COGD 2018 Analysis | Goal Setting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGD Historical Data\n",
    "## Setting Donor Goals for 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "2018 CO Gives Day is fast approaching and to kick things off we need to set some proper goals. What better way to determine how many donors can get on COGD than by doing a little bit of math.   \n",
    "\n",
    "## The Analysis \n",
    "Below you’ll see how many donors we had each year we ran COGD (Table 1). As you can see, we have some pretty consistent growth from the beginning (awesome!). It’s interesting to note that there was a significant dip in participation in 2017 (because of the non election year status?). Yet, 2017 was a standout fundraising year: COGD raised over $20,000 over the course of the entire year!\n",
    "\n",
    "As always, we'll read in the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas.compat import StringIO\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.pyplot import figure\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Up the Data\n",
    "We'll read in the data and take a peak at the dataframe. We see that every row is a unique transaction with the donor names, transaction data, and amount. \n",
    "\n",
    "We'll need to manipulate the data to summarise "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"COGD giving history 2013-2017 - Sheet.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Received On</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12/7/2011</td>\n",
       "      <td>$15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/4/2012</td>\n",
       "      <td>$250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12/8/2013</td>\n",
       "      <td>$100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12/9/2013</td>\n",
       "      <td>$100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12/9/2013</td>\n",
       "      <td>$200.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Received On   Amount\n",
       "0   12/7/2011   $15.00\n",
       "1   12/4/2012  $250.00\n",
       "2   12/8/2013  $100.00\n",
       "3   12/9/2013  $100.00\n",
       "4   12/9/2013  $200.00"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:,2:4].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conver the Received On column to datetime so we can perform operations on these data!\n",
    "data['Received On'] = pd.to_datetime(data['Received On'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Received On</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>$15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>$250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>$100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>$100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>$200.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Received On   Amount\n",
       "0       2011.0   $15.00\n",
       "1       2012.0  $250.00\n",
       "2       2013.0  $100.00\n",
       "3       2013.0  $100.00\n",
       "4       2013.0  $200.00"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We only care about the year of the gift, so we'll simplify the Received On column to the year\n",
    "data['Received On'] = data['Received On'].dt.year\n",
    "data.iloc[:,2:4].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(data.index[927])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of Unique Donors per Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Received On</th>\n",
       "      <th>Number of Donors</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Received On  Number of Donors\n",
       "0       2011.0                 1\n",
       "1       2012.0                 1\n",
       "2       2013.0               116\n",
       "3       2014.0               198\n",
       "4       2015.0               195\n",
       "5       2016.0               224\n",
       "6       2017.0               126"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_donors = data.groupby('Received On')['Contact Name'].nunique().to_frame().reset_index()\n",
    "num_donors.columns = ['Received On', 'Number of Donors']\n",
    "num_donors = num_donors.drop(num_donors.index[7])\n",
