                 object\n",
       "HPCP                float64\n",
       "Measurement Flag     object\n",
       "Quality Flag         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boulder_precip_2003_2013.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e6de19e5-c6bd-48f1-846f-dc213780f236",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>HPCP</th>\n",
       "      <th>Measurement Flag</th>\n",
       "      <th>Quality Flag</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-01-01 01:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>g</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-01 01:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>g</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-02 19:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-02 22:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-02-03 02:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-22 01:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-23 00:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>]</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-23 02:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-29 01:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-31 00:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>]</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1840 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         STATION     STATION_NAME  ELEVATION  LATITUDE  \\\n",
       "DATE                                                                     \n",
       "2003-01-01 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2003-02-01 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2003-02-02 19:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2003-02-02 22:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2003-02-03 02:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "...                          ...              ...        ...       ...   \n",
       "2013-12-22 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03380   \n",
       "2013-12-23 00:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03380   \n",
       "2013-12-23 02:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03380   \n",
       "2013-12-29 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03380   \n",
       "2013-12-31 00:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03380   \n",
       "\n",
       "                     LONGITUDE  HPCP Measurement Flag Quality Flag  \n",
       "DATE                                                                \n",
       "2003-01-01 01:00:00 -105.28111   0.0                g               \n",
       "2003-02-01 01:00:00 -105.28111   0.0                g               \n",
       "2003-02-02 19:00:00 -105.28111   0.2                                \n",
       "2003-02-02 22:00:00 -105.28111   0.1                                \n",
       "2003-02-03 02:00:00 -105.28111   0.1                                \n",
       "...                        ...   ...              ...          ...  \n",
       "2013-12-22 01:00:00 -105.28110   NaN                [               \n",
       "2013-12-23 00:00:00 -105.28110   NaN                ]               \n",
       "2013-12-23 02:00:00 -105.28110   0.1                                \n",
       "2013-12-29 01:00:00 -105.28110   NaN                [               \n",
       "2013-12-31 00:00:00 -105.28110   NaN                ]               \n",
       "\n",
       "[1840 rows x 8 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import data using datetime and no data value\n",
    "boulder_precip_2003_2013 = pd.read_csv(file_path,\n",
    "                                       # Make sure the dates import in datetime format\n",
    "                                       parse_dates=['DATE'],\n",
    "                                       # Set DATE as the index so you can subset data by time period\n",
    "                                       index_col=['DATE'],\n",
    "                                       # Mask no data values so they are not plotted / used in analysis\n",
    "                                       na_values=['999.99'])\n",
    "\n",
    "# View the data\n",
    "boulder_precip_2003_2013"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5f9770e9-f2ab-471b-a4c0-e8a592db20c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>HPCP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1840.0</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>1840.000000</td>\n",
       "      <td>1746.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.033851</td>\n",
       "      <td>-105.281106</td>\n",
       "      <td>0.111856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.093222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.033800</td>\n",
       "      <td>-105.281110</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.033800</td>\n",
       "      <td>-105.281110</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.033890</td>\n",
       "      <td>-105.281110</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.033890</td>\n",
       "      <td>-105.281100</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.033890</td>\n",
       "      <td>-105.281100</td>\n",
       "      <td>2.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ELEVATION     LATITUDE    LONGITUDE         HPCP\n",
       "count     1840.0  1840.000000  1840.000000  1746.000000\n",
       "mean      1650.5    40.033851  -105.281106     0.111856\n",
       "std          0.0     0.000045     0.000005     0.093222\n",
       "min       1650.5    40.033800  -105.281110     0.000000\n",
       "25%       1650.5    40.033800  -105.281110     0.100000\n",
       "50%       1650.5    40.033890  -105.281110     0.100000\n",
       "75%       1650.5    40.033890  -105.281100     0.100000\n",
       "max       1650.5    40.033890  -105.281100     2.200000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View summary statistics == Notice the DATE column is not included\n",
    "boulder_precip_2003_2013.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f80c5c48-0435-43a9-85f7-6e2e4ad063c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "STATION              object\n",
       "STATION_NAME         object\n",
       "ELEVATION           float64\n",
       "LATITUDE            float64\n",
       "LONGITUDE           float64\n",
       "HPCP                float64\n",
       "Measurement Flag     object\n",
       "Quality Flag         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Where is the date column\n",
    "boulder_precip_2003_2013.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "be6a3b03-20c8-47cd-80ad-82f3d336f5a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2003-01-01 01:00:00', '2003-02-01 01:00:00',\n",
