diff --git a/docs/lectures/others/homework02.ipynb b/docs/lectures/others/homework02.ipynb
deleted file mode 100644
index f781907..0000000
--- a/docs/lectures/others/homework02.ipynb
+++ /dev/null
@@ -1,2302 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Homework 02 of EPS 88\n",
-    "\n",
-    "## GPS data and subsidence of the San Joaquin Valley\n",
-    "\n",
-    "The example data we are going to use today is from continuously operating high-precision GPS stations that are operated by UNAVCO which is a non-profit university-governed consortium that facilitates geoscience research and education using geodesy. \n",
-    "\n",
-    "Let's get an introduction here: https://youtu.be/yxLMk120vMU\n",
-    "\n",
-    "This data viewer gives a great summary velocity overview of the available GPS data: https://www.unavco.org/software/visualization/GPS-Velocity-Viewer/GPS-Velocity-Viewer.html\n",
-    "\n",
-    "Let's look at data from a GPS station that is in Visalia California. Visalia is in the San Joaquin Valley between Fresno and Bakersfield.\n",
-    "\n",
-    "https://www.unavco.org/instrumentation/networks/status/pbo/overview/P566"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Today we will be focused on putting tools to use that we have used before with a couple new tricks related to dealing with time-series data. \n",
-    "\n",
-    "## Using pandas to import and filter data\n",
-    "From the first week of class we have used the pandas library to import and filter data. The DataFrame object has been the most common way we have dealt with data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One of the strengths of pandas is its ability to read and write different data types. For example, we have used the `pd.read_csv()` function to import .csv files throughout the course. This function can either be pointed to a file that is one your computer or a file that is posted on the internet. There are some online databases where you can use a url to access data of your choosing using a special format (API). We took this approach to get our birthquakes earlier in the course.\n",
-    "\n",
-    "Let's import daily data since 2005 for the Visalia California GPS station. The data is in the North American tectonic plate (NAM14) reference frame which means that it takes the interior eastern part of North America functions as a fixed and stable point. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# P566_GPS_data = pd.read_csv('data/P566.cwu.nam14.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Whoops. There was an error. I kept this error in here as a reminder that I get errors all of the time. Remember that the errors are informative, but can also be a bit cryptic. In this case, it says \"Expected 2 fields in line 10, saw 4.\" So it seems like there is a mismatch between the number of columns it is expecting and the number that there are. \n",
-    "\n",
-    "Let's look at the file.\n",
-    "\n",
-    "It turns out that there are a bunch of header lines and the header row that contains the column names needs to be specified (`header = 11`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P566_GPS_data = pd.read_csv('../data/P566.cwu.nam14.csv', header=11)\n",
-    "P566_GPS_data.columns = P566_GPS_data.columns.str.strip()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We know how to take a peak at a DataFrame by applying the `.head()` function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "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>Date</th>\n",
-       "      <th>North (mm)</th>\n",
-       "      <th>East (mm)</th>\n",
-       "      <th>Vertical (mm)</th>\n",
-       "      <th>North Std. Deviation (mm)</th>\n",
-       "      <th>East Std. Deviation (mm)</th>\n",
-       "      <th>Vertical Std. Deviation (mm)</th>\n",
-       "      <th>Quality</th>\n",
-       "      <th>Unnamed: 8</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2005-11-16</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>2.11</td>\n",
-       "      <td>1.71</td>\n",
-       "      <td>7.70</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2005-11-17</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>-1.83</td>\n",
-       "      <td>0.44</td>\n",
-       "      <td>2.02</td>\n",
-       "      <td>1.61</td>\n",
-       "      <td>7.36</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2005-11-18</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>-0.64</td>\n",
-       "      <td>5.36</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1.62</td>\n",
-       "      <td>7.35</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2005-11-19</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>-0.86</td>\n",
-       "      <td>4.61</td>\n",
-       "      <td>2.02</td>\n",
-       "      <td>1.62</td>\n",
-       "      <td>7.37</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2005-11-20</td>\n",
-       "      <td>-0.15</td>\n",
-       "      <td>-1.14</td>\n",
-       "      <td>-3.28</td>\n",
-       "      <td>2.04</td>\n",
-       "      <td>1.65</td>\n",
-       "      <td>7.46</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Date  North (mm)  East (mm)  Vertical (mm)  \\\n",
-       "0  2005-11-16        0.00       0.00           0.00   \n",
-       "1  2005-11-17        0.26      -1.83           0.44   \n",
-       "2  2005-11-18        0.03      -0.64           5.36   \n",
-       "3  2005-11-19        0.38      -0.86           4.61   \n",
-       "4  2005-11-20       -0.15      -1.14          -3.28   \n",
-       "\n",
-       "   North Std. Deviation (mm)  East Std. Deviation (mm)  \\\n",
-       "0                       2.11                      1.71   \n",
-       "1                       2.02                      1.61   \n",
-       "2                       2.00                      1.62   \n",
-       "3                       2.02                      1.62   \n",
-       "4                       2.04                      1.65   \n",
-       "\n",
-       "   Vertical Std. Deviation (mm) Quality  Unnamed: 8  \n",
-       "0                          7.70   repro         NaN  \n",
-       "1                          7.36   repro         NaN  \n",
-       "2                          7.35   repro         NaN  \n",
-       "3                          7.37   repro         NaN  \n",
-       "4                          7.46   repro         NaN  "
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "P566_GPS_data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We have done a lot where we have used extracted data from a single column. We have used the syntax `DataFrameName['column_name]`. It can be helpful to look at the available columns:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['Date', 'North (mm)', 'East (mm)', 'Vertical (mm)',\n",
-       "       'North Std. Deviation (mm)', 'East Std. Deviation (mm)',\n",
-       "       'Vertical Std. Deviation (mm)', 'Quality', 'Unnamed: 8'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "P566_GPS_data.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let first look at how this point is moving north with respect to stable North America."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    6305.000000\n",
-       "mean       81.556114\n",
-       "std        47.225855\n",
-       "min        -0.860000\n",
-       "25%        41.690000\n",
-       "50%        81.630000\n",
-       "75%       120.950000\n",
-       "max       167.900000\n",
-       "Name: North (mm), dtype: float64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "P566_GPS_data['North (mm)'].describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By themselves these data are pretty cool. It looks like Visalia has moved north relative to stable North America by ~170 mm (17 cm) over the past 18 years (the data starts in November 2005)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Pandas timeseries\n",
-    "\n",
-    "Pandas is good at dealing with time series data. We need to make sure that the data type of the 'Date' column is a time series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'2005-11-16'"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "P566_GPS_data['Date'][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "str"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(P566_GPS_data['Date'][0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Right now, pandas thinks that the values in the data column are strings (a sequence of characters) rather than datetime values. We can convert them to be datetime values using `pd.to_datetime`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P566_GPS_data['Date'] = pd.to_datetime(P566_GPS_data['Date'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Timestamp('2005-11-16 00:00:00')"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "P566_GPS_data['Date'][0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas._libs.tslibs.timestamps.Timestamp"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(P566_GPS_data['Date'][0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Making plots using `matplotlib`\n",
-    "\n",
-    "We have relied on `matplotlib` to make plots throughout the course which we have imported as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting with pandas\n",
-    "\n",
-    "One thing that you can do using pandas once you have imported matplotlib that we haven't done very much is use built-in plotting functions on the DataFrame. In this case we can use `.plot`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Axes: xlabel='Date'>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGYCAYAAABoLxltAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABPrElEQVR4nO3deVhUZf8G8HvYhkXAhV0BcUFTScV9R3NDzdTcLUXN1Mxf5lZapuaC2puvldnbYmpqaYtLmZr7bi6476jgBoigMLINMPP8/jAmR2CYwVnODPfnurhiznnOzPcbcOb2nGfOkQkhBIiIiIgkxM7SBRARERE9iwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkx8HQDQ4cOIBPPvkEMTExSExMxMaNG9GrVy/NeplMVuR2ixYtwpQpUwAAERER2L9/v9b6AQMGYN26dXrVoFarkZCQAHd392Jfj4iIiKRFCIHHjx8jICAAdna6j5EYHFAyMzNRv359DB8+HK+++mqh9YmJiVqPt23bhpEjRxYaO2rUKHz88ceaxy4uLnrXkJCQgMDAQAMrJyIiIim4c+cOqlSponOMwQElMjISkZGRxa738/PTerx582a0b98e1apV01ru6upaaKy+3N3dATxp0MPDo1TPQUREROalUCgQGBioeR/XxeCAYoj79+/jzz//xKpVqwqtW7t2LdasWQNfX19ERkZi5syZxRasVCqhVCo1jx8/fgwA8PDwYEAhIiKyMvpMzzBpQFm1ahXc3d3Rp08freVDhgxBSEgI/Pz8cOHCBUybNg1nz57Fzp07i3ye6OhozJ4925SlEhERkYTIhBCi1BvLZIUmyT6tdu3a6NSpE7744gudzxMTE4PGjRsjJiYG4eHhhdY/ewSl4BBReno6j6AQERFZCYVCAU9PT73ev012BOXgwYO4evUq1q9fX+LY8PBwODo6IjY2tsiAIpfLIZfLTVEmERERSZDJAsry5cvRqFEj1K9fv8SxFy9eRF5eHvz9/Y1ag0qlQl5enlGfk6yHk5NTiR9jIyIiaTI4oGRkZOD69euax3FxcThz5gwqVqyIoKAgAE8O4fzyyy/49NNPC21/48YNrF27Ft26dYOXlxcuXbqESZMmoWHDhmjVqtVztPIvIQSSkpKQlpZmlOcj62RnZ4eQkBA4OTlZuhQiIjKQwQHl5MmTaN++vebxxIkTAQDDhg3DypUrAQDr1q2DEAKDBg0qtL2TkxN2796Nzz77DBkZGQgMDET37t0xc+ZM2Nvbl7INbQXhxMfHB66urryYWxlUcDG/xMREBAUF8XeAiMjKPNckWUvRNclGpVLh2rVr8PHxQaVKlSxUIUlBeno6EhISUKNGDTg6Olq6HCKiMs+QSbI2d4K+YM6Jq6urhSshSys4taNSqSxcCRERGcrmAkoBHtIn/g4QEVkvmw0oREREZL0YUIiIiEhyGFAIUVFRxV4NuCRt27bFjz/+aNyC9KBUKhEUFISYmBizvzYRkS1bd/w2Gs3ZiWRFjkXrYECRkKioKMhkMixYsEBr+aZNm4wynyI+Ph4ymQxnzpx57ucCgC1btiApKQkDBw40yvMZQi6XY/LkyXjvvffM/tpERLbmwr103FfkYMmua3h/w3mkZuai6fzdFq3JpDcLJMM5Oztj4cKFGD16NCpUqGC0583NzTXacxX4/PPPMXz4cItdrXXIkCGYMmUKLl++jBdeeMEiNRARWbO0rFw0+LjoG/VaWpk4giKEQFZuvkW+DL3MTMeOHeHn54fo6Gid43777TfUrVsXcrkcVatWLXTV3qpVq2Lu3LmIioqCp6cnRo0ahZCQEABAw4YNIZPJEBERobXNf/7zH/j7+6NSpUoYN26cztsEpKSkYNeuXejZs6fWcplMhq+//ho9evSAq6srXnjhBRw9ehTXr19HREQE3Nzc0KJFC9y4cUOzzaxZs9CgQQN8//33CAoKQrly5TB27FioVCosWrQIfn5+8PHxwbx587Req1KlSmjZsiV++uknnf+viIhImxACF+6lSzacAGXkCEp2ngp1PvrLIq996eMucHXS/3+zvb095s+fj8GDB+P//u//UKVKlUJjYmJi0L9/f8yaNQsDBgzAkSNH8NZbb6FSpUqIiorSjPvkk08wY8YMfPjhhwCAt99+G02bNsWuXbtQt25drUvA7927F/7+/ti7dy+uX7+OAQMGoEGDBhg1alSRdR46dEgTQJ41Z84cLF68GIsXL8Z7772HwYMHo1q1apg2bRqCgoIwYsQIvP3229i2bZtmmxs3bmDbtm3Yvn07bty4gb59+yIuLg6hoaHYv38/jhw5ghEjRuCll15C8+bNNds1bdoUBw8e1Pv/LxERATsu3cfo1brn8NWrrPtCaqZWJgKKtenduzcaNGiAmTNnYvny5YXWL168GC+99BJmzJgBAAgNDcWlS5fwySefaAWUDh06YPLkyZrH8fHxAJ4cefDz89N6zgoVKmDp0qWwt7dH7dq10b17d+zevbvYgBIfHw9fX98iT+8MHz4c/fv3BwC89957aNGiBWbMmIEuXboAAN555x0MHz5caxu1Wo3vv/8e7u7uqFOnDtq3b4+rV69i69atsLOzQ61atbBw4ULs27dPK6BUrlxZ0xcREeln0+l7JY7ZMr6NGSopXpkIKC6O9rj0cReLvXZpLFy4EB06dMCkSZMKrbt8+TJeeeUVrWWtWrXCkiVLoFKpNPc0aty4sd6vV7duXa17Ifn7++P8+fPFjs/Ozoazs3OR61588UXN976+vgCAsLAwrWU5OTlQKBSaSx1XrVoV7u7uWmPs7e21ApCvry+Sk5O1XsvFxQVZWVn6tEhERACSFTnYdiFJ55jr8yLNVE3xykRAkclkBp1mkYK2bduiS5cumD59utZREeDJucNnP9VT1FwXNzc3vV/v2XvVyGQyqNXqYsd7eXnh0aNHJT5XQZ1FLXv6+Yt6fX1qevjwIby9vYutk4iI/pWhzC/x0znxC7qbqRrdrOtdu4xZsGABGjRogNDQUK3lderUwaFDh7SWHTlyBKGhoTrvCG3Me9M0bNgQSUlJePTokVE/bWSoCxcuoGHDhhZ7fSIia/EoMxcN5xSeFGsnA/w9XXAvLRsxH3a0QGVFY0CRsLCwMAwZMgRffPGF1vJJkyahSZMmmDNnDgYMGICjR49i6dKlWLZsmc7n8/HxgYuLC7Zv344qVarA2dkZnp6epaqtYcOG8Pb2xuHDh9GjR49SPYcxHDx4EHPmzLHY6xMRWYuPt1wqcvmpGZ3g7GiPnDwVyrs6FTnGEsrEx4yt2Zw5cwqdvgkPD8fPP/+MdevWoV69evjoo4/w8ccfFzoV9CwHBwd8/vnn+PrrrxEQEFBoHosh7O3tMWLECKxdu7bUz/G8jh49ivT0dPTt29diNRARWYuNxUyMLe/qBGdHe0mFEwCQCUMv1CEBCoUCnp6eSE9P10yyLJCTk4O4uDiEhIQUO4mTjOP+/fuoW7cuYmJiEBwcbPbX79evHxo2bIjp06cXuZ6/C0RET6w4HIfZfxQ+glI3wAN//p/5Pq2j6/37WTzFQ6Xm6+uL5cuX4/bt22YPKEqlEvXr18e7775r1tclIrIWmcp8DP7uGM7eSSty/dW5XeFgoSuB64MBhZ7L85wmeh5yuVxzAToiIvrXw8xchBcxGfZpg5sFQe5QustgmAsDChERkQ3pveywzvW/jGmB+lXKm6eY58CAQkREZIUWbLuCy4kKDG4WhBereMLf0wVHrqfgVqrui1c2qVrRTBU+H5sNKFY495eMjL8DRGTL/rf/yU1X9197AODJvXMu3FPo3GbOK3VNXpex2FxAKbj6aFZWFlxcXCxcDVlSbm4uAOi8eB0RkbVJyVBix8X7hZYXF05uzO+GW6mZ8PN0tqqrqltPpXqyt7dH+fLlNfdscXV1LXRZeLJ9arUaDx48gKurKxwcbO7XnIjKsNe+O4YrSY/1Gtu+ljfs7WSo5l3OxFUZn03uuQvu1PvsjeWobLGzs0NQUBADKhFZNSEEYpMzkJCWjV9i7uodTvo3roIZPeqYuDrTscmAIpPJ4O/vDx8fH+Tl5Vm6HLIQJycnrbshExFZo+8Px2NOMZepL87Od9uipq97yQMlzCYDSgF7e3vOPyAiIqv1/aE4g8PJ1v9rY/XhBOC9eIiIiCTpcU5esTf4K84rDQJQJ0D3JeSthU0fQSEiIrI2Qgi0XLAHiek5OseNblcNDQMrYMyaGADAhI41MbJ1iDlKNAsGFCIiIom4cC8dPb44VOK4PZPaaT6Zs3RwQ4T6uiPUBk7rPI0BhYiIyMKEEDh1Ow2vfnVE57hVI5qiXai31rIeLwaYsjSLYUAhIiKysDaL9uLuo+xi18cv6G7GaqSBAYWIiMhCms3fhfsKpc4xP41qbqZqpIUBhYiIyMyEENh56b7OcPLn/7VG3QBPM1YlLQZ/zPjAgQN4+eWXERAQAJlMhk2bNmmtj4qKgkwm0/pq3lw7/SmVSowfPx5eXl5wc3NDz549cffu3edqhIiIyBrkq9Ro/599eHN1TLFjZr5cp0yHE6AUASUzMxP169fH0qVLix3TtWtXJCYmar62bt2qtX7ChAnYuHEj1q1bh0OHDiEjIwM9evSASqUyvAMiIiIrkZKhxIhVJxGfmqVzXJ/wKmaqSLoMPsUTGRmJyMhInWPkcrnmfjjPSk9Px/Lly7F69Wp07NgRALBmzRoEBgZi165d6NKli6ElERERWYXGc3fpXF8WJ8MWxyRXkt23bx98fHwQGhqKUaNGad20LyYmBnl5eejcubNmWUBAAOrVq4cjR4r+eJVSqYRCodD6IiIisiZ5KnWx6z7qUQebx7UyYzXSZ/RJspGRkejXrx+Cg4MRFxeHGTNmoEOHDoiJiYFcLkdSUhKcnJxQoUIFre18fX2RlJRU5HNGR0dj9uzZxi6ViIjIpGLvP8aoH05iTLvqeH/D+SLHnJvVGR7OjmauTPqMHlAGDBig+b5evXpo3LgxgoOD8eeff6JPnz7FbieEgEwmK3LdtGnTMHHiRM1jhUKBwMBA4xVNRERkAtM3nkd8alax4WROr3oMJ8Uw+ceM/f39ERwcjNjYWACAn58fcnNz8ejRI62jKMnJyWjZsmWRzyGXyyGXy01dKhER0XNRqwUeZeVi1A8ncep2ms6xXuXkGNw0yDyFWSGTB5TU1FTcuXMH/v7+AIBGjRrB0dERO3fuRP/+/QEAiYmJuHDhAhYtWmTqcoiIiEwiettl/HryLlIzc0scGxfdrdizBvSEwQElIyMD169f1zyOi4vDmTNnULFiRVSsWBGzZs3Cq6++Cn9/f8THx2P69Onw8vJC7969AQCenp4YOXIkJk2ahEqVKqFixYqYPHkywsLCNJ/qISIisiaPc/Lw9f6beo9nOCmZwQHl5MmTaN++veZxwdyQYcOG4auvvsL58+fxww8/IC0tDf7+/mjfvj3Wr18Pd/d/77L43//+Fw4ODujfvz+ys7Px0ksvYeXKlbC3tzdCS0REROaTlZuPsFk79Brr5GCHqV1qmbgi2yATQghLF2EohUIBT09PpKenw8PDw9LlEBFRGbX3ajKGrzih9/iyfp0TQ96/eS8eIiIiA/0acxeTfzmr9/iqlVyxidc5MQgDChERkQGEEAaFEwDYOzmC804MxIBCRERUArVaYPKvZ1HH3wNz/7xc4vgd77ZFbr4ag7/9GxM6hjKclAIDChERUQkOXU/BhlP3sAH3dI47+WFH5KsE/DydAQBnPuoMOzuGk9JgQCEiItLhUWYu9l5NLnHcV0PC4VVO+6KiDCelx4BCRERUjIeZuQifs7PEcWX90zmmwIBCRET0jDsPs9Bm0V69xnaq42viasomBhQiIqJ/qNRPLg2mTzi5NjcSx+JS0Si4QoljyXAMKERERHjySZ0Gs3fgsTK/xLETOtaEk4Md2tT0NkNlZRMDChERlXlCCKRm5uoMJy/4e2BKl1BkKFXoWtfPjNWVTQwoRERUpiSmZ+NhZi7qBngCAJIVOWg6f3ex41+q7YPlUU3MVR79gwGFiIjKhMuJCkR+dlDzeG6vejh6MxV/nkssdpu329fAZN7czyIYUIiIyOblqdRa4QQAPtx0ocTtGE4sx87SBRAREZnaiJX633GYpIEBhYiIbNrB2Ac4GJui9/jjH7wEPw9njI2obsKqqCQ8xUNERDbryPUUvL78uN7ju9T1hY+7M45O68Ab/FkYAwoREdmE43EP4e/pjMCKrsjKzcebP8Tg0HX9j5wAQLcwfwBgOJEABhQiIrJ615Mz0P/rowCAF6t44tzddIO2/+b1RniQoUTP+gGmKI9KgQGFiIis2o0HGZj48xnNY33CyWcDG+D07TRcSlTgxzeawcGeUzKlhgGFiIis1sn4h+j7v6N6j5/SpRbebFsNjvZ2eKVBZRNWRs+LAYWIiKzWl3uv6zWuhk85/P52K7g68W3PWvAnRUREVunBYyX2Xn2g19id77blxFcrw5NuRERkdYQQaDJvl15jW9fwYjixQjyCQkREVuXCvXT0+OJQoeWXPu4CO5kMG0/fw5VEBRzt7dAwqAK6hfHOw9aIAYWIiKxGVm5+keHkw+4vaOaXDGoaZO6yyAR4ioeIiKzG9eSMIpe/0aaamSshU+MRFCIikjwhBMauOYXtF5MKrVszspkFKiJT4xEUIiKSvJ+O3ykynDSvVhGta3pZoCIyNQYUIiKSnDyVGmq1AADsvZKM6RvPFzmuQWAFc5ZFZsRTPEREJCnKfBU6Lt6POw+z0Si4AmJuPSp2rIAwY2VkTgwoREQkGTl5KtSesV3zWFc4AYDqXuVMXRJZCAMKERFJxtDvj+s1bvXIpjh9Ow2vNqpi4orIUhhQiIjI4tRqga/238DxuIc6x301JByRYf4AgDY1vc1RGlmIwZNkDxw4gJdffhkBAQGQyWTYtGmTZl1eXh7ee+89hIWFwc3NDQEBARg6dCgSEhK0niMiIgIymUzra+DAgc/dDBERWRchBNYeu4Vq07fik7+ulji+S11eFbasMDigZGZmon79+li6dGmhdVlZWTh16hRmzJiBU6dOYcOGDbh27Rp69uxZaOyoUaOQmJio+fr6669L1wEREVkdIQQ2nLqLkGlb8cHGCyWOH9OuOi5/3BV2drynTllh8CmeyMhIREZGFrnO09MTO3fu1Fr2xRdfoGnTprh9+zaCgv69/LCrqyv8/JiEiYjKijN30pCYlo0XA8tj5MoTuJL0WO9t3+taizf8K2NMPgclPT0dMpkM5cuX11q+du1arFmzBr6+voiMjMTMmTPh7u5e5HMolUoolUrNY4VCYcqSiYjIyHZduo83fjhZqm1DfcsxnJRBJg0oOTk5eP/99zF48GB4eHholg8ZMgQhISHw8/PDhQsXMG3aNJw9e7bQ0ZcC0dHRmD17tilLJSIiE1GpRanCSTUvN3zQ/QU0CubF2MoimRCi1Fe5kclk2LhxI3r16lVoXV5eHvr164fbt29j3759WgHlWTExMWjcuDFiYmIQHh5eaH1RR1ACAwORnp6u83mJiMiyJv18Fr+dumvQNpvHtcLlRAVeesEX3u5yE1VGlqBQKODp6anX+7dJjqDk5eWhf//+iIuLw549e0osIjw8HI6OjoiNjS0yoMjlcsjl/CUlIrI2hoSTm/O7QSZ78o/f+oHlTVcUWQWjB5SCcBIbG4u9e/eiUqVKJW5z8eJF5OXlwd/f39jlEBGRhey9klziGE8XRwRVdMWakc34CR3SYnBAycjIwPXr1zWP4+LicObMGVSsWBEBAQHo27cvTp06hS1btkClUiEp6cndJytWrAgnJyfcuHEDa9euRbdu3eDl5YVLly5h0qRJaNiwIVq1amW8zoiIyCLuPMzCsn3X8dPxOzrHOTva4ezMzmaqiqyNwXNQ9u3bh/bt2xdaPmzYMMyaNQshISFFbrd3715ERETgzp07eO2113DhwgVkZGQgMDAQ3bt3x8yZM1GxYkW9ajDkHBYREZnPkRspGPztsWLX92oQgGndXsCXe6/j9ebBqOlb9Kc3yTYZ8v79XJNkLYUBhYhIetRqgWrTtxa7vrq3G3ZPijBfQSQ5Fp8kS0REZcuOi0l4c3VMkesaB1dAtzB/DGtZ1bxFkVVjQCEiolIRQuBiggI5eapiwwkATO5SC82rlfyBCaKnMaAQEVGpfH84HnO2XNI55svB4QwnVCoMKEREVColhZMt41ujXmVPM1VDtsbguxkTEVHZ9dfFJFR9/098vf9GiWMZTuh58AgKEREVSaUWsJMBJ+If4UqSAnO3XEauSg0AiN52pchthrUIxt1H2ZjUuZY5SyUbxIBCRERaVGqBh5m5aDJvl0HbHZjSHkGVXE1UFZU1DChERIQHj5VoMm8XqlZyxe2HWVCX4gpZ5Zz5lkLGw98mIiLCW2uffEw4PjWrVNtPi6yNim5OxiyJyjgGFCKiMk6lFjgR/6hU27ap6YXVI5sZuSIiBhQiojJLCIHZf1zCyiPxpX6O+b3DjFcQ0VMYUIiIyqjunx/CpURFqbevXN4FgRU5KZZMgwGFiKgMepiZa3A4uTm/G+zsZACA5Mc58HRxNEVpRAB4oTYiojIpfM5OneuvzOmK/VMiULWSKxzsZPjxjWaacAIAPu7OkDvYm7pMKsN4BIWIqAzIV6lx7l46qnm5Ycqv54od93L9AHw+sAFkMhmCK7lh35T2ZqyS6F8MKEREZUDHxftL/AhxdJ8wDGoaZKaKiHRjQCEismFHrqfg1sOsYsPJl4PDUc3bDbX93CGTyYocQ2QJDChERDZs8HfHdK7v/qK/mSohMgwnyRIR2ahZv1/Uud7FkZNcSbp4BIWIyIYkpmej3/+O4rXmwTovwNY21BsL+vAiayRdDChERDbk0x3XcPdRNhZsu1Lk+kmdQtGvcSD8PJ3NXBmRYRhQiIhsQIYyHyq1QEqGUue48S/VNFNFRM+HAYWIyMqp1QL1Zv5V4jivcnIzVENkHJwkS0QkMUIIzff3FTlovXAPlu6JBQBk56rQafF+zNh0Adm5KmQo85GRm1/ic/asH4B1b/Kuw2Q9ZOLpvwQroVAo4OnpifT0dHh4eFi6HCIio8nNV6Plgj1IyVBiy/jWWH30FtafvAMA8PWQ475C9ymcopyd2Zn3zSFJMOT9m6d4iIgk5K+LSZp5JD2+OKS1rjThJH5Bd6PURWRuPMVDRCQRNx5kYPxPpy1dBpEkMKAQEUnEO+uMG07WjOScE7JePMVDRCQBf5xNwIV7CqM934353WBvx3vrkPXiERQiIgs7fzfdqKd2JnYKZTghq8cjKEREFvby0kMlD3qGn4czJnepBTcne6w4HI/j8Q8BAN8NbYyOdXyNXSKR2TGgEBFZyL6ryYhacaLIdTfnd8N3h27icU4+vthzHS/XD8DdR1n4+vVG8HKTw+6pIySRYf5ISMvG1aTHiKjlba7yiUyKAYWIyALO3EkrNpxM7VoLdnYyvNm2OgBgUudaJT5fQHkXBJR3MWqNRJZk8ByUAwcO4OWXX0ZAQABkMhk2bdqktV4IgVmzZiEgIAAuLi6IiIjAxYvat/xWKpUYP348vLy84Obmhp49e+Lu3bvP1QgRkbXIV6nR68vDhZYvGdAA8Qu6462IGhaoikhaDA4omZmZqF+/PpYuXVrk+kWLFmHx4sVYunQpTpw4AT8/P3Tq1AmPHz/WjJkwYQI2btyIdevW4dChQ8jIyECPHj2gUqlK3wkRkZX4Jabof5D1aljZzJUQSddzXepeJpNh48aN6NWrF4AnR08CAgIwYcIEvPfeewCeHC3x9fXFwoULMXr0aKSnp8Pb2xurV6/GgAEDAAAJCQkIDAzE1q1b0aVLlxJfl5e6JyJrIoTALzF38WIVT/i4OyN8zs5CYxoHV8CvY1taoDoi87HYpe7j4uKQlJSEzp07a5bJ5XK0a9cOR44cwejRoxETE4O8vDytMQEBAahXrx6OHDlSZEBRKpVQKv+9xLNCYbxrBRARmYoQAp/tjsX6E3eQmJ6jc+yaN3hRNaKnGTWgJCUlAQB8fbU/4ubr64tbt25pxjg5OaFChQqFxhRs/6zo6GjMnj3bmKUSEZlMnkqNmFuP8N5v53ArNavE8bxfDlFhJvkUj0ymfYEgIUShZc/SNWbatGmYOHGi5rFCoUBgYODzF0pEZCQ3HmTg1K1H+DXmLk7fTkOuSm3pkoismlEDip+fH4AnR0n8/f01y5OTkzVHVfz8/JCbm4tHjx5pHUVJTk5Gy5ZFn3+Vy+WQy+XGLJWIyGj+vpmKgd/8XaptD7/fwcjVENkGo17qPiQkBH5+fti5898JYLm5udi/f78mfDRq1AiOjo5aYxITE3HhwoViAwoRkVTdfZRV6nByYXYXVOa1S4iKZPARlIyMDFy/fl3zOC4uDmfOnEHFihURFBSECRMmYP78+ahZsyZq1qyJ+fPnw9XVFYMHDwYAeHp6YuTIkZg0aRIqVaqEihUrYvLkyQgLC0PHjh2N1xkRkRm0XrjXoPHucgesHdUMni6OKCfntTKJimPwX8fJkyfRvn17zeOCuSHDhg3DypUrMXXqVGRnZ+Ott97Co0eP0KxZM+zYsQPu7u6abf773//CwcEB/fv3R3Z2Nl566SWsXLkS9vb2RmiJiMh0cvJUWH/iDppUrYhunx/Ue7sRrULwZttq8PN0NmF1RLbjua6DYim8DgoRmVtcSiZWHo7DqqO3DN62Xag3Vo1oaoKqiKyLxa6DQkRkiz7dcRVf7Lle8sB/zO5ZF5cSFLiZkoEvh4TDy42T/IkMxYBCRFQCQ8LJm22rYVjLqqYrhqiMYEAhIirC5UQFfjh6Cw8e674CbIHYeZFQqQWcHTmXjsgYGFCIiIoQ+Zn+E2ABwNHeDswmRMbDgEJEZUZiejYquDoZ9SjHlvGtUc3bzWjPR0RPMKAQkU1LzVDi/9adRnp2Hi7cU6CGTznsmthO5zYqte4PN87uWRf/+esqtr7TBoEVXY1ZLhH9gwGFiGxao7m7tB5fT84ocZsJ68/oXD+sZVVOhCUyMQYUIrJZecXcsO/ojVT4ezrDz9O50OmepPQc/HE2ocjtXq4fgNebBxu9TiIqjAGFiGxW72WHi1w+6Nsn986RyYAPur2A15oHY/XRW7j9MAur/y76QmzX50XCwd6oty8jIh0YUIjIpjzMzMXqo7ew41ISLiYoihxTzcsNCenZyMlTY+6fl7Hj4n0cj39Y7HPWDyzPcEJkZgwoRGQz7jzMQptFum/eF7+gOwBg+4VEjFlzCgB0hpMfRjRFg6DyRquRiPTDgEJEVutQbAr+upiEUW2qIaiSa4nh5GklfFAHAHB8+kvw8eDN/YgsgQGFiKzSo8xcvLb8GABg9d+3ULm8S7FjW9WohBGtQlDDp5xmmTJfVeJrMJwQWQ4DChFZHbVaoOGcnVrL7qVlFzt+Xq8wVPXSvphaTl7Rn/ApcG1uZOkLJKLnxllfRGR1Dl1PMWi8nUxWaJl3Od13GHZy4O6RyJJ4BIWIrE5xn84pjrNT4bDx0gs+qO7thhsPMjXLmoZUxJBmQahX2fO5aySi58OAQkRW5cK9dCzcfqXEcV7l5JgWWRvZeSr4uBeeSyKTybBrYjuETNsKADg/qzPcnR2NXi8RlQ4DChFZlR5fHNJ67Opkj6zcfye8nvmoE1RqARcne7g66d7FyWQyxHzYEXkqwXBCJDE8yUpEkpf8OAdJ6Tk4GPug0Loj73fQfL9nUjuUd3VCpXLyEsNJgUrl5PDz5Kd1iKSGR1CISNIOX0/BkO+OFb3u/Q4o7+qEuOhukBUxEZaIrBcDChFJklot8Me5BLyz7kyR66d0qaW59gnDCZHtYUAhIou6kqSAvUyGmr7uAICEtGx0XXIAipx8nduNa1/DHOURkYUwoBCRRZy6/QiTfj6LuJQnH/O9NjcS3xy4gf/suGbhyohIChhQiMgi+iw7ovU49MNtem97dFqHkgcRkVVjQCEis9pxMQn/23/D4O2uzOmK388moKZPOfh7Fn/fHSKyDQwoRGRWb66OKdV2zo726N840MjVEJFUMaAQkVkIITD3z8ul2vY//eobuRoikjoGFCIymTsPszBsxXGMaBWCfJUayw/F6bWdnQwIq+yJupU9MaBxIOoHljdtoUQkOQwoRGQyc7Zcws0Hmfhw0wWd4xoElkd2rgpX7z8GAFyZE8m7CROVcQwoRGQyOy7dL3FMLV93bBrXCsCTGwH6eToznBARAwoRGdePx25j+sbzeo+PqO2t+b5eZU9TlEREVoj/TCEiozIknACA3J67ISIqjHsGIrIoHjUhoqIYPaBUrVoVMpms0Ne4ceMAAFFRUYXWNW/e3NhlEJGZ5OarkZOnAgDsvZJc4vifRjVH/ILu+GtCW3zarz461fE1dYlEZIWMPgflxIkTUKlUmscXLlxAp06d0K9fP82yrl27YsWKFZrHTk5Oxi6DiMzgy73X8clfVwEAnw9qiH3FBJQ+DStjw+l7mNKlFlpUrwQAqOXnjlp+7marlYisi9EDire3t9bjBQsWoHr16mjXrp1mmVwuh5+fn7FfmojMKCdPpQknAPB/P50uNGbPpHao5l0OADCnVz24yTkvn4j0Y9K9RW5uLtasWYOJEydCJpNplu/btw8+Pj4oX7482rVrh3nz5sHHx6fY51EqlVAqlZrHCoXClGUT0T+SFTlQCaG5983J+Ifo+7+jaBBYHg2DyuvcNn5Bd63HDCdEZAiT7jE2bdqEtLQ0REVFaZZFRkaiX79+CA4ORlxcHGbMmIEOHTogJiYGcrm8yOeJjo7G7NmzTVkqET1DpRZoOn83AODyx10x5Lu/cep2GgDgzJ00nLmTZrniiMjmyYQQwlRP3qVLFzg5OeGPP/4odkxiYiKCg4Oxbt069OnTp8gxRR1BCQwMRHp6Ojw8PIxeNxFpzy+p4++BS4n6H7ns+IIPvhvWxFSlEZGVUigU8PT01Ov922RHUG7duoVdu3Zhw4YNOsf5+/sjODgYsbGxxY6Ry+XFHl0hItN4en6JIeEEAD7t18DI1RBRWWOygLJixQr4+Pige/fuOselpqbizp078Pf3N1UpRGSgWb9fNGh8u1BvDG4WBCd7O7SvXfx8MiIifZkkoKjVaqxYsQLDhg2Dg8O/L5GRkYFZs2bh1Vdfhb+/P+Lj4zF9+nR4eXmhd+/epiiFiAx052EWVh6J13v87kntUM3LTWsiPBHR8zJJQNm1axdu376NESNGaC23t7fH+fPn8cMPPyAtLQ3+/v5o37491q9fD3d3Xg+ByJJi7z/GpUQFfo25a9B21f/5GDERkTGZdJKsqRgyyYaIdMvJU+GXk3cwY7P+p3Uc7GSoXMEFXwxqiBerlDddcURkUyQxSZaIpGff1WRErTiB5tUq4l5aNu6nK5GrUhv8PNfndzNBdURE/+LNAonKkKgVJwAAf998iDsPs0sMJxveaom2odpXh/55dAuT1UdEVIABhYiKFR5UAd+83gij21bTLGsaUtGCFRFRWcFTPERlxK3UTIPGbxrXCgDg7GiP9yNro0oFF9QPLG+CyoiICmNAISoDvj1wE/O2XjZomwZPhRGZTIbXW1Q1blFERDrwFA9RGaBvOJnUKdTElRAR6YdHUIhs2Jk7afhq3/Ui1x2Y0h5BlVxR9f0/NcvGRlRHXGommlTlPBMisiwGFCIbo8xXYfflZByMTcFPx28XOSZ+wb+3oPhiUEOM/+k0JnYKhYO9HRb3b2CmSomIiseAQmTF8lVq3EzJRE2fcpDJZEjNUOL15ccNurnfy/UD0LamNzxdHU1YKRGRYRhQiKzYhPVnsOVcIgDg+Acvoem83aV6HoYTIpIaTpIlslILt1/RhBMA6P3lEb222zWxnalKIiIyGh5BIbIyQgjsu/YAX+27obX8Xlp2idvueLctavjw5n5EJH0MKERWIik9B/uvPZn8+vSRE31V83JDqC/vGk5E1oEBhchKvPrVEb2OkhQluk8Y+oRXNnJFRESmw4BCZAXWHrtV6nBSTu6AQU2DjFwREZFpMaAQSZAQAtM3nsdPx++Uavv+jaugZXUvzPz9Ir55vZGRqyMiMj0GFCIJ+vvmw1KHE18POeb3DoODvR1eaRAAmUxm5OqIiEyPAYVIQo7dTMWjrDyMWRNT+ueY3lHzPcMJEVkrBhQiCRnwzd96jetUxxdTutRCTZ9yyM5TISEtBx0X70fXun4mrpCIyDwYUIgkIF+lxu4ryXqNDfFyw7dDG2seuzo5oIZPOZyf1Rnl5PyTJiLbwL0ZkQXk5KnwS8xdtKxeCX+eS8R9RQ7WHiv6xn5P83aX44/xrYtc5+7My9UTke1gQCEyE7VaQCUEHO3t8M660/jr4n2Dn2P/lAi4OvHPlohsH+/FQ2Qmfb46gmbzd+POwyyDw4mPuxw/jWrOcEJEZQb3dkQmJoRAvlrgzJ00AECbRXv12m7l8Cb4fHcsFvV9ETV8eIl6IipbGFCITCg9Kw+tFu6BWgiDtqvj74GIWj6IqOVjosqIiKSNAYXIBHLyVMjJU6HBxztLtf2vY1sYuSIiIuvCgEJkRBnKfOy7moy3fzxt8LZ1/D1QztkBlcu7cK4JEZV53AsSGVG9mX/pPdbD2QEnPuyILKUKf5xLQN9GVRhMiIj+wb0hkZEocvL0Hts21Bs/jGgKAJA72GNoi6omqoqIyDrxY8ZERvLirB16j63j72HCSoiIrB+PoBCV0rX7j/Hu+jOwk8lwOVFh0LZtanqZqCoiItvAgEKkp4eZufjkr6v46XjJl6TXZeXwJmhVgwGFiEgXBhQiPbVeuAdZuSqDt6vt546fRjXHoG//xrj2NXhtEyIiPRh9DsqsWbMgk8m0vvz8/r0FvBACs2bNQkBAAFxcXBAREYGLFy8auwwio1GpBUasPGFwOKnpUw57J0dg07hWqODmhO0T2uLl+gEmqpKIyLaY5AhK3bp1sWvXLs1je3t7zfeLFi3C4sWLsXLlSoSGhmLu3Lno1KkTrl69Cnd3Xs6bpGfKr2ex50qyQdv8NaEtqnm7wdGe89CJiErDJAHFwcFB66hJASEElixZgg8++AB9+vQBAKxatQq+vr748ccfMXr0aFOUQ/RcNpy6Z9D4mj7lUMuPYZuI6HmY5J93sbGxCAgIQEhICAYOHIibN28CAOLi4pCUlITOnTtrxsrlcrRr1w5Hjhwp9vmUSiUUCoXWF5Ep5anU+P1sAjafKT6crH+zueb7yuVdAABzetXD+tG8TD0R0fMy+hGUZs2a4YcffkBoaCju37+PuXPnomXLlrh48SKSkpIAAL6+vlrb+Pr64tatW8U+Z3R0NGbPnm3sUomK9e76M9hyLrHIdW5O9tg0rhVq+rojfkF3AEBuvhrZuSp4ujqas0wiIptl9IASGRmp+T4sLAwtWrRA9erVsWrVKjRv/uRfnDKZTGsbIUShZU+bNm0aJk6cqHmsUCgQGBho5MqJnth16X6R4eSjHnUwonVIkds4OdjByYHzTYiIjMXke1Q3NzeEhYUhNjZWMy+l4EhKgeTk5EJHVZ4ml8vh4eGh9UVkCl/uvY43fjhZ5Loe9f3NXA0RUdll8oCiVCpx+fJl+Pv7IyQkBH5+fti5899b0Ofm5mL//v1o2bKlqUsh0mnb+UR88tfVYtf7uDubsRoiorLN6Kd4Jk+ejJdffhlBQUFITk7G3LlzoVAoMGzYMMhkMkyYMAHz589HzZo1UbNmTcyfPx+urq4YPHiwsUshKtKjzFw0i96N3Hw13o+sjRvJGbiQoNB5ufrfxjJAExGZk9EDyt27dzFo0CCkpKTA29sbzZs3x99//43g4GAAwNSpU5GdnY233noLjx49QrNmzbBjxw5eA4XM5sfjt5GbrwYALNh2Ra9tGgVXMGVJRET0DJkQQli6CEMpFAp4enoiPT2d81FIL2q1gCInD++uP4O9Vx8YtK2niyPOzuxc8kAiItLJkPdv3ouHbF6+So12n+zDvbRsvcbX9CmHvya0xf5rD7Dz8n28Ucwnd4iIyHQYUMjmLdx+Re9wAgC9GlaGnZ0M7Wv7oH1t3tiPiMgSeOEGsilqtcDeK8nIUOYDAGJuPcK3B+MMeo4RrXjEhIjI0ngEhWxKtelbNd9/0vdFTPn1nEHbfzu0MVyc7EseSEREJsUjKGQTbj7IQNjMv7SW6RtOfhvbEt1f9EeIlxva1PQyRXlERGQgHkEhm/D68uN4/M9pHX180O0FjGpbTfO4UXCFEm+5QERE5sOAQlZt+sbzuPkgw6BJsN8NbYyOdQrfWoHhhIhIOhhQyGpdSlDgx2O39R6/4a2WCA/iBdeIiKwB56CQVTp9+xG6fX5Q7/He7nKGEyIiK8IjKGSVei87Uuy69W82x4Bv/gYA/PF2a5y+8wgdXyj+btlERCQ9DChkdeZsuVTkcne5A05/1AkO9naIX9Bdszysiqe5SiMiIiNhQCGrsO18Ir4+cBNn7qQVO+b87C7mK4iIiEyKc1BIctKz8/DD0XikZCghhEC+So2xa0/pDCcnPuhovgKJiMjkeASFJGfi+jPYfSUZH22+qNf4FVFN4O0uN3FVRERkTgwoJCkrDsdh95VkvcfveLctQn3dTVgRERFZAk/xkKTM/qPoCbDFseO11YiIbBIDCllcnkqNiwnpaBG9W6/xW8a31nzv4exoqrKIiMiCeIqHLOruoyy0XrhX7/Fx0d0gk8nw7dDGyMrNh4+HswmrIyIiS2FAIbPLzlXhj7MJeMHfAy8vPaT3di/V9tHcL6dTEffSISIi28GAQmY3+4+LWHfijkHbRLWsilk965qoIiIikhoGFDKLST+fxZk7j3DjQabB216fFwkHe06XIiIqSxhQyORUaoHfTt0t1bbnZnVmOCEiKoMYUMjkqk/fqte4NjW98OCxEmMjqkPuYIfm1SrxUzpERGUUAwqZ1PYLSXqN61rXD/97vZGJqyEiImvBgEIms//aA4xZE1PiuA+7v4DhrULMUBEREVkLBhQyqgv30jF/62UcuZFa5PoudX1x6nYaHjxWonm1iugTXgX9GweauUoiIpI6BhQyqh5fFH9dk9k962JYy6rmK4aIiKwWPx5BRpH8OAdV3/+z2PVjI6oznBARkd54BIWe2wcbz2Ptsds6x7zdvoaZqiEiIlvAIyj0XI7cSCkxnET3CYObnFmYiIj0x3cNMkjy4xy8/eNp9A2vgqm/ndM51sPZAQtffRGRYf5mqo6IiGwFAwrpLSdPhabzdgMAjsc9LHZciJcb/jugARoEljdTZUREZGsYUEinB4+V2Hc1GR1f8EXT+btKHN+0akX8PKaFGSojIiJbZvQ5KNHR0WjSpAnc3d3h4+ODXr164erVq1pjoqKiIJPJtL6aN29u7FKolHLyVFh77BYS0rLRZN4uTPn1HBrO2Yk8ldC53ZQutfDt0MZmqpKIiGyZ0Y+g7N+/H+PGjUOTJk2Qn5+PDz74AJ07d8alS5fg5uamGde1a1esWLFC89jJycnYpVApHLuZiqV7r+NgbIpB2wV4OmMcP6lDRERGYvSAsn37dq3HK1asgI+PD2JiYtC2bVvNcrlcDj8/P2O/PD2HTGU+Bnzzd6m2XT+ap3WIiMh4TD4HJT09HQBQsWJFreX79u2Dj48Pypcvj3bt2mHevHnw8fEp8jmUSiWUSqXmsUKhMF3BZZgiJ0+vcaPahOCD7nUAAEIIyGQyU5ZFRERlkEwIoXtiwXMQQuCVV17Bo0ePcPDgQc3y9evXo1y5cggODkZcXBxmzJiB/Px8xMTEQC6XF3qeWbNmYfbs2YWWp6enw8PDw1TllxnKfBVWHYnH94fikaTIKXH8yQ87wqtc4Z8TERGRLgqFAp6ennq9f5s0oIwbNw5//vknDh06hCpVqhQ7LjExEcHBwVi3bh369OlTaH1RR1ACAwMZUIwgKzcfDT/eCWW+Wu9tzn7UGZ6ujiasioiIbJEhAcVkp3jGjx+P33//HQcOHNAZTgDA398fwcHBiI2NLXK9XC4v8sgKPR+1WqDOR38ZvJ3ckRcgJiIi0zL6O40QAm+//TY2bNiAPXv2ICQkpMRtUlNTcefOHfj784qj5pCpzMep249QbfpWneM61fHFd0MbY3S7alrL5Q4MKEREZFpGP4Iybtw4/Pjjj9i8eTPc3d2RlJQEAPD09ISLiwsyMjIwa9YsvPrqq/D390d8fDymT58OLy8v9O7d29jl0DOycvPRafF+JKTrnmsyr3c99G8cCEd7O3Ss44tRbaphya5r8HF35qRYIiIyOaPPQSnuzWvFihWIiopCdnY2evXqhdOnTyMtLQ3+/v5o37495syZg8DAQL1ew5BzWARErTiOfVcfoMeL/thyLrHE8dMia2N0u+pmqIyIiMoSi85BKSnvuLi44K+/DJ/3QKW37+oDACgxnPw1oS1q+bmboyQiIiKdOJnAxs3dckmvcV+/3ojhhIiIJIM3C7RhKRlKfHcoTueYyHp+mNWzLnw9nM1UFRERUckYUGzEqduP8OOx23BysMPHPevi/L109F52ROc2a0Y2Q8Og8nCT89eAiIikhe9MNuDI9RQM/u6Y5vGPx27rHH96RidUcOPNGYmISLoYUGzAjM0X9Bq3oE8YBjYNMnE1REREz4+TZK2UEAKbz9xD7P3HuPEgs8Txbk726FqPd48mIiLrwCMoVkilFmi7aC/upWXrNf7C7C5wsreDE68AS0REVoIBxUpsPnMP76w7Aw9nByhy8vXe7urcrpA72JuwMiIiIuPjP6mtwMWEdLyz7gwAGBROLn/McEJERNaJR1Ak7sFjJbp/fkjv8QemtEeuSoUaPrzoGhERWS8GFAkSQiA1MxebTt/D3D8v673d1K61EFTJ1YSVERERmQcDioQIIfD68uM4dD3F4G1jPuyISuXkJqiKiIjI/BhQJGTtsdsGh5POdXzx9euNir2LNBERkTViQJGAQ7EpeGfdaaRm5uoc52Anw7j2NVDN2w3fHYzD+Xvp6Nc4kOGEiIhsDgOKBQkhcCL+EV5bfqzEsb0bVsa0yNrw+eemfp3r+CE+NRO1eQdiIiKyQQwoZpaenYch3/2NbmH+CKnkhrFrT5W4zZ5J7VDNu5zWMhcne7zg72GqMomIiCyKAcXMVhyOw4V7Cly4p9A57uDU9jh7Nw15KnWhcEJERGTrGFDMSK0WuJigO5i0ruGFr19vBDe5AwIr8iPDRERUNjGgmFHkZwdx9f7jYtef/agzPF0dzVgRERGRNPFS9yYkhNB8P+v3izrDycJXwxhOiIiI/sEjKCaQm6/G8kNxWLj9Coa2CMbmMwlIz87TuU3fRoFmqo6IiEj6GFBM4PvDT8IJAPxw9JbOsZ4ujtg0rhXs7XgtEyIiogIMKEakUguo1AJ7LifrNT5+QXcTV0RERGSdGFCe07GbqXB0sMOWs4n4/nCc3tt1C/MzYVVERETWjQGllG6nZqHtJ3sN3u6Tvi+iebVKCCjvYoKqiIiIbAMDSilsOZeAt388bdA2Xw4OR/cX/U1UERERkW1hQDGAEAK/nLyLqb+dM2i7DrV9GE6IiIgMwOuglOBeWjbWHruFnDwVNp9JMDic/F+HGvg+qomJqiMiIrJNPIJSgm6fHUR6dh4+2HjB4G3b1/LGxM61TFAVERGRbeMRlBKUdIG1Ex90LHL5yuFNsGJ4U1OUREREZPMYUHRYdSRe5/qvX28Eb3c5+jaqUmhdRC0fE1VFRERk+xhQipCelYfVR+Mx8/eLxY55rXkQutR9ci2T//Srj5vzu2F+7zAAgIujvVnqJCIislWcg1KECetPY+/VB8Wu/6Tvi+jXWPveOXZ2MgxoEogKro5oEFTexBUSERHZNoseQVm2bBlCQkLg7OyMRo0a4eDBg5YsR0NXOAGAFtUrFbnc3k6GyDB/+HvyImxERETPw2JHUNavX48JEyZg2bJlaNWqFb7++mtERkbi0qVLCAoKslRZxc47+XVMC1TzLgdFdh6qVHA1b1FERERljEwIISzxws2aNUN4eDi++uorzbIXXngBvXr1QnR0tNZYpVIJpVKpeaxQKBAYGIj09HR4eHgYraZ8lRo1PthWaPmCPmEY2NRyoYmIiMgWKBQKeHp66vX+bZFTPLm5uYiJiUHnzp21lnfu3BlHjhwpND46Ohqenp6ar8DAwEJjjOH6g4wil/MqsEREROZlkYCSkpIClUoFX19freW+vr5ISkoqNH7atGlIT0/XfN25c8ckdZV3cSpyubuzo0lej4iIiIpm0U/xyGQyrcdCiELLAEAul0Mul5u8Hj9PZ3w+qCH+76fTqFLBBaG+7nixiqfJX5eIiIi0WSSgeHl5wd7evtDRkuTk5EJHVcytZ/0A9Ajzh51d4aBERERE5mGRUzxOTk5o1KgRdu7cqbV8586daNmypSVK0sJwQkREZFkWO8UzceJEvP7662jcuDFatGiBb775Brdv38aYMWMsVRIRERFJhMUCyoABA5CamoqPP/4YiYmJqFevHrZu3Yrg4GBLlUREREQSYbHroDwPQz5HTURERNIg+eugEBEREenCgEJERESSw4BCREREksOAQkRERJLDgEJERESSw4BCREREksOAQkRERJLDgEJERESSY9G7GZdWwbXlFAqFhSshIiIifRW8b+tzjVirDCiPHz8GAAQGBlq4EiIiIjLU48eP4enpqXOMVV7qXq1WIyEhAe7u7pDJTHfnYYVCgcDAQNy5c8cmLqnPfqTNlvqxpV4A9iN17Ee6nu1FCIHHjx8jICAAdna6Z5lY5REUOzs7VKlSxWyv5+HhYfW/JE9jP9JmS/3YUi8A+5E69iNdT/dS0pGTApwkS0RERJLDgEJERESSw4Cig1wux8yZMyGXyy1dilGwH2mzpX5sqReA/Ugd+5Gu5+nFKifJEhERkW3jERQiIiKSHAYUIiIikhwGFCIiIpIcBhQiIiKSHAYUIiIikhwGFJIsW/qAmS31QmROtva3Y2v9mFKZDSjXrl3DmDFjcPDgQUuXYhR37txBTEwMEhISLF2KUSQnJ2tuCglY9x91eno6VCqV5rE19wIA169fx86dOy1dhtFwXyBdtrQfALgvMFSZCyhqtRrvvvsuGjRogMzMTK1ffmuUl5eH0aNHIzw8HCNGjED9+vVx+PBhS5dVavn5+Rg5ciSaNm2Kjh07YsiQIUhJSTHpTSFNJS8vD+PGjUO3bt3QrVs3zJkzByqVyip7KXDu3DmEhoZi0KBBuHXrlqXLeS7cF0iXLe0HAO4LSqvMBZRt27bhxIkT2LZtG1avXo1u3bpp1llbms3IyEDfvn0RGxuLHTt24Oeff0Z4eDhmzJgBwPr6yc/PR1RUFC5duoRVq1Zh0KBBOHfuHPr06YPLly9bujyD7Ny5E3Xq1MHFixcxZcoUBAYGYu3atZg1axYA6/vZFMjNzUWXLl3g6OiIRYsWWbqc58J9gTTZ0n4A4L7guYgyplevXmLcuHFCCCH27dsnPvzwQ7FixQpx69YtC1dmuGPHjomaNWuKPXv2aJZ9++23omfPnkKlUlmwstK5ffu2qFmzpli9erVmWWJioqhcubIYP368SEpKsmB1+ktPTxdvvPGGGDdunMjNzRVCCKFUKsXMmTNFly5dRGZmpoUrLL2vv/5aDBo0SOzevVs4ODiIY8eOWbqkUuO+QJpsZT8gBPcFz6vMHEFRq9V4/PgxUlJS8NJLL2Hu3LkYOHAgzp8/j48++ggdOnTAH3/8YekyDZKbm4vr169r7nGQkpKCL7/8EgEBAfj++++RnZ1t4QoNk5qairt376J58+YAAKVSCT8/P0ybNg07duzAgQMHLFyhfoQQaN26Nd544w04OjpCCAEnJyfk5OQgOzsbrq6uVvuvJrlcjuDgYHTo0AFNmjTB7NmzAQAKhcLClRnG1vYFeXl5NrMvsJX9AACoVCqb2hc8Xas59gU2G1Dmz5+PmTNnYt26dQAAOzs7uLu7Izc3F9999x2uXbuGDRs24Ndff8WtW7dQvXp1fP/997hy5YqFKy/as/0AQOvWrdGuXTsMHz4ckZGR8PX1hZ+fH5ycnDBt2jQMGzYM58+ft2DVxdu6dSsA7V/4WrVqwc/PD2vWrAHw5GcGAOPGjYO7uzu2bdsGpVJp/mJLUNCLWq0GAHh6emLYsGFo0KCB1vL09HRUq1YNACR97rmon02BU6dOISMjAwCwdu1abN++HZGRkejSpYtk/3aK6sfd3R15eXlWuS8oqp9WrVohIiLC6vYF33zzDb799lut0FGzZk2r3A8A//azf/9+AECFChUwdOhQq90XPNuPTCbT9GCWfYHRj8lY2LFjx0RQUJAIDw8XkZGRwt3dXfTt21dcuXJFCPHksKdMJhOhoaEiOTlZs92BAweEv7+/OHLkiKVKL1JJ/SgUChEbGytatmwp/vOf/2i2O336tKhWrZr4+eefLVV6kbZs2SIqV64sZDKZOHz4sBBCaA5BZ2VlialTp4rQ0FBx//59IYQQ2dnZQgghVq1aJcqXL695LAW6enmaWq0WQgjRrFkz8d1332ktk5Ki+imos+C/AwcOFLt27RJCPPlbcnFxEY6OjuLXX3+1TNE6FNdPQS/Lly+3qn1Bcb9vBb9zGRkZVrMv+PHHH4WPj49o0aKFaNCggfD29hbz5s0TQjw5LWJN+wEhdPeTn5+vGWct+4Ki+pk/f74Q4skpKiHMsy+wuYAyceJE0b17dyHEkz/e8+fPi+DgYDFmzBiRkpIibt26Jdq0aSPq1KkjEhMTNdtlZ2eLcuXKiV9++cVSpRepuH7Gjh0rEhIShBBCnDhxQtSqVUskJydrftnz8/NFhQoVtHZUlnbw4EHRtWtX8fbbb4vIyEjRuHHjQmN27dolmjRpIt566y0hxL9/vHv37hU+Pj7i7NmzZq25OPr08rS4uDjh7e2tCZZCCHHjxg0hRNGhxtxK6qegxmHDhonXX39dNGnSRHh7e4s5c+ZI7vdMCP1+PpcuXRIRERFWsS/Q9/ft1KlTkt8XrF27VtSvX1/873//E0IIce/ePbF06VLh5uYm0tPThRBC7Ny50yr2A0Lo7kehUBQaL/V9gb79mGNfYDMBRa1Wi7S0NNG6dWsxefJkIcS/P+xly5aJ8PBw8eWXXwohhNi0aZNwcnISM2fOFHfv3hVCCLF+/XrRokULTWK3tJL6adSokViyZIkQQogrV64ImUwmYmJiNNtv3LhRhIeHi1OnTpm/+GcU7FyuXbsmFi9eLG7evClOnjwpXF1dNf+KyMvLE0I8eXP473//K9zc3MSGDRs0aX3u3LkiIiLC4v/a0KeXonYyX331lQgPDxdCPHkTadq0qfD29tb0bSmG9JOVlSV69+4tKlWqJMaNG6f521mwYIGQyWQiLi7OIj08TZ9+Cv5Fm5+fLzZt2iTkcrlk9wWG/r5duXJF2NnZSXJfUNDLypUrxZtvvimysrI06w4dOiRCQ0PF0aNHhRDS3w8IoV8/RU0clfq+QJ9+zLUvsOqAEhMTI9LS0rSWNW7cWIwePVoIIUROTo4QQojc3FzRp08f8corr2hm6C9ZskQEBASIWrVqid69ews3NzfNITlLMbSfXr16iVu3bonMzEwxYMAA4erqKsaMGSOGDh0q3N3dxUcffWTRP+Si+il4c8jLyxOTJk0S3t7emr4K1ikUCjF16lTh7u4u2rVrJ/r16ydcXFw0AdMSPRnaS4GCWsePHy/69u0r3n33XWFnZydGjhxZaKw5lfZnc/z4cXHx4kWt7XJycsSiRYss+q8/Q/t5utbPP//cKvYFuvop+D1LTU0VgwYNktS+ICYmRjx69EjzOC0tTeu0hxBCnDlzRvj5+YmHDx9qlklxPyBE6fuR8r6gNP2YY19glQHl119/FVWqVBHVq1cXQUFB4qOPPtIkuM8++0yUK1dO8/GtguT922+/iSpVqohDhw5pnufYsWNi2bJlYtq0aeLq1avmb+Qfz9NPwXnyzMxMMXXqVBEVFSWGDh0quX4KDqE/PQfg5s2bIjAwUEyaNEkIUfjIw88//yxmzpwpxowZIy5fvmzeJv5R2l6e3nmqVCoRHBwsZDKZiIiIKPRHbU6l7efZHZZUGOt37e+//5bsvsDQfrKyssSUKVMsvi94tpcZM2ZofUT46ZoXL14sWrVqJYT4dx9X4JdffrH4fkAI4/Qj5X2Bvv2YM0xZXUA5ceKEqF27tliyZIk4e/asWLZsmfD29hZjx44VaWlp4tatW6J69eqaow4Fnz0XQohKlSqJ5cuXW6r0Ij1vPwWHeQtY+jChrn5SU1OFEP++2anVarFs2TLh4OAgbt68KYR48sdccB7a0ozRS2ZmpsjOzhbz588Xf/31l8V6EcJ4/RSstzRj9FPUHAFLed5+cnJytPqx5L5An15UKpWmxt69e2uuSSNFxuonLS1NREdHW8W+QAo/H6sJKAU7xK+++kpUqVJF601s6dKlomnTpiI6OloIIcSXX34p7O3txf79+zVjbty4IapXry5+++038xZejLLWT/PmzcWcOXMKbZeamipatmwpXnnlFRETEyM6d+4sVq9ebdE3QGP10qlTJ62LTVmKLf1shGA/BaTYj6G9qFQqoVarRfXq1cWWLVuEEEJcvXpVDBw4UNy+fdu8xReB/Vi2H6u5DkrBZ8Xj4uIQGhoKBwcHzbqoqCg0adIEmzdvxrVr1zB27FgMHDgQAwYMwMcff4wzZ85g0aJFcHV11Vz8x9LKWj+NGjXCtm3bcPHiRQDQ3DCrYsWKGDVqFH7//Xc0adIETk5OePXVVy16bQBj9SKXy9GnTx/zN/AMW/rZAOxHyv0Y2oudnR1OnDgBV1dXhIeHY8KECXjxxReRmpoKHx8fi/TwNGP24+3tbZEenmZ1Px+TR6BS2rFjhxg/frxYsmSJ1kzozZs3C2dnZ83HsgoOee7YsUO0bNlSLF68WDN2/PjxokGDBqJGjRoiPDxcnDt3zrxNPIX97BCtWrXS6kepVIovv/xS2NnZiXbt2okLFy6Yt4l/2FIvBfWxH/ZjDsboZebMmUImkwl3d3dRp04dcfLkSfM28RT2I61+JBdQEhISRI8ePYSPj48YMmSICAsLE56enpr/udnZ2aJ27drizTffFEJoT+Rp06aNGDt2rOaxSqUSmZmZWp83Nzf2o91PwXUNhBAiKSlJvPPOO2LVqlXmbeIfttSLEOyH/ZiPMXuZO3eu8Pb2tujpavYjzX4kFVAyMzPFsGHDxIABAzQTv4QQokmTJiIqKkoI8STp/fDDD8LOzk5zNcUCQ4YMEe3bt9c8tvS5Zfajux9LsqVehGA/7Md8jNFLRESE5vHTV/G1BPYj3X4kNQfF1dUVcrkcUVFRCAkJQX5+PgCgR48emtts29vbo3///njllVfwxhtvYP/+/RBCICkpCbGxsRgyZIjm+Sx9bpn96O7HkmypF4D9sB/zMUYvr732mub5LD03g/1IuB9LJaPiPP0x2oIjBq+99poYNWqU1rLs7GwREREhfHx8ROfOnUVAQIBo3ry5JGZKP439SLcfW+pFCPbDfszHlnoRgv1ItR+ZENK/13Pbtm0xYsQIREVFQQgBtVoNe3t73L9/H+fOncOJEydQtWpVDB482NKl6oX9SJct9QKwH6mzpX5sqReA/UiCZXKR/m7cuCF8fX21Zg4/e6VBa8J+pMuWehGC/UidLfVjS70IwX6kQlJzUJ4m/jmwc+jQIZQrVw6NGjUCAMyePRvvvPMOkpOTLVmewdiPdNlSLwD7kTpb6seWegHYj9Q4lDzEMgomhB4/fhyvvvoqdu7ciTfffBNZWVlYvXq1JC7iYwj2I1221AvAfqTOlvqxpV4A9iM5ljp0o4/s7GxRo0YNIZPJhFwuFwsWLLB0Sc+F/UiXLfUiBPuROlvqx5Z6EYL9SInkJ8l26tQJNWvWxOLFi+Hs7Gzpcp4b+5EuW+oFYD9SZ0v92FIvAPuRCskHFJVKBXt7e0uXYTTsR7psqReA/UidLfVjS70A7EcqJB9QiIiIqOyR7Kd4iIiIqOxiQCEiIiLJYUAhIiIiyWFAISIiIslhQCEiIiLJYUAhIiIiyWFAISIiIslhQCEik4iKioJMJoNMJoOjoyN8fX3RqVMnfP/991Cr1Xo/z8qVK1G+fHnTFUpEksSAQkQm07VrVyQmJiI+Ph7btm1D+/bt8c4776BHjx7Iz8+3dHlEJGEMKERkMnK5HH5+fqhcuTLCw8Mxffp0bN68Gdu2bcPKlSsBAIsXL0ZYWBjc3NwQGBiIt956CxkZGQCAffv2Yfjw4UhPT9ccjZk1axYAIDc3F1OnTkXlypXh5uaGZs2aYd++fZZplIiMjgGFiMyqQ4cOqF+/PjZs2AAAsLOzw+eff44LFy5g1apV2LNnD6ZOnQoAaNmyJZYsWQIPDw8kJiYiMTERkydPBgAMHz4chw8fxrp163Du3Dn069cPXbt2RWxsrMV6IyLj4b14iMgkoqKikJaWhk2bNhVaN3DgQJw7dw6XLl0qtO6XX37B2LFjkZKSAuDJHJQJEyYgLS1NM+bGjRuoWbMm7t69i4CAAM3yjh07omnTppg/f77R+yEi83KwdAFEVPYIISCTyQAAe/fuxfz583Hp0iUoFArk5+cjJycHmZmZcHNzK3L7U6dOQQiB0NBQreVKpRKVKlUyef1EZHoMKERkdpcvX0ZISAhu3bqFbt26YcyYMZgzZw4qVqyIQ4cOYeTIkcjLyyt2e7VaDXt7e8TExBS6jXy5cuVMXT4RmQEDChGZ1Z49e3D+/Hm8++67OHnyJPLz8/Hpp5/Czu7JlLiff/5Za7yTkxNUKpXWsoYNG0KlUiE5ORlt2rQxW+1EZD4MKERkMkqlEklJSVCpVLh//z62b9+O6Oho9OjRA0OHDsX58+eRn5+PL774Ai+//DIOHz6M//3vf1rPUbVqVWRkZGD37t2oX78+XF1dERoaiiFDhmDo0KH49NNP0bBhQ6SkpGDPnj0ICwtDt27dLNQxERkLP8VDRCazfft2+Pv7o2rVqujatSv27t2Lzz//HJs3b4a9vT0aNGiAxYsXY+HChahXrx7Wrl2L6Ohoredo2bIlxowZgwEDBsDb2xuLFi0CAKxYsQJDhw7FpEmTUKtWLfTs2RPHjh1DYGCgJVolIiPjp3iIiIhIcngEhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgkhwGFiIiIJIcBhYiIiCSHAYWIiIgk5/8BBQ+xErxvp6cAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "P566_GPS_data.plot(x='Date', y='North (mm)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plotting using plt functions\n",
-    "\n",
-    "We have made a number of different plot types using `matplotlib` such as `plt.hist()`, `plt.plot()` and `plt.scatter()`. When dealing with timedate values, one can use `plt.plot()`, but not `plt.scatter()`. \n",
-    "\n",
-    "Let's visualize both the north and east columns using `plt.plot()`. The data are from every day between Nov. 16, 2005 and now."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkkAAAHFCAYAAADmGm0KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABp1UlEQVR4nO3deXhM1/8H8Pdkm+ybyEZW+xKx1V4SS4jYlSpVW9XSUkWVlqJaVL8tutFW7ftOUbtYKogl1iJIJCQRIvuezPn94ZepkUkyEzOTTPJ+Pc88jzn3nJvPyXLn49xzz5EIIQSIiIiISIFBWQdAREREVB4xSSIiIiJSgkkSERERkRJMkoiIiIiUYJJEREREpASTJCIiIiIlmCQRERERKcEkiYiIiEgJJklERERESjBJIo26du0aRo0ahRo1asDMzAxmZmaoVasWxowZg4sXLyrUnTNnDiQSifxlYmICLy8vfPzxx0hKSlKoe/78efTt2xfu7u6QSqVwcnJC69atMWXKlFLHOnz4cHh6epaq7a+//orVq1eX+msXZ+bMmXB3d4eRkRFsbW218jV0af78+di9e3ep22dkZGDOnDkIDg4udGz16tWQSCSIjIws9flLy8/PT+H318zMDL6+vliyZAlkMpm83qu/5wUvU1NTped99uwZPv74Y3h6esp/1wMDA/H8+fNCdc+cOYPu3bvDzs5O/rc2b948lfswcuRIdOvWDQCwdOlSSCQSHDx4sMj6f/zxByQSCXbu3InIyEhIJBKt/R0o+9m+zt+sulJSUvDNN9+gefPmsLa2hlQqhaenJ0aOHInLly8rbfPjjz9CIpGgYcOGSo8nJibC1tb2tf4eSMcEkYYsX75cGBkZiQYNGoilS5eKo0ePimPHjomff/5ZtG3bVgAQ9+7dk9efPXu2ACAOHjwoQkJCxOHDh8WkSZOERCIRrVq1EjKZTAghxL59+4SBgYHo2LGj2LRpkwgODhabNm0SU6ZMEdWqVSt1vMOGDRMeHh6latugQQPRoUOHUn/touzevVsAEF988YU4c+aMCA0N1fjX0DULCwsxbNiwUrd/+vSpACBmz55d6Fh8fLwICQkRWVlZpQ+wlDp06CC8vb1FSEiICAkJEXv27BGBgYECgJg2bZq83qu/5wWv8+fPFzrn48ePhbe3t6hdu7ZYsWKFOHnypNixY4f46KOPRGxsrELdDRs2CAMDAzFo0CCxd+9ecfz4cfHHH3+IuXPnqhT/5cuXhYGBgfx37NmzZ0IqlYoBAwYU2aZ169aiatWqIicnR2RlZYmQkBARHx+v0tdT16pVqwQAERERIS+7d++euHz5sla+3svu3bsnvL29haWlpZg6darYt2+fCA4OFqtXrxbdu3cXAERSUlKhdr6+vgKAACDOnTun9Nxz5swRNWvWFNnZ2druBmkAkyTSiDNnzggDAwPRs2fPIv/4t27dKh4/fix/X/Dh8fTpU4V6Q4cOFQDEmTNnhBBCtG/fXtSoUUPk5uYWOmd+fn6pYy6PSdLXX38tAIgnT56UWDcjI0PjX18btJkklaUOHTqIBg0aKJTl5OQIb29vYW5uLnJycoQQRf+eK9O7d29RrVo18fz582LrPXr0SFhYWIhx48aVOv6BAweKVq1aFSozMTERz549K1T/33//FQDElClTSv011aEsSdKFvLw84ePjI6ytrcX169eV1jlw4IBIT09XKAsNDRUARFBQkAAgRo8erbRtXFycMDIyEhs2bNB47KR5vN1GGjF//nwYGhrit99+g4mJidI6AwYMgKura4nnatWqFQDg4cOHAICEhAQ4ODjAyMioUF0DA9V+hVevXo06depAKpWiXr16WLt2rdJ6c+fORcuWLWFvbw9ra2s0bdoUf/75J8RL+0B7enri5s2bOHnypPzWScEtgKysLEyZMgWNGzeGjY0N7O3t0bp1a+zZs6fEGD09PTFz5kwAgJOTEyQSCebMmSM/1qNHD+zcuRNNmjSBqakp5s6dCwC4ceMGevfuDTs7O5iamqJx48ZYs2aNwrmDg4MhkUiwceNGfPbZZ3BxcYGlpSV69uyJJ0+eIDU1FR988AEcHBzg4OCAESNGIC0trcSYr1y5gh49esDR0RFSqRSurq4ICgrCo0ePAAASiQTp6elYs2aN/Hvl5+cHAHj69CnGjx+P+vXrw9LSEo6OjujYsSNOnz4tP39kZCSqVq0q/9kUnGP48OEAir7dtnLlSvj6+sLU1BT29vbo27cv/v33X4U6w4cPh6WlJe7du4fu3bvD0tISbm5umDJlCrKzs0vsuzLGxsZo1qwZMjIy8PTpU7XaRkZGYu/evRg9ejTs7OyKrbtixQqkp6fjs88+K1WcT548wa5duzB06FCF8lGjRiEnJwcbN24s1GbVqlUAXtyiK4j31dttT58+xQcffAA3NzdIpVJUrVoVbdu2xdGjR+V1jhw5gt69e6N69eowNTVFzZo1MWbMGDx79qzEuJXdbvvll1/Qvn17ODo6wsLCAj4+Pli0aBFyc3NV/XYo2L17N65fv44ZM2YUedssMDAQ5ubmCmV//vknAGDhwoVo06YNNm/ejIyMjEJtnZyc0KVLFyxfvrxU8ZFuMUmi15afn48TJ06gefPmcHFxee3z3bt3DwDkH46tW7fG+fPnMXHiRJw/f17ti9/q1asxYsQI1KtXDzt27MDMmTMxb948HD9+vFDdyMhIjBkzBlu3bsXOnTvRr18/TJgwQWGex65du+Dt7Y0mTZogJCQEISEh2LVrFwAgOzsbz58/x9SpU7F7925s2rQJ7dq1Q79+/YpMzF4+76hRowAABw8eREhICN5//3358cuXL+PTTz/FxIkTcfDgQfTv3x937txBmzZtcPPmTfz444/YuXMn6tevj+HDh2PRokWFvsbnn3+O+Ph4rF69Gt9//z2Cg4PxzjvvoH///rCxscGmTZswbdo0rFu3Dp9//nmx8aanp6NLly548uQJfvnlFxw5cgRLliyBu7s7UlNTAQAhISEwMzND9+7d5d+rX3/9FQDkc2xmz56N/fv3Y9WqVfD29oafn598/pGLi4t8jsyoUaPk55g1a1aRcS1YsACjRo1CgwYNsHPnTixduhTXrl1D69atER4erlA3NzcXvXr1QqdOnbBnzx6MHDkSixcvxrffflts34tz//59GBkZFUp0fHx8YGhoCCcnJ7z33nuIiopSOH769GkIIeDq6op33nkHlpaWMDU1hZ+fH0JCQhTqnjp1Cvb29rh9+zYaN24MIyMjODo6YuzYsUhJSSkxxsOHDyM3Nxf+/v4K5Z07d4aHhwdWrlypUJ6fn49169ahVatWqF+/fpHnHTp0KHbv3o0vv/wShw8fxooVK9C5c2ckJCQofH9at26NZcuW4fDhw/jyyy9x/vx5tGvXrlSJzf379zF48GCsW7cO+/btw6hRo/Ddd99hzJgxap8LePG9AYA+ffqo3CYzMxObNm3CG2+8gYYNG2LkyJFITU3Ftm3blNb38/PDP//8U2juJZVDZT2URfovLi5OABCDBg0qdCwvL0/k5ubKXwXzjIT47zZEXFycyM3NFYmJiWL9+vXCzMxMuLm5iczMTCHEi7kS7dq1k9/rNzY2Fm3atBELFiwQqampxcaWn58vXF1dRdOmTRW+dmRkpDA2Ni72dlt+fr7Izc0VX331lahSpYpCe1VvtxX0f9SoUaJJkyYl1i/q1oyHh4cwNDQUd+7cUSgfNGiQkEqlIioqSqE8MDBQmJuby+dNnDhxQgAQPXv2VKg3adIkAUBMnDhRobxPnz7C3t6+2FgvXrwoAIjdu3cXW0/V220F36tOnTqJvn37ysuLu9326i2ZxMREYWZmJrp3765QLyoqSkilUjF48GB52bBhwwQAsXXrVoW63bt3F3Xq1Ckx3oLbbQW/2zExMWL69OkCgMK8nrVr14pvvvlGHDhwQBw/flwsXLhQ2NvbCycnJ/Ho0SN5vQULFggAwtraWvTu3VscPHhQ7NixQzRq1EiYmpqKq1evyuvWqVNHmJqaCisrKzF//nxx4sQJsWjRImFmZibatm2r8LuqzLhx44SZmZnSegW/gy/P/fnrr78EAPHHH3/IyyIiIgQAsWrVKnmZpaWlmDRpUonfuwIymUzk5uaKhw8fCgBiz5498mPKbreVdIu84G927dq1wtDQsMTblsp069ZNAFBrntvatWsFALF8+XIhhBCpqanC0tJSvPnmm0rrHzlyRAAQf//9t9rxkW4xSaLXVlyS9PJERgDiu+++kx8ruBi/+mrbtq24efNmoXOFhoaKhQsXirfeeks4ODgIAMLT07PYuR63bt0SAMT//ve/Qsc6dOhQ6IJ77Ngx0alTJ2FtbV0orri4OHm94pKkrVu3ijZt2ggLCwuF9qampkXG+er3RFmSpCzJcnR0LJQQCCHEli1bFC7CBUnSb7/9plDvt99+EwDEoUOHFMpnzJghABSbhCYlJQk7OztRp04dsWzZMqU/MyGKT5KWLVsmmjRpIqRSqcL3qm7duvI66iRJBw4cUJr4CPEicXRycpK/HzZsmJBIJPJkvMD06dNV+ll16NCh0O+IsbGxGDJkiNJJvS87f/68MDAwUEhOv/nmGwFA1K9fX+Tl5cnLY2JihLm5uRgyZIi8rFatWgKAWLBggcJ5lyxZIgCII0eOFPv1e/fuLdzd3ZUei4yMFAYGBuKjjz6Sl/Xr109YWFiIlJQUeZmyJKljx47C1tZWzJs3T4SEhMjnZb3syZMnYsyYMaJ69erCwMBA4fu3cOFCeT1Vk6TLly+Lnj17Cnt7+0I/j6ImTxenNElShw4dhJmZmcLPfcSIEQKAuHv3bqH6V69eFQDEihUr1I6PdIu32+i1OTg4wMzMTD6H6GUbN25EaGgo9u7dW2T7o0ePIjQ0FGFhYXj27BnOnDmjdEi/efPm+Oyzz7Bt2zbExMTgk08+QWRkpNLbSgUKhvmdnZ0LHXu17MKFCwgICADw4lHnf/75B6Ghofjiiy8AvBhSL8nOnTsxcOBAVKtWDevXr0dISAhCQ0MxcuRIZGVlldi+OMpuZSYkJCgtL5j79fJtDgCwt7dXeF8wf6yo8uJitrGxwcmTJ9G4cWN8/vnnaNCgAVxdXTF79myVbpv88MMPGDduHFq2bIkdO3bg3LlzCA0NRbdu3VT6XitT0N+ivievfj/Mzc0LPYovlUpV/lnVqFEDoaGhuHjxIm7cuIGkpCSsX78eNjY2xbZr0aIFateujXPnzsnLqlSpAuDFLS9DQ0N5uYuLC3x9fRUeOy+o27VrV4XzBgYGAkCRj6gXyMzMLHIJAg8PD3Tq1AkbN25EdnY2nj17hn379mHAgAGwsrIq9rxbtmzBsGHDsGLFCrRu3Rr29vZ47733EBcXBwCQyWQICAjAzp07MW3aNBw7dgwXLlyQfx/U/blHRUXhzTffxOPHj7F06VKcPn0aoaGh+OWXX0p1PgBwd3cHAERERKhU/969ezh16hSCgoIghEBSUhKSkpLw1ltvAUChW5cA5N/70v6ek+4UnglLpCZDQ0N07NgRhw8fRmxsrMIHVEGyU9w6Nr6+vnBwcFDraxobG2P27NlYvHgxbty4UWS9gg+Tgov0y14t27x5M4yNjbFv3z6FDxB11jRZv349vLy8sGXLFkgkEnl5aScCv+zl8xWoUqUKYmNjC5XHxMQAgNrfV3X5+Phg8+bNEELg2rVrWL16Nb766iuYmZlh+vTpxbZdv349/Pz8sGzZMoXygvlMpVHw8y7qe6Lp74epqSmaN29eqrZCCIUHDxo1aqRW3ZcTrJfrASU/0ODg4FBsIjVq1CgcOXIEe/bsQUxMDHJycuTz5Uo675IlS7BkyRJERUVh7969mD59OuLj43Hw4EHcuHEDV69exerVqzFs2DB5u4J5iOravXs30tPTsXPnTnh4eMjLw8LCSnU+4EXi+fvvv2P37t0l/g4DL5IgIQS2b9+O7du3Fzq+Zs0afP311wqJb8F8PG3/fdLr40gSacSMGTOQn5+PsWPHlvqpkqIo+8ADIH9aqbgn5urUqQMXFxds2rRJ4Qm1hw8f4uzZswp1JRIJjIyMFC5mmZmZWLduXaHzSqVSpf8LLFgU8+WEJi4uTqWn20qjU6dOOH78uDwpKrB27VqYm5vLnxTUNolEAl9fXyxevBi2trYKH8DFfa+kUqlC2bVr1wpNUi6oo8r/ulu3bg0zMzOsX79eofzRo0c4fvw4OnXqpHKftOncuXMIDw9X+Pm0bNkS1atXx+HDh5Gfny8vj4mJwdWrVxXq9u/fHwDw999/K5z3wIEDAFDiz71u3bpISEhAcnKy0uN9+vRBlSpVsHLlSqxatQq1a9dGu3bt1Oqju7s7PvroI3Tp0kX++1Dwd/Hqz/23335T69wFlJ1PCIE//vijVOcDgN69e8PHxwcLFiwo8j9ghw4dQkZGBvLz87FmzRrUqFEDJ06cKPSaMmUKYmNjC/2cHjx4AADFToKn8oEjSaQRbdu2xS+//IIJEyagadOm+OCDD9CgQQMYGBggNjYWO3bsAABYW1urfe6uXbuievXq6NmzJ+rWrQuZTIawsDB8//33sLS0xMcff1xkWwMDA8ybNw/vv/8++vbti9GjRyMpKQlz5swpdLstKCgIP/zwAwYPHowPPvgACQkJ+N///lfogg78N4KyZcsWeHt7w9TUFD4+PvLH9MePH4+33noL0dHRmDdvHlxcXAo9WaUJs2fPxr59++Dv748vv/wS9vb22LBhA/bv349FixaVeNvndezbtw+//vor+vTpA29vbwghsHPnTiQlJaFLly7yej4+PggODsZff/0FFxcXWFlZoU6dOujRowfmzZuH2bNno0OHDrhz5w6++uoreHl5IS8vT97eysoKHh4e2LNnDzp16gR7e3s4ODgoXXnZ1tYWs2bNwueff4733nsP77zzDhISEjB37lyYmppi9uzZWvt+FMXX1xfvvvsu6tWrB1NTU1y4cAHfffcdnJ2dMW3aNHk9AwMDLF68GAMHDkTv3r0xbtw4pKenY968eTAxMcGMGTPkdQMCAtCzZ0989dVXkMlkaNWqFS5evIi5c+eiR48eJSY0fn5+EELg/Pnz8lvML5NKpRgyZAh++uknCCGwcOHCEvuZnJwMf39/DB48GHXr1oWVlRVCQ0Nx8OBB9OvXD8CL5KxGjRqYPn06hBCwt7fHX3/9hSNHjqj67VTQpUsXmJiY4J133sG0adOQlZWFZcuWITExsVDd4OBg+Pv7Y/bs2fKlNZQxNDTErl27EBAQgNatW2PcuHHw9/eHhYUFHj58iO3bt+Ovv/5CYmKi/D8o3377rXxpi5c1bNgQP//8M/7880/06NFDXn7u3DlUqVIFPj4+peo36VDZTIWiiiosLEyMGDFCeHl5CalUKkxNTUXNmjXFe++9J44dO6ZQV9VF9rZs2SIGDx4satWqJSwtLYWxsbFwd3cXQ4cOFbdu3VIprhUrVohatWoJExMTUbt2bbFy5Uqlk0BXrlwp6tSpI6RSqfD29hYLFiwQf/75Z6EJpJGRkSIgIEBYWVkJAArnWbhwofD09BRSqVTUq1dP/PHHH/K+lqS4idtBQUFK21y/fl307NlT2NjYCBMTE+Hr66swmVaI/yZub9u2TaG8YHLsqyt7q/KzuX37tnjnnXdEjRo1hJmZmbCxsREtWrQQq1evVqgXFhYm2rZtK8zNzQUA+YT37OxsMXXqVFGtWjVhamoqmjZtKnbv3q3053L06FGFCd4FE8GLWnBwxYoVolGjRsLExETY2NiI3r17F5pYPmzYMGFhYVGoX6r+rJQtJqnMoEGDRM2aNYWFhYX8icqxY8eKmJgYpfV3794t3njjDWFqaipsbGxEr169lE6Kz8jIEJ999plwc3MTRkZGwt3dXcyYMUOlCcf5+fnC09NTjB8/vsg6BZOLDQ0Nlcb66sTtrKwsMXbsWNGoUSNhbW0tzMzMRJ06dcTs2bMVFl68deuW6NKli7CyshJ2dnZiwIABIioqqtDkfFUnbv/111/C19dXmJqaimrVqolPP/1U/P333wKAOHHihEI9vPQEWkmSkpLEvHnzRNOmTRWuO++++674559/hBAvngI1MTEpdtXxQYMGCSMjI/mDHzKZTHh4eIgJEyaoFAeVLYkQL92DICKiSuH777/HN998g8ePH8PMzKysw9G6adOmYdOmTQgPDy9y0rouHDt2DAEBAbh58ybq1q1bZnGQajgniYioEvrwww9hY2MjfxKsojtx4gRmzZpVpgkSAHz99dcYOXIkEyQ9wTlJRESVkKmpKdatW4crV66UdSg6ERoaWtYhIDExER06dMD48ePLOhRSEW+3ERERESnB221ERERESjBJIiIiIlKiTJOkU6dOoWfPnnB1dYVEIim0srFEIlH6+u677+R1/Pz8Ch0fNGiQjntCREREFU2ZTtxOT0+Hr68vRowYIV9B9mWvrrT8999/Y9SoUYXqjh49Gl999ZX8vbqPs8pkMsTExMDKykrp1g9ERERU/gghkJqaCldX1xK34ymNMk2SAgMD5RsyKvPqish79uyBv78/vL29FcrNzc2VbmCqqpiYGLi5uZW6PREREZWd6OhoVK9eXePn1ZslAJ48eYL9+/djzZo1hY5t2LAB69evh5OTEwIDAzF79uwSd6t+WUHd6OjoUm2bQURERLqXkpICNzc3tT7z1aE3SdKaNWtgZWUl3wOowJAhQ+Dl5QVnZ2fcuHEDM2bMwNWrV4vdCyg7O1thV/aCXcetra2ZJBEREekZbU2V0ZskaeXKlRgyZEih1VJHjx4t/3fDhg1Rq1YtNG/eHJcvX0bTpk2VnmvBggWYO3euVuMlIiIi/aYXSwCcPn0ad+7cwfvvv19i3aZNm8LY2LjYHddnzJiB5ORk+Ss6OlqT4RIREVEFoBcjSX/++SeaNWsGX1/fEuvevHkTubm5cHFxKbKOVCqFVCrVZIhERERUwZRpkpSWloZ79+7J30dERCAsLAz29vZwd3cH8GJS1rZt2/D9998Xan///n1s2LAB3bt3h4ODA27duoUpU6agSZMmaNu2rc76QURERBVPmSZJFy9ehL+/v/z95MmTAQDDhg3D6tWrAQCbN2+GEALvvPNOofYmJiY4duwYli5dirS0NLi5uSEoKAizZ8+GoaGhTvpAREREFRM3uMWL0SobGxskJyfz6TYiIiI9oe3Pb72YuE1ERESka0ySiIiIiJRgkkRERESkBJMkIiIiIiWYJBEREREpwSSJiIiISAkmSURERKQ1mTn50NfVhpgkERERkVbciUtFvS8PwmvGAeTkyco6HLUxSSIiIiKNiX6egTVnI3HwRiy6LjklL1937mEZRlU6erHBLREREZV/2y5G49Pt15QeO377CUa189JxRK+HSRIRERG9lrx8GTaHRmPm7htF1snMyddhRJrBJImIiIhey+AV53Eh4nmxdVaNaKGjaDSHc5KIiIjotZSUIO3+sC1szIx1FI3mMEkiIiIirQls6IzGbrZlHUapMEkiIiKiUklIy4bn9P1FHvesYo5l7zbTYUSaxSSJiIiI1PY4KRPNvj5abJ2/P26vo2i0g0kSERERqa3twuNKy6/M6oK+Taph4/stYWZiqOOoNItPtxEREZFaittmxM7CBIvfbqy7YLSII0lERESklkM3n5R1CDrBJImIiIjUMnb9pUJlllIjrBzevAyi0R7ebiMiIqISCSEQ8SwdHb8/WejYT+80QZCPCwwMJGUQmfYwSSIiIqIiPUnJwszdN3DkVtG32Bq72Va4BAlgkkRERETFaDn/WLHHl7zdGG725jqKRrc4J4mIiKiSE0LgZkwysnIVN6FNz84rtt3akS3Qp0k1bYZWpjiSREREVMntCYvBpC1hAIA+jV3xVZ+GePgsAz1/PlNsO3sLEx1EV3aYJBEREVVya0Mi5f/eHRaD3WExxdY3MTLA++280LCajZYjK1tMkoiIiCqxv6/H4nJUkkp1D3/SHrWdrLQbUDnCOUlERESV1LO0bIzbcFnl+p5VLLQYTfnDkSQiIqJKJl8mEJmQjn9jU1Rus21sa5gYVa6xFSZJRERElcis3Tew7txDtdpELOgOiaTirYNUksqVEhIREVViV6OT1E6Qrs4OqJQJEsCRJCIiokphS2gUPttxXa02lXUEqQBHkoiIiCq4Sw8TS0yQOtSuWuh9ZU6QAI4kERERVVhZuflY+U8EFh28U2y9X4c0RXcfF6w5G4n/Hb6DDe+3hE8FXwNJFRIhhCjrIMpaSkoKbGxskJycDGtr67IOh4iISCOaf30Uz9KyizxuZmyIAx+/CS+H/x7tl8mE3mxWq+3Pb44kERERVTBCCPT46UyRCVIDV2tM6FgLXRs4Fbqlpi8Jki4wSSIiIqpArj1KQq+f/yny+Kdd6+BD/5o6jEh/MUkiIiKqAPJlAl2XnMK9+LQi67TytsfItl46jEq/MUkiIiKqAGp8fqDIYw6WUvz98ZuoaiXVYUT6r0yXADh16hR69uwJV1dXSCQS7N69W+H48OHDIZFIFF6tWrVSqJOdnY0JEybAwcEBFhYW6NWrFx49eqTDXhAREZWtcesvFXv84szOTJBKoUyTpPT0dPj6+uLnn38usk63bt0QGxsrfx04oJgpT5o0Cbt27cLmzZtx5swZpKWloUePHsjPz9d2+ERERGVuWfB9/H0jrqzDqJDK9HZbYGAgAgMDi60jlUrh7Oys9FhycjL+/PNPrFu3Dp07dwYArF+/Hm5ubjh69Ci6du2q8ZiJiIjKg7TsPMz76xa2XIxWeryVtz1Gv+mN+q5c2qa0yv2K28HBwXB0dETt2rUxevRoxMfHy49dunQJubm5CAgIkJe5urqiYcOGOHv2bFmES0REpBM/HL5bZIIEAGM61ECnek5wsTHTYVQVS7meuB0YGIgBAwbAw8MDERERmDVrFjp27IhLly5BKpUiLi4OJiYmsLOzU2jn5OSEuLiihx6zs7ORnf3f2hEpKSla6wMREZE2rPwnQmn5tTkByMzJh5O1qY4jqnjKdZL09ttvy//dsGFDNG/eHB4eHti/fz/69etXZDshRLH7zSxYsABz587VaKxERETatuDAvzj67xP89E5TpcdPT/OHtakxrE2NdRxZxVTub7e9zMXFBR4eHggPDwcAODs7IycnB4mJiQr14uPj4eTkVOR5ZsyYgeTkZPkrOrro4UoiIqLy4rdTD3D/aTq6/3ha6XE3e3MdR1SxleuRpFclJCQgOjoaLi4uAIBmzZrB2NgYR44cwcCBAwEAsbGxuHHjBhYtWlTkeaRSKaRSPgpJRETl36WHidgb9hjm0uI/she91UhHEVUeZZokpaWl4d69e/L3ERERCAsLg729Pezt7TFnzhz0798fLi4uiIyMxOeffw4HBwf07dsXAGBjY4NRo0ZhypQpqFKlCuzt7TF16lT4+PjIn3YjIiLSN3n5Miw6dActvewxas3FEutHLgzSQVSVT5kmSRcvXoS/v7/8/eTJkwEAw4YNw7Jly3D9+nWsXbsWSUlJcHFxgb+/P7Zs2QIrKyt5m8WLF8PIyAgDBw5EZmYmOnXqhNWrV8PQ0FDn/SEiItKEPWEx+P3UA/x+6kGJdYuZgkuvSSKEEGUdRFlLSUmBjY0NkpOTYW3N9SSIiKhseU7fr3Ldz7rVxTi/GlqMpvzS9ue3Xs1JIiIiquj+PKP80f5XLX7bFz7VbFGjqoWWI6q8mCQRERGVA0kZOei+9DRikrNUqt+5nhOs+Ki/VjFJIiIiKkMymUCjuYeRlp2ncpvb87rB1Jhzb7VNr9ZJIiIiqmimbLtaYoK0asQb8n9/0N6bCZKOMEkiIiLSsYcJ6fj+8B2cCX+GXVcel1jfv44jBr3hBjtzY3zQ3lsHERLAp9sA8Ok2IiLSrRbfHEV8anaxdYwMJNg5vg1qOVrBzOTFyFFevgxGhhzfKMCn24iIiCqYkhIkADgx1a/QNiNMkHSLSRIREZGO3H+ahk7fnyy2zhfd62E0b6mVC0xJiYiIdCAxPafEBAkAajpa6iAaUgVHkoiIiLQoLTsPQ1acx9XopGLr9W7sisiEDLSt6aCbwKhETJKIiIi0JDUrF1/v+7fEBOnB/O4wMOAmbOUNkyQiIiIt2HctBh9tvKJSXSZI5ROTJCIiIi34ZEtYkcd6NHJBmxoOaO5pB2tuLVJuMUkiIiJ6DUIISCT/jQQduhmHMesuFVl/+9jWaO5pr4vQ6DXx6TYiIqJSeJKShR8O30HdWQfx+6n7EELg/tO0YhOko5M7MEHSIxxJIiIiUlN8ShZazj8mfz//wG3MP3C72DbV7cz4eL+e4UgSERGRmlq8lCCpqrqdmRYiIW1ikkRERKQDDV1tyjoEUhNvtxEREakoKzcfb/9+TuX628a2hpmxIf6+EYvxfjW1GBlpA5MkIiIiFQghUHfWQbXavPH/k7QbVuMokj7i7TYiIqJX5ObLcONxMoQQAICz95+h3pfqJUik/ziSRERE9Iov99zEpgtRAIA/hzXHqDUXVW47M6geqlpJ0YBzkPQekyQiIqKXCCHkCRKAEhOkjnUd8fYbbpi0OQwTOtXE+296aztE0hEmSURERP9vx6VHmLLtqsr1fxjoi35NqwMArs8JgJEhZ7FUJEySiIiI/p86CdL9+d1h+NLGtEyQKh7+RImIiNR09+tAhQSJKiYmSURERACmbFVtFGnx274wMeLHZ2XAnzIREVV6R249wY7LjwqV25gZFyrr26S6LkKicoBzkoiIqFJbejQci4/eVSj7ZXBTBDVyQVZuPh4nZSIsKgnNPe1QzZb7r1UmTJKIiKjSmr3nBtaEPCxU3rGuIwDA1NgQNapaokZVS12HRuUAb7cREVGlpSxB+m1oM5iZGJZBNFTecCSJiIgqHZlM4NyDBKXHCkaRiJgkERFRpTN8dShO3X1aqPzQpPYw5npH9P/4m0BERJXKkVtPlCZIAFDH2UrH0VB5xiSJiIgqtKzcfJy6+xRZufnIzZdh9FrVN6ulyo2324iIqEKbtfsGtl0qvAYSUUk4kkRERBXWr8H3mCBRqXEkiYiIKqxFB++UWOfPYc0hkQC1HDkfiRQxSSIiogpHJhP4ZGtYifUiFwZpPxjSW2V6u+3UqVPo2bMnXF1dIZFIsHv3bvmx3NxcfPbZZ/Dx8YGFhQVcXV3x3nvvISYmRuEcfn5+kEgkCq9BgwbpuCdERFQe5MsEDlyPhffnB7AnLKbYur18XXUUFemrMh1JSk9Ph6+vL0aMGIH+/fsrHMvIyMDly5cxa9Ys+Pr6IjExEZMmTUKvXr1w8aLikwmjR4/GV199JX9vZsa9dYiIKpu7T1IRsPiUSnU3vt8SzTzttBwR6bsyTZICAwMRGBio9JiNjQ2OHDmiUPbTTz+hRYsWiIqKgru7u7zc3Nwczs7OWo2ViIjKrz9OPcA3B/5VqW4Td1u0qemg5YioItCrOUnJycmQSCSwtbVVKN+wYQPWr18PJycnBAYGYvbs2bCyKnoCXnZ2NrKzs+XvU1JStBUyERFpwf2naej0/Uk087DD1egk5MmEym27N3TRYmRUkehNkpSVlYXp06dj8ODBsLa2lpcPGTIEXl5ecHZ2xo0bNzBjxgxcvXq10CjUyxYsWIC5c+fqImwiItKw5+k56PT9SQDApYeJKrdztJLC2swYg1q4aSs0qmAkQgjV028tkkgk2LVrF/r06VPoWG5uLgYMGICoqCgEBwcrJEmvunTpEpo3b45Lly6hadOmSusoG0lyc3NDcnJysecmIqKylZ6dh6XHwvH7qQcqt3GwlOJZWjYufN4JjtamWoyOdC0lJQU2NjZa+/wu9yNJubm5GDhwICIiInD8+PESvwlNmzaFsbExwsPDi0ySpFIppFKpNsIlIiItuROXiq5LVJuYXWBAs+qY16ch0rPzUMWS131ST7lOkgoSpPDwcJw4cQJVqlQpsc3NmzeRm5sLFxfecyYiqkjm7L2pct3QLzqjqtV/SZGpsaE2QqIKrkyTpLS0NNy7d0/+PiIiAmFhYbC3t4erqyveeustXL58Gfv27UN+fj7i4uIAAPb29jAxMcH9+/exYcMGdO/eHQ4ODrh16xamTJmCJk2aoG3btmXVLSIi0oKQBwkl1nG2NsWnXesoJEhEpVWmSdLFixfh7+8vfz958mQAwLBhwzBnzhzs3bsXANC4cWOFdidOnICfnx9MTExw7NgxLF26FGlpaXBzc0NQUBBmz54NQ0P+r4GIqCLIys3HkVtPSqy3bEhTBPrwLgJpjtpJUnZ2Ni5cuIDIyEhkZGSgatWqaNKkCby8vNT+4n5+fihu3nhJc8rd3Nxw8uRJtb8uERHph7P3n2HwH+eLPL5mZAucuB2Pa4+S0Kmekw4jo8pA5STp7Nmz+Omnn7B7927k5OTA1tYWZmZmeP78ObKzs+Ht7Y0PPvgAY8eOLXaNIiIiIlXIZKLYBGlyl9roULsqOtSuqsOoqDJRae+23r1746233kK1atVw6NAhpKamIiEhAY8ePUJGRgbCw8Mxc+ZMHDt2DLVr1y52jSIiIqKSPErMwNVHSUUetzM3xkf+NXUXEFVKKo0kBQQEYNu2bTAxMVF63NvbG97e3hg2bBhu3rxZaBNaIiIidbT79kSRx8K/CYSxYZnuz06VhEpJ0ocffqjyCRs0aIAGDRqUOiAiIqqc4lOzcDU6GeeLeYqtmYcdEyTSmdd6ui0tLQ0ymUyhjCtWExFRabT45lixx3s0csH3A311FA2RinOSXhYREYGgoCBYWFjAxsYGdnZ2sLOzg62tLezs7LQRIxEREZa83RhSIy7vQrqj9kjSkCFDAAArV66Ek5MTJBKJxoMiIqLKIS45C5EJ6Xiaml1svT+HNYcRb7ORjqmdJF27dg2XLl1CnTp1tBEPERFVcDLZizXwRq4JRfCdpyXWb+llj/Z8zJ/KgNpJ0htvvIHo6GgmSUREpLJ78akYufoiPu5UC9N2XEO+rPjFggEgcmGQDiIjKpraSdKKFSswduxYPH78GA0bNoSxsbHC8UaNGmksOCIi0k/5MoHkzFxYSA2x8/JjzNh5HQAwZdvVMo6MSHVqJ0lPnz7F/fv3MWLECHmZRCKBEAISiQT5+fkaDZCIiPRP4NJTuPskrdTt32pWXYPREJWO2knSyJEj0aRJE2zatIkTt4mISKnSJkhO1lLM690QHes6ajgiIvWpnSQ9fPgQe/fuRc2aXA6eiIgKi36eUeq25z/vrMFIiF6P2s9TduzYEVev8p4yEREpepKShY82Xsabi4reUoRIn6g9ktSzZ0988sknuH79Onx8fApN3O7Vq5fGgiMiIv2Qnp2HlvOLXzG7JPP7+mgoGiLNkAghSn4O8yUGBkUPPunrxO2UlBTY2NggOTmZ26oQEZXCgOVnERqZqHL9bg2csXxoMwBAbr4M95+moY6TFee5klq0/fmt9u02mUxW5EsfEyQiInp9JSVIl2d1QZCPi/z9t/3/Wy7G2NAAdZ2tmSBRufNaG9wSERFN33GtyGNDWrrj6z4NIZFI8PPgJlic3xj5MgEzE+7BRuVfqZKkCxcuIDg4GPHx8ZDJZArHfvjhB40ERkRE5ZMQAouPhuPm42QMbe2BzaHRSut93KkWPulSW/5eIpHAxIijRaQ/1E6S5s+fj5kzZ6JOnTqF1kniUCkRUcV34k48fjwWDgA4djteaZ0ve9THyHZeugyLSOPUTpKWLl2KlStXYvjw4VoIh4iIyqu07Dx8f/gOrkYnKT3et0k1LH67sU5jItImtZMkAwMDtG3bVhuxEBFROfb94TtY9U9kkcfHdqihu2CIdEDtp9s++eQT/PLLL9qIhYiIyql8mSg2QQJebClCVJGoPZI0depUBAUFoUaNGqhfv36hxSR37typseCIiKhsnbgdj33XYhHUyLnYehM71oStuYmOoiLSDbWTpAkTJuDEiRPw9/dHlSpVOFmbiKgCG7E6FACw4/Ijpcffbu6GBf18YGDAzwKqeNROktauXYsdO3YgKChIG/EQEZGe2D62NZp72pd1GERao/acJHt7e9Sowcl5REQVVVJGDhYfuYuHCenF1svJlxV7nEjfqZ0kzZkzB7Nnz0ZGRoY24iEiIi3Ky5chJilT/l4mE4h+rng9n7HzOpYeC0eH74KLPVdtJytthEhUbqh9u+3HH3/E/fv34eTkBE9Pz0ITty9fvqyx4IiI6PVl5ebD1PjFNiAj11zEqbtPseH9lmhb0wGz9tzAhvNRWNjPB/VcrFHH2Qqn7j4t9nybP2iFarZmcLDk02xUsamdJPXp00cLYRARkTZsvRiNaduvoVNdR3weVE+eAA1ZcV6h3vSd11U634mpfvBysNB4nETlkUQIIco6iLKWkpICGxsbJCcnw9rauqzDISLSGM/p+zV2Lk7UpvJG25/fas9JUgXzLiKisrfg7381er7azpyDRJWLSklSvXr1sHHjRuTk5BRbLzw8HOPGjcO3336rkeCIiKj0fjv5QKPnszBRe4YGkV5T6Tf+l19+wWeffYYPP/wQAQEBaN68OVxdXWFqaorExETcunULZ86cwa1bt/DRRx9h/Pjx2o6biIiKcenhc42e79+vusGQC0ZSJaNSktSxY0eEhobi7Nmz2LJlCzZu3IjIyEhkZmbCwcEBTZo0wXvvvYd3330Xtra2Wg6ZiIiKsyfsMT7eHKax880MqgczE0ONnY9IX3DiNjhxm4gqFk1O1r7wRSc4Wplq7HxEmqTtz2/eYCYiqkD+PBNRqKxvk2rYdeWx0vpfdK+HpMwcDHrDHdXtzPA8PQcHbsRh6dFw9GnsygSJKjUmSUREFUBmTj6GrbyAC5GF5yJVtzND5MIgZOfl48bjZGTk5ONRYiZqVLVECy/FR/qrWEoxtJUH3m3pzg3MqdJjkkREVAEE/XgaD54V3mvN1twYYzu82G9TamSIZh6qrXPEBIlIS+skqerUqVPo2bMnXF1dIZFIsHv3boXjQgjMmTMHrq6uMDMzg5+fH27evKlQJzs7GxMmTICDgwMsLCzQq1cvPHr0SIe9ICIqWzl5MqUJUuTCIIR9GQALKf8/TFQaZZokpaenw9fXFz///LPS44sWLcIPP/yAn3/+GaGhoXB2dkaXLl2QmpoqrzNp0iTs2rULmzdvxpkzZ5CWloYePXogPz9fV90gIipTtWf+XajsDU+7MoiEqGJR+78XhoaGiI2NhaOjo0J5QkICHB0d1UpOAgMDERgYqPSYEAJLlizBF198gX79+gEA1qxZAycnJ2zcuBFjxoxBcnIy/vzzT6xbtw6dO3cGAKxfvx5ubm44evQounbtqm73iIj0Qk6eDCZGRf8/9/Pu9XQYDVHFpPZIUlErBmRnZ8PExOS1AyoQERGBuLg4BAQEyMukUik6dOiAs2fPAgAuXbqE3NxchTqurq5o2LChvE5RsaakpCi8iIj0QUZOHn44chf1vzyIoX+eL/Jx/ybuHEkiel0qjyT9+OOPAF5M5luxYgUsLS3lx/Lz83Hq1CnUrVtXY4HFxcUBAJycnBTKnZyc8PDhQ3kdExMT2NnZFapT0F6ZBQsWYO7cuRqLlYhI27Lz8rHhXBS+2ndLXnY6/JnSurfnddNVWEQVmspJ0uLFiwG8GElavnw5DA3/W33VxMQEnp6eWL58ucYDfPUJCyFEiU9dlFRnxowZmDx5svx9SkoK3NzcXi9QIiINE0IgPScfYVFJmLTlCp6lFb9/JgDsGNcapsZcHZtIE1ROkiIiXixQ5u/vj127dml9+xFnZ2cAL0aLXFxc5OXx8fHy0SVnZ2fk5OQgMTFRYTQpPj4ebdq0KfLcUqkUUqlUS5ETEb2e+NQsnLzzFEuPheNRYqZabeu72GgpKqLKR605Sbm5uXj48CFiYmK0FY+cl5cXnJ2dceTIEXlZTk4OTp48KU+AmjVrBmNjY4U6sbGxuHHjRrFJEhFRedbim2P4dPs1tROkHePacI81Ig1S6+k2Y2NjZGdna2yRsbS0NNy7d0/+PiIiAmFhYbC3t4e7uzsmTZqE+fPno1atWqhVqxbmz58Pc3NzDB48GABgY2ODUaNGYcqUKahSpQrs7e0xdepU+Pj4yJ92IyLSJwsO/FuqdoNbuqOZBydrE2mS2ksATJgwAd9++y1WrFgBI6PXW6Ds4sWL8Pf3l78vmCc0bNgwrF69GtOmTUNmZibGjx+PxMREtGzZEocPH4aVlZW8zeLFi2FkZISBAwciMzMTnTp1wurVqxXmTBER6YPo5xn47dQDtdp8P8AXb3jao7qdmZaiIqq8JKKoZ/qL0LdvXxw7dgyWlpbw8fGBhYWFwvGdO3dqNEBd0PYuwkRERcnLlyE1Kw/Jmbnw+1+wyu2+6F4Po9t7ay8wIj2g7c9vtYeCbG1t0b9/f40HQkRUmaRm5UImgBGrLuByVJJabacH1mWCRKQDaidJq1at0kYcRESVghACJ+8+xfBVoWq3dbA0wVe9G6JLfaeSKxPRa+Ouh0REOnToZhzGrr+scv3Rb3phRmA9XIlOQgNXa66BRKRDpUqStm/fjq1btyIqKgo5OYqLm12+rPofPxFRZXP8drxK9fo0dkVtZyt88KY3DAwkfHKNqAyovXfbjz/+iBEjRsDR0RFXrlxBixYtUKVKFTx48KDIzWqJiCq7GTuvY+if57H14iOV6i8Z1ATj/WrCyFDtyzQRaYjaf32//vorfv/9d/z8888wMTHBtGnTcOTIEUycOBHJycnaiJGISK/FJmdi04WoIvdae9WWD1ppOSIiUoXaSVJUVJR8NWszMzOkpqYCAIYOHYpNmzZpNjoionIsX6baCioqVkMTd1vc+yYQLb2rvEZURKQpaidJzs7OSEhIAAB4eHjg3LlzAF6slq3mkktERHonOTMXz9Ky8d2h22g89zBuxaSU2CY2qfjtRXyq2eDt5m7YNb4tb68RlSNqT9zu2LEj/vrrLzRt2hSjRo3CJ598gu3bt+PixYvo16+fNmIkIioXcvNl8J17WKFs5u7r2Dm+bbHt3loeUuSx8X41MK1bXY3ER0SapXaS9Pvvv0MmkwEAxo4dC3t7e5w5cwY9e/bE2LFjNR4gEVF5cehmnNptIp6lF3nM3MQQo9p5vU5IRKRFam9LEhUVBTc3t0Kb3AohEB0dDXd3d40GqAvcloSIVOE5fb/S8reaVUd1OzP4VLNBx7qOCtfHotpELgzSSoxElUm525bEy8sLsbGxcHR0VCh//vw5vLy8kJ+fr7HgiIjKg7x8GR4lFj2vaPul/x7r3/1hW9yKScHnu67Du6qF0vqzetTXeIxEpHlqJ0lCiEKjSACQlpYGU1NTjQRFRFRebDwfhc93XS/y+NJBjfEoMRMrz0QgIT0HfX75R37swdPCt9qq2ZrhrWbVtRIrEWmWyknS5MmTAQASiQSzZs2Cubm5/Fh+fj7Onz+Pxo0bazxAIqKy8vZvITgf8bzI4z7VbNC7cTUAwHeH7pR4vptzu8LcxFDpfzSJqPxROUm6cuUKgBcjSdevX4eJiYn8mImJCXx9fTF16lTNR0hEpAP5MoFlwfdQ19kaneo54lFiZpEJUsNq1rjxOAUDmqs3ImQh5XaZRPpE5b/YEydOAABGjBiBpUuXcoIzEVUof5x+gP8dvgsAsLcwwfP0nCLrbhrdCjdjUtDC017l8++f2O61YyQi3VJ71bJVq1YpJEgpKSnYvXs3bt++rdHAiIh0JSEtGwv//u8aVlyCBABWpsZo5V0FBgaq3zZr4GpT6viIqGyonSQNHDgQP//8MwAgMzMTzZs3x8CBA+Hj44MdO3ZoPEAiIm1r9vVRrZ6/ngtH3on0kdpJ0qlTp/Dmm28CAHbt2gUhBJKSkvDjjz/i66+/1niARET6YEZg4VWzHa2k8HWzxdqRLcogIiJ6XWrPIkxOToa9/Yv78AcPHkT//v1hbm6OoKAgfPrppxoPkIhIm5IzctWq/3l35VuIjOlQA5suRCEyIQMAsOfDtvB1s33d8IioDKmdJLm5uSEkJAT29vY4ePAgNm/eDABITEzkOklEpFeycvPh+9XhYuvM7+uD0+FP8WnXOnC1NYOpsWGRdVePaIFZe25gnF8NJkhEFYDaSdKkSZMwZMgQWFpawsPDA35+fgBe3Ibz8fHRdHxERFqz7WK0wvsgHxfsvx4rf//70GYIaOCMwS1V227J08EC60a11GiMRFR21E6Sxo8fj5YtWyIqKgpdunSBgcGLaU3e3t6ck0REeiEqIQOmJgZY+U+kQvm3bzXCu6088M4f5/Dz4CYIaOBcNgESUbmg9ga3FRE3uCWqPIracBbgprNE+kbbn99qP91GRKSvfjlxr8hj4d8E6jASItIHXCOfiCq05IxcLDz4LzZdiC6yjoOlFMaG/D8jESlikkREFcK9+FScDn+Glf9EYE7PBuhUzwnrzz3EzN03SmzbtYGTDiIkIn3DJImI9J4QAp1/OCV/P2rNRZgaGyArV6ZS+xnd62krNCLSY6UaXz59+jTeffddtG7dGo8fPwYArFu3DmfOnNFocEREJcnKzUdKVp6SctUSpB3j2sBSyv8vElFhaidJO3bsQNeuXWFmZoYrV64gOzsbAJCamor58+drPEAioqJk5uSj4exDaPftcbXatfC0R+TCIDyY3x3NPOy0FB0R6Tu1k6Svv/4ay5cvxx9//AFjY2N5eZs2bXD58mWNBkdEVJwbMcnIkwmkKhlJKk4zzxeJkYGBRBthEVEFoXaSdOfOHbRv375QubW1NZKSkjQRExFRiW7HpeCLXddL1fbjTrU0HA0RVURqJ0kuLi64d6/wWiNnzpyBt7e3RoIiIlJm+6VHGLk6FGnZeei25DTuPklTqd26US3Qsa4jNr7fEne+7lbs/mtERAXUnq04ZswYfPzxx1i5ciUkEgliYmIQEhKCqVOn4ssvv9RGjEREAICp264CAFrNP1ZsvRpVLXD/aToAwM3eDG/Wqoo3a1XVenxEVLGonSRNmzYNycnJ8Pf3R1ZWFtq3bw+pVIqpU6fio48+0kaMREQK0rKLnoN0aWZnVLGU4klKFk7efYpevq46jIyIKpJS792WkZGBW7duQSaToX79+rC0tNR0bDrDvduIyq/gO/GwtzBBcmYuhv55ocT63H+NqPLQ9ue32iNJycnJyM/Ph729PZo3by4vf/78OYyMjJhkEJHGRD/PwPBVoWUdBhFVUmpP3B40aBA2b95cqHzr1q0YNGiQRoIiIgKAyIT0sg6BiCoxtZOk8+fPw9/fv1C5n58fzp8/r5GgiIgAqHR7rW3NKvi0ax0AQGM3Wy1HRESVidpJUnZ2NvLyCk+azM3NRWZmpkaCepmnpyckEkmh14cffggAGD58eKFjrVq10ngcRKR9Qghk5earXL+usxU2vN8KH/rXROgXnbF9bGstRkdElY3ac5LeeOMN/P777/jpp58UypcvX45mzZppLLACoaGhyM//76J548YNdOnSBQMGDJCXdevWDatWrZK/NzEx0XgcRKRdWbn5qDvrIACgcz1H/PhOE6X1Pu5UC//ce4akzFwcmPimvLyqlVQncRJR5aF2kvTNN9+gc+fOuHr1Kjp16gQAOHbsGEJDQ3H48GGNB1i1quLaJgsXLkSNGjXQoUMHeZlUKoWzs7PGvzYR6c72S4/k/z76bzzqf3lI4fjA5tXxbf9GkEgkmNS5FoTgtiJEpF1q325r27YtQkJC4Obmhq1bt+Kvv/5CzZo1ce3aNbz55psln+A15OTkYP369Rg5ciQkkv8ujsHBwXB0dETt2rUxevRoxMfHF3ue7OxspKSkKLyISPeO3HqCPr/8g+fpOZi5+0aR9XaMa4NFb/nK/+4lEgkTJCLSulKvk1QWtm7disGDByMqKgquri8WiNuyZQssLS3h4eGBiIgIzJo1C3l5ebh06RKkUuXD73PmzMHcuXMLlXOdJCLtufskFdO2X8MnXWqjQ+2qmPvXTaz6J1Kltkcnt0dNRyvtBkhEekfb6ySVKkmSyWS4d+8e4uPjIZPJFI4p2/xWU7p27QoTExP89ddfRdaJjY2Fh4cHNm/ejH79+imtk52djezsbPn7lJQUuLm5MUki0iLP6fvl/17+blOMXX9Z5bYhMzrCxcZMG2ERkR4rd4tJnjt3DoMHD8bDhw/xan4lkUgUJllr0sOHD3H06FHs3Lmz2HouLi7w8PBAeHh4kXWkUmmRo0xEpHnXHiUpvFcnQQIAJytTDUZDRKQatZOksWPHonnz5ti/fz9cXFwU5gZp06pVq+Do6IigoOK3HEhISEB0dDRcXFx0EhcRlezov8XPEyzOg/ndOf+IiMqE2klSeHg4tm/fjpo1a2ojHqVkMhlWrVqFYcOGwcjov5DT0tIwZ84c9O/fHy4uLoiMjMTnn38OBwcH9O3bV2fxEVHR4lOz8OOxokd2X9XCyx5rR7aAqbGhFqMiIiqZ2k+3tWzZEvfu3dNGLEU6evQooqKiMHLkSIVyQ0NDXL9+Hb1790bt2rUxbNgw1K5dGyEhIbCy4iRPovKgxTfHVK7raCXF1jGtmSARUbmg9kjShAkTMGXKFMTFxcHHxwfGxsYKxxs1aqSx4AoEBAQUmv8EAGZmZjh06JCSFkRU1m7FpKC6vXqTracH1tVSNERE6lP76TYDg8KDTxKJBEIIrU7c1iZtz44nqmzOP0jA27+fU6vNW82qY2E/HxgZqj3ATUSVVLl7ui0iIkLjQRCRfjod/hTP0rLRp3E1CAFk58lQ78uDap/n3jeBTI6IqNxRO0ny8PDQRhxEpGeepmZj6J8XAACfbLmqUpvPu9fF/AO3FcqOTenABImIyiWVkqS9e/ciMDAQxsbG2Lt3b7F1e/XqpZHAiKh8e5KSpVb9fk2r4YP2NZCalYefjr94+KOxmy1qVLXURnhERK9NpSSpT58+iIuLg6OjI/r06VNkPX2dk0RE6lN3rf65vRoAAKYE1EG+TODX4PuYGVRPC5EREWmGSknSy1uPvLoNCRFVPk9SstDz5zNqtbEy/e9J2Gnd6mJip1p81J+IyjWNTARISkrSxGmISA8IIdByvuprHwGAm5KlAJggEVF5p3aS9O2332LLli3y9wMGDIC9vT2qVauGq1dVm7xJRPorO0/5aPLQVooPdXw/wFf+bw97C63GRESkDWonSb/99hvc3NwAAEeOHMHRo0dx8OBBBAYG4tNPP9V4gERUfuy7FoO/rsYUKm/ibot5fRri2pwAeZmJkQG2jmmNbg2csegtzS8yS0SkbWovARAbGytPkvbt24eBAwciICAAnp6eaNmypcYDJKKyly8T2HQhCjN33yh0rKWXPbaMaQ0AsH5p3lGj6jbwqGKBFl72OouTiEiT1E6S7OzsEB0dDTc3Nxw8eBBff/01gBfzFPhkG1HF03/ZWVx6mFjkcRMjxQHpC190wvP0HHhU4S02ItJvaidJ/fr1w+DBg1GrVi0kJCQgMDAQABAWFoaaNWtqPEAi0p3FR+5CamyAcR1qIOp5BpYeCy82QQIKT8B2tDKFo5WpNsMkItIJtZOkxYsXw9PTE9HR0Vi0aBEsLV8sBBcbG4vx48drPEAi0o1dVx5h6bFwAMDFyEREPc/Avfi0YttUszXDrKD6ugiPiEjn1N7gtiLiBrdU2d2MSUbQj+qtewQAkQuDtBANEZFqtP35zQ2TiCq5p6nZpUqQiIgqOrVvtxFRxXA6/CnSsvIwbsPlUrX/96tuGo6IiKh8YZJEVAnFJWdh6J8XStV2zcgWcLc3h5kJV8wmooqNSRJRJZMvE5i4+Uqp2zd2s4WNmXHJFYmI9Fypk6ScnBzEx8cX2vDW3d39tYMiIs1Ky87DmfCnSM/Ox7KT90t8au1VBya+ie4/ngYAWHAEiYgqCbWTpPDwcIwcORJnz55VKBdCQCKRcEFJonJo4qYrOH47vlRtgxq5oL6rNS7N7AyJRAIjQz7vQUSVg9pJ0vDhw2FkZIR9+/bBxcUFEolEG3ERkQaVNkECgF8GNwUAVLGUaiocIiK9oHaSFBYWhkuXLqFu3braiIeINORxUiam77iGlirunfZJ59rwrmqB+0/TsOToi0UlB7fk7XMiqrzUTpLq16+PZ8+eaSMWItKguXtv4nT4M5wOL/7vtZ6LNX4c1Bi1nKzkZb7VbbH98iNM61pH22ESEZVbKiVJKSkp8n9/++23mDZtGubPnw8fHx8YGys+5cIVq4nKXvTzDBy+9USlumtGvlForzX/uo7wr+uojdCIiPSGSkmSra2twtwjIQQ6deqkUIcTt4nKTlxyFlKycpGUkYvEjByMWXdJpXbnP+/EzWiJiIqgUpJ04sQJbcdBRK+h1YJjardZObw5nKyZIBERFUWlJKlDhw7yf0dFRcHNza3QU21CCERHR2s2OiJS6lFiBmbsvI733/RWeWJ2AXMTQ5gYGaB9rapaio6IqGJQe+K2l5cXYmNj4eioOF/h+fPn8PLy4u02Ih2YvuM6ztwreVJ2AZ9qNohPzYKxoQGOT/GDgOB6R0REJVA7SSqYe/SqtLQ0mJpy6J5Im3LzZdhw7iHO3FPvCdOOdR0xzq8GjAy4GCQRkapUTpImT54MAJBIJJg1axbMzc3lx/Lz83H+/Hk0btxY4wES0QtCCNSe+TeEUL/tmA7eMDXmdiJEROpQOUm6cuXFhphCCFy/fh0mJibyYyYmJvD19cXUqVM1HyFRJZeXL8OJO08R9TyjVAmSTzUbmJtwL2siInWpfOUseMJt+PDh+Omnn2BlZVVCCyJ6XcF34jF8VWip2h75pD22X36E4W08NRsUEVElIRFC9f+b5uXlwdTUFGFhYWjYsKE249KplJQU2NjYIDk5mYthUrlx7VESev38j8r1//2qG8xMDJGXL4NMACZGnHtERBWbtj+/1RqDNzIygoeHB59gI9KBjzeHqVw3cmGQ/N+cmE1EpBlqX01nzpyJGTNm4Pnz59qIh4gAXI1OQsSz9LIOg4ioUlN7NuePP/6Ie/fuwdXVFR4eHrCwsFA4fvnyZY0FR1TZPHiaBhszY/T+RfXbbEREpB1qJ0l9+vTRQhhElVNadh6O/fsE5x48x6YLUaU6x/pRLTUcFRERAWpO3K6oOHGbdE0IgbYLjyMmOUvttsPbeGL12UgAwNqRLdC+NrcXIaLKSduf36We4Xnp0iWsX78eGzZskK+hpGlz5syBRCJReDk7O8uPCyEwZ84cuLq6wszMDH5+frh586ZWYiHSpPD4tFIlSL18XTE5oDYiFnTHxZmdmSAREWmR2rfb4uPjMWjQIAQHB8PW1hZCCCQnJ8Pf3x+bN29G1aqavWg3aNAAR48elb83NPxv1eBFixbhhx9+wOrVq1G7dm18/fXX6NKlC+7cucN1nKjcOngjDmPXX1K73dnpHeFqayZ/72Ap1WRYRET0CrVHkiZMmICUlBTcvHkTz58/R2JiIm7cuIGUlBRMnDhR4wEaGRnB2dlZ/ipIwoQQWLJkCb744gv069cPDRs2xJo1a5CRkYGNGzdqPA4iTRBCqJ0gfdG9HiIXBikkSEREpH1qJ0kHDx7EsmXLUK9ePXlZ/fr18csvv+Dvv//WaHAAEB4eDldXV3h5eWHQoEF48OABACAiIgJxcXEICAiQ15VKpejQoQPOnj2r8TiINCE8Pk3tNq1rVNFCJEREVBK1kySZTAZjY+NC5cbGxpDJZBoJqkDLli2xdu1aHDp0CH/88Qfi4uLQpk0bJCQkIC4uDgDg5OSk0MbJyUl+rCjZ2dlISUlReBFpU1ZuPh4nZeK9Py8oPd6upgP+N8AXANC1wYvf6Sbuttj7UVs0rGajsziJiOg/as9J6tixIz7++GNs2rQJrq6uAIDHjx/jk08+QadOnTQaXGBgoPzfPj4+aN26NWrUqIE1a9agVatWAACJRKLQRghRqOxVCxYswNy5czUaK1FxOv4vuMiJ2u1qOuCP95rDzMQQbzWrDgCIT82Cg4UUBgbF/y4TEZH2qD2S9PPPPyM1NRWenp6oUaMGatasCS8vL6SmpuKnn37SRoxyFhYW8PHxQXh4uPwpt1dHjeLj4wuNLr1qxowZSE5Olr+io6O1FjPR+nMPlSZI4d8EInJhENa/3xJmJoYKxxytTJkgERGVMbVHktzc3HD58mUcOXIEt2/fhhAC9evXR+fOnbURn4Ls7Gz8+++/ePPNN+Hl5QVnZ2ccOXIETZo0AQDk5OTg5MmT+Pbbb4s9j1QqhVTKJ4NI+65EJWLm7htKjxlzjzUionJN7SSpQJcuXdClSxdNxlLI1KlT0bNnT7i7uyM+Ph5ff/01UlJSMGzYMEgkEkyaNAnz589HrVq1UKtWLcyfPx/m5uYYPHiwVuMiUsWduFT0/VX5QwRc34iIqPwrVZJ07NgxHDt2DPHx8YUma69cuVIjgQHAo0eP8M477+DZs2eoWrUqWrVqhXPnzsHDwwMAMG3aNGRmZmL8+PFITExEy5YtcfjwYa6RRDolkwk8S8+G1NAQNuYvHmq49igJvX4uev+1z7rV0VV4RERUSmpvSzJ37lx89dVXaN68OVxcXApNkt61a5dGA9QFbktCr+PzXdex8fyLfdeGt/HEP/eelfiof+TCIF2ERkRUoWn781vtkaTly5dj9erVGDp0qMaDIdJHBQkSAPmeasWZHlhXi9EQEZGmqJ0k5eTkoE2bNtqIhUivnLgdj91hj9Vu19zDTgvREBGRpqmdJL3//vvYuHEjZs2apY14iMq9q9FJmLfvFi4+TFSp/s25XWEgkeDPMw9gYCBBMyZJRER6Qe0kKSsrC7///juOHj2KRo0aFVp9+4cfftBYcETlUe9fip6QrYyF9MWf2Ucda2kjHCIi0hK1k6Rr166hcePGAIAbNxTXfylppWsifXftUZJa9es48UlLIiJ9pXaSdOLECW3EQVQuTdl6FTsuP8K1OQEwMTQo9rF+ZQ590l5LkRERkbaVejFJoopMCIHDt55gx+VHAIBGcw6r3LZdTQecufdMW6EREZGOMEkiUqLfsrO4EpWkcv0u9Z3wXmsPWEiNUMfJCstP3ke3hs7aC5CIiLSOSRKREuokSE3cbfHz4CaQGv23Se2UAK6oTUSk75gkEf2/f2NTsODv27iuxuTshtWssWt8W+0FRUREZYZJEtH/6/3LP8jJk5Vc8f9xaxEioorNoKwDICoP9oQ9VitBWjakqRajISKi8oBJElV6OXkyfLw5TOmxj/xrwsTwvz+TXwY3xQftvdG1ASdlExFVdLzdRpXexYfPlZbXc7HG1K51MLVrHQgh5IulBjVy0WV4RERURpgkUaWUkJYNqbEhvtn/LzZdiFJa5++P35T/m6vJExFVPkySqNJJyshBs6+PFlsneKqfboIhIqJyi3OSqMLbE/YYvwbfAwCkZ+dh68XoYutP7FgTng4WugiNiIjKMY4kUYVXMCl70cE7Jdbd8H5LtPKuouWIiIhIHzBJogorJ0+G/svOqtWmbU0HLUVDRET6hrfbqMI6cSce1x8nl3UYRESkp5gkUYWUkydDWHRSifXa164KXzdbrcdDRET6h7fbqEJ5np6DpvOOqFx/7cgWiErIwKw9NzCmg7cWIyMiIn3DJIkqjBuPk9HjpzMq1x/Z1gsA4F7FHGtGttBWWEREpKeYJJHe+/3UfZwOf4bT4c9UbmMlNcK0bnW0GBUREek7Jkmkl3LzZTA2NEBOngzzD9xWq+27rdwxM6g+TI0NtRQdERFVBEySSO+E3E/AO3+cK1XbdaNa4M1aVTUcERERVURMkkjvTN4aVqp2Rz5pj1pOVpoNhoiIKiwmSaRXsvPyEZucpVLdiZ1qISw6Cd+91QjJmblMkIiISC1Mkkiv1Jl5UKV61+YEwNrUWP7eydpUWyEREVEFxcUkSW+cUfHptT/ea66QIBEREZUGR5KoXEvJykV+vsCakEgsORpeYv1VI96Afx1HHURGREQVHZMkKrfy8mVoNOdwkceXv9sM3Ro6y9+nZ+fBQspfaSIi0gzebqNy6+qjojen/WVwU4UECQATJCIi0ih+qlC5k5yRiw/WXcT5iOdF1glq5KLDiIiIqDJikkTlSmJ6DpqUsEHtkrcb6yYYIiKq1Hi7jcqVjt8Hl1inl6+r9gMhIqJKjyNJVKYS0rKRmpWHa4+TsefKYyRm5BZZ17e6DXaMawMDA4kOIyQiosqKSRKVmbtPUhGw+FSJ9bo1cMY3fRvC3sIEEgkTJCIi0g0mSVRmVEmQfhvaDF0bOJdYj4iISNPK9ZykBQsW4I033oCVlRUcHR3Rp08f3LlzR6HO8OHDIZFIFF6tWrUqo4ipODKZQKfvg+E5fT+m77hWYv1uDZwRUN9JB5EREREVVq5Hkk6ePIkPP/wQb7zxBvLy8vDFF18gICAAt27dgoWFhbxet27dsGrVKvl7ExOTsgiXirD85H0E34lHbr7A/afpAIDNodHFtlkzsgU61K6qi/CIiIiUKtdJ0sGDipuZrlq1Co6Ojrh06RLat28vL5dKpXB25i2Z8uZxUiZ+Ph6OTReKT4iUaehqrYWIiIiIVFeub7e9Kjn5xQrM9vb2CuXBwcFwdHRE7dq1MXr0aMTHxxd7nuzsbKSkpCi8SPPGrb+kUoJkamyAP95rjioWJmhX0wFHJ7dHFUupDiIkIiIqmkQIIco6CFUIIdC7d28kJibi9OnT8vItW7bA0tISHh4eiIiIwKxZs5CXl4dLly5BKlX+QTtnzhzMnTu3UHlycjKsrTmCoSme0/erVC9yYZCWIyEiooooJSUFNjY2Wvv81psk6cMPP8T+/ftx5swZVK9evch6sbGx8PDwwObNm9GvXz+ldbKzs5GdnS1/n5KSAjc3NyZJGpKdl4/912IxeetVleozSSIiotLQdpJUruckFZgwYQL27t2LU6dOFZsgAYCLiws8PDwQHh5eZB2pVFrkKBO9ntSsXPjMOVzWYRAREb22cp0kCSEwYcIE7Nq1C8HBwfDy8iqxTUJCAqKjo+Hiwg1QdY0JEhERVSTleuL2hx9+iPXr12Pjxo2wsrJCXFwc4uLikJmZCQBIS0vD1KlTERISgsjISAQHB6Nnz55wcHBA3759yzj6yiUnT1ZigjSxUy341+Fj/UREpB/K9UjSsmXLAAB+fn4K5atWrcLw4cNhaGiI69evY+3atUhKSoKLiwv8/f2xZcsWWFlZlUHElY8QArP23MD6c1HF1nuvtQcmd6kNALj2KAlDVpxHalYePvSvoYswiYiI1KY3E7e1SdsTvyqa5MxcWJsaITkzF5O3XsXx28UvudCnsSuWDGqiUCaEwINn6fB2sOB+bEREVCqcuE3lyonb8RixOlSlur8NbYZnadkY0Myt0DGJRIIaVS01HR4REZHGMEkitaiaIPX0deXGtEREpNfK9cRtKl9kMtXuzPZo5IKlbzfWbjBERERaxpEkUtmUbSUvDrl1TGu84WnHeUZERKT3OJJESmXl5uPYv08Qm/xiuYXo5xnYdeVxkfUbu9lix7jWaOFlzwSJiIgqBI4kUSE/HLmLH4+9WLFcIgGGtfbE6rORSuvamRvj0Cft4WhlqsMIiYiItI9JEhVSkCABgBAoMkF6ML87DAw4akRERBUTb7dRqTFBIiKiioxJEgEAzj9IwOStYUhIyy6x7pgO3tg5vo0OoiIiIio7vN1WiQkhIJFIsPdqDCZuugIA2Hm56MnZALB2ZAu0r83914iIqOJjklQJZeflo8lXR5CRk69Wu8+712WCRERElQaTpEqozsyDarc585k/qtuZayEaIiKi8olJUiUT/iRVrfr7J7ZDjaqWMDU21FJERERE5ROTpErk5fWPSlLdzgzf9m+EBq42Wo6KiIiofGKSVIFl5uTj5N2nCLn/DGtCHqrV9sxnHbUUFRERkX5gklSBfbDuIk6HP1OrzY5xbeDlYKGliIiIiPQH10mqwFRNkN7wtJP/u5mHHewtTLQVEhERkd7gSFIFky8TqPH5AZXqXp7VBWlZeXCykWLIH+fxhpe9lqMjIiLSH0ySKpDE9Bw0mXekxHrj/Grggze9YWdhIh812j6OK2gTERG9jLfb9NzOy4/QbckpRD/PUClBqmJhgs+61YUdb6kREREViyNJem7y1qsAgDcXnSiyztvN3TC3dwPsuvIY/nUcdRUaERGRXmOSVMHN6lEfo9p5AQDeaeFextEQERHpDyZJemzK/48iKT3WpTY+6lgTEolEhxERERFVHEyS9NSUrVex4/KjIo9P6FRLh9EQERFVPJy4rSfSs/Nw7kECcvNl2H8tttgEyZuLQRIREb02jiTpgb1XYzBx0xUAgEQCCFF8/QMfv6mDqIiIiCo2Jkl6oCBBAopPkJa/2wxVrUxgamyog6iIiIgqNiZJ5ZgoacjoJRM71UK3hs5ajIaIiKhyYZJUjgghEJucBTtzE9T78qBabQc0q66lqIiIiConJknlxJFbTzB67UW12rjbm2Pn+DYwMpDA1pwraBMREWkSk6Ry4NuDt7Es+L5aba7ODoCNmbGWIiIiIiIuAVDGTtyOVztB+vGdJkyQiIiItIxJUhla/U8ERqwOVavN3F4N0MvXVUsRERERUQHebtMhIQQ+3hwGF1tT9GtSHXP+uqVW+1k96mNYG0/tBEdEREQKmCTp0KWHidh7NQYA8NvJB2q1bVfTQb5RLREREWkfb7fp0PP0nGKPX5rZGUGNXAqVD2npjvXvt9RWWERERKQEkyQdEULgg3WXiq1TxVKKHwb6olF1G8W22gyMiIiIlOLtNh2ITc7E48TMYuvc+qorAEBqZIi9H7UDAHhO3w8AaO5hp90AiYiIqBAmSTrQesHxYo9HLgxSWn7yUz+ERSehZyM+zUZERKRrFeZ226+//govLy+YmpqiWbNmOH36dFmH9No8qligd+NqMDCQlHUoRERElU6FGEnasmULJk2ahF9//RVt27bFb7/9hsDAQNy6dQvu7u5lGtuG8w+Vli/q3whJmTnwr+Oo44iIiIhIFRKhzlbz5VTLli3RtGlTLFu2TF5Wr1499OnTBwsWLCixfUpKCmxsbJCcnAxra2uNxZWcmQvfuYcLlRd1e42IiIhUp63P7wJ6f7stJycHly5dQkBAgEJ5QEAAzp49q7RNdnY2UlJSFF7aoCxBIiIiIv2g90nSs2fPkJ+fDycnJ4VyJycnxMXFKW2zYMEC2NjYyF9ubm66CJWIiIj0iN4nSQUkEsXJzUKIQmUFZsyYgeTkZPkrOjpaKzEdnPRmoTLfV9ZAIiIiovJJ7yduOzg4wNDQsNCoUXx8fKHRpQJSqRRSqVTrsdVxskITd1tciUrCov6NcDL8KaZ3q6v1r0tERESvT++TJBMTEzRr1gxHjhxB37595eVHjhxB7969yzCyF6Nbu8a3lb8f+AZv6xEREekLvU+SAGDy5MkYOnQomjdvjtatW+P3339HVFQUxo4dW9ahERERkZ6qEEnS22+/jYSEBHz11VeIjY1Fw4YNceDAAXh4eJR1aERERKSnKsQ6Sa9L2+ssEBERkeZxnSQiIiKiMsAkiYiIiEgJJklERERESjBJIiIiIlKCSRIRERGREkySiIiIiJRgkkRERESkBJMkIiIiIiWYJBEREREpwSSJiIiISAkmSURERERKVIgNbl9XwfZ1KSkpZRwJERERqargc1tb29AySQKQmpoKAHBzcyvjSIiIiEhdqampsLGx0fh5JUJb6ZcekclkiImJgZWVFSQSida+TkpKCtzc3BAdHa2V3Yp1qSL1BWB/yjv2p3yrSP2pSH0BKn5/hBBITU2Fq6srDAw0P4OII0kADAwMUL16dZ19PWtr6wrxywpUrL4A7E95x/6UbxWpPxWpL0DF7o82RpAKcOI2ERERkRJMkoiIiIiUYJKkQ1KpFLNnz4ZUKi3rUF5bReoLwP6Ud+xP+VaR+lOR+gKwP6+LE7eJiIiIlOBIEhEREZESTJKIiIiIlGCSRERERKQEkyQiIiIiJZgkqWHBggV44403YGVlBUdHR/Tp0wd37txRqCOEwJw5c+Dq6gozMzP4+fnh5s2bCnWys7MxYcIEODg4wMLCAr169cKjR48Kfb39+/ejZcuWMDMzg4ODA/r166e3/bl79y569+4NBwcHWFtbo23btjhx4kS57M/vv/8OPz8/WFtbQyKRICkpqdDXSkxMxNChQ2FjYwMbGxsMHTpUab3y3pfIyEiMGjUKXl5eMDMzQ40aNTB79mzk5ORorC+67M/LsrOz0bhxY0gkEoSFhel1f/TlWqBKf/TlWvD8+XNMmDABderUgbm5Odzd3TFx4kQkJycrnEcfrgWq9EWfrgWq/mwKvNa1QJDKunbtKlatWiVu3LghwsLCRFBQkHB3dxdpaWnyOgsXLhRWVlZix44d4vr16+Ltt98WLi4uIiUlRV5n7Nixolq1auLIkSPi8uXLwt/fX/j6+oq8vDx5ne3btws7OzuxbNkycefOHXH79m2xbds2ve1PzZo1Rffu3cXVq1fF3bt3xfjx44W5ubmIjY0td/1ZvHixWLBggViwYIEAIBITEwt9rW7duomGDRuKs2fPirNnz4qGDRuKHj166F1f/v77bzF8+HBx6NAhcf/+fbFnzx7h6OgopkyZorG+6LI/L5s4caIIDAwUAMSVK1f0tj/6dC1QpT/6ci24fv266Nevn9i7d6+4d++eOHbsmKhVq5bo37+/wtfSh2uBKn3Rp2uBqj+bAq9zLWCS9Bri4+MFAHHy5EkhhBAymUw4OzuLhQsXyutkZWUJGxsbsXz5ciGEEElJScLY2Fhs3rxZXufx48fCwMBAHDx4UAghRG5urqhWrZpYsWKFDnujvf48ffpUABCnTp2S10lJSREAxNGjR8tVf1524sQJpRf6W7duCQDi3Llz8rKQkBABQNy+fVuv+qLMokWLhJeXl8ZiV0bb/Tlw4ICoW7euuHnzplaSpFdpqz/6dC14WVH90ddrQYGtW7cKExMTkZubK4TQz2tBUX1RRh+uBQWK6s/rXgt4u+01FAzt2dvbAwAiIiIQFxeHgIAAeR2pVIoOHTrg7NmzAIBLly4hNzdXoY6rqysaNmwor3P58mU8fvwYBgYGaNKkCVxcXBAYGFhoaFtf+lOlShXUq1cPa9euRXp6OvLy8vDbb7/ByckJzZo1K1f9UUVISAhsbGzQsmVLeVmrVq1gY2Oj1nnUoa2+FPW1Cr6OtmizP0+ePMHo0aOxbt06mJubay7oYmirP/p0LVCFvl8LkpOTYW1tDSOjF9ue6vO14NW+FFVHX64FyvqjiWsBk6RSEkJg8uTJaNeuHRo2bAgAiIuLAwA4OTkp1HVycpIfi4uLg4mJCezs7Iqs8+DBAwDAnDlzMHPmTOzbtw92dnbo0KEDnj9/rnf9kUgkOHLkCK5cuQIrKyuYmppi8eLFOHjwIGxtbctVf1QRFxcHR0fHQuWOjo5qnUdV2uzLq+7fv4+ffvoJY8eOLX3AJdBmf4QQGD58OMaOHYvmzZtrLugSvqa2+qNP1wJV6PO1ICEhAfPmzcOYMWPkZfp6LVDWl1fp07VAWX80dS0oOoWkYn300Ue4du0azpw5U+iYRCJReC+EKFT2qpfryGQyAMAXX3yB/v37AwBWrVqF6tWrY9u2bcX+YpeWNvsjhMD48ePh6OiI06dPw8zMDCtWrECPHj0QGhoKFxcXzXXk/2m6PyWdo7TnUYW2+1IgJiYG3bp1w4ABA/D++++X6hyq0GZ/fvrpJ6SkpGDGjBmvHaeqtNmfinAteLW+Pl4LUlJSEBQUhPr162P27NnFnqO487wubfelgD5dC4rqj6auBRxJKoUJEyZg7969OHHiBKpXry4vd3Z2BoBC2W58fLw8K3Z2dkZOTg4SExOLrFNwoahfv778uFQqhbe3N6KiovSuP8ePH8e+ffuwefNmtG3bFk2bNsWvv/4KMzMzrFmzplz1RxXOzs548uRJofKnT5+qdR5VaLsvBWJiYuDv74/WrVvj999/f72gi6Ht/hw/fhznzp2DVCqFkZERatasCQBo3rw5hg0bpoEeKNJ2f/TpWqAKfbwWpKamolu3brC0tMSuXbtgbGyscB59uhYU15cC+nQtKK4/GrsWqDWDqZKTyWTiww8/FK6uruLu3btKjzs7O4tvv/1WXpadna10ovOWLVvkdWJiYhQmOicnJwupVKowWTMnJ0c4OjqK3377Te/6s3fvXmFgYCBSU1MVzl+7dm3xzTfflKv+vKykidvnz5+Xl507d06jkzV11RchhHj06JGoVauWGDRokMITiZqkq/48fPhQXL9+Xf46dOiQACC2b98uoqOj9a4/+nQtUKU/+nYtSE5OFq1atRIdOnQQ6enphc6jT9eCkvoihH5dC0rqj6auBUyS1DBu3DhhY2MjgoODRWxsrPyVkZEhr7Nw4UJhY2Mjdu7cKa5fvy7eeecdpY/MV69eXRw9elRcvnxZdOzYsdAj8x9//LGoVq2aOHTokLh9+7YYNWqUcHR0FM+fP9e7/jx9+lRUqVJF9OvXT4SFhYk7d+6IqVOnCmNjYxEWFlbu+hMbGyuuXLki/vjjD/mTOFeuXBEJCQnyOt26dRONGjUSISEhIiQkRPj4+Gj0sV9d9eXx48eiZs2aomPHjuLRo0cKX0uTdPmzeVlERIRWnm7TZX/06VpQUn/06VqQkpIiWrZsKXx8fMS9e/cUzvPytVofrgWq9EWfrgWq/mxeVtprAZMkNQBQ+lq1apW8jkwmE7NnzxbOzs5CKpWK9u3bi+vXryucJzMzU3z00UfC3t5emJmZiR49eoioqCiFOjk5OWLKlCnC0dFRWFlZic6dO4sbN27obX9CQ0NFQECAsLe3F1ZWVqJVq1biwIED5bI/s2fPLvE8CQkJYsiQIcLKykpYWVmJIUOGqPR4fXnry6pVq4r8Wpqky5/Ny7SVJOmyP/p0LVClP/pyLSgYDVP2ioiIkNfTh2uBKn3Rp2uBqj+bl5X2WiD5/6CJiIiI6CWcuE1ERESkBJMkIiIiIiWYJBEREREpwSSJiIiISAkmSURERERKMEkiIiIiUoJJEhEREZESTJKISK/5+flh0qRJZR0GEVVATJKIqNIIDg6GRCJBUlJSWYdCRHqASRIRERGREkySiEhvpKen47333oOlpSVcXFzw/fffKxxfv349mjdvDisrKzg7O2Pw4MGIj48HAERGRsLf3x8AYGdnB4lEguHDhwMAhBBYtGgRvL29YWZmBl9fX2zfvl2nfSOi8odJEhHpjU8//RQnTpzArl27cPjwYQQHB+PSpUvy4zk5OZg3bx6uXr2K3bt3IyIiQp4Iubm5YceOHQCAO3fuIDY2FkuXLgUAzJw5E6tWrcKyZctw8+ZNfPLJJ3j33Xdx8uRJnfeRiMoPbnBLRHohLS0NVapUwdq1a/H2228DAJ4/f47q1avjgw8+wJIlSwq1CQ0NRYsWLZCamgpLS0sEBwfD398fiYmJsLW1BfBidMrBwQHHjx9H69at5W3ff/99ZGRkYOPGjbroHhGVQ0ZlHQARkSru37+PnJwchUTG3t4ederUkb+/cuUK5syZg7CwMDx//hwymQwAEBUVhfr16ys9761bt5CVlYUuXboolOfk5KBJkyZa6AkR6QsmSUSkF0oa9E5PT0dAQAACAgKwfv16VK1aFVFRUejatStycnKKbFeQSO3fvx/VqlVTOCaVSl8/cCLSW0ySiEgv1KxZE8bGxjh37hzc3d0BAImJibh79y46dOiA27dv49mzZ1i4cCHc3NwAABcvXlQ4h4mJCQAgPz9fXla/fn1IpVJERUWhQ4cOOuoNEekDJklEpBcsLS0xatQofPrpp6hSpQqcnJzwxRdfwMDgxfMn7u7uMDExwU8//YSxY8fixo0bmDdvnsI5PDw8IJFIsG/fPnTv3h1mZmawsrLC1KlT8cknn0Amk6Fdu3ZISUnB2bNnYWlpiWHDhpVFd4moHODTbUSkN7777ju0b98evXr1QufOndGuXTs0a9YMAFC1alWsXr0a27ZtQ/369bFw4UL873//U2hfrVo1zJ07F9OnT4eTkxM++ugjAMC8efPw5ZdfYsGCBahXrx66du2Kv/76C15eXjrvIxGVH3y6jYiIiEgJjiQRERERKcEkiYiIiEgJJklERERESjBJIiIiIlKCSRIRERGREkySiIiIiJRgkkRERESkBJMkIiIiIiWYJBEREREpwSSJiIiISAkmSURERERKMEkiIiIiUuL/AFblFnrS73CWAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "plt.plot(P566_GPS_data['Date'],P566_GPS_data['North (mm)'])\n",
-    "plt.ylabel('north since start (mm)')\n",
-    "plt.xlabel('date')\n",
-    "plt.title('GPS data from station P566 (Visalia, CA)')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlUAAAHFCAYAAADbiAxsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABnlUlEQVR4nO3deXhM1xsH8O9km0xWWWSzJHYiEVsRVOxbbKValFKq1qK0aqvYQ2m11RallarWrqr2LVQIYgkJtSeCJJaIJEL28/vDL1Mjk8lMzExmku/neeZ55J733nlPEnfe3HPuuRIhhAARERERvRaTkk6AiIiIqDRgUUVERESkBSyqiIiIiLSARRURERGRFrCoIiIiItICFlVEREREWsCiioiIiEgLWFQRERERaQGLKiIiIiItYFFFJerixYsYNmwYqlWrBplMBplMhho1amDEiBE4c+aMQuysWbMgkUjkLwsLC1SpUgXjx4/HkydPFGJPnTqFt956C5UrV4ZUKoWrqyv8/f0xadKkYuc6ZMgQeHl5FWvfH3/8ESEhIcV+b1VmzJiBypUrw8zMDOXKldPJe+jTggULsH379mLv/+zZM8yaNQtHjhwp0BYSEgKJRILY2NhiH7+4WrdurfD7K5PJ4Ofnh2+++QZ5eXnyuFd/z/NflpaWSo/76NEjjB8/Hl5eXvLf9S5duuDx48cFYsPCwtC1a1c4ODjI/6/NnTtX7T4MHToUnTt3BgB8++23kEgk2Lt3b6Hxq1atgkQiwbZt2xAbGwuJRKKz/wfKfrav839WU6mpqZg/fz4aN24MOzs7SKVSeHl5YejQoTh37pzSfb777jtIJBL4+PgobU9OTka5cuVe6/8D6ZkgKiErVqwQZmZmom7duuLbb78VBw8eFIcOHRLff/+9aNGihQAgbty4IY8PCgoSAMTevXtFeHi42L9/v5gwYYKQSCSiWbNmIi8vTwghxM6dO4WJiYlo27atWL9+vThy5IhYv369mDRpkqhQoUKx8x08eLDw9PQs1r5169YVAQEBxX7vwmzfvl0AENOnTxdhYWEiIiJC6++hb9bW1mLw4MHF3v/hw4cCgAgKCirQ9uDBAxEeHi4yMjKKn2AxBQQEiKpVq4rw8HARHh4u/vrrL9GlSxcBQEyePFke9+rvef7r1KlTBY557949UbVqVVGzZk2xevVqcfToUbF161YxduxYkZCQoBD7+++/CxMTE9GvXz+xY8cOcfjwYbFq1Soxe/ZstfI/d+6cMDExkf+OPXr0SEilUtG3b99C9/H39xfly5cXWVlZIiMjQ4SHh4sHDx6o9X6aWrNmjQAgYmJi5Ntu3Lghzp07p5P3e9mNGzdE1apVhY2Njfj000/Fzp07xZEjR0RISIjo2rWrACCePHlSYD8/Pz8BQAAQJ0+eVHrsWbNmierVq4vMzExdd4O0gEUVlYiwsDBhYmIiunfvXujJYtOmTeLevXvyr/M/bB4+fKgQN2jQIAFAhIWFCSGEaNWqlahWrZrIzs4ucMzc3Nxi52yIRdW8efMEAHH//v0iY589e6b199cFXRZVJSkgIEDUrVtXYVtWVpaoWrWqsLKyEllZWUKIwn/PlenZs6eoUKGCePz4scq4u3fvCmtrazFq1Khi5//OO++IZs2aFdhmYWEhHj16VCD+33//FQDEpEmTiv2emlBWVOlDTk6O8PX1FXZ2diIqKkppzO7du0V6errCtoiICAFABAYGCgBi+PDhSvdNTEwUZmZm4vfff9d67qR9HP6jErFgwQKYmppi5cqVsLCwUBrTt29feHh4FHmsZs2aAQBu374NAEhKSoKzszPMzMwKxJqYqPcrHxISglq1akEqlaJOnTpYu3at0rjZs2ejadOmcHR0hJ2dHRo2bIiff/4Z4qXnlHt5eeHSpUs4evSofCgnf0giIyMDkyZNQv369WFvbw9HR0f4+/vjr7/+KjJHLy8vzJgxAwDg6uoKiUSCWbNmydu6deuGbdu2oUGDBrC0tMTs2bMBANHR0ejZsyccHBxgaWmJ+vXr49dff1U49pEjRyCRSPDHH3/g888/h7u7O2xsbNC9e3fcv38faWlp+Oijj+Ds7AxnZ2d88MEHePr0aZE5nz9/Ht26dYOLiwukUik8PDwQGBiIu3fvAgAkEgnS09Px66+/yr9XrVu3BgA8fPgQo0ePhre3N2xsbODi4oK2bdvi2LFj8uPHxsaifPny8p9N/jGGDBkCoPDhv19++QV+fn6wtLSEo6Mj3nrrLfz7778KMUOGDIGNjQ1u3LiBrl27wsbGBpUqVcKkSZOQmZlZZN+VMTc3R6NGjfDs2TM8fPhQo31jY2OxY8cODB8+HA4ODipjV69ejfT0dHz++efFyvP+/fv4888/MWjQIIXtw4YNQ1ZWFv74448C+6xZswbAiyHD/HxfHf57+PAhPvroI1SqVAlSqRTly5dHixYtcPDgQXnMgQMH0LNnT1SsWBGWlpaoXr06RowYgUePHhWZt7Lhvx9++AGtWrWCi4sLrK2t4evriy+//BLZ2dnqfjsUbN++HVFRUZg6dWqhw3hdunSBlZWVwraff/4ZALBw4UI0b94cGzZswLNnzwrs6+rqig4dOmDFihXFyo/0i0UV6V1ubi5CQ0PRuHFjuLu7v/bxbty4AQDyD1N/f3+cOnUK48aNw6lTpzQ+WYaEhOCDDz5AnTp1sHXrVsyYMQNz587F4cOHC8TGxsZixIgR2LRpE7Zt24bevXvj448/Vpin8ueff6Jq1apo0KABwsPDER4ejj///BMAkJmZicePH+PTTz/F9u3bsX79erRs2RK9e/cutJB7+bjDhg0DAOzduxfh4eH48MMP5e3nzp3DZ599hnHjxmHv3r3o06cPrl69iubNm+PSpUv47rvvsG3bNnh7e2PIkCH48ssvC7zHtGnT8ODBA4SEhOCrr77CkSNH0L9/f/Tp0wf29vZYv349Jk+ejN9++w3Tpk1TmW96ejo6dOiA+/fv44cffsCBAwfwzTffoHLlykhLSwMAhIeHQyaToWvXrvLv1Y8//ggA8jlCQUFB2LVrF9asWYOqVauidevW8vlT7u7u8jk+w4YNkx/jiy++KDSv4OBgDBs2DHXr1sW2bdvw7bff4uLFi/D398f169cVYrOzs9GjRw+0a9cOf/31F4YOHYqlS5di0aJFKvuuys2bN2FmZlagMPL19YWpqSlcXV3x/vvvIy4uTqH92LFjEELAw8MD/fv3h42NDSwtLdG6dWuEh4crxP7zzz9wdHTElStXUL9+fZiZmcHFxQUjR45EampqkTnu378f2dnZaNOmjcL29u3bw9PTE7/88ovC9tzcXPz2229o1qwZvL29Cz3uoEGDsH37dsycORP79+/H6tWr0b59eyQlJSl8f/z9/bF8+XLs378fM2fOxKlTp9CyZctiFUI3b97EgAED8Ntvv2Hnzp0YNmwYFi9ejBEjRmh8LODF9wYAevXqpfY+z58/x/r16/HGG2/Ax8cHQ4cORVpaGjZv3qw0vnXr1jh+/HiBuaNkgEr6UhmVPYmJiQKA6NevX4G2nJwckZ2dLX/lz5MS4r9hkcTERJGdnS2Sk5PFunXrhEwmE5UqVRLPnz8XQryY69GyZUv5XAVzc3PRvHlzERwcLNLS0lTmlpubKzw8PETDhg0V3js2NlaYm5urHP7Lzc0V2dnZYs6cOcLJyUlhf3WH//L7P2zYMNGgQYMi4wsbKvL09BSmpqbi6tWrCtv79esnpFKpiIuLU9jepUsXYWVlJZ/3ERoaKgCI7t27K8RNmDBBABDjxo1T2N6rVy/h6OioMtczZ84IAGL79u0q49Qd/sv/XrVr10689dZb8u2qhv9eHSJKTk4WMplMdO3aVSEuLi5OSKVSMWDAAPm2wYMHCwBi06ZNCrFdu3YVtWrVKjLf/OG//N/t+Ph4MWXKFAFAYV7S2rVrxfz588Xu3bvF4cOHxcKFC4Wjo6NwdXUVd+/elccFBwcLAMLOzk707NlT7N27V2zdulXUq1dPWFpaigsXLshja9WqJSwtLYWtra1YsGCBCA0NFV9++aWQyWSiRYsWCr+ryowaNUrIZDKlcfm/gy/PXfr7778FALFq1Sr5tpiYGAFArFmzRr7NxsZGTJgwocjvXb68vDyRnZ0tbt++LQCIv/76S96mbPivqCH7/P+za9euFaampkUOoyrTuXNnAUCjeXpr164VAMSKFSuEEEKkpaUJGxsb8eabbyqNP3DggAAg9uzZo3F+pF8sqkjvVBVVL0/cBCAWL14sb8s/eb/6atGihbh06VKBY0VERIiFCxeKt99+Wzg7OwsAwsvLS+VclcuXLwsAYsmSJQXaAgICCpygDx06JNq1ayfs7OwK5JWYmCiPU1VUbdq0STRv3lxYW1sr7G9paVlonq9+T5QVVcqKMhcXlwIFhBBCbNy4UeGknV9UrVy5UiFu5cqVAoDYt2+fwvapU6cKACqL1idPnggHBwdRq1YtsXz5cqU/MyFUF1XLly8XDRo0EFKpVOF7Vbt2bXmMJkXV7t27lRZKQrwoNF1dXeVfDx48WEgkEnnxnm/KlClq/awCAgIK/I6Ym5uL9957T+kk5pedOnVKmJiYKBSz8+fPFwCEt7e3yMnJkW+Pj48XVlZW4r333pNvq1GjhgAggoODFY77zTffCADiwIEDKt+/Z8+eonLlykrbYmNjhYmJiRg7dqx8W+/evYW1tbVITU2Vb1NWVLVt21aUK1dOzJ07V4SHh8vnlb3s/v37YsSIEaJixYrCxMRE4fu3cOFCeZy6RdW5c+dE9+7dhaOjY4GfR2GTxVUpTlEVEBAgZDKZws/9gw8+EADEtWvXCsRfuHBBABCrV6/WOD/SLw7/kd45OztDJpPJ50C97I8//kBERAR27NhR6P4HDx5EREQEIiMj8ejRI4SFhSkdYmjcuDE+//xzbN68GfHx8fjkk08QGxurdJgrX/6wg5ubW4G2V7edPn0aHTt2BPDi1vHjx48jIiIC06dPB/DiEn9Rtm3bhnfeeQcVKlTAunXrEB4ejoiICAwdOhQZGRlF7q+KsqHVpKQkpdvz5669POwCAI6Ojgpf589/K2y7qpzt7e1x9OhR1K9fH9OmTUPdunXh4eGBoKAgtYZxvv76a4waNQpNmzbF1q1bcfLkSURERKBz585qfa+Vye9vYd+TV78fVlZWBZY2kEqlav+sqlWrhoiICJw5cwbR0dF48uQJ1q1bB3t7e5X7NWnSBDVr1sTJkyfl25ycnAC8GIIzNTWVb3d3d4efn5/Cbfz5sZ06dVI4bpcuXQCg0Fv+8z1//rzQJR08PT3Rrl07/PHHH8jMzMSjR4+wc+dO9O3bF7a2tiqPu3HjRgwePBirV6+Gv78/HB0d8f777yMxMREAkJeXh44dO2Lbtm2YPHkyDh06hNOnT8u/D5r+3OPi4vDmm2/i3r17+Pbbb3Hs2DFERETghx9+KNbxAKBy5coAgJiYGLXib9y4gX/++QeBgYEQQuDJkyd48uQJ3n77bQAoMJQKQP69L+7vOelPwZm8RDpmamqKtm3bYv/+/UhISFD4QMsvjlStI+Tn5wdnZ2eN3tPc3BxBQUFYunQpoqOjC43L//DJP6m/7NVtGzZsgLm5OXbu3KnwgaPJmjLr1q1DlSpVsHHjRkgkEvn24k58ftnLx8vn5OSEhISEAtvj4+MBQOPvq6Z8fX2xYcMGCCFw8eJFhISEYM6cOZDJZJgyZYrKfdetW4fWrVtj+fLlCtvz52MVR/7Pu7Dviba/H5aWlmjcuHGx9hVCKNxoUa9ePY1iXy7IXo4Dir6Bw9nZWWXhNWzYMBw4cAB//fUX4uPjkZWVJZ/vV9Rxv/nmG3zzzTeIi4vDjh07MGXKFDx48AB79+5FdHQ0Lly4gJCQEAwePFi+X/48Sk1t374d6enp2LZtGzw9PeXbIyMji3U84EWh+tNPP2H79u1F/g4DL4omIQS2bNmCLVu2FGj/9ddfMW/ePIVCOX8+oa7/f9Lr45UqKhFTp05Fbm4uRo4cWey7bgqj7AMSgPxuLlV3FNaqVQvu7u5Yv369wh18t2/fxokTJxRiJRIJzMzMFE5+z58/x2+//VbguFKpVOlfmfmLmL5cACUmJqp1919xtGvXDocPH5YXUfnWrl0LKysr+Z2UuiaRSODn54elS5eiXLlyCh/Yqr5XUqlUYdvFixcLTMrOj1Hnr3p/f3/IZDKsW7dOYfvdu3dx+PBhtGvXTu0+6dLJkydx/fp1hZ9P06ZNUbFiRezfvx+5ubny7fHx8bhw4YJCbJ8+fQAAe/bsUTju7t27AaDIn3vt2rWRlJSElJQUpe29evWCk5MTfvnlF6xZswY1a9ZEy5YtNepj5cqVMXbsWHTo0EH++5D//+LVn/vKlSs1OnY+ZccTQmDVqlXFOh4A9OzZE76+vggODi70D7Z9+/bh2bNnyM3Nxa+//opq1aohNDS0wGvSpElISEgo8HO6desWAKic9E+GgVeqqES0aNECP/zwAz7++GM0bNgQH330EerWrQsTExMkJCRg69atAAA7OzuNj92pUydUrFgR3bt3R+3atZGXl4fIyEh89dVXsLGxwfjx4wvd18TEBHPnzsWHH36It956C8OHD8eTJ08wa9asAsN/gYGB+PrrrzFgwAB89NFHSEpKwpIlSwp8AAD/XaHZuHEjqlatCktLS/j6+sqXPRg9ejTefvtt3LlzB3PnzoW7u3uBO8+0ISgoCDt37kSbNm0wc+ZMODo64vfff8euXbvw5ZdfFjkM9Tp27tyJH3/8Eb169ULVqlUhhMC2bdvw5MkTdOjQQR7n6+uLI0eO4O+//4a7uztsbW1Rq1YtdOvWDXPnzkVQUBACAgJw9epVzJkzB1WqVEFOTo58f1tbW3h6euKvv/5Cu3bt4OjoCGdnZ6Ura5crVw5ffPEFpk2bhvfffx/9+/dHUlISZs+eDUtLSwQFBens+1EYPz8/DBw4EHXq1IGlpSVOnz6NxYsXw83NDZMnT5bHmZiYYOnSpXjnnXfQs2dPjBo1Cunp6Zg7dy4sLCwwdepUeWzHjh3RvXt3zJkzB3l5eWjWrBnOnDmD2bNno1u3bkUWQK1bt4YQAqdOnZIPeb9MKpXivffew7JlyyCEwMKFC4vsZ0pKCtq0aYMBAwagdu3asLW1RUREBPbu3YvevXsDeFHMVatWDVOmTIEQAo6Ojvj7779x4MABdb+dCjp06AALCwv0798fkydPRkZGBpYvX47k5OQCsUeOHEGbNm0QFBQkX6pEGVNTU/z555/o2LEj/P39MWrUKLRp0wbW1ta4ffs2tmzZgr///hvJycnyP2gWLVokXyrkZT4+Pvj+++/x888/o1u3bvLtJ0+ehJOTE3x9fYvVb9KjkpnKRfRCZGSk+OCDD0SVKlWEVCoVlpaWonr16uL9998Xhw4dUohVd1HEjRs3igEDBogaNWoIGxsbYW5uLipXriwGDRokLl++rFZeq1evFjVq1BAWFhaiZs2a4pdfflE66fWXX34RtWrVElKpVFStWlUEBweLn3/+ucCE2djYWNGxY0dha2srACgcZ+HChcLLy0tIpVJRp04dsWrVKnlfi6JqonpgYKDSfaKiokT37t2Fvb29sLCwEH5+fgqTh4X4b6L65s2bFbbnTwZ+deV2dX42V65cEf379xfVqlUTMplM2NvbiyZNmoiQkBCFuMjISNGiRQthZWUlAMgn+GdmZopPP/1UVKhQQVhaWoqGDRuK7du3K/25HDx4UGFCe/7E98IWiFy9erWoV6+esLCwEPb29qJnz54FJtIPHjxYWFtbF+iXuj8rZYt/KtOvXz9RvXp1YW1tLb/jdOTIkSI+Pl5p/Pbt28Ubb7whLC0thb29vejRo4fSmwCePXsmPv/8c1GpUiVhZmYmKleuLKZOnarWBOvc3Fzh5eUlRo8eXWhM/mRqU1NTpbm+OlE9IyNDjBw5UtSrV0/Y2dkJmUwmatWqJYKCghQWyrx8+bLo0KGDsLW1FQ4ODqJv374iLi6uwM0I6k5U//vvv4Wfn5+wtLQUFSpUEJ999pnYs2ePACBCQ0MV4vDSHXpFefLkiZg7d65o2LChwnln4MCB4vjx40KIF3fJWlhYqFxVvl+/fsLMzEx+o0teXp7w9PQUH3/8sVp5UMmSCPHSGAcREZESX331FebPn4979+5BJpOVdDo6N3nyZKxfvx7Xr18vdJK+Phw6dAgdO3bEpUuXULt27RLLg9TDOVVERFSkMWPGwN7eXn6nXGkXGhqKL774okQLKgCYN28ehg4dyoLKSHBOFRERFcnS0hK//fYbzp8/X9Kp6EVERERJp4Dk5GQEBARg9OjRJZ0KqYnDf0RERERawOE/IiIiIi1gUUVERESkBSyqiIiIiLSAE9WLIS8vD/Hx8bC1tVX6KBAiIiIyPEIIpKWlwcPDo8jHMxUHi6piiI+PR6VKlUo6DSIiIiqGO3fuoGLFilo/LouqYsh/8vqdO3eK9RgVIiIi0r/U1FRUqlRJ/jmubSyqiiF/yM/Ozo5FFRERkZHR1dQdTlQnIiIi0gIWVURERERawKKKiIiISAtYVBERERFpAYsqIiIiIi1gUUVERESkBSyqiIiIiLSARRURERGRFrCoIiIiItICFlVEREREWlBmi6off/wRVapUgaWlJRo1aoRjx46VdEpERERkxMpkUbVx40ZMmDAB06dPx/nz5/Hmm2+iS5cuiIuLK+nUiIiIyEhJhBCipJPQt6ZNm6Jhw4ZYvny5fFudOnXQq1cvBAcHF7l/amoq7O3tkZKSotUHKiekPEd2joBHOUuYmZbJepeIiEhndPX5na/MfXJnZWXh7Nmz6Nixo8L2jh074sSJE0r3yczMRGpqqsJLF1ouCkWrxaGIf5KBS/EpyM0rc/UuERGR0SpzRdWjR4+Qm5sLV1dXhe2urq5ITExUuk9wcDDs7e3lr0qVKukkt/wiqtXiUAR+F4alB67p5H2IiIhI+8pcUZVPIpEofC2EKLAt39SpU5GSkiJ/3blzRx8p4vvQG6g6dRfm7ryM9MwcZGTn6uV9iYiISHNmJZ2Avjk7O8PU1LTAVakHDx4UuHqVTyqVQiqV6iO9AvIE8HNYDH4OiwEAfD+gAbrV8yiRXIiIiKhwZe5KlYWFBRo1aoQDBw4obD9w4ACaN29eQlmpb+wf50s6BSIiIlKizBVVADBx4kSsXr0av/zyC/7991988skniIuLw8iRI0s0r+1jWqgV90PoDR1nQkRERJoqc8N/APDuu+8iKSkJc+bMQUJCAnx8fLB79254enqWaF71K5XDvgmtcOvhU4z6/VyhcYv3XYWrnSXeblRRj9kRERGRKmVynarXpet1LvIlp2eh/ddHkZSepbTdw94SBycFwMqiTNbGREREGtH15zeLqmLQV1GVL/ZROhysLOA3Z3+BtooOMkxoXxPtarvAwdpC57kQEREZKy7+SfBytoa9lTmOTW5ToO1u8nN8uvkCGsw9gEvxKSWQHREREQEsqoxKJUcrTOxQs9D2wO/CMGrdWeRxJXYiIiK9Y1FlZMa1q4GfBjUqtH1PdCKqTtuNVf/c0mNWRERExDlVxaDvOVXKRN9LQbdlYWrFhk9tC3d7mY4zIiIiMmycU0VK2Vmaqx3rH3xYh5kQERERUEbXqSoNKjtZYem7fihvY4m/Iu9h89m7KuMfp2fhfmoG7GXm8CjHq1ZERETaxuG/YjCE4b9XeU3ZpXZs7MJAHWZCRERkmDj8R2rZ+XFLfNHNW61Yrym78G9Cqo4zIiIiKltYVJUSPhXsMaxlFZyc2g5veDnA291O5bMEu3x7DJk5uXrMkIiIqHTjnKpSxs3eEptHNlcrttaMvbg8p5P8MTe5eQJZOXmQWZjqMkUiIqJSiVeqSrn+TSqpbPeeuQ9Tt11E6JUHqDZtN+rM3IvUjGw9ZUdERFR6sKgq5b7o5o3g3r4qY9afvoMPQiLkX/8QekPXaREREZU6vPuvGAzx7j91/PTPTSSmZOKX4zFFxvIOQSIiKm10/fnNoqoYjLWoetnvp25j+p/RKmNYWBERUWnCJRVIJ/q9UbnImI/WnkFCynMIIZDGeVZEREQqsagqo0xNJEVeidp/+T78gw9j2p/R8J21H1F3U/A0MwfpmTl6ypKIiMh4sKgiuavzOivdvv50HADg080X4BO0D/Vm70duHkeNiYiIXsaiiuSkZqbYNMK/0Par99MAvFjPqtq03dh05g76rjiBJ8+y9JUiERGRwWJRVcb9NqwJKpST4bdhTQAATao44sinrdXad/KWi4iITcbETRfA+x2IiKis491/xVAa7v4rStLTTDSad1CjfaJnd4KNlIv0ExGRYeLdf1QinGykeKdxRY32+enoTR1lQ0REZPhYVFGhZnavi8aeDgAAWzWuQH13mCuxExFR2cWxGiqUjdQMW0Y1x/OsXMgsTPHjkRvYERmPK4lphe5zPzUDyc+yMCzkDCa0r4G+jVU/e5CIiKi04JyqYigLc6pUuZKYij1Rifj20PUiY7kqOxERGQpdf37zShVprLabHWq72aGDtysszEzQcek/hcbuupiArw5cxeK366GRp6MesyQiItIvzqmiYvOpYI+arrYqY8b8cQ63HqbjnZUn9ZQVERFRyWBRRa9tTJtqRcbk5gmEHI/RQzZEREQlg3OqiqGsz6lSJjUjG/Vm7Vc7/ufBjdGujqsOMyIiIlLEdarIKNhZmsPOUv0pesN+PYMHaRk6zIiIiEi/WFSR1pya1h52lmYY3boaZnX3LjK+yfxDfLwNERGVGhz+KwYO/6mn49KjuHb/aZFxtxZ0hYmJRA8ZERFRWcbhPzJa+z8JwI6xLYqM67/qJK9YERGR0eOVqmLglSrNpGZkw1ZqhjwBfHPwGpYV8TibFQMbYsXRW0hKz8ThSa1hbsran4iIXp+uP79ZVBUDi6rXN3D1KYTdeFRkXFdfN/z4XiM9ZERERKUdh/+oVFr3YVM0r+ZUZNzuqEQ0W3AIoVce6CErIiKi4mNRRSXmj+HN1IpLTM3AByER2B2VgLw8XlglIiLDxKKKStTxKW3Vjh39+zlsPHNHh9kQEREVH4sqKlEe9pbyfwf6uhcZ//eFeF2mQ0REVGwsqqhESSQS/P5hU6wc1Ag/vNcQ1+Z1QW03W7zXtLLS+BM3k7D8yE09Z0lERFQ03v1XDLz7Tz9+CYvBnJ2XlbaFT20Ld3uZnjMiIiJjxiUVDBCLKv15lpWDoL8uYfPZu0rbF79dD30bV9JzVkREZIy4pAKVaVYWZljUp16h7Z9tuYhL8Sl6zIiIiEg5FlVk8ExMJJjXy6fQ9sDvwnDkKtexIiKiksWiioxCYRPX8w1ZEwGvKbtw9vZjPWVERESkiHOqioFzqkpO+M0k9F91Uq3Yi7M6ws7SXMcZERGRseCcKqKX+FdzQuzCQByc2KrI2Hqz9iM7N08PWREREbGoIiNV3cUWMcFdi4wb9PMpPWRDRETEooqMmEQiKTLm5K3H8JqyCz2/D8Ox6w/1kBUREZVVLKrIqIVPbYsfBjTEzQVdYW5aeJF14W4KBv18Gt8evK7H7IiIqCzhRPVi4ER1w/ToaSaOXH0IJxsLfLAmQmXs8SltUaEcV2QnIipLOFGdSE3ONlK83agi2tRywZK+fipjWyw8jPdWn8TzrFw9ZUdERKUdiyoqld5uVBGxCwNVxhy/kYQ6M/ciIeW5nrIiIqLSjEUVlWqxCwPRw89DZYx/8GGEXuGK7ERE9HpYVFGp913/Brgws6PKmA9CIsDphURE9DpYVFGZYG9ljsiZHfC+v2ehMV/uu6rHjIiIqLTh3X/FwLv/jN/DtEy8Mf9gge39m1TGF93qwMrCrASyIiIiXeLdf0Q6UN5WqnRF9vWn4+A9cx9+DospgayIiMiYsaiiMksikSB6didUdbYu0DZ35+USyIiIiIwZiyoq02ykZjj8aWulbT/9cxMX7jzB/F2X8SwrR7+JERGR0eHEEaJCLNh9Rf7v+6mZ+K5/gxLMhoiIDB2vVBEB2DLSX2X7jgvx2HcpEUII5OXx3g4iIiqId/8VA+/+K52WH7mJRXuvFB34f0Oae2FGYB2YmfJvEyIiY6Drz28O/xH930etqqJJFUc8SM3AqN/PFRkfciIWqRnZ+Pqd+rpPjoiIDB6vVBUDr1SVfkIIpDzPRv05B4qMvT6/C8x5tYqIyOBxnSqiEiCRSFDOygKta5UvMrbG9D3YcDpOD1kREZEhY1FFpMKXfeqhfqVy6FzXTWXclG1R8A8+hLSMbD1lRkREhobDf8XA4b+y6XZSOr746xJGBVRDeVsLtP/6H6Vx9jJzHJjYCr+F30bvhhVRRcniokREpH+6/vxmUVUMLKoIALym7FIrbusof9xOeobeDSvqOCMiIlKFc6o04OXlBYlEovCaMmWKQkxcXBy6d+8Oa2trODs7Y9y4ccjKyiqhjKks6LM8HBM3XcDfF+K5xhURUSlW6pZUmDNnDoYPHy7/2sbGRv7v3NxcBAYGonz58ggLC0NSUhIGDx4MIQSWLVtWEumSEds6yh9XE5/i+I1H2BWVUGT8x+vP4/CVB1j6bn3dJ0dERHpX6ooqW1tbuLkpn1S8f/9+XL58GXfu3IGHhwcA4KuvvsKQIUMwf/58DuWRRhp5OqKRpyMGNK2MvlcfwMlaiu7fh6nc58/z91hUERGVUqVq+A8AFi1aBCcnJ9SvXx/z589XGNoLDw+Hj4+PvKACgE6dOiEzMxNnz54t9JiZmZlITU1VeBG9rHUtF/hWtC/pNIiIqASVqqJq/Pjx2LBhA0JDQzF27Fh88803GD16tLw9MTERrq6uCvs4ODjAwsICiYmJhR43ODgY9vb28lelSpV01gcybsG9fYuMOXD5vh4yISIifTP4u/9mzZqF2bNnq4yJiIhA48aNC2zfunUr3n77bTx69AhOTk746KOPcPv2bezbt08hzsLCAmvXrkW/fv2UHj8zMxOZmZnyr1NTU1GpUiXe/UdKJadnYe6uy6hW3gYP0zIRciK2QIy3ux2eZ+diy0h/ONlI9Z8kEVEZVOaf/Td27NhCi518Xl5eSrc3a9YMAHDjxg04OTnBzc0Np06dUohJTk5GdnZ2gStYL5NKpZBK+cFH6nGwtlB4HmBQd2/svJiAj9efl2+7nPBiCHnF0ZuYHuit7xSJiEgHDL6ocnZ2hrOzc7H2PX/+xYeYu7s7AMDf3x/z589HQkKCfNv+/fshlUrRqFEj7SRM9AqJRILufh4KRVW+VcdiMK1rHUgkkhLIjIiItKnUzKkKDw/H0qVLERkZiZiYGGzatAkjRoxAjx49ULlyZQBAx44d4e3tjUGDBuH8+fM4dOgQPv30UwwfPpzDeFRiqkzdjQdpGTh2/SEMfDSeiIhUMPgrVeqSSqXYuHEjZs+ejczMTHh6emL48OGYPHmyPMbU1BS7du3C6NGj0aJFC8hkMgwYMABLliwpwcyJgCbzDwEAvuvfAD38PIqIJiIiQ2TwE9UNER9TQ8Wx7NB1fHXgmlqxW0c1RyNPBx1nRERUtvAxNUSlxNi21XF6WjvELgwsMrbP8hN4nJ6FLt8ew7Q/o/SQHRERvS5eqSoGXqmi1/UsKwfzdv2LPVEJSH6WXWR8z/oe+LZfAz1kRkRUevFKFVEpZGVhhgVv+eL8zI5YqMaCoX9FxuN5Vq4eMiMiouJiUUVUwvo1qYyr8zoXGVdn5l5cv5+mh4yIiKg4WFQRGQCpmalacR2W/oNHTzOLDiQiIr3jnKpi4Jwq0oVHTzORkZ2Lig5WyMrJQ6svQ5GYmqE09pchjbHi6C10r+eOljXKo0I5GSzM+DcSEZEquv78ZlFVDCyqSB8yc3JhKpHgWXYu6s3arzL2zRrO+G1YUz1lRkRknDhRnaiMkpqZwszUBHaW5pjXy0dl7LHrj/SUFRERFYZFFZERGNjMs8iYuTsv41xcMrJz8/SQERERvUqj4b+rV69i/fr1OHbsGGJjY/Hs2TOUL18eDRo0QKdOndCnTx9IpVJd5msQOPxHJeF5Vi4mborEnuhElXHvNa2M+W8VvUwDEVFZYxBzqs6fP4/Jkyfj2LFjaN68OZo0aYIKFSpAJpPh8ePHiI6OxrFjx5CamorJkydjwoQJpbq4YlFFJenotYcY/MtplTG3FnSFiYlETxkRERkHgyiqPD098dlnn2HAgAFwdHQsNC48PBxLly5F/fr1MW3aNK0makhYVFFJ+yvyHsZviCy03a9SOczv5QOfCvb6S4qIyMAZRFGVlZUFCwsLtQ+qabyxYVFFhuBhWibaLjmCtMycQmOuzO0MS3P11sAiIirtDOLuP00LpNJcUBEZivK2UkTN7oQFKuZP1f5iL+4mP9NjVkREZVex1qk6ffo0jhw5ggcPHiAvT/FOo6+//lpryRkqXqkiQ/PkWRbqzzlQaPutBV0BgPOsiKhM0/Xnt5mmOyxYsAAzZsxArVq14OrqConkv5P0y/8mIv0pZ2WB2IWB8JqyS2l71Wm7AQDNqjqiT8OK6Nu4kj7TIyIqEzS+UuXq6opFixZhyJAhOkrJ8PFKFRmqxJQM7LhwDwt2X1EZd2N+F5iZcpk6IipbDGJOlcIOJiZo0aKF1hMhotfnZm+J4W9WxaAiFgutPn0Pou+l6CkrIqKyQeOi6pNPPsEPP/ygi1yISAskEgnm9vJB7MJAlXHdloVhwKqTiLqbwlXYiYi0QOPhv7y8PAQGBuLatWvw9vaGubm5Qvu2bdu0mqAh4vAfGYv3fzmNf649LDKuRXUn/P5hMz1kRERUcgxuovrHH3+M0NBQtGnTBk5OTpycTmTA1g5tAgCYv+syVh2LKTTu+I0kpGfmwFqq8SmBiIj+T+MrVba2ttiwYQMCA1UPLZRmvFJFxuZ5Vi5+PHIDzas5Y0NEHP6KjFcaV9SQIRGRMTO4ieqOjo6oVq2a1hMhIt2RWZhiUsda8K/mhG/7NSg0zmvKLqz65xZy8wSKsYQdEVGZpnFRNWvWLAQFBeHZM67STGSshraoUmjb/N3/otq03Wg49wBO3Hykx6yIiIybxsN/DRo0wM2bNyGEgJeXV4GJ6ufOndNqgoaIw39UWmTm5CLor0vYEHGn0JjYhYF4lpUDKwvOtyIi42ZwE9V79eql9SSIqGRIzUwR3NtXZVH18irtv3/YFC2qO+sjNSIio1OsZ/+VdbxSRaXNncfP8OaXoWrF7hrXEnU97HWcERGR9un68/u1iqqnT58WeKByWSgyWFRRaXTn8TMcvfYQM7ZHqxUfE9yVS6oQkVExuLv/YmJiEBgYCGtra9jb28PBwQEODg4oV64cHBwctJ4gEelHJUcrDGzmiT4NK6oVX2XqbnhN2YVHTzN1nBkRkXHQeE7Ve++9BwD45Zdf4Orqyr9UiUqZr97xw5s1nDFhY6Ra8Y3nHcQPAxoisJ67bhMjIjJwGg//2djY4OzZs6hVq5aucjJ4HP6jsuDlCerq2D6mBbadu4tJHWrB3sq86B2IiPTM4O7+e+ONN3Dnzp0yXVQRlQWnprVD2PVHaF/HFTaWZjCRAGPXn8euiwlK43v9cBwAkJ6Zi6/e8dNnqkREBkHjK1U3b97EyJEjMXDgQPj4+BRYp6pevXpaTdAQ8UoVlVUX7jxBz/8XT6rwcTdEZIgM7u6/kydPYsCAAYiNjf3vIBIJhBCQSCTIzc3Vdo4Gh0UVlWXxT57D2UaKQT+fwqmYx0pjfCvYw9nGAkHd68LL2VrPGRIRKWdwRZW3tzfq1KmDyZMnK52o7unpqdUEDRGLKiJACIHG8w4iKT1LZdyucS3h7W7Hm1qIqMQZXFFlbW2NCxcuoHr16lpPxliwqCL6z89hMZi787LKmB/fa4iuvrw7kIhKlsGtU9W2bVtcuHBB64kQkXEa1rIKto9poTJm9O/nsOlM4Y/CISIqDTS++6979+745JNPEBUVBV9f3wIT1Xv06KG15IjIOPh42KFeRXtcvJtSaMzkLRfRumZ5uNhZ6jEzIiL90Xj4z8Sk8ItbnKhOVHYJISAEMGFjJGq52WLxvqtK434b1gR2lubwq1ROvwkSUZlncHOqiEUVkToSUp7DP/hwoe3X5nWBhZnGMxCIiIrN4OZUERGpw91ehtPT2xXaPnD1KaXbz8Q+xv5LibpKi4hIZ9QqqjZs2KD2Ae/cuYPjx4teHJCISj8XW0t42CufQ3U69jHafXUEA1efwupjt/A0Mwdf77+Kt1eE46PfziIu6ZmesyUiej1qFVXLly9H7dq1sWjRIvz7778F2lNSUrB7924MGDAAjRo1wuPHyhcEJKKyZ9voFpjUoabStpsP0xF24xHm7foXPkH78N3hG/K2+JTn+kqRiEgr1J5TtXPnTixbtgwHDx6EtbU1XF1dYWlpieTkZCQmJqJ8+fL44IMPMGHCBLi4uOg67xLFOVVEmrvx4Cnaf31Uo31uLugKUxMuGkpE2mFwE9WTkpIQFhaG2NhYPH/+HM7OzmjQoAEaNGig8s7A0oRFFdHr8ZqyS6247/o3QA8/Dx1nQ0RlhcEVVcSiiuh1Xbufho5L/1Erdte4lqjrYa/jjIioLODdf0RU6tR0tcWOsapXYc8X+F0Y+LcfERkDXqkqBl6pItKOjOxcPM/KhYO1BfZfSsRHv51VGtervgf6NamMplUckZMnYG7KvweJSHMc/jNALKqIdGP96Ticu52MjnXdMHztmULjto5qjkaeDnrMjIhKAw7/EVGZ0b9JZSzu64fabrYq4/osP4EHaRl6yoqISD0aF1Vz5szBs2cFF+V7/vw55syZo5WkiKhss5YW/az3zWfucq4VERkUjYf/TE1NkZCQUGAtqqSkJLi4uPCBykSkFd8duo614bfx6Gmmyrg5PevC1tIMeXlAn0YV9ZQdERkjg5tTZWJigvv376N8+fIK2w8fPox3330XDx8+1GqChohFFZH+/PTPTSzYfUXt+Ovzu3AiOxEpZTBzqhwcHODo6AiJRIKaNWvC0dFR/rK3t0eHDh3wzjvvaD1BIirbur+0+OfWUc2LjK8xfQ8yskv/FXMiMjxFT1z4v2+++QZCCAwdOhSzZ8+Gvf1/i/FZWFjAy8sL/v7+OkmSiMoud3sZRreuBisLUzTydMDC3r6Ysi1K5T4j151FyAdN9JQhEdELGg3/5eTkYN26dWjfvj0qViy7cxc4/EdUsmpM343sXNWnrtXvN0Z7b1c9ZURExsBghv8AwMzMDKNHjy4Tk9GJyHBtHPHiqvjYNtVxaFIA+jepVCDmw7Vn4DVlF5LTsxS2b4q4g3+ulf65n0SkfxpPVG/Tpg3Gjx+PXr166Sglw8crVUQlLyM7F5bmpvKvz95ORp/lJwqN79+kMjZGxCHv/2e82IWBuk6RiAyMrj+/1Z5TlW/06NGYNGkS7t69i0aNGsHa2lqhvV69elpLjoioMC8XVADQyNMB83r5YMb2aKXx60/HKXydmJIBN3tLneVHRGVPsZZUKHAQiQRCCEgkkjIxNMgrVUSG62lmDnyC9qkV+8OAhgis567jjIjIUBjclaqYmBitJ0FEpC02UjOcnNoObb86gmdZqv/IG/PHOXT17QqJRKKn7IioNOMDlYuBV6qIDF/Ks2z4zdmvVuys7t4Y0qKKjjMiopJmcCuq57t8+TLi4uKQlaV4Z02PHj20kpghY1FFZDzqzdqH1IwcAMBfY1qg5w/HC43lauxEpZvBFVW3bt3CW2+9haioKPlcKgDyy+ecU0VEhuR0zGMMX3sGs3p4460GFfHoaSYazztYaPzPgxvjzRrlYWHG4oqotDGodaoAYPz48ahSpQru378PKysrXLp0Cf/88w8aN26MI0eOaD1BIqLX0aSKIyJndsBbDV4sWOxsI1UZP+zXM6g5Yw8u3HmC8JtJyM3jDAkiUo/GRVV4eDjmzJmD8uXLw8TEBCYmJmjZsiWCg4Mxbtw4XeRIRPRaXp2IXse96L9Qe/5wHP1XncSKozd1lRYRlTIaF1W5ubmwsbEBADg7OyM+Ph4A4OnpiatXr2o3OyIiHfj9w6b4fkADnJnRvsjYxft4XiMi9WhcVPn4+ODixYsAgKZNm+LLL7/E8ePHMWfOHFStWlXrCRIRaZujtQW61fOAs40UKwY2KjJ+0qYLesiKiIydxutUzZgxA+np6QCAefPmoVu3bnjzzTfh5OSEDRs2aD1BIiJdal/HpciYrefu4p3GFeFkYwE7mTlcbLkSOxEVpJV1qh4/fgwHBwedLqA3f/587Nq1C5GRkbCwsMCTJ08KxMTFxWHMmDE4fPgwZDIZBgwYgCVLlsDCwkIeExUVhbFjx+L06dNwdHTEiBEj8MUXX2iUO+/+IypdHqdnQQgBM1MT+M0uem2rNUPeQJvaRRdjRGRYDO7uv6FDhyItLU1hm6OjI549e4ahQ4dqLbFXZWVloW/fvhg1apTS9tzcXAQGBiI9PR1hYWHYsGEDtm7dikmTJsljUlNT0aFDB3h4eCAiIgLLli3DkiVL8PXXX+ssbyIyfI7WFnCykcJeZq5W/AchEVh97JaOsyIiY6PxlSpTU1MkJCTAxUXxr7RHjx7Bzc0NOTk5Wk3wVSEhIZgwYUKBK1V79uxBt27dcOfOHXh4eAAANmzYgCFDhuDBgwews7PD8uXLMXXqVNy/fx9S6YvbqhcuXIhly5bh7t27al+t4pUqotIr6WkmgvdcQa/6FTDw51MqY60tTJGelYsDn7RCDVdbPWVIRMVlMFeqUlNTkZKSAiEE0tLSkJqaKn8lJydj9+7dBQotfQoPD4ePj4+8oAKATp06ITMzE2fPnpXHBAQEyAuq/Jj4+HjExsbqO2UiMkBONlIs6euHljWccWFmR7zTuGKhsen/f7Zg4Hdh+kqPiAyY2hPVy5UrB4lEAolEgpo1axZol0gkmD17tlaT00RiYiJcXV0Vtjk4OMDCwgKJiYnyGC8vL4WY/H0SExNRpYryZ39lZmYiMzNT/nVqaqoWMyciQ2VvZY4v3/bDgrd8cfjKA3z021mlcVm5eZi14xIqO1rhvWaVITUz1XOmRGQI1C6qQkNDIYRA27ZtsXXrVjg6OsrbLCws4OnpqXCVSB2zZs0qshCLiIhA48aN1TqesuE7IYTC9ldjXn3MjjLBwcElWjASUckyMzVBx7puWDesaaFDgiEnYgEAj55mYnLn2nrMjogMhdpFVUBAAAAgJiYGlStX1sqdfmPHjkW/fv1Uxrx6Zakwbm5uOHVK8WSXnJyM7Oxs+dUoNzc3+VWrfA8ePACAAle5XjZ16lRMnDhR/nVqaioqVaqkVl5EVHq0rOGM2IWB8Jqyq9CYH4/cxI9HbmJIcy/M6lFXj9kRUUnT+O6/f//9F8eP//eU9x9++AH169fHgAEDkJycrNGxnJ2dUbt2bZUvS0v11oPx9/dHdHQ0EhIS5Nv2798PqVSKRo0ayWP++ecfZGVlKcR4eHioLN6kUins7OwUXkRUdl2f3wUdvAv/Qwx4ceXKa8oubD9/T2m7EILPFSQqZTQuqj777DP5nKKoqChMnDgRXbt2xa1btxSu5mhbXFwcIiMjERcXh9zcXERGRiIyMhJPnz4FAHTs2BHe3t4YNGgQzp8/j0OHDuHTTz/F8OHD5UXQgAEDIJVKMWTIEERHR+PPP//EggULMHHiRJ2usUVEpYu5qQl+fK8hTkxpW2TshI2ReP7/Ce35nmXloMrU3ag2bTei76XoKk0i0jONl1SwsbFBdHQ0vLy8MGvWLERHR2PLli04d+4cunbtWmB4TVuGDBmCX3/9tcD20NBQtG7dGsCLwmv06NEFFv98+W6/qKgojBkzBqdPn4aDgwNGjhyJmTNncvFPIiqW9Mwc1A3aV2TchaCO8nWwRq07iz3R/50rv37HD70bFn6XIRFph64/vzUuqhwdHREWFgZvb2+0bNkS77//Pj766CPExsbC29sbz54903qShoZFFRG9TNUcq5ddmdsZlxNS0fvHE0rbw6e2hbu9TJupEdFLdP35rfGz/1q2bImJEyeiRYsWOH36NDZu3AgAuHbtGipW5F9aRFR29azvgZhH6bh4V/mQXu0v9qK6i02h+/sHHwYA7B73Jrw9+AcbkbHReE7V999/DzMzM2zZsgXLly9HhQoVALxY0bxz585aT5CIyNDtnfAmlvVvgG/7NcCOsS1xbV4XrB3aRGnsjQdP4WxjgT3j3yz0eF2/OwYtPJaViPRMKw9ULms4/EdE6ridlI6AxUcKbP/xvYZoVtUJDeceKHTf9nVcsXqwemv0EZF6DOYxNUREpBlPJ+sC21pUd0JXX3dYS1Wvun7w3/uYsT0KD1IzdJUeEWkZr1QVA69UEZG6zt5ORp/l/01MPzWtHVztXqy/dzrmMX47eRtO1hZ4nJ6FHRfilR7jzRrOWD6wEWykGk+DJaKXGNzdf8Siiog0d+1+GpysLeBkI1XanvIsG35z9qs8RkxwV66pR/QaOPxHRFQK1HS1LbSgAl48vPmP4U3hW8G+0JgqU3fDa8ouHL32EDm5ecjMyS00loj0r9hXqm7cuIGbN2+iVatWkMlkBR5cXJrxShUR6YoQAsdvJBX64OZXHZzYCtVdbHWcFVHpYHBXqpKSktC+fXvUrFkTXbt2lT9r78MPP8SkSZO0niARUVkikUjQsoYz5r/lo1Z8+6//QVpGto6zIiJ1aFxUffLJJzAzM0NcXBysrKzk2999913s3btXq8kREZVV7zX1RExwVwxq5llkbJdvj+khIyIqisZF1f79+7Fo0aICq6fXqFEDt2/f1lpiRERlnUQiwdxePhjfrobKuLvJz+E1ZRf+LuTuQSLSD42LqvT0dIUrVPkePXqk8OBiIiLSjk861FQr7uP15/Hn+bs6zoaICqNxUdWqVSusXbtW/rVEIkFeXh4WL16MNm3aaDU5IiJ6YfX7jVHL1RYLe/uqjPtk4wXU/mIPPl5/nncHEumZxnf/Xb58Ga1bt0ajRo1w+PBh9OjRA5cuXcLjx49x/PhxVKtWTVe5Ggze/UdEJUUIgUV7r8LTyQoBNcuj+cLDKuNvzO+CvZcS8YaXo3zRUaKyyiAX/0xMTMTy5ctx9uxZ5OXloWHDhhgzZgzc3d21nqAhYlFFRIbi91O3Mf3P6CLjyttKETG9vR4yIjJcBllUlXUsqojIkNxOSoe9zBz15xT+gGaAK7ITGdw6VWvWrMHmzZsLbN+8eTN+/fVXrSRFRETq83SyRjkrC/Tw81AZN2rdOT1lRFQ2aVxULVy4EM7OzgW2u7i4YMGCBVpJioiINNfFx01l+95LiTh67aGesiEqezQuqm7fvo0qVaoU2O7p6Ym4uDitJEVERJrr4O2K7n4e+KCFF8a1ra40ZvAvp3E65rGeMyMqGzQuqlxcXHDx4sUC2y9cuAAnJyetJEVERJozMzXBsv4NENS9LiZ2rIUDn7RSGvfOynDEPEpHTm6enjMkKt00Lqr69euHcePGITQ0FLm5ucjNzcXhw4cxfvx49OvXTxc5EhFRMdRwtcXX7/gpbWuz5AiqT9+D9MwcPWdFVHppfPdfVlYWBg0ahM2bN8PMzAwAkJeXh/fffx8rVqyAhYWFThI1JLz7j4iMScrzbPjN3l9o+8mp7eBmzzWsqPQz2CUVrl27hgsXLkAmk8HX1xeenkU/9LO0YFFFRMao2YJDSEzNUNo2pLkXWtcqj9a1XPScFZH+GGxRVZaxqCIiY9X5m39wJTGt0PYLQR1hLzPXY0ZE+mNwRVVubi5CQkJw6NAhPHjwAHl5ihMdDx9W/ciE0oBFFREZq9w8geFrz+DwlQeFxowIqIo2tVyQkZ2La/fTMKR5FViYaTwFl8jg6Prz20zTHcaPH4+QkBAEBgbCx8eHq/MSERkRUxMJfhnyBg5cvo9vD11D9L3UAjErj97CyqO35F/LLMwwqFnZmeJBVFwaX6lydnbG2rVr0bVrV13lZPB4pYqISoOc3Dy0+/oobic9UxlXy9UW+wpZnoHImBjcY2osLCxQvbryReWIiMh4mJma4MinrYuMu3o/Ddn/X9OK03CJCqdxUTVp0iR8++23/I9FRFQKSCQSTOxQs8i4GtP3wGvKLlSZuhu5eTz/Eymj8fDfW2+9hdDQUDg6OqJu3bowN1e8S2Tbtm1aTdAQcfiPiEojrym71IqrUE6Gub3qom1tVx1nRKRdBjdRvVy5cnjrrbe0nggREZWsJl6OOB1b9HMB7z15jqEhZ7D6/caoX7kcnG2kesiOyPBxnapi4JUqIiqNHj3NxPpTcehR3wPdloXB3NQEbWq5YOu5uyr3+6qvH/o0qqinLImKz+DWqSIWVURU+gkh5EvmZGTnovYXe4vcJ7i3L/o3qazr1IiKzSCG/xo2bIhDhw7BwcEBDRo0ULk21blz57SWHBERlYyXz/OW5qbYOqo5+iw/oXKfqdui0KpmeVQoJ9N1ekQGSa2iqmfPnpBKX4yZ9+rVS5f5EBGRAWrk6YCY4K6oMnW3yri+y0/gxNR2esqKyLBw+K8YOPxHRGXZ94evY8n+a4W2B3X3xgctqugxIyL1GNzin3fu3MHdu/9NWjx9+jQmTJiAn376SauJERGRYRrbtobKIb7Zf1/Gg7QMPWZEZBg0LqoGDBiA0NBQAEBiYiLat2+P06dPY9q0aZgzZ47WEyQiIsOzdlgTeDlZFdreZP4hXIpP0WNGRCVP46IqOjoaTZo0AQBs2rQJvr6+OHHiBP744w+EhIRoOz8iIjJA1crb4MhnbRA9u1OhMYHfhSEjO1f+iBui0k7joio7O1s+af3gwYPo0aMHAKB27dpISEjQbnZERGTQbKRm2DehFcrbKl8AtPYXexH43TE9Z0VUMjQuqurWrYsVK1bg2LFjOHDgADp37gwAiI+Ph5OTk9YTJCIiw1bLzRYR09tj7dAmStuv3X8qf15gcnoW8vjsQCqlNC6qFi1ahJUrV6J169bo378//Pz8AAA7duyQDwsSEVHZ06pm+ULbqk3bjVO3ktBg7gF8vP68HrMi0p9iLamQm5uL1NRUODg4yLfFxsbCysoKLi4uWk3QEHFJBSIi5bp8ewz/JqQWGXdtXhdYmGn8dz3RazG4JRUAwNTUVKGgAgAvL68yUVAREVHh/viwKX4Y0BCL+viqjFt+5KaeMiLSH/6ZQEREWuNgbYHAeu54943KiF0YWGjcd4evQwgBrj9NpQmLKiIi0pmThTyyJjdPoMrU3agydbd8EjuRsWNRRUREOuNmb4mVgxqpjKk2bTf2RifqKSMi3dG4qFq7di0yMzMLbM/KysLatWu1khQREZUeneq6Ye+EN/FZp1qFxoxcdxYbTsdhxvYodP7mH2Rk5+oxQyLt0PjuP1NTUyQkJBSYlJ6UlAQXFxfk5pb+/wi8+4+IqHh2RyVg9O/nioz7+h0/9G5YUQ8ZUVlicHf/CSEgkUgKbL979y7s7e21khQREZVOXX3d8ffYlvjnszYq4yZuuoDnWaX/j3QqXczUDWzQoAEkEgkkEgnatWsHM7P/ds3NzUVMTIx8dXUiIqLC+FZ88Qd4vzcqYUPEnULj6szci1sLusLEpOAf8kSGSO2iqlevXgCAyMhIdOrUCTY2NvI2CwsLeHl5oU+fPlpPkIiISqcZ3bxR3cUGwXuuFHoHYNVpu+X/3vhRMzT2coQpiywyUBrPqfr111/Rr18/+UOVyyLOqSIi0q56s/YhNSOnyDgzEwmuzuvCwoqKxeDmVLVt2xYPHz6Uf3369GlMmDABP/30k1YTIyKisiNyZke14nLyBNafjtNxNkTFo3FRNWDAAISGhgIAEhMT0b59e5w+fRrTpk3DnDlztJ4gERGVfiYmEpyY0hYtqzvD3FT1VagZ26P1lBWRZtSeU5UvOjoaTZo0AQBs2rQJvr6+OH78OPbv34+RI0di5syZWk+SiIhKP49yMqz7sKn86+zcPFxNTEO3ZWEFYn8Lj8Ugfy/k5gkOBZLB0PhKVXZ2tnw+1cGDB9GjRw8AQO3atZGQkKDd7IiIqMwyNzWBTwXlS/V88dcleE3ZhWrTduOXsBg9Z0aknMZFVd26dbFixQocO3YMBw4ckC+jEB8fDycnJ60nSEREZdu8Xj4q2+fsvMw1rcggaFxULVq0CCtXrkTr1q3Rv39/+Pn5AQB27NghHxYkIiLSloHNPHFrQVeVMbN2XNJTNkSF03hJBeDFYp+pqalwcHCQb4uNjYWVlVWBx9eURlxSgYhI/6Zui1J559+/czpDZmGqx4zI2Oj687tYRVVZx6KKiKhk5OTmYeq2KGw+e1dpe+zCQD1nRMZE15/fGt/9BwBbtmzBpk2bEBcXh6ysLIW2c+eKflAmERFRcZiZmmBxXz8420rxW/htPM1UXDDUa8oudPFxw/KBjUooQyrLNJ5T9d133+GDDz6Ai4sLzp8/jyZNmsDJyQm3bt1Cly5ddJEjERGRgs8710b07E7YO+HNAm17ohPhE7Sv0EffEOmKxkXVjz/+iJ9++gnff/89LCwsMHnyZBw4cADjxo1DSkqKLnIkIiJSqrabHX4e3LjA9qeZOZi0KRIhx2Nw7PpDZOXkIY9FFumYxnOqrKys8O+//8LT0xMuLi44cOAA/Pz8cP36dTRr1gxJSUm6ytVgcE4VEZFhOXzlPoaGnFEZ4+1uh93jC17ZorLD4J795+bmJi+cPD09cfLkSQBATEwMOOediIhKQtvarmjk6aAy5nJCKrym7MLXB67pKSsqa4r1QOW///4bADBs2DB88skn6NChA95991289dZbWk+QiIhIHVtHNS9yPSsA+O7Qdcz++xKeZ+UiPTMHf5yKw8O0TD1kSKWdxsN/eXl5yMvLg5nZixsHN23ahLCwMFSvXh0jR46EhYWFThI1JBz+IyIyXKduJeHdn06qFftO44rYdOYuarjY4MDEAB1nRiWN61QZIBZVRESGLTElAw7W5pCamaLNkiOIeZRe5D7f9W+AHn4eesiOSorBzakCgGPHjmHgwIHw9/fHvXv3AAC//fYbwsIKPkmciIhI39zsLSE1e7G6euinrRE+tS0C67mr3Gfc+vMF1r0i0oTGRdXWrVvRqVMnyGQynD9/HpmZL8ah09LSsGDBAq0nmG/+/Plo3rw5rKysUK5cOaUxEomkwGvFihUKMVFRUQgICIBMJkOFChUwZ84cTrAnIirl3O1l+GFAQ1yd11llnE/QPj1lRKWRxkXVvHnzsGLFCqxatQrm5uby7c2bN9fpaupZWVno27cvRo0apTJuzZo1SEhIkL8GDx4sb0tNTUWHDh3g4eGBiIgILFu2DEuWLMHXX3+ts7yJiMhw5F+9UoXrWVFxafyYmqtXr6JVq1YFttvZ2eHJkyfayEmp2bNnAwBCQkJUxpUrVw5ubm5K237//XdkZGQgJCQEUqkUPj4+uHbtGr7++mtMnDgREolE22kTEZGBmdShJo7deITTMY+VtofdeIRWNcvrOSsqDTS+UuXu7o4bN24U2B4WFoaqVatqJanXMXbsWDg7O+ONN97AihUrkJeXJ28LDw9HQEAApFKpfFunTp0QHx+P2NjYQo+ZmZmJ1NRUhRcRERmnj9vVwKYR/ri5oCt2ftwSH7TwUmhP57wqKiaNi6oRI0Zg/PjxOHXqFCQSCeLj4/H777/j008/xejRo3WRo9rmzp2LzZs34+DBg+jXrx8mTZqkMM8rMTERrq6uCvvkf52YmFjocYODg2Fvby9/VapUSTcdICIivTE1kcCngj2CutfFlC615ds5aEHFpXFRNXnyZPTq1Qtt2rTB06dP0apVK3z44YcYMWIExo4dq9GxZs2apXRy+cuvM2dUP3bgZTNmzIC/vz/q16+PSZMmYc6cOVi8eLFCzKtDfPmT1FUN/U2dOhUpKSny1507dzToJRERGbohzb3k/7a1NC88kEgFjedUAS/uxJs+fTouX76MvLw8eHt7w8bGRuPjjB07Fv369VMZ4+XlVZwUAQDNmjVDamoq7t+/D1dXV7i5uRW4IvXgwQMAKHAF62VSqVRhyJCIiEoXS/P/JrCbmfBSFRVPsYoq4MWDlRs3LvhkcE04OzvD2dn5tY6hyvnz52FpaSlfgsHf3x/Tpk1DVlaWfOX3/fv3w8PD47WKNyIiMn61XG1x9X4acnj3HxVTsYsqfYuLi8Pjx48RFxeH3NxcREZGAgCqV68OGxsb/P3330hMTIS/vz9kMhlCQ0Mxffp0fPTRR/KrTAMGDMDs2bMxZMgQTJs2DdevX8eCBQswc+ZM3vlHRFTGmZm++BxgUUXFZTRF1cyZM/Hrr7/Kv27QoAEAIDQ0FK1bt4a5uTl+/PFHTJw4EXl5eahatSrmzJmDMWPGyPext7fHgQMHMGbMGDRu3BgODg6YOHEiJk6cqPf+EBGRYckf9svJzSsikkg5PvuvGPjsPyKi0qfP8hM4ezsZKwY2Qmcf5esdknEzyGf/ERERlTam/79Slfo8u4QzIWPFooqIiAiQr7A+eetFZGTnlnA2ZIxYVBEREb3i/V9Ol3QKZIRYVBEREQGo6/HfHJvTMY+Ry7sASUMsqoiIiABsHdVc4euZf0WXUCZkrFhUERERQXFVdQD4/VQc5u68XELZkDFiUUVERFSIn8Ni4DVlF/I4FEhqYFFFRET0f2dntFe6vf+qk3rOhIwRiyoiIqL/c7KRKt1+6v/LLRCpwqKKiIjoJWuGvKF0+7OsHD1nQsaGRRUREdFL2tR2wZFPW2NYyyoK271n7iuhjMhYsKgiIiJ6hZezNb7o5o0LQR0Vtp+LSy6hjMgYsKgiIiIqhL3MXOHr3j+eKKFMyBiwqCIiIlLh8861Fb72mrILXlN2YdOZOyWUERkqFlVEREQqDH+zitLtk7dchBBcv4r+w6KKiIhIBTNTE6wf3kxpW5Wpu3Ho3/tcHJQAsKgiIiIqkn81JwTWc1faNuzXM6g6bTc2RsTpOSsyNCyqiIiI1PDDgIboVkhhBQCfb43CrB2X8DST61mVVSyqiIiI1PRdvwYq20NOxMIniOtZlVUsqoiIiNRkYiJB7MJArBzUSGVcRnaunjIiQ8KiioiISEOd6rrhytzOhbbX/mIvMnNYWJU1EsH7QTWWmpoKe3t7pKSkwM7OrqTTISKiEiKEwN3k56joIMOivVex4uhNhfYzM9rDuZCHNJP+6frzm1eqiIiIikkikaCSoxUkEgmmdKldoL3xvIOIvpcCIQRaLDyM0b+fxcfrz+Ps7cclkC3pGq9UFQOvVBERkTLzd13GqmMxasXGLgzUcTb0Kl6pIiIiMhLTA71LOgUqQSyqiIiItGj/J63UiktOz9JxJqRvLKqIiIi0qKarLT5uW73IuFG/n9VDNqRPZiWdABERUWkzunV15AmBk7ce4+ztZKUxJ29xsnppw6KKiIhIy2QWpvis04u7AXNy85CYmoHvD9/Ahog7JZwZ6RKH/4iIiHTIzNQEFR2ssLBPvQJti/ZewbJD10sgK9IFFlVERER6Mq+Xj8LXy4/cxFcHrmHylgt4kJpRQlmRtrCoIiIi0pOBzTyVbt905i76/XRSz9mQtrGoIiIi0qNjk9so3X7rUbqeMyFtY1FFRESkR5UcrWBqIlHa1u+ncOTl8UEnxopFFRERkZ5Zmin/+D156zGqTtuNp5k5es6ItIFFFRERkZ519nFX2R7wZaieMiFtYlFFRESkZ3N61sWCt3xxelo7pe1J6VloPO8gou6mICsnT8/ZUXFJhBAcvNWQrp9yTUREZUv0vRR0WxZWaHu72i5YPrARLAoZNiT16Przmz8dIiKiEuZTwR4xwV2xblhTpe2HrjxAzRl7cOpWkp4zI02wqCIiIjIAEokELWs4o15F+0Jj3uVaVgaNRRUREZEB+WtMC5XtXlN2wWvKLj1lQ5pgUUVERGRAJBIJYhcG4n1/5auv54u+l6KnjEhdLKqIiIgM0JyePvi8c+1C27stC0MuFwo1KLz7rxh49x8REenLhTtPkJ6ZgwGrTxUac2VuZ1iam+oxK+Ok689vFlXFwKKKiIj0LTElA2P+OIezt5NVxgXULI/lAxvCysJMT5kZDxZVBohFFRERlZTElAw0Cz6kVuyOsS1Qr2I53SZkRLhOFREREcm52Vsi7PM2asX2+P64jrOhl7GoIiIiMjIVHawQuzAQ3/VvUGRs6JUHesiIABZVRERERquHnwc8naxUxnwQEoGlB67pKaOyjUUVERGREfusU60iY749dB2cQq17LKqIiIiMWLd6Hjj6WWts/KiZyrgqU3frKaOyi0UVERGRkfN0skbTqk5Y+q4fvN3tMLRFFaVxvkH7kJiSoefsyg4uqVAMXFKBiIgMWWpGNurN2l9oe+zCQD1mYzi4pAIRERFpxM7SHDHBXVHbzVZp+6p/buk5o7KBRRUREVEpJJFIsGvcm9igZK7V/N3/wmvKLmw+cwc9vw9D/JPnJZBh6cOiioiIqJQyNZGgWVUnXJvXRWn7Z1su4sLdFMzYHq3nzEonFlVERESlnIWZCW4t6Irl7zVU2n74ygOkZmTrOavSh0UVERFRGWBiIkEXX3eYmkiUtrf76igAIC0jGxnZufpMrdRgUUVERFSGzOvlo3T7w7RMeE3ZBd9Z+1H7i70srIqBRRUREVEZ0tXXXa24r/Zf1XEmpQ+LKiIiojLEXmaOyJkd0O+NSirjVh2L4dUqDXHxz2Lg4p9ERFSaeE3ZVWhbaVoolIt/EhERkU6pKpw6fH0Us3ZcwqOnmXrMyDjxSlUx8EoVERGVNjcfPsWTZ9lwsZXizS9DlcbcmN8FZqYmuPHgKSqUk0FmYarnLF+Prj+/zbR+RCIiIjI61crbFBlTffoe+b/rethh17g3dZmS0eHwHxERESm4MrdzkTGX4lP1kIlxYVFFRERECizNTfF559pFxnlN2QWvKbsQdv2RHrIyfCyqiIiIqIBRravB2cZCrdiBP5/ScTbGgUUVERERKfXP5Db457M26NOwYpGxD9IykJdXtu99M4qiKjY2FsOGDUOVKlUgk8lQrVo1BAUFISsrSyEuLi4O3bt3h7W1NZydnTFu3LgCMVFRUQgICIBMJkOFChUwZ84c8AZIIiKigqwszFDZyQpfveOHy3M6YVy7GnirQQWlsU3mH0LVabtx5/Ez3Hn8rEx+thrF3X9XrlxBXl4eVq5cierVqyM6OhrDhw9Heno6lixZAgDIzc1FYGAgypcvj7CwMCQlJWHw4MEQQmDZsmUAXtxK2aFDB7Rp0wYRERG4du0ahgwZAmtra0yaNKkku0hERGTQrCzMMLFDTQDAwGae6LP8hNK4/OUYxrWrIY8vK4x2narFixdj+fLluHXrFgBgz5496NatG+7cuQMPDw8AwIYNGzBkyBA8ePAAdnZ2WL58OaZOnYr79+9DKpUCABYuXIhly5bh7t27kEiUP7n7VVynioiICNgbnYiR684W2m5oq7FzRfVCpKSkwNHRUf51eHg4fHx85AUVAHTq1AmZmZk4e/asPCYgIEBeUOXHxMfHIzY2ttD3yszMRGpqqsKLiIiorOvs46ayPTs3T0+ZGAajLKpu3ryJZcuWYeTIkfJtiYmJcHV1VYhzcHCAhYUFEhMTC43J/zo/Rpng4GDY29vLX5UqqX4IJRERUVnxacfCh/hqTN9Tph7KXKJF1axZsyCRSFS+zpw5o7BPfHw8OnfujL59++LDDz9UaFM2fCeEUNj+akz+6Keqob+pU6ciJSVF/rpz547GfSUiIiqNxratgcOTAgptr/3FXly480R/CZWgEp2oPnbsWPTr109ljJeXl/zf8fHxaNOmDfz9/fHTTz8pxLm5ueHUKcV1MpKTk5GdnS2/GuXm5lbgitSDBw8AoMAVrJdJpVKFIUMiIiL6j5eTtcr2nj8cx6I+vnj3jcp6yqhklGhR5ezsDGdnZ7Vi7927hzZt2qBRo0ZYs2YNTEwUL7L5+/tj/vz5SEhIgLu7OwBg//79kEqlaNSokTxm2rRpyMrKgoWFhTzGw8NDoXgjIiIi9ZmYSPBtv/p4mpmD95p6wmvKrgIxn2+Ngr3MHJ193EsgQ/0wirv/4uPjERAQgMqVK2Pt2rUwNf3vqdhubi8myeXm5qJ+/fpwdXXF4sWL8fjxYwwZMgS9evWSL6mQkpKCWrVqoW3btpg2bRquX7+OIUOGYObMmRotqcC7/4iIiAr3LCsHS/Zdwy/HYwq0leQdgbr+/DaKoiokJAQffPCB0raX04+Li8Po0aNx+PBhyGQyDBgwAEuWLFEYuouKisKYMWNw+vRpODg4YOTIkZg5c6bayykALKqIiIjUsf38PUzYGFlg+65xLWFpbopq5W30mg+LKgPEooqIiKhoQgj8fTEB49afV9o+qnU1tR7crC1cp4qIiIiMkkQiQQ8/D6wf3kxp+/IjN0vVkgssqoiIiEin/Ks5wb+qk9K22l/s1XM2usOiioiIiHRu9eDG6F3Iw5jfWRmu52x0wygeqExERETGzVpqhq/frY+7yc9xOvaxQtvpmMfo8u0xpDzLwvvNvdCtnjsqOliVUKbFx4nqxcCJ6kRERMWXnZuHGtP3qIzRxdILnKhOREREpYq5qQmuz+9S0mloHYsqIiIi0jtzUxOMaVOtpNPQKhZVREREVCImdqiFFQMb4g0vhwJtuXnGNzuJE9WJiIioRJiaSNDZx13+PMBHTzPReN5BAEBWTh5kFqaqdjc4LKqIiIjIIDjbSEv02YCvi8N/RERERFrAooqIiIhIC1hUEREREWkBiyoiIiIiLWBRRURERKQFLKqIiIiItIBFFREREZEWsKgiIiIi0gIWVURERERawKKKiIiISAtYVBERERFpAYsqIiIiIi1gUUVERESkBSyqiIiIiLTArKQTMEZCCABAampqCWdCRERE6sr/3M7/HNc2FlXFkJaWBgCoVKlSCWdCREREmkpLS4O9vb3WjysRuirXSrG8vDzEx8fD1tYWEolEJ++RmpqKSpUq4c6dO7Czs9PJe+gT+2PY2B/DVZr6ArA/hq409UdZX4QQSEtLg4eHB0xMtD8DileqisHExAQVK1bUy3vZ2dkZ/S/2y9gfw8b+GK7S1BeA/TF0pak/r/ZFF1eo8nGiOhEREZEWsKgiIiIi0gIWVQZKKpUiKCgIUqm0pFPRCvbHsLE/hqs09QVgfwxdaepPSfSFE9WJiIiItIBXqoiIiIi0gEUVERERkRawqCIiIiLSAhZVRERERFrAokqHgoOD8cYbb8DW1hYuLi7o1asXrl69qhAjhMCsWbPg4eEBmUyG1q1b49KlSwoxmZmZ+Pjjj+Hs7Axra2v06NEDd+/eLfB+u3btQtOmTSGTyeDs7IzevXsbZV+uXbuGnj17wtnZGXZ2dmjRogVCQ0O11hdt9uenn35C69atYWdnB4lEgidPnhR4r+TkZAwaNAj29vawt7fHoEGDlMYZQ39iY2MxbNgwVKlSBTKZDNWqVUNQUBCysrKMsj8vy8zMRP369SGRSBAZGWnU/TGGc4E6fTGWc8Hjx4/x8ccfo1atWrCyskLlypUxbtw4pKSkKBzHWM4F6vTHmM4F6v588r3WuUCQznTq1EmsWbNGREdHi8jISBEYGCgqV64snj59Ko9ZuHChsLW1FVu3bhVRUVHi3XffFe7u7iI1NVUeM3LkSFGhQgVx4MABce7cOdGmTRvh5+cncnJy5DFbtmwRDg4OYvny5eLq1aviypUrYvPmzUbZl+rVq4uuXbuKCxcuiGvXronRo0cLKysrkZCQYHD9Wbp0qQgODhbBwcECgEhOTi7wXp07dxY+Pj7ixIkT4sSJE8LHx0d069ZNa33RZ3/27NkjhgwZIvbt2ydu3rwp/vrrL+Hi4iImTZpklP152bhx40SXLl0EAHH+/Hmj7Y+xnAvU6YuxnAuioqJE7969xY4dO8SNGzfEoUOHRI0aNUSfPn0U3stYzgXq9MeYzgXq/nzyvc65gEWVHj148EAAEEePHhVCCJGXlyfc3NzEwoUL5TEZGRnC3t5erFixQgghxJMnT4S5ubnYsGGDPObevXvCxMRE7N27VwghRHZ2tqhQoYJYvXq10ffl4cOHAoD4559/5DGpqakCgDh48KBB9edloaGhSj8YLl++LACIkydPyreFh4cLAOLKlSu66YzQXX+U+fLLL0WVKlW0lrsyuu7P7t27Re3atcWlS5d0UlS9Slf9MZZzwcsK64uxngvybdq0SVhYWIjs7GwhhPGeC/K92h9ljOFckK+w/rzuuYDDf3qUf6nR0dERABATE4PExER07NhRHiOVShEQEIATJ04AAM6ePYvs7GyFGA8PD/j4+Mhjzp07h3v37sHExAQNGjSAu7s7unTpUuByuzH0xcnJCXXq1MHatWuRnp6OnJwcrFy5Eq6urmjUqJFB9Ucd4eHhsLe3R9OmTeXbmjVrBnt7e42Ooyld9aew98p/H13RZX/u37+P4cOH47fffoOVlZX2klZBV/0xlnOBOoz9XJCSkgI7OzuYmb14xK6xnwte7U9hMcZyLlDWH22cC1hU6YkQAhMnTkTLli3h4+MDAEhMTAQAuLq6KsS6urrK2xITE2FhYQEHB4dCY27dugUAmDVrFmbMmIGdO3fCwcEBAQEBePz4sVH1RSKR4MCBAzh//jxsbW1haWmJpUuXYu/evShXrpzW+/I6/VFHYmIiXFxcCmx3cXHR6Dia0GV/XnXz5k0sW7YMI0eOLH7CRdBlf4QQGDJkCEaOHInGjRtrL+ki3lNX/TGWc4E6jPlckJSUhLlz52LEiBHybcZ8LlDWn1cZ07lAWX+0dS4ovOQkrRo7diwuXryIsLCwAm0SiUThayFEgW2vejkmLy8PADB9+nT06dMHALBmzRpUrFgRmzdvVvkfoTh02RchBEaPHg0XFxccO3YMMpkMq1evRrdu3RAREQF3d3ftdeT/tN2foo5R3OOoS9f9yRcfH4/OnTujb9+++PDDD4t1DHXosj/Lli1Damoqpk6d+tp5qkuX/TH2c8Gr8cZ4LkhNTUVgYCC8vb0RFBSk8hiqjqMNuu5PPmM6FxTWH22dC3ilSg8+/vhj7NixA6GhoahYsaJ8u5ubGwAUqKYfPHggr7rd3NyQlZWF5OTkQmPyTy7e3t7ydqlUiqpVqyIuLs6o+nL48GHs3LkTGzZsQIsWLdCwYUP8+OOPkMlk+PXXX7Xal9ftjzrc3Nxw//79AtsfPnyo0XHUpev+5IuPj0ebNm3g7++Pn3766fWSVkHX/Tl8+DBOnjwJqVQKMzMzVK9eHQDQuHFjDB48WAs9UKTr/hjLuUAdxnguSEtLQ+fOnWFjY4M///wT5ubmCscxtnOBqv7kM6Zzgar+aO1coNEMLNJIXl6eGDNmjPDw8BDXrl1T2u7m5iYWLVok35aZmal0cvfGjRvlMfHx8QqTu1NSUoRUKlWYnJqVlSVcXFzEypUrjaovO3bsECYmJiItLU3h+DVr1hTz58/XSl+01Z+XFTVR/dSpU/JtJ0+e1PrkVH31Rwgh7t69K2rUqCH69euncNemNumrP7dv3xZRUVHy1759+wQAsWXLFnHnzh2j64+xnAvU6YuxnQtSUlJEs2bNREBAgEhPTy9wHGM7FxTVHyGM61xQVH+0dS5gUaVDo0aNEvb29uLIkSMiISFB/nr27Jk8ZuHChcLe3l5s27ZNREVFif79+ytdhqBixYri4MGD4ty5c6Jt27YFliEYP368qFChgti3b5+4cuWKGDZsmHBxcRGPHz82qr48fPhQODk5id69e4vIyEhx9epV8emnnwpzc3MRGRmplb5osz8JCQni/PnzYtWqVfI7lc6fPy+SkpLkMZ07dxb16tUT4eHhIjw8XPj6+mr9Nmp99efevXuievXqom3btuLu3bsK72WM/XlVTEyMTu7+02d/jOVcUFRfjOlckJqaKpo2bSp8fX3FjRs3FI7z8nnaWM4F6vTHmM4F6v58XlbccwGLKh0CoPS1Zs0aeUxeXp4ICgoSbm5uQiqVilatWomoqCiF4zx//lyMHTtWODo6CplMJrp16ybi4uIUYrKyssSkSZOEi4uLsLW1Fe3btxfR0dFG2ZeIiAjRsWNH4ejoKGxtbUWzZs3E7t27tdYXbfYnKCioyOMkJSWJ9957T9ja2gpbW1vx3nvvqbVUgSH2Z82aNYW+lzH251W6Kqr02R9jOReo0xdjORfkX21T9oqJiZHHGcu5QJ3+GNO5QN2fz8uKey6Q/D9pIiIiInoNnKhOREREpAUsqoiIiIi0gEUVERERkRawqCIiIiLSAhZVRERERFrAooqIiIhIC1hUEREREWkBiyoiKlNat26NCRMmlHQaRFQKsagiIirEkSNHIJFI8OTJk5JOhYiMAIsqIiIiIi1gUUVEpVZ6ejref/992NjYwN3dHV999ZVC+7p169C4cWPY2trCzc0NAwYMwIMHDwAAsbGxaNOmDQDAwcEBEokEQ4YMAQAIIfDll1+iatWqkMlk8PPzw5YtW/TaNyIyPCyqiKjU+uyzzxAaGoo///wT+/fvx5EjR3D27Fl5e1ZWFubOnYsLFy5g+/btiImJkRdOlSpVwtatWwEAV69eRUJCAr799lsAwIwZM7BmzRosX74cly5dwieffIKBAwfi6NGjeu8jERkOPlCZiEqlp0+fwsnJCWvXrsW7774LAHj8+DEqVqyIjz76CN98802BfSIiItCkSROkpaXBxsYGR44cQZs2bZCcnIxy5coBeHH1y9nZGYcPH4a/v7983w8//BDPnj3DH3/8oY/uEZEBMivpBIiIdOHmzZvIyspSKHwcHR1Rq1Yt+dfnz5/HrFmzEBkZicePHyMvLw8AEBcXB29vb6XHvXz5MjIyMtChQweF7VlZWWjQoIEOekJExoJFFRGVSkVdhE9PT0fHjh3RsWNHrFu3DuXLl0dcXBw6deqErKysQvfLL7x27dqFChUqKLRJpdLXT5yIjBaLKiIqlapXrw5zc3OcPHkSlStXBgAkJyfj2rVrCAgIwJUrV/Do0SMsXLgQlSpVAgCcOXNG4RgWFhYAgNzcXPk2b29vSKVSxMXFISAgQE+9ISJjwKKKiEolGxsbDBs2DJ999hmcnJzg6uqK6dOnw8Tkxf05lStXhoWFBZYtW4aRI0ciOjoac+fOVTiGp6cnJBIJdu7cia5du0Imk8HW1haffvopPvnkE+Tl5aFly5ZITU3FiRMnYGNjg8GDB5dEd4nIAPDuPyIqtRYvXoxWrVqhR48eaN++PVq2bIlGjRoBAMqXL4+QkBBs3rwZ3t7eWLhwIZYsWaKwf4UKFTB79mxMmTIFrq6uGDt2LABg7ty5mDlzJoKDg1GnTh106tQJf//9N6pUqaL3PhKR4eDdf0RERERawCtVRERERFrAooqIiIhIC1hUEREREWkBiyoiIiIiLWBRRURERKQFLKqIiIiItIBFFREREZEWsKgiIiIi0gIWVURERERawKKKiIiISAtYVBERERFpAYsqIiIiIi34H9bcX1hs6qE+AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure()\n",