    "num_donors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6e268d86d8>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = num_donors[\"Received On\"]\n",
    "B = num_donors[\"Number of Donors\"]\n",
    "fig = plt.figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "plt.plot(num_donors[\"Received On\"], num_donors[\"Number of Donors\"], marker='o', color='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting the Number of Donors for 2018\n",
    "A LOT goes into predicting participating in any fundraising campaign -- there’s just so many variables that influence giving year to year, season to season. For the purposes of this analysis, I just wanted to get a feel for the general growth of the program over time, so I plotted a linear regression line on top of the data, shown below (Figure 1).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/thebbennett/anaconda3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6e247b04a8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(num_donors['Received On'],num_donors['Number of Donors'])\n",
    "ax = sns.regplot(x=\"Received On\", y=\"Number of Donors\", data=num_donors, color='b', \n",
    " line_kws={'label':\"y={0:.1f}x+{1:.1f}\".format(slope,intercept)})\n",
    "\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "251.57142857142753"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting the number of new donors\n",
    "year = 2018\n",
    "(32.14285714285714 * year) -64612.714285714275"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can expect to reach **250 donors** this year if we continue growing at the same rate.\n",
    "\n",
    "But will we?\n",
    "\n",
    "## Hold Up...Wait a Minute.\n",
    "250 is a LOT of donors compared to last year ‘s reach (126 donors). Is this really feasible? \n",
    "We had a pretty big dip in donors last year in 2017 that but a bit stop to our pretty awesome linear growth. What could impact the number of donors we reach this year?\n",
    "\n",
    "Here are my questions:\n",
    "\n",
    "**Staff Turnover** Have we lost a lot of staff that had ties to previous COGD donors? \n",
    "\n",
    "**How many have already given** How many donors from our go to lists have already given this year? Are we still waiting on any gifts from donors?\n",
    "\n",
    "**How big our the staff lists?** Can we reliably expect to bring in new donors from our staff? If so, how many historically?   \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Money Raised per Year  \n",
    "\n",
    "It's all about the dollar, dollar bills, y'all!\n",
    "\n",
    "We have to do a bit of data cleaning to take the Amount column in the dataframe and make the values numeric (and take care of all those pesky dollar signs!)  \n",
    "\n",
    "Once we do that, it's pretty easy to group the data by year and amount to find the annual amount raised!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "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>Received On</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Received On  Amount\n",
       "0       2011.0    15.0\n",
       "1       2012.0   250.0\n",
       "2       2013.0   100.0\n",
       "3       2013.0   100.0\n",
       "4       2013.0   200.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Amount'] = data[data.columns[3]].replace('[\\$,]', '', regex=True).astype(float)\n",
    "data.iloc[:,2:4].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>Received On</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>2018.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Received On  Amount\n",
       "922       2018.0    25.0\n",
       "923       2018.0    10.0\n",
       "924       2018.0    20.0\n",
       "925       2018.0    25.0\n",
       "926       2018.0    10.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:,2:4].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Received On</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012.0</td>\n",
       "      <td>250.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013.0</td>\n",
       "      <td>9625.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>15938.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015.0</td>\n",
       "      <td>14538.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016.0</td>\n",
