nb1_jupyter-pandas_solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook 1 - Introduction to Jupyter, exploring/manipulating data with Pandas and plotting with Matplotlib\n",
    "\n",
    "- Feature engineering: Date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to Jupyter notebooks\n",
    "\n",
    "*Optional*\n",
    "\n",
    "You can add extended functionalities by installing `conda install -c conda-forge jupyter_contrib_nbextensions`. This will add a tab to your dashboard, from where you can select the extension. By far the most useful in my opinion is the collapsible heading extension in combination with the table of contents extensions, which allow you to get a lot more organized!\n",
    "\n",
    "For changing the look of the notebook, you want to run `pip install jupyterthemes`. This will allow you to select different themes. For more information visit: https://github.com/dunovank/jupyter-themes\n",
    "\n",
    "### How to use these notebooks\n",
    "\n",
    "Do **NOT** just execute all the cells and feel good about it! To learn, open a new notebook and type everything yourself. Use the Jupyter notebook functionalities (see below). Looking at the solution is allowed but **no Copy-Paste**.\n",
    "\n",
    "If you are not that familiar with Python and the concepts presented here, your goal should be to do replicate the steps in these notebooks with as little peaking as possible. If you are more advanced, try to solve the additional challenges below.\n",
    "\n",
    "\n",
    "### Basic usage\n",
    "\n",
    "To start a Jupyter notebook, simply type `jupyter notebook` in the command line. If you do this locally (e.g. on your laptop) this should automatically open up a browser window with the dashboard. We will cover remote usage below.\n",
    "\n",
    "On the dashboard you can see all files in your current directory. (Note that you can click on any file and edit it.) Jupyter notebook files end in `.ipynb`. To create a new notebook, click on `New` and select a Python version. Now a new tab will appear with your empty notebook.\n",
    "\n",
    "### Cells\n",
    "\n",
    "Jupyter notebooks have cells such as this one. There are different types of cells. The two you will use most frequently are code cells and Markdown cells. This here is a Markdown cell, which allows you to write formatted text using Markdown. For a quick Markdown introduction, check out: https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet\n",
    "\n",
    "Note that in markdown you can also type Latex:\n",
    "\n",
    "$x=\\frac{a}{b}$\n",
    "\n",
    "To switch the cell type, either use the toolbar above or press `y` for code and `m` for markdown in command mode, which we will get to now.\n",
    "\n",
    "### Modes\n",
    "\n",
    "To navigate Jupyter notebooks there are two modes: Command mode, which you can enter by pressing `esc` and edit mode which you can enter by pressing `Enter`. In edit mode you edit inside a cell, while in command mode you can edit the cells themselves. If you get confused about all the keys just press `h` in command mode, which will bring up a list of all keyboard shortcuts. Of course, you can also use the mouse and the toolbar.\n",
    "\n",
    "### Executing a cell\n",
    "\n",
    "To execute a cell, press `Shift + Enter`. This will run the code or render the text and jump to the next cell. If you would like to insert a cell below, type `Alt + Enter`.\n",
    "\n",
    "Now let's start by actually writing some code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import modules just as you would in a regular Python script\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# This will allow you to display plots inside the notebook.\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign variables\n",
    "a = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of the last object in the cell will be displayed\n",
    "b = 2\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You do not have to work from top to bottom. You can always edit cells above.\n",
    "\n",
    "But pay attention: This means that the notebook might throw an error when executed in after restarting the kernel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get function docs\n",
    "\n",
    "Next, let's create a basic plot and also note a super-handy functionality of the notebook: When using a function and pressing `Shift + Tab` when the cursor is inside the function parenthesis, it will bring up the list of arguments. Pressing it twice will bring up the doc string.\n",
    "\n",
    "Similarly, executing `?function_name` will bring up the doc string in a separate window. `??function_name` will bring up the source code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAADkJJREFUeJzt3X+o3fV9x/Hna+raUR0qXsUmkSsl\nK7WljeUiDv9xtbT+KEYLDmWzoRPSPxQUhC22MDuGkNHVjrLNkU7RMqsTVAzVrabOIYX5I7rMRqNr\naDO9TTDp3KpFcETf++N+M0/Tm3vOveecnHs/ez7gcs/53M855x0Jz3z9nh83VYUkqV2/NukBJEnj\nZeglqXGGXpIaZ+glqXGGXpIaZ+glqXGGXpIaZ+glqXGGXpIad+ykBwA45ZRTanp6etJjSNKK8uyz\nz/6sqqb67VsWoZ+enmb79u2THkOSVpQk/zHIPk/dSFLjDL0kNc7QS1LjDL0kNc7QS1LjDL0kNc7Q\nS1LjDL0kNc7QS1LjlsU7Y6V+pjc9PLHH3rP5kok9tjQKHtFLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMM\nvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1rm/ok6xJ8niSXUleSHJ9t/7V\nJD9NsqP7urjnNjcl2Z3k5SSfHecfQJK0sEE+j/4gcGNVPZfkBODZJNu6n32jqv68d3OSs4ArgY8C\nHwS+n+S3quqdUQ4uSRpM3yP6qtpXVc91l98EdgGrFrjJeuDeqnq7qn4C7AbOGcWwkqTFW9Q5+iTT\nwNnAU93SdUmeT3JHkpO6tVXAqz03m2XhfxgkSWM0cOiTHA/cD9xQVW8AtwEfAtYB+4CvH9o6z81r\nnvvbmGR7ku0HDhxY9OCSpMEMFPokxzEX+bur6gGAqnqtqt6pqneBb/He6ZlZYE3PzVcDew+/z6ra\nUlUzVTUzNTU1zJ9BkrSAQV51E+B2YFdV3dqzfnrPtsuBnd3lrcCVSd6X5ExgLfD06EaWJC3GIK+6\nOQ+4Gvhhkh3d2peBq5KsY+60zB7gSwBV9UKS+4AXmXvFzrW+4kaSJqdv6KvqB8x/3v2RBW5zC3DL\nEHNJkkbEd8ZKUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMM\nvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1\nztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuP6hj7JmiSPJ9mV5IUk13frJyfZluRH3feTuvUk+WaS3Ume\nT/LJcf8hJElHNsgR/UHgxqr6CHAucG2Ss4BNwGNVtRZ4rLsOcBGwtvvaCNw28qklSQPrG/qq2ldV\nz3WX3wR2AauA9cBd3ba7gMu6y+uBb9ecJ4ETk5w+8sklSQM5djGbk0wDZwNPAadV1T6Y+8cgyand\ntlXAqz03m+3W9h12XxuZO+LnjDPOWMLomoTpTQ9PegRJizTwk7FJjgfuB26oqjcW2jrPWv3KQtWW\nqpqpqpmpqalBx5AkLdJAoU9yHHORv7uqHuiWXzt0Sqb7vr9bnwXW9Nx8NbB3NONKkhZrkFfdBLgd\n2FVVt/b8aCuwobu8AXioZ/0L3atvzgV+fugUjyTp6BvkHP15wNXAD5Ps6Na+DGwG7ktyDfAKcEX3\ns0eAi4HdwFvAF0c6sSRpUfqGvqp+wPzn3QEumGd/AdcOOZe0bEzqCeg9my+ZyOOqPb4zVpIaZ+gl\nqXGGXpIaZ+glqXGGXpIaZ+glqXGGXpIaZ+glqXGGXpIaZ+glqXGGXpIaZ+glqXGGXpIaZ+glqXGG\nXpIaZ+glqXGGXpIaZ+glqXGGXpIaZ+glqXGGXpIaZ+glqXGGXpIaZ+glqXGGXpIaZ+glqXGGXpIa\nZ+glqXF9Q5/kjiT7k+zsWftqkp8m2dF9Xdzzs5uS7E7ycpLPjmtwSdJgBjmivxO4cJ71b1TVuu7r\nEYAkZwFXAh/tbvPXSY4Z1bCSpMXrG/qqegJ4fcD7Ww/cW1VvV9VPgN3AOUPMJ0ka0jDn6K9L8nx3\nauekbm0V8GrPntluTZI0IUsN/W3Ah4B1wD7g69165tlb891Bko1JtifZfuDAgSWOIUnqZ0mhr6rX\nquqdqnoX+BbvnZ6ZBdb0bF0N7D3CfWypqpmqmpmamlrKGJKkASwp9ElO77l6OXDoFTlbgSuTvC/J\nmcBa4OnhRpQkDePYfhuS3AOcD5ySZBa4GTg/yTrmTsvsAb4EUFUvJLkPeBE4CFxbVe+MZ3RJ0iD6\nhr6qrppn+fYF9t8C3DLMUJKk0fGdsZLUOEMvSY0z9JLUOEMvSY0z9JLUOEMvSY0z9JLUOEMvSY0z\n9JLUOEMvSY0z9JLUOEMvSY0z9JLUOEMvSY0z9JLUOEMvSY0z9JLUuL6/YUrSZExvengij7tn8yUT\neVyNj0f0ktQ4Qy9JjTP0ktQ4Qy9JjTP0ktQ4Qy9JjTP0ktQ4Qy9JjTP0ktQ4Qy9JjfMjEFagSb01\nXtLK1PeIPskdSfYn2dmzdnKSbUl+1H0/qVtPkm8m2Z3k+SSfHOfwkqT+Bjl1cydw4WFrm4DHqmot\n8Fh3HeAiYG33tRG4bTRjSpKWqm/oq+oJ4PXDltcDd3WX7wIu61n/ds15EjgxyemjGlaStHhLfTL2\ntKraB9B9P7VbXwW82rNvtlv7FUk2JtmeZPuBAweWOIYkqZ9Rv+om86zVfBuraktVzVTVzNTU1IjH\nkCQdstTQv3bolEz3fX+3Pgus6dm3Gti79PEkScNaaui3Ahu6yxuAh3rWv9C9+uZc4OeHTvFIkiaj\n7+vok9wDnA+ckmQWuBnYDNyX5BrgFeCKbvsjwMXAbuAt4ItjmFmStAh9Q19VVx3hRxfMs7eAa4cd\nSpI0On4EgiQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBL\nUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMM\nvSQ1ztBLUuMMvSQ1ztBLUuOOHebGSfYAbwLvAAeraibJycDfA9PAHuB3q+q/hhtTkrRUozii/52q\nWldVM931TcBjVbUWeKy7LkmakHGculkP3NVdvgu4bAyPIUka0LChL+DRJM8m2ditnVZV+wC676cO\n+RiSpCEMdY4eOK+q9iY5FdiW5KVBb9j9w7AR4IwzzhhyDEnSkQx1RF9Ve7vv+4EHgXOA15KcDtB9\n33+E226pqpmqmpmamhpmDEnSApYc+iQfSHLCocvAZ4CdwFZgQ7dtA/DQsENKkpZumFM3pwEPJjl0\nP9+pqn9M8gxwX5JrgFeAK4YfU5K0VEsOfVX9GPjEPOv/CVwwzFCSpNEZ9snY/9emNz086REkqS8/\nAkGSGmfoJalxhl6SGuc5ekm/ZJLPPe3ZfMnEHrtlHtFLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1\nztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuMMvSQ1ztBLUuP8DVOSlo1J\n/Xar1n+zlUf0ktQ4Qy9JjTP0ktQ4Qy9JjTP0ktS4Ff+qm0k9Sy9JK8XYjuiTXJjk5SS7k2wa1+NI\nkhY2ltAnOQb4K+Ai4CzgqiRnjeOxJEkLG9epm3OA3VX1Y4Ak9wLrgRfH9HiStGSTPAV8NN6sNa5T\nN6uAV3uuz3ZrkqSjbFxH9JlnrX5pQ7IR2Nhd/UWSl0fwuKcAPxvB/RxNznx0rMSZYWXO7cyLkD8b\n6uYfHmTTuEI/C6zpub4a2Nu7oaq2AFtG+aBJtlfVzCjvc9yc+ehYiTPDypzbmY+eJNsH2TeuUzfP\nAGuTnJnk14Erga1jeixJ0gLGckRfVQeTXAd8DzgGuKOqXhjHY0mSFja2N0xV1SPAI+O6/yMY6amg\no8SZj46VODOszLmd+egZaO5UVf9dkqQVy8+6kaTGNRX6JH+a5PkkO5I8muSDk55pEEm+luSlbvYH\nk5w46Zn6SXJFkheSvJtkWb9aYSV+HEeSO5LsT7Jz0rMMKsmaJI8n2dX93bh+0jP1k+T9SZ5O8m/d\nzH8y6ZkGleSYJP+a5Lv99jYVeuBrVfXxqloHfBf440kPNKBtwMeq6uPAvwM3TXieQewEPg88MelB\nFrKCP47jTuDCSQ+xSAeBG6vqI8C5wLUr4L/128CnquoTwDrgwiTnTnimQV0P7BpkY1Ohr6o3eq5+\ngMPepLVcVdWjVXWwu/okc+87WNaqaldVjeJNbuP2fx/HUVX/Axz6OI5lraqeAF6f9ByLUVX7quq5\n7vKbzEVoWb8jvub8ort6XPe17LuRZDVwCfC3g+xvKvQASW5J8irwe6ycI/pefwD8w6SHaIgfxzEB\nSaaBs4GnJjtJf90pkB3AfmBbVS37mYG/AP4QeHeQzSsu9Em+n2TnPF/rAarqK1W1BrgbuG6y076n\n39zdnq8w97+/d09u0vcMMvMK0PfjODRaSY4H7gduOOz/spelqnqnO927GjgnyccmPdNCknwO2F9V\nzw56mxX3i0eq6tMDbv0O8DBw8xjHGVi/uZNsAD4HXFDL5DWvi/hvvZz1/TgOjU6S45iL/N1V9cCk\n51mMqvrvJP/M3HMjy/lJ8POAS5NcDLwf+M0kf1dVv3+kG6y4I/qFJFnbc/VS4KVJzbIYSS4E/gi4\ntKremvQ8jfHjOI6SJAFuB3ZV1a2TnmcQSaYOvcotyW8An2aZd6Oqbqqq1VU1zdzf539aKPLQWOiB\nzd2pheeBzzD3rPRK8JfACcC27qWhfzPpgfpJcnmSWeC3gYeTfG/SM82ne5L70Mdx7ALuWwkfx5Hk\nHuBfgA8nmU1yzaRnGsB5wNXAp7q/xzu6o87l7HTg8a4ZzzB3jr7vyxVXGt8ZK0mNa+2IXpJ0GEMv\nSY0z9JLUOEMvSY0z9JLUOEMvSY0z9JLUOEMvSY37X5h3hoeumqItAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23a60ac2a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.random.normal(size=1000))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?plt.hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "??plt.hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sharing a notebook\n",
    "\n",
    "Here are several ways to share a Jupyter notebook.\n",
    "\n",
    "**GitHub**\n",
    "\n",
    "If the notebook is in a GitHub repository (which it probably should be!), GitHub will simply render the notebook for you like this: https://github.com/raspstephan/ESS-Python-Tutorial/blob/master/notebooks/jupyter-intro.ipynb\n",
    "\n",
    "**NBViewer**\n",
    "\n",
    "Another option is to copy the GitHub URL to http://nbviewer.jupyter.org/.\n",
    "\n",
    "**As HTML or PDF**\n",
    "\n",
    "You can also export the notebook to HTML or PDF by typing `jupyter nbconvert --to FORMAT notebook.ipynb` where `FORMAT` can be `pdf`, `html` or many others.\n",
    "\n",
    "More information here: https://ipython.org/ipython-doc/3/notebook/nbconvert.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remote setup\n",
    "\n",
    "Now let's see how we can use Jupyter notebooks remotely.\n",
    "\n",
    "** Port tunnel**\n",
    "\n",
    "If you want to run Jupyter on a remote computer, you need to create a port tunnel in order to open the notebook locally in the browser. The command to do this is:\n",
    "\n",
    "`ssh -L localhost:8888:localhost:8888 user@host`\n",
    "\n",
    "This will create a tunnel for port 8888. If you do your actual computations on a different node, you can do the same thing to create a tunnel to that node:\n",
    "\n",
    "`ssh -L localhost:8888:localhost:8888 c-3-39`\n",
    "\n",
    "Now locally in your browser, go to `localhost:8888`. Most probably it will ask you for a token. You will find the token in the terminal where Jupyter is running, where it will look something like this:\n",
    "\n",
    "`http://localhost:8888/?token=940f1a436d91bde5ef15e3640dd66bd7d3f724a68513c99f`\n",
    "\n",
    "Just copy and paste the token after = and you should be good to go.\n",
    "\n",
    "**Screen**\n",
    "\n",
    "Since remote connections die sometimes (or all the time), it is really handy to use screen. Screen enables you to run processes remotely, which will continue even after your connection has timed out.\n",
    "\n",
    "To start a screen session, just type `screen`. To execute screen commands, you need to press `Ctrl + a` followed by a command. `c` will create a new window. `n` and `p` will go to the next and previous window. k will kill the window. Finally, `d` will detach from the window. To log back into a screen session after detaching or having been disconnected, simply type `screen -r`.\n",
    "\n",
    "Here is more information about screen: http://aperiodic.net/screen/quick_reference\n",
    "\n",
    "**Mounting**\n",
    "\n",
    "Finally, to edit remote files in your preferred editor (PyCharm, VSC, etc.), you might want to mount a remote directory. For Linux/Mac you can use sshfs. On the Mac you have to install OSXFuse. Here is a link outlining the process: https://blogs.harvard.edu/acts/2013/11/08/the-newbie-how-to-set-up-sshfs-on-mac-os-x/\n",
    "\n",
    "Then you can mount a remote directory: `sshfs srasp@gplogin1.ps.uci.edu:/export/home/srasp/ greenplanet_home/`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring and manipulating data with Pandas plus some basic plotting - Post-processing NWP forecasts\n",
    "\n",
    "Our first dataset will be a postprocessing dataset. Our aim is to correct biases of NWP temperature forecasts. The forecasts are ensemble means of the ECMWF TIGGE archive. The corresponding observations are taken at DWD surface stations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATADIR = './dataset1/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pp_sample_submission.csv  pp_test.csv  pp_train.csv\r\n"
     ]
    }
   ],
   "source": [
    "# Command line with !; $to use local variables in command line\n",
    "!ls $DATADIR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pp_sample_submission.csv', 'pp_test.csv', 'pp_train.csv']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unfortunately this might not work on Windows depending on your setup, so we will use os\n",
    "os.listdir(DATADIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first dataset we will use in this workshop is tabular. Tabular data are what you would use in Excel. To work with tabular data in Python, we will use Pandas. Pandas is very widely used which means that you will find an answer to any question you can think of online. Here is a quick overview of basic functionalities: https://pandas.pydata.org/pandas-docs/stable/basics.html\n",
    "\n",
    "Tabular data files are often saved as `.csv` files which we can easily read into a Pandas `DataFrame`.\n",
    "\n",
    "There are two datasets: a training and a test datset. First, let's ignore the test set and open the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# Don't worry about this statement. This just suppresses warnings later on.\n",
    "pd.options.mode.chained_assignment = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",t2m_obs,time,station,t2m_fc_mean,orog,station_alt,station_lat,station_lon,u_pl500_fc_mean,v_pl500_fc_mean,gh_pl500_fc_mean,u_pl850_fc_mean,v_pl850_fc_mean,q_pl850_fc_mean,cape_fc_mean,sp_fc_mean,tcc_fc_mean,sshf_fc_mean,slhf_fc_mean,u10_fc_mean,v10_fc_mean,ssr_fc_mean,str_fc_mean,d2m_fc_mean,sm_fc_mean\r\n",
      "0,1.2,2011-01-01,3,-0.022290079,107.43946,202.0,50.7827,6.0941,4.7604675,-4.907918,5619.513,5.040613,-2.4240947,0.0020729504,2.517037,101379.51,98.05601,634981.4,39645.008,2.2089093,0.75530154,2433034.8,-3513225.0,273.57892,374.63348\r\n",
      "1,1.6,2011-01-01,44,2.0864146,19.397774,44.0,52.9335,8.237,14.442179,-10.303844,5579.092,16.77781,-6.1201735,0.0017127863,1.8534747,101709.04,98.7418,1592483.9,370638.6,6.223993,0.800821,2427059.2,-1702554.9,275.06873,388.6035\r\n",
      "2,-6.3,2011-01-01,71,-6.609096,604.885,759.0,48.2156,8.9784,0.25433925,-6.2813725,5614.9375,0.8365133,-0.2675586,0.002297013,1.3541166,94432.85,91.60234,-1416809.8,-239365.98,-0.39441925,-0.95084035,4320586.0,-4435227.5,265.47473,437.023\r\n",
      "3,-3.0,2011-01-01,73,-5.0717525,461.4418,340.0,48.6159,13.0506,2.4893465,-10.044441,5588.1655,6.1789904,-1.3422626,0.002404796,1.821984,97368.55,91.13937,48696.496,-570603.4,1.9234713,0.33606747,5658946.5,-6691302.0,267.4964,366.52255\r\n",
      "4,0.5,2011-01-01,78,1.7435802,47.632523,65.0,52.4853,7.9126,11.60049,-9.078665,5588.1724,14.216161,-5.2134333,0.0016373554,1.9884366,101419.82,99.178986,1435826.9,616228.75,4.472726,0.6738203,2416039.5,-1872194.9,274.56433,263.73572\r\n",
      "5,-2.0,2011-01-01,91,-1.6467652,348.8699,300.0,50.7446,9.345,4.7360325,-7.3809066,5600.502,6.91943,-3.6368911,0.0016977584,0.991969,97728.27,99.15859,-437949.38,-131503.19,2.346411,1.1555793,2039684.1,-1838326.1,269.76184,373.94714\r\n",
      "6,3.4,2011-01-01,102,3.7684197,-0.19300345,32.0,53.8617,8.1266,21.955555,-13.723398,5553.8613,21.089575,-8.558664,0.0022098175,3.0073986,101625.16,98.13913,459276.16,-818385.75,12.63234,-0.44567385,2508306.2,-3847206.0,276.1935,\r\n",
      "7,,2011-01-01,125,-7.9482865,662.68945,756.0,47.8342,10.8667,0.3899494,-7.4064136,5603.9883,2.9416766,0.05649725,0.0024515719,0.5690889,94748.22,68.470856,-190734.56,-231717.19,0.4879644,1.1171349,6003780.5,-7940518.0,264.27023,372.93637\r\n",
      "8,-1.5,2011-01-01,131,-0.37933877,296.8392,296.0,51.0881,12.9326,8.423413,-12.893658,5563.9995,12.519774,-6.05727,0.0014666808,0.6320709,98477.74,85.5063,3846844.2,200367.44,4.465709,1.2320244,3020785.2,-5267415.0,271.5818,363.70914\r\n"
     ]
    }
   ],
   "source": [
    "# Command line option\n",
    "!head $f'{DATADIR}pp_train.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ",t2m_obs,time,station,t2m_fc_mean,orog,station_alt,station_lat,station_lon,u_pl500_fc_mean,v_pl500_fc_mean,gh_pl500_fc_mean,u_pl850_fc_mean,v_pl850_fc_mean,q_pl850_fc_mean,cape_fc_mean,sp_fc_mean,tcc_fc_mean,sshf_fc_mean,slhf_fc_mean,u10_fc_mean,v10_fc_mean,ssr_fc_mean,str_fc_mean,d2m_fc_mean,sm_fc_mean\n",
      "\n",
      "0,1.2,2011-01-01,3,-0.022290079,107.43946,202.0,50.7827,6.0941,4.7604675,-4.907918,5619.513,5.040613,-2.4240947,0.0020729504,2.517037,101379.51,98.05601,634981.4,39645.008,2.2089093,0.75530154,2433034.8,-3513225.0,273.57892,374.63348\n",
      "\n",
      "1,1.6,2011-01-01,44,2.0864146,19.397774,44.0,52.9335,8.237,14.442179,-10.303844,5579.092,16.77781,-6.1201735,0.0017127863,1.8534747,101709.04,98.7418,1592483.9,370638.6,6.223993,0.800821,2427059.2,-1702554.9,275.06873,388.6035\n",
      "\n",
      "2,-6.3,2011-01-01,71,-6.609096,604.885,759.0,48.2156,8.9784,0.25433925,-6.2813725,5614.9375,0.8365133,-0.2675586,0.002297013,1.3541166,94432.85,91.60234,-1416809.8,-239365.98,-0.39441925,-0.95084035,4320586.0,-4435227.5,265.47473,437.023\n",
      "\n",
      "3,-3.0,2011-01-01,73,-5.0717525,461.4418,340.0,48.6159,13.0506,2.4893465,-10.044441,5588.1655,6.1789904,-1.3422626,0.002404796,1.821984,97368.55,91.13937,48696.496,-570603.4,1.9234713,0.33606747,5658946.5,-6691302.0,267.4964,366.52255\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Windows-safe option\n",
    "def print_head(fn):\n",
    "    with open(fn) as f:\n",
    "        for i in range(5):\n",
    "            print(f.readline())\n",
    "print_head(f'{DATADIR}pp_train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load this dataset into a Pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.09 s, sys: 388 ms, total: 4.48 s\n",
      "Wall time: 4.01 s\n"
     ]
    }
   ],
   "source": [
    "# Time how long it takes to read file\n",
    "%time df_train = pd.read_csv(f'{DATADIR}pp_train.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Before we play around with the data, let's save a copy\n",
