content/notebooks/2017-06-30-pdf-scraping.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "name = '2017-06-30-pdf-scraping'\n",
    "title = 'Extracting data from PDF files'\n",
    "tags = 'pdf, text processing, pandas'\n",
    "author = 'Denis Sergeev'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nb_tools import connect_notebook_to_post\n",
    "from IPython.core.display import HTML, Image\n",
    "\n",
    "html = connect_notebook_to_post(name, title, tags, author)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Some organisations still release their data in PDF format\n",
    "* PDF was not designed as a data format. It was designed as an \"electronic paper\" format.\n",
    "* Main purpose: presenting elements exactly how creator want them to be, independent of operating system or time.\n",
    "* PDF documents are not aware what tabular data, or even words are.\n",
    "* Even when it's made using Microsoft Excel, the PDF document doesn't retain any sense of the \"cells\" that once contained the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**All these features make it very hard to extract text and especially tables from PDF documents**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One possible solution: store data as [attachments](http://blogs.adobe.com/insidepdf/2010/11/pdf-file-attachments.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Otherwise, you have to find a way to parse PDFs programmatically, especially if there are more than just a few of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Today we had a brief look at some of the most active/developed Python libraries that allow PDF text mining."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below are the two PDF documents that will be used in this notebook:\n",
    "1. [Sea Surface Temperature and Altimeter Synergy for Improved Forecasting of Polar Lows (STARS) manual](http://polarlow.met.no/documents/stars-dat_user-manual_p2d-4_v3p1.pdf)\n",
    "1. [Moore, G. W. K., D. H. Bromwich, A. B. Wilson, I, Renfrew, and L. Bai, 2016: Arctic System Reanalysis improvements in topographically-forced winds near Greenland. Q. J. R. Meteorol. Soc., 142, 2033-2045, doi: 10.1002/qj.2798](http://polarmet.osu.edu/PMG_publications/moore_bromwich_qjrms_2016.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define paths to these files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stars_file = '../pdfs/stars-dat_user-manual_p2d-4_v3p1.pdf'\n",
    "moore2016_file = '../pdfs/moore_bromwich_qjrms_2016.pdf'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [PDFMiner](https://github.com/euske/pdfminer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> PDFMiner is a tool for extracting information from PDF documents. Unlike other PDF-related tools, it focuses entirely on getting and analyzing text data. PDFMiner allows one to obtain the exact location of text in a page, as well as other information such as fonts or lines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This package is not extensively documented, but following [basic examples](https://euske.github.io/pdfminer/programming.html), you can start extracting useful information from a PDF file. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple example: extract table of contents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pdfminer.pdfparser import PDFParser\n",