-    "plt.plot(P566_GPS_data['Date'],P566_GPS_data['East (mm)'])\n",
-    "plt.ylabel('east since start (mm)')\n",
-    "plt.xlabel('date')\n",
-    "plt.title('GPS data from station P566 (Visalia, CA)')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What is going on with that drop midway through 2019? Let's take a look. I played around with what values were zoomed in that drop and found that `[4900:4950]` gave a good zoom-in:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10,5))\n",
-    "plt.plot(P566_GPS_data['Date'][4900:4950],P566_GPS_data['East (mm)'][4900:4950],'.')\n",
-    "plt.ylabel('east since start (mm)')\n",
-    "plt.xlabel('date')\n",
-    "plt.title('GPS data from station P566 (Visalia, CA)')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What happened on July 6, 2019?\n",
-    "\n",
-    "https://earthquake.usgs.gov/earthquakes/eventpage/ci38457511/executive\n",
-    "\n",
-    "![alt text](https://static.temblor.net/wp-content/uploads/2016/05/eastern-california-21.jpg)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Calculations using `numpy` and `scipy`\n",
-    "\n",
-    "We have used both the `numpy` and `scipy` libraries of functions to make numerical and statistical calculations. In particular we have put the numpy array data structure to work for us."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Fitting a line with scikit-learn\n",
-    "\n",
-    "Scikit-learn has a function called `LinearRegression` that can be used to calculate best fit lines using the .fit() method. We have used this to fit lines to data in the past.\n",
-    "\n",
-    "We can also consider higher order curves by using the `PolynomialFeatures` function in scikit-learn. \n",
-    "\n",
-    "This function can be used to transform the data into a higher order polynomial space and then use the `LinearRegression` function to fit a line to the transformed data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can calculate days by making a new column in the data frame that is the 'date' value minus the initial date. This will be the number of days since the first date in the data set (Nov. 16 2005). "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P566_GPS_data['days'] = (P566_GPS_data['Date'] - P566_GPS_data['Date'][0])/np.timedelta64(1,'D')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's take a look at our DataFrame and make sure it has a new column `days` and that the column looks good."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "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>Date</th>\n",
-       "      <th>North (mm)</th>\n",
-       "      <th>East (mm)</th>\n",
-       "      <th>Vertical (mm)</th>\n",
-       "      <th>North Std. Deviation (mm)</th>\n",
-       "      <th>East Std. Deviation (mm)</th>\n",
-       "      <th>Vertical Std. Deviation (mm)</th>\n",
-       "      <th>Quality</th>\n",
-       "      <th>Unnamed: 8</th>\n",
-       "      <th>days</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2005-11-16</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>2.11</td>\n",
-       "      <td>1.71</td>\n",
-       "      <td>7.70</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2005-11-17</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>-1.83</td>\n",
-       "      <td>0.44</td>\n",
-       "      <td>2.02</td>\n",
-       "      <td>1.61</td>\n",
-       "      <td>7.36</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2005-11-18</td>\n",
-       "      <td>0.03</td>\n",
-       "      <td>-0.64</td>\n",
-       "      <td>5.36</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>1.62</td>\n",
-       "      <td>7.35</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2005-11-19</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>-0.86</td>\n",
-       "      <td>4.61</td>\n",
-       "      <td>2.02</td>\n",
-       "      <td>1.62</td>\n",
-       "      <td>7.37</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2005-11-20</td>\n",
-       "      <td>-0.15</td>\n",
-       "      <td>-1.14</td>\n",
-       "      <td>-3.28</td>\n",
-       "      <td>2.04</td>\n",
-       "      <td>1.65</td>\n",
-       "      <td>7.46</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Date  North (mm)  East (mm)  Vertical (mm)  North Std. Deviation (mm)  \\\n",
-       "0 2005-11-16        0.00       0.00           0.00                       2.11   \n",
-       "1 2005-11-17        0.26      -1.83           0.44                       2.02   \n",
-       "2 2005-11-18        0.03      -0.64           5.36                       2.00   \n",
-       "3 2005-11-19        0.38      -0.86           4.61                       2.02   \n",
-       "4 2005-11-20       -0.15      -1.14          -3.28                       2.04   \n",
-       "\n",
-       "   East Std. Deviation (mm)  Vertical Std. Deviation (mm) Quality  Unnamed: 8  \\\n",
-       "0                      1.71                          7.70   repro         NaN   \n",
-       "1                      1.61                          7.36   repro         NaN   \n",
-       "2                      1.62                          7.35   repro         NaN   \n",
-       "3                      1.62                          7.37   repro         NaN   \n",
-       "4                      1.65                          7.46   repro         NaN   \n",
-       "\n",
-       "   days  \n",
-       "0   0.0  \n",
-       "1   1.0  \n",
-       "2   2.0  \n",
-       "3   3.0  \n",
-       "4   4.0  "
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "P566_GPS_data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we can do a linear regression between the days (`P566_GPS_data['days']`) and the distance traveled north (`P566_GPS_data[' North (mm)']`)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
-      ],
-      "text/plain": [
-       "LinearRegression()"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from sklearn.linear_model import LinearRegression\n",
-    "model_north = LinearRegression()\n",
-    "model_north.fit(P566_GPS_data[['days']].values,P566_GPS_data['North (mm)'].values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can get the best fitting slope and intercept of the line using the `.coef_` and `.intercept_` attributes of the `LinearRegression` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slop, intercept = model_north.coef_[0], model_north.intercept_"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The slope is 0.03 and the intercept is 0.25\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f'The slope is {slop:.2f} and the intercept is {intercept:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**What are the units of this slope?**\n",
-    "\n",
-    "*write your answer here*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " ### Make a plot of prediction\n",
-    "\n",
-    "**Plot a best-fit line for the data.** *Recall that you can use model.predict() to predict the values of the best-fit line.*\n",
-    "\n",
-    "**Calculate and plot the residual.** *Recall that the residual is the difference between the actual data and the values obtained with the linear model.*\n",
-    "\n",
-    "**Use the same function to predict how far north (relative to stable North America) the Visalia station will go in the next 10 years.** *There are 365.25 days in a year.*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1200x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "y_pred = model_north.predict(P566_GPS_data['days'].values.reshape(-1,1))\n",
-    "\n",
-    "plt.figure(figsize=(12, 5))\n",
-    "plt.plot(P566_GPS_data['Date'],P566_GPS_data['North (mm)'],label='data')\n",
-    "plt.plot(P566_GPS_data['Date'],y_pred,label='model')\n",
-    "plt.ylabel('north since start (mm)')\n",
-    "plt.xlabel('date')\n",
-    "plt.title('GPS data from station P566 (Visalia, CA)')\n",
-    "plt.legend()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1200x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "residuals = P566_GPS_data['North (mm)'] - y_pred\n",
-    "\n",
-    "plt.figure(figsize=(12, 5))\n",
-    "plt.plot(P566_GPS_data['Date'],residuals)\n",
-    "plt.ylabel('residuals (mm)')\n",
-    "plt.xlabel('date')\n",
-    "plt.title('GPS data from station P566 (Visalia, CA)')\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Evaluating the model using $R^{2}$\n",
-    "\n",
-    "We'd also like to know who well this model fits our data (i.e. how correlated the data are). The $R^{2}$ correlation coefficient can be helpful in this regard. $R^{2}$ is zero for uncorrelated data, and 1 for perfectly linear data (so no misfit between the model line and data). \n",
-    "Let's calculate the $R^{2}$ value for our model.\n",
-    "Recall that the $R^{2}$ value is calculated as follows:\n",
-    "\n",
-    "$$R^{2} = 1 - \\frac{\\sum_{i=1}^{n} (y_{i} - \\hat{y}_{i})^{2}}{\\sum_{i=1}^{n} (y_{i} - \\bar{y})^{2}}$$\n",
-    "\n",
-    "where $y_{i}$ is the actual data, $\\hat{y}_{i}$ is the predicted data, and $\\bar{y}$ is the mean of the actual data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The R^2 value is 0.998779\n"
-     ]
-    }
-   ],
-   "source": [
-    "y = P566_GPS_data['North (mm)']\n",
-    "y_pred = model_north.predict(P566_GPS_data['days'].values.reshape(-1,1))\n",
-    "\n",
-    "R2 = 1 - np.sum((y - y_pred)**2)/np.sum((y - np.mean(y))**2)\n",
-    "\n",
-    "print(f'The R^2 value is {R2:.6f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Defining a function\n",
-    "\n",
-    "When you may be doing a calculation more than once it is a good idea to define a function. Let's define a function that will take an east magnitude and a north magnitude and return a direction between 0 and 360."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def GPS_direction(east_magnitude, north_magnitude):\n",
-    "    direction_rad = np.arctan2(east_magnitude, north_magnitude)\n",
-    "    direction = np.rad2deg(direction_rad) % 360\n",
-    "    return direction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "180.0"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "GPS_direction(0,-1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Calculate the slope for the east data. Use this slope and the slope for the north data to calculate the direction of the motion of the station using the `GPS_direction` function.**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The slope is -0.03 and the intercept is 3.79\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_east = LinearRegression()\n",
-    "model_east.fit(P566_GPS_data['days'].values.reshape(-1,1),P566_GPS_data['East (mm)'].values)\n",
-    "\n",
-    "slop_east, intercept_east = model_east.coef_[0], model_east.intercept_\n",
-    "\n",
-    "print(f'The slope is {slop_east:.2f} and the intercept is {intercept_east:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The direction is 308.52 degrees\n"
-     ]
-    }
-   ],
-   "source": [
-    "slop_north, intercept_north = model_north.coef_[0], model_north.intercept_\n",
-    "\n",
-    "direction = GPS_direction(slop_east, slop_north)\n",
-    "\n",
-    "print(f'The direction is {direction:.2f} degrees')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Making a map with `cartopy`\n",
-    "\n",
-    "At the start of the course, we made a number of maps using the `cartopy` library. I have put in code below that will make a map showing the location of the P566 GPS station. I put in a number of bells and whistles including using terrain imagery tiles from Stamen and putting on text that labels the station location.\n",
-    "\n",
-    "Let's define variables giving the latitude and longitude of the P566 GPS station."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "P566_lat = 36.32445\n",
-    "P566_lon = -119.22929"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import cartopy.crs as ccrs\n",
-    "import cartopy.feature as cfeature\n",
-    "import cartopy.io.img_tiles as cimgt\n",
-    "\n",
-    "# Replace these variables with your station's longitude and latitude\n",
-    "P566_lon = -120.0  # Example longitude of station P566\n",
-    "P566_lat = 37.0    # Example latitude of station P566\n",
-    "\n",
-    "# Add a background image from Google Maps\n",
-    "tiles = cimgt.GoogleTiles()\n",
-    "\n",
-    "# Set up the map\n",
-    "plt.figure(figsize=(10, 10))\n",
-    "ax = plt.axes(projection=tiles.crs)\n",
-    "ax.set_extent((-125, -114, 32, 42.5))  # Set the geographical extent (lon_min, lon_max, lat_min, lat_max)\n",
-    "\n",
-    "# Instread of adding features, we add the Google Maps image\n",
-    "# ax.add_feature(cfeature.LAND)\n",
-    "# ax.add_feature(cfeature.OCEAN)\n",
-    "# ax.add_feature(cfeature.STATES)\n",
-    "\n",
-    "# Add the Google Maps image to the map\n",
-    "ax.add_image(tiles, 6) # The number 6 is the zoom level. The higher the number, the closer in you zoom\n",
-    "\n",
-    "# Plot the station's location\n",
-    "ax.scatter(P566_lon, P566_lat, transform=ccrs.PlateCarree(), color='red', s=50, label='P566 Station')\n",
-    "\n",
-    "# Annotate the station with its name\n",
-    "ax.text(P566_lon, P566_lat, 'P566\\nstation\\n', transform=ccrs.PlateCarree(),\n",
-    "        color='red', horizontalalignment='center', verticalalignment='bottom', size=12)\n",
-    "\n",
-    "# Annotation for the direction of motion using an arrow\n",
-    "ax.arrow(P566_lon, P566_lat, np.sin(np.deg2rad(direction)), np.cos(np.deg2rad(direction)), head_width=0.1, head_length=0.1, fc='blue', ec='blue', transform=ccrs.PlateCarree())\n",
-    "\n",
-    "plt.legend()\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Plot the vertical component of the GPS time-series"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1200x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12, 5))\n",
-    "plt.plot(P566_GPS_data['Date'],P566_GPS_data['Vertical (mm)'])\n",
-    "plt.ylabel('vertical since start (mm)')\n",
-    "plt.xlabel('days since start')\n",
-    "plt.title('GPS data from station P566 (Visalia, CA)')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**What do these data show? What is happening to the land surface? Why?**\n",
-    "\n",
-    "https://earthobservatory.nasa.gov/images/89761/san-joaquin-valley-is-still-sinking\n",
-    "\n",
-    "https://www.earthdate.org/californias-sinking-valley"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's look at a subset of the data for the past 5 years between 2018-01-01 and 2023-01-01. We have done a lot of this filtering using pandas. However, the syntax is hard to remember.\n",
-    "\n",
-    "It can be helpful to remember how this is actually working under the hood. When we are passing in a conditional statement like `P566_GPS_data['Date'] >= '2006-01-01'` we are asking pandas to tell us, at every value in the `P566_GPS_data['Date']` column is it true or false that the date is greater than 2018-01-01? "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0       False\n",
-       "1       False\n",
-       "2       False\n",
-       "3       False\n",
-       "4       False\n",
-       "        ...  \n",
-       "6300     True\n",
-       "6301     True\n",
-       "6302     True\n",
-       "6303     True\n",
-       "6304     True\n",
-       "Name: Date, Length: 6305, dtype: bool"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "P566_GPS_data['Date'] >= '2018-01-01'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is a list of true/false values. We then use these true/false values to filter the values in the DataFrame only returning those that are true. We can link multiple conditionals together with the `&` symbol such as in the example below `(P566_GPS_data['Date'] >= '2006-01-01') & (P566_GPS_data['Date'] < '2010-01-01')`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "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>Date</th>\n",
-       "      <th>North (mm)</th>\n",
-       "      <th>East (mm)</th>\n",
-       "      <th>Vertical (mm)</th>\n",
-       "      <th>North Std. Deviation (mm)</th>\n",
-       "      <th>East Std. Deviation (mm)</th>\n",
-       "      <th>Vertical Std. Deviation (mm)</th>\n",
-       "      <th>Quality</th>\n",
-       "      <th>Unnamed: 8</th>\n",
-       "      <th>days</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4374</th>\n",
-       "      <td>2018-01-01</td>\n",
-       "      <td>111.65</td>\n",
-       "      <td>-132.47</td>\n",
-       "      <td>-156.00</td>\n",
-       "      <td>1.86</td>\n",
-       "      <td>1.55</td>\n",
-       "      <td>6.83</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4429.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4375</th>\n",
-       "      <td>2018-01-02</td>\n",
-       "      <td>112.04</td>\n",
-       "      <td>-132.45</td>\n",
-       "      <td>-158.25</td>\n",
-       "      <td>1.87</td>\n",
-       "      <td>1.54</td>\n",
-       "      <td>6.79</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4430.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4376</th>\n",
-       "      <td>2018-01-03</td>\n",
-       "      <td>111.85</td>\n",
-       "      <td>-132.73</td>\n",
-       "      <td>-155.05</td>\n",
-       "      <td>1.88</td>\n",
-       "      <td>1.55</td>\n",
-       "      <td>6.83</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4431.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4377</th>\n",
-       "      <td>2018-01-04</td>\n",
-       "      <td>110.52</td>\n",
-       "      <td>-131.16</td>\n",
-       "      <td>-160.25</td>\n",
-       "      <td>1.90</td>\n",
-       "      <td>1.56</td>\n",
-       "      <td>6.91</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4432.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4378</th>\n",
-       "      <td>2018-01-05</td>\n",
-       "      <td>111.68</td>\n",
-       "      <td>-132.28</td>\n",
-       "      <td>-156.70</td>\n",
-       "      <td>1.87</td>\n",
-       "      <td>1.54</td>\n",
-       "      <td>6.79</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4433.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           Date  North (mm)  East (mm)  Vertical (mm)  \\\n",
-       "4374 2018-01-01      111.65    -132.47        -156.00   \n",
-       "4375 2018-01-02      112.04    -132.45        -158.25   \n",
-       "4376 2018-01-03      111.85    -132.73        -155.05   \n",
-       "4377 2018-01-04      110.52    -131.16        -160.25   \n",
-       "4378 2018-01-05      111.68    -132.28        -156.70   \n",
-       "\n",
-       "      North Std. Deviation (mm)  East Std. Deviation (mm)  \\\n",
-       "4374                       1.86                      1.55   \n",
-       "4375                       1.87                      1.54   \n",
-       "4376                       1.88                      1.55   \n",
-       "4377                       1.90                      1.56   \n",
-       "4378                       1.87                      1.54   \n",
-       "\n",
-       "      Vertical Std. Deviation (mm) Quality  Unnamed: 8    days  \n",
-       "4374                          6.83   repro         NaN  4429.0  \n",
-       "4375                          6.79   repro         NaN  4430.0  \n",
-       "4376                          6.83   repro         NaN  4431.0  \n",