    "df_train_raw = df_train.copy()\n",
    "# But watch out for RAM usage for larger datasets!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "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>t2m_obs</th>\n",
       "      <th>time</th>\n",
       "      <th>station</th>\n",
       "      <th>t2m_fc_mean</th>\n",
       "      <th>orog</th>\n",
       "      <th>station_alt</th>\n",
       "      <th>station_lat</th>\n",
       "      <th>station_lon</th>\n",
       "      <th>u_pl500_fc_mean</th>\n",
       "      <th>v_pl500_fc_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>sp_fc_mean</th>\n",
       "      <th>tcc_fc_mean</th>\n",
       "      <th>sshf_fc_mean</th>\n",
       "      <th>slhf_fc_mean</th>\n",
       "      <th>u10_fc_mean</th>\n",
       "      <th>v10_fc_mean</th>\n",
       "      <th>ssr_fc_mean</th>\n",
       "      <th>str_fc_mean</th>\n",
       "      <th>d2m_fc_mean</th>\n",
       "      <th>sm_fc_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.2</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.022290</td>\n",
       "      <td>107.439460</td>\n",
       "      <td>202.0</td>\n",
       "      <td>50.7827</td>\n",
       "      <td>6.0941</td>\n",
       "      <td>4.760467</td>\n",
       "      <td>-4.907918</td>\n",
       "      <td>...</td>\n",
       "      <td>101379.51</td>\n",
       "      <td>98.056010</td>\n",
       "      <td>634981.400</td>\n",
       "      <td>39645.008</td>\n",
       "      <td>2.208909</td>\n",
       "      <td>0.755302</td>\n",
       "      <td>2433034.8</td>\n",
       "      <td>-3513225.0</td>\n",
       "      <td>273.57892</td>\n",
       "      <td>374.63348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.6</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>44</td>\n",
       "      <td>2.086415</td>\n",
       "      <td>19.397774</td>\n",
       "      <td>44.0</td>\n",
       "      <td>52.9335</td>\n",
       "      <td>8.2370</td>\n",
       "      <td>14.442179</td>\n",
       "      <td>-10.303844</td>\n",
       "      <td>...</td>\n",
       "      <td>101709.04</td>\n",
       "      <td>98.741800</td>\n",
       "      <td>1592483.900</td>\n",
       "      <td>370638.600</td>\n",
       "      <td>6.223993</td>\n",
       "      <td>0.800821</td>\n",
       "      <td>2427059.2</td>\n",
       "      <td>-1702554.9</td>\n",
       "      <td>275.06873</td>\n",
       "      <td>388.60350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-6.3</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>71</td>\n",
       "      <td>-6.609096</td>\n",
       "      <td>604.885000</td>\n",
       "      <td>759.0</td>\n",
       "      <td>48.2156</td>\n",
       "      <td>8.9784</td>\n",
       "      <td>0.254339</td>\n",
       "      <td>-6.281372</td>\n",
       "      <td>...</td>\n",
       "      <td>94432.85</td>\n",
       "      <td>91.602340</td>\n",
       "      <td>-1416809.800</td>\n",
       "      <td>-239365.980</td>\n",
       "      <td>-0.394419</td>\n",
       "      <td>-0.950840</td>\n",
       "      <td>4320586.0</td>\n",
       "      <td>-4435227.5</td>\n",
       "      <td>265.47473</td>\n",
       "      <td>437.02300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.0</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>73</td>\n",
       "      <td>-5.071753</td>\n",
       "      <td>461.441800</td>\n",
       "      <td>340.0</td>\n",
       "      <td>48.6159</td>\n",
       "      <td>13.0506</td>\n",
       "      <td>2.489346</td>\n",
       "      <td>-10.044441</td>\n",
       "      <td>...</td>\n",
       "      <td>97368.55</td>\n",
       "      <td>91.139370</td>\n",
       "      <td>48696.496</td>\n",
       "      <td>-570603.400</td>\n",
       "      <td>1.923471</td>\n",
       "      <td>0.336067</td>\n",
       "      <td>5658946.5</td>\n",
       "      <td>-6691302.0</td>\n",
       "      <td>267.49640</td>\n",
       "      <td>366.52255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>78</td>\n",
       "      <td>1.743580</td>\n",
       "      <td>47.632523</td>\n",
       "      <td>65.0</td>\n",
       "      <td>52.4853</td>\n",
       "      <td>7.9126</td>\n",
       "      <td>11.600490</td>\n",
       "      <td>-9.078665</td>\n",
       "      <td>...</td>\n",
       "      <td>101419.82</td>\n",
       "      <td>99.178986</td>\n",
       "      <td>1435826.900</td>\n",
       "      <td>616228.750</td>\n",
       "      <td>4.472726</td>\n",
       "      <td>0.673820</td>\n",
       "      <td>2416039.5</td>\n",
       "      <td>-1872194.9</td>\n",
       "      <td>274.56433</td>\n",
       "      <td>263.73572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   t2m_obs        time  station  t2m_fc_mean        orog  station_alt  \\\n",
       "0      1.2  2011-01-01        3    -0.022290  107.439460        202.0   \n",
       "1      1.6  2011-01-01       44     2.086415   19.397774         44.0   \n",
       "2     -6.3  2011-01-01       71    -6.609096  604.885000        759.0   \n",
       "3     -3.0  2011-01-01       73    -5.071753  461.441800        340.0   \n",
       "4      0.5  2011-01-01       78     1.743580   47.632523         65.0   \n",
       "\n",
       "   station_lat  station_lon  u_pl500_fc_mean  v_pl500_fc_mean     ...      \\\n",
       "0      50.7827       6.0941         4.760467        -4.907918     ...       \n",
       "1      52.9335       8.2370        14.442179       -10.303844     ...       \n",
       "2      48.2156       8.9784         0.254339        -6.281372     ...       \n",
       "3      48.6159      13.0506         2.489346       -10.044441     ...       \n",
       "4      52.4853       7.9126        11.600490        -9.078665     ...       \n",
       "\n",
       "   sp_fc_mean  tcc_fc_mean  sshf_fc_mean  slhf_fc_mean  u10_fc_mean  \\\n",
       "0   101379.51    98.056010    634981.400     39645.008     2.208909   \n",
       "1   101709.04    98.741800   1592483.900    370638.600     6.223993   \n",
       "2    94432.85    91.602340  -1416809.800   -239365.980    -0.394419   \n",
       "3    97368.55    91.139370     48696.496   -570603.400     1.923471   \n",
       "4   101419.82    99.178986   1435826.900    616228.750     4.472726   \n",
       "\n",
       "   v10_fc_mean  ssr_fc_mean  str_fc_mean  d2m_fc_mean  sm_fc_mean  \n",
       "0     0.755302    2433034.8   -3513225.0    273.57892   374.63348  \n",
       "1     0.800821    2427059.2   -1702554.9    275.06873   388.60350  \n",
       "2    -0.950840    4320586.0   -4435227.5    265.47473   437.02300  \n",
       "3     0.336067    5658946.5   -6691302.0    267.49640   366.52255  \n",
       "4     0.673820    2416039.5   -1872194.9    274.56433   263.73572  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first five rows. .tail() for the last five\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "980562"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many rows\n",
    "len(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['t2m_obs', 'time', 'station', 't2m_fc_mean', 'orog', 'station_alt',\n",
       "       'station_lat', 'station_lon', 'u_pl500_fc_mean', 'v_pl500_fc_mean',\n",
       "       'gh_pl500_fc_mean', 'u_pl850_fc_mean', 'v_pl850_fc_mean',\n",
       "       'q_pl850_fc_mean', 'cape_fc_mean', 'sp_fc_mean', 'tcc_fc_mean',\n",
       "       'sshf_fc_mean', 'slhf_fc_mean', 'u10_fc_mean', 'v10_fc_mean',\n",
       "       'ssr_fc_mean', 'str_fc_mean', 'd2m_fc_mean', 'sm_fc_mean'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](images/variables.png)\n",
    "https://arxiv.org/abs/1805.09091"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>t2m_obs</th>\n",
       "      <th>station</th>\n",
       "      <th>t2m_fc_mean</th>\n",
       "      <th>orog</th>\n",
       "      <th>station_alt</th>\n",
       "      <th>station_lat</th>\n",
       "      <th>station_lon</th>\n",
       "      <th>u_pl500_fc_mean</th>\n",
       "      <th>v_pl500_fc_mean</th>\n",
       "      <th>gh_pl500_fc_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>sp_fc_mean</th>\n",
       "      <th>tcc_fc_mean</th>\n",
       "      <th>sshf_fc_mean</th>\n",
       "      <th>slhf_fc_mean</th>\n",
       "      <th>u10_fc_mean</th>\n",
       "      <th>v10_fc_mean</th>\n",
       "      <th>ssr_fc_mean</th>\n",
       "      <th>str_fc_mean</th>\n",
       "      <th>d2m_fc_mean</th>\n",
       "      <th>sm_fc_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>908857.000000</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>9.805620e+05</td>\n",
       "      <td>9.805620e+05</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>9.805620e+05</td>\n",
       "      <td>9.805620e+05</td>\n",
       "      <td>980562.000000</td>\n",
       "      <td>886600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.531618</td>\n",
       "      <td>3726.726257</td>\n",
       "      <td>7.305595</td>\n",
       "      <td>295.234511</td>\n",
       "      <td>289.700186</td>\n",
       "      <td>50.812609</td>\n",
       "      <td>10.184040</td>\n",
       "      <td>8.823780</td>\n",
       "      <td>-0.302459</td>\n",
       "      <td>5607.319530</td>\n",
       "      <td>...</td>\n",
       "      <td>98167.966711</td>\n",
       "      <td>61.148280</td>\n",
       "      <td>-1.767204e+06</td>\n",
       "      <td>-8.152601e+06</td>\n",
       "      <td>0.792879</td>\n",
       "      <td>0.643888</td>\n",
       "      <td>1.928963e+07</td>\n",
       "      <td>-9.371092e+06</td>\n",
       "      <td>277.832031</td>\n",
       "      <td>343.767861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.579644</td>\n",
       "      <td>2719.500011</td>\n",
       "      <td>6.718977</td>\n",
       "      <td>234.161776</td>\n",
       "      <td>278.959584</td>\n",
       "      <td>1.915865</td>\n",
       "      <td>2.084653</td>\n",
       "      <td>10.804031</td>\n",
       "      <td>10.906147</td>\n",
       "      <td>148.716669</td>\n",
       "      <td>...</td>\n",
       "      <td>2799.084576</td>\n",
       "      <td>31.656075</td>\n",
       "      <td>4.201333e+06</td>\n",
       "      <td>5.924629e+06</td>\n",
       "      <td>2.601944</td>\n",
       "      <td>1.994099</td>\n",
       "      <td>1.228819e+07</td>\n",
       "      <td>3.442328e+06</td>\n",
       "      <td>6.616596</td>\n",
       "      <td>61.335377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-26.200000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-21.508630</td>\n",
       "      <td>-1.427856</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.398600</td>\n",
       "      <td>6.024400</td>\n",
       "      <td>-34.023964</td>\n",
       "      <td>-51.873432</td>\n",
       "      <td>4976.940000</td>\n",
       "      <td>...</td>\n",
       "      <td>84283.160000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.243611e+07</td>\n",
       "      <td>-5.462550e+07</td>\n",
       "      <td>-14.955598</td>\n",
       "      <td>-15.886876</td>\n",
       "      <td>3.300497e+05</td>\n",
       "      <td>-2.136808e+07</td>\n",
       "      <td>247.132420</td>\n",
       "      <td>64.123955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.800000</td>\n",
       "      <td>1639.000000</td>\n",
       "      <td>2.331773</td>\n",
       "      <td>67.390170</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>49.244500</td>\n",
       "      <td>8.576100</td>\n",
       "      <td>1.466554</td>\n",
       "      <td>-6.884149</td>\n",
       "      <td>5507.763700</td>\n",
       "      <td>...</td>\n",
       "      <td>96367.070000</td>\n",
       "      <td>34.416545</td>\n",
       "      <td>-4.712416e+06</td>\n",
       "      <td>-1.300108e+07</td>\n",
       "      <td>-1.076624</td>\n",
       "      <td>-0.712025</td>\n",
       "      <td>7.668573e+06</td>\n",
       "      <td>-1.182197e+07</td>\n",
       "      <td>273.451382</td>\n",
       "      <td>308.552310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.800000</td>\n",
       "      <td>3348.000000</td>\n",
       "      <td>7.676022</td>\n",
       "      <td>296.438720</td>\n",
       "      <td>238.000000</td>\n",
       "      <td>50.736400</td>\n",
       "      <td>10.048000</td>\n",
       "      <td>8.571194</td>\n",
       "      <td>0.297703</td>\n",
       "      <td>5629.717300</td>\n",
       "      <td>...</td>\n",
       "      <td>98335.900000</td>\n",
       "      <td>67.456060</td>\n",
       "      <td>-1.958288e+06</td>\n",
       "      <td>-7.106672e+06</td>\n",
       "      <td>0.762717</td>\n",
       "      <td>0.707770</td>\n",
       "      <td>1.845740e+07</td>\n",
       "      <td>-9.528052e+06</td>\n",
       "      <td>278.301150</td>\n",
       "      <td>358.254030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>5277.000000</td>\n",
       "      <td>12.487657</td>\n",
       "      <td>436.711360</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>52.178900</td>\n",
       "      <td>11.813300</td>\n",
       "      <td>15.717412</td>\n",
       "      <td>7.203771</td>\n",
       "      <td>5719.493700</td>\n",
       "      <td>...</td>\n",
       "      <td>100424.337500</td>\n",
       "      <td>90.847860</td>\n",
       "      <td>8.736628e+05</td>\n",
       "      <td>-2.717170e+06</td>\n",
       "      <td>2.310319</td>\n",
       "      <td>1.861252</td>\n",
       "      <td>2.953434e+07</td>\n",
       "      <td>-7.014966e+06</td>\n",
       "      <td>282.778200</td>\n",
       "      <td>385.054400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>30.400000</td>\n",
       "      <td>15555.000000</td>\n",
       "      <td>26.242172</td>\n",
       "      <td>1268.067900</td>\n",
       "      <td>2964.000000</td>\n",
       "      <td>55.011000</td>\n",
       "      <td>14.950600</td>\n",
       "      <td>53.232000</td>\n",
       "      <td>37.145084</td>\n",
       "      <td>5940.681000</td>\n",
       "      <td>...</td>\n",
       "      <td>104708.470000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>1.657920e+07</td>\n",
       "      <td>9.008404e+06</td>\n",
       "      <td>21.202560</td>\n",
       "      <td>18.126524</td>\n",
       "      <td>5.411776e+07</td>\n",
       "      <td>1.604214e+06</td>\n",
       "      <td>293.593700</td>\n",
       "      <td>541.179440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             t2m_obs        station    t2m_fc_mean           orog  \\\n",
       "count  908857.000000  980562.000000  980562.000000  980562.000000   \n",
       "mean        7.531618    3726.726257       7.305595     295.234511   \n",
       "std         6.579644    2719.500011       6.718977     234.161776   \n",
       "min       -26.200000       3.000000     -21.508630      -1.427856   \n",
       "25%         2.800000    1639.000000       2.331773      67.390170   \n",
       "50%         7.800000    3348.000000       7.676022     296.438720   \n",
       "75%        12.500000    5277.000000      12.487657     436.711360   \n",
       "max        30.400000   15555.000000      26.242172    1268.067900   \n",
       "\n",
       "         station_alt    station_lat    station_lon  u_pl500_fc_mean  \\\n",
       "count  980562.000000  980562.000000  980562.000000    980562.000000   \n",
       "mean      289.700186      50.812609      10.184040         8.823780   \n",
       "std       278.959584       1.915865       2.084653        10.804031   \n",
       "min         0.000000      47.398600       6.024400       -34.023964   \n",
       "25%        72.000000      49.244500       8.576100         1.466554   \n",
       "50%       238.000000      50.736400      10.048000         8.571194   \n",
       "75%       440.000000      52.178900      11.813300        15.717412   \n",
       "max      2964.000000      55.011000      14.950600        53.232000   \n",
       "\n",
       "       v_pl500_fc_mean  gh_pl500_fc_mean      ...           sp_fc_mean  \\\n",
       "count    980562.000000     980562.000000      ...        980562.000000   \n",
       "mean         -0.302459       5607.319530      ...         98167.966711   \n",
       "std          10.906147        148.716669      ...          2799.084576   \n",
       "min         -51.873432       4976.940000      ...         84283.160000   \n",
       "25%          -6.884149       5507.763700      ...         96367.070000   \n",
       "50%           0.297703       5629.717300      ...         98335.900000   \n",
       "75%           7.203771       5719.493700      ...        100424.337500   \n",
       "max          37.145084       5940.681000      ...        104708.470000   \n",
       "\n",
       "         tcc_fc_mean  sshf_fc_mean  slhf_fc_mean    u10_fc_mean  \\\n",
       "count  980562.000000  9.805620e+05  9.805620e+05  980562.000000   \n",
       "mean       61.148280 -1.767204e+06 -8.152601e+06       0.792879   \n",
       "std        31.656075  4.201333e+06  5.924629e+06       2.601944   \n",
       "min         0.000000 -3.243611e+07 -5.462550e+07     -14.955598   \n",
       "25%        34.416545 -4.712416e+06 -1.300108e+07      -1.076624   \n",
       "50%        67.456060 -1.958288e+06 -7.106672e+06       0.762717   \n",
       "75%        90.847860  8.736628e+05 -2.717170e+06       2.310319   \n",
       "max       100.000000  1.657920e+07  9.008404e+06      21.202560   \n",
       "\n",
       "         v10_fc_mean   ssr_fc_mean   str_fc_mean    d2m_fc_mean     sm_fc_mean  \n",
       "count  980562.000000  9.805620e+05  9.805620e+05  980562.000000  886600.000000  \n",
       "mean        0.643888  1.928963e+07 -9.371092e+06     277.832031     343.767861  \n",
       "std         1.994099  1.228819e+07  3.442328e+06       6.616596      61.335377  \n",
       "min       -15.886876  3.300497e+05 -2.136808e+07     247.132420      64.123955  \n",
       "25%        -0.712025  7.668573e+06 -1.182197e+07     273.451382     308.552310  \n",
       "50%         0.707770  1.845740e+07 -9.528052e+06     278.301150     358.254030  \n",
       "75%         1.861252  2.953434e+07 -7.014966e+06     282.778200     385.054400  \n",
       "max        18.126524  5.411776e+07  1.604214e+06     293.593700     541.179440  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t2m_obs             float64\n",
       "time                 object\n",
       "station               int64\n",
       "t2m_fc_mean         float64\n",
       "orog                float64\n",
       "station_alt         float64\n",
       "station_lat         float64\n",
       "station_lon         float64\n",
       "u_pl500_fc_mean     float64\n",
       "v_pl500_fc_mean     float64\n",
       "gh_pl500_fc_mean    float64\n",
       "u_pl850_fc_mean     float64\n",
       "v_pl850_fc_mean     float64\n",
       "q_pl850_fc_mean     float64\n",
       "cape_fc_mean        float64\n",
       "sp_fc_mean          float64\n",
       "tcc_fc_mean         float64\n",
       "sshf_fc_mean        float64\n",
       "slhf_fc_mean        float64\n",
       "u10_fc_mean         float64\n",
       "v10_fc_mean         float64\n",
       "ssr_fc_mean         float64\n",
       "str_fc_mean         float64\n",
       "d2m_fc_mean         float64\n",
       "sm_fc_mean          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the data type of each column\n",
    "df_train.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each column is a Pandas Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.2\n",
       "1    1.6\n",
       "2   -6.3\n",
       "3   -3.0\n",
       "4    0.5\n",
       "Name: t2m_obs, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['t2m_obs'].head()  # same as df_train.t2m_obs except for assignement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "537"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Home many unique stations\n",
    "df_train['station'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also do computations column-wise, such as min, max, mean, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2011-01-01', '2015-12-31')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.time.min(), df_train.time.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7.531618395413346, 7.305594593821011)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.t2m_obs.mean(),df_train.t2m_fc_mean.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas Series objects have some build in plotting functionality with is based on Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train.t2m_obs.hist(range=(-30, 40), bins=100, label='obs')\n",
    "df_train.t2m_fc_mean.hist(range=(-30, 40), bins=100, alpha=0.5, label='fc')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another convenient way to plot tabular data is Seaborn: https://seaborn.pydata.org/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# Only take every 1000th value\n",
    "sns.regplot(data=df_train.iloc[::1000], x='t2m_obs', y='t2m_fc_mean');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clean the dataset\n",
    "\n",
    "Next we need to check whether there are missing values and what to do with them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "t2m_obs             0.073126\n",
       "time                0.000000\n",
       "station             0.000000\n",
       "t2m_fc_mean         0.000000\n",
       "orog                0.000000\n",
       "station_alt         0.000000\n",
       "station_lat         0.000000\n",
       "station_lon         0.000000\n",
       "u_pl500_fc_mean     0.000000\n",
       "v_pl500_fc_mean     0.000000\n",
       "gh_pl500_fc_mean    0.000000\n",
       "u_pl850_fc_mean     0.000000\n",
       "v_pl850_fc_mean     0.000000\n",
       "q_pl850_fc_mean     0.000000\n",
       "cape_fc_mean        0.000000\n",
       "sp_fc_mean          0.000000\n",
       "tcc_fc_mean         0.000000\n",
       "sshf_fc_mean        0.000000\n",
       "slhf_fc_mean        0.000000\n",
       "u10_fc_mean         0.000000\n",
       "v10_fc_mean         0.000000\n",
       "ssr_fc_mean         0.000000\n",
       "str_fc_mean         0.000000\n",
       "d2m_fc_mean         0.000000\n",
       "sm_fc_mean          0.095825\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What is the fraction of missing values for each column\n",
    "df_train.apply(pd.Series.isna).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two columns with missing values: `t2m_obs` and `sm_fc_mean`. The first one is the variable we want to predict. If we don't have the answer, we cannot use it for training our algorithms. For this reason we will throw these rows away. The second is just a predictor variable. Here we don't want to throw the entire row away. One way to deal with these missing values is to set the value to the mean over all samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows where observations are missing\n",
    "df_train.dropna(subset=['t2m_obs'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace missing sm_fc_mean values with the column mean\n",
    "df_train['sm_fc_mean'].replace(np.nan, df_train.sm_fc_mean.mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split data into features (inputs) and targets (outputs)\n",
    "\n",
    "To train our algorithm we need to split the variables we want to give the algorithm to make a prediction and the variable it should predict, in our case `t2m_obs`. Furthermore we will throw away the variables which we are not using at this time: `time` and `station`. These variables might be useful, but are not simple continuous variables and require some more thought."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split features and targets\n",
    "# At the moment we only want continuous variables in the data set\n",
    "# so we will throw away the station ID, along with the time\n",
    "X_train = df_train.drop(['t2m_obs', 'time', 'station'], axis=1)\n",
    "y_train = df_train['t2m_obs']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalize input data\n",
    "\n",
    "Many machine learning algorithms require all the features to have the same order of magnitude for reasons we will see later. The simplest way to achieve this is to subtract the mean and divide by the standard deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = (X_train - X_train.mean()) / X_train.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>t2m_fc_mean</th>\n",
       "      <th>orog</th>\n",
       "      <th>station_alt</th>\n",
       "      <th>station_lat</th>\n",
       "      <th>station_lon</th>\n",
       "      <th>u_pl500_fc_mean</th>\n",
       "      <th>v_pl500_fc_mean</th>\n",
       "      <th>gh_pl500_fc_mean</th>\n",
       "      <th>u_pl850_fc_mean</th>\n",
       "      <th>v_pl850_fc_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>sp_fc_mean</th>\n",
       "      <th>tcc_fc_mean</th>\n",
       "      <th>sshf_fc_mean</th>\n",
       "      <th>slhf_fc_mean</th>\n",
       "      <th>u10_fc_mean</th>\n",
       "      <th>v10_fc_mean</th>\n",
       "      <th>ssr_fc_mean</th>\n",
       "      <th>str_fc_mean</th>\n",
       "      <th>d2m_fc_mean</th>\n",
       "      <th>sm_fc_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.085537</td>\n",
       "      <td>-0.806304</td>\n",
       "      <td>-0.319616</td>\n",
       "      <td>-0.014388</td>\n",
       "      <td>-1.988359</td>\n",
       "      <td>-0.375583</td>\n",
       "      <td>-0.420998</td>\n",
       "      <td>0.082273</td>\n",
       "      <td>0.032273</td>\n",
       "      <td>-0.589303</td>\n",
       "      <td>...</td>\n",
       "      <td>1.153825</td>\n",
       "      <td>1.165866</td>\n",
       "      <td>0.571970</td>\n",
       "      <td>1.383392</td>\n",
       "      <td>0.551740</td>\n",
       "      <td>0.058768</td>\n",
       "      <td>-1.371728</td>\n",
       "      <td>1.699146</td>\n",
       "      <td>-0.637595</td>\n",
       "      <td>0.524749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.772045</td>\n",
       "      <td>-1.182264</td>\n",
       "      <td>-0.890327</td>\n",
       "      <td>1.113170</td>\n",
       "      <td>-0.962599</td>\n",
       "      <td>0.520713</td>\n",
       "      <td>-0.916043</td>\n",
       "      <td>-0.189695</td>\n",
       "      <td>1.584633</td>\n",
       "      <td>-1.304124</td>\n",
       "      <td>...</td>\n",
       "      <td>1.271600</td>\n",
       "      <td>1.187512</td>\n",
       "      <td>0.800075</td>\n",
       "      <td>1.439388</td>\n",
       "      <td>2.107265</td>\n",
       "      <td>0.081871</td>\n",
       "      <td>-1.372214</td>\n",
       "      <td>2.224538</td>\n",
       "      <td>-0.412719</td>\n",
       "      <td>0.764039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.064770</td>\n",
       "      <td>1.317913</td>\n",
       "      <td>1.692322</td>\n",
       "      <td>-1.360192</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>-0.792744</td>\n",
       "      <td>-0.547005</td>\n",
       "      <td>0.051487</td>\n",
       "      <td>-0.523761</td>\n",
       "      <td>-0.172229</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.328921</td>\n",
       "      <td>0.962162</td>\n",
       "      <td>0.083173</td>\n",
       "      <td>1.336190</td>\n",
       "      <td>-0.456843</td>\n",
       "      <td>-0.807191</td>\n",
       "      <td>-1.217983</td>\n",
       "      <td>1.431614</td>\n",
       "      <td>-1.860869</td>\n",
       "      <td>1.593406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.836220</td>\n",
       "      <td>0.705375</td>\n",
       "      <td>0.178854</td>\n",
       "      <td>-1.150334</td>\n",
       "      <td>1.341568</td>\n",
       "      <td>-0.585835</td>\n",
       "      <td>-0.892244</td>\n",
       "      <td>-0.128645</td>\n",
       "      <td>0.182835</td>\n",
       "      <td>-0.380077</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.279698</td>\n",
       "      <td>0.947549</td>\n",
       "      <td>0.432299</td>\n",
       "      <td>1.280152</td>\n",
       "      <td>0.441155</td>\n",
       "      <td>-0.154016</td>\n",
       "      <td>-1.108971</td>\n",
       "      <td>0.776981</td>\n",
       "      <td>-1.555712</td>\n",
       "      <td>0.385818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.823013</td>\n",
       "      <td>-1.061694</td>\n",
       "      <td>-0.814473</td>\n",
       "      <td>0.878201</td>\n",
       "      <td>-1.117882</td>\n",
       "      <td>0.257640</td>\n",
       "      <td>-0.803640</td>\n",
       "      <td>-0.128599</td>\n",
       "      <td>1.245830</td>\n",
       "      <td>-1.128760</td>\n",
       "      <td>...</td>\n",
       "      <td>1.168232</td>\n",
       "      <td>1.201311</td>\n",
       "      <td>0.762755</td>\n",
       "      <td>1.480936</td>\n",
       "      <td>1.428789</td>\n",
       "      <td>0.017412</td>\n",
       "      <td>-1.373112</td>\n",
       "      <td>2.175315</td>\n",
       "      <td>-0.488854</td>\n",
       "      <td>-1.374796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   t2m_fc_mean      orog  station_alt  station_lat  station_lon  \\\n",
       "0    -1.085537 -0.806304    -0.319616    -0.014388    -1.988359   \n",
       "1    -0.772045 -1.182264    -0.890327     1.113170    -0.962599   \n",
       "2    -2.064770  1.317913     1.692322    -1.360192    -0.607706   \n",
       "3    -1.836220  0.705375     0.178854    -1.150334     1.341568   \n",
       "4    -0.823013 -1.061694    -0.814473     0.878201    -1.117882   \n",
       "\n",
       "   u_pl500_fc_mean  v_pl500_fc_mean  gh_pl500_fc_mean  u_pl850_fc_mean  \\\n",
       "0        -0.375583        -0.420998          0.082273         0.032273   \n",
       "1         0.520713        -0.916043         -0.189695         1.584633   \n",
       "2        -0.792744        -0.547005          0.051487        -0.523761   \n",
       "3        -0.585835        -0.892244         -0.128645         0.182835   \n",
       "4         0.257640        -0.803640         -0.128599         1.245830   \n",
       "\n",
       "   v_pl850_fc_mean     ...      sp_fc_mean  tcc_fc_mean  sshf_fc_mean  \\\n",
       "0        -0.589303     ...        1.153825     1.165866      0.571970   \n",
       "1        -1.304124     ...        1.271600     1.187512      0.800075   \n",
       "2        -0.172229     ...       -1.328921     0.962162      0.083173   \n",
       "3        -0.380077     ...       -0.279698     0.947549      0.432299   \n",
       "4        -1.128760     ...        1.168232     1.201311      0.762755   \n",
       "\n",
       "   slhf_fc_mean  u10_fc_mean  v10_fc_mean  ssr_fc_mean  str_fc_mean  \\\n",
       "0      1.383392     0.551740     0.058768    -1.371728     1.699146   \n",
       "1      1.439388     2.107265     0.081871    -1.372214     2.224538   \n",
       "2      1.336190    -0.456843    -0.807191    -1.217983     1.431614   \n",
       "3      1.280152     0.441155    -0.154016    -1.108971     0.776981   \n",
       "4      1.480936     1.428789     0.017412    -1.373112     2.175315   \n",
       "\n",
       "   d2m_fc_mean  sm_fc_mean  \n",
       "0    -0.637595    0.524749  \n",
       "1    -0.412719    0.764039  \n",
       "2    -1.860869    1.593406  \n",
       "3    -1.555712    0.385818  \n",
       "4    -0.488854   -1.374796  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocessing in one function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preproc(df_in, means=None, stds=None, drop_vars=['time', 'station']):\n",
    "    df = df_in.copy()\n",
    "    if 't2m_obs' in df.columns: df.dropna(subset=['t2m_obs'], inplace=True)\n",
    "    df['sm_fc_mean'].replace(np.nan, df['sm_fc_mean'].mean(), inplace=True)\n",
    "    \n",
    "    y = df.pop('t2m_obs') if 't2m_obs' in df.columns else None\n",
    "    X = df.drop(drop_vars, 1)\n",
    "    \n",
    "    if means is None: means = X.mean()\n",
    "    if stds  is None: stds  = X.std()\n",
    "    \n",
    "    X = (X - means) / stds\n",
    "    return X, y, means, stds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, means, stds = preproc(df_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training, test and validation sets\n",
    "The first thing you will notice is that the test set does not have the `t2m_obs` column. So what are these different datasets?\n",
    "\n",
    "- Training set: Data used to optimize the machine learning algorithm\n",
    "- Validation set: Data used to independently verify the skill of the algorithm\n",
    "- Test set: Another independent set used for final verification. In this case the solutions are hidden (I have them, hehe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 692 ms, sys: 20 ms, total: 712 ms\n",
      "Wall time: 714 ms\n"
     ]
    }
   ],
   "source": [
    "# Read test dataset\n",
    "%time df_test = pd.read_csv(f'{DATADIR}pp_test.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(908857, 182218)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_train), len(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2011-01-01', '2015-12-31')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.time.min(), df_train.time.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2016-01-01', '2016-12-31')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.time.min(), df_test.time.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no target information in the test dataset. Only I have it, haha! \n",
    "\n",
    "Which means we cannot use this dataset for checking the skill of our algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Index(['t2m_obs', 'time', 'station', 't2m_fc_mean', 'orog', 'station_alt',\n",
       "        'station_lat', 'station_lon', 'u_pl500_fc_mean', 'v_pl500_fc_mean',\n",
       "        'gh_pl500_fc_mean', 'u_pl850_fc_mean', 'v_pl850_fc_mean',\n",
       "        'q_pl850_fc_mean', 'cape_fc_mean', 'sp_fc_mean', 'tcc_fc_mean',\n",
       "        'sshf_fc_mean', 'slhf_fc_mean', 'u10_fc_mean', 'v10_fc_mean',\n",
       "        'ssr_fc_mean', 'str_fc_mean', 'd2m_fc_mean', 'sm_fc_mean'],\n",
       "       dtype='object'),\n",
       " Index(['time', 'station', 't2m_fc_mean', 'orog', 'station_alt', 'station_lat',\n",
       "        'station_lon', 'u_pl500_fc_mean', 'v_pl500_fc_mean', 'gh_pl500_fc_mean',\n",
       "        'u_pl850_fc_mean', 'v_pl850_fc_mean', 'q_pl850_fc_mean', 'cape_fc_mean',\n",
       "        'sp_fc_mean', 'tcc_fc_mean', 'sshf_fc_mean', 'slhf_fc_mean',\n",
       "        'u10_fc_mean', 'v10_fc_mean', 'ssr_fc_mean', 'str_fc_mean',\n",
       "        'd2m_fc_mean', 'sm_fc_mean'],\n",
       "       dtype='object'))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns, df_test.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a validation skill that fairly represents operational conditions, we will also split by time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_date = '2015-01-01'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's important to use the same means and stds to normalize each dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train, means, stds = preproc(df_train[df_train.time < split_date])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_valid, y_valid, _, _ = preproc(df_train[df_train.time >= split_date], means, stds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test, _, _, _ = preproc(df_test, means, stds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((728008, 22), (180849, 22), (182218, 22))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, X_valid.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save preprocessed datasets\n",
    "\n",
    "Let's save these datasets so we can load them in a different notebook. The easiest way to save Python objects is Pickle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.makedirs('./tmp/', exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./tmp/preproc_data.pkl', 'wb') as f:\n",
    "    pickle.dump((X_train, y_train, X_valid, y_valid, X_test), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# To load\n",
    "with open('./tmp/preproc_data.pkl', 'rb') as f:\n",
    "    X_train, y_train, X_valid, y_valid, X_test = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Also save the processed datasets\n",
    "with open('./tmp/dfs.pkl', 'wb') as f:\n",
    "    pickle.dump((df_train, df_test), f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Our first Kaggle submission\n",
    "\n",
    "Let's just always predict the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_sub(preds, fn=None):\n",
    "    df =  pd.DataFrame({'Id': range(len(preds)), 'Expected': preds})\n",
    "    if fn is not None: df.to_csv(DATADIR + fn, index=False)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sub = create_sub([df_train.t2m_fc_mean.mean()]*len(df_test), 'mean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>Id</th>\n",
       "      <th>Expected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7.279572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7.279572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7.279572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7.279572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>7.279572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  Expected\n",
       "0   0  7.279572\n",
       "1   1  7.279572\n",
       "2   2  7.279572\n",
       "3   3  7.279572\n",
       "4   4  7.279572"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id,Expected\n",
      "\n",
      "0,7.279571712763294\n",
      "\n",
      "1,7.279571712763294\n",
      "\n",
      "2,7.279571712763294\n",
      "\n",
      "3,7.279571712763294\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_head(f'{DATADIR}mean.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your turn\n",
    "\n",
    "Now it's your turn. If you are not very familiar with what I have just presented, just open up a new notebook and try to replicate all the steps with as little \"cheating\" as possible. If you are a pro and this was easy, here are some challenges for you!\n",
    "\n",
    "1. We will use the mean squared error (MSE) as a metric for our forecast skill. What is the MSE of the raw forecast?\n",
    "2. How much can you improve the MSE by simply correcting the bias? Submit your bias-corrected values to kaggle.\n",
    "3. Does the bias depend on the time of year?\n",
    "4. Is there a geographical pattern?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute the MSE of the raw forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8667748393126327"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df_train.t2m_obs - df_train.t2m_fc_mean) ** 2).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple bias correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25204668264974084"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bias = (df_train.t2m_obs - df_train.t2m_fc_mean).mean(); bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create new column with bias corrected forecasts\n",
    "df_train['t2m_fc_bc'] = df_train.t2m_fc_mean + bias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8032473090780905"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((df_train.t2m_obs - df_train.t2m_fc_bc) ** 2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.35519345212747"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Score for predicting the mean always\n",
