notebooks/tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e75534c3-2828-42ad-8803-2dc4c75c7e8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from uncertainties_pandas import UncertaintyArray, UncertaintyDtype\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from uncertainties import ufloat, umath, unumpy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39d49925-e1f8-453d-bf1c-faf722a182d3",
   "metadata": {},
   "source": [
    "# Object creation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42fb9e40-e614-4506-b3af-85fa5b04f302",
   "metadata": {},
   "source": [
    "Uncertainties-Pandas provides `UncertaintyArray`, which is used by pandas to store and operate on `uncertainties` objects.\n",
    "Create an `UncertaintyArray` by passing a list of ufloats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "64602486-db25-4ab6-9444-c2303122df9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<UncertaintyArray>\n",
       "[4.0+/-0.04, <NA>, -5.0+/-0.05]\n",
       "Length: 3, dtype: UncertaintyDtype"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufloats = [ufloat(i, abs(i) / 100) for i in [4.0, np.nan, -5.0]]\n",
    "uarr = UncertaintyArray(ufloats)\n",
    "uarr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b09e576f-bd8a-460a-ae71-56539e6bc4c7",
   "metadata": {},
   "source": [
    "The `UncertaintyArray` can be stored in a `Series` or `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0e5c7365-551c-4e68-9b70-35fadf9ffda3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4.00+/-0.04\n",
       "1            <NA>\n",
       "2    -5.00+/-0.05\n",
       "dtype: UncertaintyDtype"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(uarr)\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be0a6f1e-8180-497b-9071-131a392c27f0",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\"> Series and DataFrame both accept a list of ufloats as an input. If no dtype is specified, pandas will store the ufloats as objects, bypassing Uncertainties-Pandas. Notice the object dtype:   </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d4dcb83-df8c-4648-bea2-3907e8992e28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4.00+/-0.04\n",
       "1       nan+/-nan\n",
       "2    -5.00+/-0.05\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(ufloats)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e96067-82ea-4859-8634-7e53d9c61fce",
   "metadata": {},
   "source": [
    "An alternative to using `UncertaintyArray` is to specify the dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c961007f-0399-453e-a341-ebf203e4fb0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4.00+/-0.04\n",
       "1            <NA>\n",
       "2    -5.00+/-0.05\n",
       "dtype: UncertaintyDtype"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(ufloats, dtype=\"UncertaintyDtype\")\n",
    "pd.Series(ufloats, dtype=UncertaintyDtype())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a70efc34-9dea-44c0-8638-0cacd4054c23",
   "metadata": {},
   "source": [
    "# Operations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33312260-45e6-47e5-8688-12c656947a44",
   "metadata": {},
   "source": [
    "Let's create a `DataFrame` to show how operations behave.\n",
    "\n",
    "Columns A1 and A2 use the same objects, while column B uses different objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "448edc7c-fb00-4e35-bc77-487aad118448",
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000+/-0.010</td>\n",
       "      <td>1.000+/-0.010</td>\n",
       "      <td>1.000+/-0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.000+/-0.020</td>\n",
       "      <td>2.000+/-0.020</td>\n",
       "      <td>2.000+/-0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.000+/-0.030</td>\n",
       "      <td>3.000+/-0.030</td>\n",
       "      <td>3.000+/-0.030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A1             A2              B\n",
       "0  1.000+/-0.010  1.000+/-0.010  1.000+/-0.010\n",
       "1  2.000+/-0.020  2.000+/-0.020  2.000+/-0.020\n",
       "2  3.000+/-0.030  3.000+/-0.030  3.000+/-0.030"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uarr_a = [ufloat(i, abs(i) / 100) for i in range(1, 4)]\n",
    "uarr_b = [ufloat(i, abs(i) / 100) for i in range(1, 4)]\n",
    "\n",
    "df = pd.DataFrame({\"A1\": uarr_a, \"A2\": uarr_a, \"B\": uarr_b}, dtype=UncertaintyDtype())\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c04db629-1431-4d20-ae50-f3effb8c7887",
   "metadata": {},
   "source": [
    "Equality checks show the object stored in A1 are the same as A2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "99deba2c-bc51-4a19-9488-552c9a52c7d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    True\n",