       "      <td>13272.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017.0</td>\n",
       "      <td>20149.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Received On    Amount\n",
       "1       2012.0    250.00\n",
       "2       2013.0   9625.43\n",
       "3       2014.0  15938.49\n",
       "4       2015.0  14538.00\n",
       "5       2016.0  13272.00\n",
       "6       2017.0  20149.11"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "byyear = data.groupby('Received On')['Amount'].agg('sum')\n",
    "byyear = byyear.to_frame().reset_index()\n",
    "byyear = byyear.drop(byyear.index[0])\n",
    "byyear = byyear.drop(byyear.index[6])\n",
    "byyear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Growth of Colorado Gives Day Since 2013')"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "x = byyear['Amount']\n",
    "figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "plt.bar(byyear['Received On'], byyear['Amount'], align='center')\n",
    "plt.ylabel('Dollars Raised')\n",
    "plt.title('Growth of Colorado Gives Day Since 2013')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd9d67ef9e8>"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IAZSXy14d6hkGHiIiIh/Q2Ch7dVpaPF0T38TAQ0RE5MWsVrn6qqrK0zXxbQw8REREXqqhQfbqtLZ6uia+j4GHiIjIy1gs8vyrmhpP18R/MPAQERF5ER72qQ4GHiIiIi/Q2iqHrxoaPF0T/8TAQ0RE5EHcQFAbDDxEREQeUlcnh684KVl9mgWe5uZm/P73v8epU6cQGhqK/v37Y82aNUhMTMSiRYtw4cIFREREAABycnJwzz33AADKy8uxbNkynD17FsHBwXjiiScwceLEPpURERF5UmsrcPEiUF/v6ZoEDk17eO644w5MmTIFiqJg8+bNWLVqFTZt2gQA+OMf/4jp06c7PGf9+vUYP348Nm7ciKNHj+KRRx7Bnj17YDAYel1GRETkCbadkisrOXylNc0+/UNCQjB16tS2n6+++mr84x//6PZ5O3fuxL59+wAA48aNQ1xcHI4cOYLJkyf3uqw7FpWmxtvuq9b9iW2sFbazNtjO2tCqnQN9+ErNdhai+3t6rLvjjTfe6NCjs27dOvz5z3/GyJEjsWTJEgwdOhSVlZWwWq3o169f23UJCQkoLi7udZkr8vPz3fAKPXd/Yhtrhe2sDbazNtRq59ZWBRUVBjQ16VS5v68pLCx0+z0VRWDYsK6v8Ujgefnll3HmzBmsWbMGgAw78fHxEELgzTffxEMPPYTt27cDABRF6fBc0a4PsLdl3UlLS4Ner3f5eldZLBbk5+erdn9iG2uF7awNtrM21Gpnq1WuvKqsBKKj3XZbn2WxWFBYWIjk5GS3v5+FsKCxsevAqnng2bhxI3bv3o3XXnsNRqMRABAfHw9AhpQ777wTa9euRWVlJWJjYwEAFRUVbb01Fy5cQHx8fK/LXKHX61X9y0Xt+xPbWCtsZ22wnbXhznaurZXDV2YzoGPHTgdqvJ9d6dPQ9H/Dq6++im3btuHVV19FVFQUAMBsNqOsrKztml27dqF///5toWXOnDl48803AQBHjx5FWVkZJkyY0KcyIiIiNbS0AOfOAcXFMuyQ99Csh6ekpATPPfcchg4dirvuugsAEBwcjH/84x948MEH0draCkVREBsbi7/97W9tz1u6dCmWLVuG2bNnIygoCOvWrWtbadXbMiIiIneyWuXqq6oqrr7yVpolgMGDB+PEiRNOy7Zs2XLZ5/Xv379t6bq7yoiIiNyl/fAVeS92eRAREfVCc7PcPLCx0dM1IVcw8BAREfUAh698EwMPERGRi2pq5PAV94L0PQw8RERE3eDwle9j4CEiIroMqxUoK5PDV+TbGHiIiIicqK6WYYfDV/6BgYeIiKidpiY5fNXU5OmakDsx8BAREUH25JSVyZ4d8j8MPEREFPDq6nQoKvJ0LUhNPNKMiIgCVlMT8MMPQHl5EOfq+Dn28BARUcBpP3zFoBMYGHiIiCigVFXJnZIZdAILAw8REQWExka5+qq52dM1IU9g4CEiIr9mscjjIGpqPF0T8iQGHiIi8ltVVXKujtXq6ZqQpzHwEBGR3+HwFXXGwENERH7DbJY9Ohy+os4YeIiIyOcJYV99xeErcoaBh4iIfFpjI1BaCrS0eLom5M0YeIiIyCeZzXL1VW2tp2tCvoCBh4iIfAqHr6g3GHiIiMhnNDTI1VccvqKeYuAhIiKvx+Er6isGHiIi8lpCAJWVQEUFh6+obxh4iIjIK3H4ityJgYeIiLyK2SyDTl2dp2tC/oSBh4iIvIJt+Kq8XH5P5E4MPERE5HH19XJSMoevSC0MPERE5DGtrTLocPiK1MbAQ0REmhNCrryqqODwFWmDgYeIiDRVXy8nJbe2eromFEgYeIiISBOtrTLo1Nd7uiYUiBh4iIhIVRy+Im/AwENERKrh8BV5CwYeIiJyOw5fkbdh4CEiIrfh8BV5K52nK0BERP6hrg4oKuJOyaStixeBzZu7v449PERE1CccviKtNTUBe/cCJhPw2WeA0QhkZ3f9HAYeIiLqFQ5fkZasVuDIERlydu7s+e7cDDxERNRjdXXySAiuviK1nTkD5ObKr3PnHMuDg4Fbbun+Pgw8RETkMg5fkRaqq2UvztatwNdfO79mwgQ5jDVnDhAZ2X2PDwMPERF1Swg5GbmyksNXpI7WVuDTT+WQ1f79QEuL4zWJiTLkZGUBw4bZH3flPcnAQ0REXaqrk706ZrOna0K+7MQJ4MsvI6HTAamp8jEhgGPHZMj58EMZqjuLiABuvVUGnQkTAEXp3e9n4CEiIqdaWmTQaWjwdE3Il126BKxdC5w+rUNj4wDk5ekwZAhw5ZVypVVhoeNz9HrgpptkyJkxAwgN7Xs9NAs8zc3N+P3vf49Tp04hNDQU/fv3x5o1a5CYmIjy8nIsW7YMZ8+eRXBwMJ544glMnDgRAFQpIyKiy7Na5corDl+RO8iwI99XdXV6XLwIfPstsGeP47UpKUBODpCRAQwY4N56aNrDc8cdd2DKlClQFAWbN2/GqlWrsGnTJqxfvx7jx4/Hxo0bcfToUTzyyCPYs2cPDAaDKmVERORcba38FzmHr8gdCgqA/HygqgqoqQGs1hCHawYMAObPl/NyRo9Wry6affqHhIRg6tSpbT9fffXV+Mc//gEA2LlzJ/bt2wcAGDduHOLi4nDkyBFMnjxZlbLuWCwWt772zvdV6/7ENtYK21kbWrZzIA9f8f3sfkVFQF6egnfeUVBRYZt0Y598oygCkZHAnXda8fDDgK0vorf/C4To/oke6+544403MH36dFRWVsJqtaJfv35tZQkJCSguLlalzBX5+flueIWeuz+xjbXCdtaGmu1stQI1NXrU1BgCfviq0NlkEnJZba0en30WjY8/jkFhYZjTa0JDLYiIMCMiwgKdDhg16gJOnmzq8+9WFNFh1ZYzHgk8L7/8Ms6cOYM1a9agqakJSqcp16Ldnzo1yrqTlpYGvV7v8vWuslgsyM/PV+3+xDbWCttZG2q3s234KiICGDLE7bf3GRaLBYWFhUhOTub7uYdaW4GDB4G8PB0++ghobXVcQhUWJhAeDkRHC1itzQgNDYWiBCMpSWDOnBFuqYcQFjQ2dv0PA80Dz8aNG7F792689tprMBqNMBqNAICKioq2HpkLFy4gPj4esbGxbi9zhV6vV/VNr/b9iW2sFbazNtzdzp2Hr/i/UOL72TVCyEnHtqXklZWO10RGAnPnylVWiYkK1q2TE5cbG2WHRFKSgsceU9z23vO6fXheffVVbNu2Da+++iqioqLaHp8zZw7efPNNLF68GEePHkVZWRkmTJigWhkRUSCyWuU+J1VVXH1FPVdaCuTlySMeTp50LNfrgSlTgMxMYOZMIKTd/OT164GCAisOH76ESZMSkZqqfbDULPCUlJTgueeew9ChQ3HXXXcBAIKDg/Huu+9i6dKlWLZsGWbPno2goCCsW7eubTWVGmVERIGGq6+oNxoa7KeSHzrkPCinpsoVVhkZQP/+l79XSgpgtdYiJUW9+nZFswQwePBgnDhxwmlZ//79sWnTJs3KiIgCRSCvvqLesVqBw4dlT87Onc7fO1otJXcndnkQEfkhDl9RT50+LUNOXh5w4YJjeUiIPJU8Oxu4/nr7UnJf4WPVJSKi7nD4ilxVWQls3y6HrI4edX7NpEmyJ2fOHLmiz1cx8BAR+QkOX5ErWlqATz6RIefAAbm0vLPhw2XIycyUJ5T7AwYeIiIfx+Er6o4Q8oiH3Fy5lLyqyvGaqCi5lDwnB7j66t6fSu6tGHiIiHxYTQ1QVsbhK3KuuBj44APZm3PqlGO5wQDcfLMMOdOnA8HB2tdRKww8REQ+qLlZDl81Nnq6JuRt6uvlSeQmE/Cvfznv9Rs7Vk4+njcPiIvTvo6ewMBDRORDOHxFzlgscim5yQTs3u18HtfAgXIpeXY2kJysfR09jYGHiMhH1NTI1Vc81JtsTp2SIScvDygpcSw3Gu1LyX/yk8A+RoSBh4jIyzU3y14dDl8RAFRUyKXkW7cC//mPY7miAJMny1VWs2f79lJyd2LgISLyUlYrUFFhwJkzgf0vc5JLyT/+WIacjz92Pkl9+HA5+TgzExgyRPMqej0GHiIiL1RTI4coamuZdAKVEHIzQJNJ9ug4W0oeHS0nHmdnA+PG+d9Scndi4CEi8iLtV19xrk5gunBBzskxmYDvv3csNxiAqVNlyJk2zb+XkrsTAw8RkRewWuV+Os7+FU/+r65Orq4ymYDPP3d+zVVXySGruXOBfv20rZ8/YOAhIvIwrr4KTBaL3CfHZJL75jiblD5okJx8nJUFjBqlfR39CQMPEZGHNDcDpaVAU5Ona0JaOnnSvpT84kXH8rAwuboqO1se3MkJ6+7BwENEpDGLxb55IAWGigp5hpXJBHz7rWO5bSl5To7cNyc8XPs6+jsGHiIiDVVXy7k6HL7yfy0twEcfyZDzySfOl5InJcmQM38+EB+vfR0DCQMPEZEGOHwVGIQA/v1vuV/Ojh0y4HYWE2NfSp6WxqXkWmHgISJSkcUie3ScffCR/zh3Ts7Jyc0Fioocy4OC5BLy7GxgyhQuJfcEBh4iIpVw+Mq/1dUBO3fKkHP4sPNrrr5arrCaOxeIjdW2ftQRAw8RkZs1NcnVNxy+8j8WC3DokJyXs3ev8//HQ4bI4x2ysuQcHfIODDxERG7C4Sv/deKEDDkffCD3TOosLAyYM0cGncmTAZ1O+zpS1xh4iIjcoKpKLjXn8JX/KCuzLyU/dsyxXFGAG26QPTm33CJDD3kvBh4ioj5oapKrr5qbPV0TcofmZmD/fhlyDh50HmBHjZKTj+fPBwYP1r6O1DsMPEREvWCxyKGNmhpP14T6Sgjg+PEw/N//Kdi5E6itdbwmNhbIyJBBZ+xYLiX3RQw8REQ9VFUlhzusVk/XhPri7Fm5wspk0uHsWcfZxUFBwIwZcshqyhT5M/kuBh4iIhc1NsrVVxy+8l21tXIpuckEfPml7dGO3TXjx8uenFtvlZsEkn9wOfDMnDkT+/btc3h89uzZ2L17t1srRUTkTcxmOXzlbKiDvJ/ZDHz2mQw5+/Y5D6wDBrTgttsMyMnRYfhwzatIGnA58FRUVDg8JoRAFU+/IyI/JYR99RWHr3zP8eP2peRlZY7ltqXkWVkWhIUVYuzYMTyZ3I91G3iWLFkCAGhtbW373ub8+fNITk5Wp2ZERB7U0CCHr1paPF0T6olLl+xLyY8fdyzX6eRS8uxsYNYswGiUE9CdLTsn/9Jt4BkxYoTT7wHguuuuw5w5c9xfKyIiD+Hwle9parIvJf/0U+dLya+8Uk4+zswEBg3Svo7ked0Gnt/85jcAgKuuugrTpk1Tuz5ERB4hBFBZCVRUcPjKFwgBHDkiQ86OHfJcq8769ZN75WRnA2PGcCl5oHN5Ds+0adNw9uxZHDt2DA0NDR3KsrOz3V4xIiKt1NfLXh0OX3m/s2dlyMnNld93FhQEzJwpe3NuvplLycnO5cCzceNG/Pd//zeSkpJgNBrbHlcUhYGHKMAcPQocPBgNnQ645hpP16b3Wltl0HHWO0Deo6bGvpT8yBHn11x7rezJmTMHiI7Wtn7kG1wOPJs2bcJbb72FcePGqVkfIvJiJSXA0qXA8eM6NDTEY/NmHUaPBtav960t9oWQQ1cVFfJ78j6trR2XkjvrfUtIkCEnKwu44grt60i+xeXAoygKUlNT1awLEXk5GXY6Pnb8uHx882bP1Kmn6uvl6qvWVk/XhDoTQq6WMpnkSqvycsdrwsPlhoA5ObJXh6eSk6tcDjwPP/ww/vKXv+C3v/0t9NyogCjgHD3qfJkvIB8/ehTw5g7g1lYZdOrrPV0T6uziRblXjskEFBY6lut0wE03yd6cmTOB0FDt60i+z+XA8/LLL6OyshKbNm1CdKcB0k8//dTtFSMibVks8stqdfyvEEB+vr1XRFHsZbaVLz/84J2BRwjZU1BZyeErb9LYKIeqtm4FDh1yvjIuOVn25GRkAAMHal9H8i8uB54///nPataDiDzEagXOn5cfQF2JjOy4v4nZrHSYV6HXAxcuAMHBHb88OeRQVycnJXP4yjtYrXLS8datchKys962/v3tS8lHj9a+juS/XA48kyZNUrMeROQBFgtw7pxrh2GOHg0kJQGnTzuWjRy3QNVyAAAgAElEQVQpv5ytdjIYZPAJCZFLhG1ByKDi0cUtLXKYpNMOGuQhRUVyGXlurgzXnQUHy6GqnBzgxhvVfW9Q4HL5bfXSSy9dtsy2OSER+Q6zWYadnuw989hjwNq1HUNPUhKwbFnXv8dsdgwfOp38oGsfgmw/97ZXyGqVK684fOV51dVyQ0CTCfj6a+fXTJhgX0oeFaVt/SjwuBx4Tnf6Z92lS5fwzTffYMaMGW6vFBGpq6VFhh2zuWfPGzBALkEvKLDi8OFLmDQpEampvVvEYLXKIwGamhzLbL1Cnb+c/cvfYpE9S3V1MlQx6HhOaytw8KAMOfv3Ox9KTEy0LyUfNkz7OlLg6tMcnr179+KTTz5xa4WISF319XI/HWfnDbkqJQWwWmuRkuK+erV3uV4hRZE9QLav5ubu5x6RuoQACgrsS8krKhyviYyUS8mzsmSvDo94IE/o00jpjBkzsGLFCjz55JPuqg8RqcBikbvVVlf79vEJQsj6+/Jr8BelpUBenpyXc/KkY7leb19KPmMGl5KT57kceFo6/Q3T1NSErVu3IiYmxuVf9vTTT2P//v04f/48PvjgAyQnJwOQwSk4OBghISEAgIceeghz584FABQVFWH58uWorKxEZGQknnvuOYwaNapPZUT+yGKRgcBqtS8lt1rlqd+1tRzqob5raAD27pUh53JLyceMkSFn3jw5BErkLVwOPOPGjYPSrh9SCIH4+Hg888wzLv+y9PR0PPDAA/j5z3/uUPbiiy+2BaD2Vq1ahYULF2LBggXYuXMnVq5cibfffrtPZUS+qqVFfug0Nsr5Ee33yyFSg9UKfPGFHLLaudP5yrcBA+RS8qwsLiUn7+Vy4Nm3b1+Hn8PCwhAbG9ujX3bdddf16Pry8nIUFBRg06ZNAGRgeuqpp3Du3DkYjcZelSUmJnb7ey0qfXrY7qvW/cn/2tg2j8X21dNJxmrxt3b2Vp5s5++/B/LyFOTlKSgudpx0ExIiMGuWQGamwPXX2yeU++Jbgu9nbajZzkJ0f0+XA09CQgIAoLS0FKWlpQgKCup9zZxYunQphBAYN24clixZgn79+qG4uBgDBw6E4cc/SYqiID4+HsXFxTAajb0qcyXw5Ofnu/W1aX1/8t02FgJoblbQ2KhHU5MOLS3ePbuz0Nk5AOR2WrVzba0en30WjQMHYnDyZJjTa1JT6zF1ahWuv74a4eFyTMvZHB5fxPezNtRoZ0UR3a76cznwlJWVYcmSJTh8+DAiIiJQV1eH6667Dn/6058woI8DtZs3b8aQIUPQ2tqKF154AY899hheeeWVH19Ex7/wRbuJCL0t605aWpoq54VZLBbk5+erdn/y/jYWQvbStLbav9r/bDbLwxG9ncViQWFhIZKTk72ynf3FsWNWfPnlBUycOARjxqizZXVLC/Dpp0Burg4HDgCtrY4he+hQgexsgfnzBRITQwEM/vHLP/D9rA0121kICxobu/6HrsuB58knn0RCQgI+//xzREVFobq6GuvWrcOaNWu63JTQFUOGDAEABAUF4e6770Z6ejoAID4+HiUlJTCbzTAYDBBCoKSkBPHx8TAajb0qc4Ver1f1Ta/2/Un7NradQ2WxyNDS/mfbY7Zg0xVfe1vwvayOS5dsGzzq0Ng4AHl5BiQlKXjsMfdMBBYC+M9/5OTjDz+UGzV2FhUll5JnZwPXXKM4/CPSH/H9rA13trPBILeo0Ou736LC5cDzxRdf4MCBA20rqaKjo/H4449j2rRpfakrGhoaYDabEfXjNpvbtm1DamoqACAuLg6pqanIy8vDggULsGvXLiQkJLQNS/W2jKg7tonAnQOMszBjWx1F5C6dd7MG5M9r18qNH3urpMS+lPy77xzLDQbg5ptlyJk+XR4HQuRptlDTfmd2215ctl3ZLRaguLib+7j6C8PCwlBSUoIrrrii7bHS0lKEhTkf53VmzZo12LdvH8rKynDvvfciLCwMmzZtwuLFi9smMSUmJmLt2rUdnrNixQps2LAB4eHhbimjwGMbSuqqJ6b9zwww5CnHjzs/rwyQjx8/3rOVUPX1wJ49MuT885/O39tjx8oVVhkZQFxc7+pN1Be23dU7B5rgYPdtVOly4Lnjjjtw33334Z577kFCQgIuXLiA119/HT/72c9c/mWrV6/G6tWrHR43mUyXfU5SUtJll5P3toy8j23PmPb/deX7zo+ZzUBpaRDOnpV/SGyPMcCQr+juX6nFxd0HHqsV+PxzuZR8927nS8kHDrSfSu5kRxAit7Ltkh4aakVMjNyIsn240WLE1OXA8+CDD6J///744IMPUFpaikGDBuHhhx9GTk6OmvUjD+oqgPQ0iHT3mLtYLEBTkw6Njb43H4YIALqbathV+alTsicnL895cAoNBW65RYac66/nnxHqOUWR7xudTv63/ffdPWaxANXVrRg40DPvvR4dLbFgwQIsWLBArbpQN3obKnr7HCLS3ujR8gR6Z8NaI0c69u5UVgLbt8venKNHnd9z0iQ5ZDVnDhAR4f46k+/pbWjx5bnrPQo8p06dQkFBARo69Y/ecccdbq2UL+lJmGhtBaqr9Sgrk2+gnoYTIgoMjz3mOHE5KQlYtkx+39ICfPKJDDlyKbnjPYYPlz05mZnAj9uokZ/R6VwPLZ3LA5HLgeevf/0rNmzYgJSUFIS2OwVOURS/CzylpfK/rvSY9ITFAlRVGVBRwa5kIrq8AQPkaqyCAisOH76ESZMSMWaMHvn5wN/+BmzbBlRVOT4vOhqYO1cGnauv9u1/jQeSngYW2/f8/9szLgee119/HVu3bsXIkSPVrI9XqK3lG4mIPC8lBSgtbcLHHytYssT5MJfBAEyZYl9KHhysfT1JUhTZ/q4GlkDubfEElwNPZGQkhg4dqmZdiIgIcin57t3A1q06HD6cDCEc/wV21VX2U8n79fNAJQmA3KsoLEx+GY0MMN7M5cCzZMkSPP300/jVr36Ffp3+dAXznxRERH1isXRcSi53je0YdAYNknNysrOBUaM8Us2Ap9PJcBMRIY+B4fQE3+Fy4Bk0aBAOHjyId999t+0xIQQURcGxY8dUqRwRkb87dUqGnLw8uRNyZyEhVqSnAzk5OkyezA9YTzAYZLiJiJBhh1MefJPLgWfFihXIycnBvHnzOkxaJiKinqmokBOPTSZ5plVnimJbSm7FsGHHce21KQEXdAwG2ZvS2uqZbTJCQuy9OPzI8w89Oi198eLFAXGAHBGRu7W0yCXkW7fKJeVms+M1I0YAOTlyB+QhQwCLReDYMf/fkyI8XM5/CQ52vvOu7eiX1taOXy0t7tlJ/cQJ4MiRSISFAT/5iaxPUFDf7knex+XAk56ejk8++QRTp05Vsz5ERH5DCODf/5Y9Odu3A9XVjtfExMil5Dk5QFpa4AyXhIbKE9kjI7sfprOtbHJ2mKntnLyWlo5ByPZ9V2Goqsp+Kn1TU3/s2qXD6NFyS4DBg/v2+sj7uBx46urqsHjxYlxzzTXo379/h7I//elPbq8YEZGvOn9ezskxmYCiIsfyoCBg6lQ5+Xjq1MBZSh4cLANOZKT7XrPtjCZnPTK2DV9bWuSXEPZepOBgYNEi4MyZjjsIHz8OLF0KbN7snvqR93A58CQnJyPZyQlz5eXlbq0QEZEvqqsDdu2SIefwYefXjBsnj3iYOzdwlpLr9faeHK3nwtj2xXEWro4eleHGmePHZfm4cerWj7TlcuD5zW9+0/a9xWLBRx99hPfffx8HDx50egI6EZG/s1iAf/5Thpw9e4CmJsdrBg+WIScrS56FFQh0OjnhNzJSzofxRj/80H05A49/6dFZWoWFhdiyZQvy8vJQWVmJjIwMvPXWW2rVjYjIK508aV9KfvGiY3lYGDB7thyymjw5cDajCw+XvTkREd4/F2nYsL6Vk+/pNvDU1NTggw8+wPvvv4+CggJMmDABv/3tb/HCCy9g+fLliIuL06KeREQeVV4OfPghkJsLfPutY7miANdfL3tybrnFe3s23E2vl2d4xcTIpeS+Ytw4efK8s2GtMWPYu+OPun173njjjQgLC8M999yDF198EYmJiQCAl156SfXKERF5UnMz8NFHsjfnk0/kEFZnSUn2peTx8drX0VNCQ2XIiYz0/t6cy1m/Xk5Qbh96Ro8Gnn/ec3Ui9XQbeG666SYcPHgQ27dvh16vR2ZmJgZzvR4R+SkhgK+/liFnxw6gpsbxmpgYICNDDllddZXvfuD3lKLI4arYWP/YjG/wYLka6+uvrfj442JMnToC11wTYDs8BpBuA8/f/vY3VFRUIDc3F1u2bMELL7yACRMmoL6+Hs3NzVrUkYhIdefOyeGq3Fy5VLmzoCB5GnlWljydPFCWkgO+O2zlqnHjAKu1msNYfs6lt26/fv1w77334t5770V+fj62bNmCwsJCZGZmIjs7G3/84x/VricRkdvV1gI7d8qQ88UXzq+5+mrZk3PrrbJnI5AEB8vXHBUVOL1Y5L96nNXT0tKQlpaGFStWYO/evdiyZYsa9SIiUoXZDBw6JEPOnj1ynk5nQ4bYTyUfMUL7OnpaeLjszQmUidcUGHrdORkcHIy5c+di7ty57qwPEZEqTpyQ83I++AC4dMmxPCwMSE+XIWfSpMBZSm5j2yAwOjqwhusocPjhaCwRkVRWJpeSm0zAsWOO5YoC3HCDDDmzZsnQE0gURfbiREfL185hK/JnDDxE5Feam4F9++SQ1cGDzpeSjxpl3/140CDt6+hJiiLDTUSE/Oru4E4if8HAQ0Q+TwjgyBEZcnbskJORO+vXz76UPDU1sHozdDrZkxMRIf8baMN1RAADDxH5sLNnZcgxmeT3nQUFATNmyJBz883OT9T2V3q9DDeRkRyuIgIYeIjIx9iWkptMwJdfOr/mmmvsS8mjo7WtnyfpdDLg2EIOEdkx8BCR1zObgc8+kyFn3z7nS8kTEuzzcoYP17yKHhUWJldY+fIxD0RqY+AhIq91/Lh9KXlZmWN5eLjsxcnOBiZMCKy5KUFBMuRERQXWUB1RbzHwEJFXuXRJBhyTSe6d05lOB9x4oww5M2cCRqP2dfQU21lWtmXkROQ6Bh4i8rimJjlUZTIBn34KWK2O1yQny5Azfz4wcKD2dfSkoCAgLk725nAZOVHvMPAQkUdYrXIpuckkJyHX1TleExcnA052NjB6dODNTwkPBwYObMWIEQw6RH3FwENEmjpzxn4q+blzjuXBwXIpeU6OHLoKtPkpOp3syYmJkSHn0iUn3V1E1GMMPESkupoauSGgyQR89ZXza669VoacOXPkB36gCQqSISc62j752tku0UTUOww8RKSK1taOS8lbWhyvSUyUy8izs4Fhw7SvozcIC5NBJyLC0zUh8m8MPETkNkLIQzpNJnloZ3m54zURER2XkgfavByg47AVTyYn0gYDDxH1WWmpXEqemwsUFjqW6/XATTfJ3pyZM4HQUO3r6A2Cg+WQVfthKyLSBgMPEfVKc7OCbdsU5OYChw45X0o+erTsycnIAAYM0L6O3kCvlzsgR0UFbtAj