    "((df_train.t2m_obs - df_train.t2m_fc_mean.mean()) ** 2).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Does the bias depend on the time of year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['error'] = df_train.t2m_obs - df_train.t2m_fc_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f95c7bfedd8>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_train.time[::5000], df_train.error[::5000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['month'] = pd.DatetimeIndex(df_train['time']).month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f96255e5940>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train.groupby('month').mean().error.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus: Plot station locations on map with cartopy\n",
    "\n",
    "cartopy is the new basemap. It is similarly awkward to use but at least there is active development and a decent documentation. If you are interested, here is how to plot the station locations on a map of Germany."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get unique stations lats and lons\n",
    "stats = list(np.unique(df_train.station))\n",
    "lats = [df_train.station_lat[df_train.station == s].iloc[0] for s in stats]\n",
    "lons = [df_train.station_lon[df_train.station == s].iloc[0] for s in stats]\n",
    "alts = [df_train.station_alt[df_train.station == s].iloc[0] for s in stats]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f9624285358>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n",
    "ax.scatter(lons, lats, c=alts, cmap='gist_earth')\n",
    "ax.add_feature(cfeature.LAND)\n",
    "ax.add_feature(cfeature.OCEAN)\n",
    "ax.add_feature(cfeature.COASTLINE)\n",
    "ax.add_feature(cfeature.BORDERS, linestyle='-')\n",
    "ax.add_feature(cfeature.LAKES, alpha=0.5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Plot with MSE coloring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['mse'] = (df_train.t2m_obs - df_train.t2m_fc_mean) ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "mses = [df_train.mse[df_train.station == s].mean() for s in stats]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f960493fc18>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAT8AAADxCAYAAAC9M7DbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzsnWd0FFUbgJ872zc9JCShhl6k9yIiRSkqqICCNBEVPoqKiNhRsItiF0UUFKRYKSpdkF6kd6S39L59d+73Y0MKu4EEEkDNc84cTmZmZ+4MM++8/QopJaWUUkop/zWU6z2AUkoppZTrQanwK6WUUv6TlAq/Ukop5T9JqfArpZRS/pOUCr9SSinlP0mp8CullFL+k5QKv1JKKeU/SanwK6WUUv6TlAq/Ukop5T+Jtig7l4mqKFMSzpTUWEoppZQbk5NSytirOUB1IaS1kPueh6VSyq5Xc77CUCThl5Jwhs9+O1lSY/lHYclM47VR3XjtjYn069X+eg8nH7d2GUrNxh3p2HPIdR1H4vmTvPn4XdzbdwCvPDeU8LCg6zqeC7z94Ty+nTGLp6csQKc3XnJfmyWDl4a2x5KVht5gwmA0oTfkWYz51+kMJvR6I3qjOfdvgzFnu8Fo9v5tvLAtd51WZ0AIcY3uQuH5X/fKla/2GFZgWCH3fRkirvZ8hUEUpbZXCCFLhV8uR/ZuYfqbI/jjj5+pUinqeg8nhy3bj9C71yAmfL6KgKCQ6zqW5Pgz/Dp7Cnu3rWbo8OGMe6wvZpPhuo1n07bD9Ok9kLHv/EB0hWqF+o3q8SAU5YYUTNeC/3WvjJTyqi6+vBByRCH3fQH+klI2u5rzFYZSn99VUKNeC9p1H8CgIU/jcnuu93ByaNGkBm1v7czSeR9d76FQJqoCg558l8dfm82Kletp2Lgrn0xfhMejXvOxpGdYeWjoY/R++MVCCz4ARaP5zwq+4kIBTIVcruWYSrkKut0/GofLw/MTp13voeTjzUmPsWHFDySeP3W9hwJA+Sq1GfnKDAaOmcz0aTNp2uoefli4DlW9dl2FHh4xiSq1mtCy073X7JyleBGArpDLtaJU+F0likbDkHEfMG/WTJau2nG9h5ND5QqR9B30IAtmvHm9h5KPmg1aM+69Bdx+32ief24it94+hDUb9pX4eT/6YgF7d+/gvv9NLPFzleKLwBtgKMxyrSgVfsVAWEQMD4x+k5EjxpCQlH69h5PDS08P4diB7Rzdv+16DyUfQgia3Nydlz5bTr1W3Rk88FHu6jOGPQdLxp+8Y88x3n79dYY+8wkGo7lEzlHKpSnV/P6lSAmWjC5YLV2oW2cC/foZOHTo+t/a4CATT4x7kp++fJUbsWmtRqvjljsG8MqXawiPqUW3Ln0YPGwSZ84lF9s5MrPsDH7wcXoMfIoKVeoU23FLKRqlmt+/lMWzNMybqsWW9RIe9wmWLJlD585mjh+//k7ykUN7oHpc/PXn4us9lAIxGM106/cYEz5fSZZDS6tWXXnyuY9JTcu66mOPeOJNoipU5+ZuDxTDSEu5Uko1v38hdiss/1GD0y4AI/A58BZW60Hee09/nUcHGo3CKxOf4ZcZb+Jy2q/3cC5JUEgZ+gybwPj3F7H/4EkaN72d19/7DofDdUXHm/7tEjZtWMsDo98ojdZeZ0qjvf9CEs4JNPl09erAi6jqcDZtcl6nUeWnR9eWxFaryYqfbqyIdEFExlTiwXEfMPKVGfz660oaNOnOF9/8XqTI8P7Dp3nlxZd5ePwnmAKCS3C0pRSGUs3vX0hohMTto5j0BeqSkDiATz/9mtWr1xMfn1jsfjeHA06c0GKx+Go1GRmZrFjxJ5MmvUf37v3Yt30Df/76bbGev6SpWK0eo1/9lr4jX+OTj76gWZveLPh902V/Z7M7GTR4DF3vH0nlmg2uwUhLKQzF5fMTQnwlhEgQQuz1s+0pIYQUQly2SuRa+hf/lQSHQoOWKnu2KLicF4SQQKsbikY3kuPHT/Hrr8vZv/8wGo1C3bq1qFOnBnXr1qJu3ZrUrl2DLGsIaRkaqsc60RXi0yclfPhhKJMnhyMleDzQq9cROnZcxpYt29i4cRtHj56gUaN6tG7djKeeGkFEVHl69BhYoveipKjT+GZqNVzEX2sX89TY55nyfiyvvzqONs1r+93/8XFTCAiJpEPPh67xSEspiAuaXzExA/gY+CbfOYSoCNwGFCq5tVT4FQODx7qZ95mWLX94FenAYOjWL4TlP6i8884EAKSUxMUlsH//YQ4cOMzmzX8xbdocDhw4hiqi0OjroTXcxMB+lRkyoDLVq8ei1/v3Gc6ZE8ibb6Zgty8C1gJrmT07ifXrWzB4cAPefnsCjRrdhMGQW0aWlJKJw24r6VtRYiiKQvP2PWjcpivrlnxHv/uH0KTlzbw+8XHq1KiQs993P65mxdLfeO6j30r9fDcQF6K9xYGU8k8hRKyfTVOAp4EFhRpTaW1v8eFygt3mFX52awbPDmpF3LmdBe5/W+8K7Nyrxe08Bu494NmLIndTLmInSYlnqVKlMnXr1qROnZrUrl2ds2fPs3HjNhYt2oHHowHa5VluwmyGkyePofXzlDldbqKjb+KTRcf+FULBbs1ixU/TWL1oBl3uuodJLw7HanPQqdO9PPLcVKrf1Px6D/FfQ3HU9tYRQs4o5L6tClHbmy38Fksp62X/3QPoJKV8XAhxAmgmpUy61DFKNb9iRKf3LgBGcxAel4ssi4PAAN9C/iPHdOw7pMftUUBT07vQCykkzTtk8cmbJzl8+CgHDhxm375DzJgxj3LloujSpQPLln2E1VoV7/c0F5dLYrEohIT41s3qdVo0Gi0upwO94dKdTP4JGM2B3DlgDLfcMYDf5nxAi5ZdCQ4Np0OPIddV8KkqnDikw5KpULWOk4CgGy+/8npQRLM3QgiRNzP/CynlFwUeWwgz8Dxwe1HGVCr8SgghBIEhYZxPSKFGlRif7fGJWnQ6ie2i7BMpBafPaTGZjDRseBMNG97k89tZs2JYv973QxwR4SE4WEVK+PzzED76KIzkZIVGjRy89loSBlMAWWlWzp0ORRFQo749R1j/UwkOi6TviFfp2HMouzevoOPdQ6/bWBLOafjw+XCyMhSEAI9bcEf/TLr0sVy3Md0oFNHsTSpiV5dqQBVgV7ZVUwHYLoRoIaWMK+hHpcKvBAkMDiMuPtWv8Ktfx4HT6SvADHqVjjdfuu3jpEnJ3HmnEZtNcMEaMZlU3ngjESFg4sRwPv88FKvV64PcvNlEjx7lEZpAXhkejKLNDYQ9+lwSdRqXbP6flJJTf9twOlRia5nR6Yo/yaBs+Sp0vveRYj9uYZESPn4xnJQEDXktxN++C6RyDRe1G90YaU/Xi2IOeORDSrkHKJtzrkKavaWpLiVIUEgZ4hNS/G4LCVZ5YlgqZlOuiarTqYSGqAwbdOn64MaNHSxZcoYuXSzExLhp08bG3Lnn6dnTQlaW4LPPcgXfBaw2gSUzCKfDit2q5CxTJ0VgySy5x+DcSTsvDDnAlPFH+fTl4zzddx/b16WV2PlKEpdTsnG55Ot3VBbMVEmOzzVpTx/VkpGqcLFrzOkQrFlUWk9cnOVtQog5wEaglhDijBDiitT9Us2vBAkMLUNiUsEv+vjRqdSt6eSTr0JJStXQ7CYbTes5OHxIT8uWdi4Vl6hf38mcOXEcOaJl2zY9breKxwOnT+vQav34maQAAgHfkrHt60y061b8pln8GQ1vj7XjsEWAjAe8gn7Gu6coF2skusI/x/dos0refkKSkgROO2i0sOoXyf8mQO1GAptFQfj9hgiySvDj8k+hODU/KWW/y2yPLcxxSoVfCRIYHE5i0qWL9O/qYqFLBwuDB8ewcF4Qv/4QiJSCypVdLFx4ljJl/Df9VFUYMSKUBQvMaDQSISAkRGXOnGTcbj9SU0iQAUB+YexxC2xWhZRkDWtWBHL2tJ4q1R3c0jGLoOArazgqJfz4VSirfw3E444CJVsYezYBaXjckvVLUuj1cLkrOv71YPmPkqR4chLaPW7v8vVkyRvfQGwtF6qffrY6g0rjtjd2WeG1QHBtS9cKQ+knqQQJCA4nMTH1svu9/34Yq1ebsNkUsrI0WCwKR47oGTmy4Nb4s2aZWbDAjN0usFgUsrIUzp/XMHJkGP36ZWIy5RdcBr1E0bQEeoFsAnI0yLkIzSnCotxMfDaGlUuD2bfbxNJFwbw8PobEBM0VXff+7UbWLgnE41YALQidd9G0AED1QEbaldXrXi+2r8VPJY+3tjvhLBiMkt6PZqAzqAjhFfZ6g0pktIebuxR26p5/LwLQaQu3XCtKhV8JEhgSTnKyf59fXmbODMFuz/9f4XIJVq0yY7X6t30nTQrBbs+/TVUFR47oGDUqhWHD0ggIUFEUSbVqTmbPPk+vXs9hMifhTY6vhKLMQcimzHynCY6E/ngyPwH3DlxOic2q8NOcsCu67nXLAnE6/D1aChCOwahQr/k/q95WX8C0I1LN3daum40n30yhRUcbdZvauffhTMa/n8Rl5kj6TyAEaLWFW64VpWZvCRIUEs6JvZfX/C4WYnlxuQSQ34e3c6eB5GSNz3oAISRut2DChBReeikFpxMuFHp06GBjSc8A5s1rgFDq063rQJKSBS9MTgPXBnCtB/snoJ5Faluye0MbDrSrSZXajTGaAwt93U5Hwdej1WuIqWykSdvQQh/vRqD9nYL5UyVOR+46oUB0RQgvm3u9sbVcPFgrHYtNIcuqoJS+YYBX+OmuzJAoMUr/a0qQwOBwEuJT2bHLQJ1aToxG/wmvXbtamD8/yMdXV6OG02/C8qLFAUgUwNfJpNVC9epuwPvA5a2QUxTo3t1C9+4WfvwxkFGjynrtEXNZMNYCY/ZUl2oSuDai0/zJkrnvc+LIPqIrVqVqnWZUrduManWbERZRrsCATIv2Fv7eb8B5kTYrFMHdDxq4pXs0Gu31qTKxWTP5c9E3bP7jJ6rVbU6z9j2o3KAle6L1nAtUiLGoNErwoL/otre+Df7eB3/96b2PCAgIhGEv5L8Oh1PwzcJwdh82o1EkQoF7O6XSrul/O9fvguZ3I3GDDeffQ3qqwtyva3L+73R69CsHUvD2xET69cn02ffFF5NZtcpMerqCzaZgMKjodPDxxwmAN4CwYYOOrVt1REerICUajRaPW+Vi7W/gQK9zffKHYXz6ZShp6Qp1azl585Ukbm7tre1NTlYYNapsrqmtAQzkFowoERhD7mD00LY898QwrDYH6zbt5891f7F6+WJmvPcKqmpCMbWh+k3N6Nm3LrHV66BovJ/2ZrdY2fxHAMcOGnDYFTQaiaKRDH4ilaY3lynuW10oLJnp/LHwa9YsmknTVu2Y/O4k1m/cyfdfTeJ8Vgq6e3ui9O2NsX4DlsTqeGyHg1BH7r1VFMHgJwXd7pccPwjB4VCrASia/MLPK/hMuD0Ct8e77YflYYSFeKhX/b8b+BACdNdvxlK/lNb2lhCvPR3N2ZOZyMwqEHgcRBlMJpUFc87SvInDZ/+MDMGcOcFs3mykZk0nDz6YQXS0B4cD+vQJZft2LQ6HwGCQaDQKVltQtgPegzeFRAAa5v4Yx+q1ZmZ+F4zNlqt5mUwqv31/lkYNHMyeHcT48RFYLNl2iACCAa1Er5coCnRqZ+WrD+LyaY4nz2hp27uSt4WW+wg41qO416GX68Bzjiq1G1G1jlczrHZTSw7tDmbPNhOBQSqtO1mIjHGX2P0uiMz0ZFb9PJ21v8+mTfvbeOGZR2lwU2zO9qEaM0sPH8f5w8+4v/8JAF2fe6l1W09GOPLP1Z1i0+CREGHy+NV6LTaFZ6aUzxF6eale0c7YBxP8jlFKicvpLY28Eeuui6O2t5leyG2RhdtXnLs28/aWan4lwNlTOhLitEjKgO5BcDwLxi+w2wWfTQ+leZN4n98EB0uGDUtn2LD8Cc5ffGFi2zZtjiDzmsYSESjhQjT1AoHwzqdh7NtlwH5RwMFuF7z1fhhzvopDVcmfjCuBdO+hOt1hZdLLyVSL9Q1tfvJtqLcqRQjQ1QRdTVSGIA0qi6fv4tDhraxbv53vPn6GTvc8wq13PUj95tdH20lPiWflT9NYv2w+t97eneUrf87X/eUCqxQdok4tDC8+g/6F8ajbd+Ke/xN7+/fhjcAImrXvSWyzu5l/uiFxFq33O2HwMLRRMlVC81dtZFq8Wq4/4Zea6f9V27DMxoKZVjLTJeYAQfd+Zjr0NN6QQvCqKM62LsXEdRvOBY3zWv0nnz1u5dTfVsLL6qlRPwhFKfi8TruHE0eyMJm1VKhqLvIYM9MVNBecu4ZJYKkDnk1ITSvOnS/aLZ8925RPgwNACKRBgB648P7pAQX2HTB4k5wvUi6lFBw45LU7br/dyrhxvucy6SRjR6dSLdaFqmb7tvKw56ABl58cQoNOkmkpS//eHb1LRibCf8ZvieBywt4dJqwWheiYY2xb/Qlb/viF2++8hzV/LqK6n/LCC2iAC/qoEAJN08ZomjYm8LUJTPx1BbPnLmbKmK7I8HpQsx9U702yGsEHW8oysf05gg25zsGIMDf+nhQhJDUq+34Etq62M+8zS04QxZIpWTDTglCgQ48bLSuuGPivC7+zxy3M+eRvjh3IQKdXaNU5il5Dq6A3lkwoyONW+fzVoxzcmYFQhDcZOFzHk2/VJiTcN+d84/IE5n52HEURqKokJFzPqIm1KVuu8A9jparO3JwwEQKGyWD/H4awzXTpVDTHt+ovz/iCp0LBO21IHqKj3MSd9v1vFUJSt7b3LYuK8vDmm4k8+2wkHo83RUavlzz0UDq7zxjoPymG+BQt5cu6eOnRZO673VsV0rCOg7/2GH0EoNMlqJ5HU3Q6nOgKyg0pZk4e1fPBa5F4XMdxZ72F6vyRWvUGsGHD71SueHk76w7VxSJFhyvPB04nJXcKlZ7dWqGN7cSGKqFY/l4Oh7+DDc9ATFvctfuyvuKtdMszIZxWA3d3SuWnFWE4XV7hL4TEqJd0b+dbsrjwW2u+6DGA0wG/fWfl1rv+Zdqf1ytzQ3FN8/zSkhxMfmoXR/dnICU4HSobl8cz9dX9JXbO5T/GcXBnBi6nxGlXcdhUkuIczHj3mM++J49kMefT4zjtKnarB6ddJem8nQ+eO1Ck+SPMAZLuvdLRX9AKtP0QShh65RMeGpBRpPH37Wv3EyWWhBptPonMZpPKM4+l8NDAdJ9tRqNk/BO5aTcPPpjJxo2nePbZFMaNS2Hp0jNUa+7ihU8iiE/xCs+zCTqeeKcsP68KAGDEwDQMekneIIvRoNLlFgsVY9y43OBwgsPhQHsNvNuqCh+9moItaSjO5JaoMgrMhzmd8D6H/658+QMAL3tsxEqVACnRS0mAlMRKlVc83uDQuQwtHsUAVXtA17kw5AzUegDPoXn8+nw9pr81il0bl+FyeaVYo2o27miZTmyMgzIhblrWt/DsI+eJDMsfmXciSK4bAvdGQq38tb+WTInn2rtH8yGl5PTRffw258PiOeANOHflNdX8Vi86h8ud/6V0OVX+3ptB3Gkr0RWLvwB87e9JuJz5hYfqgSN7srBZ3JgCcm/Bml/jcDvzj09KsGS6OH4wi2p1gwp93q73ZFK+sotVvwWRmaGhavU32L6yO6kZLQkJKZtvXylh7Voja9caKFNGpVcvC5GR3nGMGGFl6VI9+/Z55+owm0Grlfz0bQILlgczbVYoUoJOJ3lmdAr3351Fnx5ZRJTx8Mm0MFLTFOrVcfDGy0k0rJ9fzYiNdTNmTG65W68XymG9KD3F5lB49csy3NPRQqXybn6beYbxb0SyZZeRAJPKkD7pPNw/nQdeiGb55gCkBPN5Qbk6xW+2uZx2zp86wpljBzh7/ABH9u7HkvA36B6DwKMgvLmDVivM/C6YFi3tzFsbxN6TehpWcdCnbSbB5vzPQhiSFe5M1gktW1N1JO40UkuRuBopEOyhecWLaqz1gVCrPwH1+zGp3X4Sdv7CLz9P49v3x2EO7kFy6iD0xltwu7W0aGfhgTtTcl0g2ZwVOqboysIYQCPAI2FHJkw4BiqEhCtoddde63PabRzatYG9W1eye8tK9HoDbdt3KJ6DC7wZBTcQ1zTa+9ELe9m/3Tfp12jW8ODYWjRsXfxpEOP77yQj1fczqtEK3vi2AUEhuabvxy8dYN8230YERrOGB5+qTsNW4Vc1lgUz3saaepoF89/NWed2Q79+kWzcaMRiERiN3mjrnDmJ3HKL10+kqrBqlZ5t23RER3u45x4HISHe/ze7Q5CSqhBZxnPJ+T/iEzScPqOlelUXoaG+trTbDZEdq3Fxg1QAnVaSsOqo3+N6PNDywUqcitPhuuDoP9odQ/nhvPFaY0wF5DZejoy0JM4eO8DpY/s4d/wAZ44fIOHcCaLKxVKtVm3q3VQbvaEh077qRpbVN2G6dSsbh8P0WB0Cm1PBrFcxG1VWTjpDpUjf5+GLlcG89GOE1xQS3g/SewMS6Ncmi36zY1hzzIQt25Q1alWqR7hYNex0TuLu6CdtzJmzBI9jLsjzoLkfjfF+uvauxZ3356Y3SeBlXQwJQks+qWrzwOdn0S1Lov/oQFp2vDZlISkJZ9mzdRX7tq7iyJ4tVK1Vj46dO3Bvj1tpeFMVFEUQFlbj6qO9AUJuq1u4fcW2f2G0t1KNQA7vScPtyv9CeNyS6Iol4+Bt2DqUDUuT8FyUDxwZY8gn+AAatArjyJ4MnI78wsHtUqlWp/BaX0F06zuaif/rzM+/buSeO1oDMG9eABs2GHNaUF2o9njwwQgOHz6DVusNPHTu7KRzZ9+ecA6nYNUmM6kZGto1s9Gobn7tzuGAEU9E8euSAAx6icMpGDIog9cmJOULaGi1EB3hIS7J95GILVdwHe4ff5mJT9HkCj4A1Y5HNbFlVwDtW2YhJWxdY2bVoiBsFoVGrazc3iuTgCCV86dg8ax4Th87gNG8A5NpB3Fn9uN2OahcrQ6169ah622tadLoQZo2rE6AOVd9sNoEn3/lWyZnNqukmRVSsxTU7HfW6lRwuAXjZ0Yw56n8/S2PxuuY8GMEDld+rffJWWXpUNfGt33P8+WWEGZuC8bpEfRpkMmotmk5gk9K+HnhTXhEfTCOA/UQeObgsQ7it1kq0nkHzdr3IKZSDRKFljShwSdXxqRBe3cED9Z30OTmklORVI+HE4d3smfLSvZuWUlaSgLN27RnYP+7ufuOd4gsU0Jlh//1aO+td5VjzaJzuN2eHLeRTi+o1TCEqAol0/PsroHl2bctg6wMN067ilYn0GgFg8dW8dm3VadI1iyKI/G8A1e2+as3KHS9vzyBIVffkEdvNHHf8Fd47pmX6dJxIWaTgTlzAn1674E3pWX7dj0tWhTcBHPTTiN9RpXz+k9dAq1W0v1WC1+8Gp8j2J5/OYLflgbgcCg4suXiN7OCqVTBxf8eye+Ef+mRZMa+F4ktT5qMyaDy8rCCO9McO6vLL/gApAO3aiIuwXvPvv8ylPXLAnHaM4HdrPh5B2sW7yQ0fDvxZ44A5YCGKEoDdLoxfDWzLF1vi7hkRB7AbJJ8PDmekU9G4XYLXG5BgFmlaWM765wm1Iu0WI8qWLkrwOc4v2wLxK36nksAi3cE8HCHDIa3Tmd4a/99FqX0CuIclFqgvAzaCSjKX5iUqXz4fH+CQsvQbvCz0OYBv8cpW0lLk5jiF3w2Swb7t69l39aV7N26mpDwSG7p0IF3351Ih3YN0GmvUSTiBgt4XFPhFxKuZ9x7jZg/9ShH9qSjMyi07RJNz8GxJXbOoBAdL029iS1/JPP3viyiyhtp2yXSb6RXb9Dw9JT6rFsSz451KZiDtNx6VzR1mxRfHWqDlp3ZsHQuE17/incm/Q+Nxr9ZKCU+vqK8eDwwcGw0WXkEp8st+H1NAD8vD6RXlyzcbpg9L9inaYLVpvDJ56E5wm/dJhOffR1CYpKGbk2y2HbcxLlELbHlXLw8PJnu7QqOUN9U1YFWc1FmjWpHy3lIP8YP03ayesFxpLoTSATqo6oN8ShNcdoeBBoDXq1aVcHhkHz+qY3uXc4VfPF5uOdOCw3rnWbWvCBSUjR06Wzhtg5Wyg2oinpeeAcWDJQBhNeEvxi3x9ug4GJUvALzcigK1L/Jwe69F5mqQtCixU18+fEzuNzj+HHhOl587lnYuAqGT4HAkJxddVKlpVq8JXCnj+5jzaIZbF//O7XrN6XzbR2Y8sZoalUvX6znKRT/dc0PIKaSmcdfr39Nz2kwamjXrSztupUt1L6d7i5Hp7t9e81JCUlxAr0BQsKL5stKTVI4sF2P3XaCtORETp44DcCAAVls22bw0f5MJkmjS7Q+/2uv0SeRGbyC7dufg+nVJQu7Q2Q3RvAznjSvZP18RjAT34nI1lwEew4YiIlyc3ThMYILMflOmwZ2alZysv+YPtds1JhRTz1JWnhNQoIbYDD2xe54C6gGwntetxtSkyW+PkbBpk1F83dVjXXx0vjc7jnbdhiQ2wS4JagC4gAT6Jqo9G7jW154RxMLHywJw3bRvRJAt4aFE0hvv5bEvX3L4XCCx+NNdjYaJG+84u2krtNq6Htve7p0XMQDT0xm0yMN0IydhqfZ7RikSjnpooPHd2xFxeNxs2vjMtYsmkHi+VP07vcAU99fToWY61NWmEOp8PvncmSvwox3jGRmCKQKFaqoPPysnTJRlxcQS+ab+HV2AJL5eFxPodON4703+wIuevWysnSpid9/N+NyiZzystmzE9BovD67bdsMaLXQrJkjRxtUJTl94y7mQm5gYIAktpKLo8cvnqVI0qqFjcwswStvR2DLoxna7Qrn47V8/V0Ijw+7fLt5IWDhe2d5dXoZ5i0Lwq0Kug1fzKv/S6FsuIe/thvoeW95cOYX1IpGIsDHFwsQdBUznkkJD4+Kxp13fhQVsEnC0z1M6u87rUO9Ck6Gd05j6opQHG6BIkCrkTzbI5lKEYXLOWnZ3M6qJaf54JMw9u3X06iBg8dGpFGtqtdfarMLflwYyMq1UTSv/REd2v7KB+88RHjLrnQb+jyNDJqryjvLykhl/dK5rFn8DWWjy/PwI4MYdF8nDIaSmjmjiPyQw6aXAAAgAElEQVTXo73/VFKTBC8/asaZp/WUUCRhEZJJ060olzBPTxzW8u7TWtzOp/BOOzATaExgoMqhQ3GYs1Mvdu7Us26dgfBwlbvushIUJFm61MQjj3gnG5LSqw1+910CzZo5cbmg1m1VSM3If3KzSeXdZxPpe6dXi1j9p4kHhsTgcAhUVaDRSExGydJFZ0hJ09B/WDQZmb4X0KKJjaU/nPV7TfEpGmb+FsyBE3qa1rIzsFsmIYH+uz5LCa1vrsjfR/V48vgGTSaVO7tmsWhRYD6z3GRSGT06lWefvXwrMH+cOKmlzW2V8gn0C1Sr4mTbmlMF/nbPKT0L/gpEo0juaZ5F7UsEeopCRqbgtnsrcua8FqtVQaeTaLWSj948xNx5L7Hzr80MHDOZmvVbFfnYZ44f8Jq2636jdfvbeGzkQG5p7Tvj39VQLNHeUCG3tS/cvmLhvzDa+09l/VKtT4tyqQqsmXBol4Y6TfyoL9n8Pu8wbuejQBtgC95m3t6286tWGbjzTm86S6NGznxm7tmzGoYMichX2paVBb16RbF//xkCAiRfvxXHA2NiUKU35SXAJLm5mY0+3XLNp1tvsbF0wRmmfBzGoSN6mjayM2Z0KlVi3ew7qPff8h5J2Uj/17T3mJ7uY8rjdAscToWlmwL4YH4Yqz45Q8WyvlqSEPDzj+cYPCSa3Xu8GqzRKPnog3g6tLdhtSqsWGHGYJA4HIIePbIYN+7KBB+AVucVuP644O+z2QU/Lw5kzwEDtWs46XVXJoEBkvqVnNSvdPnms0Xl069COXlaiyNb+3W5vO6IZybW4OCmN5j78x+88MxjNLm5Oz0Hj0erN5Fm0RBgVDHofC9G9XjYvXk5qxd+TfzZ4/Tq9wCfvbuMCuWus2l7KUrN3pIjI01w+m+FkDKSClWubO6JgkiOU3D78Z2pKqQl+/8gSilZMvdrdm/8BHgfRP88b6UTKdVLNjGdNy/Ar0moqvDbbyb69LHSvqWNnYtP8tPSQJLTNLRvYaVNk9ykXClh3l9BfLomlPTyCt26WBjbOZXIIO+B69ZyUrmii8M+Wplk+BD/Ju8T75Ul05rd0A5vErTTJXjp8zJ8/aJvwwaAmGgPy34/y9lzGrKyFKpXc+WY77NmxXH6tJbjx3VUr+6kXLmCPySFoUI5N9WquNh/SJ+veYPJpDLogQziEjR0ursC6RkKFqsGs0nl1XfDWfHTGSpXLJmyil9+DcwRfHmx2RQOH9XTv3cHOt2ymOGjX+XFYd1x1pmBJ6QtUgpa1LLQ99YUdBqwZKaxfulc/lz8DeGRUQwdOojBD9yO8UYxbS9FMZa3CSG+Au4EEqSU9bLXvQPchbfa/SgwREp5Sb/NP174SQk/TdezepEOrc5bvRFVQWXUJDvBoVfuO8pLzQYetq/X5jN7L5w7tpbvy5qRlsS3U8Zy+u9MNNpNeDzVvBtypJIOmx6m/BLBxO8UbmttZdyQFKIjco+VnKz4ndfX7YbU1NynKDLcw7B+/lMwnlsQwTebgrFmv3hfrQ9hwa5ANj59ilCzihAw/6vz3PdQDCdP69BqJC634OWnk2jbwrcQ3+4U7DySt/GfF48qWL7VN4XkYsqX8+CvAWvFim4qFqPgmTE1ju69y2OzezUsjQI3t7bx8KB0ho+NIiFRm9N5xWpTsDsETzxXlp+/LVyEuagEBPh/Dj0eCDB7P9TRZUN57PlP6Dt2I67N90G5AVBjElsPmclK2E9I0gf8tXYxLdt1Ytr0j+hw87UNGl41xav5zcA7F8M3edYtB56VUrqFEG8BzwLjL3WQf7zw27pay5+/6nC7RE4zgXMnFKa/aWDMm0Vvp6R6PNismViz0rFmZRBVoSrN2gewaLYJt1uP6pEgnegNkvot3MRUyv9gH9i+lm+mjKVbj3tIPf8u6Wl+8hfLCpSyOvYf8wqlbxcFs2hNABtnnaJMdvVFp052Zs4M8vbOy4MQ0L697bLXkZCp4esNwTjceVJhVEGKRWHij+G83S8JrdarKa3//TQHDutJTVNoWM9BYAEvq0bxBmM8fhRro77oH5r0dIUFCwJJStLQtq2NFi0uPV1nYale1cXeTSdYujKA83Famjex07ihNxnn95UBPi2nVFXw50YTHs+l04uulEcGpjP2sB5rHheGokhqVHNRqUKu0J/8SziuyN7Qtj3sHwHrG+MyRLHbcpj+g+7no41LqFyhkE3xbjSKMeAhpfxTCBF70bplef7cBPS+3HFuWOHndMChnQZUFWo1dGI0+3+5Vv2i85kzwuNxcHR/Kkf2xKNo0rBZMrKFmVeg2bLSsVrSveszc9dbsjJw2q0YzYGYA4PR6fUYTMEEhy8nyxKCVLJ76QkzHe5Jokf/3Ow2t8vJom/fZcsfP/Peh+9wT/fWtG7j503SAGHg8uTPz8vIUpj2YwjPDPX6u2691U7Llg42bcpNgzGbVe67z0KtWpfXknadMWDQShwX7er0KMxcFcIvnwYx/d04OrSxIYTXBL4cOi3c2TaLX9cH4MwjVI16lYFdLz3R+sVs2WKkd+8YPB6R06S1fXsr334bV2QBJCWsWWNi1SozZcp46NMnk3LlPNzlZy7igo6tKL5FF4UlLkHDmo0mggIkHdtZMRryP6v335PJ5r+MzPkpKNvvKAgL9TBr6vl8+51OzH4dDZHQaD4cfg4Sf8fc5RhjxsRTOeafNeNdPq6tz+8hYN7ldrohhd+B7Xo+fy3M+zBKr6YxaEw6zW7x1eQsmQLYCowFkvB25VTxuEP5ZkoQoeHBBAWFEBgcTEhIMKGhIVSOiiA0tBrhYcEYdCGEh4VQuXIgEeHBhIcGotF4X2xVlbRq9wx7t4xEZU722yGQUrJ9fRg9B3of3sTzJ/nqrdGUiSjD2rULcnKqHn0knRdejMiXw6eYJULjrWXPi8OpsGarOUf4KQrMm5fADz8EMHduAHq9ZNCgLO64w7/Wl5SskJquoUolF1otxIS4/VYtoIKaJUhN1zBgVAzblpwkpmzh/WzvjE5k+2EjZxK8x9Yokjb1bYwfVPggharCoEHRZOaJMlutgjVrzMydG0T//oXPd3O74YEHYtiwwYTF4hWib70VzsyZcdx2m++Ukb3vyuS7H4Nw5vHB6bSSbp2zfPoXFoYpU0N566Nwbxmi8M7Z8f3087RonPusCgFTXkvkieGpbNluJLqsh7YtbT7na17DzrkUrbckTwioPgHOzkRmHaRCROEnkLohKZrwixBCbMvz9xdSyi8KdRohnsfbonH25fa94YSfNUsw9dUwn8lvvnkvlCq1EikTlf9FrVJnL0lxg4A3gJZACGCiTBnJwb1pBX7pjx3TMWxYWXbt8ibU1q/v4PPP44ksk/t1VRSB0fAlqqcbKBNBTMjeIkhP0RB/VsupI9/z/RcTeWTESF54amBOSVZCqoZ4rYbQyh4cR0SONhBZzk2cTovnIn+eokhiy+f/smu10Levhb59C060Tc9QeOTJKP7caEKrBb1O8vaERHrflUWNsk72nTPkF4IqkJ2t5FFh/sIgHn/48vl84NWwRk0rS6KiwRMkwAM6kyS2hrtIZu+ePQYsFj8J2laFWbOCiyT8fvghiPXrTTkfGEe2FfDww1EcOXI8Xxt+gFeeSeKvXQaOn9Tjcnvz+cJDVO7obCEuXkN0VOE/BJu3G3nn03AcTgVHHsX5/kdiOLTB99yVK7qpXDGrwOM90yuFZTsDsDqyO21rjGirjqK263UMutcLPa4blsJLm6QrSXURQgzGGwjpJAuRw3fDzdu7c4P/7H5Vha2r829LijvF4V39CAqehNHYE4hBozFhMsGHH1oKFHw2m6BLl/Js327MSTvYscNA167lfebJlaoZNL+A+hWoc/NsyeSXr55gydwPmTt/Bi89PShH8P19RkfLYZV4/4cwzoXoUBpLPFUk736SwPYNp6hf0+FTZmXQSUbcXzghlJeBI6NZs8GEw6lgsXq1v8eeK8vm7UZ+ePQcbarZ0AjpVTUdwC4g+/1zOBUSUwpvY67db2LtPrM3gKIDjGCXCnPWBnHobOEjjpd6LIuQdgrAvHn+a6OlhK1bfZ+l4CDJmkVn+O6L8zw1IoWIIJXkeA1PPRtJo1aVeXZCRKHH8O38IL8Re48H/txU9Fr1muVdrJh4mjuaWSgb4qZhrJ0PJ93D8T3LOHPm/OUPcCNzIdpbmOVKDi9EV7wBjh5SykLNEn/DCT+7Tfjk1AF43GDL85BnpCby0QsDeXj4MHbv6sRTT9m4+WYX/fo5WLYsg+7dC/aPLFoUgM2moObRiKQU2O0KCxfmNy/uvy8Dk7ksaBaBOhrkJpDbcNubERUp2LjuZ9q2qJPvN898HkG6Rckp93JpFOyBCl+tCUEImPvOeW5uYsWg87ZZigxz8+Ur8dSv6VUfLFbBohUBfP9rIMmpBf8XnTqjZesOY07X4Jx76BB8PD2UiECVW8tY0RyXcBxvAkBulgoBZpVbWl4+eHKBFbvMWPzMyatKWL2n8C97/foOn2ar4PVpDihis9eC2nhdqjZaCGjX2sYff5g5e9abeJyZqcHhUPhmVjDzfyqciWmxKhSU+2uzXZkDsXYFF9+OiePQZydY/foZ+nXS0b9/L6ZOnXFFx7thKMZmpkKIOXgrBmoJIc4IIYbijf4GAcuFEDuFEFMvd5wSNXtdTjtutwu93ohGWzjNoG5TB7987dtWR2+UNMhOv7BZMvj4pUHc0bMnL4wbAEjGjrUzdmzhorunTun8PpwWi+DUqfy35OGHMli0OJB9++thyfwKPHeBEDz62Cu88fJtfo+/dpfZz0sh2HrQiMcD4SEqP71/nqRUhQyLhsoxuXlvqzeZGPBEjLd0TQrcHnj1qSSG3u8rFOISteh0Eruf+TpOndGycF0Ak+eE47xQA6zgjbhFgjlDpXE9O53bFeojCUBYgAe9VuK8KDFap4EQc+FzKzUa+OabOHr3Loeqett4mc2SNm1s9O1btPrWAQMy8pm9FzAaJc0vMXlSXLyGv3YYfZK8rTaFz74I5f5eBZunF7i7WxbL1wRguejcLpfgltaF/6hcjuHDH6Rdu7sYN24kISEl1HKqpCneaG8/P6unF/U4xS780lPi2b15JXs2L+fwns0AuBzeh1CnN6DTG9EZjOj0BvR6A3qDEa3eiF5vyF5vJDImkLgzQageE2BEozUSVV7HmeMqCeeMbFg2j8ZNmzH51ZH5zr1nj56XJ5bhr+1Gypb1MHZMCvf1yfKJ4jVq5MBkkj5pJAEBkoYN80sSo1Hy26KzrFhpZt2Gm4mL/4AhQyrQpoVv44MLmAwqTrev2qHXyXxO7ogwlYiwXKGRaREMeDwGy0UTFr0wOYI2Te3UqZ4/Ilu7usNv4wK9TqV9Gxsf/hCG9eLmBwoIs+SZIckM65deJCd/n5uzeOdn34auqgp3NL+8sMhLq1Z29uw5wc8/B5KcrKFNGxtt2hQ91eWuuyysWJHJDz8Eoapk9z+UzJ596ahxVpZS4Pb0jMLdlDtvt/DtDzY2bTNhsSpoFO/Un689l0RIcPEl2leoEEOXLh346qs53HbnaN79Kox9RwzcVMPB2IdSqVfj8pH6686/scJDSsm5E4fYvXk5ezYvJ/7sCZq1bk//fj3oNWcyEeHedkUOpwuL1Y7V6sRitWOx2rHZnVitdqw2BzabA6vNjs3mwG53cOigm93bVVxuBxUrJxIemUXGeQdWq5XEs3/z+rz38vV7239AT9c7K2T77ATpGRqeHFeW83Fanngsvy+tY0cr1as7OXhQjyNbOBgMKlWrOunc2VcT0migy+1WutxuBVpc9p4M6JLB9MUh2PNEFA06lfs7ZF7y5V72Z4DfZgVut2DuoiBeGZO/r15wkOTJ/6Xy/udhOTlkWq0kMFAy8qE0Fozzb76ZjZJunaw+DvnLUaGMmxoWJ7uFAS5otlIi9khsmQpBpqJVZ4SFqTz0UNHM3AvEJ2uIT9JQrZKLDz9MZPjwdP7800RoqMqdd2YRGHhpx12VWBdmk+qjMep0Kt27FK6Ti0YD8744z9LVZhYvDSQk2EP/3plULO/G4QRDEe6vlN6PSEECedSoofS8eyhv/zARp8uIKgVHT+lYti6AHz46R+tGN/iE6P8W4edxuziydwt7Nq9g9+blSKnSrsNtvPTSWLp1aua3k4RBr8Og1xFeDK3xXnzxTd5552Pef//VnHVvvR2WbcrmSherVWHye+EMfzQ93yRAigKLF59l8uRw5s8PQkro0yeTceN851u4El4YlMLhU3rW7jah03rncW1Wy87rw3w7iuTFahM5nYfz4vZ4/YD+GDcylZpVnXz0ZRiJKRo63mxl3MgUoiI93NrYyncrgn2SenVaSewV5IwdPKjjyHo9OASE4W1ImyrwGARffx3M+PFXXpNbWLKsgkdfiWLVZjN6nffejn8ohccHpFG3buE1II0GPnovgYeGR+PMbkNlMqqEhXkYM7rw16HRQPdOVrp3srJ+h5EHJ0Rz6rwOISQ9O1h4d1wCQQUkjQNkZgmefTmC738JwuUStGxm573XE6lzUd5lvXq1cSmNsCd9B8FDAVClwGoXPDM5gjWzzhR6zNeFG1D4FbmrS4sOPdm7dTXR5WPpeFtH7u3RiZZNa162625