    "from pdfminer.pdfdocument import PDFDocument"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Open the first PDF document in as a file stream in binary mode. Then we pass the file stream object to create a `PDFParser` instance and create a `PDFDocument` accordingly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fp = open(stars_file, mode='rb')\n",
    "parser = PDFParser(fp)\n",
    "document = PDFDocument(parser)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, if a document has a built-in table of contents, we can extract it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pdfminer.pdfdocument import PDFNoOutlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    outlines = document.get_outlines()\n",
    "except PDFNoOutlines:\n",
    "    raise Exception('No outlines found!')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result, `outlines`, is a generator object which contains section titles and their levels, as well as other technical info."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1. Introduction\n",
      "2 1.1 Purpose of this document\n",
      "2 1.2 Background\n",
      "2 1.3 Structure of this documentation\n",
      "2 1.4 Glossary\n",
      "2 1.5 Reference Documents\n",
      "1 2. STARS-DAT Data Model\n",
      "2 2.1 Format\n",
      "2 2.2 Organization\n",
      "2 2.3 Content\n"
     ]
    }
   ],
   "source": [
    "for i, (level, title, *_) in enumerate(outlines):\n",
    "    if i < 10:\n",
    "        # Print only first 10 lines\n",
    "        print (level, title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fp.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another example of using `pdfminer` package is taken from [this gist](https://gist.github.com/jmcarp/7105045)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter#process_pdf\n",
    "from pdfminer.pdfpage import PDFPage\n",
    "from pdfminer.converter import TextConverter\n",
    "from pdfminer.layout import LAParams\n",
    "\n",
    "from io import StringIO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def pdf_to_text(pdfname, codec='utf-8'):\n",
    "    \"\"\"\n",
    "    Extract text from a PDF document\n",
    "    \"\"\"\n",
    "    # PDFMiner boilerplate\n",
    "    rsrcmgr = PDFResourceManager()\n",
    "    sio = StringIO()\n",
    "    laparams = LAParams()\n",
    "    device = TextConverter(rsrcmgr, sio, codec=codec, laparams=laparams)\n",
    "    interpreter = PDFPageInterpreter(rsrcmgr, device)\n",
    "\n",