       "               '2003-02-02 19:00:00', '2003-02-02 22:00:00',\n",
       "               '2003-02-03 02:00:00', '2003-02-05 02:00:00',\n",
       "               '2003-02-05 08:00:00', '2003-02-06 00:00:00',\n",
       "               '2003-02-07 12:00:00', '2003-02-10 13:00:00',\n",
       "               ...\n",
       "               '2013-12-01 01:00:00', '2013-12-03 20:00:00',\n",
       "               '2013-12-04 03:00:00', '2013-12-04 06:00:00',\n",
       "               '2013-12-04 09:00:00', '2013-12-22 01:00:00',\n",
       "               '2013-12-23 00:00:00', '2013-12-23 02:00:00',\n",
       "               '2013-12-29 01:00:00', '2013-12-31 00:00:00'],\n",
       "              dtype='datetime64[ns]', name='DATE', length=1840, freq=None)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the index for your data frame\n",
    "boulder_precip_2003_2013.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0f01f10d-6104-491f-8c92-3acf9a540ed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>STATION</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>HPCP</th>\n",
       "      <th>Measurement Flag</th>\n",
       "      <th>Quality Flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003-01-01 01:00:00</td>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>g</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2003-02-01 01:00:00</td>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>g</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003-02-02 19:00:00</td>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003-02-02 22:00:00</td>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2003-02-03 02:00:00</td>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1835</th>\n",
       "      <td>2013-12-22 01:00:00</td>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1836</th>\n",
       "      <td>2013-12-23 00:00:00</td>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>]</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1837</th>\n",
       "      <td>2013-12-23 02:00:00</td>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1838</th>\n",
       "      <td>2013-12-29 01:00:00</td>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1839</th>\n",
       "      <td>2013-12-31 00:00:00</td>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03380</td>\n",
       "      <td>-105.28110</td>\n",
       "      <td>NaN</td>\n",
       "      <td>]</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1840 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    DATE      STATION     STATION_NAME  ELEVATION  LATITUDE  \\\n",
       "0    2003-01-01 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "1    2003-02-01 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2    2003-02-02 19:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "3    2003-02-02 22:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "4    2003-02-03 02:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "...                  ...          ...              ...        ...       ...   \n",
       "1835 2013-12-22 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03380   \n",
       "1836 2013-12-23 00:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03380   \n",
       "1837 2013-12-23 02:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03380   \n",
       "1838 2013-12-29 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03380   \n",
       "1839 2013-12-31 00:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03380   \n",
       "\n",
       "      LONGITUDE  HPCP Measurement Flag Quality Flag  \n",
       "0    -105.28111   0.0                g               \n",
       "1    -105.28111   0.0                g               \n",
       "2    -105.28111   0.2                                \n",
       "3    -105.28111   0.1                                \n",
       "4    -105.28111   0.1                                \n",
       "...         ...   ...              ...          ...  \n",
       "1835 -105.28110   NaN                [               \n",
       "1836 -105.28110   NaN                ]               \n",
       "1837 -105.28110   0.1                                \n",
       "1838 -105.28110   NaN                [               \n",
       "1839 -105.28110   NaN                ]               \n",
       "\n",
       "[1840 rows x 9 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boulder_precip_2003_2013.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6129c370-dba0-4902-9544-db5442c4201d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boulder_precip_2003_2013.plot(y=\"HPCP\",\n",
    "                              title=\"Hourly Precipitation\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e79cda31-c843-40be-b26d-2c53fff50385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>LATITUDE</th>\n",
       "      <th>LONGITUDE</th>\n",
       "      <th>HPCP</th>\n",
       "      <th>Measurement Flag</th>\n",
       "      <th>Quality Flag</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2005-01-01 01:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>g</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-02 06:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-02 08:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>}</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-05 08:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-12 04:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-01 01:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>g</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-03 00:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-06 19:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-07 01:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-08 13:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         STATION     STATION_NAME  ELEVATION  LATITUDE  \\\n",
       "DATE                                                                     \n",
       "2005-01-01 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-01-02 06:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-01-02 08:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-01-05 08:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-01-12 04:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "...                          ...              ...        ...       ...   \n",
       "2005-12-01 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-12-03 00:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-12-06 19:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-12-07 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-12-08 13:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "\n",