xkpaWTvPmt7Nw4Szq1KkBQP1GlTnjJ+IYEKCyZuVpqlW79gmih0/rOHhST/UKLurG+n8xM62CFz6NYP6KIJxugXSCzBDelJRsAkwqsz84T/tWRfMjnUnUcsuoimRZlexuyxKTQfL+6ATu61g0MxVgwYIARo8umy8/7wJduliYO7fkI5KDn49i6bqAfC3nzUaVT1+Ip2fHojcDPXBIzxfTQzh5Ssutt9gY3D+DkJCim6xHTuq4dUjFfBNAGXQqrRra+eXDgsvmuvUqz45dhpzaXyEkgYEqW/84RdRF+Zfh9U8h40dCpX2QZ15kISTJm48WS3WMP4qlq0sFIbc9Vrh9xfgbtKtLp/bN+GzKU1StHF0S4ykUoaEhPPnkcCZMeIv5878EIDbW5Vf4ud0Q6afbyLWgZkUXNSsWLHSlhN7jy7HzsCE3YquVECYRaSA9ggCTyu23WIoUlb1AhUg36z89xUc/hvLnLjOVyroY3TuN1jcVzkSS0ttL8MABA9WqOale3eW3C4zBoNKwYcmbXemZCkvWBfhEt612hQ9mhV2R8KtTy8mUtxOvemyfzg3FcVHupsOlsHmPkaOndVTz8xzs3qtn915DvqYHUgpcTsGM74LzTTUKEBJ5C2mJAWBdDAE9ctYHB6olJviKjRtQ8yvycJ5+vG9JjKPIDB3an2nTZrFmzQbat2/D00+l8td2Y74WUCaTSp9emYXqSFxYXC5YvjyAc+c0NG/u8AmQFIUdhwzsPWq46GUW6PUqDZs7qBXt5O4uFjq2sV7xwx1TxsPrjyYDBc/D4Q+LRdCrVzn27jUgpTc9pEoVF40a2dm+3ZjjKxXC6+QfMuTKfHdFITVDQasBp5/vSXwh8hXdKvy8P5Dv9wVh0koGNcqgY9Urv7d5OXRC77flvV4rOXVe61f4/X1M7zfgZHco7N3vGxod8UA673z4FK7Ed3KEn8moMrxv0fNDrzk3YDPTGy7Pr7Do9XpeeukpXnzxTVRVpd3NNj79OJ7oKDd6vcRkUhnYP4N33rr6r/oFjh/XUr9+LMOHR/HiixF0716efv1icF2hRX34lN7PRJHgdClUquTmo4mJdGqb+3Kej9OwZ58+ZyKikmTixDLs3OmtxLBaFSwWhUOHdERGenjggUxMJhUhJK1b21i69CzR0VfXiqowVIhyZ0+anh+NImnX5NKasSrh/nkxjPmtLMv/DmDhwUAG/RjNiyuLpwdeywY29Dpfc9nhFNSu4t/lUbuW02/bMqNBpXEDX036ySGpDOjXGeE5i5m1GPUq/e7IYNzQkve1XjXFmOdXXPxjhR9Az55dMRoNzJ+/AIC7e1jYv+cEB/Yc58Tfx3jrjaQiRzQvxYMPRpOYqCEzU8Fu9wqFNWtMTJsWcvkf+6FWZSf+dFKTQaVhjVwJl5amcE/fcjRuXZnu95Sner2qfP1tyeZ7zZ0blKPdXcDlUvj990AmT07k3LljJCcf5ddfz1GnztWnWuw7rufjn0L5ZkkwaZn+H0utFt58IhGTUeXC9H9ajSTQrPLsw5duQrr8bzObT5uw5NGyrS6F6X+FcDLt8m/c6RQtH68I5b0lYRw45/tQDeuTjtkoUZTc/1GTQeW+rpnEFNAYtm4tJ62a2zAacoWmokhMJsngB3w1aY0GRvTZQXSEjQkjjnHg9xO8+0xSiXSiKXZKhV/xIoRg0qRnefXVKdhs9ux1EB6uXpXQO3FKy5SpobzxQTg793p19XPnNBw6pM9XFQLehpTffFM04Sel1xfZqKaD+tUcGPJoDIqQGPWSAd1yH/7Bj0azfqPX1MzK0pBlUXjh5QjWrC2ZuY6BAic+8nhy5wgpDnNRShjzUSS3janAxBnhPPtFBDcNjmXNTv/Xdl/XLL5/9xydW1mpWdnJwB4ZrP3mNJXLXdqvu+xvcz7BdwEBrDl+6eqU7zYG0eKVSkxaGM4bi8Pp9FYFJv6SP98xOsLDqumnubN9FiGBHipGu3jukWSmPH1py2P29DiGDEwnJNiDXq/S+VYrKxeepky4rxa5f/9hevQYyHPPPcajQ9oSWoy5hCVOCZe3XQn/aOEH0LJlE5o2bcD06Zdt4lAovv0+iNbdK/HGh+FM/jSM7v3K88ykCNxuUeDL7iyk4uN0wvOvlaFCvaqUrVWNm7tXZNx9KTzQNROzUUWrkXRoZmXFp2cIy36wz5zVsnmrEdfFTn6bwoefFd+Umhdz220Wn2k1L5i52mL8Oi/bamb+H0HYnAout4LV7l0GvRqNowB3QtvGdr5/7zyb55zivXGJVIy+fEAr3KSiU/yZzBBsLNhkT8zUMHZuJHaXgtOt4FYFNpfC1NWh7DiZ34lVpYKbma/Fc2LZcXb/dJJRD6RfViszGSWvv5TM9nUnGfdUKk6N4NNZofx9In/wbufOvdxzz2AmThzPgAF9Lnu9Nxylml/JUL58DHb71UcbE5M1PP1KJHaHgsvlrf212RW+/T6Yc4laovx0/DAYVHr1KlxJ1qjxUXw9OwSrzVsTuv+QgYHDY3jkjnTO/naMxOVH+eGt81TN090lKVmDvoCOKefPl9yT8vrrSZQp48GcXbZmMqmEhKhMmVJ0H6qqwolELcl+zNk5K4LypYdcQAIb9hSfZtuvQSYaP8JPEZIu1Qsu8Vu6x4zGz0fP4RL8tK142kydi9fQ6q5KvPt5GKs3mpkxP4Rb7q3I2s3e69+8eTt9+gzlvfcm0rv3XcVyzmuOAIyFXK4R/wrh98cf6+nQ4eacv6WE3/8wM/CxaPqPjmbxioAcU+1SrFhj9juJuM0u+OW3QL78Mo7AQBWjMVsgmFViY1089tjlo23xiRoW/h7gM6uY0yn44POCNbia1Z355te4gE6ncmt7/y+t3SF4d3ooTXtWoknPSrw9LQzbJeYL8Uf58h62bTvJxIlJ3H9/Br0HZ/LWtESiKxQtbWjJLjN1xsXS5uVK1H06lnunxJCSlXsP/F1bzrZCTBheWKID3IxrkYJJqxJk8BCk91DG5OGHfucw+Zkk6ALeFo4FbBfeZ23m8iDqPBpL+H3VaDq6Eos3X76lf15e/zCc1DRNzjzMbrf3ozv6hbKsWbORAQP+x9Spk7njDv+15P8ISs3e4ufcuTji4xNp1KhezrrRL0byyFPRLF4RyG8rAxk+Poph46Mu26pI0fibRNv7Amg0kmbNHGzffpK7e2VhCFZxG+BYqo5hY6MuWw964pQOg8F3AB6PYN/BgnMAzGbJi88kY87TBUWnkwQHqTz2P1+hKyXcM6Ick78M59hpPcdP65nyVRh3PVrO7wdgR5KBEX+WpdfSGKbtD8aSx9cXFCSp2tTFr5mB/HIqkKe+jqTWkCr8uLZwGs++M3oe+iKahAwtNqfXbFx3yMx9H+bWRd/fKZMAo+/AVFXQtr43guv2UKAJXBimLg2hxqgqvD83DPUIVHa6+KJHPAefOE7z8vlD5xmZgl8WBbBgcQAZmYIu9Sx+hbBBJ+nVLIsvlwTz3MxI4lK1SCk4Fqfn0Q+jWPpX4bvcLPvTt7U+wNlTS3nooTHMmPEhnTq1K/qF30iUmr3Fz+rV62nfvjWabOfK7v16fvotKF9zAItN4bdVAfy1+9KJRl1utfpNPTDoJX16eCsiTp/TsmBVIA6NgguvebxyjZlBIy6d9B0c4MGaIbw9ZvPIQK1G0sRPWkNehj+czswv42jX1kqN6k4eGpTOupWn/TbeXLvVxJ6DhhwtArx5YwePGli1Mf8L+d3hIO74tTzz/g5i1dkAJmyNoOPCimRlC8B0i8KAt2LIsilk2jRk2jTYnAqjPinLqYTLP6VTV4T4dIBxeQQHzuo5cNYbkereykKXlhbMBhWBxKBTMRlUpo2Pw+EWPPR5FOVGVqP8qGrc/kb5nN8VluW7zEz6vgxWh/caHE6FQ3/r+fyXULQXPf0LFgdQu1EVRo8ty6gny1K7URU2/GliygMJGHUqRp2KTuP9d2SnNBpUcPDm/DI+zSNsToWJ3xU+hSYowM9Xyb4Qd9JQpn89lbZtWxbpmm9YbjDhd4PlXBedP/5Yz623ts39e6PZbxWC3S74Y4OZZpdISg4NUZk6OZ7h46IQeH1VQsDjj6bSqJ73dx9OC8V+UU87p0thy3YjJ09rfaY/lBJefLEMX34ZgpACXNkZw0GAAgaD5LFHL282d+5gpXOHy7ef2rbX6DM+AItNsG67kY6trSgKWN2CcZsiseWZS8TmUTiVpeXrgyGMrp/Gr5sD/AZ5/s/eeUdHUb3//3VnZ/tuekISQmjSQQSkKIgIiqBiFxVBUbErHxULVtwl5kcAACAASURBVETF9rF3UWzYUZpgQUGRplSRpiAtkN6z2b4z9/fHpm12A4EPKp7v733OnpPM7uzO3Ln3uU99P5oOs35yMunCA+eX7S42xtSaVIMkr1ylS8sAigJv3VXImt8tfL/WRrxd5/yTXaQnaQx5JItteaaa0jxYu8vCiCdbsm7aXlKczYt0vvRVQl33uloEQwqrtlvILzeQkRjeQPILDNwwsUWUW+K6W1rw6897WTd1L/PXOwhoghE93HRMD1LtFVTFIFIF2FPYfHLX68ZV8tAzyRG/rehbsDmT6d61VbO/56jGEWxdeaTwr9b8dF3nxx9XcMop9cLPaddR1Wjz0mSSxDkOvmDOGeHmtx/38ug9JUy5o5SVC3O4u0Gh+54cY0wCS5NRklcQvZfMnu3gnXfi8fsVggERZkPRJcItGdjfw9ef7ad92yNXd5yZFopqoAOAgJe/SqDjhLa8v9jJxhJzmOG5EXyawpd7wj6raq8SUxMOhgRVTRAtNMTgzl7MaozE36Dg2Oz6TUgI6NfFx73jyrjp/AoykjVW77Sws8gU0ehJIgiEBDOXNT/HsaCJHD6jQVLSoEZ53gIHehNukXlfOshM0Lh+aCUTT6ugY3r4ednMkrgmeAzbtGj+M51waSUXnOHCbNKJc2jYrDrH9b+Vyy8bwtlnX05x8aFV5xyV+P9m75HF5s3bSEiIJzs7q+7YuSNiF+wLAeeNbF4xf2qyxlVjqrjhykrato7U5E4a4MUYI5M/EBAxO6C98UZ8DJp1gVmVvPZUET0OgYmkOTh7WDUmk2xEjRXWNjWbQqnLwOR3Uln/uxmtiVr1xJqk26G9PLFcoNjMkhF9D66FTjilknhbOIWn7lyTzlVDKkmNO3BFyO6i2JqTL6iwNbf5dVJDu3swxghigaBjRv3Ye7xKTIshFBJNMuooCtw9uhSbOXI+WE06D445uMCqrFZYvM7Ghh1mXnykmLVf5/D6k4V8+9F+Fn+ay6OP3sbZZ5/OqFGXUVh45CqV/hHUlrc15/U34V8t/BpHeQGSEnQ+fDkfu03HbNIxm3VsVp13nysgLSX2glu30cwdD6Zwy92pLP7JdsDAyE0TKnDYZURU2GbVufmaipgElpWVsXV9gwGqmkmaeSiwWSVfzdhP53YBzCY9HKk0Csik7ml7/Qoz58eRZQ+hNKoxsak613YNt6E8JjPINSMrw/64GmFqt+iM7OtmQOeDpxYlOXSWPrCPywdVkZUUpHuWn6cvK2ba6IMLhq5Z/piamNWk06dt89Oabh1VTpxNw2iofzY2k84jlxbTkHnttKFuTDGivkZVMjwGx2MtrhlRxWNXFJORFEIISbv0ANMnFnJ6nwNvDq/Njafz2DZc9XgLzrmnJb2vziYIjDzFQ/eaTVQIweTJE7nggrMYNWos+fmFzb7vow5Hoeb3r/b5/fDDCq699vKo42v+tBBKhpBHhBe/FXLKY9/q068k8uwrifgDAl2HOQudjBzmZvrzhTH9XelpGku/3MdTLyayZJmN5CSdmyeUc9E5sbXKs86q5pVXEqJKxcxmSaeD9MrVNFix3kpJuYH+Pb20bGZnsc7tgqyctY9tu4ycfFc2sYo18kpUlg3fx3nfZFLkVTEISUAX3HpsOcOy6hfuw1eUMryPhw+XOAmGBBcNdjG8T/PJANITNJ4Ze+hay7HZAfq287F6pwVfTYK3QUgcZp1LT2w+1X16gsaKaft48asEftxsIyMpxMQzKhjcNbIWuEe3AOPGVPHBJ3F1LQ6sFsm4MVV0O0D5nhBwxWkurjit+de0YpOFR99LxhdQ8NV8tafQyAX3Z7LurZyosb3zzptRVSNnnXUZ8+bNJCsro9m/ddTgKGR1OWQ+v/LyHX/h5TQfHo+XTp1OYMuWZcTFOeuOb91t4tQbs/A2FjYmnXUz99KyQZ3l/jyV44dlRwkmm03nkzfzOekI9GGoqFAYMqQVRUUGvF4FgyHMgvLWW4WccUbTFEw7c4ycfUMmVdUGQBIMCSZcWMkjt5Y2W/DoOnS6pg0lVdGzrn8nL988mouUsK7YTKnfQN9UH0kx0k4i7set8OFyJxv3mumRHWDsoCoSm+FLPRx4A4LH5yXx4Yo4fCHB8O5uHhldSlbSX0NRJiUsX2nl088dGAww+gIXJw44dGp9AK9fsPI3K0pNX+OGrM7jH2vBvOUOGvsU7BadL5/IpVfH2EG5l1+ewYwZHzJv3kyys1se+kUdJo4In183Idd+1LzPiuOOUj6/owWrVq2lR48uEYIPYP5SO4EYqo4i4KsVDq45t7Lu2OKfbBhiWJ5er2DhInuzhV9ZhUJOrpE2WUESGhFhJiToLFuWw4cfxrF4sY1WrUJMmFB5QNZhKeGS2zLIL1YjgivvzIlnwHE+zjqlebx1igJTx5Zyx1upeGsjnhoYPTontPCSm2+gZYbG8WnNo4nZU6Qy7NFWeAMCb0BhwXqd5xYm8t39+2l/CA7+5sJqkjx8USkPX/TXO/ylhLc/jePJ15MoKTeQnqpx4snewxJ83/5iY8K09DpXgRDw3oMFDKlhnimtNBDLmaookorqpl0hN998NapqYNSoscyb9x5t2mQf+sX9kzhC0V4hxNuE+/MWSSm71xxLAj4F2gB7gNFSygOmI/xrfX4//LA8IspbC0VpuuC+cX8Mq0WPYOGohcFAXVnXgRAKwcQHUuk6pA1nX5lJlyFtuOvRlKhkYqdTcv31lcyalc+zzxYflG79910m8orUqKiyx6vw1qxDI1EYc4qL6RML6ZrtxxbUULZJ1DyY/mYCfU5tzdOvJjb7u+76MJUKt1InSL0BhQqPwh0zUw/pmo5GzPg0jgefTaG4LDzu+UUqtz2SxuxvDpzQLSWs+t3Cne+kcPd7KSxaZ+XKR9LDuZEeAy6PgSq3gTFTMiiv8fGedaIbqzl6foU0wfEH8aVef/14brllAqNGjWPXrr11x/PLDBSWH2W5JA1xZH1+7wIjGh2bDCyWUnYAFtf8f0D8a4XfkiXLo4IdAGcPro6Z6iIlnHFipMY0Ypg7Zs8MVZVccv7BfTjTXkri84VO/AEFV3W4POmDOXG88Nb/Rjjg9oqYZXYAVe5Df2Rn9Xfz/bT9KHsEekjg9Sp4vAr+gMKzryWyZkPzQmw/brVFjZeUgp+2WQ+52XgtcgIqy6qtFAWbt3A9fsH+MpXQEaQPlBKefC0pqsbY61OY9lJ0t7qGuOf9FC58MpMZ38Xz1qJ4xj6fgb+2FtsIOMIvryqY9Hp4kxh3ehWt04N1AlAIidWsM/WqEpy28Lkur2DuKjufL3dQ3qgmesKEy5g06QZGjRrLwh/2MWBiNr1uaE3P61sz5I4sduY1P8fwb8MRjPZKKX8CGnOYnQO8V/P3e8C5B/uef6XZW1BQRH5+Ib169Yh6r2N2kE5JfjbmWiIsi9suLiezEa9anFPywWv5jLsho04rDIUED95Zyo+/2njva5X+3X2MHOTG2GikpIS3PkqISor1ehVefT+B25qRuNwUju3kj6m9Ws065x/EsV4rhBqf/8NyW8zOcP6A4KPZcfTtdfCghEmVdQnHDWGMsdkcDB5dcNXedJZWWzEJiV8KLkl08UzL4phEAoEQ3P1hKh+vdKIIsBglUy8qYdzgQ+vzGwuhEJRWxBa++wuaFiQbd5t5/4e4Ok1YStBDAkwyvIiNNJiDggVrHHyw2MPYYS4WP7efj75z8uVKB2kJGhNGVdK/a1jr+2adjaufSw9bJTKsET5zTTFjTqm/1/HjL6HaK7l8/CT03mvrenr8ttvMyPtasmn6HmL0Efvn8NcHPFpIKfMBpJT5Qoi0g53wrxR+P/64gsGDB9SVtDXEZ3Oc7FhrhpAAO+FSMrfk8/ed3Dm+PEoonHKSlz9W72bJTzYCQUFaRojLHsgIF5f7Fd77Uqd1RpBvXt1ftytDOJjQVP5XZVX4utZvNfP1cjtWs+T8U120adk8R73JCC89UMT1U1oQDApCmsBm1WnTMsjxXX2cd3UmGzabSU/VmHR9GRedVU1JucKdT6Wy8EcHUsKpA908c3cxmTVNcPwBEVM7CzPXNM+xNfoEFx8td+IP1Qt8k6pzYf8Dt+SMhcm5KSyttuKTCr6a6/qs3Ek7U5CJadEbx10fpvLZSmdd4yJvAO7+KJW0eI3Teza/8XosGI3QIkWjoDh6ObRt1bSL4pv1Nvy1/mVJuHSxdn+NUYUX1ARPfZbE2GEubBbJhFFVTBgVSVpa5lK46tn0eh9tDSa9lcoJXby0bUDfZWlzDVL/BMoXQ1KY9EBKgTcg+Gq1g/MGHnqTqr8Mhyb8UoQQaxv8P11KOf1IX9K/Uvg1ZfICvD0zrq6HLXVrSLA/T+XPXUY6xOjiZrdJRo0Im8T9xmbjctcLVbdXYec+I89/kMgD19Zr2gYDdDkmwNYd0Xp6z24+7vhvCh99HYffLzAY4L/vJvL0pGIuO6t5msrZQ910aruPd2fHkV+sMnygh85tAowa3xJPTYvOSpeBW6ekUVBk4N0F8eTkGesK5L9bYefU8RbWz9mLxSwZcqInZhKv3aZz3hnNWyQPjy5hyz4Tm/eF+3pIoEvLAI9deuCWnI0RlPBZhRO/bKQ1S4U3ShKihF+1T/BJA8FX9/mAwtNfJkUIv9rrOtRmgg/+p4RJj6ZFaPJWi86UW5sOtijUELsKIgVfzOYEYRSUG5hr8mJCMChoIq7RGCxc3URJoSb4YoWTOy6o9+HvLzEhs+6EnKfqhB+AP6CQW3KULe1DE34lhxHtLRRCZNRofRlA0cFO+Nf5/HRdZ+nSlTGDHQC+GNxwECatjFXz2hB5xQZyYpSo+YMKn3/vjDr+1APF2BoETRRFYrPqjLnQxcdfx+H1KehSEAwJfH6FO55Opayy+UPeqW2QxyeV8u4ThYwZ5eLp12upqervw+NVeOzlZApL1AhmEE0TVLkV5i0Ol6olJug8fn8xFkttxYXEZtMZNtjNaU1QYzWGwyKZflUhaYEQoWJQCiV//GJi4dJD47ULSEGoicyJKj16fMqqDTFNYYB9peHnFdDggV+TaTWnHSmz2nPyoixWl4TJ4QIhWLLZysL1diqbqMW99OxqXppaRNtWAYyqpFM7PzOeKmDkkPDYbNpq4tKr0ul8fBtOP68li5faCBYTlrSSBoKvEbzAduo2YjXby2tWNy9ZqhkdV8YqNVKz9AaUmOw7IR08jTT0fp382FpfAp5t4FpXd9xklPTucPQ1MZeG5r0OE/OBK2r+vgKYd7ATjrLt4eDYsuUPHA47rVvHLvi+4BwXO3YZo4Sg2azHLD9rCNVAk4772gCElLBzlxEp4cQ+Pr75aD/PTk9k63Yzx3bxM+m6Mt6cE483hqBVVfhulY2LmyjBOxh+3WKJWVesaYJgjGwVt0fhj1319tf4S1wM6OPj07lOXG6FM091M2Rg89M5altt5uQZ0XVBrQ496YVUOrUJ0Ltz81Jm7IqknTnIDn+kbSiQnGiPTi/KSAhFlMjVfV5Ijm8fXuQ3r2nBglx7HVHDbxUWzluayUsdCrnjzbS6AElQEzx+aTHjh0Rr4BeMrOaCGCWQGzeZOePClnh9AikFhUUq464106ptEFwCsqJOqYcR8AA5gKaT+VQOgQb711R7FV9UJmGv0UNO6+VmysxoRhirSTKyb2TAbngfN+0zE9na+la0nP9Ct0+wmHSOa+/jhC5Hl/CTCgSOEFGpEOJjYAhh83g/MAV4AvhMCHE14dE+KN31v074hVNcYpu8ANeMr+SLeQ527THh9iio8TrCCWOuqMIbEDisTTvn05I0urYL8Nt2c0RU02LWGXdmFRs3mrni6nATI4DUVI13ZxTwzrORZUeqGp7bMWLOqAfZ2ap8gg37LCRYdY5tGRn4aJ0VJL8JKimLWeL2Nkqatel0OSZS4HfuEGTKnQdu9lOLjdvNLFxhx2KSnDfERUW1gfxSNaqPiT8gmD4nntfvOailUYfnWhZx0e5MAhI0FIxILIrOIxnRJrRRhQcvKOWBz1LqfGFCSGwmyb3nllHgNTB/vx1/I63RF4TrX00PE0o0wL2fpNL3GD/dsppXV/3Q48l1roZaeL0Ke3cawSihSkB7ou0oSVjwAeggisDWJVLoGoBfjEGGBsPuk7bpIW4+u4JXFoT7AOsyXEt9wSAXx3eI3FxUA3w1LZenPrqIV+59gkzH74w/O4Wbz6k46vr4SgGhWEm1MXHgNDMp5aVNvDXsUK7pXyf8fvxxBRMmjG3yfZtV8v38/cz72sGjnyeR5zWCgHeWJvDesnjmTMmjd4emNZS3Hypg5E1ZuH2CQFCgGqBvNx+Xn1FJ775tqKqql1579yqcc14mmzbuiegNPPp0FzPnx0Vpf5omOPWEpk3MN1fEMWVhCkaDRNMF6XEhPp+QR5vksJP7uKE+ft5ogYYRV4Mks0cQkw/25BoJ1vj1VIMkwalx9tDD0zInv5zC+wvj8AdqfJYzE7n8rMqYVPC6FBTWmp8hWPKnDZdPYVA7LxlNEBgMdPhY0mEfrxQnsM1n4nibj5tSK2llih0UunpoFRmJGk/NTySvXOX4dj7uP7+MrlkBVhVbMBsk/kZrRi9WiFUgHAgKPvjJyePNIB8A2LDJTCw/nq4LLCaJLyCgEGhBpACUQH79v8Kk4//Tgq2hjxLQGm2T911SxvDebj5d6iSoCS4YWM1J3WNr6A6r5OGrg6i5F1FV9SiTLnyoWff0d0MKgdbs5i9HluyjKfyrhJ/X62PNml95552XDvg5kwm0eEFJSCVUo6XUpmiMfTKDzdP3xGwWDdC2ZYjfZu3h25V2cotUenfx07ebjw8+dMZm/dAE8+Y5GDe2fkfv09XPzWPKeenDRCRhf6MuYfpDhcQ3UQq2areFKQtT8AYVvDX25O5SIxe8lcnau8L1nosr7NBLwCYgSHg9thHktVZZ95+9PPpyMvOXONB1GDnYzRN3lESUVTUXq7dYmPlVXF2JoB6CIIL3voydYG016ww/wc2vuWbOfzuToA5IQVCH204u5+5hsRPtO1uCvNSq+XW/Z/Ryc0av6OqWY5xB/DFScIQmw0GJRsc1KajwNN+5lJ4WiklQoRokU+8o4aFnUzBoEn+pgBTQhEBzAXki7POrgQwoGBtVwmhAv1D0Q2qbGqRfSx9mk6TPMQcvsbvuussZMGAkkyffQkrKkelFfKShHWU9Nv9Vwm/VqjV069aZ+Pjo4ENjzPw+LophF6DKo7B5j4lj2zW9u5iMMOrkyEVWWKTii5ES4vMJCgujh/Hea8q5ZGQ13620YTZJRg2pJjmhaXX+zRXx+BqV5elSUFilsjHXzHFZfsq9Sti/1JKw8FMBJRx5NlrgjYeLeOPh2Kbnb5qJDwIOXFIwyuhhhOptMiI650dHbJ9lTXe5JWvtdQnBFpNORkqIS0dU0ff51pR7Iyf4iz8lMrCtjxPbeMnNU3E6dBIOMA6Hg1SLxskJXhYV22gYGZEJseOudrPOqD7N14jv/E85E+9Mq88iIBwJvuKyKq4ZW8WY8138usVMQrxO1w4BVq6xcNGVmXUECRCuLc86yYWzRRCfrG9XMdFrJ75RxPfNT+N44LmUCPfJB8/kM6R/0+WWLVqkcs45I3jzzQ+4557/NPve/i5IBNpRxmb6rxJ+P/ywgqFDY0d5G0NrYn0JISOior9tM7H0FxtJCRqjTq0mzhHbJzigvw+rVeJ2Ry4nq1UyYEBs53K7rCDXja6M+V5jlFQbkE3Ue5bXRChP6eBl9kYHGiIijyzFrtHC2XTJwxt+J1P9ifgR6Ai+DNkZqPr4yFoUM4pqUGRsn6UQnDnIw+VnunhjdjyllQbOHlzN1edU8luhBX8MzdgTFDzxZSK73mtBRYWCpsOQwV7eeLHwiAlBKeH3r02QIMKVnUagHNTfJb2yfWzab8EXDAcrbCadPm18h5QbeMHZ1RQXG3jsmSQ0LeyHu2x0FQ/fG/ZP2m2SgX3r58DAfj6en1bE5IdT8fsFmgbDh7p5+ali9lXHs8IYwCzh1KCFlnqkQNj6p4kpz6fgDyj4G+zPY2/PYNui3TjtTfusb7llAiNGXMzEiddgtze/h8jfAYkg9P+F3+HB5/OzYMEi3nzzWTxegdcrSErUmzQHLhni4rfd5mh2F6OkZzs/ug7X39eCBYvthEICo1Ey+YlUZr2Wx4Be0cJs4Ile+vT2sWatBW+NBmC16vTp5WPgiQcnQJASFq+0MXNuHD6/4MKRLs47rbquB+4Z3dyszbHgbZTLFgwJercK+yjvP62U7/+wUe0XBDUFgyIxqZLnzytuchxKdYWH/In4Gjij3CisCFn4JmTjTGO0ELhoWDVvz4+OWGsajDjBTYJT57T+ked5mmhyDoJV663oBfXv/7DUysWXZ/Dt/NyoT+/YbWTGx/Hsy1M55UQPl5zjwnGABQ+QW6ZSVGGAYqAB6VAIhTzFyOxJeby8MIFla614diqsWmXl9A1ZvPlUAW2zm5d4fv3VlVw1rpKCQpXkZA277cDXNPrcas4/q5p9uSqJCXod4UU3zUg3renSi08WOKP6nkB401603M4FpzetsbZv34YTT+zLBx/M4rrrrmjyc/8EJILA38lU2gz8a/L8Hn/8Bbp06crL7w+nbZ92dB3Ull7DWrN0ZezerpcNraJ/J19NZzCJxaRjM+u8M6kQgwHmLnKwcEm4lWQwJPB4Fao9CmNvTY9J3S4EzPokjwfvL6V7Nz/duvp54L5SZn2aVyd4lu+zcvasTLq/2ZrL5qWzqbhePbv/2WSuuCud+YsdLFpu57ZH07j01oy6nK5x/avITgxhrWGJFkhsRp0HR5YSX9O5TUpoGx9ECwhESOJUdd64sJBhHZvWYn7SLDF3ODcK84KxtYOeHf3cemk5FpNe11DIYtZ5bXIhCU30zjihjbfOv9oQqpTI3ZHHgkGFTVvM/LEjUggsWmrj5AtaMeOTeL5a4mDKMykMOi+bioPkRlqMepMU9BajpFumn5WfWnFtUAgVC4JBwYbNZkZclhWhXR0MJhNktwodVPBJCSsCFu7zpvBucjy59ubrGB6viDn/dCkizOimMHHiNbzyyjsEg0eeZed/Qa3Z25zX34V/hea3evUGPv10Lm2OXc2GxY46yqq9+4yMuT6DxV/so3OHyIdtVOGLB/P4aZOVnzZZSUvQuGBQNakJ4Zk1c44zwodTC39AsG6zhX49o7U/kwmuv7aS66+NNmUX7LBz7dct8NaUfuW5VH7ca2PB6FwSghpvfx4f0VHN7VVYtcHKklU2Th3owW6SLP7PPt7/JY4Fm+2k2DWuHVTJwHbh6wiEYMQrWRS5DHVpOJUehYmz0ji5wx7iLLEXpDWmMQ0KEodo2uy88qIqWh0X5M8/TGRZQpw1yE1qYtOmtdMs+e+oYm6flxrWXITAKCRmt071zugJbVQlubkqnWqem6bBTfdFVlh4vAr5RfDSOwk8cGvT6TkpcTq92/lZ86clomGS1aRz9bBK5nztrCnvq39P1wUen2DhYgfnN7O9QXMgJdzsSmOuz4EXgQK86YnnAUcpN9gO7gIZNdTNJwviotKWNA2GDTy4qd6nT09at87iiXnfkDzmPDKlZERQ+zt7gTeJo83nd9Rrfh6PlxtvvIvb73iY37ZlE2hU8+gPwKvvxGZRURQY0tPLg2PLuP6syjrBB0TlqtVCQMyd90CQEib/mFIn+CC803lCClOWJbN0tS2mWer2Kny7vF77spskN5xUycIb8njv8sI6wQfwzVY71X4RkX9Y29Dniw2xA0AhDXoH/IgYGYdmJGNN0YteSpi8OoUeX7Rm8pZUXgsk8LnRiekgpidA9R6BspFwlLMQ5O8SbavAFIMg1R8QdO9Wr3b9uccYczMKBBTmLzp4BcmMGwtonRrEYdGwmzUsRp3Tj3NzzWmV5OSqMb/b51fYl3dk9/8VQStzfQ48KDXajsCLwtTqZAq1gy/+wf28nD7YXdOnWaIoEqtF557ry8hIPfjE9AJVd97ASy+9yXMmAw9ajAx2WNh9qPV+Rxi1Pr/mvP4uHPWa37Rpz9GzZzc6dB6F0SjxNlLINE1hx65Dz+e4dJSLtb9ZohaFwQDH9zi07HhXQFDojj2U6wssjO9YFZM01ahKkg7SyKcW+8qNTQQUFPaURZqPmg7TZifxxuIEgiGBs4uG9Uo9XMFCOG3lPnM5vQ3RNt/7O+KYuSMOv67U5c2tLTFz44o0PjyloMnr8/gED09PCVfW1JRyhVBQVB1DEqgF9YEmm1Vn3Jgq0hosZrtNxizrgib62jZCZpLGmidzWPG7ldxylV5tfHRqGdYqe3XzY7fpuBuVtplNkl7dmleV0lzM89nxxtC1DcD3ARuXWSOTnKWEUo+C3SSxGiVCwFuPFfLDz1bmfufAapFcepaL47o27zpfN6kUDz2R4EU70YNBPGYzXiS3WYzM9fw9+XOxEN4Iji5xc3RdTSOsXLmG2bMXsmLFAoJaAH+M9AuzSWfA8YdON3/RmS7mfmdn+Wobbq/AYpYoCrzzdAHGQ6QCshklJkXG9Hml2UKMGOyOSSdlMEguHdU8ooPjsnyYDBBsJCsdZp0+rSKF9ZRZybz9Q3xdNUTZZhXLAxo33lbOMdlBTjZ4SVXCAqVMU5jtcVCuGxho9vLq1gQ8WqSQCOgK3+faWCSrMZg1jgvaSJaRg7RtlylmAnQgpNCps5/+J/tYtNhOQoLGjddWMPaSyPvOygjRtWOAjVtNaA1+32bVuXZc8yLmigIndY2eC6cPcdM6K8jOPUb8NWNiMet06eBnUD8veYUG7DZJ/AF6AUsJn//u4KW1iZR6FYZke5l8Yhmt4iIDJhYRzi1svKUpSMyN5sD3O23c9lUqRTVEGud1qeaZkcXYTZKhJ3gZehhtFOaYDLhX/Yrh2C4IczjAIIXgD4NCqYDkw+Rd/F8RhKiuGgAAIABJREFUDngcRtLpX4ijVvi53R5uvnkyzzzzMElJiXh8Or2O87F6rbXOZA0TCUiuv6J5i6MhDAb4+MUCVq6z8OPPNpITNM4fWU1a8qGzZKoKXNWzkhkb4yNMX5uqM6l/OTar5POX87n01gyCNWtF0wWvPFRIm6wDRxu9PsG8rx1s/d1IC3OIPF3FV/MbRoNOil1jaMf6nERfUEQIvrrjHgOLP7Yz+f76COsKn4WLizPRAZ8UWIUk1ITSGxKS6WopZosf3Qqjfcmc569Ppk1N1OqqSyIhyc4I8cJTxYTDsU3j/RfyOeeqlhQUqShCEggJLj23ioubuUE0BVWFbz7cz9OvJzHrSyeKIrn0XBe9u/voMaw1ZRUGdF0wbJCb1x4vitmF7/FVibyyNhFPzdh/uk3l6112VlyeQ4ajfs6MtriY4Y2P0v50BMNN9c/ptwITl3+eHjFf5m5zUOE38MnofA4XEgitWovxhD6H/R1/BcKMX0eXz++oFX5Tpz5N//59OOOMYXh8gtOuzGLXPiO6SUAgzFvUvZuf918ooEUzfCGxIAQMPN7HwOObNnM1DV57LZ4330ygulph6FAPDz5YSqtWkUJrykmleEOCDzfHYVDCD/v2fuWM6RZeuP16+vjju938/KuVQBAGHOfDdoA6Y4C8AgOnnteKKpeC26Ngc+ioXSRpXUJU+BRCfkFxkUKXe9vx8LklXDm4ilJX027cvSX12pomYXxJOu4GCbYeKTAkSpRCid5o8RqtAaTDR63y/ampjOX72/JFaTIuXaG31UeHjgF+/90cIQRtFsnNFzeP2LVlusaahTms/tVCQZGBPsf6qfYIPpnvJCsjxMDjvU1W5hwMTrtk6qRSpk4Kl7Rt3WHi1IuzIgIsi5fbuOTGDL7+IDIFp9Kv8NKaRHwNNFJNCtxBwSvrEnj05PoyuR7GAPfZS3nEnYyBsManI3g3voC4BprxC6sSo6pS/JrCj7ut7K9SyYo7vCZN5wQ1nlm5FvXic+qOCSnpoMt/TOuruYr/b/Y2B8uW/czChYtYsWIhAB996WT3fmM4WmoBLOFJs73IRELiX9M5rBa33JLG3LmOuty+OXMcLFliY/XqvSQn1/+2qsDTw0qYclIpxW6VTGcISyOGY1WFQcd78WvwTY6dAo9KvxY+eqXE9ufc9VAqRSUGtJpF4qlWMKyXWMs16BxmDXbX7Kb3zU4hOyXE4I6emAwoIOnegJhzQ8CMT0ZLEq2tQCmVKJpElwJF6AiDTpehmyKCNr/t7M6S0mSCNRxEa71WrFfqdH/Xz7Y/TBjV8PmTrylljTDzzJeJtIkLcn2PSrokHbgVZP9ePkIhuOrOdL5fbkMxhFN/0lI0FrydS0ba/85h/9p78QQaER4Eggobt5rZvstIx3b12QPbSkyYDBJfo58NaArL90WnWt1kr+R8SzWLAzbMQnK6yR0h+AD+LDPGbKFgNkj2V8YWflLCrjIjQQ06pQZjBtFu8AV5atU6kp6bil9KbISJpZ/1/nP+Pvg/VOFRWFjMou/9vPtRHIVFBnr19HPV2CoyM0JUVyuoqo7ZHHsbCoU0br75Hp5/fhoJCeFa0gU/OKLo4iEcMFi7ycKwA5AF1CIYgv/OSuKdRXF4fAqDj/Xw2JUlEcy4jbFvn8rs2Y6I1paaJvB4BDNmxHPXXdE1q06TxGlqOsdqe4WRM79qiU8TBDWBQYHBGV5mDstHbXSLi5bY6wRfw98v3mmAYyKPewMKLyxKYFhXD/ecU8ajs5PxNDB9rSbJ/efXayjhs2M8AwvoXYFtAkWXJCa66XraBkypLkDSha20Cuznp9LT6gRfLQJ2QY/7/LxnKKCk3EBSC43Tv8yicr2CT1NYka8z608n755WwPDsAz+z1z+M5/sVtogkdW+uwjWTW7Dg7TwAyisVPv3Syd48IwOO83LGKe5m+2t37jVFpMXUwmiU7M9XI4RfpjNEIEbtMEhyhcomt4ke9kjhkmHQGGtt2lzvn+VjW5GZYGOGHE3QKSVaUP1eZGTcxxnkVqkIIMGqMeOiQga0jrRaivbkkKIaeDklhU3+EC2lZGRQ42io9/g/IfwGDryEsnIFWZNdlLsLFs4N14YGa3wcDrtOegsNg0EiGm1h559/JqeddnLd/8mJGkLIKC47XQoSmhktnfBcC75bZ6/zhS1aa+fnrVZ+eSmHtITY37FliwmzWeJvpJj5fOEqAThgZ7yYuGJJOqW+BqVsOvyUb2XGtniu6xbpuwyTpMZYdE1kLeRVhB/nDcMrSXJoTPkshWKXAauic1m/Krpk1i+q40x+rEJS3Vj+aUCFAHPYT+UTKuakaiTQj1/oyHZ2+TqgKkGCmqnRqYKNPjOt2oVo1SLEHctSKPMb6ha4JhW8IZi4NI2tY/dE1BYv2mHjsR+T2FtupFNqgJxv1agNT9MEazZaKa8MtwodNSGTUDDcbmDm7Dhatwzyzfv7D1gCVouBfT2s32SuC4DUwu8XdG/E+5gdF+KEll5W5FoJNAwGKYKSVAMjN2XxQ899dLA2P7F44gkVfLrJiRZQ6jRAm1FnfK9KEq2R1owvKDjr7SzKPErdvPEEFS6cmcmGW/eS2sDnuHr1r/Tv15tTNMkp2l/T3/hwcDRqfkc8zy8YhApXW6R5Oti31b2kbStB4+9g3grmrQSUrSRl/MbPP3/DqlVfR7ymTLkj4juvGV2JpZGmKIQkJUGjdzNSAHYXqCxqIPigJmM+IJjxTVyT52Vnh3DHyA9DSI7pcOhmRI5LZY/LGJV27AkpvL89+jpGjXBjNEbet1GVGDKiv1tVJIM71UcHV6yz4spV0IsF7kIDM7+K54zJLesCLgYB76cW4BA6NqFjQIYdgZVE9MVye60UrO2AxVNFJ/kHRjRaWAoI6dEqloIk3eqluoZH5dsce5RmA1AVUMhx1e+7c7fYGT8rnY35Fip8Bn7ZZyW/kwoxOmIKIfH6BBMmt8BVbajTDN0ehZ17jTw/I9yKMxiCd5bEcdrUlgx/uCUzlzojOr5de1kVDrse4SKwWXWuuKiKtJTozfDdUQUMae0BIcOrRiXM4ecQ+HTBM/ua3wIUICsuxJKr9nNWx2oSLRrtEgM8emoJj54aTbP1zR82/CGi5o2mw2cbI3MgV69eT79+vQ7pWv4OSAR+zM16/V044sIvZ78RYegC+rZG74gIjSUQUNi8xczmrQcPfw/o6eOhW0qwmHWcdg27Vad1ZpDZL+c1i7RxW44pZocxf1Bh7famc99tKTp6HNGjpEDbAfW7vJSw1mXmv/sSeT0vnsJA7B0uJEWT3R1iCYknpxTTplUQh13HaNRx2HXaZAd54O4SbKZ67cCAxCZ0bqmhjtqZZ2TWUmcEq403oLAj18SXq+oXywCzj80t9/B4YgkT7eUYd0jYRY32R1gQSijdkU2v655A1iT+JZgq6Je8CpPSKFCk6JRk7+bi+GLmmzzEm2L7YzUdnMb6kr37v4tMEA/flICe0c+sZXoIXRIzOdkfUPj8KydSwsXPZnD/Ryms3WllzZ9WJn+QyuUvptcxdacma/z4+X4uPruKFikhOrYLMO3uEp64N3Y/kjiz5L5Ty3D00+FYoA9QE+zWEGxwR8+jSq/Co18n0e+pbE55IYtP1jojmMLbJwV578JCdk3azbobc7iyd1XM+VxYrcacH76QQl5V5DiEhV/vmPfwT+L/RHlbcqIGIpbwI8rFZFAlOTlGuh+kiTfAtaOruOQMF2s2WUmIC2t8zRF8uW4DywJWPMcoUEBEWxOjqtM1O/ZvhzRYud2K9WSJ52cRPhfACvQWbCqryaGScP2ONBaUOfDpApOQPJyTzNsdCxiRFOnXausMkmLV2FcdudAtBp3R7aP9Q0mJOqu+zWHxTza27zTRsX2AYYM9GAzQrW2Q575O4LcNZrylCkFVMOTaVjx4VSlmq4wZFXX7FJZssHL+SfWVHfGKzuWOKnDAD6qNX0vMsLPBRiUkKf0ku4s60ZEg1EzOa455lcR9pSwuGIFXs2JzVJPddhfYvfiBN6wuTutfxJZFWZHkq0KSkKaRXGPa+TVBvquJaRgX1sY8XgWzSUdV4bVpRVFtRBtCVSXLtllZvcMa4fP0+BWWbrWx5k8L/Wr6W2RlhHh52sFTcGrR0hwiqAga14oJJMdYI+eROyA45YUs8irVum53k+aYWL3XwrMXNJ/DEGBAti8m/ZjdpDOwbWQuYGJiAuvXb6JPn56H9Bt/Nf5PmL0JCTp9+7VByMbCT0YJv0BA0K2ZmesAcQ7JsBM89OnWPMG3aL+NvnNb8/aeeLR2AvpJOIG6hW1S4ZozIv1sK3ZbOOGlVqRNbc/ti1MJJgL9gTMJ94g/DYwtJBmJYfvx63I7C8sceHQl7COTCl5dYcL2dLyNnORCwIwhBThUHYshvPjtqk6nhAA3do+dDmIwwPBTPNw8oYLhp4QFH8Cp3Ty0E0FClQJNC/u9qtwGHpiewt48I0qMpGqTqpOR3LQf6OEOJbCTMPunVvMKCbavMTH/rLcoWAq6J2wSqorGJa0/4oG+DzJwwAq6HPsbdme9UA0AW9pXItrroMgw3btBQqKk4kRJbg2Vk9kgcTahIWYnhph2Zwmjz6zi1qvLWTN/L/16+miRotHlmEBd46haWM06486rYsXvVtwxEuIDQcHKPw6/yjXZqHNucjUWJfJ6LYpkUstI/+9n65wUVqkRbT49AYWP1jrJKa+X3ps1I8/745gecFIUo3kTQM9MP8OO8WAz1v+uRdXpnBrg9EakFk88NYWpj7/MyUskw7e2ZGaxk9A/muJSj/8T5W0vPp3MwIFbEWYdoxoOVEhN4vPWO3etFp2Rp7tp3QSlUEiDNbss6Dr0be/DdIhXGtDgmmUt6hraAKCGU9xFNhxr8PPsdcVkp9X//qYCE6M/yMRTQyvl1wRYwwuWclE3WqpBMu6kcL/VT4qcuGNMWkVIVlRZOTUxcnL2TfOz4aI9fLbTSa5b5cR0H6e3ckdEeitdCsVlBlplBJtkYq72CmYtcUY57L1+he9/sWE1S9y+yCCRwQAXDmm6iH/FNitGAY3d9qqAgNfBffs/5Yo/XmVg959RDDpl1WlkGUagWBUa72xSQJkikX0kdNXC42eTkAAWJH/qRloqGkLAfwaW8/SypLpxh7Dz/+6TyxhznIvxF1bRGI9MKuGy/2QQCAp0PZxG1K+njxsvr+D9pXFYTRJvo1QWs1GS2swAWUCDBX86+K3IRPuEIOd1qsZhkrx4TBHxe3RmFsURlIIsU5D/tiuhtzNyE/9hhy3ifmphNEjW7rXQKqGayb5EZgadBBGoSKaQyJuWEs4yRUfC3xldwMx1cby7Lo6QJhjd08W1AyojyialhCniJAJnT+S3p26HqfPZ4jGzsMzBxx3z/9G+Hv9nytvat0vDbPLyw7e/Egim0LpVkJdfjeeFV5Pw+MBikVx9RSVT7ovdQ2HVDgtjX62vhhAC3rmugKHdmsebt3yXlU/+cMSkNkcVDBzh4cvT86Leeu6nRHyNqhRUWwhh01G9RgyEAwuvTSikbY3QjKVh1aKpuZZi1bmxe3RVij8Atz+WyhffOFEN4fuefH0pN42N/mxZVdPtHPNLVRY8k8uYRzPIL1URItwu0t9NMHB2Nr1a+HhpWBGdkyPFXKnbUEf33xC6hBvaVzDP5+DD9RP5cP1NdEn0ML1vBSlxfl4T0WacRUKa38FGJCFrzSZSe58I2in1v33rwAoCmuDlVYkEtTAN1T0nlzLmuGhXgJQw5cVkpn8WHzbtVYmQgsfvKmbceeHm6RcMqOahT1OizlUUydl9D87gUuZVOPWjLIo8BtxBAzZVZ+ryZL67dD9tE0I82a6EaW1L8OoChyJjCpVWiUGMiozhqwv3ZlmuWfgg6MRbY3yFambLdb4Ufjfuw9loXqkGuLJfFVf2i94IarHSZWGly0rwoslwU29Y+hmeIRezzGXll2oLA5z/XEe3I2n2CiFuAyYQ3nE3AVdKKQ/55v4S4SeEoGPHdhQX7WDAgDgm35vCzA/j8NQUlgc0eP/9OK6bUElWy0jNr9KjcNELmbgbkZCOfTWDDY/tpUV80zt3UIPR72WyJseCWxHQCmKNtyVmEjBsKzTVaaaWRA89Ll9PfOuwOZPktXL+/myGpioRPqdLU118V27HE0P7GxTffLZggLufSmX2t2FtrlaPeOzVZDLTNM4bHrloM1NCmIwSTyOvgRCSvl19dMwKsua1HH7fZ+TiuZnkoYarNiSsK7Aw4vMsNlyxl8QGjCvDu3j4eG0c7kbapJRwec8qHkwuY59HRRWSTFvNc5AK430O3rNUEyCs8VmkoK2m8h8dFiHrFjaABZ3hqpdWSv1zFALuPrmc2weVU+EzkGTVYhJBAPzwi5UZX8RHabxTX01hzNkuVBXibToPjCzlsa+TwkEmAXE2nQ/+k4/zIFU1AA8tS2a/SyVY80w9IQWfJrj52zQWXhzeNFUBzibmEcBVA6p4Z1V8hPAzCEmqI8QJbX1M9CXHJEBQgB9DVkY1IJndXGDipeUJbC8x0T/by80DK8mKj7aYVrqseHUBRhPc9hY8fB70PhV/XBIrXNZ/XPj5j0BtrxCiJTAR6Cql9AohPgMuAd491O/6yyitOnZsz/btOyktVXj3/XrBB2E6Ka9X4ZVXoxvifLneHvP7pA6z1zjCRJHbLdzzSQpTv0hiW279gL6/Jo7Vey3hxesT4ZXYCDZV5/IOsXfPnpl+DEIiFJ1+ty0nvk0ZiipRVEmFw8OnnbYTVCOF7/BEDxekuLAqOkYkVkXHqui826kAswIrN1g476ZMjh3VmnF3pbN5R+wJ4PUJPlngjOD8A/D4FJ59OzqNQjXA1GtLsJrrhZciJDaL5L7xYY1aCCgSKuVGQ5j6vnYsEQQ0wSfbIqmwhnX0cGyKD7NHr2ugZTPpjB9QRduUEEJAtj1UL/hqMNpv58nqRIYGzfQLmrjF6+TZ6kSOUTQW2As4XvEhkNjRudrkYro1tsPfaIBAALblmwg04Zp8f25cTRvJSARDsOpXKzn7VXoNa82jDyejrYHQRhgS7+a3Z/bQu13z/MvzdzjqBF8tdClYnWfF2yRjdSTapwZ5//ICUh0h7CYdixpuRTr/+nCGgox2gdeh4fEf/rQy/M0sPt/k5Nc8C2+vTmDgy634syQ61SjFqGGp9YN2GQCDL4bpkzArklT1f6+K+V9Qa/Y259UMqIBVCKECNiDajGvml/wlqBV+7Y+JnSgcDApWr4kuDarwGGJOfF9IUOoycPO7qcxd68QbECgKvL44gakXlHDtsCo+XBcX6WfZT1j7E2G6d0XAhW1cnJUd3QEM4PbB5SzY6sDepRDVEkJpqDUKCCFZbqpkeCCp/rCAF48p5pqMSpZU2Igz6JyTXE2SUeerpTYm3J9el6y7v1BlySobC97IpVejQE+lS2nSJ5NfHNtcuHyki/QkjWc+SmR/kUrfLj7uuaKMTq3rTcrdlUa0GKvMG1L4o6xeELu9gnEPpbNhswVFASUgSc8O8cytxZze9eAabHfNRHdPtGA/zhDgO0cBUobHqqRc4ZE3k/lmuZ2kOI0bLqnk3KHVlFYrjH8nnbV7LBgNYUH+1IXFXNwvUuP1+QVNORT8AcG4m9LZnxfZW/iHRXY+O8HJmAuaR5BwIOq7Q/GbndrZw+8P7GFHsRGbSdIqsX5ijza5mROy42mcuwecoobdO1LCrfPTIlobBHVBKKDw0KJkPhgTSTF2XlI1D+Y0MPfHPwrXdUdfu4hzjuvY/Av/i3AkzF4pZa4Q4mnCjcm9wCIp5aLD+a6/UPgdw4oVH3PtdaGoGkoI+1+OOSY6zeTkzh5UQ1IUdZPNJEl2arz2fUJdCoOmh9lRHvwihXOOd0dOzABh4fcbKHFwyflV3DCoks4JTWfhd0wNsuDqXKYUuhGGGAScQlKoxE6N6WEPRJQ4SQl3P50aUaUgZZg9+MEXk/ny9TwWb7TxylfxFFeqnNrTjdUi8cUwY/sd27S5Mry/h+H9mxZO3VP8sTuYGXX6pNd/792vpLBykxV/g4VWnm9g8wYTI7odmvkeC0KEBfzJ41tRUmEgEFTYCWyZZmbTdhPLPDY27jcT1AT+Ghlx26dptE0N0a9t/XVeOKKaZetsUTyMmibITg+yfacpiqjW41V484N4RpzmZvNOMxkpITq0anoeXNTZxbub4iKqOQxCMjjbE1WvfTAoCnRqEf1bJxl8XGqs5qOggwCC2tT31y0ldf6+Sp8SMw1ISsGKPdGKQ4Kq80WnPK74Mx2XpoDdhum2VzC/ci3quAWg/nNFbofo80sRQqxt8P90KeV0ACFEInAO0JZwNuosIcRYKeUHh3pNf5nw69QprPlltwoxeJCXpcusETWyZrPk4otdPPZ8Inv2GRnU38eFo1z0yA5wTp9qvlzvqPP7iRpz8sdN1qgIHoSDEIs32xjbp4rfC014XApsp65hq+6BWTPiGN26Gg4g/ACOy/TzRCs/j6uSxkaSRQqO0Zo3gdxeQUFx7OHdsNXCKwvjeezz5LpE5B15RqypOlafXle1oCgSq1ly/03hkostO0wsXmXjt70mtpWZsVgk40+tZMwQF021RO3Twk/PND/rC834axazKiTxZo3zO4a1qpAGny9xRgg+CEeO35yfwB1jm8fKcjDMmBNHWVVY8NXC41N4eU4Chu5EBVt8QcErS+Lpd3W98Dt3WDWfLHTy869W3F4FoypRDZKXHihC6kRq6w2wN0+l6yVtMJskwZDg2GP8fPxwPokx6KvuH1TKL3kW/iw3EdAEZoMk0aIxoW0ll01LZ3e+kf5dfNx2YTnZLQ6vhEwIeNpaxhWmahaFrNjROdfoIb2BL9SiyhoW7ug5H2+NnR7Uz+ljy3F72OwxIYBufTpxw/rePPbY80ybdu9hXeuRwCEKvxIp5fFNvHcqsFtKWQwghJgNnAgcPcKvdessiotL8Hi8vP1WAZPuTGXOPAdIQXpGiOuur2DcLRmEQmE2jYXfOXj+jQSWzN7Pq1cWcVJHL7e/F+4HIf2C0gqVJS57uC1hIwjApEpGH+/im9/tfP+trYYVuC5TF69fcNtLaayengOA3w9ffetgf65K7+N8nDigvjF0Z81GW83CToOPYM0urEpBgmZk5aYsnixw0Noe5LqOlXSNj60JWs0SkzGyTWYtkhM0ps1Kjii384cUsMK5F1Sz73cjOXkqfXv4uPu6Mjq2DXLTQ6nM+dZZ04Kx5t4y4fd9JpZstPHO7YUxr0MI+PycPJ74JYmPtjkJaoIz2rl5aGAp9prSuWBIxLxOgOpY5X2HiSW/2KJ8mgBGi4xpT0oEuRWRU1RV4bPn81m8ysY3y2wkxetcepaLdq2C6Do4bHqEfzl8jo5LKISCCv6avW/9H2aueaIFnz8WzZ3nNEmWXLafn3KsbCkx0yY+SLAQrn4sva4F5o5cE18sc/DDs/tpn3n4zYJ6GAL0iMGoDeGo9/ndq5mzxRGRL2gz6tx4QtMbkiLg2AZWyGOP3cuJJ57J+eef+Y8lP9eWtx0B5AADhBA2wmbvMGDtgU+Jjb9M+KmqStu2rfnzz10ce2w3Xn+1iOefLcbrFcTH6/Qa2jrCdPF4FXILVJ55LZFHJpeyN1cFN8gGDuaQT4AqIzdCL7iLFCY+kcaLLROYcmUpy+ZZ61IIGmJ3vhGXR1BcqDLinJZ4fQK/X8FklPTo7mfOJ3lYLBKB4L7q1swxl7DUXIGGpJc3gZfm96bMY8anKRiEzqwcJ2+fUMCIzGiz0GCAqy6sZMbn8RGmr82ic85IF++tjadx4o4/qLC9wsSSt/ZHHJ/3vZ253zmj2nCSDx6zwrfr7GzaY6JHm9iLyGaUPDyolIcHlfJnuZGf8yxsKDIzLNuD0RAW1J1bB9iyO3JyCiE5sZeHz3Y4KPMZGJjppUfy4VMjZWeEWLVRRpml0iNi9lk2qzpDO0ePraLAaQM9nNaooY+iwOv/LWTsjRkEg4JgSGCz6oQEBOyRYxcMKSz71UpppUJyfPSPKwKGtPYypLUXXYeuj7SJ2KxCmsDtVXjk/STenRx74zkSeHpUMeU+A0t3WjEZJH5NMK53FRP6NZ/ANzk5iUcemczEiffx449zMB4qVfkRwJFKdZFS/iKE+BxYD4SADcD0w/muv7SBUadO7fnjj511/1ssksREnf35KkWl0QMRCCjM/Tpce/r9r/YoMwwNzJrEaJDYTDrmgA7lElnD7LFpl4UxUzMIxsjuh3AzbotJMuHGFpSWGaiuNhAMCtwehV9/M/Pia/WNkEwoXOxP49WqjrxR1Yk9v3Sj2G2uI7TUpIJXU5i4Ji2ibeKPQmWMameY6kSd6ObcM11YTDp2m47NovOfK8oZe7YrZj4dSDKTos2oD+bGxWzAgwT8Yf/iqm2xW3jWQpdw8/epnPRRK+5emso137Sg+ztt2F7T/+O5W4uwW3SMNekbJqOOLUPn59Y2bl+WykM/J3P63CyuXdyiyTaRB8MNoyswmyJPNhgkHbIC3DWiPKJe2WjQSbDpXHfyobF0nzLIy8qFOdx0VTnnn+niyQeKSe2qxZzpqgGq3AdfAkUVBiqqoz+nS8HyzQce9/8VdpPkk8vyWT0xh48vy2fLpD08cWZJXfmiX8LKkJn1mumAz+Wii84mMzOdBx59m6822NhT9PcnHB+p2l4p5RQpZWcpZXcp5Tgp5WE1YvlLR6A24vv/2DvvMCnKrO3/nqrqHCbnIWcQRJKioCIKipgjpjWioL5mTGvOGFZcXcPighhWxYCCmDAgBjIiOQ8Mk3Po3FXP90f1hJ5uUHfxffdzva9rxOmp3FWnTrjPfTrCYd/3sJoWdeP89ChrkAlKFkoY3p1Wwo5qK/c+l0GogyBnOKqYJTNB3A1vsxicNaaJhgaFDZtsCd5HMKjw+ptept31F7yfAAAgAElEQVQQ36bkiwreLfLw+k4PkYhiblO0/7vCrmYLPTwRXlasPKY6CMRCuF1WhZS7Qnx/TQ3RapXC3CiO2IjJvoVh1u22xYWbDqtk6oTEcCa0P3qFNDtOclL3n3uau8XN+1s9cWrEvojk/AV5LL9wD8P7h1jy4h6efy+VjbusDOsX5A2Ll6pgu5vRgI+KXLy73c1ZvX79uMeBvcM8/+cKrn0hG98gBSPTNMr9eoa56uh6+uWFee6rVKqaVMYN8HHdsfVkuH+9WG2XTlHuuaVNmmZViZ3XPvEmhPYuu/GLcnYe576PIbOD11gfVZhf76JRVzna62dAh55fw4BvtznYXGalb16YUb1+mTp1p9QonTp8xwvCTqYGzequBLzC4E1nZdIw2hdSaOjzAi++MIrXdlxM1NWPcYP8zLyy/Fd3T/0raJne9p+E39z4zZu3MOHzrAydwQeFWLXWFjesxuEwuPQ8800/dWI9X/zojCtwaKqkf+cwR/QLMiIa5PpHk2gegWmgfIALkBJNg6MGB3jsqmqamxQzidy+QT0IGCQY5BK/ythPOtEcVfC15Fx0TOJ07LB0aSqUBCDO8AFEhKBRwhspVu50x1ds37yljPOfzGX9HhsW1QwFH7qwisP7xi/36nceljfZzf7Yjt0CArBLbBY4flhy+k4L/rEupXX+RAskgtJmjW11FnqnR+iWH2X6NaaqyY9VNmbOTxwJ6o8qvLrZm2D8ohLKQhppFh33fsi/ww8NIosERsQ8ASlg3m435Z9qzJtYyoRBbeexs8jCXfdksHSVg04FEW6aWsfRowLoOixe7aCyTuOwgwJ0zd+/AZt2QS0LvnPR5FMIRRQUIbFZJU/fULXPQhGYnMNFS5zU1amMPdjPF2udcdGI02Zw3eltL8tvmhxM2pHXej0eKk3nnIwmnupUhRBQ71eY+JcCdlebaswWFTpnRPjoxhJS92Ngk2GXoTE5mBmX3mmWglP9OWx0F2PrcKvc9kYmP1V6oO/d+JZeBaMXs2idkyfmp3PHafueiXyg8F/T3taCTp17sm79TuobFVI7VNX+8XQ5E88voLpWQ0pzVsbxY3xcfr5p/Eb2CzL90ipun52FEJKILhjYNcTr00xuk6ZCmsegtjHJ3dsiydQIWAQH9Q7y1n1mYttp10kv1ClrT4JOBbXZ4MzT4nlgt67MojoYTxBu3b4GFiEZnhEk266zRqhJL2ZYCBYLC3cSb9SyU3Q+v7+EogqN2maVfp3CODqEhN9ttXPr3CxCKQp4Y+djtOU8bYWS/Lwor08rx7afNM6eoMZOp8XUn/MB1ZjZEsx3QMeWPoCoQdJ5v+bf4pd/o9zDn3dmEjQEhoQzspt5sldVG+G2HWZuSCGsQ3v3OaQrLC+3s7nOQt80s3iwfaeFMad0wh8QGIZgd7GFVWvt3HpDLS98moovYO5L1wXnjmviqRuq9snBy8vU+eHve3hpXiqL1zjomhfh6jPrGZSEatWCDVusnHJhAeGwQJcmrSr3kCjlPg2rxawYX31qHece08TmSgtf73Rwf00mgWwlrqvo7VoPx6f4GJ/i5/a5mWwttxCJvfBDUdhWYeG2tzN54eLKfRxJIiQGC8RWJmfMJSDtfO0bw9ZQX0AQkYIvog4mWNoyyroBc5d6zKio+9VQ/E8oeolAt6uY9bX3f8X4wX+JkrOU8PBz6Tw7ewzBor30GVPA2RODPPXnqlaZ8YI8nVWL9vDtMgel5RqHDAzSp2d81eyCsU2cObqZzcVW0j16nAiBEHDLpFoemJ0Rp1uHgWkkYv8vwpJu7apxpZUqNUascbbdw2J4BGecGW/8FpU6Ew2feYY4FEkvb5h/jDST3VnSYF+PUq5sM/wbqq3c+10GK8rsZDh0rh9WxwX9m5I+uH9dlGZ6vgLoEzuveoFqM3jh+goG9QjTKz/5LIcW/NhkY+LaAoKe2EIuIBPYBoTAphoM6CCbrhuQIXSSNQU4NYNze7ddpy9rHdy8PYtAu46I96rc6BJe6Jv4QK+vthFO0gqoKpLt9dZW4/fwX9JbDV8LAgGF+6dnYBRC+y/v7UUeBvcNsqbczgcr3AjgjJFN3HVWDd5YGiUz1eCOi2u54+J9X6sWSAnnTs6jpi4+x1H1o8bj91YxYGCYngVhPA7JtfOyeHedB0OaLzoEcAQQc5r9hsIbNR7Gp/h5f7WbiCFQDw2hHhFCTdGxEeTLOp3vNYPDO2oaJkFTcxPrM96ms20P3ZUQhoQRzqXMbzyZDxrPQAdqO4wXiOq0hfxChSEzYcnRkHsSPmsSZdzfAP81oytnz/Xyt1dTCYYVUAsI+3fx7se98bgMHp7WJmagKHDkz8wmtVslg3u05TMNCXMqvLxUlkJjvsKgCUE2fW7DF1CQOhiNZvWwdX2bZEo7ntqCr9xJGfxSgSfeTWfWbRWtxkRTaB3c3R6agE/G7mVQmmk0vvnWwT/meLHcEiHaV0dvpzjgkJIrDfP4t9VZGD+3EH9EIBE0hFVuW5zF3iaN2w+rIwhsVhRSpaSrlDGaR8tNC6SYP067pGunKL0Lfp5iceO2rHjVmdjzLAok9j2Sl8ZXxPXRfrDMxc2zsvAFFSK6QEmXaP0kYSFwWSTDs4NM6t3WHvhkcXqc4aMCgmsV3vZ7GHRciEuOacTRToX7kOwgi0scCQbQF1bIsbW93H5Y4UjIywIYMZmt9neuP6hw29tZ6AqtYxLmfO3lhy0Onr+1go9rXKjAqTnN9HD+/DX7aaONuvp2uY2W/QQU3vvQzYWnm1HEe+vcvL/e0zpKtBVLgfFtq0djEYZhgGWyD7V/BBErrIewIgvC3Gg3mBSWXBfct3e0cOEXXH3tLaguneOu6c7Rl3fDnW7FLkKckjKPb3xjCOipjNLiowybBQZ2DrF2exhql0LNt4ACS09h+EWzMEUqf1v8J+b8fnW1V8r9lJRieHpWWlt1UusHkU0Eggqz30kh+m+OFbhxRxZ3FmWyKWCjJGJh1UF2nDcbrH19F+tfKWJIt1Cr4rPbZfDknVUMP7jNeEaigqTZFUOwcKOL+z5va107vXMT1g7abRbF4JTOza2G7/Gn05h0cR4fLHBTfmoq0WUWRAjcUuKUkrv1AEdI86SfWG6qxrQv4vijCn9dncZr0sJQh4sLbA6OtzuZaHNw6BAf1iSdJlFDxM3j2BeiEn5sTsKtEmbXy9IL9nBMl7aXz4ptNqY8n0N1o0YgrBDVBVqdpMveKNceXM+c48p498RSLO3u4eJgOyu0HpgLbAJZJHjglQyOva0Qf7vq+6X9G4lGY82tLTCAZnjjG1PKv9qnkJq+n17UjnetA8KGaDV8YBa+tlZYGfthIdN3pfNYUTqjlnfixeJ9jy1oQTAkErQCW//WjrY0e6U3qWwVEUwVbMClGJybbnrKQ8fEG76WkwlHrTQaFl63SUo7qLnUNim8sMDJuLOf4bob7uehdy/g+vcPY+/6Rm7s8QmzpqymZFMjulQZYv+JP1ma6aqY91tpaTnvvfcRt956P/5PhsMn+YitD4HUUYf/HXuX0/jp1aOZNeufv+i5/ndwgHt7Dwh+9Z7Gjj2DyZMv5NRTJ2C3Jyct1tS1ezos/SC6CTiNSMQkG3t+ZYtQC4pDGm9WeeIqvBGpUG/A+80ers5vYNHre9m9V6OuUaVfz1CCHt6Eo5p58Pl0EqBAJFPhxaWpXD+6nlSHwQNDalhbZ2NHkxVdmh5foSvC48PMxvzKKpUnZ6S1da7UCXwnp+LsEeGK+2uYOqY5TvR3ZYUdPYnYgpYX5gGHlVC7+HWTohA8KYj3K50GP615IodmcObAJn7Y6GDUgAB2676vpQpYFUkoiQeVohl07jAe8ZkFaQQ7jnOMKuwp1hjqDbK9wVS9GVPob/UWR3iDlFRpGBEBX9OaSwQIhhV2lVl4dZGXK080rYFdSJQiMLKF2ZIugTqQlYIPa9xUaSqLtjsRXYDt0vTyYrDbDAwHhDu47qpdJk1PRCJAjQL5ZmEqIgX37MjkxCw/hfZ9v4UPOSiYNJXgsBuc2W6AenK6EuacDwOciuQ4r48TU80iTrcTa1mrJTO+gohuRVUiLLdITo19B0s32zn9z5LQ6nMwFDfOoWsoMuZz8JAqrpoznPryIF88v5OHx3xDp4PTGXtBF7pU/8CVS1fx3dJV1DX5KRgygmMOH8KzM04ivWAQr3ybzYa9VoZ1D3H5MQOpKx/KlCnTWLDgM5555mEKCn67MPg/Lef3qz2/adOuYe7cDxk06CgeeOApSkoSGfJDDwrSqk0R8/wA8nOjuJ3/+htmbbMNa5I3csBQmFvsYcrcbO74KAOfpjC4v2n4QmGoqlNbK7ndCqPcfGkdon1FUgG6Ah6wqpJNlabFTLEazDyigusG1HF1v3peO7KM7yYUkxZTUvl+qT1hwBCAf4eFtW+4O6qd0zM1Yl6XDhGVMSRAx649XQhKNMGsu0u47MgGeuWE6ZsewqiGeYvdXPqXHHpf3pWv1iaGLKU1Kl+scbKzzMKk7CZsCcrDBhfnJfLndpYnDlcC02hc+UkOdy/N4JJFOYx4qzOVfvNGvq1LLU5VIspl0rspEFb4sN3cEFWRZt/1bmATsBmoMC+LLyJYtN1JSFcIFiow1CS12x0GNqvBhHE+Xn66HEdMJBdMukquN4pFSeLPq0C8cA0K8HF1cuWgFlit8PzjFTjs7fbjNBjYL8QF7YRVzx7UFKeu3AKHKrmxbx3v9izlH90qUAQstNazIbUGRU3m0UoUYaAA3tjtZBgwado6AktGYKSfAIM/wm9k8947xyJ102dJzbVzxn39+UvRCYw6pwur/v4FK1aswTtsFNWPf4r+cSU7H1rInIkP8FrncfTIV3ngnBreu6mMO06rJTtFp0+fnnz22duMHDmMo48+lTfffP838QJ/FzM8jj/+GI4//hi2bdvJzJmvMWrUSRx11EiuuOICDj98BEIIHripmhMuLiQYAsPSD5qfw2E3mH77vityLdB1ePdbN28t9mDRJBeObWLCCFO0IN8aTTroGSnZsNPG2nV2VCGZvTyFx06sYuMyG6986MUwwOMyuH9qNZOOb+amS+v4PmDnyyVOs+Kbh1lNBcK6oDAlSkiHi7/P4+tKBxYhiUrBilo7wzKDuGIPRGqKkbQcoqqSzCQTwAoyI/HTLqNgCxpkZUWoS3JhNEB6JI+cVUNpTT3D/qcLoXYtWgAXPJ7H+ueLSPMY6Dpc/0IWc7/xYItVJIf2C3LYKQGWN5vnEZaCcek+bu3SVuHbWG7l+rlZbKy1QpJeUsOAkM3kT4Z0c3DO9d9k8cbx5fR0RvjikGJuCWSyRDqTGE9JqqvtWrhtklE9AyzZ5oibm2vTDCJWMNorbx8E9BX0cYd4/7JS0lJNQ/Ndjz3MWeilpFLjmOEBircpPPxtpmnsWleXYMVU9ekAdT8CtC2YMNbP9x/t4dW5XqpqVMaO9nPicT60dk/M+UMaeXedhx/LbPjCClbVQFVgztnlHFvY1n0SQfKqowYvCoQ6J92fzRJEkXBERNDU1MzdD86mYflcOOgNSB/TutyObV347I0/ccKFs1GkCgI0q8ItZz1I+uld8BmC3tu7xUVHASl4t9HDKR4fY12JHTOapnHzzVczfvwYrrpqGvPnf8Zf/vIA2dmJgrD/Kv4Tc37i11h5IYSsq9sW91ljYxNvvjmPmTNfw2q1ctT4S/ih7DK2laRiAMLfQODHziz4dCMjh+w/TyUlTHokjyXrHK0VXKfN4Kwjm3h6ShVSwqi1ndjqt8YJZKJL+EaYNI4Y1F0SS7WM6yV12AxeeaCc4w7z82OpjQkvF8TJBdk0g1FdA7xzURn3rM3gpe0pcaRgm2JwTpcmZgw3w95oFPoP6UpVdbwr57AbfDp/LwMHtJ3vB7tdTP0uJ55rJyVdHBEuP7eMZ21Wgh0MoE1Klgd8pAB//TCVh95MT+h6cdoMHrmkmovGNvLXeak88lZ6XBuczWJwyshmbplcx/aAhX7OMF0cbSFfVbPKsMc60xhUzEFDFbEvouV8FAmdhOkZt4MmJKWX7WjN/0kJQ6/uTFGFJU4632kzeP22Mo4+uC23WNagMuHpAqqbVXTDFBvtkxdmq27FlySH1jU1zJqr9yR83rLfbod0oyGkQi9aX2IEgfMkpHa4porB2pG7ybHtO6dYG1R4aXMKi8ucdHZHmNq/gYMzkjcR6AYs2ubkqx1Osl1RzhncREEHwd1iJcw0TzFBIWmKutka6I0hzRm8Akm2pQZt9WqOXLiUDV9+z7p1GxkyfBQr1VkERH7CPof1CrDw4R3UWHagSgsZ0e4oMT9mYbOTq8pyaDISDc2Znib+nr//VrxQKMT06c/x2mtzmT79bk455QTS0nohOw7N/pXIHNZFnrTy9l+07GwxZdV+hA0OGP7t7KLX62Hy5Au5/PLzeerF1Tzy+OsYTdMh4xLIngrergivhwcza3lfurHu5xJ+s87Bt+sdcdQVf0jh7cUerjyxgX6dw7zfv5TLtuawosmOIsAIQ2ilEmf40EEvA71DRTEQUpg+O53jDvMzOD/E9LFV3P1lJr6o+QCe0MfHM6ea9Iw5O71xhg8gZCi8tdvD08NMD7a0QWPa9Foevz8df5WCUCTRqGD6g1Vxhg/gmfVpCSRjhKA8rHGSX+fvK6zUz3OiRwSWCUG8xwa41gjTIvda26Qk7fSI6tAUa+R/cWFKQv9vKKIw73s3z1xdSc8klc45yzyEowIQpueUDTQKCEtzNGMWrSMa20NCXOFICHhyWhVTXsymea+KKiXhiOCmM+viDB9AXorOyrv28NVmJ3tqNQYVhjioIESvp7sn7EcVklFdAugGNIQUvDYjbt5JICho9sW6etZjen7S/NHyJNoZslVLEODJPlX7NXwVAZUj53eiIaKY/MMqO/P3uHlhVAUnd0kkkqsKjO/jZ3yffct+pUgVPZYG8mjNDHUtp2lrFVWLfiL6+WqqvvmRLgV5OMaM4uabpzJy5DAcDgeDpmaxt8MkTafN4KKxjViwkxsZkLCvfT9eyRIaibDZbNx1142ccMIxTJlieoEHAv+J09sOWGlFURTeXnE6Ro9zIbQTKv8Gm4aD+whk2MGPW3cwu/AwJot9U1u+WOPEF0z8ikJRwaUv5HDRcY1cMLKR+QeVUh1R8OkK983L4P1qN3Ff+37YDHvKNQwDbrg9i7fe82CxSBQL9Owc5olrq/DEaBkdJ6+1IGwIglGY8koOn65zYVUl4eGCg7JD3HhoLaNHBnG7Er3pyn1QGFQFHv9rBlUfewjFKolyhZWCD61MmV4KCuyq1pi11mueYodNKwocM9h88Jr8iV4TmD2owbCCLUl+anO5LZ6qoQHpoFjA6EZM0Tk+FBZIDssNYIud0p6gxnkb8tgZsKBONJP9F2sNXHdQPeme5J0LqgLHdhBJvX9sNX9elNlaQdWE2cOdlxqlx/PdCEQFdlVyw4g6rhtejxBmq2R6mk5VdexWbre7frvCvHZoOR9Xu1CF5IRMH5nW/SsaP7Uujbqw2io/byAI6IIblmYzodOuOMP7S+GVKv3Lo3z5zfc0L/qOqs/XY0R0Co7rw1GnHcGdjz9KTnZit9KrN5dzyv356IYgFBFYNcnRg/xMOnrfoqxHOQNJU0NOITk3Zd/zPzpi2LDBLF78AQ8++NQvXufn8J9m/A6osMH2vTEGs607dHoCBu6GlIkQNQht2co/25UAQmF49WMPZ9yWx6UP5vDdWjvpHh1rkkqwIWFzhY0HFmRwxKOdqfUpZFoMutijXDK8EVvHdawkfQUKIRncJ8Ss17y8M89DKKTQ3KwSrFPYssHGldfltC47KjuQpMNBMjQjyJMfp/PpOhfBiEJjUCUYUVhfaePzKndSwwdwVK4/aa5Ja5S8+5EnTvlFDyhsWWPn65jS9dS3cmiUKjjbE7Mlmia5YEwj/TqZXuZRgwJJKRpdcyKkuJIboSGdgziSGEUjipmfDMU6YVrSI6okxWYw4ygz9K9GctqODDb5NQKGQrOu4pMKf9dTKVN++bt1ebmd94vdKGkSd4ZOTlqE8w5u5PrRdTy3Oo2GkEpYV2gMqzy+NJ0X15g+sRBw5001WPMNk7wde74cdoN7b62hsyPKZYUNFKNx6OYu5P/Yg2EbOvNlY3Ju22d7nUkHhId0wa4m8/6WEuqCCoEknTGty4dCfPPND9x33xOMGXMaL/c5l6Y5n5AzIIMTFk7m/L33Mmb2eXj/1J2iwuQOweAeITa8UMQTl1dx16QaPrinhNduKUfbjw1xKpKX88txCAOHMNCQOITBud4mxjh/fgBY3LacDh5++M5ftc6+0JLz+92OrsxO06mobbdJ1QVZV0DDHMSAg1o/DkfgxJsK2VRkxR8045RPl7q48vT6fQyuEeCAQEShogFmLErjvlNMsvToHgHyc6Ps2mttMwwqcJCA9bLdwOyYMOjlNVw2OTdBJSUSFSz5wUF9vYLTZRBcKpD5mLNmVQGGxGWRPDW0ilMeKiDYITcVjCj8c6mHJ89NXtSZdnAdHxW78UUUItJ04Ryq5CS1mfeEJ2F5X0Dw2fcuRgwOsnKP3QxaMjEVzJrNa+LI0pl+WVtcdN9F1Wa+NCwIRxQ0xdQUfHqKGcqHdJi1I4U3izxoCvypewPnDmniL1+mEY6KVhqOVZVENTC02IkEMHk+qqRrfoTF44vBZnCdM8RKzUA5dDfdJFSuy6dpr0kjChswszSFv/SqwpDwcqOX5xrSqDcURtkD3JteQ0+r6aIvLbNz+vx8Au08UF0THNk7wN1fZSSkC/xRhSeXpXHVkAaWbHZw17eZKAPNbh4pobAxwozrqjhmtPmw31KcxVs1HgKxIsCOkJULduQxv3cJQ13xubx0m0FREs2GqGFW/5eUOLjuqyz2NlsQwMTuzTx9dCUeq2THjiI+/vgLvv76O5YtW0mfvvkcffQoHnzwdoYMH8RDWV8TEfGeZ0TofG3bwaHh5IUQt0Ny7lG/TH6/BePdftZ23828JjfNhuBYl5+B9n9diuxA4Hff23vTebXcOzMzZtBisBkQ3oCjbx8mxfpb3/va087wAQj8IcHf3k3l2VsruPHFbAwpzMHTQpg5p9iiYV1h/lpXq/ED8GYZZs4nGFvODhSAPcUgr0invl7lkL5B7r6iloG9wmaOKAkUYRqdFz9NY9VqO6wR0BPIANEAw+xBBp4RTjoQG8zwXDeIezN//L2TGW+mUVWnMn6YD/VgyZpGO51cUa47qI7StRofqO6EbVk0SZpXjzekApMbFxOTtrrixyZ2y43yw4w9vLgwheWb7fTpFGHqSfX0zI9gSDh1cQFr62yts4w3NVj5PNfFF/+zl7vmZ/D5Fhc2TTKkW4DvG5zxoX8UiAoO1YJ4rZKpzhA/aaZWnqKZnmPOoBKifhuBWhc6CiUxAvQdNZm82uTFHzM+C/0ulgQdfFtYTCctyl3fZ8QZPjBnjNz1fSYVvuSeQHVApSEgmPRsXtukv9g/1dkaXfuYRZ2GqMKbNR6CHdR/glLwRFka/+xZTkTCwqiTtbqVgb2DbFphjTseiyI5NDtIfVDh3I/y4ozxR7tcVDZlMmr73cyc+RonnTSWCyY38sLbdlJTfSDmI8JBooFB6Mnp9TQr/5Ii036Rpelckfbr5MB+S/zu29suP7kRf1Dh8X+m4wsLsIAyoQi5WWFoVhoXC/PLWPCdK95AxmDVJDYB22bv4uNVLibPySGiioQQ1muPv4nGdPOxqcFCuINEh9JZ8O09xTg0ydyFbqbekU1NnYozzUCtkegdCgiZGTr5uTqvfJ5ituYBbDD/kQi+Vx34Q4IRPYJ8W+4AlzCNbiMQgEM6h+IM37Nvp/DI7IzWc91TbsH7lcG3M/eQF6PC+EYLbnw8Md+jqXDuCU24rJJDuwb4YZcjLpdjVQ3OHtLmESy2BHnV3kyV16DHlVU8EfTQT29TO1hU7mRdvS1uiLtfV1hU7uTGfiqzLzKrgFLCk5+k8VW1KyEp4tAMBmWHKBMG6zWDjvUXoUrSelYSWN4Np2IwLt1Hra4wu8kbR72QCAKGwnP1qTyaWc3GmuRk+Sq/So/UMNuqbLCLVj4gWdBpcIRP17qSii/ohuDN792MG+znyz1OlIA0veZ2xysRbAlaqZcK45pzKZMazSg4sw1kdwPrdnPEacQQDEgL848jy3lgaUbCLOhQyXq+m/EnZN80Fi/+gJweLyGtxSAitCSfpfULNKMLXqM79WriPJZO0UT1nN8b/hOpLgc05ycEXHdOPUXv7mT1a7uZ8W4Jk3otZ0DfnrynNLRWejO8etLclK4LhDQlmk49zMchvUKoHeSRnFaDq442jeh2qTLBSOWlQzS42Id1og9chjnzQzN48IhqnBbJY8+nccP92WzcZqOiWmN3hQXDBbaYEdVUidNh8NfHKxGChC6H9qjxK2zXrGbjuhWzLTJLYkk3eHJS20hGX0Dw8KyMOCMf1QVNfsFf32q72V0Oydynykjz6nhc5o/TYfC3uyooyI/ypdA44eJaMrqGcdt0VCFxWQ16Z0e4fZzJ1Ztv9TPd2UCRquNTJD9ZItzsrmWz2lb5+bbSYc4y7pAfDeuC76vacrG3vpTJU7PSMJpihj0GRUgcmuT8AU3UKLLjZgDz+9ccEWyKQZ4tyqTcJjaHrdiSGKgIgmVBc7+5ruTdFnZNcv/oGsRaaQ4njGIeU4UkuFyh3qck7ZiJ6DDnBy+nPFvAjA/T8H+qwAJorzyhIDnYGeLBYCpFMcMH4BcKof6SARP8zDm6nK9OLObzCXvJsBtsqbO27U+PwFcPwstjsY2+hhufnkNefhbS+hGIDiGmCCJtb3FyYAAWqbQWrYQEi1Q5Mdgv6fn/3vD/fXvbL9qoCt3So3Qjim/LOux9e8aFZ5dMbGTulx4C7cNHKQn4BJedk8t5k5p44muM3iQAACAASURBVPEqZl9azqnPFlBSp6EoknBUMGlEI+cMb6JJCibKNOoRyJiahiiI4jqjmaO+FVw9qIHD84M0NgtmzEqL4/sZhkDTJIOGhvBqBt27RZh8cQM9u5vG4vjhPuZ+40kQv+xdGOa9rR7qQ0r8zAlFINIkXbLajM2W3Va0JLp2kajC4tVOoC1sP3RgkC0LdrF0rYM1ZVbervFwRUkW9rAVq91A9YJxXSPDaiSjVyoMzA8ztrcfRQEdyT/szXSMxEPAy/YmHvels0q38mahEzrrpvdTC/ykQtgM01cX26AP7C7XmPOZ1+QS/oQpgZUDCEm/lDCvnVVGit3AoiskzfUb4Kx3cHOnOiYX1ONSJSt22mi0KgnD4xUkvSzm9bp1WC3XL86OCzWdmsGUQXU4fBJbWBJsb+SkwO9X0IOCZDRVIaDGrxIV7d7tdRKWCRht/mpXJLfk1XFyNIdIBx9AIlin2RiSFmydpAZwWF6A1RU2wmUb4Z2LwZkBV69GZhTQP2M3pqe3j7Y54WNANJfLmg/lC/s2qhUfhXoKxwZ7k2sk5nx/b/hdU132hc2btzNwYP+4zwb3DvHwlCrueD4LPYo52lIXyG0QDgneestDXl6UabfUsfSOPazabaO8QeOQzkEK0kx35H1pI0S8SIAUApvT4MLxjRwee/tu22XFoiWOhIxGBb6wwmf/LAEgEBK8/pWH5VvsFGRFSPfoNAcU/CGTImLR4LlrKrlzeUaiigdgUyVrym0c081Msmen6zH+XEdICrMTHxCLBq5Cg0eXmfkvx5/qkE4Z18e6IQPOOdbPcUabZ9EgDELJOhYE7FCjlBkqp/hy8VnaHXM6MEKHJSpIwQeam4WlbsJSwGQJ/8Q0kFtjPwj6HxWma0xJ2IngiqCFmfYILcwkRQevgDdSg2SkmBd7SbGDxxZnwCEirhILYBOSa1PNdpez+zRTH1J4eHksrBSQnR/lJZnK7G/NTpWO8AUVSis0ph5bz3OLUmMcSHNdqUC0Y1BjCNglsY6WDHGFeKiwmkxVJ1wRe5F5ZRIDDYZoIKKuQ5FeLus/gBeeeR4WPw3jHoFhl+GwSM7q3USuSwfsYHQHdXv8hqRARIcD0E1P53LfoYnf1+8c/5XGb9OmbZx99skJn198YhNnjGmm77CuhJviScqBgMILL6Qy7ZY6hIBhXUPQYZDkDqkmDHwGCCPYE7vIUsL7ipumcJLoXki25mjcGEjn2nADp95RSE2Dii+kYLcaaKrkign17K220LsgzEXHNpLqNTB+AGHIVm+zBboUZLVr4yrMjnLYQUF++MluikjG4LBJrj0n+eStx5alE4wKRIqOkqojOhx2QAheVayc2874uaWyT/JqtqEyJ+xO9EVaikJeCemCaJ4wO2YE0B+4DbiL1ktu0ST5GfFbuTBkoXNUcN92D+tXZhMJaGQ7oiwaUsM5Pcxy6YzVqaY3twoYCORj7iMoeTqvktRag0fmpbG3QuOoEQE2XLCLNc12ztmaTxEW02C5MSvuHeh5LrtB78IwF45r4sPdLrbvtpnhpBuTpJ3koigSygbvRFHg7yVeJuwsiIljC3PdrgakSFQkI9Ugwv4atfY5CKmxdVOI2y6pYqC1P+mPfc2yUF+81ihXDarnykFthQXV/2d09xQgDEIHaQFsKIEb9/Et/ffgPy3n95saPyklmzdvo2/fXkn/7nZIAhVtOZD2aGyMf/JraxXeetvDriILI0YEGXBiBJfFwNfhDW9BMiD2uL9V72GWkRKjvRBPfrZC9ALJ6xE381730FyjtiqnmMUOyRdrXSx5ohiAzVUWRj3TGX9UIFseRAGoZrN+19QIB2XF53peubecyx/IYcmPDiwaYJeMm+hnjd9GXk2UHhnxbOyN1Vaz5UmTSa8JQKDDU61KwRG1aXzlakba27Znk/CnoJtZhoVQstSuNI+dXEwaS+sGhbnyCAFLzI80VXLRuESC7LYN6WxZldl63SoDFm5Ymo1FgdO7NVPa1I54vBZYZ+7TLQyauikcdl9noro5uvSDzz3MmJ1G9zvDBIl5cWCG3g4BEdmqvK0Iid1qcNroZnQDdvitZidKC0LQUVlWCNk6L2OL38I9uzLNIkz761yk4BoQwWsxeN7zKdX6HL77oIEv5wf44oMg1z+QwvlXQHqzFcGuxGsKCP1g1KY3MWyvIdXtiOhAlND5CJmddPn/FhgohA/M6EoAhBCpwEzMDnAJXCql/OHXbOM3NX6lpeXY7TYyMpJISGFGGwMHhvjpp476JzC4nQbfunVWTjylgGhUEAgovPGmTt4TOukLfYS9kkjsQbFh0Budw2NWbkZlqkmvuBrzMq2KKY84gZsMOAjCKIRXqu34gK1Hx5YSK3VNCqlugwvfyaM2oMQ3CUmJhmRQTpjXTitL4PeluA3mPlZGZa3Kyyu8zFiVxoJSF/NLXDz8dTo3j67jptFtSgd9M8LsbdaQNSoyLBAd5KpsUnJKO6/v0xon127NxqcrhCU405voNXQLqdYoVwTcjIza2KwG+SjqxN/RAFok9qFNBP0eEupeNoHaTeJYabaS/e2GCrp