-       "4377                          6.91   repro         NaN  4432.0  \n",
-       "4378                          6.79   repro         NaN  4433.0  "
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "P566_GPS_18_23 = P566_GPS_data[(P566_GPS_data['Date'] >= '2018-01-01') & (P566_GPS_data['Date'] < '2023-01-01')]\n",
-    "P566_GPS_18_23.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(P566_GPS_18_23['Date'],P566_GPS_18_23['Vertical (mm)'])\n",
-    "plt.ylabel('vertical since start (mm)')\n",
-    "plt.xlabel('date')\n",
-    "plt.title('GPS data from station P566 (Visalia, CA)')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Estimating the slope and associated uncertainty\n",
-    "\n",
-    "Like we did for the directional data when estimating the median and its uncertainty, we can get an estimate of the uncertainty of slope of the subsidence by bootstrap resampling the data a large number of times, and calculating the regression line through each bootstrapped dataset. This chapter in Inferential Thinking provides good explanations for this approach: https://www.inferentialthinking.com/chapters/16/Inference_for_Regression.html\n",
-    "\n",
-    "We can collect all the slopes and plot their empirical histogram. We will use the `.sample()` function on the `P566_GPS_06_09` DataFrame and grab as many samples as the DataFrame is long (`len(P566_GPS_06_09)`) and replace the values each time they are sample (`replace=True`).\n",
-    "\n",
-    "> The Bootstrap: Resampling from the Sample \n",
-    "\n",
-    "> What we do have is a large random sample from the population. As we know, a large random sample is likely to resemble the population from which it is drawn. This observation allows data scientists to lift themselves up by their own bootstraps: the sampling procedure can be replicated by sampling from the sample."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We need to put together a few different programming elements to make this work. Let's break them down.\n",
-    "- make a placeholder list that we with subsequently append values to: `slopes_06_09 = []`\n",
-    "- resample the DataFrame using `.sample`\n",
-    "- calculate a linear regression using `np.polyfit()`\n",
-    "- extract the slope which is the first element of what is returned from `np.polyfit()`\n",
-    "- append that value to the `slopes_06_09` list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The slope is -0.05 mm/day\n"
-     ]
-    }
-   ],
-   "source": [
-    "slopes_18_23 = []\n",
-    "\n",
-    "resample = P566_GPS_18_23.sample(n=len(P566_GPS_18_23),replace=True)\n",
-    "model_vertical = LinearRegression()\n",
-    "model_vertical.fit(resample['days'].values.reshape(-1,1),resample['Vertical (mm)'].values)\n",
-    "slop = model_vertical.coef_[0]\n",
-    "print(f'The slope is {slop:.2f} mm/day')\n",
-    "\n",
-    "slopes_18_23.append(slop)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[-0.04635289530599001]"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "slopes_18_23"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But what we did above just does the resampling and slope fitting once. The idea of the bootstrap is that we are going to do it many many times.\n",
-    "\n",
-    "To do this we will use a `for` loop. To loop through a set of code a specified number of times, we can use the `range()` function,\n",
-    "The `range()` function returns a sequence of numbers, starting from 0 by default, and increments by 1 (by default), and ends at a specified number. Anything that is indented under the for loop will then be repeated for the number of times that are in the range."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n",
-      "6\n",
-      "7\n",
-      "8\n",
-      "9\n"
-     ]
-    }
-   ],
-   "source": [
-    "for n in range(0,10):\n",
-    "    print(n)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the bootstrap resampling and calculation of slope for the bootstrap resampled data, we want to do it many times. So let's do `for n in range(0,1000):`.  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "slopes_18_23 = []\n",
-    "\n",
-    "model_vertical = LinearRegression()\n",
-    "for n in range(0,1000):\n",
-    "    resample = P566_GPS_18_23.sample(n=len(P566_GPS_18_23),replace=True)\n",
-    "\n",
-    "    model_vertical.fit(resample['days'].values.reshape(-1,1),resample['Vertical (mm)'].values)\n",
-    "\n",
-    "    slope = model_vertical.coef_[0]\n",
-    "    slopes_18_23.append(slope)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will now have a large number of slope values for the bootstrap resampled data. We can visualize them using `plt.hist()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1200x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(12, 5))\n",
-    "plt.hist(slopes_18_23, bins=20, edgecolor='white')\n",
-    "plt.ylabel('Frequency')\n",
-    "plt.xlabel('Subsidence (mm/day)')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Code for you to write**\n",
-    "\n",
-    "**Address the question: Is the rate of land subsidence increasing? Specifically, was the rate of land subsidence greater during the last 5 years (2018-01-01 and 2023-01-01) than it was in the first 5 years of the record (2006-01-01 and 2011-01-01)?** \n",
-    "\n",
-    "To do so:\n",
-    "- Filter the DataFrame to only include those years\n",
-    "- Use the bootstrap method to estimate the slope and its 95 percent confidence bounds (using the `np.percentile` function will help)\n",
-    "- Compare the the slopes between the two age ranges. Which one appears to be greater? Is this results significant or do they have overlapping confidence bounds? Plotting histograms of the bootstrap slope values is a good way to make this comparison.\n",
-    "\n",
-    "*Bonus: fit a linear regression to the whole time series, calculate and plot the residuals. What does the residual correspond to?* "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "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>Date</th>\n",
-       "      <th>North (mm)</th>\n",
-       "      <th>East (mm)</th>\n",
-       "      <th>Vertical (mm)</th>\n",
-       "      <th>North Std. Deviation (mm)</th>\n",
-       "      <th>East Std. Deviation (mm)</th>\n",
-       "      <th>Vertical Std. Deviation (mm)</th>\n",
-       "      <th>Quality</th>\n",
-       "      <th>Unnamed: 8</th>\n",
-       "      <th>days</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>2006-01-01</td>\n",
-       "      <td>2.37</td>\n",
-       "      <td>-2.75</td>\n",
-       "      <td>4.95</td>\n",
-       "      <td>2.19</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>8.02</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>46.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>2006-01-02</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>-2.49</td>\n",
-       "      <td>7.68</td>\n",
-       "      <td>2.11</td>\n",
-       "      <td>1.68</td>\n",
-       "      <td>7.51</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>47.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>2006-01-03</td>\n",
-       "      <td>1.98</td>\n",
-       "      <td>-2.50</td>\n",
-       "      <td>4.52</td>\n",
-       "      <td>2.32</td>\n",
-       "      <td>2.02</td>\n",
-       "      <td>8.48</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>48.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>2006-01-04</td>\n",
-       "      <td>1.91</td>\n",
-       "      <td>-1.75</td>\n",
-       "      <td>-0.37</td>\n",
-       "      <td>2.26</td>\n",
-       "      <td>1.96</td>\n",
-       "      <td>8.32</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>49.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>2006-01-05</td>\n",
-       "      <td>2.94</td>\n",
-       "      <td>-1.34</td>\n",
-       "      <td>-4.20</td>\n",
-       "      <td>2.13</td>\n",
-       "      <td>1.78</td>\n",
-       "      <td>7.71</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>50.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         Date  North (mm)  East (mm)  Vertical (mm)  \\\n",
-       "46 2006-01-01        2.37      -2.75           4.95   \n",
-       "47 2006-01-02        0.65      -2.49           7.68   \n",
-       "48 2006-01-03        1.98      -2.50           4.52   \n",
-       "49 2006-01-04        1.91      -1.75          -0.37   \n",
-       "50 2006-01-05        2.94      -1.34          -4.20   \n",
-       "\n",
-       "    North Std. Deviation (mm)  East Std. Deviation (mm)  \\\n",
-       "46                       2.19                      1.80   \n",
-       "47                       2.11                      1.68   \n",
-       "48                       2.32                      2.02   \n",
-       "49                       2.26                      1.96   \n",
-       "50                       2.13                      1.78   \n",
-       "\n",
-       "    Vertical Std. Deviation (mm) Quality  Unnamed: 8  days  \n",
-       "46                          8.02   repro         NaN  46.0  \n",
-       "47                          7.51   repro         NaN  47.0  \n",
-       "48                          8.48   repro         NaN  48.0  \n",
-       "49                          8.32   repro         NaN  49.0  \n",
-       "50                          7.71   repro         NaN  50.0  "
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "P566_GPS_06_11 = P566_GPS_data[(P566_GPS_data['Date'] >= '2006-01-01') & (P566_GPS_data['Date'] < '2011-01-01')]\n",
-    "P566_GPS_06_11.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The slope is -0.03 mm/day\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_06 = LinearRegression()\n",
-    "model_06.fit(P566_GPS_06_11['days'].values.reshape(-1,1),P566_GPS_06_11['Vertical (mm)'].values)\n",
-    "print(f'The slope is {model_06.coef_[0]:.2f} mm/day')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "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>Date</th>\n",
-       "      <th>North (mm)</th>\n",
-       "      <th>East (mm)</th>\n",
-       "      <th>Vertical (mm)</th>\n",
-       "      <th>North Std. Deviation (mm)</th>\n",
-       "      <th>East Std. Deviation (mm)</th>\n",
-       "      <th>Vertical Std. Deviation (mm)</th>\n",
-       "      <th>Quality</th>\n",
-       "      <th>Unnamed: 8</th>\n",
-       "      <th>days</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1872</th>\n",
-       "      <td>2011-01-01</td>\n",
-       "      <td>47.68</td>\n",
-       "      <td>-55.55</td>\n",
-       "      <td>-56.53</td>\n",
-       "      <td>1.96</td>\n",
-       "      <td>1.56</td>\n",
-       "      <td>7.18</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1872.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1873</th>\n",
-       "      <td>2011-01-02</td>\n",
-       "      <td>48.56</td>\n",
-       "      <td>-55.01</td>\n",
-       "      <td>-65.94</td>\n",
-       "      <td>2.05</td>\n",
-       "      <td>1.64</td>\n",
-       "      <td>7.46</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1873.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1874</th>\n",
-       "      <td>2011-01-03</td>\n",
-       "      <td>48.74</td>\n",
-       "      <td>-56.17</td>\n",
-       "      <td>-64.03</td>\n",
-       "      <td>1.98</td>\n",
-       "      <td>1.60</td>\n",
-       "      <td>7.29</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1874.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1875</th>\n",
-       "      <td>2011-01-04</td>\n",
-       "      <td>48.48</td>\n",
-       "      <td>-56.14</td>\n",
-       "      <td>-62.31</td>\n",
-       "      <td>2.02</td>\n",
-       "      <td>1.58</td>\n",
-       "      <td>7.23</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1875.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1876</th>\n",
-       "      <td>2011-01-05</td>\n",
-       "      <td>50.01</td>\n",
-       "      <td>-55.84</td>\n",
-       "      <td>-67.32</td>\n",
-       "      <td>1.94</td>\n",
-       "      <td>1.52</td>\n",
-       "      <td>7.00</td>\n",
-       "      <td>repro</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1876.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           Date  North (mm)  East (mm)  Vertical (mm)  \\\n",
-       "1872 2011-01-01       47.68     -55.55         -56.53   \n",
-       "1873 2011-01-02       48.56     -55.01         -65.94   \n",
-       "1874 2011-01-03       48.74     -56.17         -64.03   \n",
-       "1875 2011-01-04       48.48     -56.14         -62.31   \n",
-       "1876 2011-01-05       50.01     -55.84         -67.32   \n",
-       "\n",
-       "      North Std. Deviation (mm)  East Std. Deviation (mm)  \\\n",
-       "1872                       1.96                      1.56   \n",
-       "1873                       2.05                      1.64   \n",
-       "1874                       1.98                      1.60   \n",
-       "1875                       2.02                      1.58   \n",
-       "1876                       1.94                      1.52   \n",
-       "\n",
-       "      Vertical Std. Deviation (mm) Quality  Unnamed: 8    days  \n",
-       "1872                          7.18   repro         NaN  1872.0  \n",
-       "1873                          7.46   repro         NaN  1873.0  \n",
-       "1874                          7.29   repro         NaN  1874.0  \n",
-       "1875                          7.23   repro         NaN  1875.0  \n",
-       "1876                          7.00   repro         NaN  1876.0  "
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "P566_GPS_11_16 = P566_GPS_data[(P566_GPS_data['Date'] >= '2011-01-01') & (P566_GPS_data['Date'] < '2016-01-01')]\n",
-    "P566_GPS_11_16.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The slope is -0.03 mm/day\n"
-     ]
-    }
-   ],
-   "source": [
-    "model_11 = LinearRegression()\n",
-    "model_11.fit(P566_GPS_11_16['days'].values.reshape(-1,1),P566_GPS_11_16['Vertical (mm)'].values)\n",
-    "print(f'The slope is {model_11.coef_[0]:.2f} mm/day')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What is the main economic activity around P566? And what resources does that activity require?\n",
-    "\n",
-    "What does the vertical component of the GPS time-series tell us about the land movement in the San Joaquin Valley?\n",
-    "\n",
-    "What are the implications of the land subsidence in the San Joaquin Valley?\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![alt text](https://upload.wikimedia.org/wikipedia/commons/1/1c/Drought_area_in_California.svg)\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "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": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/docs/lectures/others/homework03.ipynb b/docs/lectures/others/homework03.ipynb
deleted file mode 100644
index 5575292..0000000
--- a/docs/lectures/others/homework03.ipynb
+++ /dev/null
@@ -1,1017 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Homework 03 of EPS 88\n",
-    "\n",
-    "## Learning from a bigger classification of basalt source\n",
-    "\n",
-    "In the 2006 paper\n",
-    "\n",
-    ">Vermeesch, P. (2006). Tectonic discrimination of basalts with classification trees. Geochimica et Cosmochimica Acta, 70, 1839-1848. https://doi.org/10.1016/j.gca.2005.12.016\n",
-    "\n",
-    "Vermeesch wrote:\n",
-    "\n",
-    "> *\"If a much larger database were compiled, the trees would grow and their discriminative power increase, but they would still be easy to interpret\"*\n",
-    "\n",
-    "In a more recent paper, Doucet et al. compiled many more data. Rather than 756 basalt data points, they compiled 29,407 of which 22,005 correspond to the categories of Vermeesch (2006).\n",
-    "\n",
-    "> Doucet, L. S., Tetley, M. G., Li, Z.-X., Liu, Y., & Gamaleldien, H. (2022). Geochemical fingerprinting of continental and oceanic basalts: A machine learning approach. Earth-Science Reviews, 233, https://doi.org/10.1016/j.earscirev.2022.104192\n",
-    "\n",
-    "Your task in this assignment is use the data of Doucet et al. (2022) to evaluate whether the predictive power of the classification tree approach increases within this increase in data size as predicted by Vermeesch (2006)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Import scientific Python libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.metrics import accuracy_score\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.tree import plot_tree\n",
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.svm import SVC\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn.pipeline import make_pipeline\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Import data\n",
-    "\n",
-    "We will import the data from Doucet et al. 2022 that is provided as their supplemental table 1.\n",
-    "\n",
-    "For comparison, we will also import the data from Vermeesch (2006)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Read Doucet et al. 2022 data\n",
-    "Doucet_data = pd.read_csv('../data/Doucet2022.csv',header=11)\n",
-    "\n",
-    "## Read Vermeesch (2006) data\n",
-    "Vermeesch_data = pd.read_csv('../data/Vermeesch2006.csv')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## The first task for data science is to inspect the data\n",
-    "\n",
-    "As we have two dataframes, if we want to develop a machine learning model that can be applied to both datasets, we need to make sure that the column names (feature names) are the same.\n",
-    "\n",
-    "Let's first inspect the column names of the two dataframes. If they are different, we need to make them the same.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['X1', 'type', 'location', 'SiO2', 'TiO2', 'Al2O3', 'MgO', 'Fe2O3',\n",
-      "       'FeO', 'FeOt', 'MgO.1', 'MnO', 'CaO', 'Na2O', 'K2O', 'P2O5', 'NiO',\n",
-      "       'Li', 'Be', 'B', 'Ti', 'Ni', 'V', 'Cu', 'Zn', 'Cr', 'Li.1', 'Rb', 'Sr',\n",
-      "       'Y', 'Zr', 'Nb', 'Mo', 'Cs', 'Ba', 'La', 'Ce', 'Pr', 'Nd', 'Sm', 'Eu',\n",
-      "       'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Ta', 'Pb', 'Th',\n",
-      "       'U'],\n",
-      "      dtype='object')\n",
-      "Index(['affinity', 'SiO2_wt_percent', 'TiO2_wt_percent', 'Al2O3_wt_percent',\n",
-      "       'Fe2O3_wt_percent', 'FeO_wt_percent', 'CaO_wt_percent',\n",
-      "       'MgO_wt_percent', 'MnO_wt_percent', 'K2O_wt_percent', 'Na2O_wt_percent',\n",
-      "       'P2O5(wt%)', 'La_ppm', 'Ce_ppm', 'Pr_ppm', 'Nd_ppm', 'Sm_ppm', 'Eu_ppm',\n",
-      "       'Gd_ppm', 'Tb_ppm', 'Dy_ppm', 'Ho_ppm', 'Er_ppm', 'Tm_ppm', 'Yb_ppm',\n",
-      "       'Lu_ppm', 'Sc_ppm', 'V_ppm', 'Cr_ppm', 'Co_ppm', 'Ni_ppm', 'Cu_ppm',\n",
-      "       'Zn_ppm', 'Ga_ppm', 'Rb_ppm', 'Sr_ppm', 'Y_ppm', 'Zr_ppm', 'Nb_ppm',\n",
-      "       'Sn_ppm', 'Cs_ppm', 'Ba_ppm', 'Hf_ppm', 'Ta_ppm', 'Pb_ppm', 'Th_ppm',\n",
-      "       'U_ppm', '143Nd/144Nd', '87Sr/86Sr', '206Pb/204Pb', '207Pb/204Pb',\n",
-      "       '208Pb/204Pb'],\n",
-      "      dtype='object')\n"
-     ]
-    }
-   ],
-   "source": [
-    "## print the column names\n",
-    "print(Doucet_data.columns)\n",
-    "print(Vermeesch_data.columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the column names are different. We need to make them the same.\n",
-    "\n",
-    "There are multiple ways for data cleaning. Here we will use the following steps:\n",
-    "\n",
-    "1. Rename the 'affinity' column to 'type' in the Vermeesch dataframe to match the column name in the Doucet dataframe\n",
-    "2. Remove the units from the column names\n",
-    "3. Keep only the columns that are present in both dataframes\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['type', 'SiO2_wt_percent', 'TiO2_wt_percent', 'Al2O3_wt_percent',\n",
-      "       'Fe2O3_wt_percent', 'FeO_wt_percent', 'CaO_wt_percent',\n",
-      "       'MgO_wt_percent', 'MnO_wt_percent', 'K2O_wt_percent', 'Na2O_wt_percent',\n",
-      "       'P2O5(wt%)', 'La_ppm', 'Ce_ppm', 'Pr_ppm', 'Nd_ppm', 'Sm_ppm', 'Eu_ppm',\n",
-      "       'Gd_ppm', 'Tb_ppm', 'Dy_ppm', 'Ho_ppm', 'Er_ppm', 'Tm_ppm', 'Yb_ppm',\n",
-      "       'Lu_ppm', 'Sc_ppm', 'V_ppm', 'Cr_ppm', 'Co_ppm', 'Ni_ppm', 'Cu_ppm',\n",
-      "       'Zn_ppm', 'Ga_ppm', 'Rb_ppm', 'Sr_ppm', 'Y_ppm', 'Zr_ppm', 'Nb_ppm',\n",
-      "       'Sn_ppm', 'Cs_ppm', 'Ba_ppm', 'Hf_ppm', 'Ta_ppm', 'Pb_ppm', 'Th_ppm',\n",
-      "       'U_ppm', '143Nd/144Nd', '87Sr/86Sr', '206Pb/204Pb', '207Pb/204Pb',\n",
-      "       '208Pb/204Pb'],\n",
-      "      dtype='object')\n"
-     ]
-    }
-   ],
-   "source": [
-    "## rename type to affinity for Doucet data\n",
-    "Vermeesch_data.rename(columns={'affinity':'type'},inplace=True)\n",
-    "print(Vermeesch_data.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['X1', 'type', 'location', 'SiO2', 'TiO2', 'Al2O3', 'MgO', 'Fe2O3',\n",
-      "       'FeO', 'FeOt', 'MgO.1', 'MnO', 'CaO', 'Na2O', 'K2O', 'P2O5', 'NiO',\n",
-      "       'Li', 'Be', 'B', 'Ti', 'Ni', 'V', 'Cu', 'Zn', 'Cr', 'Li.1', 'Rb', 'Sr',\n",
-      "       'Y', 'Zr', 'Nb', 'Mo', 'Cs', 'Ba', 'La', 'Ce', 'Pr', 'Nd', 'Sm', 'Eu',\n",
-      "       'Gd', 'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Hf', 'Ta', 'Pb', 'Th',\n",
-      "       'U'],\n",
-      "      dtype='object')\n",
-      "Index(['type', 'SiO2', 'TiO2', 'Al2O3', 'Fe2O3', 'FeO', 'CaO', 'MgO', 'MnO',\n",