    "    # Extract text\n",
    "    with open(pdfname, mode='rb') as fp:\n",
    "        for page in PDFPage.get_pages(fp):\n",
    "            interpreter.process_page(page)\n",
    "\n",
    "    # Get text from StringIO\n",
    "    text = sio.getvalue()\n",
    "\n",
    "    # Cleanup\n",
    "    device.close()\n",
    "    sio.close()\n",
    "\n",
    "    return text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's apply it to the same PDF document:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mytext = pdf_to_text(stars_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And print only an excerpt from the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "duct is \n",
      "included in the sea ice files. More details about the sea ice concentration product are given in \n",
      "the OSI SAF Sea Ice Product Manual [RD-10]. The file name convention for these files are \n",
      "listed in Table 4.\n",
      "\n",
      "5.6   Satellite radiometer imagery\n",
      "\n",
      "The NOAA AVHRR satellite radiometer imagery data files in STARS-DAT are from the local \n",
      "receiving station at met.no. These files are only used for tracking polar low events. There is \n",
      "one file for each satellite passage, and the files are multi-layered TIFF files, one layer for \n",
      "each channel. The AVHRR data have two fixed visible channels (0.6 and 0.9 um) and two \n",
      "fixed infrared channels (10.5 and 11.5um). There is one “flexible” channel that depends from \n",
      "the different missions. This channel is either 1.6um or 3.7um, and for some satellites there is \n",
      "a switch between the two channels so that 1.6um is used at daytime and 3.7um at nighttime.\n",
      "\n",
      "The file name convention for these files are listed in Table 4.\n",
      "\n",
      "5.7   In situ data\n",
      "\n",
      "The availabl\n"
     ]
    }
   ],
   "source": [
    "print(mytext[25000:26000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [PDFPlumber](https://github.com/jsvine/pdfplumber)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A package that builds on `PDFMiner` and enables extraction of not only text, but tables and curves from a PDF document, is PDFPlumber. Thanks to [PythonBytes](https://pythonbytes.fm) podcast for letting me know about it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a nutshell,\n",
    "<ul>\n",
    "<li>Plumb a PDF for detailed information about each char, rectangle, line, etc - and easily extract text and tables.</li>\n",
    "<li>Visual debugging with <code>.to_image()</code></li>\n",
    "<li>Extracting tables\n",
    "<ul>\n",
    "<li>pdfplumber's approach to table detection borrows heavily from Anssi Nurminen's master's thesis, and is inspired by Tabula. It works like this:</li>\n",
    "<li>For any given PDF page, find the lines that are (a) explicitly defined and/or (b) implied by the alignment of words on the page.</li>\n",