3mE0rJTy2Nh1/hxbAgK7wwJoM+tnDHF4QYHu9tY00bJg/uip49PH0uGKQL6Cws9jC3nkaxnHt86nAhaB+DBSZL4XeeWH+Z0IdNk2ao1g0GU849hJLqZoG02ExsFskT51tFqTervAkJTJbheSUnVsY9ON7XLPoRVZ8X87BI6wcPdHO/9znJa9QQ8oGoupWLHqfhPVbIIzOqIH/uwHh/6k4wGHvDOATKeWZQggrrQSwX47f1Pht3rx9n17fxh1W7n42g23CiugpkTVAnUBVJTab5NFH2yqnk6fk0NSk0OIN+Hwqe3YLLnwKxL0B5mNHRXIWQW4RvlYjVB2NXWw7cA1gNcwQKov4531fV0GCpkl21lkoadKSdEcKeqWF+eKivUlXb0FUg2dWpcUNno4Y8MSSNE7s20zfWKHk1kNr+a7EQSCqEPrIg/20RlBAaOCUks7S4PKYKvQmn5VLNuXGqSj7a7wEvxvE20OLaQmGz7b6eCqcSlAK2jRoJJotjDUzgNIg8NfFa8w5hcGUQ+vIPyTIlp46qzToGlXo106OPyqhJpT8Zi5q1Bj3VKEpuNxZmjm4mLFxagZXdK5nZiCR3hEMKSgrDTiuwx/cYDlb8kBJFXfdmcXebRrTFmdxx51ZvDW7lAsObuTVtd62XKwKtlyDwSlmweLwHgEuPqKRtFiXS9AQbVG0YcC2FbBiPpGVC/igrhh9/JGccVEuT70l8KTEG3chFaTY/7CoP5CIA5nzE0J4gSOBiwGklGESfP2fx29s/JKHvLv2aoy/shBfQJgKIBqouZKUnCjHDvJzw/V19O1rGoTKSpVduyx0TOKEwwoff+Bmw/01PE7yMYqHuwMsaHC3PfQWARmJunOW0Tp8rJijKWPQFMkRAwJ4HJKS5n03jOuG4PLPc/h4lwtFwJm9mrh3ZA0ptjZDsXCLK+n6UV3wwUY3fY8yvb9huSHePKmMW77OYmuxBf/LqVj7BbF6DU5PCXBfp6ZWmuiLJSmEO3Bmowh2BaxsaLYx0G3eCy4hWeQq5WA9i3DIXNvqDGJzBRACbKkBInXu1oqnEmuHKu4W4A2Lih8FBXhNs3BrJMQlMSluiwI5Dp3yQJJbKCRojpWf7cUGIw8JUBrWyHHq/M/QOmyK5FkjLen1LHBH2atocUbdoRgcZ/Vx1x1ZBINK3CioMy/M58dlu9nbqPHVLidWVRKMCmy6ZO0OOxZV8sMOB9lenfMPM3mRxzgqmbnsS8LLF8LKj8CTAcMnol35DN9fmE9nJwSs7+FzvETHXLMUOpbofwc15UBCckBzft2BKmCWEOJgzKzydVLKX/VW+s2N3yGHDEz4/C+vphEIijjpI10K/FaF6Y9Xk9KuGV7TEmf3tv1t//u/K7eWr5qcBAzFTOj7gXQQMlawAFQk2Sfq9NhlsHKrHamAzAO3x+C+y83Wsd6ZEVLsRoJkuV0zKI+q7Nzubp3T8PoWOz9Uh/jgortJD07BYnTe5/FLSBg0fWSnAN2UCEX1GuFqjdBuNyHgFYubky6McHhX89HfHbSgJ8nlaUJSGtJajR9AqmLgdDVjT9J3Z0Qlw5qDrHLb0REcbfNzTlot91g0/LHlDczmmUctNk6K6mTGEmV3H1LDTcuy4ojT6EA7RzgYUCjbrrHyz6Yk1U9RKxMaC9DzgN1teTzAHOp+bh2unga37cqiLqqgCDgvuxHH26ZiTsLxG7DkGwevn1XOvLUuFqxz8dVGJw0+FQNBMBat3zQrwJZv3mLj8s9YtmwV6f2GUzP4FCJn3o6S1wObIpnWpZbOTrMYZQ+fSNC6EF0tARE0S8hYcAWuQfwvzLz4/eFXtbdlCiFWtvv9JSnlS+1+14AhwLVSymVCiBm0lel+MX5z4zdp0ukJn6/cYI8TsmyB1SLZUWxhSP+2t216usHBg4KsXmNHb5eXs9sNjjzWzzn35FFao3HsUB9Xn14fN0S6py3Ckt7FPFWRxlK/g+7WMOdampgl3Hyrm3nGMWqAZ9w15N2tM3utl9t2ZKIICCiCcUsLub1/Ldf1rufl08o565/56NIU83RZDbJcUSo1Le6hjOg2ius7sWSvk8O7XU9O49+Z0Eflrs8TdaEsquTU/vEvq/Imla+3Owh3zKVFBE8vSePwrqZy9lFpfpY22gl2kO0KGYLBnnhvxQYcZBj8pMTzE6UOkR9t/DjDzuyryzl+sB8h4DaLlWQCTRrwjapyum56f5N6NmFVJQ+tSWevz0LED+wVrfMrWlDV1PbGv8OfaRap7gNuBAKmCoww4ORjm5l0UhOKAidn+KiLKrhVA5sCN9RnJTd+UlDfqHDJrBw+3+jC30pQF1C9Aoo/gJL5hPylfDjoOO6deib/+MfTeDwevm+w836VG5tSz7k5TXEvDIGN1Oa/EbJ+TsjyHYqRhiN8Mtp+cn1/YN/4lWFv9c/o+e0F9kopl8V+fwfT+P0q/GbGT0rJli3b6dcvMezt1SXM5l1WOuojhiKCwtxEDtzMFys44aQCGhsVolGBokBB1wjvbPYQjPG7thZbeGORl2+fKyYrtY0X0cUaZUanqrjtnYKPsDRDWUvsEAK64O7dGWZlVNJKrXhsUzqjMwOM7BRkzdW7eWudh5JGjdFdAiwut/PSl2lmcl3DVIT2QFS3sL26FyO7LafZNp9g6vmcNKGEDxYWxLTmBKoiuXFUHf2y41MV5U0aVk0SSlBeEuypb/u6Lslr5KWSVKoj5hwTAKdicGFuIzlJZJseDYc4y+6gOQzCCjIEMiQIvuJBhhUeeTedEw4xTZ6DGMctYStg71CGPqNbM2d0a0ZKGHB3F8oa4gcICyTDu7YFqiujsYpfAfAGZsBSAxwkmTGosnWyniIgo53azPHH+Zg7z4Ovg2yXroOeLVi00IW/PaUpVAOfHAa9JsOIv0HmYXTqE+LUU0tbFzkiNcgRqYntZm3HbsUePhF7+MR9LvMHfhkkgtAB6u2VUpYLIYqFEH2klFuAscDGX7udA9re1h5795bhdDpJS0tMbN94UT12W/xDZLcZTBjtI7vD1K61W2zc9HQW4XxBzgCd8y5qZO5bpZSkW2LKKubTEo4o1DcpPPPOL+uTtIo2wwfwVUXyYlFIF7y+2ywIZLl0rjmsnkfGVXNsDx+fLXDBDmkSgisxqRyloKkRuqTvAhFhl2UT/+OpZfdRe+g/bTUZw8rplubjzO5NHN89MUXRKzOcMBQczOb6I9oZkRTNYPGQYi7Lb6CLPcIgV4gne1XxSI/qhHUB+kuD96qDROc7CS+3EXzHTfN1WchK06DurmozWmdEo/u8TY/Wk+vhCQHTz6yOyWOZ360iJE6r5N52IhSpop1JVYERwAlg7yTZz9x1jhvj57ARAZzOlvXNlsRrJtfzxU4nvo5cTluGafiEBtlH4LAJjh/wR6Hi/wq/wfS2a4HXhRA/AYOBh3/tMf1mnt/++H0H9wnx2iNl3Ph4NiUVGpoqmTShiUeui/fQ1myyceLVBQSCpndXDZR+r5HTS0/o+QVz0tgXq5w8cHkNgZBg6x4rmalRCrL2L2AJZgUwGbXOQOBPEm69852HymoN2hsqAyiSpPeu4YjuS5BSY4XSqbX1rOyTfKq/zqE8orAdeHeFh5vG1ZGTHeXp79Ko9qsMLwxy0dAGXl2V0ppjVGOintePros7hiyrziM9anikRw2/BD0dOrYPXdQlUUrpnR/mnTVuXlnmJRIVHDHCz5JRYTS1TUP1xVBwv3yCiQf7eGdqKU9+msauagtDu4S45fhaeue00WSm2ut5NJBOoN1714HBJfaG/c53URR4a1YZH3zk5t0P3TgckgvObmToIUGueWsfHLrBD8IH/bD2vYzc7v256PBfLub5Bw48DiTVRUr5I/BvSd0fUONXX9/Ajh1F7NhRxLx5H5Obm6hW0oJjDg2wZu5umvwCh02aJOAOuO/5jFbD14JASOHFt1IJ70MPPyddZ+aHXu79e2ZrP/Ch/YO8cnc5qe0KKTuKLNQ1KAzoE8ZhlxyV5U/K/XKpBicXJlaT5y1zx8ntt0BVo1w9eAYCg4Bu4zNlLAjw7XJR/XUORti8AQzMPN6jH6ejZchWyaUvtjv5wergz8dW8/aPXip9KqO7Bbj9mFo6pUaJGvDZdic7a630ywozprsfRUBNs8Iby7xsq7QwomuQ04c24+zAE1QVuOOMGu55MzMuRHRYDby5Bte9k936ubPUxqDVQS6ZUolTgTF6lP3PPDNxeI8gh09NnOjXgmvsDew1NOaEvNiEJCQFJ1t93OOo3ec6bdcWTj+5mdNOauavM1O4+LpcfAEFl8vA0tUgkh//fWiOdHKO/jPq1ql89bc5eBLppH/gfwm/u/a26uoa7r33cbZu3cmOHUWEw2G6d+9Cjx5dyc3NYt68T/jxx/UMHnxQ0vWFAO8+lI8BftxsIxnJpNkvGNQ3xNrt9rjZqU6bweiBfu55KTPOMP2w3s7FD+Qyb3oppRUq512dx9adpvCAIQWP3l7FBWc08cDAau5Zn0lYF+gIFCEJC/hbcSqZDp3hqWYhIWxA0NbiD8Ufn00LkZNazpqiobxUeSWB4TYEUL86HSPJcJ6oAVG/QksBUcYqlGvKHXw1JZ4/WNGsMv6VQmr8CiFdYFOhc0qEJ4+r5Jzn8wnrgmBE4b1VHqZ/ks4XN+8ly6NTFVZZUOsiIgXjR/nw2Ct5bF46ZXUaffLDXDq+gdsWZsUNc/JHFNYV20nbZOW4vvueT/FroQiY7qrhdkcdOw0LnZUoWYrpmW83NFYYVnKFzpFKCLXdpQ1F4dVVXub+5KFqpUrJdxrh2Hfc0KCibZRoqoFaAFosbfvm5DIO7zGeMWNeYNHHCzjjjIkH7Dz+wK+DRKAnGar0f4l/y/h98smXFBeXcu+9t9CjR1eyszMR7WKXY44ZzaRJV/LJJ2/SpUvbDMHKKpWn/prKoi9cZGboXDOlnoknJOZj8rKiNDQnXjBVgTl3lnHpo3n8uN2GphnousKf/1TDe1+48bdPiiumksrSDXb2Vqqcc2U+W3ZY0NtVU6c9lEXvHhGuGNzIyMwgD25MZ1G1C10RGKrg2zonp6yw886wUhw2yRm78gn2BlbTYV6IxB9xcems10EI7Hk++g75CVU1zDnBikzsJBHmMeLFVEyOmLSfVSWJrUDXL8yipFFrzQlGdNhea+H8f+aZ09daSOBhhbAueHBBOkcf52fqthyEMLsy7i7K4Lautax+Yk/rdl/6NiWBctOyncXbHAfU+LUgTTEYGhPxNCRcHU5nnu5oHWmcKgwW2CrpouhEdTh5VgHrym0Ewgp8SwKlNRoRdKqMMnVKPR67wcSDfaQ4DEDlscfu5vLLb+D444/B5frVjQB/4ABAGoJQ8MC1tx0I/FvGb+3ajYwfP4bDDx+e9O8TJ46jtLSCs866nE8+eZP09DSqaxRGH9uJunqVSESwYxesu8bG9dfWccv18TmtaZfWcc1D2XFtUA67waWnNZCbqXP3s2/zTU0JzbUe8nqUk7b1SFb9dVz8QRiAMIVSV6x1UFQcb/gAgiHBi6+mMGJwkAHeMOvDNvQOYXXAULhrSyZFHgt1umpWK0cB3wCqxKlIVEUSKRAEY4oEwTIXZZ91In/8HtKH11A+v7CtL7gFAjgTszlHwRzmvVTSIz2+nUw34PMdrgSKUFhXqDXiUwMAEV3w4Vo3b3eNqRi3M27T96ZzbJqfg1ymBUl36VhUSahDod2mGWS5fz5f+u/itaiLD3UHwXZ5QL8UXBTKZLGjgo+3uNhQYTM9U519DqiqqlJbtR7bY+TIYRx++HCeeuoF7rrrD4GB/wtIKdCj/1me379V7f3pp40MGtR/v8tMnnwhJ5wwlkmTriIQCPLC31NpaFCItJMB9gcUnnomjYYOYganjW3mnqnVpLh1HDYDh83gopMbuXdKDatsK9hgW09KQRUFA3eiOP08+UK3pPpuSFMo1WUzkmrsSSkoqzDfAz5dUBbQTKPZYdF1ISuR9p8dClwDYiIccZGf1+4oQ+3Af634spCNjx9CdVEO3mOaQJXYLAYum4FFk4hxQApgwax+dgZ1JNw4Kv5FIFv/kwT7KBRIJfmA7rAheLe6TTp/wgAfSpJtqAJOG9LER2Ens4JeNuv7rscGpeCOYBqdGzuT1diFk3w5bNrP8u3xctSd0HtsINgqNYoNlS+3tavmqsA+cnc9uyd2OJVHVJ6uTMU+5VFemvUm23fu/kXH9AcOMCToUfUX/fxv4V/2/HRdZ8OGzQwc+POtPvfcczOTJ9/E5Mk3UlL9PqEkElNWi2T9RitHHBbPu5p8ZiOXnNpIZa1KeoqBwyaRSNba1xIV8a7K3o2dSG4JJLdeVMOhg4OEk8y+tdsMxh/tY6fPwknL8jHa2BQmYhFlqtWIKY60gwNkb4HDC6O7BCnwRtlZa2lHV5GE6u1U1BaYPcWnglEquaVTLe/muFmndniSNRBdYUBhPFFZU2B01wDfFMXL2WuKJFOLUq1ocRQZu2Zw6MEBvk1SnzWgtSMFwGmVfHBlKefNMsNnISQWBe6ZVM0xRiGhZjMHKoFTrM38zVWVYCz/FMjim6i91Xv7Trcz3pfHUncJ+cr+vceOSjUtUIAggmx3FItqmOoxVswa3/fEyVzZ7Qb3T4uvei9pdnDurjx0IKRloh17IafNXMKKB7tiT6Ik/gd+O0gpiEZ+J57fjh1FZGVlkJLi/dllFUXhuecepb6+kUDTY4gk3kgkIsjNTv6QWDQoyNZxxLiBBgaRWNInHLIQ8JkGxJOZnMpgtUiuOq2BFK/BrVNrcTraqr42q0F2ps7F5zRy3qpcyoMaCQZUglM1uKmgNjZ5LR4uYXBqajNCwEd/2svYHn4ssTBYeDHloVrSHQ6gh6Cik0qVNfm7x6pIM7SOoaxOpaRWZcaESjKcOs4Y+ddlNcj3RHn3wlK6ZERw2wycFgOn1WB4tyDTJ1ajt1xqidkNuRvsIcmpGfEV7MGFIdbfWcSCKSW8d0UpW+7exYy8VGqlSjMqARSCKMwPu3k7HD9waYeuxRk+c3eCEPD38M+rFJ+q+rEloVR7MP4fe+cdJkWZbvHfV1WdJ+eBYcg5CigoIigGgiKIgbwGzK55zRF114hZFBMmVGANiAkVJCoiOWcmMAyTp2emc9V3/6hJPd0gerkud+U8Tz889HRXVVd3nXq/N5xDWxFiQu8q09UvFjM90A04U0KixG436NnVx0evHeD0UxusGXUJl+ek45FKvX9I6IQzKVi1jLdLf/s3exxHGwJD147o8WfhD+9p/frNv7nkbQybzcY555zO6jUF5ByQeL0NJGKxSHp099O2TZB8v8qzaxP5dbuddJvOpb0rGdbeE9YDpqKiVKYz880R7FjX2fS0ySymx8jVLJ1xJgFfQ4uusBs4Lqjk+Vi43Qc3X1lBt04BXn0vnpIylWGn13DNpEqKhUqux9JI+aTx/iT3tCtlYloVhV6NVwMJBGpVUlyKQR+Hj/PiTTJJcRl8NO4A3qBgXqmL2/NTqW5S5QoiyPFb6B/vZV5NTMQ+rULSTAuxq9DC5dMz2HHAghCQnhwiLlmnzKOgCEmSQ+f9MQV0yQjyy725LNnpIKfUQo8sPydkm5HjY61KuHdTCv4vhCkYK8xl73flTvpc4KeoRmXBbieKgKHtauhVG3Fu0y0cNCKVbDwovO2LY6ytgTy3GxYsSJrOSgRQWK//dpL7BksVn+tO9kuoQcGKKRU2w1aGIiA7McRpXTws2OdqGM9rIxBtJD0y/Xw7bn/ENrf4rBGObfQYiPHIeD4qsnLtobuwjuP/AhI4xnJ+f5j8zHxf19/1npycfPqd1JLh5xbxj3tS0XVBMAQn9fXx9quFPFScxAufJiLLqK+KLst1MLmXm8fPbphckBLeevw6dhU40WvvFAfzMykvSeLycQW89XG2qX0ZAls/D84ry5htg+YGjA/AmQM9nDkwvIKZU6mFtVY0RqeYAMHVgg4ftUZVJHoqZA8N0qOrn9GJ1ZwbXxOmBwrgsEhOTvQRzIvcqEMxOC3OyxmJHr73uPBI6glQMySBJYLW21sTLBMEQg0CEDmGBUqoJ4B8v8aoN7PYcMc+XFbJ4I5ezJJxA67IdDPjpQR2Vlrqt6MDL36dSCkK7+2NR1XMWPe271J5aVgRYzpXE5DikMsCfxNC7KAE6zUVG8OKQQ/1t20Z44Rksb2Qz3QnP+p2WogQk7Qashstl1cX2WnaBS0RrDlopyogiG3S06iJKON5sQmQ3ZHA1l+ha8vfPK7jOIowBPiOLd/eP7zsPZJiR1Pk5OSRnd2Ciy6oZufGvSyYn8+6n3OYN6eAlXYH07cmIMtFWDuIN6gwc20cOY3mWtfk2MgviqknNqncCQAAIABJREFUvnqENBwJsaS9k4dI0yEe/NscFF7YmpLP4njnMEFIl9gAlro8kMRspTDArhh09Ad46qMkPH6FKq9KMFehcKaG41PJqIRI4qtDti3E2JQqnErDZWgRBimazvhUN+2tQRY1z+M8ezUJQR31gESfD54NCtVVKv5gI+UbO5geFY2ECRD4Q4J5myJ9f+uwu9BC3kEtYo7a41d48/sE/LqCJ6hQE1TwhRRu+DqNg9UqXdUAtijpCQcGF1vDl8zt1BADVR/2xnTjkagHBBNkw2u37LPy6LtJPDIzifW7wr8Mu4CxmodXbWXca3WHER8QtSBTh7o/GQYs2OjkhpmpzPw0jvhSnaZVIq3XINpv+e7QGzuO/zuEjvDxJ+EPUbGUkg0bttCz5++L/HJz82nZMgsAiwU6d2yozr1WGU9gvxL1wwsBy3IctEww9dj2lVii+rUGQirbSiwcuKsZRpkGsuFVla+kUNguAC2iD7JrCkzveZCJ32QQyhPmnUpIHM0MtmyyRkxz+AIKny2P4elri4lxSAyjVhm+ye1kWqti+rh8zDiYQLWhcG5iNbdklhNbW3VODemsm27H4xboimh0JUNY7lElai3HExDkVxz6a6z0KKiHSO7LaOca+GKniyknuHkj5iATqjIIYS5hXRh0UgNcZo/Mrb7jLOYhXwLve2PxzFURa1WEJhmkZ3PDOeU4/ZInZiURDJmOa9M/T+DqkRU8eNlvT3YAXNy5ijfWxeNv1KakCkm/Zl5irOb5n/xqBj9ucVLjN9MCFk3iOFminmAQkgIBnDCgH5X/fh6YckT7PY6jBFPQ75jCHyK/vLwC7HYbaWkpR/weKSW5ufvJzm4e9e+VhmJGN3WDE42gCEhsZFTeo4U/6vC/w2KQpYWQbjVMJw4wZeE/iYObDq3iESoTaPk0WFZKga9QIecQAbIiIKfEwrSfEvliSwy6hAGtvDw3spg2tf4cioBJaVVMSquKuo2Xf4insFIlIJXDq0sHiTZQgssqOSHr0EvLri38kW8CVFVixNWean/t9h1gqNRPzQy2ePklIZcPfbEUGBqnW70Ms0SPdB1C8oSjHPG5yrvr4vCGGmaiX/wykVChCJOk8voFr32ewJjB1XRr/dsivHedXMayPAe7yq34QwK7xSDGInllaBEA32921hMfmFJX/qDAtsLgidNL8NkVTnZ5aZHdnm73rMfv92OzHVtNt//VOAbJ7w8tezds+H3FDoCiohIcDgexseYSzQOsUwV5tcu481zVWNsakUckJfjhzSVxXPpeOot3OmifHmRIF0+tgogJVZHE2g1OaePHHs3KUQoyyw+fcH1iSWKELaU3pBBIEIgo0ZPDLpnyeTpfbHERNASGFCzf5+CsGVlU+o7s1M79NdaUt2+6ebX2UfeHAAhdhkW8Ns2gfWqAM9odegLDZoFplxbhsBootefFYTVIjw9hTZawHVONZjuwDkIFcE7bhu01V3Rud1YwLaaE86wm8ZXoCmV65OcL6fDu0riwMTkAX5VCKEohPxASfLniSCaGIcYqWTghn/dGHuC+U0t56ewi1k/ZR3aceUXNW+2qJ77G0FSw5UuuTKmkmyNAfHws7du3YfXqDUe03+M4SpCYN9gjefxJ+EOR3x/L9zUsed+2qEyzW1AxP2t33eApWcnH1XHknKwR+Fkx7ewN0KrM8aeFO1yA5LutLq47rYK3pxTy/IIE3l4ajzegcHa3Gh4YVYpTk+iH6OW7uP/hJY32V0ZvytVUieaS+GqU+gkLh83g8gsrmb4pgWAjQVFDCrwhwbRliVhtEk2RjO5UTYfkyG91U6GVnIrafTZuqq4b2IiR2A1JuiOIG4FzQDV2p8C9xoVNwtheVdw8uBzlN3j2olOq6dAsyGsL4iko0zinVw0TB7k59b5scquEGSXX7lspEGzdaaXNSZHHuzVg5arSdHYEzWp6N6ufN5ILaW0xCcgTEOFG73U5nGjCgJhRsfY7CoCKgNNbejm9pTfiby6bRBEyrAcSTNe2pgIPAwacxPLlKw85mXQc/weQNHUE+I/jD5Hf+vWbmTjxwt/1npycPFq2zGKxqjDNbsHbKHG/TlW4y2Xhx+Z5fBgXyzftXGilkuyiIB8siqemPpIQeIKCFxcnMOHESrLSdfp195ERF+LSE900qxUxfWByKY+9n4w3YBYMnDaDNs2CjD09+tKzDj0z/Sza46DpMtFplXz3VB7Pzklk+Q4H9hMkrfoG2V5qRY9yYXuFwstrE5CYF+xzKxO5f2Ap150YPnr1zx+SMOw03O0CmJMeiqm02r+Nj8cvKeLWtqArEBCCIOC8oJJLfTo3+6OzyrJ8BzPWx1PiVRnepppLu7np2crPK1cV1b+muFLl4IHI9EAwJHjh8wRGnBR+o3AbguEHm1MpGxzs1gVsDD2YxYbm+7AJiLVLMuJD5JVaoJpG87fSbE5u8uNXVcno08yCiJTw1Y8u3pobh9encNGwKsaPdGNrJCxYFRB8tj2GgmqNPhl+zmjlqS+ETBjg5r1lcXgDTW98gjO6mpGsrkO1V+HUU/vxyitv849/RD19x/F/gWNw2fu7ye/aa+9gzZoNPPXUg7/rfTk5+WRnZ/GmTQsjPoCQEKxTFSpUuCLezRXxbmgO13yYFilSiTnVcN6MLMr8Kp6AgqZIZv4Sz2sXHeS8bjVcd34lvdr5eePLeMqqVEaeXM24IVX1TdJNEdLhh41O+iR4+Umz4wtBHQE6LAYPDSmhbbMQU68vZci2LPJCFrYZNqzSINC06VkBLBK99nlDms5lU5ckcWaXHbR3pSHqyKPAbqqqOjA7VAQmEarwxU35nNrOz9s2hRJFJdDonHmF4C27yuSAQVKTj/TK2nge+ym5Nt8mWFtk493N8Sy8JI+YRhFQWZWCpoI/yjKjqDIyHPvUE0sAEdb3ZyDwSMFXnhhGu8wm7yfHFzPp2QxCgcbzxrVpA5skIbWGxN7FBNwWbuiq1Vti3vVUCu9/HofHa37f67fa+PjLWOa/vh9Ng83FVs6d3Ry/LvAWCqweSYuMEAtvziPOLumRHeD+0aU8/EkyFgVz5YDgoxsKsFsk/3orkVdmJ+IPCOKdY6hafQtbtuygS5cOUX8Tx3GU8d9AftnZzVFVlTvumMqkSRdx9tmDsVh+e4YzNzefXr268cshFCs1oExAs0YXc5JLRxUNRFKHkCE4WK3Wy7eHDEHIENzwSRrndNqLVYNTuvo4peuhixt12F1oYcQ/m1PjM3N2hgYZLUMEVIWs+CB3DipneEczCnpsfxL5QY1Q7X4DiQrESoRbIusKMJqMWpWVIsg7uesZfdJiutRcj9NoRsvEIAeqNHBhEmAIEGCzS3q1MMOmxZqCL8o5s0jYqAoGhRpOWKVf4ZEVyfga5eN8IYX8Ko13NsXRKRBkR46FDi2DnNrbgxYlj6mpktN7RC4rc0Oa6YHcBH6pkNeo5WhoTw/NHDq57vDfhJQCzWpw0ss/Yk/wY1FgnRDsrWkH++J495O4WrN4Ex6fwqadNr5a7GLkkBqu+DKDihoFVprN2gFDsHubhR6rW/HT9FwyU3SuPbOSC0+qZtEWB06rZEg3Dw6r5LE3knhldkK9QEaJOwlL2uMMH3EpJ/fvznlT/s7BZqeSYDUYlVVNou0Q6/Tj+OM4Bsnvdxc87r77Jlav/p7zzx/KSy+9Rbdup/Hgg0+yc+ee+tfouk5ZWTm7d+9j1aq1fPvtIlavXk9yciKnhXQsUdUHoH0TXaVJJ1Vh1SJfGzJEWJ6tDlLC8gIHy0M2dh3hmMzE5zMoqlSp9ql4/AqBGgX3bpVH+pew+Kr8euJbWO1gZnU8IatijqrVTcENEIiWEGMzsGsGHVMCUauhQkgU1Y9fKWVTzLMY6NwxuKyhaKMAVnA4DSb2dhNTG6VmGBIlyvnSBaQ0eXrtQRuWKMIN3hqFxx5I5tJ7M3joFfPfQZdmc3r7GjM0rdu+lLhsBrePiWw/6W314xKRpGAVkt7W8PXsoVrypGIQCgkMi4FfNahRdJ5x7WbZGgdKlNxfjUfh++VOCqpU9lVqsEOYy+m69j1dUFmp8PdpDUrOqXE6F/ev5tzeNTiskmAIps9JCFMGAgjG30Cz07ZT1HY4N1xzC1P/Noa73v+Vrl+0ZMnB4+5sRx115Pf/vc/P5XIybtwFjBt3ATt27Ob99+dy7rkTsNlsVFfX4HZXERPjIikpgcRE85GcnMgtt9zPNfkHiL35CqrtNnMpJyUO4G5fkKaNB50zAky7oIjbPklDVSQScFokWclB1hRE/kB9hmBsMA27xyCIoJsS4CNnEUlK9Dv5noMWcoot0RuAf4hn/EAzR7jRZ2ViXiZG+Ixd/VWu9oJNk/cRrxpsL7Uw+J0WEU5jUir067wcBBgEqdC2cHq77rx4fhH3fJNChVdBVeBvfd1MbTTNMilg8LU13KtWlZLmhqSLHk50BbkaNd8oUI6ZO2wPdALWSHwVoj46DQRhV47Grp2xppFJAuYvwStQKyRp8ZGl2XMcNbTSguwKWkyTJ8COQVeLnwG28Ehx1MnVTJ8fT6BJ5dya4MeREV6ZDggDX2INioicN7NoktRkvaGvu4DI4okULNrqZMgTWRRUaJzUxss955bRMdNcTrurFUKHuKByHHFwwi3IrjfBrx8TmH0nqPcydsQd7H64Nw7rsTWO9f8ex1jk97+eN+nQoS1Tp97J/fffSk5OPgkJcSQkxKNFMdXdtm0nDz30FL7X36Pfo3dSfPFIMiRcEQhxcpPKQY1fsPughTM6eNn+4F5W7nPgsBj0a+Xjqy0urpljC/PRFUISihHIFEGgVi57nWHjMm8qn7sORj12X1AccnKgceL8pdIE/E1zewJQTJPv/i4v8ap5/B2Tg9w3sJRHlyQjhQ5CR0rB30c/Q2Ks6QkrpaTEHeDeJ9P4/PsYDAln9PfwxO3FZGeGE09XXfKYR+chp2qqsQDtdMn0mlBYhLVlj5V/TE3F8NU+GwC2YRru5tGwLK+F7lfMqE8HChueD7oEy1Y7GNwvnNA0AV+n5/N0ZRJzPTEowHiXm5vjKiK8N24ZU878lS4KyzVqfAo2i4GhGpxw9y8RrxVAn4GVWK0SPOGNjJoqmTjSTWaMTtvEIFuklYi4Mg6MVFiTY4pbzF8Xww9bXHx/Rx6dMoMkxBo4HTJqbtPWE8p1xexM7zcBThwHm77C/+0/6fP1Ae68ZQpjx4463g94NFBn/nwMQchDLEGjvlgIWV6+83+90yVLfuL++x/HZrPyyCN3069f77C/P/d1Ak/OT0Kr9eAY0s3DjCkHcdkk6wptfLQplmV77ewosGKVJvH5rYLgOAOSwvdlw2B9zH7So8gqGQZ0vrEVRe5worZbDG4/v4zbRppkdcaeLNb6oojISUm61FnYIY9mjewi97tVnl6WyI+5CglJu5k8bAYdWjScN6lbuX34XPbk2gnWRkeqKklL0ln9SQ4Oe5TpFWCnKoiTkhZRAtkrHkrns0UxEa0eKBKhg2yqIF07vtcUsS6dlx8q4rwh0duCcss0fEFBu9TgYVtsvH7BJ8tiWLrZQau0IJ2G5fJl9h78TZbOVqnwcmU3du5wMPamTCqq1Pp+xFcfKWL4YPM4tpdaOO3OFgRyRXiFuo2k6VC2EJLzelXzzpXmTe/tz+O47+WUcFFcm0H3W/z8Uh25gojVdO62fc6id15g06ZtXHvtpVx66dj6HtW/GhIT2yObLo9+J0TrvpKHf/3tFwL8Taz+Dd9ehBAq8CuwX0r5h/wJ/iPkB2AYBrNnz+PRR6fRp08PHnzwH7Rp05LPfnVx/dvpYQY7Ns1gxAk1dOke4JmfEvGHzFZfuyZp5wxgrIItG23IbAnXSDip4TO5MPjBdYCOasOt3+MXrNjhQFMkIR0mv5CJbpjuby6bQau0IN8+kI+rNu92T2Eyb5QlRAzvW5Bsar+XtEbN1luLrJzzdnP8IUFAV9DUIJoa5PEbbqVd1i4UaWX3wkncfeslVDfxoHU5DJ6+q5ixww/fkgPg1+GDbXHM3RWLy2Kw+R0rBw5EFp5inTrd2/lZuc4RpgItDImiE2YED2C3Gmz6eh/JieEkta9UY9LbmewqtqAIiLUbzJhwkNPaRxZH6pBfqXHP1yl8t8OJpkC7Pnm0HLUOwxFAkaAhuNyTzSlB844lpVnl9foFfbr5sDb5OPklKqff0AJ3lUIgoOBwGXgzRNTB3/S4ENse31f//08XxvD420kUFGt0bu3n4WtLKUpUue6XdDxNGrbjLDo7Ru7FpsLGjVt57rnXWLx4BZddNo6//30KcXG/LdP134SjQn6t+koeOELyu+KIyO9WTGXHuP935FcHr9fH9Okzefnlt7jkkvNZLB9lS0nkCJzFZqC0Jmy2E4CghK8F1K1sbRL+acBg83MloLMzNq++CDF/tYtr3kg3o4vaUdpnJxezY7+F/WUag7p6GXliNY2l9vYHVQbszqbKUOrVV5zCYFKim8czwn1yR73XjCV7HRFSUJ2z9zDj+mfJDJzGzNdP51+vJkcQD8ANE8t55KbDW1EGdRg2rzlbymx4ayNHZZlE5hKRv7RZDRa+lsfIvzen3K1i1M4s26ySTpkBduZY8XhNAVO7TXL3NaV06xPg2/Uu4hw6Y0+ppmVqkB6PtKTQrYVFlk6rwc935tIiMTKZU+0X9H2+JSU1an213qYatMzwcsVNP4Pfyo/ftOeHtWlYVbi4WxUPDykJa8fRdVi91U4wBH27+LBZwR+A+ctjWL/TRouMEPd/n2xOyDRB75Y+frgzP+L5xtikBLnm53R25McTCinYFIkQ8P6AA5yREU7qe/fm8NRTL7Ny5RpmznzxiER8/1twVMivZV/JvUdIflcfnvyEEFnAO8BjwK1/lPz+4xozDoedW2+9hkmTLuKJJ15k6we9oMNd0PYGUBvlWhpJuYVBFdCKBvLzC5imIAaHsCN5xl5aT3z5ZRpXzkivNTtvwI0z09j8zD7inZHrQD8SbCG+apPHkweTWeJxkqDoXJ9cweWJkQP+K3IiiQ9gW25rulTfRHmVwvRvE6I2R7scOl3aRp9zLfMrvLk3nhUlDoRfhhEfgNFVQL4MUzd22AwuOaeKxAQDn0vBqKumaQLDJbE2l0ybUMTn38cQH2sw+QI3Ly9N4ImXkqnxCywqvPhNIledU4nbp0YsqUM6fLAylruGhkvuA8zZEEuVXwlrU/LrCvuLHbTZ2Ilr56dT7jWJMaDD++tj2XDQyoK/7UcIWLfdxti7MqmpJWYkvHpvEcMH1jDm9GrGnG42R29wW5m7KjbsO3VYDW4bdnjBhA9sHt6ze7CcWUmrkhi8efG01WBGehBLADbvtdIqI4jLYZJx69YteeWVJ5k79wsuOH8y919yPhOvvRQlO+uw+zmORjh6BY/ngDsw5W3/MP7j5FeH1NRknn76IfISb2HBrKmw+2XIuhhcrcHZGs3SEpGSTb3HYx2i9Q8VwPmyhhti3PRRG8jk3z/HRHUpA/hyjYvxpzYsNyWSGTaDmbX5Nxmrc0lcAW/5FJRDNnOY6soVvsgqoV0zKfGZTxKpNM01mhy3JMYpOf/MSI/g/R6VwQtbUB1S8BkKokoimxqpJ4L9bIOUjQb5+RpxLoOrxlRy56VlPPlxEiEDUzeqFkFdYfM+G106B7hkhFnxmL/aFSYOENRNkYPp3ySgJUTJQ+oKueXRezw3HAgvSNXBkDBrXRyeYDgxBnSFLUU2fi2w0S0lwKhbmkU4902Zms7P7+aSndlw4p6+pBgkzFkVi6qARZU8OKqU4T0OPe9cLHTetXuo68N2pVXjSqvG6xdc/3BHlv4Yh9UiCemCmy4p546J5ab7ncfLBbM/pZvfw2Wvv8dPb7zHk+eeRcyMZxFH0Ov6l8bv6/NLEUI0DhNnSClnAAghzgWKpJSrhRCD/zeHdMyQXx0evzKZnw9+Qk3+CvSiJVD2C8r+2Th37qK0pBCcqRDfGuJbQUJriG0NG1uD3gqULBAa8bEGb8WVRESKFTUKgaakgRnBVHnDL9TZVoO37RJfo5d/bJO4pMFV/kO3QFzap5JXVyaECSTYNYMJvdwIAV//6iKkK5AJlGIqPACKC958shBnlGLHY1uSKQ+o6LWkKw/RKG5Jh2kTihmS5QkrRmzeZ8UfhYhURbKrwEr3WuOfuStjoooDWFVJIEBEV6jLajCofXSS6ZwWwGExIkQOVAHugIhKjABbi2wU7NSiRsYhHWZ9E8tdlzVEmjYLvDipmLG9q1i43kmr1CCj+kTeQBpjlSWIEkUhZ/e0llQuiSMUVOqrwy/MTiQrLcSEc6qQ9z0KK36hQyDAAuB2Hc784nteuG8a/Z6487D7/MvDoKnO7uFQcphl7wBgpBBiOKYOVJwQ4n0p5cTfe0jHHPm1Tgux9IE8pn3Vg5929qNlSpCbhpUzoIOPr7ZbuWKWDpV7MMpzCJbug7ULkaX7wNgHRhFCbU5iXHNuvDGDli2zaNEii5YtzccZ3azMWJiAx9+0Ogindwu/iN9sQnwAPgHv2iRX+mX9iFpT3D24jD1lFhbsdJmkoQtOa+1l6llmHi+udhmFCqRRLyhgsUratYguafHdQWc98QHmV974h+QE7JJqofBpkYueaX7S7A3r397tfSxc6wyboAAzqstMDeIJCJxWic0iiaabpSiS/m18rC5w1BeibJpBi8Qgo3o1EE1elcbrG+PZWmala5Ifm1XiDzWIDVgUgxYJQc5o62VFvjNs2U7tXtunBNieZ42aDw2GFEqaKPPoOkx5Op0Fv7oIhgRWi+TeN1L4ZGoBfTtGn6S3SxFxYzQCgrL5acgm58jjU3juo0TGn1kBsz8zk46APyaJyglvsrviAENnPUCGdRDv3DSYk1KOsX6OYwWSsJTMH96MlHcDdwPURn63/xHig2OQ/ACyU0I8N7k44vnhHQNsv0fw3Z6uBI1uDGlVwwcfxPHMK0noOoCf8edvYtjpm8jL209OTj7ffruIvLx8cnLyqaqqRo1pgaq1QXe0AWcrrPEtGXpKGkkWF1Im1Zuulx9iZVtTu1qtW+SsKrIxc1s8FQGFka2quaBNNe9cdJDcCo3txVbaJQdondQQ718zooLb30jF3ryULuduxJHgIe/nNiQdyCY9UWdPoYUD5SrdsgPEu8zwJ1YzKGl8HWuY2Y5qiRoPugYIswI+Jy+OxcVOfjkrF1ftdMylQ928/HkigZA0Cx6AVTPABee/bxaXRnar4aJ+buavjsHTRBxAAB9ffYA5a2N5c3k8vqDC6BOquH5QBbbaX9DaIhvnfd6cgA5BQ2Hpfge2ZEnfVB+/5thRFRjZpYanRhQjBDy9PAm/XkuMEjTFoG1SgP5ZPtLQo1qQuhwGQ/qF36TmLIllwWpXvdhsnRbhxH9msuXtfVHbcU4OWiMUxAyPekhr0JIK1WTZoPk9SmDMDQvY2qwbumaDNidTOOMiRmxewU9v3ES7pOPN0VFxjDU5/8ervUcDwSAUl6okJ+ocrh+1psZDTs5+5i4qYcFPRfgq9pGs7sFXsY+cnHxCoRDZ2VlkZzdnW7vmuNs2R2udhdbK/FeJj6WFDl9UmVf8K5vieXRNMr6QOfDv1Ax6JPmZN3y/OVxft1+f4KOFsSzd6KBleoii9Dyco5eiWHQUVaL7NBKrE1ny6Cg27rNjUc3+xhvPLeeuC8t5bXc8Uzcn421U6bYoBn0SfKytsONvMupnVwwe7lbCVe0aCjL7CjXueTOFH9c5sWiSGpeCniLql7I2zeDU1l66x/p59bsEhDCVkiWCj28uYEDHw0c0g2ZnsaEkvBdSEZKhLWt4d2ghiggvWO0us3Drl6ks+d4Be4EQtM4K8tStJQzp7+GOZ1P44Ou4+t48p92gTxcfnz5TgNqIW869pxnLN0Xac7rsBl88tp8T2keP/tZoAe53VYEEKSQhCbtG9KG8JDx3J4TkrBM9fPzYAYwzzoeNW1ib3YeRN/+Ix9ao789biXj3MlJr9rHg42do2bLFYc/XsQiPx8v69ZspL69kyJBT65u7j0q1t1lfyZQjrPY+8tutLkcDx2Tk93thsUCzjN+OqV0uJ126tOeBLu154PrIv1dWusnNNaPE5Xl5fLQnn+qFKwntzSe0dz/CZmXQTVPgpmso8ylMXZ0c1nrjCSlsKLPx2d4YLmprLgfLqxROv7UFxRXm7LBFM5BaDEM77qRZb7MVQ7WH+PjRARzYYycUUupXtC99lUinFkGu7FfJpkorc/NisSmSoBR0jQ8wKquGDZWRzdc+Q+GeTakIFa5sbRJgq4wQs+41CxtXz05j7vrYsEjHH1JYvtfBtFuKuXSQm4WbncTaDYb2qiEmSh4ybH8hwebSyLuOIQU/5jvDpP0PSIViqdAuMUjnkgCr8uz1y9+9+VYm3Z3BZy8U8MTNJQw+0cM7X8Tj8wsuPruKi8+uCiM+IKx3sTGE4JDFLYDeISufVCaxyhIgAPQJWVh6fQlXPZ6O129WQlTFbP95cIqZshDPPIIcPYnctHaoRpPfmyMeedW/Sfr1Sc466yKef/4xhg0bctjz9p+Eruts376bNWvW8+uv61mzZgO7du2lU6f2OBx2brzxbi65ZBSTJl10dHZ4DAob/FeQ39FCfHwc3bt3oXv3LpwLjFcl0+06O1VI2b2fFf0vZPKpJwOwotCBVZH4m1wDnpDCvH0N5PfMnEQOlKr1c67BkAIhhUWPnc34uW8hBHgrHOzf2ByjSQ7M41d4+csERvev5qU+xdzVuZyNlVaynSG6xgdYWuw45HieLgUPbUmhfUyQwanhmeZdJdbISRDMwkZehcaA1j4uGxzdAzkaNEVGVd8B6j2G3VIwJZjAMmnDiiTkBf/nMbXSVw3w+gVPvpXI3GcPMPxUD8NPPXTVFmD8GW427LZFeKzYLAa92h5ePdOG4NRgA2lVrsiBAAAgAElEQVSfe2oNnzxewDOzEtlTYKFZ8xAJnQ1m7opjvKOKXif0gB+/oPvMrwhaIsneoUnGX3EF/S5qw+WX38TPP6/m/vtvjTrq+Z/Avn25vP32R6xZs4H16zeRmppCnz496dOnBxMmjKF79y7Y7ebn2rs3h/ff/zejR196dHZ+DI63/WH3tr8CuuuCV2o0viqVlE64ndtvvpo+fXoCEGuNLpagIElsNOo2/6eYiAF/AJ/bTtUB0zzbX21DUZuMfbXzkvmvvZQ/uYdRsX4+s4Zo7gwyLNND13gz6T4gxUumPYQWTbZfAY+u8NKuhIg/9W/pxaJG6WnUBZ3SwvsMf9plZ+RzzehydysufDGT1fsiL3pNgQvaVWNrIiBh1wwu62qS6FWhBJZKG34EVSh4itUGG1cVSATSgTTByhx7bfT12xg3pIr+Xbw47aYUtt1q4LQZzLzzYESUWIdf8uyMeKcZLZ9ozSmvtmDe1gYp/f7dfHz82AG6nB9gbYqd+QUxvLkunuGzmvPKqnhEq2zaPXQNw1oHcTQ6h5qQxGoGk9u4OemkE1i8+DM2b97GyJGTKCgojHIUfy7mzJnHmWdeiBCCm2++mnXrFrF69ffMmPEMV1/9N0488YR64gOzr/H++29l48bFR+8g/htUXf5qeOyx54iPj+f66y+vf25AhhebKqlqUqC1qZK/dWqImpyH0oYzBJrd/KbjMitRLQah2jujtZWP5k/uRaldbuYDTzu8lAiDKf74+k0oAr48bT8Tf87gl7I69yfCFGcKdcG3lhABAScHVVKk4PpTK/hgdRxVRkMl1mkxmHSim2RXw/F+t9nJ32Zk1LerHKhUWbHLwf3nlPL+Z3Fsz7GSHK9zy9hynhhRTF61xpoiO5oiCeqCM7M9/KNPGcVSYYlhI9C4ipxm2hSgAMk0SPcL0xh90hMZzH3gwOG/GEwZ/LkPHWDpBgeLN9sIdS/GyHbzWa6TbQuSuegUH/ExDZ9pVb6NUe83q/9M7iKVaz5Lp9xbzN96m32e3+9xsmifs15BvM6aYOqSZC7qUk2qS2dGv4O8tD2Bt3bHU+5TcXp0Ug7ovLsijitOqSQ5OYnZs9/g6Wem03/gGOxDX8eXPYKTs31MPbOEjql/jllFdXUNd9zxMKtWreOTT97+3V7bRy1qPQaXvccjv9/ADz8sZc6cz5k+/QmURqXDkBDcPbiMeJtOjEUn1qJjVw0ePLGEPqkNy60rR1RGEKCqSNI7FqMFrPzy+ml8edcYOrV1Y7cYCCFJHF+EagtfT/uElXfsEh/hggOpdp1PTy3AYZUm6dXpDAJxzSsxhu3mcWeAZ+wBRsd5mWUN0ixeZ9H1eYzsWk2SU6dNcoBHhpXwrxHho3p3z0lp0qcn8AYV7v13Clv22NB9gqICjQdfTub5DxKYP6qAHy7MZ/oZRawYm8t7QwuxqFAqFSxNSqnCDlwUhFhZt+l6GAhWbHGwLe/IGoeFgD69qim6ag2bT9zH1pYl7O6bz/zhGznp7iR+2dSQF334h+SI3kNvUGHqDyn1vYXzdrgaWSc0wKLAwn1mk72mwM2dKxgWqsFYC0UbLWzJs/PPBUmc81IW/hAoikJ5jwfxtp1E8adXUvXOaSyYP58hMzLIKf+/jzvWr9/M4MGjUFWVRYs+/d3Ed1Tx32Jg9FdBYWER119/J2+88SwpKcn1zy/wOplSkg6APBWMCrjZVcGVLSpJaEJ0fzvHzartdj5dGmOa9QhITwzx9PgQE6+fgD+oEAoplKgSVZGc1tVLUfdKAkIxjesK7eg1GvZML8JpcND6Iy0DI8L24dQkD3ct4aEtKfVD+jZLkPRBueiqNPuoa8nlVUeQviGVDskh3h4fXeoLQDdgd1F08pFCQBX1qjABv8K015IYe2Y1nbMDdE4KXzq3ESGMQgVjng12K5AgEcOCWC4PELNZUB5FkCFkCH7ZYadTo97H9QdsvLUmjuIalREda7iwa1V9q81sWymFIgS10bLqMJCGQesH1jP5qgFs+bfZ9rLpYPR2gJqAoMKnkOw0cFmimyEhJC5LA4nnlmm8uzIurKHdF1TYU2rhs/UxnN3Fw5tf7yW06R0Y8wNU7IS1z1Oz5HauyL2C2f8aQVJSYtTj+d/AMAxeffUdpk2bzuOP38+FF5531Pfxu3GU+vyOJo6T3yGg6zpjL7sD++Arubr8YrovDXB3tzLTLKkkA2+dpLsAEuE5kciVlsqI7SgKvPD3Igb28LBut52B3T2M6Odh1D+bUeNtWJ8GdUFQF1R5FHq5SlnhzqTgw1aE3JoZn4cESf0OktLvJVboo3kiGM8uQ6ObEuQuSyVXtnbTISbIS7sTKPRpdOlczHZFRjTVB4GvrSE6+KwcDoqAeIdBpTdK4iyPSDksA258JI2vX98f8fKcQgvBxx0QqJWjcoN8W8EyCsaeWsVbnyVETKAEdXjsqyQuGFBNjF3y/rpY7vg2Fb9u2g0s3ufk9V/j+XryfhwWyQprFbKJLL9QwJntwWcLsnGnjZ4d/WTFh6hUVbPBXADFQJUp6RdXe+Oa0N3NextM7+FwCIa0bijA/LTXHtUKwBNQ+G6bizRHGfq8S2Dw85Daw3y0HwNFa9mz7Vl69z6T888fytVX/+2oeYkUF5dy3XV3UlFRwfffz6VVq+yjst3/NY4ve///4Lqpb7KxTCNn8FQKvBYWHHAyfGFzns1POGQLxTxPpN7b1lwrXa9sxZ1vpjLrx1imPJvBvz5K5OftkS5xAGv32LjcV0nhnCyCZRZkUEX6VaSu4F6VzPP7JnOhP5Ulhp0CNBYYdkb40/hZtzIo1cuc/gdYOjiP4c2ro1y8tUW3aAUSYHOFlR8POqgIKAgB1w+pwNmksKMgTaXoCAhWbbQTiLJsefzzJIJ1xFeHgEDMs3LtmZW19pVNjkkTVOsqc36JpToguOPbVLwhpT4a8wQVdpRa+XCDOdsexe2g9rAk6A1uxz36+qC3hBaYj16gdpBcfVIFllqe75Ee4IHTSrGpBjEWgxirToxV5+/9y3l/WxwbS8wbR2qMHlWz36JIMuOCvProbZB9BnQaH34O03txxrUvs2rVArKyMhkz5jLOP38yX331A7r+x8OjRYuWMWjQ+XTv3pmvvvrw2CE+ML9e7xE+/iQcJ78oWLFiFf9+512Myz+gzlxCIvDoCvO2xYQn7msRlFDZpNnYMOCiRzIpqlCp8qpUe1X8QYWX5yVijeKHAWC3ShJLeiBLNWhiGBQMWnnRMgAvTfJwKNwXDK/qlu6Mbfp285iCCj2rwqO+Qq/KaQtacPYPWfxteQad57Xi6S2J3Da0nCmDKnBYDFxWA6fVYGxfN4cchSC68s4vu+xRW2uQEDAUbh5XhtJ4NeoAEs0IauUeO6v326N7kwQVPttq3nDOCCSgNCL7gNvKwVUZ7P8mG2tQo0d7P3k+jU+8seaAsYJJXCqITBjWNzyXek3fSjZek8O0c4q497RSrLHw4sYEHliRzDmfZHHpt+mc2tZLjE2aqjONoKkS67bnKDqQzzlXPopdC/+ubZrkpgHlpKYm849/3MD69YuYMGEMzzzzCn37nsX06W/jdv+2pmP9Zw0EePDBJ7nhhruYPv1JHnjgtiMyFftTUbfsPZLHn4Tj5NcEZWXlXHXVbRiT3oTESF3BkioNR5TISRMw2B7ek7Zml43Kmkal11p4/AoZcTqOJlGV3WIwcbAbd0DDKqIsNxWJzxG9BWSTEU5oS3fFUfRjBkZQIA1TKNQICHy7YqnZFx6hTlyeydZKKx5dwR1S8RkKz25N5NtCJw+PLmP5fbmc2aMah8NgWa6T9h0DtdaQDVBVyRn9PFiiJFKykqOvd0I6pMTq9O/ox5EhzVaXdCAeUMypk/bpAWKsRtQeQpAk2M1zOMqfSFfDAX6F3f9uz7Lbz2DrWz3Z8UlXZDfYXWJhQZkz6g/eEPB1mSvi+VSXzoWdq3ljawJlfoXqoIpPV/CGFL7LcfHh9ji+uGY/bZKD2IMGlkIDS4HB6Lhvee/1V5g58wXevLiSi7tXYdMMNEXSKjHABxcfoHtGQ17UarVy8cXn8/33c3nttWdYtWo9PXuezp13TmX37n1Rz10d9u7NYdiwcWzbtpPFiz9n0KBTDvv6/xiOQQOj4+TXCFJKrrvuTkaNGk7MCUOjvibVFuJsR02Yk5lTGIxxVtHdGp7o9/gVlCg5IYD0hBDDetdgsxjEOXXsFoPB3T1MHV9Kl2R//YxxYwihoxxizZ3c6Ja5Q62kMK4Y95pkct9tR/mqFCrWJrH/362p/KY5SfaGY8+p1thcaSXU1MRJV5i+PYFKr8Kw57P4ckMspTUauWUW8pIsxCYYuJwGmiqJcRpkpoZ47t4iouH288qjEv2oE6uJdxqc0s5Ls4QQlia2nxYNJp5SRe9mfpIcOqJJxOm0SK7oY+ZZLSg84Mli5PpO7F/QFhlS0f0aKIJSj8rFMzIbF8LDoACWQ6QCdldaKKiJfKcnpDBzSxztUoNc1bECuRP0g4Lg/lI+fO5qWvV/hRYtWuCwSJ4/r5jcO/ew6/a9rLkhl9PbRl/bCSE46aQTeOut51i27AtiYmIYOvQSLrnkShYtWkbTUdTZsz/nrLMu4uKLR/LRRzPCinLHHI5B8jte8GiEV1+dSUlJKe+++xKO7ZW8vCMhbJ7WqRrc0qmcy5PdzPe6+LAmFgWY4HIzzOFhZ5GFCq9C92YB7BZJ3w4+QlHUSZw2gzGnVnPZOW7ySjR2FlhomxGkZVrDNz/ttGKu+zHVHNSXCooWwhbjJ92VR74vmxANkaETg1ssZm/hEmshcxx7iT8hDmVTNoESByU/1mkgStIdOgObNVx8FUE1epM0UBpQef+nWNxehVCjMTKfVJBdDZ44s5jyEpW2LYIMPa0matQHMKSbh6cmFHP/7BR8QYEh4YKTqnhmstlaoygw/5b9XPduGku2m3O67dMDvDK5iPRaJ7m5YwsYPasZbr+pphjQBbcOKGdQ64bPIhB8uzSVYDA8apYIiqs1WgUC0WxLUIXkh5lOnv4pCbtVMu4sN1OvKsVplwQNccgIIWgISisVHnw9BX9AAanD3onIxAlsKb+Q71cVcnatEINVBWuUxvJDoXnzTO6//1Zuv/065s79gvvuexxdD3HVVZM599yzefDBJ1m9ej2ffjqT7t27HPF2/2Ooa3U5hvBfIWxwNLB27UYuvngK3303h1atstENuGldKh/tiUPWpoiu6VjB1K6lEXmt/AqNsW9lsqfEgqaYLRJPjS5mXN8qPv4xhlteTSMQEuiGwGkz6Jwd4MtH87H9RlpmtOcg29e0wFflIKVtEc165qFadXZVtqfI0wIV8zd1o1bFHRY3QaFzW/wvBGqj0pzNrfn1m5ORFQKjSEXocEILH89cUEKvFmYvol+Hdp+3obqpzaRicEPHCravtPLlhshCTqxN5/lxxYzu3SBpJSWsyLOzPMdBilPngi7VJDgaLviQDgXlGkkxOjF2iZSROcJqn1n1TnRFEoUh4adcO+VelZOzvSRHUd4e8VJzVuyJYkpk15l1eSGl8QrXbE9HxRRt0CXwAwRWiHoLAJvFoF9XH58/VYAhocs7rTjoaWJypRrcfVIZzcpD3PJcGtVeBQoeBvci6Pg9CI3xZ7t5+R/Ro+HfCykly5at5NVX3+HbbxcxbtxoHn/8flyuSFGHo42jImwQ11fS9wiFDRYdFzb40+B2V3HFFTfz1FMP1VfIVus2Pm0VA1kSIyBQbQbvabFcarhp28gMSUoY83ozdhdbwvJSt32SSsf0AJcMrqZ76wAzv4ujuEJl2Ik1jDqlOsycR0rYXm4aA7VPCNYTQkymmy7nbow43o7xO/mw3MK8HbHEBiSjutUgEmG/6glTmW7ZdS9Vu+LY9lU3CJqLxjU5Doa91JzvbsqnW7MANhWe7l3ELavT8OmmOo1dMUix69zQoYIX9iZgVQ0CTbxTDCnITm44DyEDxs3O5KdcB56gwK5JHlyYwtxxBfTLMkdXNBWaJ4Z4emYi02cn4K5W6NbOz+O3lHBKL/M1poiCRDdg+jfxvPZdAlVehcFdPTx0SSkDWh5+QPT8ntWszbNFNDLrhqB3tg+nVTIwYR/flLoISsHORRpvrkoI8z7xBxVWbbWzea+Vrq0DvHl2IZfMb4YuwacruCwGHRMDXNm9koUrnWbBo3IBFL8GnVeD0FAUicN+5JHeb0EIwcCB/Rk4sD81NZ4/hfSOKo7B2d6/fOQnpWTKlFuIiYul/SuP8rFN4BUQ9NjZX5qCHmpgKYFkqMXDrNiGWc31+TZGTG9OTRMRTCEkF51QxWvjDn/n//WgjUu/zaDcpyKBNIfOe8MO0D0lwAvOApZZqmgsXCIkBH5ow8pZHVFEQ9310VEljBxUxMOxawnWRn6hgMrnN12C7m8aYko6ZAZY+Y+8+mdWl9p4dWcC+R6NszNruLytm3irQV6ZRv9/Zoe56VlUSacMP4vvyK8n6plr4rj3+5QIheZ0V4gtN+2rF2C4/ekUZn0Vh7eRjaTTbvDNq/l079CQM/37G6n8++dYvLX7VYTEZpdcdUMFZ2V5OCXBF7Wy7A0IznmhObtLrHgCivk+TfLMhcWMOzGygjr+gQy+/ikyso1x6rx4axGjBplV4IMelY+3x1JQrTGwuZdzWtWgKaYQQ7uRFjxrToI2H0HcYMD0T/lq2n56dTi8uML/BxyVyM/VV9LlCCO/X/+cyO8vX/B47705bNu2i9Bz9/GWXVCqCDxCEHD6SWl+ANHI71ciWBI0l1RSQq6usadGQ41S1JBScKDy8IF1hU9h9Lzm7K+24AmZVcScKo3zPmtOTVAwyZtGgtSw1f7urFJAsZOVH3bEH1LwBhV8tY/7PkvBUxxDlu6k7nA8pa5D6E0Ldh60sjK/YeyrT7Kf1/sf5Osz9jMypYbpC+O5f14yueUan1xXQLu0AFZVYlElgzt6+PT6gjDymbUhNqo0fXVQYdNBsxJdWaXw/vxw4gOTQJ6e2TDpcKBcZc5PDcQHtfO1fsHz3yRyyYZmXLg+k2CUwMphlSy4aT9PjC5mWNdqJvVz8+2N+VGJD6Bnez+2KCIVui7okN0Q2aY7dW48oYLHB5Ywoo1JfAABn5vsmguwZt1ETMZAXA4dm8Xg7sml/xXEd9RwDLa6/KWXvVu37mTq1KeZ8fUsbotz4G90NQsBUhg446qoqWjooYsRBj/4nfy9Ko0KQ0FPFATPA74gLKx3WAzO6Rzd+LsOc3fFmDmnMAhCUvDFnhjGdqziBXdrllor2an6aWFY2bW8FUujBOu6AfPWxXBdSmdedm1lv+ohMd6PHkVRBkCqMGt9bP2StA6zfonltn+nohuCkA5vrYhnRLcaVt6TS1mNgs0iiY2i73ckYUFuoYbVIuuU4BuORQo2725o9NuSZ8OmyXofjXoYAoqgRhf8XOHg/YI4LsuKlN6yWyQT+1UxsV844UkJ3xY6eXtvPJ6QYExWNROGu5n+SQKBoGzI+VkNTurio0tr80B1Cd8XOvm51E6mQ+e8lBJWL13EnDnzWLRoORdcMILv/nkhC1cX4fEpnN7HQ3rSMTbLdSzgGJvw+MuSn8fj5fLLb+Thh+9A79IOC9D0Pq0oYLX766UEHBiMslQzqTIjrNFYyZaICyXG++bFY9cMMuJCTO53eE28gzVa1CkMf0hw0GNWLO0onBVI5Kzav71oKIeYMBHoBsRJK3dX9+Sg4qVKBCno4GXxVhcR9OQQ+Jvsu8KjcNvc1LBZVU9A8OUmF4t2OBnS6dDaehN7udlUFOnYFms16JZukkh2RohgMPLzKkLSrV3D2W+ZGqyXo2/yEc0eQMBjKLx3IDr5HQr3b0w2ia82f7mm3M5H8bF89Vw+97ycyvINDmxWyfiz3TxUK2Dq0wUjlzZjc7mGZ8tS1JUfcOeaT+nZrROXjz+X559/jIQE86BGDjz8ze4vjWNwvO0vS36zZn1C69YtGT9+DDv0Q3wvEghoxKHjQ9CMEJ/WxOBrkmwyFIEtw6BnHy/+QoXzulcz5RQ3sbUVzX01GkFD0D42GLZUPDnTi0NNwNvkQrcokpMzo2eHh3ev4Z9fJRFsElioimREj4aLL91wkI6D+84oZ/FeV0NUqgBOcDoNLuga7nK2aIcTTZURJ8MTEHyyNuaw5HdiMx+dk/1sKbYRMAQ2TaIIeO/CAygCftlh519zk1BaglItMcpE/d3GZpPcdmnDzFy7zCAntvOxcoc93JBcBf6gMEmuR+ONPfFhkv8eXWFTpY2dmpXPniyIeI+Ukofm57Dmw5fRV34Mceno/cbDQw9TlpXOpHNyontJH0ckjsGCx1+W/DZu3MIZZwxECEFHAzrrsEmVBBv9mp1I3pTVrLZJHitPJsewErIIiDJ8YVMkt42u4GxbA0Fsd1uYvDyTPI+GABKsOm/0P8jJqeavoJMrgHenAE+tY1oikCLBC70P4QLWNi3IP84u44nlSYQsAlENlpDk1rPKaZ8evk70BQTjH800v+U6S4nanr4hrWs4q204mVmUaHbrZgrAeojh2UqvwrhZmazbb8OiSkIhwQnNfUzsXcWoLtXE2w0Wb3Iw7ulMvHVGuTYgQyKKoGdLP/+6uYTu7QPkqeUst+3B/T/tnXd4VFX6xz/n3umTnkAKhBZqCB0MSDUUESxgRUXs3UUFf9ZdWXR1xVWsuyqroriu2LCAohQBKdKLkJBAKIFQQkiZJNNn7vn9MSFtAgRkNZr5PM88mbm598yZO3O/9z3nvEVxcufDicS+1Z9vNoTj0QSECTifKsvPomhMTGy41bem0IxOBFv3dr/CoiNWLmsRbLW9/faHvP3Mm/gH3QZTl0BSl6r/Fbk1civ0dAhvZM5rjZWQ5dd4yMrKYcKE8VWv/1Xh5/YwN5mqGYkgHCdTXB4GiHAetEXgOmExnEjAWUcl3FLQXVd9abn9MHZZC4rdKickxeFUuOLHJD4YfITBsU4umtESSql2djsGHBOIEsmyLRZG9gu2tIrdCvP1YSi9QadJ/AgymjmYMig428C366043QrYBFQAMYAqMDo0MtKdQVbLBZ0c9cbgmnWSCX3rXzC454vmbMo34vErOCt/3FlHjEh/OZGVrh6PfxBXa/ECAp+5U283y/4RqGOyRX+Qryw/48OPFHAoupTOj+zhhaIL2F4UzsTsRDQETk1gViTpkU4mJpZR7FEwqxJzPbG/NYky+INicCEQ2dHMVP/8XFJSPOZ2Pam4/G9B/5NwUufwECfhHImfECIZmAMkELgiZ0kpXznTdpqk+Gmaxs6du2ulEfranE+EWkQvEVjVVdFYbBJ08LYn11cjbtZP4KzVKG9rQeM6cxkJavVF9P0RK+5Kv7maOP2C69Yloi+TuGxKsJevBl4F8gp0bPcYyPEa6KD30sMQENa7N8WTaTPirSFSy4otvL0nkjvb106plX9ch+vEHJsbOHLiqUJ+YfBXbzVK5tx8hBveTUQogcQMUsK9w0pIbxtsiZa5BEt2W/HUGbY7vAqvrIrmpn4By2xnfv3ps3IOGZAS/EJjvmU7XlF9/nxCowIPG8NzGaXvwo64PL4sCKPQozIgyonPJ0hf2YrDzoAbz6WJFcxMO0b4SSzUjHgHRgUq6tQl1imSia3rtyD79OmBtvcJzIofp1bT3Je0MPtoY21kpkxj5txGePiAqVLKzUKIcGCTEGKxlDLrTBppkuKXl3eQ6OgoIiMra2jgZ7mhCK+QGKUHM07KCccjFBaaD6PSvXbRcDegA0WVdNR5uNtcyg3mco45VN7NimDTMRNeQWByP8iQEnj8Ak8R1FucVgHNIvggNYInC+JQkWgIuhvcvBN1hOXHLLWED8DpV5hVj/j1bu/GqJdBIXZWk0bvDvUPqzM6Ocn6634W7rBi9whGdHbQ+iSJCeyVfnT1rfXuL9bx8tIoHhheSmy4n+NlwT+1mHANIaBArV98/EIjW3+UUa4uROo0bmwR2C+7XE/GumScNebv5h+1UuhO5Mv02nN3J6JIDAp8OfgQ16xOxOZV0Qis7r7cq4D2Jxm6JiQ0JybcTCvPdrYau+PVAu0YFMn76UdD831nwjlMZiqlPELlrVxKWS6E2Am0AELidzoyM3Po2rVT1etSxYde+rnB9y5DtRUAOLDwvnojOepQrrBWMM8ehrvGCq/Zr/GQtZgpkaVAIAB+xLyWOP0Ct19Br8iAH1o4wWdZB1gIzB3W1RVNYr1MI0dnwF0jJ9Vmt5FptrjK4P7gq668HpeWgV2dpLVxs22vEZfnRIZnjbYJXkb1CQypff6AONQsLxlp1phwEr+4miSE+4mx+DlcVue9JeATzFgUw9hudiZfXMJzn8fWqrBmMWpMvqQ48Fwa8KNReLA56xcOoOhQM6Lji+g7ei1tk+v4xQD/2heFp859w60prCsxsdeup53Vy8o9Zh5dEEdWgYEos8Y9A0uZMqyEd88r4OqfEtE0gV6RTNkSj1Ep4LKW9a/U9uvXk1HaEv46OJZ1RWYSTD7GJtlPO8wOUYf/0ZyfEKIN0AtYd6bHNlnxS02tFr8YTc+N/n+Trq3GUGmbG7Bxl/9NPpSxhCcayTmUTpYrCgMStxRcZq5gckRpVRuPr4nD5lGqhrneyrAMxSnRwmuIlULgrCcBWwX4aoiZlBgNEn8fUUtoATwoLHCFkWDyk+eo/T+dkIxOCL54hYAvph3m1S+j+O+yCDQNrhxSztQrSjhUrOOB95vz485A+cvRPe3MnFRIs4iG356FgFcvK+TaDxMDq8+iMuREAp5AnZOvt4UxZWwJNofKG99GgQhYY3dfVMqfxgYs1WjNgju3A1+8OQqfVwUUKkrCObKnJYOvz4F2td93l92Av55khQZFcsCpw1aicM2cxKoQt1Knyksroimwq3wiwyn31V6xuntTPD2jD9C6nmFs37492Qq0r3gAABoXSURBVLhxKxMmjKNfTMhp+azROJNEpXFCiJrhILOklLPq7iSECAM+Bx6QUjZ89auSJil+WVk5XHppdcoqPXYG+lejitpWhhEPg+WHvGEJ5/z2e+juimGwrT/pOkELXW2RWHnIHDS/ByB9EK/zUqDpQF+50lmZRNM0QiN+q4/DR/UIoEsLN2/dVsBgb/0ZeF1ScEmLCt7dG4lXE3hlIA43Qq/xWGpxvceYjZJHrinhkWuqF0QqXIIRf2tJcYWKJgNZVr7bZmXn3w2se+ZALSvwdAzv4OCe80p4bVV0IAzvRFqiGpouBPz56mKmjivhWKlK8yh/oOBSDdbOuwCft2YYnsDn1fOv+R25+f4DtfYdEO1kS6kRTx0BdGuCLuEeHvi2WfVcZyVOr8Kc3REYUoItNp8GH+WF82hq8KJRnz49+OSTrxp4NkKckobfV4+fLrxNCKEnIHwfSinnnU13mmR4W91hL6IQ5ST3gVj/MQwrctFyDmM0FVIQuypI+CCwIlofUoItU4UyAfrqfHUmRaN1nI+f/nKQnBf3sfOFfSz/Sz6dEr0MNTkC6eJrNQRUCN49EMmg5g5uSyllRLydR7oUs3bkARLNDf9lzVsfjsOt1FrZ9fkFBTaVH3acOmBeyuCpylsHlGHwBVx08FIVcKxTJZf2qMAhBZ96rbwjwzkao2KqLAJkE06y9EfIV0vYeTQ4EwtAbpGe17dHsr2oetHkzjY2LDpZ6xyZFY0rUorItzrJdij13ogUPUF5CwG8UqHEU3+R3+7dU9m9ey8Ox6+YX/2Pimzg4zSIQLLLd4CdUsqZZ9udJmf52e0ODh8+SkpKm+qNMjhjMwRWOwu+KCXmnrfB68eXmkD+/DvYpBvMDGcztvqMtFZ8PGIu5sZUG2/8HIWrRvYTISXSEajoxX6gXCASJXGRfu5JKeW2VraAm4a19jc+I/o4IwpaUqEJvCiBIYMG5Accc1cWWfhr13y6RATPhzWEnYcMtRIVnMDrE+wp0FdFk9Rkn0vH1D3NWWEzoxOS8bEVzGh3nEidRnK0j2mXHGfa/LgqYVQFPDKqGF8spJW3xIPAg0CPZIDq4trYJfxsPIAqFaSQGK2peMqCsymjwFMbY9EpMKa1nVlDC0gw+Vk28CDTs2NZXmQhwugldWgOuRFOpklB1EPbSV4bx8GP2tWqGyJtQHLdq0vSPm4vI9sUIomhbvY+s9lE584d2LYtkwED/uex9iEaxkDgBmC7EGJr5bbHpZTfnkkjTc7yy8nJpX37tki9xm7Dz2w3ruWYWojfew9SVlsfmgSfA356SkOxuVAcXvRbD+Gcup5Lylqx2GuhUOrY6DcxqSKBDmkeMpIdmFStqoYvHgI+dicoArlDYNwheaBdKWEnsRbb6b1sSMyjm8MdKBZ0FMim2kNXwLri6qQEfuAHncIbBh0LdQqnk8SWyV5MzbWgb1+vQmrL4KNtPoURPyezwmZGQ+CRCvOKwrg0Mwkp4bP8MP5eHos+XSLbQvPOfuZcvIqLyv7L9aWRlKBgR8GLwIHCKr+RDzxR+ISGW/HhEX56Dl+P3lBn1VUA4QKvVHD6FRYesPLpnkAGljYWH7N7F7Bv5D4mXLiT4ggnHgF2RYJOEt2viGYZ1UXPLXqNO/ofoW/nn9BX+mO2jtnHa1ffwZ/HPI7WeibLIp6mSJcb9Pn79OnBxo1bg7aH+G2QUq6SUgopZXcpZc/KxxkJHzRByy8zM4d2XVvxWeSbSCQ+fOjQkeBNZrj7KXS6WSAKyVvvY8U9ZRTnVB8rfBrLxz6AU4pa/nlOFKa5Y8kelce+Mj3ZxQZiDD7Gvd0CTz21Xz0d/dyuRWBFcr1w0k8ET7THqhoXKQ52HDTiqVMYSRWSeFPgGBtwjdXIEUXgJFD7J0JKPrW7SaijrUfcKhO3J5LlM+DOEAGFXyvgABh0Gm2bexjcOXh499GxcJyaQKsxlPRKhT1OAx8cCefRbc0CGa/1oGvuZPjLl/Lz7tWsS2lL/vk3BeY6a+BGZbs9la4R1YKSNmwLTruFzGX9AufUVxnVUaMuk8On8MGuCK5pXx2W50WyzOCmbsiwYtRoMeIIxT8kEm3xc9+gUkYN30YLy0pSWmSzLbcX9w96EbMu4OytAR7hZbN1NkPKHkEv9ZQbluFWs+kywMWyLzcAtwWdmxC/X5qc+O3IzEbf24m3xuKGDy9H9QfY5cugo3sBAMvuHktxVvAC0pGuvestUWaTCqVSISXSS0qkFyklXWJK2X48unpuTUgM1zuwt/HxNSYEki+liSmygslKsOhc36qMl3ZH17LkBBKLKhnRPOCq8rxRT54iqsLy7ARCKJ8wGXjHWX2klHDl1iRy7IaAz+KJb/58icWjcXXnCqZfdRyf8LNXV4hE0s7XDAM6djgMtXzqqtoE5uRH4KrhRzj808dI3rUKndeFx+mp35cRqJseTggYOHYts/ub2bS3Gff+FI+9nuQGNdPpA3iQ9aamB7BG+Dj+zJ6q1zmV96yOydn0Tt6ACVfQzKBE47BhJarxMzRRgRRuUgZrzHhyL251D0Z/ykneLcSpObPl3l+DJjfs/TlrB/HdI4O2+4SP3YbqrMntRg1DNQRHJkQUBAfAA6heOFSmYvcJ/H4/t976IJlPJ6B+MQTDlmfQFW3G1MWD0tqPVxe45CQCJ4IXCONYPa4biQYbn/TdQoLRi0XVMCsancI9LBy4E5OyFJWVLNb7a8UjA/iFYJVOqeVQv6PCQJ5TX9tZG1B0cOlEOy9NKqQg/CgzIr/lM+sGPrdsZEbkt2TpD9HD6saiBEuMANze2lEsvVbORu8NOFDH7M/Fejw4masBH12s2UHbTVJHks7M2PZ2jPVMCVh0Gte0r/Y/PLR6Pd+Pv4VRbS+n31WPE565t7pvEnp5aydxbe1rhVsq2KUFRfqpb3ZdEz78+mX4RSlSBIbHLVMEbpfGjqLng/YP0VBOhHg05PHr0KTET0pJTuYekrqdvspV7/tuxRQbjWqszDMnBDqziXsrdmOuaWtooGZKPMsUxixqSftPWzNs0tMUF5ewe9dPvPf8TQxJ2EvksiuR01rjmXIv2ldfIG3VPoI6JD9SM6+9F7PtL0Qe688YhpDfI5qVfV9mzQUH2HDBS3QL74dJPIRJTGaNozd9/afPkFvg0aHWE4uqIch36XAID3Ot6/AIH27hw614kcLFZ5aNjIk/hkXRUGuIhUFodDR7uLxZBaYawqjzVEeOdOoHH+RcR7ivDJM34IdoRSNN9XJ+WDZ6GVhhVaRAL1WucPRCQaBTYPYFR7HotMDcKRKrTqNvMxcTOwas8dwFi/jyqls5uHw1xrwjJM5bztD0W4nckoNOglUK7nRVL6A4gUeVaF523s8bznuY47oJWc8NR5UGVDUfaoTaCSFISzezZeNuNEJpq86Oxle+rUkNewsKClGEQmx8NK46P2JV6mjvSat6bY6NYeLqBWyb9QH7l/xIWFI8ve65haT0rhidRTzrisEnBe7dAnFY4NMEFX4FPn+QrLy9TH/7Q6Ki/IwZPZQxo4cCj3FfbiEfL1mJ/78fIv90H6JbN5ThI/CPGIGle+tqN5jyGRicXyJwgwwEgvSSf8apHkUvXkMINydWP6KA/zpvoLt1My4RWLBRpGSAT6spp/QKd+Gux83DpGiMjHWQpT9U6ZsnaclBWnAYgcSPyq4wWNo9jEf2xfFDqRW9kFzZrJyn2xxH0wTv5kVyzB0oQL4vNYOUHd+TPlYyYhIYTBvZn92e/0ZO4JCxDb2TRpFhDsNbPoSNxgPs0RUS47fS39OWZlp1OvkhSU42X5nHp3vCOe5SGJzo4oIWjkDqfilZ/vBT+JzVQqtoEmF30edPL1E25QbG5DtodtnFEBkOwH2qhRWKvtJKVsiRndni701fdRsIb+V50xPhb4Gq5OMXtZNKdEs3sX2dE0Y0qUvmHNL4yrc1qRoeS5eu5NVX/83bC55jUdgnSCR+fOjQ09yXxBDHaPTSjMLpq917JRzxqwyY1xrHidCyBdPg56/ggeW0iLOyY1xerWMypcoYGYMLgXQ6kWtWoy1eBEsXE2cvZ8TwwYwYPpDLuj5OTGRwNIEvMhJpqUCI2j59FYTxf8aZfKG7iHCPh6Gff8t9y9cS1SaZqAnj0FXWc312bzT/PBCNo3L+zig0mhv9rDzvIDusOSwxZZEo8mjBIdSa1q3U0cNxDYnenvWeixKPwj/3RPHNUSvJhbsY9vggpv6rDGMd1z2Jiid+HM6Oz532/J4Kt62MWR36o3kDF5MHOEjAmyiPwOySRVGw6PV0H38RpqRE/hPbHH9UJCIyEqKiEFGRqBFh3BiTzZhmmxGKJMnTh2RPOjbjJ9hM85A15oXXLnLyztMOlsxf/Yv6/nvknNTwEN0lNHRBNjlUve1ck5mZTWpqR5r5k7iy7C7267NxCQcW6eGYcRlrIn5CIIj39KWj80pU6s9GAoEFzOZCw3UiG/KSF2Hzx/Dgj2CJosgdPEfWVfh5SpbzJOHozEYYnoE6PIMPxaPE7tvDkiUr+Oijz5m8xk2PznDR4MCjZ5dAVmlwBAkfgEVq3OIpoXVpCecPuwLTwUO47Q6OmYwUzniNNl/Nwdy7O4+1LaF7uIc3DkRR7FMYG2fn3lalROo0Onjj+cGURRKHawsfgPCRa1p0UvGLNmj8uUsxf+5SDJjxDHwLdfck6t7pBX50tg2n+IYahgfYK2APAcE7SiC3URtgCGAAXJqG2+NBydmDN6UdouAY/pzdSJsNSsuQpaVIWxlv2mx8ExnOK688Q+shAwCIcl+FW7cLly4z4CcooGffBLK3rMXr9aLXn/7mGKIuksa24NFkLD+brZwrrrgZr9dLmzbJKIqCEAp+1UGpcReKCkIBoQgUoWAmhjitC4qiVD4EqqpWHieqtr+bG4Wt1AZZCwPCFx3IGjqgmZNvRx6qty+lUrAKAxYkg/BgqHlPlRr6gwNZue44C1fCwh+hzA6jB8HIES0YeXExMdG1f0RSGnHIxRyZ/h+K33gPWadIhiGlNe03LEacJg3JAvMGFMOnlZlaaqNKAyNtzzTgTIPwFhOxbjBCBvsMeiPTsXf/T4PaOYHD4WTDhi2sWrWOlSvXsX17Fq3Cw4gvKqaVz08ynPQ2ZYyM4Nr9m+mjj6hVowVAJyXXaW6GfLuQKVOeZNSoYUyf/jAREYGhslvZh0fdi06Lx12cTLe0oWzZspS4uNPPGf+RODeWX5qETxu4d+qvYvn94cVPSslXX33H448/w9ChA8jIGIzBoEfTJJqmkaf/AZvIQ9MkUqPqL35BG8dYFL8RKTU0Tas6xu/3Vz3Ptal8tj8MX9+JENcOQSCx5vwRh+gde3aB8HrnN1hsjyIq837nHoCFK/UsWNudn9ZtJS1VMnqkxoUjoGd3Ez5xO14ms6vHMLwHqgXXRsA6ytWp2C6/mPHXXc7Qoeef9H01NJZGTMevBCdRjfS1Qr98MF6vj7AwC2FhVqxWK1arBYvFHCSs1jXXoXNvQNQYW0iXEe/i/ugXrQevF9/AQTj/8iRacnKtY51OV5DYde3amUGD0hk06DzOO683JoOeZQ/9leyPv0TR6fDa60+xb01ozm07V/OsauI9xYizsp+KlFiBRd4yWiKx2cqZNm0GS5b8yEsvPc3IkUNrtTNt2vOUlJTy6qvPnvT8/VE5N+LXVcJHDdy7R0j8AJYs+ZEVK9bQq1c3evZMo23bVqe1YE6Ql3eQhx6aTn7+YWbOfKre8KR14X/Hrh4J2q5KE70q7iPCX3+SgZqsLzTxQmY0u2wGesS4eLhbCV2jzi707AQ69xqMFa+j+vPx6dNwhd2Ppu+E02ln7Zq3WLrkGxYtKcZWppKRkcGIEUMoeOJZsgsKyQVyCfj8pQAdFIWuU+7ig8/m0759O6ZPf7hWIteaHNJvItPyOZqoHrIqUo/r615Mve0F2rVrg93uoKKiArvdgd3uwO32YLWasVqthIVZMfp96PIPYo2ChDYQEwFWA4StF4R7BeGaRpiAMFVgDbOivPoaDkVh9er1rFq1jp9/ziI1tVMtsQsLqyf0DXDbynEcK2TlkzPIW7qyah4QQGcy0ef+2+n/6GQkMFcx8KZipEQI+ms+HvO7aFtniL98+Wruv//PDBzYj2eeeZzo6CgOHDjEsGHjWL16AYmJ8b/oe/09cm7EL1VCQy3+PiHxA5gx4zUWLVpOUlI8W7bsoKLCTs+eaVWPXr3SaNWqZS1B9Hq9vP76u7z++jvce+8t3HffLRjq8dkDyDbP5bBhLYjaF4Ei9Qyy/Q0d9QfcNxb27z/A4sU/MmfOx4iSUs4rOE6Kz0d7IBEQioK5Zxrtln6Ox+Nh9uyPePHFN7jwwgt47LH7SUpKAEC3aRmmD19EPZpH/uhUtt/cFYfVS5iWQAf7aC4fMoUpU+7isssuCuqDz+fDbndSXl7B4kefZvd3P+Dw+/ERmJ9Law5hfih3QYWsfGhQLqFCUSiLT0RNTKR//z4MHpx+SrE7Ga6SUuaNu5HSPftBCKTfT/LQ8xn7/mv1+mueiooKO08//SLz5y/iH/+Yxvz5i0hOTuKJJx48o3b+KJwb8esi4b0G7t0/JH4Ac+d+wQ8/rGLWrBcBKCwsYuvWHWzdup0tW3awdesOXC43vXql0aNHGikprfnnP2eTlJTACy9Mo02bU1tuTuU468Nn4MdTVdxHkQZauofQ3nXp//zznSv+859P+XH5Gp4oK8e+ej1oEqFTUcLDaLvwIwytq4eWNlsZL730FnPmfMItt1zLlIEdSPz344gaPnrSYMIx+QW8/S/ks8/m8+ab77F48WentLr3fb+Mhbc+EDQE1Qt4qCXo3fXX93WPH4/zuV/uQCylpGDTNmz7DxKX1pnYzh1+UXtr1mxg8uTHKSsrZ9OmJYSHh53+oD8g50b8OstAIpaGMCgkfgCrV6/n6adn8t13c0+6z9GjxyoFcQdZWTmMHz+WceMuavDw2K4cJdf0NTbdHvTSSiv3cJI85yMaVIq7cZCdvZvrrruLzZuX4ty6A+embeiTEggbMQRxktXJgwcP8+yzL7Psy6/4SyuNO1qAvobfrz8+meMzF5KePprXX3+OQYPST9mHhbc+yK55C4K2GxW4vBl0kMHiJ00mnFOm4rnxpjP8xL8OTqeLY8cKad06+fQ7/0E5N+LXUcK/Grj3yJCrC0CrVi05cCD/lPskJDRn9OgMRo/OOKv3sGoJ9HDccVbHNhY6dkyhqKiE48eLiOuZhrln2mmPSU5O4o03nmffvi95ZDe8chCeaw/jmwViYJWCg7w3ey4dO6acVvgAUE5+fUiTDmmygt2J8ATmQ6WiIE0mvOPGn/S43xqz2dSkhe/c0ficnBt9eFtSUjxFRcW43aEU4qdCURS6devCli07zvjYHq2as6gXvN4Jpu+DwZvgJxvYwuJ4ceYbPPnkQw1qJ3XCeHSW4DlSKQQJU6ZQ9v1SPBdfgjQakYqC7/zzqfj0M2RkcKx1iD8ijSu8rdGLn6qqJCUlcPBg/QkFQgTIzd3Hzp276dKl/lXcU+G6ejLSaGZULGw+D25vAdfsEJy3XjJ8+BDS0jo3qJ1WGYPocu14dGYTil6PzmxGZzZx0dy3kZNuh8hInH9/DtvPO7BlZWN/ZzZaq9Zn3N8Qv0caX2KDRj/sXbVqHTZbOT5fqEbqyZBSMnXqNKZMuYuWLRPP+HjP8KvA58H08asojjJu6BjOxf93N+8W6rn44lENbkcIQcYL0+l+83XkLf0RfZiVDpeNxhwbU9/OZ9zPEL9nGt+wt9GKn6ZpvPLKv3nrrfd5552X6PwLV+7+yHz88VeUlJRy552Tzq4BIfCMnojnwuvB5QCTBVUIbj/L/sR17URczRopIUL8r2pX/gIapfiVltq4++6HKSoqZsmSz8/KmmkqFBeXMG3aDD766C10ul/4dQoB5jPzrwsRomGEkpmelm3bMhk2bBxt2iSzYMGHIeE7DdOmPc+4cRfRu3f337orIUKcgtCc30mRUvL++x/zt7/N5IUXpjNuXHAkQYjarF69nh9+WMlPPy38rbvym+L1etm9ex9ZWTlkZe0iPb03F154wW/drRC1OHfDXiHEaOAVAqku35ZSnlWOtEYhfg6Hk6lTp7F16w4WLpxLhw7tfusu/S44cqQAh8PF3Xc/zE03TSAjYxCqWn/92T8CUkry849UidyJv3v37qdlyyRSUzuSktKWe+55hBUrvgqNGhoV52bBQwihAv8ERgL5wAYhxNdSyqwzbuu3jvDIzd3HjTf+ibS0zsyc+RRW66mLZoeoTUWFnXnzvuG99+Zy/HgxkyZdzcSJV5KQ0Py37tovwmYrIzOztsjt3LkLs9lEampHUlM7Vf3t1Kk9ZnN1Kc9//ON1Nm7cxty5sxoc5RPi5JybCI+WEiY3cO9HThrhIYQYAPxVSnlh5evHAKSUfz/TPv2mlt/XX3/H1KnTeOyx+7n55mtDP9SzICzMyqRJVzNp0tVs25bJ7NkfMWDAGAYPTuemmyYwbNhAFKXRTe1W4Xa72bVrby2Ry8rKoaysnM6d21eJ3PjxY0hN7UhsfW4zdbj//jsYNmw8n3++gCuvvORX+BQhTs85W/BoQSBx9wnygQaEHwVzxpbf2bxJiBAhftfkSSnb/JIGhBDfAXEN3N0EuGq8niWlnFXZzlXAhVLK2ypf3wCcJ6X805n26Ywsv19q+oYIEaJpIqUcfY6aygdqBlu3BM4q/KvxjodChAgRIpgNQAchRFshhAGYAHx9Ng01itXeECFChGgIUkqfEOI+4HsCri7vSikzz6atM5rzCxEiRIg/CqFhb4gQIZokIfELESJEkyQkfiFChGiShMQvRIgQTZKQ+IUIEaJJEhK/ECFCNElC4hciRIgmSUj8QoQI0ST5f0bcEnGlyX4zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n",
    "I = ax.scatter(lons, lats, c=mses, cmap='jet', vmin=0, vmax=15)\n",
    "ax.add_feature(cfeature.LAND)\n",
    "ax.add_feature(cfeature.OCEAN)\n",
    "ax.add_feature(cfeature.COASTLINE)\n",
    "ax.add_feature(cfeature.BORDERS, linestyle='-')\n",
    "ax.add_feature(cfeature.LAKES, alpha=0.5)\n",
    "fig.colorbar(I)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": false,
   "skip_h1_title": true,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}