-      "       'K2O', 'Na2O', 'P2O5(wt%)', 'La', 'Ce', 'Pr', 'Nd', 'Sm', 'Eu', 'Gd',\n",
-      "       'Tb', 'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu', 'Sc', 'V', 'Cr', 'Co', 'Ni',\n",
-      "       'Cu', 'Zn', 'Ga', 'Rb', 'Sr', 'Y', 'Zr', 'Nb', 'Sn', 'Cs', 'Ba', 'Hf',\n",
-      "       'Ta', 'Pb', 'Th', 'U', '143Nd/144Nd', '87Sr/86Sr', '206Pb/204Pb',\n",
-      "       '207Pb/204Pb', '208Pb/204Pb'],\n",
-      "      dtype='object')\n"
-     ]
-    }
-   ],
-   "source": [
-    "## remove _ppm from the column names\n",
-    "Doucet_data.columns = Doucet_data.columns.str.replace('_ppm', '')\n",
-    "Vermeesch_data.columns = Vermeesch_data.columns.str.replace('_ppm', '')\n",
-    "\n",
-    "## remove _wt_percent from the column names\n",
-    "Doucet_data.columns = Doucet_data.columns.str.replace('_wt_percent', '')\n",
-    "Vermeesch_data.columns = Vermeesch_data.columns.str.replace('_wt_percent', '')\n",
-    "\n",
-    "print(Doucet_data.columns)\n",
-    "print(Vermeesch_data.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Index(['Eu', 'K2O', 'Pb', 'MgO', 'SiO2', 'MnO', 'Rb', 'Nb', 'Cr', 'Lu', 'Y',\n",
-      "       'La', 'CaO', 'Tm', 'Tb', 'Yb', 'Nd', 'TiO2', 'Cs', 'V', 'U', 'Fe2O3',\n",
-      "       'Ce', 'Gd', 'Ni', 'Zn', 'Sr', 'Hf', 'Na2O', 'Zr', 'Pr', 'type', 'Th',\n",
-      "       'FeO', 'Dy', 'Cu', 'Al2O3', 'Ho', 'Er', 'Ta', 'Sm', 'Ba'],\n",
-      "      dtype='object')\n",
-      "Index(['Eu', 'K2O', 'Pb', 'MgO', 'SiO2', 'MnO', 'Rb', 'Nb', 'Cr', 'Lu', 'Y',\n",
-      "       'La', 'CaO', 'Tm', 'Tb', 'Yb', 'Nd', 'TiO2', 'Cs', 'V', 'U', 'Fe2O3',\n",
-      "       'Ce', 'Gd', 'Ni', 'Zn', 'Sr', 'Hf', 'Na2O', 'Zr', 'Pr', 'type', 'Th',\n",
-      "       'FeO', 'Dy', 'Cu', 'Al2O3', 'Ho', 'Er', 'Ta', 'Sm', 'Ba'],\n",
-      "      dtype='object')\n"
-     ]
-    }
-   ],
-   "source": [
-    "## keep common columns between Doucet and Vermeesch data\n",
-    "common_columns = list(set(Doucet_data.columns) & set(Vermeesch_data.columns))\n",
-    "\n",
-    "Doucet_data = Doucet_data[common_columns]\n",
-    "Vermeesch_data = Vermeesch_data[common_columns]\n",
-    "\n",
-    "print(Doucet_data.columns)\n",
-    "print(Vermeesch_data.columns)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that here we simply keep the common columns between the two dataframes, which loss some information. \n",
-    "If you want to keep all the information, you could further explore how to rename the columns in a way that can be used for both dataframes."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The Doucet et al. 2022 study includes data from additional basalt types. To test Vermeesch's hypothesis, let's filter the data to be those from:\n",
-    "\n",
-    "- ***Island arc basalts (IAB)*** *In the Doucet et al. dataset these are called `ARC-O` standing for oceanic arc.*\n",
-    "- ***Mid-ocean ridge (MORB)***\n",
-    "- ***Ocean-island (OIB)***\n",
-    "\n",
-    "The code below filters to these types and creates a new dataframe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "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>Eu</th>\n",
-       "      <th>K2O</th>\n",
-       "      <th>Pb</th>\n",
-       "      <th>MgO</th>\n",
-       "      <th>SiO2</th>\n",
-       "      <th>MnO</th>\n",
-       "      <th>Rb</th>\n",
-       "      <th>Nb</th>\n",
-       "      <th>Cr</th>\n",
-       "      <th>Lu</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Th</th>\n",
-       "      <th>FeO</th>\n",
-       "      <th>Dy</th>\n",
-       "      <th>Cu</th>\n",
-       "      <th>Al2O3</th>\n",
-       "      <th>Ho</th>\n",
-       "      <th>Er</th>\n",
-       "      <th>Ta</th>\n",
-       "      <th>Sm</th>\n",
-       "      <th>Ba</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.28</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.76</td>\n",
-       "      <td>52.80</td>\n",
-       "      <td>0.16</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>0.20</td>\n",
-       "      <td>244.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>13.68</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9.41</td>\n",
-       "      <td>52.07</td>\n",
-       "      <td>0.19</td>\n",
-       "      <td>4.1</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>260.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>14.46</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>45.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.47</td>\n",
-       "      <td>0.26</td>\n",
-       "      <td>0.54</td>\n",
-       "      <td>8.56</td>\n",
-       "      <td>52.84</td>\n",
-       "      <td>0.16</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>0.13</td>\n",
-       "      <td>167.0</td>\n",
-       "      <td>0.23</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.14</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.36</td>\n",
-       "      <td>58.0</td>\n",
-       "      <td>14.85</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.48</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.28</td>\n",
-       "      <td>37.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>8.80</td>\n",
-       "      <td>52.87</td>\n",
-       "      <td>0.15</td>\n",
-       "      <td>4.2</td>\n",
-       "      <td>0.10</td>\n",
-       "      <td>200.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>97.0</td>\n",
-       "      <td>14.80</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>55.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.52</td>\n",
-       "      <td>0.28</td>\n",
-       "      <td>0.21</td>\n",
-       "      <td>9.26</td>\n",
-       "      <td>52.58</td>\n",
-       "      <td>0.17</td>\n",
-       "      <td>4.8</td>\n",
-       "      <td>0.20</td>\n",
-       "      <td>275.0</td>\n",
-       "      <td>1.25</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.16</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.49</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>14.63</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.58</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.36</td>\n",
-       "      <td>45.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 42 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     Eu   K2O    Pb   MgO   SiO2   MnO   Rb    Nb     Cr    Lu  ...    Th  \\\n",
-       "0   NaN  0.28   NaN  9.76  52.80  0.16  4.7  0.20  244.0   NaN  ...   NaN   \n",
-       "1   NaN  0.26   NaN  9.41  52.07  0.19  4.1  0.00  260.0   NaN  ...   NaN   \n",
-       "2  0.47  0.26  0.54  8.56  52.84  0.16  2.8  0.13  167.0  0.23  ...  0.14   \n",
-       "3   NaN  0.32   NaN  8.80  52.87  0.15  4.2  0.10  200.0   NaN  ...   NaN   \n",
-       "4  0.52  0.28  0.21  9.26  52.58  0.17  4.8  0.20  275.0  1.25  ...  0.16   \n",
-       "\n",
-       "   FeO    Dy     Cu  Al2O3  Ho    Er  Ta    Sm    Ba  \n",
-       "0  NaN   NaN   19.0  13.68 NaN   NaN NaN   NaN   4.0  \n",
-       "1  NaN   NaN  112.0  14.46 NaN   NaN NaN   NaN  45.0  \n",
-       "2  NaN  2.36   58.0  14.85 NaN  1.48 NaN  1.28  37.0  \n",
-       "3  NaN   NaN   97.0  14.80 NaN   NaN NaN   NaN  55.0  \n",
-       "4  NaN  2.49   88.0  14.63 NaN  1.58 NaN  1.36  45.0  \n",
-       "\n",
-       "[5 rows x 42 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "## Filter the data \n",
-    "Doucet_data = Doucet_data[(Doucet_data['type']=='MORB') | (Doucet_data['type']=='OIB') | (Doucet_data['type']=='ARC-O')].reset_index(drop=True)\n",
-    "Doucet_data.head()\n",
-    "\n",
-    "## To make the column names consistent with Vermeesch (2006), let's rename the type names \"ARC-O\" to \"IAB\"\n",
-    "Doucet_data.loc[Doucet_data['type']=='ARC-O','type'] = 'IAB'\n",
-    "Doucet_data.head()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Data preprocessing\n",
-    "\n",
-    "- Encode the target variable 'type' using LabelEncoder\n",
-    "- Split the data into features (X) and target (y)\n",
-    "- Impute missing values using median imputation\n",
-    "- Split the data into training and testing sets\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "X_train.shape = (15403, 41)\n",
-      "X_test.shape = (6602, 41)\n"
-     ]
-    }
-   ],
-   "source": [
-    "## Encode the target variable 'type' using LabelEncoder\n",
-    "le = LabelEncoder()\n",
-    "le.fit(Doucet_data['type'])\n",
-    "\n",
-    "## Split the data into features (X) and target (y)\n",
-    "X = Doucet_data.drop('type',axis=1)\n",
-    "X_columns = X.columns ## save the column names\n",
-    "y = Doucet_data['type']\n",
-    "y = le.transform(y)\n",
-    "\n",
-    "## Impute missing values using median imputation\n",
-    "imputer = SimpleImputer(strategy='median')\n",
-    "imputer.fit(X)\n",
-    "X = imputer.transform(X)   \n",
-    "\n",
-    "## Split the data into training and testing sets. Use 30% of the data as the testing set. Set random_state=42\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
-    "\n",
-    "## Let's check the shape of the training and testing sets\n",
-    "print(f\"{X_train.shape = }\")\n",
-    "print(f\"{X_test.shape = }\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Build a logistic regression classifier\n",
-    "\n",
-    "Review the previous lectures on logistic regression and how to build a logistic regression classifier using `sklearn`.\n",
-    "\n",
-    "Try to build a logistic regression classifier and evaluate its accuracy using Doucet et al. (2022) data.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "accuracy = 0.947440169645562\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "## Build a logistic regression classifier. \n",
-    "## Remember to standardize the data using StandardScaler\n",
-    "## Hint: using make_pipeline and StandardScaler\n",
-    "model_linear = make_pipeline(StandardScaler(),LogisticRegression(solver=\"liblinear\"))\n",
-    "\n",
-    "## train the model\n",
-    "model_linear.fit(X_train,y_train)\n",
-    "\n",
-    "## predict on the test set\n",
-    "y_pred = model_linear.predict(X_test)\n",
-    "\n",
-    "## evaluate the model\n",
-    "accuracy = accuracy_score(y_test,y_pred)\n",
-    "print(f\"{accuracy = }\")\n",
-    "\n",
-    "## plot confusion matrix\n",
-    "conf_matrix = confusion_matrix(y_test,y_pred)\n",
-    "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=le.classes_)\n",
-    "disp.plot(cmap=plt.cm.Blues, values_format='d', colorbar=False);\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Build a SVM classifier\n",
-    "\n",
-    "Let's try to use the more advanced machine learning models SVM to see if they can improve the accuracy.\n",
-    "\n",
-    "First, let's try a linear kernel SVM, which is similar to the logistic regression classifier we built above. \n",
-    "\n",
-    "Review the previous lectures on SVM and how to build a SVM classifier using `sklearn`.\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "accuracy = 0.9528930627082702\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "## Build a SVM classifier using kernel=\"linear\"\n",
-    "## Remember to standardize the data using StandardScaler\n",
-    "## Hint: using make_pipeline and StandardScaler\n",
-    "model_svm = make_pipeline(StandardScaler(),SVC(kernel=\"linear\"))\n",
-    "\n",
-    "## train the model\n",
-    "model_svm.fit(X_train,y_train)\n",
-    "\n",
-    "## predict on the test set\n",
-    "y_pred = model_svm.predict(X_test)\n",
-    "\n",
-    "## evaluate the model\n",
-    "accuracy = accuracy_score(y_test,y_pred)\n",
-    "print(f\"{accuracy = }\")\n",
-    "\n",
-    "## plot confusion matrix\n",
-    "conf_matrix = confusion_matrix(y_test,y_pred)\n",
-    "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=le.classes_)\n",
-    "disp.plot(cmap=plt.cm.Blues, values_format='d', colorbar=False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we dicussed in class, the advantage of SVM is that it can handle non-linear decision boundaries. Let's try a RBF kernel SVM.\n",
-    "\n",
-    "The RBF kernel SVM is more computationally expensive than the linear kernel SVM, but it can handle non-linear decision boundaries.\n",
-    "\n",
-    "The gamma parameter controls the smoothness of the decision boundary. A large gamma value will create a more complex decision boundary, while a small gamma value will create a simpler decision boundary.\n",
-    "\n",
-    "First build a SVM classifier using a default gamma value of 0.1.\n",
-    "\n",
-    "Then try a different gamma value and see if you can find a better model. \n",
-    "\n",
-    "Report the best gamma value and the accuracy of the model.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "accuracy = 0.9792487125113601\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "## Build a SVM classifier using SVC(kernel=\"rbf\",gamma=0.1)\n",
-    "## Remember to standardize the data using StandardScaler\n",
-    "## Hint: using make_pipeline and StandardScaler\n",
-    "model_svm = make_pipeline(StandardScaler(),SVC(kernel=\"rbf\",gamma=0.1))\n",
-    "\n",
-    "## train the model\n",
-    "model_svm.fit(X_train,y_train)\n",
-    "\n",
-    "## predict on the test set\n",
-    "y_pred = model_svm.predict(X_test)\n",
-    "\n",
-    "## evaluate the model\n",
-    "accuracy = accuracy_score(y_test,y_pred)\n",
-    "print(f\"{accuracy = }\")\n",
-    "\n",
-    "## plot confusion matrix\n",
-    "conf_matrix = confusion_matrix(y_test,y_pred)\n",
-    "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=le.classes_)\n",
-    "disp.plot(cmap=plt.cm.Blues, values_format='d', colorbar=False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Build a decision tree classifier\n",
-    "\n",
-    "Review the previous lectures on decision tree and how to build a decision tree classifier using `sklearn`.\n",
-    "\n",
-    "Similar to the gamma parameter in SVM, the `max_depth` parameter in decision tree can also affect the complexity of the model.\n",
-    "\n",
-    "The default setting is `max_depth=None` which means it will keep going and going until the leafs of the tree contain a single category. \n",
-    "\n",
-    "First, let's build a decision tree classifier with the default setting.\n",
-    "\n",
-    "Then try different `max_depth` values and see if you can find a better model.\n",
-    "\n",
-    "Report the best `max_depth` value and the accuracy of the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "accuracy = 0.965010602847622\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "## Build a decision tree classifier with default setting DecisionTreeClassifier(max_depth=None)\n",
-    "model_tree = DecisionTreeClassifier(max_depth=None)\n",
-    "\n",
-    "## train the model\n",
-    "model_tree.fit(X_train,y_train)\n",
-    "\n",
-    "## predict on the test set\n",
-    "y_pred = model_tree.predict(X_test)\n",
-    "\n",
-    "## evaluate the model\n",
-    "accuracy = accuracy_score(y_test,y_pred)\n",
-    "print(f\"{accuracy = }\")\n",
-    "\n",
-    "## plot confusion matrix\n",
-    "conf_matrix = confusion_matrix(y_test,y_pred)\n",
-    "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=le.classes_)\n",
-    "disp.plot(cmap=plt.cm.Blues, values_format='d', colorbar=False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we discussed in class, the decision tree classifier is a white box model, which means we can understand the importance of each feature in the decision making process."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "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>feature</th>\n",
-       "      <th>importance</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>TiO2</td>\n",
-       "      <td>0.463511</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>MgO</td>\n",
-       "      <td>0.136978</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>Al2O3</td>\n",
-       "      <td>0.085395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>Na2O</td>\n",
-       "      <td>0.059288</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Sr</td>\n",
-       "      <td>0.053626</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>SiO2</td>\n",
-       "      <td>0.038245</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>Zr</td>\n",
-       "      <td>0.022979</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>Fe2O3</td>\n",
-       "      <td>0.017267</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>Ni</td>\n",
-       "      <td>0.015391</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Nb</td>\n",
-       "      <td>0.015360</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   feature  importance\n",
-       "17    TiO2    0.463511\n",
-       "3      MgO    0.136978\n",
-       "35   Al2O3    0.085395\n",
-       "28    Na2O    0.059288\n",
-       "26      Sr    0.053626\n",
-       "4     SiO2    0.038245\n",
-       "29      Zr    0.022979\n",
-       "21   Fe2O3    0.017267\n",
-       "24      Ni    0.015391\n",
-       "7       Nb    0.015360"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "## print the most important features\n",
-    "feature_importances = model_tree.feature_importances_\n",
-    "feature_df = pd.DataFrame({'feature':X_columns,'importance':feature_importances})\n",
-    "feature_df = feature_df.sort_values(by='importance',ascending=False)\n",
-    "feature_df.head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "How does the accuracy of the decision tree based on larger dataset from Doucet et al. (2022) compare to that using the smaller dataset from Vermeesch (2006)? Write your answer in the markdown cell below."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "What similarities and differences are there between the importance of different data fields (feature importance) between the decision tree built on the Vermeesch (2006) data compilation vs that built on the Doucet et al. (2022) data compilation?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Applying the model trained on Doucet et al. (2022) dataset to the Vermeesch (2006) dataset\n",
-    "\n",
-    "The goal of machine learning is to generalize the model to new data. Here we will apply the decision tree classifier trained on the Doucet et al. (2022) dataset to the Vermeesch (2006) dataset to see how well the model can be applied to new data.\n",
-    "\n",
-    "Let's apply the linear model, SVM, and decision tree classifier to the Vermeesch (2006) dataset and evaluate their accuracy.\n",
-    "\n",
-    "Please note that you would need to preprocess the Vermeesch (2006) dataset in the same way as the Doucet et al. (2022) dataset.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Logistic regression accuracy: 0.766\n",
-      "SVM accuracy: 0.659\n",
-      "Decision tree accuracy: 0.680\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "## Preprocess the Vermeesch (2006) dataset\n",
-    "## Split the data into features (X) and target (y)\n",
-    "X = Vermeesch_data.drop('type',axis=1)\n",
-    "X = X[X_columns] ## keep column in the same order as Doucet et al. (2022) dataset\n",
-    "y = Vermeesch_data['type']\n",
-    "y = le.transform(y) ## reuse the label encoder we defined earlier\n",
-    "\n",
-    "## Impute missing values using median imputation \n",
-    "X = imputer.transform(X) ## use the same imputer we defined earlier\n",
-    "\n",
-    "## Apply the linear model and evaluate the accuracy\n",
-    "y_pred = model_linear.predict(X)\n",
-    "accuracy = accuracy_score(y,y_pred)\n",
-    "print(f\"Logistic regression accuracy: {accuracy:.3f}\")\n",
-    "\n",
-    "## Apply the SVM model and evaluate the accuracy\n",
-    "y_pred = model_svm.predict(X)\n",
-    "accuracy = accuracy_score(y,y_pred)\n",
-    "print(f\"SVM accuracy: {accuracy:.3f}\")\n",
-    "\n",
-    "## Apply the decision tree model and evaluate the accuracy\n",
-    "y_pred = model_tree.predict(X)\n",
-    "accuracy = accuracy_score(y,y_pred)\n",
-    "print(f\"Decision tree accuracy: {accuracy:.3f}\")\n",
-    "\n",
-    "## plot confusion matrix\n",
-    "conf_matrix = confusion_matrix(y,y_pred)\n",
-    "disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=le.classes_)\n",
-    "disp.plot(cmap=plt.cm.Blues, values_format='d', colorbar=False);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<!-- Comment on how well these models perform on the Vermeesch (2006) dataset. Why do you think the models perform better or worse on the Vermeesch (2006) dataset? -->\n",
-    "Based on the accuracy, please comment on the following questions:\n",
-    "\n",
-    "- How does the accuracy of models trained on Doucet et al. (2022) dataset compare to the model trained on Vermeesch (2006) dataset?\n",
-    "Recall the accuracy you get in previous class using the model trained on Vermeesch (2006) dataset.\n",
-    "\n",
-    "- Among these models, which one performs the best on the Doucet et al. (2022) dataset and which one performane the worst? \n",
-    "Similar question for the Vermeesch (2006) dataset. Are the best and worst models the same or totally different?\n",
-    "\n",
-    "- What are the possible reasons for the difference in accuracy?  What does this imply for the predictive power of machine learning models? Hint: think about model fitting and generalization.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "base",
-   "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": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}