       "                     LONGITUDE  HPCP Measurement Flag Quality Flag  \n",
       "DATE                                                                \n",
       "2005-01-01 01:00:00 -105.28111   0.0                g               \n",
       "2005-01-02 06:00:00 -105.28111   NaN                {               \n",
       "2005-01-02 08:00:00 -105.28111   NaN                }               \n",
       "2005-01-05 08:00:00 -105.28111   0.1                                \n",
       "2005-01-12 04:00:00 -105.28111   0.1                                \n",
       "...                        ...   ...              ...          ...  \n",
       "2005-12-01 01:00:00 -105.28111   0.0                g               \n",
       "2005-12-03 00:00:00 -105.28111   0.1                                \n",
       "2005-12-06 19:00:00 -105.28111   0.1                                \n",
       "2005-12-07 01:00:00 -105.28111   0.1                                \n",
       "2005-12-08 13:00:00 -105.28111   0.1                                \n",
       "\n",
       "[177 rows x 8 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subset data from 2005\n",
    "precip_2005 = boulder_precip_2003_2013['2005']\n",
    "precip_2005"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9b4ad06a-acf1-4574-b444-e2ad6775d1ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>STATION</th>\n",
       "      <th>STATION_NAME</th>\n",
       "      <th>ELEVATION</th>\n",
       "      <th>LATITUDE</th>\n",
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       "      <th>2005-01-01 01:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
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       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-12 04:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-12 06:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-12 08:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-01 01:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.0</td>\n",
       "      <td>g</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-03 00:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-06 19:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-07 01:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-12-08 13:00:00</th>\n",
       "      <td>COOP:050843</td>\n",
       "      <td>BOULDER 2 CO US</td>\n",
       "      <td>1650.5</td>\n",
       "      <td>40.03389</td>\n",
       "      <td>-105.28111</td>\n",
       "      <td>0.1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         STATION     STATION_NAME  ELEVATION  LATITUDE  \\\n",
       "DATE                                                                     \n",
       "2005-01-01 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-01-05 08:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-01-12 04:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-01-12 06:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-01-12 08:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "...                          ...              ...        ...       ...   \n",
       "2005-12-01 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-12-03 00:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-12-06 19:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-12-07 01:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "2005-12-08 13:00:00  COOP:050843  BOULDER 2 CO US     1650.5  40.03389   \n",
       "\n",
       "                     LONGITUDE  HPCP Measurement Flag Quality Flag  \n",
       "DATE                                                                \n",
       "2005-01-01 01:00:00 -105.28111   0.0                g               \n",
       "2005-01-05 08:00:00 -105.28111   0.1                                \n",
       "2005-01-12 04:00:00 -105.28111   0.1                                \n",
       "2005-01-12 06:00:00 -105.28111   0.1                                \n",
       "2005-01-12 08:00:00 -105.28111   0.1                                \n",
       "...                        ...   ...              ...          ...  \n",
       "2005-12-01 01:00:00 -105.28111   0.0                g               \n",
       "2005-12-03 00:00:00 -105.28111   0.1                                \n",
       "2005-12-06 19:00:00 -105.28111   0.1                                \n",
       "2005-12-07 01:00:00 -105.28111   0.1                                \n",
       "2005-12-08 13:00:00 -105.28111   0.1                                \n",
       "\n",
       "[165 rows x 8 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove missing data values\n",
    "precip_2005_clean = precip_2005.dropna()\n",
    "precip_2005_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "30d00a14-750b-4826-bace-b2b8fafbc4f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data using pandas\n",
    "\n",
    "precip_2005_clean.reset_index().plot(x=\"DATE\",\n",
    "                                     y=\"HPCP\",\n",
    "                                     title=\"Hourly Precipitation\",\n",
    "                                     kind=\"scatter\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5f2c6aec-81fc-468a-b2e5-70481aec8923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data using native matplotlib\n",
    "f, ax = plt.subplots()\n",
    "ax.scatter(x=precip_2005_clean.index.values,\n",
    "           y=precip_2005_clean[\"HPCP\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5a3b7c3a-092f-4b03-a293-8028a957b9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precip_2005_daily = precip_2005_clean.resample(\"D\").sum()\n",
    "# Plot the data using native matplotlib\n",
    "f, ax = plt.subplots()\n",
    "ax.scatter(x=precip_2005_daily.index.values,\n",
    "           y=precip_2005_daily[\"HPCP\"])\n",
    "ax.set(ylim=[0, 2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7b8573f6-d5a9-4c00-a646-ed680d32433f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.dates import DateFormatter\n",
    "\n",
    "# Place your code to plot your data here\n",
    "flood_data = boulder_precip_2003_2013['2013-09-01':'2013-11-01']\n",
    "\n",
    "f, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax.scatter(x=flood_data.index.values,\n",
    "           y=flood_data[\"HPCP\"])\n",
    "\n",
    "# Define the date format\n",
    "date_form = DateFormatter(\"%m-%d\")\n",
    "ax.xaxis.set_major_formatter(date_form)\n",
    "ax.set(title=\"Optional Challenge \\n Precipitation Sept - Nov 2013 \\n Optional Plot with Dates Formatted Cleanly\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d066aba-4558-460c-a376-77d703f4d27d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}