       "1    True\n",
       "2    True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A1\"] == df[\"A2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff20a54b-7a09-49de-80da-b3b32bbc47d1",
   "metadata": {},
   "source": [
    "which are different objects to those in B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7dcf6be8-4893-47a5-8f9d-13c339d42bf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A1\"] == df[\"B\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346b1bab-8a6b-4096-a99b-3a41bff65323",
   "metadata": {},
   "source": [
    "Comparative operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2cb8756a-f700-42ce-85d8-e361735ce350",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "Name: A1, dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A1\"] > 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1fa122e-fe4a-483b-8853-2db0700b6a76",
   "metadata": {},
   "source": [
    "Arithmetic operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c59c3f27-0fe0-4e8f-81d2-1ade3e0ecc41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.0+/-0\n",
       "1    0.0+/-0\n",
       "2    0.0+/-0\n",
       "Name: A1, dtype: UncertaintyDtype"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A1\"] - df[\"A1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d51eca57-3c28-4fc2-a218-a8447182f30c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.000+/-0.014\n",
       "1    0.000+/-0.028\n",
       "2      0.00+/-0.04\n",
       "dtype: UncertaintyDtype"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A1\"] - df[\"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a6693ea-fc77-46a4-90b7-8b055785cf1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1    2.000+/-0.012\n",
       "A2    2.000+/-0.012\n",
       "B     2.000+/-0.012\n",
       "dtype: UncertaintyDtype"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31c121a2-cd87-487b-94d9-62db1253fa9b",
   "metadata": {},
   "source": [
    "# Other operations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "35cd02f5-90f3-4f02-82b8-1b952d5fc042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0  A1    1.000+/-0.010\n",
       "   A2    1.000+/-0.010\n",
       "   B     1.000+/-0.010\n",
       "1  A1    2.000+/-0.020\n",
       "   A2    2.000+/-0.020\n",
       "   B     2.000+/-0.020\n",
       "2  A1    3.000+/-0.030\n",
       "   A2    3.000+/-0.030\n",
       "   B     3.000+/-0.030\n",
       "dtype: UncertaintyDtype"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = df.stack()\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "95d5397d-9b4b-469f-b545-a287f1ac5fbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.000+/-0.010    2\n",
       "2.000+/-0.020    2\n",
       "3.000+/-0.030    2\n",
       "1.000+/-0.010    1\n",
       "2.000+/-0.020    1\n",
       "3.000+/-0.030    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e3bd0e46-f422-4833-bd26-61edbc045091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>A1</th>\n",
       "      <th>A2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.000+/-0.022</td>\n",
       "      <td>3.000+/-0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.000+/-0.030</td>\n",
       "      <td>3.000+/-0.030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A1             A2\n",
       "C                              \n",
       "1  3.000+/-0.022  3.000+/-0.022\n",
       "2  3.000+/-0.030  3.000+/-0.030"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"C\"] = [1, 1, 2]\n",
    "df.groupby(\"C\")[[\"A1\", \"A2\"]].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16a5684a-b81a-45f6-9fc9-a54ca940fd38",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "596da97a-260d-4459-a8e6-181bade084a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vJjk5mYceeijPOMMwqFKlSkHieIUOHTowf/58AJKSkhg5ciR33303Bw4csDiZiIj802srhxKT8j6Z/jZCXG4eqfwAfe4aYXWsEuOSrmFJSUkBIDQ0/58f//tbiKtXr55neXp6OtWrV8flcnHjjTcyYcIEGjRocM7tOBwOHA5H7vPU1NQCpi8e7HZ7bpGrUqUKQ4cOpVWrVhw7doyKFStanE5ERE6mHWfkkm7E+Z8Am406Dh/G3P429a9uYnW0EuWiC4tpmkRHR9OyZUvq16+fr3USExNZv349ixYtyrO8Tp06xMTEcN1115GamsrMmTNp0aIFu3btolatWmfd1pQpUxg3btxFZz/lPHVR616qQN9ADMO4qHXT09NZuHAhV199NeXLly/kZCIiUlBbdq1nyldD+N0fDNPkTncNxvVZSoA9yOpoJc5FF5aBAweye/duNm/enO91YmJiKFu2LF26dMmzvGnTpjRt2jT3eYsWLWjYsCH//e9/mTVr1lm3NWzYMKKjo3Ofp6amEhERka8cp5ynaLLImua77YFtBPnl/x/y+++/T+nSpQHIyMggLCyM999/H5utQJcfiYhIIZu1/BneTd/AKX8bZV1unqjalwfaP2d1rBLrogrLoEGDWLt2LZs2baJatWr5Wsc0TebNm0evXr3w9/c/71ibzUbjxo3Zu3fvOcfY7XbsdnuBchdHrVu35rXXXgPgzz//5NVXX+XOO+/kq6++OuO0moiIXH7JKccYvvQ+NttPgs1GPYcv49rOo3aNc1/GIJeuQIXFNE0GDRrEqlWr2LhxIzVq1Mj3unFxcezbt49+/frl6/fs3LmT6667riDx8i3QN5BtD2y7LNvOz+8uiFKlSnH11VfnPm/UqBEhISG89dZbTJw4sbDjiYjIeXzx7Vpe+GYEB+ynTwHdZV7NuL5L8fcv+W+grVagwjJgwAAWLVrEmjVrCA4OJikpCTj9SaDAwNN/iIcNG0ZCQgILFizIs+7cuXNp0qTJWa93GTduHE2bNqVWrVqkpqYya9Ysdu7cyZw5cy52XudlGEaBTst4EsMwsNlsnDplzTU4IiLeavrSgSzK3IjD3yDU6eaJiP7c3y7K6lheo0CF5e9TE7fddlue5fPnz6dv377A6Qtr//2R25SUFGJjY5k5c+ZZt3vy5EkeffRRkpKSCAkJoUGDBmzatImbb765IPFKJIfDkVsMk5OTmT17Nunp6XTq1MniZCIi3uFY8mFGLu/BFnsK2Ayuy/JjbIcYrql+vdXRvEqBTwldSExMzBnLQkJCyMzMPOc6M2bMYMaMGQWJ4jU+/PBDwsLCAAgODqZOnTosX778jNIoIiKF7/OvY3lx1xgO2Q1spkknajO23xJ8ff2sjuZ19F1CHiwmJuasBVBERC4vt8vFtGVPsjTrSxx+BhWcbgbWGEDX25+0OprXUmERERH5h6TjBxm5sgfb7OlgM7ghy58Jdy+kRtU6VkfzaiosIiIif/k4fjHTfpjEYbuBj2lyr60+I/q9o1NAHkCFRUREvJ7b5eLFJY+wLOcrcvwMKjrdPH11FJ1v7W91NPmLCouIiHi1w8f+YOSqHnxtzwTDoEFWAJPuWURE2Nm/GkasocIiIiJea93mBcz4+UWS7Aa+psl9Pjcyov//sPn4WB1N/kWFRUREvI7b5WLyooeIdX2L08+gco6bqDrPcVfLvlZHk3NQYREREa9yMHEvI9c+wLcBWWAY3JQVxIR7l1Kt0pVWR5PzUGERERGvsXbTXGbunc7RABt+pkl3v5sY2n+uTgEVAyosIiJS4jmdOUxa1JvV7u9w+toIyzEZXG847Zs9YHU0yScVFhERKdH2J/zM6Pd7sjPAAYbBzY7STLpvGVUqRFgdTQrAZnUAubCkpCQGDRpEzZo1sdvtRERE0KlTJz799FOro4mIeLRVn79Ovw+7sjPAgb/bpJd/M97qt1llpRjSERYP9/vvv9OiRQvKli3Liy++yPXXX09OTg4fffQRAwYM4Oeff7Y6ooiIx3E6cxi/8AHWmj/h8rVRNcdkyPVjuf3mblZHk4ukwuLhnnzySQzD4KuvvqJUqVK5y+vVq8fDDz9sYTIREc+078D3jP6wN9/Zc8AwaOYow6Tuy6lYLtzqaHIJvLKwmKaJeeqUJb/bCAzEMIx8jf3zzz/58MMPmTRpUp6y8reyZcsWcjoRkeJt2YZZvHrgDU7YbdjdJg8EteKZnv/Vp4BKAO8sLKdOsadhI0t+d+1vv8EICsrX2H379mGaJnXq6BtCRUTOJzvbwbiF9/O+sRe3r42IbJOhDSdxa6POVkeTQuKVhaW4ME0TIN9HZEREvNEvf+xk9EcP8YPdCRi0cJRlUo/llC9bxepoUoi8srAYgYHU/vYby353ftWqVQvDMPjpp5/o0qXL5QslIlJMLfpoGq8nzCPZbiPQ7ebB0m14us9Mq2PJZeCdhcUw8n1axkqhoaG0b9+eOXPm8NRTT51xHcvJkyd1HYuIeKUsRyZjF97POttvmD42qmfD841fouWNHa2OJpeJ7sPi4V599VVcLhc333wzsbGx7N27l59++olZs2bRrFkzq+OJiBS5H37dTu//NecDn/2YhkErR3nevf9zlZUSziuPsBQnNWrU4Ntvv2XSpEkMHjyYxMREKlasSKNGjXjttdesjiciUqTeWfcCbyS9Q4rdRpDbTe+Qjgzo85LVsaQIqLAUA2FhYcyePZvZs2dbHUVExBKZWRmMWdidD30Pgo+NmtkwrOkMml7XzupoUkRUWERExKPt3ruV8Z8/xh67G4DW2RWZ8J8VhJQOtTiZFCUVFhER8Vjz3hvP3GNLSbXbKOV281DZe3js3ilWxxILqLCIiIjHychMY9SibmzwOww+Nq7KNhjZfA431bvN6mhiERUWERHxKN/+GMfEzYPYaz9988w2OWFMfDCWUkHBFicTK6mwiIiIx3hzzSjmn1hJut1GsMvNwxW60f+ecVbHEg/gVYXl71vdezv9/yAiniYt4yQjF3XjM/8j4GPjGoeNUbe+xo21W1odTTyEVxQWn7++pTM7O5vAAtwav6TKzMwEwM/Pz+IkIiLw9Q+fMunLKH796xRQ+5xqjO+9gqCAM7+lXryXVxQWX19fgoKCOHbsGH5+fths3nmDX9M0yczM5OjRo5QtWza3yImIWOW1lUOJSXmfTLuNEJeb/pXvp+9do6yOJR7IKwqLYRiEhYWxf/9+/vjjD6vjWK5s2bJUqaJvMRUR66Sk/8mIxV2J8z8ONht1HD6Muf1t6l/dxOpo4qEKVFimTJnCypUr+fnnnwkMDKR58+ZMnTqV2rVrn3OdjRs30rp16zOW//TTT9SpUyf3eWxsLKNGjeLXX3/lqquuYtKkSdx7770FiXde/v7+1KpVi+zs7ELbZnHk5+enIysiYqn43R8yedtz/O4PhmnSwX0l4/ssI8Du+V9KK9YpUGGJi4tjwIABNG7cGKfTyYgRI2jXrh0//vjjGd8k/G979uyhTJkyuc8rVqyY+7/j4+OJjIxkwoQJ3HvvvaxatYoePXqwefNmmjQpvLZts9kICAgotO2JiEjB/Hd5FO+kf8wpfxtlXW4eD+/Dgx2GWB1LigHDvISPjBw7doxKlSoRFxdHq1atzjrm7yMsycnJlC1b9qxjIiMjSU1NZf369bnLOnToQLly5Vi8eHG+sqSmphISEkJKSkqeYiQiItZLTjnGiKVd+cKeDEBdhy9j27zNtTUbWZxMrJbfv9+XdPVpSkoKAKGhF/4+hwYNGhAWFsYdd9zB559/nudn8fHxtGuX9wus2rdvz5YtW865PYfDQWpqap6HiIh4ni++XUvPZbfzhT0ZwzS5230V7/TdqrIiBXLRF92apkl0dDQtW7akfv365xwXFhbGm2++SaNGjXA4HLzzzjvccccdbNy4MfeoTFJSEpUrV86zXuXKlUlKSjrndqdMmcK4cbqZkIiIJ5u+dCCLMzeS5W8Q6nTzRER/7m8XZXUsKYYuurAMHDiQ3bt3s3nz5vOOq127dp6Lcps1a8bBgwd5+eWX85xGMgwjz3qmaZ6x7J+GDRtGdHR07vPU1FQiIiIKOg0REbkMjiUfZuTyHmyxp4DNoL7Dj3HtY7im+vVWR5Ni6qIKy6BBg1i7di2bNm2iWrVqBV6/adOmvPvuu7nPq1SpcsbRlKNHj55x1OWf7HY7dru9wL9bREQur8+/juXFXWM4ZDewmSadqM3ovovw99d/s+XiFegaFtM0GThwICtXruSzzz6jRo0aF/VLd+zYQVhYWO7zZs2asWHDhjxjPv74Y5o3b35R2xcRkaLndrl4ecnjPPf9GA75GZR3uhkV8QQT+8aqrMglK9ARlgEDBrBo0SLWrFlDcHBw7lGRkJCQ3FveDxs2jISEBBYsWADAK6+8wpVXXkm9evXIzs7m3XffJTY2ltjY2NztPv3007Rq1YqpU6fSuXNn1qxZwyeffHLB000iIuIZjpxIYMSKbmwLSAebwfVZ/kzouICaEfWsjiYlRIEKy2uvvQbAbbfdlmf5/Pnz6du3LwCJiYkcOHAg92fZ2dk8++yzJCQkEBgYSL169fjggw/o2LFj7pjmzZuzZMkSRo4cyahRo7jqqqtYunRpod6DRURELo8NW5fy8vcTOBxg4GOadDHqM7LfO/j66vvKpPBc0n1YPInuwyIiUrTcLhcvLnmE5dlfkW0zqOh08/RVT9P5tketjibFSH7/fnvFdwmJiEjhOnzsD0au6sHX9kywGTRwBDDh7oVUD7/G6mhSQqmwiIhIgaz/8h1m/DSVRLuBr2lyn8+NjOj3P2z6njK5jFRYREQkX9wuF5MXPUSs61ucfgaVc9xE1XmOu1r2tTqaeAEVFhERuaCDSb8xas39fBNwCgyDRlmBTLx3GdUqXWl1NPESKiwiInJe722ax8xfpnEkwIafadLd7yaG9p+rU0BSpFRYRETkrJzOHCYv6sMq926cfjbCckyi6w6jQ/MHrY4mXkiFRUREzvDH4V8Y+d4D7AxwgGFwc1YpJty3lPCK1a2OJl5KhUVERPJY/fkbzPptFscCbPi7TSLtzXi25+s6BSSWUmERERHg9Cmg8QsfZK35Iy5fG1VzTJ69bgxtmnS3OpqICouIiMC+A98zZn0fdgdkg2HQ1BHMxK7LqVy+qtXRRAAVFhERr7f8k9nM+eM1TgTYsLtNHghqxTM9/6tTQOJRVFhERLxUdraDcQvv531jL25fGxHZJkMaTOC2m+61OprIGVRYRES80C9/7GTMRw/xvd0JGLRwlGVSj+WUL1vF6mgiZ6XCIiLiZZZ8PJ1XD84l2W4jwG3yYKnbeabPLKtjiZyXCouIiJfIcmQybuH9fGD7DdPXRvVseL7xi7S8saPV0UQuSIVFRMQL/PTbN4z5pB8/2V2AQavsUCbdH0vZ4ApWRxPJFxUWEZES7p11L/BG0juk2G0Eud30KnMnA7u+bHUskQJRYRERKaGyHJmMerc7H/oeAB8bNbJheNMZNL2undXRRApMhUVEpATavXcr4z9/jD12NwCtsysy4T8rCCkdanEykYujwiIiUsLMf38Cbx9dQqrdRim3m4fK3sNj906xOpbIJVFhEREpITIy0xi1qBsb/A6Dj42rsg1GNp/DTfVuszqayCVTYRERKQG+/fkLJm4awF67CcAdOVWY8MBygkuVtTaYSCFRYRERKebeXjuaecdjSbPbCHa5ebhCV/rfM97qWCKFSoVFRKSYSss4yahF3fjU/wj42KjlMBh96xvcWLul1dFECp0Ki4hIMfT1D58y6csofv3rFFC7nKpM6B1LUEApi5OJXB4qLCIixcxrq4bxv5NrybDbKONy80jl++l71yirY4lcViosIiLFREr6n4xc3JWN/sfBZqO2w8bo1m9xfa2mVkcTuexUWEREioH43R8yedtz/O5/+vmdzisY32c5AfYga4OJFBEVFhERD/ffFdG8m/YRmf42yrrcPBbWm553DrU6lkiRUmEREfFQySnHGLG0G1/Y/wSbjboOX8a2eZtrazayOppIkVNhERHxQJt3vM8L24fxhx0M0+Qu8yrG9V2Gv7/d6mgilrAVZPCUKVNo3LgxwcHBVKpUiS5durBnz57zrrNy5Uratm1LxYoVKVOmDM2aNeOjjz7KMyYmJgbDMM54ZGVlFXxGIiLF3CvLBhG183n+8IdyTjfDwx9mykNrVFbEqxWosMTFxTFgwAC2bt3Khg0bcDqdtGvXjoyMjHOus2nTJtq2bcu6dev45ptvaN26NZ06dWLHjh15xpUpU4bExMQ8j4CAgIublYhIMXTiZBKPv9mSuac2kmUzqO/w5e02C7m/XbTV0UQsZ5imaV7syseOHaNSpUrExcXRqlWrfK9Xr149IiMjGT16NHD6CMszzzzDyZMnLzYKqamphISEkJKSQpkyZS56OyIiVti4fRVTd47ikJ+BzTTpZNZm9IOLdFRFSrz8/v2+pGtYUlJSAAgNDc33Om63m7S0tDPWSU9Pp3r16rhcLm688UYmTJhAgwYNzrkdh8OBw+HIfZ6amlrA9CIi1nO7XExfPoAlpzbj8DMo73QzoPoTdG8z0OpoIh6lQKeE/sk0TaKjo2nZsiX169fP93rTpk0jIyODHj165C6rU6cOMTExrF27lsWLFxMQEECLFi3Yu3fvObczZcoUQkJCch8REREXOxUREUscOZHAY3Nv4X+OL3HYDK7P8mdeu2UqKyJncdGnhAYMGMAHH3zA5s2bqVatWr7WWbx4Mf3792fNmjW0adPmnOPcbjcNGzakVatWzJo166xjznaEJSIiQqeERKRY2LB1KdO+n0CCn4GPadLZqMeoB9/F19fP6mgiReqynhIaNGgQa9euZdOmTfkuK0uXLqVfv34sX778vGUFwGaz0bhx4/MeYbHb7djtOrcrIsWL2+XixaWPstyxjWw/g4pON0/VfIourR+zOpqIRytQYTFNk0GDBrFq1So2btxIjRo18rXe4sWLefjhh1m8eDF33XVXvn7Pzp07ue666woST0TEox0+9gejVkbyVUAG2AxuzLIzsdMiqodfY3U0EY9XoMIyYMAAFi1axJo1awgODiYpKQmAkJAQAgMDARg2bBgJCQksWLAAOF1WevfuzcyZM2natGnuOoGBgYSEhAAwbtw4mjZtSq1atUhNTWXWrFns3LmTOXPmFNpERUSstP7Ld5jx01QSAwx8TZN7fW5gZP8F2Hx8rI4mUiwUqLC89tprANx22215ls+fP5++ffsCkJiYyIEDB3J/9sYbb+B0OhkwYAADBgzIXd6nTx9iYmIAOHnyJI8++ihJSUmEhITQoEEDNm3axM0333wRUxIR8Rxul4spix9mhfMbnH4GlXPcPH3NYDq1etjqaCLFyiXdh8WT6D4sIuJpDib9xqg19/NNwCkAGmUFMqHzEiKq1LQ4mYjnKJL7sIiIyNm9t2keM3+ZxpEAG36mSVffmxjWf65OAYlcJBUWEZFC5Ha5mLiwF6vcu3H62QjLMYmuO4wOzR+0OppIsabCIiJSSP44/Asj33uAnQEOMAwaZ5Vi4n1LCa9Y3epoIsWeCouISCFY/fkbzPptFscCbPi7TXrYm/Jczzd0CkikkKiwiIhcAqczh/ELH2St+SMuXxvhOSbP1h9F26aRVkcTKVFUWERELtJvB39g1Lre7A7IBsOgqSOYiV2XU7l8VaujiZQ4KiwiIhdh+SezmfPHa5wIsGF3m/wn8Baies7WKSCRy0SFRUSkALKzHYxb+B/eN37B7WsjIttkSIMJ3HbTvVZHEynRVFhERPLplz92Muajh/je7gQMWjhCmNRjBeXLVrE6mkiJp8IiIpIPSz6ezqsH55JstxHgNnmwVGue6fNfq2OJeA0VFhGR88jOdjBmYQ8+MH7F9LVRPRuev+kFWja42+poIl5FhUVE5Bx++u0bxnzSj5/sLsDgFkcokyJXUC6kotXRRLyOCouIyFm8u34qbyQu4KTdRpDbTc/g9gzqM93qWCJeS4VFROQfshyZjHq3Ox/6HgAfG1dmw/Am02h2fQero4l4NRUWEZG/7N67lfGfP8YeuxuA1tkVmfCfFYSUDrU4mYiosIiIADEfTOCtI0tItdso5XbTp+w9PHHvFKtjichfVFhExKtlZmUw6t2ufOyXAD42rnIYjGgxi8b17rA6moj8gwqLiHitb3/+gombBrDXbgJwR3ZlJjy4guBSZa0NJiJnUGEREa/09trRzDseS5rdRrDLzcMVutL/nvFWxxKRc1BhERGvkpZxklGLu/OpXxL42KjlMBjZ6nUa1rnF6mgich4qLCLiNbb/sJGJXz7Fr3+dAmqXE874nisoFRRscTIRuRAVFhHxCm+sGsb8k2vJsNso43LTv9L9PHT3KKtjiUg+qbCISImWkv4noxZ343P/Y2CzUdthY3Trt7i+VlOro4lIAaiwiEiJtfW7j5m8dTD7/U8/v9N5BWN7LyMooJS1wUSkwFRYRKREmh37LO+krifT30aIy83jYb3peedQq2OJyEVSYRGREuVk2nFGLOnKJv8/wWbjWocP49rEcG3NRlZHE5FLoMIiIiXG5h3v88L2YfzhD4Zpcpe7JmP6LCHAHmR1NBG5RCosIlIivLLsKRZmfEaWv0E5p5vHqz3MA+0HWx1LRAqJCouIFGsnTiYxYll3vrSfBJtBfYcv49rP55rqN1odTUQKkQqLiBRbG7ev4sUdozhoN7CZJneb1zCm72L8/e1WRxORQqbCIiLFjtvlYsbygSw+9QUOf4PyTjcDqj9B9zYDrY4mIpeJrSCDp0yZQuPGjQkODqZSpUp06dKFPXv2XHC9uLg4GjVqREBAADVr1uT1118/Y0xsbCx169bFbrdTt25dVq1aVZBoIuIljpxI4LG5txDj2IzDZnB9lj9vt12qsiJSwhWosMTFxTFgwAC2bt3Khg0bcDqdtGvXjoyMjHOus3//fjp27Mgtt9zCjh07GD58OE899RSxsbG5Y+Lj44mMjKRXr17s2rWLXr160aNHD7Zt23bxMxOREueTbcvps6o9W+1p+Jgm93It/+u3lauvqG91NBG5zAzTNM2LXfnYsWNUqlSJuLg4WrVqddYxQ4cOZe3atfz000+5yx5//HF27dpFfHw8AJGRkaSmprJ+/frcMR06dKBcuXIsXrw4X1lSU1MJCQkhJSWFMmXKXOyURMQDuV0uXlr6GMscW8m2GVR0uhlUcxD3tn7c6mgicony+/e7QEdY/i0lJQWA0NDQc46Jj4+nXbt2eZa1b9+e7du3k5OTc94xW7ZsOed2HQ4HqampeR4iUvIkHT/II3Nb8m7ONrJtBjdm2Zl/5yqVFZEilPrxxxwa9BSmy2VZhosuLKZpEh0dTcuWLalf/9yHY5OSkqhcuXKeZZUrV8bpdHL8+PHzjklKSjrndqdMmUJISEjuIyIi4mKnIiIe6sMtC+m9+k6+sqfja5p0N67jf/23UT38GqujiXgFt8NB0oSJJDz1NGkbNnBy5UrLslz0p4QGDhzI7t272bx58wXHGoaR5/nfZ6H+ufxsY/697J+GDRtGdHR07vPU1FSVFpESwu1yMWVxP2Kd28nxM6ic4+apa6K5p1U/q6OJeI3sP/7gUFQUjh9PX9JRvn8/ynbpYlmeiyosgwYNYu3atWzatIlq1aqdd2yVKlXOOFJy9OhRfH19KV++/HnH/Puoyz/Z7Xbsdt1rQaSkOZj0G6PW3M83AafAMGiUFciEzkuIqFLT6mgiXiN13ToSR43GnZGBT7lyhE99gdLnuFa1qBTolJBpmgwcOJCVK1fy2WefUaNGjQuu06xZMzZs2JBn2ccff8xNN92En5/fecc0b968IPFEpJj7YHMMD73fiW8CTuFrmtzv05B5/eNVVkSKiDsri8TRY0iIHow7I4PAmxpRY/Uqy8sKFPAIy4ABA1i0aBFr1qwhODg496hISEgIgYGBwOlTNQkJCSxYsAA4/Ymg2bNnEx0dzSOPPEJ8fDxz587N8+mfp59+mlatWjF16lQ6d+7MmjVr+OSTT/J1uklEij+3y8XERb1Z5dqF089GWI5J1LVDubNFL6ujiXgNx2/7SYiKwrFnDxgG5R9/jIoDBmD4esY9Zgv0seZzXVMyf/58+vbtC0Dfvn35/fff2bhxY+7P4+LiiIqK4ocffiA8PJyhQ4fy+ON5r/BfsWIFI0eO5LfffuOqq65i0qRJ3HffffmeiD7WLFI8HUzcy4j3HmCHPQuAxo4gJt67jPCK1S1OJuI9UtauJXHsOMzMTHzKlyf8xamUbtGiSH53fv9+X9J9WDyJCotI8bNm45vM/HUmx3xt+LtNutubMCTyTWw+PlZHE/EK7lOnSJowkZS/Pv0T1KQJ4S+9iF+lSkWWIb9/vz3jOI+IeBWnM4cJC3uyxvwBl6+N8ByTZ+uPom3TSKujiXgNx969HIqKInvfr2AYVBgwgApPPI7hoW8YVFhEpEj9dvAHRq/rza6AbDAMmjqCmdh1OZXLV7U6mohXME2TlJWrSJowATMrC5+KFaj60suUatrE6mjnpcIiIkVmxadzmP37q5wIsGF3m9wf2JLonnN0CkikiLgzMkgaP56UNWsBKNW8OeEvvYjvX7cZ8WQqLCJy2WVnOxi/8AHeM/bg9rVRLcdkyA3jaN24q9XRRLxG1p49JDwTRfb+/WCzUfGppyj/6CMYtkv6lp4io8IiIpfVL3/sZsxHffnengMYtHCEMKnHCsqXrWJ1NBGvYJomJ5ct58jkyZgOB76VK1N12ssE3XST1dEKRIVFRC6bJR/P4NWDb5NstxHgNnmwVGue6fNfq2OJeA1XejpJo8eQum4dAKVubUX4Cy/gW66cxckKToVFRApddraDMQt78IHxK6avjSuy4flGk7ml4T1WRxPxGlk//sihqChy/jgAvr5UinqG0IceKjangP5NhUVECtVPv33D2E/686PdCRjc4ghlUuQKyoVUtDqaiFcwTZPkRYs4+sJUzJwcfMPDqDptGkENGlgd7ZKosIhIoVn44Yu8fvh/nLTbCHK76RncnkF9plsdS8RruFJTSRw5irSPPwag9O23Ez55Ej5ly1obrBCosIjIJctyZDL63e6s9z0APjauzIbhTabR7PoOVkcT8RqnvvuOhKhocg4dAj8/Kj87mHK9e5/za3WKGxUWEbkk3+/bxrjPHuNnuwuA27IrMPE/sYSUDrU4mYh3ME2T5AULOPLyNMjJwa9aNarOmE7gdddZHa1QqbCIyEX73weTeOvIIlL+OgXUN+RunrhvqtWxRLyG6+RJDg8fQfpnnwEQ3K4dYRMn4FMCv1NPhUVECiwzK4PR73bjI79D4GPjKofBiBazaFzvDqujiXiNzB07SBg8GOfhRAw/Pyo9P5RyDzxQYk4B/ZsKi4gUyM49m5kQN4Bf7G4Abs+uzMQHVxBcqqy1wUS8hOl28+f8+Ryd8Qo4nfhVv4JqM2YQULeu1dEuKxUWEcm3t9eOYd7xFaTZbQS73PQtfx+Pdp5gdSwRr+FMTubw88+TEbcJgDIdO1Jl/Dh8Spe2ONnlp8IiIheUkZnGiEX38alfEvjYqOUwGNnqdRrWucXqaCJeI3P7dhIGP4vzyBEMu53Kw4dTtkf3EnsK6N9UWETkvLb/sJGJW57iV38TgHY54YzvuYJSQcEWJxPxDqbbzYk33+LYf/8LLhf+NWpQ9ZUZBNSubXW0IqXCIiLn9MaqYcw/uZYMfxtlXG76VYzk4U6jrY4l4jWcJ05w+LkhZGzZAkBI53uoMno0tlKlLE5W9FRYROQMKel/MmpxNz73PwY2G7UdNkbf9gbXX9Pc6mgiXiNj6zYSnnsW17HjGAEBVBk1ipD77vWaU0D/psIiInls/e5jpmwdzG/+p593cEYwrvdyggK87x2diBVMl4vjr73O8VdfBbcb/6uvotqMGdhr1bI6mqVUWEQk15yVz7EgZR2Z/jZCXG4eq9KLXh2ftzqWiNfIOXqUw0OGkrl1KwAhXe+jysiR2AIDLU5mPRUWEeFk2nFGLO7GJvsJsNm41uHDmDvmU++qm6yOJuI10r/8ksNDhuI6cQIjKIiwMaMJ6dzZ6lgeQ4VFxMtt3rmOF74eyh92MEyTju6ajO2zhAB7kNXRRLyC6XRybPZsTrzxJpgm9tq1qTpjOvaaNa2O5lFUWES82MzlT7Mw/RNO+dso53TzeLWHeaD9YKtjiXiNnCNHSBg8mFPbvwGgbGQklYc9jy0gwOJknkeFRcQLnTiZxIhl3fnSfhJsNuo5fBnffj7XVL/R6mgiXiN90yYOD30eV3IytlKlqDJ+HCF33WV1LI+lwiLiZeK+WcPUb0dw0G5gM03uNmsxpu8S/P3tVkcT8QpmTg7HZs7kxNtzAbDXvZZqM2bgX726xck8mwqLiJdwu1y8smIQizI34fA3KO908+QVj9Gj7VNWRxPxGjmHD5Mw+FlO7dgBQLkHH6TSkOew2fWG4UJUWES8wLHkwwxf3o2t9jSwGVzn8GN8hwVcfUV9q6OJeI20zz7j8LDhuFNSsAUHEzZxImXat7M6VrGhwiJSwn2ybTkvfzeOBLuBj2lyj3Etox9ehK+vn9XRRLyCmZ3N0WnT+fN//wMg4LrrqDp9Gv4RERYnK15UWERKKLfLxctLH2epI55sP4MKTjdP1RzEva0ftzqaiNfIPnSIhOjBZO3eDUBon95UGjwYw9/f4mTFjwqLSAmUdPwgI1b24Ct7OtgMbsyyM/7ud6lRtY7V0US8RurHH5M4YiTutDRsISGET5lM8O23Wx2r2LIVdIVNmzbRqVMnwsPDMQyD1atXn3d83759MQzjjEe9evVyx8TExJx1TFZWVoEnJOLtPopfRO/Vd/KVPR1f06SbUZ/5/eJVVkSKiDs7m6QJE0l46mncaWkE3nADNVfGqqxcogIfYcnIyOCGG27goYceomvXrhccP3PmTF544YXc506nkxtuuIHu3bvnGVemTBn27NmTZ1mAbpwjkm9ul4upS/qxPGc7OX4GlXPcPHVNNPe06md1NBGvkf3HHyRERZP1448AlO/fj4pPP43hp2vGLlWBC8udd97JnXfeme/xISEhhISE5D5fvXo1ycnJPPTQQ3nGGYZBlSpVChpHRIBDR39n1KpItgdkgmHQMCuAiZ2XElFFt/YWKSqp69eTOHIU7owMfMqWJXzqC5S+9VarY5UYRX4Ny9y5c2nTpg3V/3WDnPT0dKpXr47L5eLGG29kwoQJNGjQ4JzbcTgcOByO3OepqamXLbOIJ/tgcwwzfn6JIwE2fE2Trj4NGd5/PjYfH6ujiXgFd1YWR6a8wMmlSwEIbNSIqtNexk9vwgtVkRaWxMRE1q9fz6JFi/Isr1OnDjExMVx33XWkpqYyc+ZMWrRowa5du6hVq9ZZtzVlyhTGjRtXFLFFPJLb5WLSoj6sdO3E6WejSo5JVJ0hdGzZ2+poIl7D8dt+EqKicOzZA4ZB+ccepeLAgRi++kxLYTNM0zQvemXDYNWqVXTp0iVf46dMmcK0adM4fPgw/uf5SJfb7aZhw4a0atWKWbNmnXXM2Y6wREREkJKSQpkyZQo0D5Hi5mDiXka89wA77KcvTG/sCGLivcsIr6hbe4sUlZS1a0kcOw4zMxOf0FDCX3qR0i1aWB2r2ElNTSUkJOSCf7+LrAKapsm8efPo1avXecsKgM1mo3Hjxuzdu/ecY+x2O3bdyli80JqNbzLz15kcs9vwM016+N3MkJ5v6RSQSBFxnzpF0sSJpMSuBCCoSRPCX3oRv0qVLE5WshVZYYmLi2Pfvn3063fhTyyYpsnOnTu57rrriiCZSPHgdOYwcWEvVpvf4/K1EZ5jMrjeCNo1+4/V0US8hmPfvtOngPbuA8OgwpNPUuHJJzD0huGyK3BhSU9PZ9++fbnP9+/fz86dOwkNDeWKK65g2LBhJCQksGDBgjzrzZ07lyZNmlC//pnfXTJu3DiaNm1KrVq1SE1NZdasWezcuZM5c+ZcxJRESp7fDv7A6HW92RWQDYZBk6zSTOq2gsrlq1odTcRrnFy5iqTx4zGzsvCpWIGqL71EqaZNrY7lNQpcWLZv307r1q1zn0dHRwPQp08fYmJiSExM5MCBA3nWSUlJITY2lpkzZ551mydPnuTRRx8lKSmJkJAQGjRowKZNm7j55psLGk+kxIn97FX+u38OJwJs2N0mkQEtGNzzVZ0CEiki7owMksZPIGXNGgBKNW9O+ItT8a1QweJk3uWSLrr1JPm9aEekuHA6cxj77v28xx7chkG1HJMhN4yjdeML37BRRApH1p5fSIiKIvu338Bmo+JTgyj/6KMYtgLfKF7OweMuuhWR/Pvlj92M/bAv3wXkAAbNHSFM7L6MiuXCrY4m4hVM0+Tk8uUcmTQZ0+HAt3Jlqk57maCbbrI6mtdSYRHxMEs+nsFrB9/mzwAbAW6T/wTdRnSf2VbHEvEarvR0kkaPIXXdOgBKtbqF8KlT8S1XzuJk3k2FRcRDZGc7GLMwkg+MfZi+Nq7IhucbTeaWhvdYHU3Ea2T9+COHoqLI+eMA+PhQKTqK0Ice0ikgD6DCIuIB9uzfwegND/Oj3QkY3OIox6TIWMqFVLQ6mohXME2T5MWLOTrlBcycHHzDwqg6fRpB5/mKGClaKiwiFlv44Yu8fvh/nLTbCHS76VW6HYP6zLA6lojXcKWlkThyFGkffQRA6dtvJ3zyJHzKlrU2mOShwiJikSxHJqMX9uBD2++YPjauzIZhN79M8xvy/23oInJpTn33HQlR0eQcOgR+flQaHE1onz4YhmF1NPkXFRYRC3y/bxvjPnuMn+0uwODW7ApMvH85ZYN1XweRomCaJsnvvMORl16GnBz8qlal6ozpBF5/vdXR5BxUWESK2P8+mMRbRxaRYrcR5HbTJ+QunrzvRatjiXgN18mTHB4xkvRPPwUguG1bwiZNxEf38PJoKiwiRSQzK4PR73bjI79D4GOjZjaMaDqTm69rY3U0Ea9xaudODkVH4zyciOHnR6WhQyn34AM6BVQMqLCIFIHdv2xh3MYn+MXuBuD27MpMfGAFwaXKWhtMxEuYbjd/zo/h6IwZ4HTid8UVp08B1atndTTJJxUWkcts3nvjePvYMtLsNkq73DxUvguPdp5kdSwRr+FMTibx+WGkx8UBUKbjnVQZPx6f0qUtTiYFocIicplkZKYxclFXPvFLBB8bVzsMRrV8lYZ1b7U6mojXyPzmGxIGP4szKQnD35/KI0ZQtkd3nQIqhlRYRC6Db3+MY8LmQeyzn/5u0bY54UzouYJSQcEWJxPxDqbbzYm33ubYrFngcuF/5ZVUnfkKAbVrWx1NLpIKi0ghe2P1cGL+XEO63Uawy03/ipE83Gm01bFEvIbzxAkODxlKxpdfAlDmnk6EjRmDrVQpi5PJpVBhESkkKel/MmpxNz73PwY+Nq5x2Bhz2xtcf01zq6OJeI2MbV9x+NlncR47hhEQQJVRIwm57z6dAioBVFhECsFX333CpK1R/OZ/+nl7ZzXG915BUIDe0YkUBdPl4vjrr3N8zqvgduN/9VVUmzEDe61aVkeTQqLCInKJ5qx8jgUp68j0txHicvNolQfp3XG41bFEvIbz2DESnhtC5tatAITcdx9VRo7AFhRkcTIpTCosIhfpZNpxRizuxib7CbDZqOPwYewd86l31U1WRxPxGhlbtpDw3BBcJ05gBAURNmY0IZ07Wx1LLgMVFpGLsGXXeqZ8NYTf7WCYJh3dNRnbZwkBdr2jEykKptPJsTlzOPH6G2Ca2K+5hqqvzMBes6bV0eQyUWERKaBZy5/h3fQNnPK3Uc7l5vGqfXmg/XNWxxLxGjlHjnB48LNkbt8OQNkePag8fBi2gACLk8nlpMIikk/JKccYtvQ+vrSfBJuNeg5fxrWdR+0aDayOJuI10r/4gsNDhuJKTsYWFESVCeMJuesuq2NJEVBhEcmHuG/WMPXbERy0G9hMk7vNWozpuwR/f7vV0US8gpmTw7FZszjx1tsA2OteS7Xp0/G/8kprg0mRUWEROQ+3y8UrK55iUWYcDn+DUKebAVc8Ro+2T1kdTcRr5CQmkhA9mFM7dgBQ7oEHqDR0CDa73jB4ExUWkXM4lnyYEcu7E29PBZvBdQ4/xndYwNVX1Lc6mojXSPvscxKHDcOVkoKtdGnCJk6kTIf2VscSC6iwiJzFZ1+t4KXdYzn01ymge7iWMQ8vwtfXz+poIl7BzM7m6PQZ/BkTA0BA/fpUnTEd/4gIa4OJZVRYRP7B7XLx8rInWJq1hWw/gwpONwNrDKDr7U9aHU3Ea2QfOkRC9GCydu8GILRPbyoNHozh729xMrGSCovIX5KOH2Tkyh5ss6eDzeCGLDsT7n6XGlXrWB1NxGukbthA4vARuNPSsJUpQ/iUyQTfcYfVscQDqLCIAB/HL2baD5M4bDfwNU262Oozot87OgUkUkTc2dkcffElkt99F4DAG26g6vRp+FWtanEy8RQqLOLV3C4XU5f0Z3nO1+T4GVRyunnq6ig639rf6mgiXiP7wAESoqLJ+uEHAEL7PUylZ57B8NMbBvl/KizitQ4d/Z1RqyLZHpAJhkGDrAAm3bOIiDB9u6tIUUldv57EkaNwZ2TgU7YsYS9MIfi226yOJR5IhUW80rrNC5jx84skBZw+BdTVpyHD+8/H5uNjdTQRr+B2ODgyZQonlywFILBRI6pOexm/KlUsTiaeylbQFTZt2kSnTp0IDw/HMAxWr1593vEbN27EMIwzHj///HOecbGxsdStWxe73U7dunVZtWpVQaOJXJDb5WLiO70Yse9FkvwMquSYTLp6CCN7LVBZESkijv37+T3y/tNlxTAo/9hjVP9fjMqKnFeBC0tGRgY33HADs2fPLtB6e/bsITExMfdRq9b/H3aPj48nMjKSXr16sWvXLnr16kWPHj3Ytm1bQeOJnNPBxL30fbspS907cRoGN2UFMf+e9+nYsrfV0US8Rsp777G/azccP/+MT2goEW+9RaWoZzB8dcBfzs8wTdO86JUNg1WrVtGlS5dzjtm4cSOtW7cmOTmZsmXLnnVMZGQkqamprF+/PndZhw4dKFeuHIsXL85XltTUVEJCQkhJSaFMmTIFmYZ4gTVxbzNr3wyO+trwM026+zVm6P1v66iKSBFxnzpF0qRJpKyIBSDo5psJf+kl/CpXsjiZWC2/f78LfITlYjVo0ICwsDDuuOMOPv/88zw/i4+Pp127dnmWtW/fni1btpxzew6Hg9TU1DwPkX9zOnMYt+B+xu5/haO+NsJzTF6oPYJhD+p6FZGi4ti3j9979DhdVgyDCgMGcMX8eSorUiCX/RhcWFgYb775Jo0aNcLhcPDOO+9wxx13sHHjRlq1agVAUlISlStXzrNe5cqVSUpKOud2p0yZwrhx4y5rdine9if8zKj3e7IrwAGGQRNHaSbet4wqFXRrb5GicnLlKpImTMA8dQqfihWo+tJLlGra1OpYUgxd9sJSu3Ztateunfu8WbNmHDx4kJdffjm3sMDp00v/ZJrmGcv+adiwYURHR+c+T01NJULfMSF/if3sVWbvn8PxABt2t0mPgOY82/M1HVURKSLujAySxk8gZc0aAEo1b0b4iy/iW6GCxcmkuLLkKqemTZvy7l93MwSoUqXKGUdTjh49esZRl3+y2+3Y9dXi8i9OZw7j3n2A9/gJl6+Najkmz10/lttv7mZ1NBGvkbXnFxKiosj+7Tew2aj41CDKP/IIht4wyCUosmtY/mnHjh2EhYXlPm/WrBkbNmzIM+bjjz+mefPmRR1NirF9B76n99wmrDZ+xmUYNHOUYUHXj1VWRIqIaZokL1/O7z16kP3bb/hWqkT1/8VQ4fHHVVbkkhX4CEt6ejr79u3Lfb5//3527txJaGgoV1xxBcOGDSMhIYEFCxYA8Morr3DllVdSr149srOzeffdd4mNjSU2NjZ3G08//TStWrVi6tSpdO7cmTVr1vDJJ5+wefPmQpiieIOlG17h1QNv8edfp4AeCLqVZ3rO0ikgkSLiSs8gacwYUj/4AIBSt9xC+NQX8A0NtTiZlBQFLizbt2+ndevWuc//vo6kT58+xMTEkJiYyIEDB3J/np2dzbPPPktCQgKBgYHUq1ePDz74gI4dO+aOad68OUuWLGHkyJGMGjWKq666iqVLl9KkSZNLmZt4gexsB2MX3s8Hxl7cvjYisk2GNZrMLQ3vsTqaiNfI+uknEp6JIvuPP8DHh0pRzxD68MMYNksO4ksJdUn3YfEkug+L99mzfwdjNjzMD3YnAC0dZZkcuZJyIRUtTibiHUzT5OSSJRyZ8gJmdja+YWFUnTaNoIYNrI4mxUh+/37r1oJSLC366CVeT4gh2W4j0O2mZ+m2PNXnFatjiXgNV1oaiaNGk/bhhwCUbt2asMmT8C1XzuJkUlKpsEixkuXIZMzCSNbb9mP62LgyG4Y2fomWN3a88MoiUihOffc9CdHR5Bw8CL6+VHp2MKF9+pz3VhQil0qFRYqN7/dtY9xnj/Gz3QUY3Jpdnon3r6BssO7rIFIUTNMk+Z13OfLSS5CTg1/VqlSdMZ3A66+3Opp4ARUWKRYWrJvMm0kLSbHbCHK76R3SkQH3vWR1LBGv4UpJ4fCIEaR/8ikAwW3bEDZpEj66ZlCKiAqLeLTMrAxGL+zGR76HwMdGzWwY0XQmN1/XxupoIl7j1K5dJERFk3P4MIafH5WGDqXcgw/oFJAUKRUW8Vi7f9nCuI1P8IvdDcDt2ZWY+EAswaXKWhtMxEuYbjd/xvyPo9Ong9OJ3xVXUHX6dALr17M6mnghFRbxSPPeG8fbx5aRZrdR2uWmb2hnHusy2epYIl7DmZxM4vPDSI+LAyD4zg6EjR+PT3CwxcnEW6mwiEfJyExj5KKufOKXCD42rnYYjGr5Kg3r3mp1NBGvkfnttyRED8aZlITh70/l4cMpG9lDp4DEUios4jG+/TGOiZsHsdd++l6GbXLCmNgzllJBekcnUhRMt5sTb8/l2MyZ4HLhf+WVVH1lBgF16lgdTUSFRTzDm2tGMP/EatLtNoJdbvpX7MHDncZYHUvEazhPnODw0OfJ+Os73Mp06kSVMWPwKV3K4mQip6mwiKXSMk4yclFXPvM/Cj42rnHYGHXra9xYu6XV0US8RsZXX3F48LM4jx3DCAigyqiRhNx3n04BiUdRYRHLfPXdJ0zaGsVv/qeft3dWY3zvFQQF6B2dSFEwXS6Ov/EGx2fPAbcb/6uuouqM6QRcc43V0UTOoMIilnh15RD+l/IBmf42QlxuHqn8AH3uGmF1LBGv4Tx2jIQhQ8iM3wpAyL33UmXUSGxBQRYnEzk7FRYpUifTjjNySTfi/E+AzUYdhw9jbn+b+lc3sTqaiNfIiI8n4bkhuI4fxwgMpMqY0ZTt0sXqWCLnpcIiRWbLrvVM+WoIv/uDYZrc6a7BuD5LCbDrHZ1IUTCdTo6/+irHX3sdTBN7rVpUnfkK9po1rY4mckEqLFIkZi1/hnfTN3DK30Y5l5vHq/blgfbPWR1LxGvkHDnK4cGDydy+HYCy3btTecRwbAEBFicTyR8VFrmsklOOMXzpfWy2nwSbjXoOX8a1nUftGg2sjibiNdK/+ILDQ4biSk7GFhRElfHjCbn7LqtjiRSICotcNl98u5YXvhnBAfvpU0B3m7UY23cJ/v52q6OJeAXT6eTYzFmceOstAOzXXku1GdPxv/JKa4OJXAQVFrkspi8dyKLMjTj8DUKdbp684hEi2z5jdSwRr5GTmEjC4Gc59e23AJR74D9UGjoUm11vGKR4UmGRQnUs+TAjl/dgiz0FbAbXZfkxtkMM11S/3upoIl4j7fPPSXx+GK6UFGylSxM2cSJlOrS3OpbIJVFhkULz+dexvLhrDIfsBjbT5B6uZUy/Rfj6+lkdTcQrmNnZHJ3xCn/Onw9AQP36VJ0xHf+ICIuTiVw6FRa5ZG6Xi2nLnmRp1pc4/AwqON0MrDGArrc/aXU0Ea+RfSiBhMHRZO3aDUC53r2o9Oyz2Pz9LU4mUjhUWOSSJB0/yMiVPdhmTwebwQ1Z/ky4eyE1qurbXUWKStonn3B4+AjcqanYypQhfPIkgtu0sTqWSKFSYZGL9nH8Yqb9MInDdgMf0+ReW31G9HtHp4BEiog7O5ujL71M8jvvABBww/VUmz4dv6pVLU4mUvhUWKTA3C4XLy55hGU5X5HjZ1DJ6eapq6PofGt/q6OJeI3sAwdIiIom64cfAAh9+GEqRT2D4ac3DFIyqbBIgRw+9gcjV/Xga3smGAYNsgKYdM8iIsJqWR1NxGukfvghiSNH4U5PxyckhLCpLxB8221WxxK5rFRYJN/WbV7AjJ9fJMlu4GuadPVpwPD+Mdh8fKyOJuIV3A4HR154gZOLlwAQ2LAhVae9jF9YmMXJRC4/FRa5ILfLxeRFDxHr+hann0HlHDfRdYbSsWVvq6OJeA3H/v0kREXj+PlnAMo/+igVnxqE4av/jIt30L90Oa+DiXsZufYBvg3IAsPgpqwgJty7lGqVrrQ6mojXSHnvfZLGjMGdmYlPaCjhU6dS+paWVscSKVIqLHJOazfNZebe6RwNsOFnmnT3a8zQ/m/rFJBIEXGfOsWRyZM5uXwFAEE330z4Sy/hV7mSxclEip4Ki5zB6cxh0qLerHZ/h9PXRliOyeB6w2nf7AGro4l4Dcevv5LwTBSOvXvBMKjwxBNUGPAkht4wiJeyFXSFTZs20alTJ8LDwzEMg9WrV593/MqVK2nbti0VK1akTJkyNGvWjI8++ijPmJiYGAzDOOORlZVV0HhyifYn/MxDc5uxwvwep2Fws6M0C7qsV1kRKUInV61mf7fuOPbuxadCBa6YN/f09SoqK+LFClxYMjIyuOGGG5g9e3a+xm/atIm2bduybt06vvnmG1q3bk2nTp3YsWNHnnFlypQhMTExzyMgIKCg8eQSrPr8dR7+sCs7Axz4u016+TfjrX6bqVJB30MiUhTcmZkcfn4YicOGYZ46RVCzptRctZJSzZpZHU3EcgU+JXTnnXdy55135nv8K6+8kuf55MmTWbNmDe+99x4NGjTIXW4YBlWqVCloHCkETmcO4xc+wFrzJ1y+NqrlmDx3/Vhuv7mb1dFEvEbWL7+Q8EwU2b/9BjYbFQcNpPyjj+qoishfivwaFrfbTVpaGqGhoXmWp6enU716dVwuFzfeeCMTJkzIU2j+zeFw4HA4cp+npqZetswl2b4D3zP6w958Z88Bw6CZowyTui+nYrlwq6OJeAXTNDm5YgVHJk7CdDjwrVSJ8JdfotTNN1sdTcSjFPiU0KWaNm0aGRkZ9OjRI3dZnTp1iImJYe3atSxevJiAgABatGjB3r17z7mdKVOmEBISkvuI0NenF9iyDbPovyGS7+w52N0mDwW04vV+m1RWRIqIKz2Dw88NIWnUaEyHg1K33EKN1atUVkTOwjBN07zolQ2DVatW0aVLl3yNX7x4Mf3792fNmjW0Oc83ibrdbho2bEirVq2YNWvWWcec7QhLREQEKSkplClTpkDz8DbZ2Q7GLbyf9429uA2DiGyToQ0ncWujzlZHE/EaWT/9REJUNNm//w4+PlR85mnK9+uHYSvy95EilkpNTSUkJOSCf7+L7JTQ0qVL6devH8uXLz9vWQGw2Ww0btz4vEdY7HY7dru9sGOWeL/8sZPRHz3ED3YnYNDCUZZJPZZTvqyuHxIpCqZpcnLJEo5MeQEzOxvfKlWoOn0aQQ0bWh1NxKMVSWFZvHgxDz/8MIsXL+auu+664HjTNNm5cyfXXXddEaTzHos+msbrCfNIttsIdLt5sHQbnu4z0+pYIl7DlZZG4ujRpK3/EIDSt91G2JTJ+JYrZ3EyEc9X4MKSnp7Ovn37cp/v37+fnTt3EhoayhVXXMGwYcNISEhgwYIFwOmy0rt3b2bOnEnTpk1JSkoCIDAwkJCQEADGjRtH06ZNqVWrFqmpqcyaNYudO3cyZ86cwpij18tyZDJ24f2ss/2G6WOjejY83/glWt7Y0epoIl7j1Pc/kBAVRc7Bg+DrS6XBgwnt2wfDMKyOJlIsFLiwbN++ndatW+c+j46OBqBPnz7ExMSQmJjIgQMHcn/+xhtv4HQ6GTBgAAMGDMhd/vd4gJMnT/Loo4+SlJRESEgIDRo0YNOmTdysC88u2Q+/bmfcp/35ye4CDFo5yjPpPysoG1zB6mgiXsE0TZLfXcjRF1/EzMnBLzycqjOmE3jDDVZHEylWLumiW0+S34t2vMk7617gjaR3SPGxEeR20zukIwPue8nqWCJew5WSQuLIkaRt+ASA0m3uIHzSJHz+OrosIh540a0UncysDMYs7M6HvgfBx0bNbBjWdAZNr2tndTQRr3Fq1y4SogeTk5CA4edHpSFDKNfzQZ0CErlIKiwlzO5ftjB+4xPssbsBaJ1dkQn/WUFI6dALrCkihcE0Tf6cH8PR6dPB6cQvIoKqM2YQWL+e1dFEijUVlhJk3nvjmXtsKal2G6VdbvqGduaxLpOtjiXiNZzJySQOG076xo0ABHfoQNiE8fgEB1sbTKQEUGEpATIy0xi1qBsb/A6Dj42rHQYjWszhpnq3WR1NxGtkfvstCYOfxZmYiOHvT+XhwygbGalTQCKFRIWlmPv2xzgmbh7EXvvpa6fb5IQxsWcspYL0jk6kKJhuNyfensuxmTPB5cK/enWqvjKDgGuvtTqaSImiwlKMvblmFPNPrCTdbiPY5ebhCt3of884q2OJeA3nn39yeOjzZHzxBQBl7r6bKmPH4lO6lMXJREoeFZZiKC3jJCMXdeMz/yPgY+Mah41Rt77GjbVbWh1NxGtkfv316VNAR49i2O1UGTWSkK5ddQpI5DJRYSlmvv7hUyZ9GcWvf50Cap9TjfG9VxAUoHd0IkXBdLk48eabHPvvbHC78b/qKqrOmE7ANddYHU2kRFNhKUZeWzmUmJT3ybTbCHG56V/5fvreNcrqWCJew3n8OAnPPUdm/FYAQrp0ocroUdiCgixOJlLyqbAUAyfTjjNySXfi/I+DzUYdhw9jbn+b+lc3sTqaiNfIiI8n4bkhuI4fxwgMpMro0ZS9t4vVsUS8hgqLh9uyaz1TvhrC7/5gmCYd3Fcyvs8yAux6RydSFEyXi+NzXuX4a6+BaWKvVYuqr8zAftVVVkcT8SoqLB7sv8ujeCf9Y0752yjrcvN4eB8e7DDE6lgiXiPnyFEOP/ssmV9/DUDZ7t2oPHw4tsBAi5OJeB8VFg+UnHKMEUu78oU9GWw26jp8Gd92HrVrNLA6mojXSP9iM4eHDsX155/YgoKoMm4cIZ3utjqWiNdSYfEwX3y7lhe+GcEB++lTQHeZVzOu71L8/e1WRxPxCqbTybGZszjx1lsA2OvUoeqM6dhr1LA4mYh3U2HxINOXDmRx5kay/A1CnW6eiOjP/e2irI4l4jVyEhNJGPwsp779FoByD/yHSkOHYrPrDYOI1VRYPMCx5MOMXN6DLfYUsBnUd/gxrn0M11S/3upoIl4jbeNGEoc+jyslBVvp0oRNnECZDh2sjiUif1FhsdjnX8fy4q4xHLIb2EyTTtRm7MNL8PX1szqaiFcwc3I4OuMV/pw3D4CAevWoOmM6/ldcYXEyEfknFRaLuF0upi8fwJJTm3H4GZR3uhl45ZN0u2OA1dFEvEZOQgKHoqPJ2rUbgHK9elHpuWex+ftbnExE/k2FxQJHTiQwYkU3tgWkg83g+ix/JnRcQM2IelZHE/EaaZ98wuHhI3CnpmIrU4bwyZMIbtPG6lgicg4qLEVsw9alvPz9BA4HGPiYJl2M+ozs945OAYkUETM7myMvv0zygncACLjheqpOm45/taoWJxOR81FhKSJul4sXlzzC8uyvyPYzqOh08/RVT9P5tketjibiNbIPHiQhKpqs778HIPShh6gU9QyGTgGJeDwVliJw+NgfjFzVg6/tmWAzaOAIYMLdC6kerm93FSkqqR9+ROLIkbjT0/EJCSHshSkEt25tdSwRyScVlsts/ZfvMOOnqSTaDXxNk/t8bmREv/9h8/GxOpqIV3A7HBydOpXkRYsBCGzYkKrTXsYvLMziZCJSECosl4nb5WLyooeIdX2L08+gco6bqDrPcVfLvlZHE/Ea2b//zqGoaBw//QRA+UcfpeKggRh+umZMpLhRYbkMDib9xqg19/NNwCkwDG7KCmLCvUupVulKq6OJeI2U9z8gafRo3JmZ+JQrR/iLL1L6lpZWxxKRi6TCUsje2zSPmb9M40iADT/TpLvfTQztP1engESKiDsriyOTJnNy+XIAgho3Jvzll/GrXMniZCJyKVRYConTmcPkRX1Y5d6N089GWI5JdN1hdGj+oNXRRLyG47ffSHj6GRx794JhUOGJx6nw5JMYvvpPnUhxp724EPxx+BdGvvcAOwMcYBjcnFWKSV2XU6VChNXRRLzGydWrSRo3HvPUKXwqVKDqi1Mp1by51bFEpJCosFyi1Z+/wazfZnEswIa/2yTS3oxne76uU0AiRcSdmUnShImkrFoFQFCzplR98UV8K1a0OJmIFCYVlovkdOYwfuGDrDV/xOVro2qOybPXjaFNk+5WRxPxGo69ezkUFUX2vl/BZqPCwAFUeOwxDL1hEClxVFguwr4D3zNmfR92B2SDYdDUEczErsupXF639hYpCqZpkhIbS9LESZhZWfhWrEj4tJcpdfPNVkcTkcvEVtAVNm3aRKdOnQgPD8cwDFavXn3BdeLi4mjUqBEBAQHUrFmT119//YwxsbGx1K1bF7vdTt26dVn11+FdT7P8k9n03xDJ7oBs7G6ThwJu4Y1+X6isiBQRV3oGh4cMJXHkKMysLEq1bEmN1atUVkRKuAIXloyMDG644QZmz56dr/H79++nY8eO3HLLLezYsYPhw4fz1FNPERsbmzsmPj6eyMhIevXqxa5du+jVqxc9evRg27ZtBY132WRnOxgx/14mHnqdE742IrJNpl0/iejIV3W9ikgRyfr5Z37v1o3U994DHx8qRkcT8eYb+JYvb3U0EbnMDNM0zYte2TBYtWoVXbp0OeeYoUOHsnbtWn76606TAI8//ji7du0iPj4egMjISFJTU1m/fn3umA4dOlCuXDkWL16cryypqamEhISQkpJCmTJlLm5C5/DLHzsZ89FDfG93AtDCUZZJPZZTvmyVQv09InJ2pmlyculSjkyegpmdjW+VKlSd9jJBjRpZHU1ELlF+/35f9mtY4uPjadeuXZ5l7du3Z+7cueTk5ODn50d8fDxRUVFnjHnllVfOuV2Hw4HD4ch9npqaWqi5//bJsIf4+cBWGhkGTUyo4x/BjbVuJefVeSRdlt8oIv+W/fvvZGz6AoDSt91G2JTJ+JYrZ3EqESlKl72wJCUlUbly5TzLKleujNPp5Pjx44SFhZ1zTFLSuSvBlClTGDdu3GXJ/LfklGNkbdvKHYcB/j4QdYDk+Hcu6+8VkbPw9aVSdDShD/XFMAyr04hIESuSTwn9+z8uf5+F+ufys40533+Uhg0bRnR0dO7z1NRUIiIK90Zt5UIq4tuiGdv/2M2t9e4jwD+oULcvIvlj+NgofccdBNarZ3UUEbHIZS8sVapUOeNIydGjR/H19aX8XxfKnWvMv4+6/JPdbsdutxd+4H/pMGHeZf8dIiIicn4F/pRQQTVr1owNGzbkWfbxxx9z00034ffXV7yfa0xz3VZbREREuIgjLOnp6ezbty/3+f79+9m5cyehoaFcccUVDBs2jISEBBYsWACc/kTQ7NmziY6O5pFHHiE+Pp65c+fm+fTP008/TatWrZg6dSqdO3dmzZo1fPLJJ2zevLkQpigiIiLFXYGPsGzfvp0GDRrQoEEDAKKjo2nQoAGjR48GIDExkQMHDuSOr1GjBuvWrWPjxo3ceOONTJgwgVmzZtG1a9fcMc2bN2fJkiXMnz+f66+/npiYGJYuXUqTJk0udX4iIiJSAlzSfVg8yeW8D4uIiIhcHvn9+33Zr2ERERERuVQqLCIiIuLxVFhERETE46mwiIiIiMdTYRERERGPp8IiIiIiHk+FRURERDyeCouIiIh4PBUWERER8XiX/duai8rfN+xNTU21OImIiIjk199/ty904/0SU1jS0tIAiIiIsDiJiIiIFFRaWhohISHn/HmJ+S4ht9vN4cOHCQ4OxjCMQttuamoqERERHDx4sMR+R1FJn6PmV/yV9DlqfsVfSZ/j5ZyfaZqkpaURHh6OzXbuK1VKzBEWm81GtWrVLtv2y5QpUyL/Ef5TSZ+j5lf8lfQ5an7FX0mf4+Wa3/mOrPxNF92KiIiIx1NhEREREY+nwnIBdrudMWPGYLfbrY5y2ZT0OWp+xV9Jn6PmV/yV9Dl6wvxKzEW3IiIiUnLpCIuIiIh4PBUWERER8XgqLCIiIuLxVFhERETE43llYXn11VepUaMGAQEBNGrUiC+++OK84+Pi4mjUqBEBAQHUrFmT119//YwxsbGx1K1bF7vdTt26dVm1atXlin9BBZnfypUradu2LRUrVqRMmTI0a9aMjz76KM+YmJgYDMM445GVlXW5p3JWBZnfxo0bz5r9559/zjPOk14/KNgc+/bte9Y51qtXL3eMJ72GmzZtolOnToSHh2MYBqtXr77gOsVpHyzo/IrjPljQORa3/bCg8ytu++CUKVNo3LgxwcHBVKpUiS5durBnz54Lrmf1fuh1hWXp0qU888wzjBgxgh07dnDLLbdw5513cuDAgbOO379/Px07duSWW25hx44dDB8+nKeeeorY2NjcMfHx8URGRtKrVy927dpFr1696NGjB9u2bSuqaeUq6Pw2bdpE27ZtWbduHd988w2tW7emU6dO7NixI8+4MmXKkJiYmOcREBBQFFPKo6Dz+9uePXvyZK9Vq1buzzzp9YOCz3HmzJl55nbw4EFCQ0Pp3r17nnGe8hpmZGRwww03MHv27HyNL277YEHnV9z2QSj4HP9WXPbDgs6vuO2DcXFxDBgwgK1bt7JhwwacTift2rUjIyPjnOt4xH5oepmbb77ZfPzxx/Msq1Onjvn888+fdfyQIUPMOnXq5Fn22GOPmU2bNs193qNHD7NDhw55xrRv3968//77Cyl1/hV0fmdTt25dc9y4cbnP58+fb4aEhBRWxEtS0Pl9/vnnJmAmJyefc5ue9PqZ5qW/hqtWrTINwzB///333GWe9Br+E2CuWrXqvGOK2z74T/mZ39l48j74b/mZY3HcD/92Ma9hcdoHTdM0jx49agJmXFzcOcd4wn7oVUdYsrOz+eabb2jXrl2e5e3atWPLli1nXSc+Pv6M8e3bt2f79u3k5OScd8y5tnm5XMz8/s3tdpOWlkZoaGie5enp6VSvXp1q1apx9913n/HuryhcyvwaNGhAWFgYd9xxB59//nmen3nK6weF8xrOnTuXNm3aUL169TzLPeE1vBjFaR8sDJ68D16q4rIfXqritg+mpKQAnPFv7p88YT/0qsJy/PhxXC4XlStXzrO8cuXKJCUlnXWdpKSks453Op0cP378vGPOtc3L5WLm92/Tpk0jIyODHj165C6rU6cOMTExrF27lsWLFxMQEECLFi3Yu3dvoea/kIuZX1hYGG+++SaxsbGsXLmS2rVrc8cdd7Bp06bcMZ7y+sGlv4aJiYmsX7+e/v3751nuKa/hxShO+2Bh8OR98GIVt/3wUhS3fdA0TaKjo2nZsiX169c/5zhP2A9LzLc1F4RhGHmem6Z5xrILjf/38oJu83K62CyLFy9m7NixrFmzhkqVKuUub9q0KU2bNs193qJFCxo2bMh///tfZs2aVXjB86kg86tduza1a9fOfd6sWTMOHjzIyy+/TKtWrS5qm0XhYvPExMRQtmxZunTpkme5p72GBVXc9sGLVVz2wYIqrvvhxShu++DAgQPZvXs3mzdvvuBYq/dDrzrCUqFCBXx8fM5oe0ePHj2jFf6tSpUqZx3v6+tL+fLlzzvmXNu8XC5mfn9bunQp/fr1Y9myZbRp0+a8Y202G40bNy7ydwaXMr9/atq0aZ7snvL6waXN0TRN5s2bR69evfD39z/vWKtew4tRnPbBS1Ec9sHC5Mn74cUqbvvgoEGDWLt2LZ9//jnVqlU771hP2A+9qrD4+/vTqFEjNmzYkGf5hg0baN68+VnXadas2RnjP/74Y2666Sb8/PzOO+Zc27xcLmZ+cPpdXd++fVm0aBF33XXXBX+PaZrs3LmTsLCwS85cEBc7v3/bsWNHnuye8vrBpc0xLi6Offv20a9fvwv+Hqtew4tRnPbBi1Vc9sHC5Mn74cUqLvugaZoMHDiQlStX8tlnn1GjRo0LruMR+2GhXLpbjCxZssT08/Mz586da/7444/mM888Y5YqVSr3au7nn3/e7NWrV+743377zQwKCjKjoqLMH3/80Zw7d67p5+dnrlixInfMl19+afr4+JgvvPCC+dNPP5kvvPCC6evra27dutXj57do0SLT19fXnDNnjpmYmJj7OHnyZO6YsWPHmh9++KH566+/mjt27DAfeugh09fX19y2bZvHz2/GjBnmqlWrzF9++cX8/vvvzeeff94EzNjY2NwxnvT6mWbB5/i3nj17mk2aNDnrNj3pNUxLSzN37Nhh7tixwwTM6dOnmzt27DD/+OMP0zSL/z5Y0PkVt33QNAs+x+K2HxZ0fn8rLvvgE088YYaEhJgbN27M828uMzMzd4wn7odeV1hM0zTnzJljVq9e3fT39zcbNmyY56Ncffr0MW+99dY84zdu3Gg2aNDA9Pf3N6+88krztddeO2Oby5cvN2vXrm36+fmZderUybMjFrWCzO/WW281gTMeffr0yR3zzDPPmFdccYXp7+9vVqxY0WzXrp25ZcuWIpxRXgWZ39SpU82rrrrKDAgIMMuVK2e2bNnS/OCDD87Ypie9fqZZ8H+jJ0+eNAMDA80333zzrNvzpNfw74+4nuvfXHHfBws6v+K4DxZ0jsVtP7yYf6PFaR8829wAc/78+bljPHE/NP4KLyIiIuKxvOoaFhERESmeVFhERETE46mwiIiIiMdTYRERERGPp8IiIiIiHk+FRURERDyeCouIiIh4PBUWERER8XgqLCIiIuLxVFhERETE46mwiIiIiMdTYRERERGP93+MjXfeMTQEUwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:pint-dev-311]",
   "language": "python",
   "name": "conda-env-pint-dev-311-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}