8gYMPETkMqtVnl+1dauCHTtGo7HRsZuif38gM1P25owe7YFKegFFkUvKo6LkfwNx2I7I2zDwEFG3iorsS8nPnweAjkEnJASYNUuGnBtvBAwB+jdLaKj9TCvum0PkXQL0ryUi6k5VlVxKnpsLfP2182smTBDIyVEwZ478kA9EQUH2IStOQCbyXgw8RNSmtRX45BMZcvbvlz93NmwYkJlpxZgxJzF9+ijoA7ArQ6eTq82ionimFZGvYOAhCnBCAN9+a19KXlnpeE1kJDB3rhyyuvZawGoVOHbMSRryc2Fh9iErzssh8i0MPEQBqrQUyMuTvTknTzqW6/XAzTfLVVYzZsh5OoEoOFiGnKiowJ2bROQPNP3j+/TTT2P//v04f/48PvjgAyQnJwMAioqKsHz5clRWViIyMhLPPfccRo0apVoZUaBqaAD27pW9OYcOyd6dzlJTZU/O/Pny8M5AFREBxMYCRqOna0JE7qDp1lfp6el46623kJCQ0OHxVatWYeHChdi1axceeOABrFy5UtUyokBitQL/+hewYoVcQfXoo/LIh/ZhZ8AA4L77ZI/P1q3APfcEZtjR6eTuxyNGAEOGMOwQ+RNNe3iuu+46h8fKy8tRUFCATZs2AZCh6KmnnsK5c+dgNBrdXpaYmNhtPS0Wiyrj85YfTwK08ERA1bCN7b7/HsjLU5CXp6C42PENHRIiMGuWQFaWwE9+Yh+ucaXp/K2dDQbZm+NtB3f6Wzt7K7azNtRsZ1fu6fER6eLiYgwcOBCGH/+2VRQF8fHxKC4uhtFodHuZK4HnxIkTEEK9GYmFzvbeJ7c4cyYUly5F4syZH3DFFU2ero7mamv1+OyzaBw4EIOTJ50vHxo7th5TplThhhuqERYmt0d2NofHFb7+Xg4JsSIy0oKwMCtqajxdm8vLz8/3dBUCAttZG55qZ48HHkAGkvZEu752Ncq6k5KSAkVx/1Jbi8WCwsJCJCcnB+RSXjWVlQHr1ik4fRpoampCaGgokpKAZcsE+vf3dO3U1dICHDwI5OXp8NFHgNnsGNavuEL25MyfL5CQEApg8I9fvePr7+XISDl05e1DVhaLBfn5+UhLS/PJdvYVbGdtqNnOtnt3xeOBJz4+HiUlJTCbzTAYDBBCoKSkBPHx8TAajW4vc4Ver1cl8LS/P/9Qudfzz8shHEWRwVZRFHz/vYLnnwfWr/dw5VQgBJCfL1dYffih3CSws6gouZQ8OxsYP15x+EeAO/jSe1mnk0NWMTFys0Bf4kvt7MvYztrwVDt7/LzeuLg4pKamIi8vDwCwa9cuJCQkIDExUZUy8j/HjwOnTzsvO31alvuL4mLg738H5s0Dbr8d2Ly5Y9gxGOQS8hdflBOT16wBrrkmsPeMCQqSk7KTkuR/fS3sEJF7aNrDs2bNGuzbtw9lZWW49957ERYWhj179mDNmjVYsWIFNmzYgPDwcKxdu7bDc9xdRv6luLj7cl8+xLK+HtizRy4l/9e/nC8lHztW9uTMmxeYq6ucMRrlROSICE/XhIi8gaaBZ/Xq1Vi9erXD40lJSXj77bedPkeNMvIv3Y1UujiS6VUsFuDwYRlydu+W++d0NnCgPJU8Oxu48krt6+iNFMU+Pyc01NO1ISJv4vE5PER9NXq0HK5wNqw1cqRv9e6cOmU/lbykxLE8NBS45RYgJwf4yU94IreNXm+fn8PdkInIGf7VQH7hsceAtWs7hh65SstzdXJVRQWwfbvszbncIoNJk2TImT2bQzTtBQfLYauoqMCep0RE3WPgIb8wYIBcjVVQYMXhw5cwaVIiUlO9t/ujpQX4+GMZcj7+2Pmp5MOHy+GqzEyg0+bkAc1gkMNWERHev6yciLwHAw/5lZQUwGqtRUqKp2viSAjg6FEZcrZvd76UPDpaLiXPyQHGjWOvhU1QkD3kcG4OEfUGAw+Ryi5ckGdUmUxyr6DODAZg6lTZmzNtmhymIclolPNyIiM9XRMi8nUMPEQqqKuTq6tMJuDzz51fk5YmTyWfNw/o10/b+nkzRbGfVM7eHCJyFwYeIjexWOQ+OSaT3DensdHxmsGDZcjJypIryMiOK62ISE38a4Woj06elCEnLw+4eNGxPCxMrq7KygImT+ZS8s6Cg2XIiYqyn1RORORuDDxEvVBRIc+wMpmAb791LFcUuU9OdrbcNyc8XPs6eruwMBl0uMyeiLTAwEPkopYW4KOPZMj55BPAbHa8JilJhpz584EhQ7Svo7ez7YQcGwuEhHi6NkQUSBh4iLogBPDvf9uXkldXO14TEwNkZMghq7Q0LiV3hvNziMjT+FcPkRPnzsk5Obm5QFGRY3lQkFxCnp0NTJnCpeSXw52QichbMPAQ/aiuDti5U4acw4edX5OWJjcFvPVWLiXvSliYDDqcu0RE3oKBhwKaxQIcOiSHrPbuBZqaHK+Jj5fDVZmZXEreFc7PISJvxsBDAamwENi6FfjgA+DSJcfysDAgPV0OWU2axOXSXdHr5dycmBguuSci78XAQwGjvFwGnNxcoKDAsVxRgBtukL05t9wiQw9dXlCQwKBBskeH83OIyNsx8JBfa24G9u+XIeeTT+QQVmcjR9pPJR88WPs6+prwcDkJuaGhBdHRDDtE5BsYeMjvCAF89ZXszdm+HaitdbwmNlYuJc/OBsaO5Yd2dxRFhpzYWLnyyllwJCLyZgw85DfOngVMJgXvvXclSkocJ5MEBQEzZsieHC4ld43BYN8/h/NziMiXMfCQT6utlUvJTSbgyy8BQAeg4xKh8ePlvJy5c+UHN3UvJET25kRGsveLiPwDAw/5HLMZ+OwzOS9n7145T6ez+HiB7GwFmZnyuAdyTXi4DDqcsE1E/oaBh3zG8eMy5HS9lNyK8ePP4LbbhiEoiGMwrtDp5PycmBgO8xGR/2LgIa9WViZPJd+6VQaeznQ6+1LyWbOAkBCBY8fquW+OCwwGGXKiozk/h4j8HwMPeZ2mJrmU3GQCPv3U+YqgK6+0n0o+aJD9ca4e6l5oqBy2iojg/BwiChwMPOQVhACOHJFDVjt2OF9K3q+fDDjZ2cCYMfyw7qnISNmjYzR6uiZERNpj4CGP+uEHGXJMJnlCeWe2peQ5OcBNN8mfyXU6nX1ZOduOiAIZAw9prqbGvpT8yBHn11xzjQw5c+bID2zqmaAg+/wczmciImLgIY2YzXI+jskE7NsHtLQ4XpOQICcfZ2cDV1yhfR39gdFon59DRER2DDykGiHkyiqTSS4lLy93vCY8HLj1VhlyJkxgb0RvKIoMOLGxckIyERE5YuAht7t4UQYckwkoLHQs1+nkfBzbUnJ+SPeObX5ObKxcYk5ERJfHvybJLRob5VCVySR3QbZaHa9JTpbzcjIygIEDta+jvwgKkiEnKoo9YkRErmLgoV6zWuWk461b5STk+nrHa+LiOi4lp97j/Bwiot5j4KEeKyqSS8lzc4Hz5x3Lg4OBmTNlb86NN3K4pS8URe6fExsrD/QkIqLe4UcRuaS6Wm4IaDIBX3/t/JoJE2RPzpw5criFek+vt++fw8BIRNR3/KuULqu1VS4l37pVHvXQ2up4TWKiDDlZWcCwYdrX0d8EB9vn53AnaSIi92HgoQ6EAAoKZE/Ohx8CFRWO10REAHPnypAzYQI/mN0hLEwGnfBwT9eEiMg/MfAQAKC0VC4lz811vpRcr5dLybOz5VEPXEred5yfQ0SkHQaeANbYCOzZI0POoUPOl5KPGSNDzrx5wIAB2tfRH+n1cm5OTIz8noiI1MfAE2CsVuCLL+SQ1c6dQEOD4zX9+wOZmXLIavRo7evorzg/h4jIcxh4AsT338uQk5cHXLjgWB4SInc9zs4GbriBK4PcKTxc9uZwfg4RkefwY82PVVUB27fLIatvvnF+zcSJ9qXkkZHa1s+fKYrsyYmNlT07RETkWQw8fqalBTh4UIacyy0lHzZMDldlZQFDh2pfR3/G+TlERN6JgccPCAH85z8y5Hz4IVBZ6XhNVJT9VPJrruEcEncLCZG9OZGRbFsiIm/EwOPDSkrknJzcXOC77xzL9XpgyhTZkzNjBpc+qyE8XAadsDBP14SIiLriNYFnxowZCA4ORsiPn8oPPfQQ5s6di6KiIixfvhyVlZWIjIzEc889h1GjRgFAr8t8WUMDsHu3DDn//Kfs3eksNVWGnPnz5eGd5F6cn0NE5Hu8JvAAwIsvvojk5OQOj61atQoLFy7EggULsHPnTqxcuRJvv/12n8p8jdUKHD4sV1nt2uV8KfmAAfal5Ckp2tcxEBgMcm5OdDTn5xAR+RqdpyvQlfLychQUFCAzMxMAkJ6ejnPnzuHcuXO9LvMlp08D//3fcjjq7rvlmVbtw05oKJCRAfzv/wIHDgDLljHsqCEkBBg8GBgxAujXj2GHiMgXeVUPz9KlSyGEwLhx47BkyRIUFxdj4MCBMPy4KYyiKIiPj0dxcTGMRmOvyhITE7uth8ViUWXiqcVi6fBfZ6qqgB07FJhMCvLznVfiuusEsrIEZs8WiIhof3+3VtcnudLGroqIkMNWRqP82dlO1IHKne1Ml8d21gbbWRtqtrMr9/SawLN582YMGTIEra2teOGFF/DYY4/hkUcegdIpeYh2k1Z6W9adEydOQAj1ltoUdjqsqrVVwVdfReDjj2Nw5EgkzGbHjrf4+GZMm1aFKVOqMHCgXGt+9qxqVfR5ndvYVTodEB5uQVSUGQ0NwMWLbq6Yn8nPz/d0FQIC21kbbGdteKqdvSbwDBkyBAAQFBSEu+++G+np6YiPj0dJSQnMZjMMBgOEECgpKUF8fDyMRmOvylyRkpICRXH/uIXFYkFhYSGSk5Oh0+mRnw/k5SnYtk1BdbVjwIqKErj1VoHsbIFx4wxQlP4A+ru9Xv6kfRvrezD2ZJufExMjQw91zWKxID8/H2lpaT1qZ+oZtrM22M7aULOdbffuilcEnoaGBpjNZkRFRQEAtm3bhtTUVMTFxSE1NRV5eXlYsGABdu3ahYSEhLZhqd6WdUev16sSeACgrCwIBw8akJenw+nTjuUGg1xKnp0NTJ+uIDiYm7r0hl6vd+kPVGioHLaKiOD+Ob3hajtT37CdtcF21oan2tkrAk95eTkWL17cNgaXmJiItWvXAgDWrFmDFStWYMOGDQgPD297vC9lWquvl6eSb9miw+HDyU6Hy8aOlSEnI0NOjCV1dZ6fQ0RE/s0rAs/QoUNhMpmcliUlJV12OXlvy7Rgscil5Fu3yn1zGhsBoGPQGTTIvpT8yis9Us2AotPJJeUxMUBQkKdrQ0REWvKKwONPTp2yn0peUuJYHhJixezZwIIFOkyezCXOWggKsu+fw/k5RESBiYHHDSoq5KnkW7fKM606UxRg8mQgM9OKYcOO49prUxh0NFUZQksAABJjSURBVGA0yqDDU+CJiIiBp5daWuRmfyYT8PHHgNnseM2IEXJeTmYmMGQIYLEIHDvGzVzUpChyWfmwYfKcKyIiIoCBp0eEAI4elT0527cD1dWO18TEAPPmyaCTlsaVP1qxzc+JigLq680IDfV0jYiIyJsw8Ljg/Hk5J8dkAoqKHMuDgoCpU2XImTqVB0pqKShIrraKipKhhxulEhGRMww8l1FXJ1dXmUzA5587vyYtDcjJAW69lUvJtWY02vfPISIi6g4DjxMrVwLbtgFNTY5lgwfLZeRZWcDIkdrXLZApipyAHBMDDlkREVGPMPA4sXNnx7ATFgbMni2HrCZN4lJyren19v1zDHzHEhFRL/Dj4zIUBfjJT2TIueUWrvjxhOBg+/45nPxNRER9wcDjxK9/DcydC7h41ii5WViYnJ/DkElERO7CwOPEffexR0Frtvk5sbFASIina0NERP6GgYc8Sq+3D1txfg4REamFHzHkEcHB9v1z2JtGRERqY+AhTXF+DhEReQIDD6lOUWRPTmwsd6EmIiLPYOAh1djm58TEcO8iIiLyLAYecjvOzyEiIm/DwENuYzTKM8U4P4eIiLwNAw/1mW3/HJ5vRURE3oqBh3pFp7NPRA4K8nRtiIiIusbAQz1iMNg3CuREZCIi8hUMPOQSTkQmIiJfxsBDXeJEZCIi8gcMPOQUJyITEZE/YeChNpyITERE/oqBhzgRmYiI/B4DTwDjRGQiIgoUDDwBiCeWExFRoGHgCRCKAkREcCIyEREFJgYeP2c7sTw6Ws7VISIiCkT8CPRToaEy6ERGcn4OERERA48fURQ5ATk6msNWRERE7THw+IGgINmbExXFZeVERETOMPD4sOBgIC5ODlsRERHR5THw+KCQEHm+FYMOERGRaxh4fIjRKJeVR0R4uiZERES+hYHHyymK/SDPkBBP14aIiMg3MfB4Kdv+OTExnIhMRETUVww8XiYkRPbmcP8cIiIi92Hg8RK2Yx+MRk/XhIiIyP8w8HhYeDjQvz/n5xAREamJgUdDJ04AX34ZCZ0OmDhRBh3uiExERKQ+Bh4NXLoErF0LnD6tQ3PzAOzcqUNqKrB+PTB4sKdrR0RE5P90nq5AIFi7FjhzRu6MHBQkoNMBx48DS5d6umZERESBwa8DT1FREX72s58hPT0dP/3pT/Hdd99p+vv1eqC4GDh/Xp53pevU2sePA0ePalolIiKigOTXgWfVqlVYuHAhdu3ahQceeAArV67U5PcaDMCAAcCIEUBtbdfLy3/4QZMqERERBTS/DTzl5eUoKChAZmYmACA9PR3nzp3DuXPnVPudwcFyTs6IEXKJuU4HDBvW9XO6KyciIqK+89tJy8XFxRg4cCAMBvkSFUVBfHw8iouLkZiY2OVzLRZLjzb9MxrlYZ7h4fJnq9VeNnYskJKi4MQJBUIIAIAQAooCjB4tMHasgMXSo5dGXbD82JgWNqqq2M7aYDtrg+2sDTXb2ZV7+m3gAWTIac8WOLpz4sQJCNF94gkPtyAy0oKGBoHy8stfd9ddBvzP/ySiqEiuQW9sbMTw4U1YtOgcvvnG7FKdqGfy8/M9XYWAwHbWBttZG2xnbXiqnf028MTHx6OkpARmsxkGgwFCCJSUlCA+Pr7b56akpEBRnB9gpdMBUVFyyCooyPX6zJoFfPONFQcPnsHNN1+B8eNDAcS4fgNyicViQX5+PtLS0qDnIWSqYTtrg+2sDbazNtRsZ9u9u+K3gScuLg6pqanIy8vDggULsGvXLiQkJHQ7nAUAer3eIfAYDPIgz+jo3h/mOX48AFRj/Hgd/1CpTK/Xs401wHbWBttZG2xnbXiqnf028ADAmjVrsGLFCmzYsAHh4eFYu3Ztj+9hMABxcbJXh4d5EhER+Sa/DjxJSUl4++23e/VcRZETkW2rrYiIiMh3+XXg6a2ICGDQINm7Q0RERL6PH+lODB7c+3k6RERE5H04WENERER+j4GHiIiI/B4DDxEREfk9Bh4iIiLyeww8RERE5PcYeIiIiMjvMfAQERGR32PgISIiIr/HwENERER+j4GHiIiI/B4DDxEREfk9Bh4iIiLyeww8RERE5PcYeIiIiMjvMfAQERGR3zN4ugLeRAgBALBYLKrc33Zfte5PbGOtsJ21wXbWBttZG2q2s+2ets9xZxTRVWmAaWlpQX5+vqerQURERL2QlpaG4OBgp2UMPO1YrVaYzWbodDooiuLp6hAREZELhBCwWq0wGAzQ6ZzP1mHgISIiIr/HSctERETk9xh4iIiIyO8x8BAREZHfY+AhIiIiv8fAQ0RERH6PgaeXmpub8atf/Qrp6enIysrC/fffj3PnzgEAysvLcf/992P27NnIyMjAl19+2fa8l19+Genp6Rg9ejQ++uijDvdcsWJF2/1+8Ytf4NixY5q+Jm+kVjvPnz8fWVlZuO222/DPf/5T09fkjdRoZ5utW7ciJSXlsuWBRI12XrRoEWbOnImsrCxkZWXhtdde0/IleSU12lkIgb/85S9IT09HRkYGFi1apOlr8kZqtPPPfvaztvdyRkYGUlJScPz4cfdUWFCvNDU1iQMHDgir1SqEEOKNN94Q9957rxBCiOXLl4sXX3xRCCHEv//9bzFt2jTR2toqhBDim2++EWfOnBF33nmn2L9/f4d77t27t+26/fv3i9mzZ2v1cryWGu1cXV3d9n1BQYGYNGlS2/0DlRrtLIQQxcXF4o477hALFy50Wh5o1Gjny7V9IFOjnV977TXxm9/8RjQ3NwshhCgtLdXq5Xgttf7esNmxY4fIyMhwW33Zw9NLISEhmDp1atsGhVdffTXOnj0LANi5cyd+8YtfAADGjRuHuLg4HDlypO26YcOGOb3nzJkzYTAY2q47f/48rFar2i/Fq6nRzlFRUW3f19TUcJNJqNPOAPD4449jxYoVl935NNCo1c7UkRrtvHHjRixdurTtvTxw4EC1X4bXU/v9/P777+O2225zW30ZeNzkjTfewPTp01FZWQmr1Yp+/fq1lSUkJKC4uLhH93v99dcxderUy+4YGajc1c7r16/HrFmzsHjxYvzP//wPQ08n7mjnt956C6NGjcLVV1+tZlV9mrvez+vWrcP8+fPxu9/9ru0Dh+z62s51dXWoqKjAnj17sHDhQixcuBDbt29Xu9o+x52fg6WlpTh8+DAyMzPdVj9+mrrByy+/jDNnzuD3v/89ADh8eIoebmadm5uLHTt24Mknn3RbHf2BO9t56dKl2Lt3L1544QWsW7cOLS0tbq2rL3NHO589exbvvvsuHnnkEVXq6A/c9X5et24dduzYgby8PEycOBEPPfSQ2+vqy9zRzq2trWhtbUVTUxPeeecdvPDCC3j22WdRWFioSp19kbs/B7ds2YJp06Z1CE19xcDTRxs3bsTu3bvxyiuvwGg0IjY2FgBQUVHRds2FCxcQHx/v0v22b9+Ov/71r3j11VcRFxenSp19kbvb2eaGG25AfX09/+L6kbva+ZtvvsHFixcxd+5czJgxA9988w1WrlyJd955R9X6+wp3vp9t1yj/v727i2n63uM4/gbkLwoJVmFqly7Rm5H4yAAXgqObEsEBNTFiQNHIUpM5F4xZVKzEXSwzRNxSvVgGGNEYg4mJWYdPYKzGZ7lRYAl7iIlEcYKkjaY+tWh3sZzmcJBzdrZWpH5ed/8Hfr/v75fmzyf//6/9x8RQXl7O7du38Xq9kSl8lAnXPJtMJsaPH8+SJUsAMJvNvPfee/z000+RK34UCff1ORgMcvToUZYtWxbWOhV4/oHGxkaOHz9OY2PjoHUhBQUFHDp0CICOjg76+/vJyMj4n+2dOHECp9NJY2MjZrM5YnWPNuGc54GBAW7duhXa7ujowOPxYLFYIlL7aBLOeS4uLubSpUu43W7cbjdz587l66+/Zvny5REdw2gQ7s9zf39/aLulpYWUlJTQP5w3Wbivz0VFRZw/fx6ABw8e0NnZybvvvhuZ4keRcM8zQFtbG4FAgJycnLDWqpeH/k337t3DarVisVhITEwEwDAMjhw5Qn9/P5s3b+bOnTvEx8fz5ZdfMm/ePADq6uo4dOgQHo+HxMRExo4dyw8//MDEiROZMWMGKSkpTJgwIdTP/v373+iLV7jnOSkpidWrV+Pz+YiNjWX8+PFs2LCB7OzskRzmiIvE5/nfrVq1ik8++YSPPvrolY/tdRLueU5ISKC8vJxAIEBMTAwmk4mtW7eSlpY2ksMccZH4PHs8HhwOR+hr1+Xl5ZSWlo7YGF8HkbpubNq0CYvFQmVlZVjrVeARERGRqKdHWiIiIhL1FHhEREQk6inwiIiISNRT4BEREZGop8AjIiIiUU+BR0RERKKeAo+IRJXt27ezZ8+esLd78+ZN/dCcyCg2ZqQLEJHotGrVKm7cuMGYMWMwDIOZM2dSXV3NtGnTItrvSL6DzuVysW/fPrq7u0lISMBqtfLFF1/ozdoirwHd4RGRiHE4HFy/fp1z584xceJEHA7HSJcUMQcOHGDHjh1UVlbS1taGy+ViYGCAsrIyHj58ONLlibzxFHhEJOLGjRtHYWEhXV1dg/a7XC6KiorIzMxkxYoV/Pbbb6Fjvb29bNy4kZycHObNm8dnn30WOtbR0cHKlSvJyspi8eLFtLa2ho5VVVWxa9cuAD7++GNOnjw5qM/8/HxOnz4NwK1bt7Db7bz//vvk5eWF3v0D8OzZM7Zt20ZWVhaLFi3iypUrw47P5/PhdDqprq5m4cKFGIbB5MmT2blzJ4ZhsH//fgCuXbtGTk4OBw8eZP78+WRnZ7N3797/czZF5O9Q4BGRiPP5fDQ3N/POO++E9rndbnbv3s2uXbu4du0aNpuNTz/9FL/fz/Pnz1m3bh3Jycm0tLRw8eJF1qxZA0BfXx9r166loqKCq1evUlNTQ3V1NTdv3hzSb3FxMc3NzaHtzs5OvF4vVquVJ0+eUFFRwYIFC7h48SL19fU0NDRw6dIlAL777jt+/fVXTp06RVNT06B2/tP169d59uwZBQUFg/bHxcVRUFAQahPA6/XS19eH2+2mrq4Op9NJd3f335pXEfnrFHhEJGJqamrIyMggIyOD9vZ2amtrQ8eampqw2+2kpaURFxdHaWkpMTExtLe309nZyZ07d3A4HCQlJWEYRujFgy6Xi+zsbPLy8oiLi2POnDnk5eUNuZMDYLPZuHDhAg8ePACgubmZgoICDMPg7NmzpKSksGLFCuLj45k+fTolJSUcO3YMgGPHjrFu3TomTZrEpEmTWLt27bDj9Hq9mEwm4uPjhxxLTU3F6/WGtmNjY6msrMQwDGbPns20adOG3PkSkfDTomURiZiqqirKysq4ffs2drud7u7u0Dedenp6qK2t5dtvvw2dHwgE6O3tJTY2lqlTp2IYxpA2e3p6OHPmDJmZmaF9z58/x2azDTn37bffZtasWZw6dYqSkhJOnDiB0+kMtdPV1TWknX9t9/X1YTabB7U1HJPJhNfrJRAIDAk99+/fx2QyhbaTk5MHnTNu3DgeP348bNsiEh4KPCIScRaLBYfDwbZt28jNzSUhIYGpU6dit9tZunTpkPNv3LjB77///tIAYTabKSwspKam5i/1bbPZ+PHHH7FYLBiGQUZGRqid9PR0Dh48+NK/e+utt7h79y5paWkA3L17d9g+0tPTGTt2LK2trRQWFob2v3jxgpaWFhYtWvSXahWRyNEjLRF5JaxWK6mpqRw+fBiAsrIy6uvr+fnnnwkGgzx69Ai3243P52PWrFmYzWZqamrw+XwEAgHa2tqAPwPM+fPncbvdDAwM4Pf7aW9vf+kaHoDFixfT2dlJQ0MDxcXFxMTEAPDhhx/S09PDkSNH8Pv9DAwM8Msvv9DR0QH8ueC5rq4Oj8eDx+OhoaFh2LElJSVRWVnJV199hdvtxu/309vbS1VVFU+fPg2tPxKRkaPAIyKvjN1uZ+/evTx9+pS8vDw+//xztmzZQmZmJvn5+bhcLuDPxb7ff/89fX19LFy4kPnz54fuxEyZMoX6+noOHDhATk4OH3zwAd988w1+v/+lfSYnJ5Obm8vly5cHPfZKTExk3759uN1urFYr2dnZbN++nUePHgGwfv16pk+fTn5+PqWlpRQVFf3XsVVUVFBVVYXT6SQrKwubzUYwGKSpqYkJEyaEY/pE5B+ICQaDwZEuQkRERCSSdIdHREREop4Cj4iIiEQ9BR4RERGJego8IiIiEvUUeERERCTqKfCIiIhI1FPgERERkainwCMiIiJRT4FHREREot4fOhKeCmmY2zcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "slope, intercept, r_value, p_value, std_err = stats.linregress(byyear['Received On'],byyear['Amount'])\n",
    "ax = sns.regplot(x=\"Received On\", y=\"Amount\", data=byyear, color='b', \n",
    " line_kws={'label':\"y={0:.1f}x+{1:.1f}\".format(slope,intercept)})\n",
    "\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3115.2791428571422"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## print out a more precise intercept for the calculation below\n",
    "slope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23198.981999999844"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Predicting our revenue for 2018!\n",
    "year = 2018\n",
    "3115.2791428571422 * year -6263434.328285713"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like over the course of all of 2018, we can expect to raise $23,198 from COGD!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion \n",
    "Given the spread of the data in the last two years, and a lack of knowledge about NEC’s donor lists, I suggest  compromise of 200 donors for COGD 2018. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}