    "<li>Merge overlapping, or nearly-overlapping, lines.</li>\n",
    "<li>Find the intersections of all those lines.</li>\n",
    "<li>Find the most granular set of rectangles (i.e., cells) that use these intersections as their vertices.</li>\n",
    "<li>Group contiguous cells into tables.</li>\n",
    "<li>Check out the demonstrations section.</li>\n",
    "</ul></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pdfplumber"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doc = pdfplumber.open(stars_file)  # No fussing about with binary file streams!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select page nuber 36 from the document. It is a table containing polar low start and end locations and times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "p0 = doc.pages[35]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can display it as image - very useful in Jupyter Notebook!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAANKCAIAAAAhjVxYAACLfUlEQVR4nO29X2geV5rnf95EiWWS\njMTkwsZNHLWMV94x+PXNMvYsjLwXQ2hIs5ZhJwum2SuPQgJGEAYtLG0LlmbWzE0zkMXRy140xtDB\nkCjdFyu5m8VZbJzcqQPqjSxi5N5l8C64UbfajE0nU7+LE5/f0fOcc+pU1amqU3W+n6v3LZ06f751\nqo701qv6DLIsEwAAAEAyPNd2BwAAAIBGwcoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAg\nLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAgLbDy\nAQAASAusfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAgLbDyAQAA\nSAusfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASAus\nfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAOgDb7/99v79+1944YWD\nBw+ura3JjYO9yI1LS0uDwWBpaUntqwpMTk6eO3fOUSHfRZXPrVzvg/qR8XVRSDfOnDlTpTZSc5B6\noiMDAIDuMxgM5ufn7927Nzs7OzY2pv+IXOgOHDgwMTFx4MABXmBubk7t66hQ7TI3NyeEmJubc1Ru\n7APfWOVqLITQWzxw4ECoa3tf1wj8zQcA6APPP//8//t//+/o0aO3bt364x//aCu2trb26NGjnZ2d\nR48e8b/kTpw4MTY25l/hRx99NDc39/HHH/tU7sO5c+cGg8H+/ftPnjwpt7z99tsvvPDC/v37R6OR\n3DIYDI4cOUJ2PHjwoCwwGo0OHjyoatu/f//+/fvPnDkjdzx48KD+B6L6k069ILv0lraXXgAACMDq\n6upwOBwfH5+YmFhdXdV/pF/ohsPh7OxslmWzs7PD4VAVUKg/4BwVZpY/14yV8/KOSsbHx6enp+/d\nu6e2j42NLS8vX758Wf1VJ4RYXl4m9czPz09PT2dZNj09ffnyZVnbvXv3lpeXl5eX5d+sQojLly/L\nv1NJB9QLvgvvdg/o56gAAMly+fLl8fFxfYt++Sa31vQCy8vL/FNKvUL9rwWhfUCqXhsr533gG+/d\nuyebkEvOYDBQa5utt7yesbGx1dVVfcWSy+H4+Lh861jw1AvbLj0Dn3YCAPqA+l7Jd77zHVuZc+fO\nffe731WXv+9+97v6F0MuXLhw7Ngx9UEir1DuRSr8+OOP5eLnrtzI2NiYLHPhwoWJiQn54o9//OOJ\nEyfm5+dVN5aXl1dXV//5n//ZXdurr776H/7Dfzh8+LDa8sEHHwyHw8XFRfURrjGTt99+u9AufaDp\npRYAAGrg8uXL8mItPx7Uf6QudOPj4/qPLl++PDExke39y2ZiYkKWcVSYaeuf+nTUVjnpg478C09v\nQn45Ra92bm5O/impauNVCfZ1G7lFrqbD4VD+vSgsf7BOT0+rLbZdesa3YwMAAAASAZ92AgAASAus\nfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASAusfAAA\nANICKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANIC\nKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASIsernxLS0ttd6Fp\nEhyyPwjHAcKpA6QanOCR9nDlAwAAABxg5QMAAJAWWPkAAACkBVY+AAAAaZHcyre2tjbQeOGFF2z3\nTmXJF154oWjlhXYx7q538oUXXjh58mS5CgsxGo0mJydlowcPHhyNRrJLJ0+efPvtt3N3N5bUN45G\noyrhxACZPBKfXcioVSw9yMQ4bUTlmVOlP+ok6vrcCzXfqsDz7GiYlKx3XL582fHTubk5IcT09LQs\nKYQYGxvLLelJiV2Mu+v1DIdDIcRwOHTs6B6yJxMTE0KI1dVVPZbp6enBYHDv3r3c3Y0l/XevjyDh\nSEJNiRhikVQPxzhtssozpzothhxqyjV/CXLQ7qQNeBZLkvub71e/+pUQQi4n58+fF0J8/fXX586d\ne+GFFwaDweTkpPqlVZUcjUb79+8fDAbPPffcuXPnhBDqN1zy15jahVT43HPPTU5OCiHefvtt+Vvb\n0tLSYDAgf26q3fVOrq+vCyG2t7dri+Rbfve73wkhPv7446WlpSzL/vjHPw4Gg/v372dZ9i/+xb9Y\nWlpy5EBKygr1jf/m3/wbmYYQQpY/efKkjOXcuXMqH/n6hRdeUEchKvTjopAhnDx58oUXXjh48KDc\neObMGTmo//E//gfZRY9FZSI6GwufNmLvGP/tv/23jtOHzxz1h44MRP0tyE/St99+W2554YUXZDF5\nWsm/QY1zj1diPHyRYJxvwtRnx3zzzJOHKSx5OsI09i1Swi6kMeD+7WBsbEwIsbq6mj37c+qVV14R\nQgyHQ/lL68TEBCk5HA7n5ubkTweDgXwhhOANyV1OnDhBKpSoAlmWTUxM8N/LVIt6J7Msyz1SQX4h\nGh8flw0dOHBA/mYn81H9dORASqryaqP8VfTAgQNqOLOzs2p3+eI//+f/PBgM9JJBCPjbojwuOqrz\najhZls3Pz8sJMD09LTeqQ5lpsZCRNhyLpHo4fNpkew+9+/ThM0eOdDgcLi8vCyHGx8ezLJOxkJNU\n/hK5vLw8GAxkMiol49zjlRgPX3VCTTk+37Jnn1TpfXbPN888eZiZKU9HmMa+Bckhq+FvvrRWvtXV\nVX0aDQaD6elpfZlRR0uWlB/dzM/PT0xMyGITExO2zxPULrzC4XA4MTExOzsrLxNyfuizU99dbzrL\nMjlfbR/J5g7Zn9XV1QMHDqirWJZl8kSanZ2VBRw5kJISfaP6zFaepeoKqJ9CMpzMY6UvRKhzhhwX\niQxhMBioUWTPFoPV1VX5U7KLikX/HLv5WCTVw+HTJtt76N2nD585covsmBqy8SSVF+vx8XF1KqlI\njXOPV2I8fNUJdT4aT3zeZ5/5lpsnDzMz5ekI09i36jlI8GlnJT744AOhnXX//M///NVXX3399ddC\niDfeeEP+wS6PqCx5+PDhycnJDz74YGpq6tVXXxVCnDx58v79+8L0EYTahVc4OTn59OnT//k//+c/\n/MM/CCF+/OMfD4fDN954w7i7eiG3v//++0KI48eP15bKt7zxxhsPHz6Ul+D/+3//rxDi0aNHQogz\nZ84IIdw56CUV+saHDx8KIaanpz/77DMhxNmzZ1dWVuSL//2//7es/8mTJ+LZGRgh5LhIZAhnz57d\n2dmRL4QQciBvvPGG/CnZRcWiMhFCdDcWPm2ENsbc04fPnN/85jfyhfxoVJ6PtpN0YmLiyZMnb775\nptxFRWqce7wS4+GLBON8E6Yp555vnnnyMIUpT0eYxr6FTCQoaa18xs/N5TFbW1v74Q9/KIT41//6\nX+sl5W2MK1euyGM/Go3kLbfvfe97tsp5hWfOnHny5MmJEycuXLgghPj9739/48YN2+56J5eWln71\nq1+Nj4/z8mGRn85vbW3Jj+bl1xbk2CXuHPSSZBeJPDlPnDixubkpX2xsbAghDh48qE4qeXFXn5jV\nNdSyGCePCoHfiF1bW5OXCbKLikVlIoToaCzGaSO0MeaePmTmrK2tyUvqw4cP//t//+9CiP/0n/6T\nMJ2kBw8e/Ju/+ZuFhQUhxDfffCN3V5Ea5x6vxHH4Wsd2k8/WZ+N888zz+eef52EKU56OMB19i5Gw\nf0LGgOPvYnL/TLK8vCw/LhgbG5ubmyMl5a/k6pS+d++evBI5KucV3rt3T368nmWZEEK1Ytxd/3Bf\nfuae+32q6h8FzM3NqRs2ExMTssW5uTk52Pn5eXcOekm9TrVR7vXf/tt/ky/U/QBVz71795aXl+XY\nx8bGyAenVaj1posKgaQhhFB5kvmmYlEJqA/hm4xFUjEc47TJtDHKCeM4fcjMUdHJIavpxM+p+fl5\nWWYwGKhiqhU+92SSpBLj4atOkClnnG+Zqc+O+eaZpzHMzJSnI0xj30KB+3z5BM8ofhIcsj8Ix0Fs\n4RhvGHeOeFLtR54Z7vMBAHqMvCkl/90IVAd52qB/UAMAQFvIfwcEoUCeNvA3HwAAgLTAygcAACAt\nevhp5/3791NzIn/yySdtdyFeEI4DhFMHSDU4q6urYa/qPVz5pqenU1v5hBAJDtkfhOMA4dQBUo0c\nfNoJAAAgLbDyAQAASIseftrZLmtra+SBnK2ztrZ29+7d8+fPHz16VDz7HMb2lhRWkGKqZuNg1fZy\ntY1GI/mYt7W1NSHEG2+8oX92dPr0aVlsa2vr+vXrFT9WijAcUkwOUxVrMpyiGHsitK7Kt7JvjmJL\nS0vKWKTvqCDRGZN0HxRbV2OD95MPNjf23DwJqhiZe3oBvQ+kWDeyDfuP8THQ1gMU7t27J3UezTft\nGLJ8lPuBAwfGxsbu3bs3PT0t3w4GA/lsNv0tKawqIcUy+2D17aVrU6Ko4XCoqpqenp6YmJBWlOyZ\nAeDAgQOqcD/CIcXm5+dJscbCKcHwGePj4/qDssbGxvQ0ZPdkSdUBvZjQHEY8RhKdFKEcOHBgfHxc\nJZl7UGRbpA+hCFghidQ4bXJjd+dJUDvyuSeRzhm5fXV1lRerI1s8vSyftlY+dTI337T74q5eyKdp\nq7fyYX36W1JYVUKKZfbB6ttL18Yv7nKMeiXqOajD4XB5edk2/Kxr4ZBi6no3Ozsrh9lYOKVZXl5W\nnZGvyRjlcGZnZ9UTJkmxsbExOQRjjI6jNtQWNrXRPcfqIHiqKlLHtHHE7s6TVyLhc0+inoVmK1ZH\ntnh6Wbzs7OxEaOWQSnchxO7u7pUrV+SD2yWfffYZeUsKqx+RYsI+WH179dpsjEajffv2qVbkp38l\niDAcUuzJkyfSW/T5558TCZSNUOGUY2tr6+LFi1ItMhqN3n//fTVSxebm5s7Ojhzm1tYWL/bSSy/N\nzMzYxkuiO378+MmTJ0+ePPnw4UOVZO5Befz48clnRP49TD1S27Rxx+7OU0F2tM29W7dura2tHTly\nZGNj48qVK7xYJ7LFypcEJ0+e/I//8T++8cYb6qn5EvKWFJ6cnDx58qStmEIVczftbtSG0kzrXLhw\n4enTp/L12tqavC1RmqjCIQwGg4cPH66vr8/MzPA1rIFwinLhwoWZmRl5v2d+fn57e3tycnJjY2Nj\nY0P64YQQi4uL6+vralDGYjdu3Lhz546jIRWdFOKcOnXqyZMnd+/elT/NnWNjY2Nnn3H69OnK464R\nPVIJnza5sefmyXd0zL3p6em/+qu/klY/XqwT2WLl6zmj0ejgwYNXrlyRk/Lx48dbW1tCiN3d3bNn\nz5K3pPDCwsK7777L9yJNqGLuphXu2oQQv//972WB7e1tqa9z8NZbb3kFYSLCcAjPP/+8WrqOHTsm\nGgynHHfu3FEuyUuXLi0sLCwsLBw+fPjw4cNKGHTt2jVV/tixY8ZiR48e/f73v//FF1/wJkh0T58+\nXV9fv3r16tTU1K1bt2SZ3Dm2b9++pWdE+hWMZ+iR2qZNbuyOPBVkx+eee47MPcmRI0eEEFevXp2Z\nmfntb3/Lp2g3sg374WkMtKgIIbdbmmzX9iPd8iU/ixdCyM/97927R96SwqoSUkw1ahys2l66tuFw\nOBgMyNciSHPyPtxgMMgNvFvhkGLy6wbj4+PT09MNh1MC6Wzj29XtJXkHSMrexsfHSfdIsSzLeJnM\ndNRkbUoQODExkXtQ9Gtg8HM2YKokUj5tJiYmPGPPLHnaduRzjxw+qQ7lxerIFt9wySceOVZjJDhk\nfxCOA4RTB0g1OPiGCwAAAFAJrHwAAADSAisfAACAtMDKBwAAIC16+NzOR48eRfvvkzWxurradhfi\nBeE4QDh1gFSD8+mnn4atsIcr36uvvprayifgA3OCcBwgnDpAqmEJnic+7QQAAJAWWPkAAACkRQ8/\n7WwLt26tLVr3z/GPKarL+ULJ5yIMx1hnrqJPmKRoDSv6HJY4PQp3yMFNcnr9vIedMMmRoRlnjnFy\n6jLCcqnaZqnbj9iJVPEMlzAM7bq1BrANeRiBf04+4si/tlz/nL98rnPhGOv0UfSVTqk+k1xmisId\nchbaJMcPIulhTZa+gFWRoRlnDp+cJPlyqdpm6ZD5EeHni4K2Vj71IvhjAHNxXNzVi2Gz/rnBYKD2\nmpub868t1z/nL59T+xq3RxiOsU5PRV9WKqX6THKZKQp3yFlokxyvn/ewymBtBEyVDM04c/jkJMmX\nS9U2S0lzxtYrjNgMnl4WKQ7dWou06J9TijghxP3790vUZiSgfC7CcIx1dkjRp1vihCkKd8gitEmO\n1096GL9JjgzNOHP45CTJl0vVNktJc7z1+FMVuM8XllzdWiuU9s9NTU2tr6/n+udkMbJ9ZmZm//79\n+/bte/r06Z//+Z+7G7XB/XMXLly4ePGifO1508JNVOEYm5b+M9n6hQsXlIhHYlP0hU3JE26SI/iE\nfOPGjT/7sz87fvy4o6H5+fk/+ZM/mZycfPz4sbBHxOsnPZQmOfk6TpMcGZpj5rgvPiVSffnllx2z\nlDSnv40/VYHvdobCU7fWMC3653Z3dxcXFz/88MNDhw6dP39ebQ8o56son4swHAmps0OKPt0SZ8Qd\nsiwT0CTH6yc9jN8kR46+ceb4XHxKpLp//37jLCXN8dbjT1UIfMMlEG7dWt3Yhtyif07tRYpVl/P5\ny+c6F46xTk9FX7mU6jPJ8S5xbR5PL6xJjidJeqhfCQOetmFT1YfGZ87ExIRxcpLk5YtCqRrbytiJ\nw1uvI1V8wyWfBOVYCQ7ZH4TjAOHUAVINDr7hAgAAAFQCKx8AAIC0wMoHAAAgLXr4Xw1fffVVtP9E\nUhPyP36AEYTjAOHUAVINzurqatireg9XviNHjqS28glYUZwgHAcIpw6QauTg004AAABpgZUPAABA\nWmDlAwAAkBY9vM/XFnH6+USzFjq3uIt3THfLOfxzRuNXdf9ctMmQSkjhzvn5HJ10pOpWynnm6TYd\nxm+SK5Hq3bt3yS7+fj49RpkVb1rsdQTyYvGnKgSeXhaI2dlZJamSprEmcQx52KCFbpgn7lLou/PK\ncxV9WZb5W/pi8PP5J8Pz4YW74ufLNNmbsYwjB+FUyvE85WD1Z8hlHqbD+P185VIlu7jD1NvS85FT\niIcz3Ovt48Xg52uHtlY++cI9t2rCvfKpF8OaLXT8VDTWRnY3Vs53JA/D9Lf0xeDn809GolfCC3fF\nz6fL3oxlHDm4lXKOY+c4Og7TYVjaTZW/9fTz8Vk6Ozs7OztLfpUn3j5eDH6+hLh169ba2tqRI0c2\nNjbi8fOJZi10ueIuhb47rzyXIP65OJOR6JWUUD/G4OcjsjdjGcfQ3Eo5suPx48elDe7hw4eOo8M1\nfp0wyRVNlb/19PORVDc3N3d2duRUlMoLCfH28WKdSBUrXzCmp6f/6q/+amxs7IMPPmi7L5TSFrqT\nJ0/aiilUMbK78S3HXbnNP/f06VP5em1tTWlcShBzMsamyfa68ymEbr+bn5/f3t6enJzc2NjY2Ng4\nd+4cLyOxDe3GjRt37txxNKd23N7eFkKcOnXqyZMn+l0uo+lwfX19ZmZG/jYgTXKSaE1yJVLlIeeG\nqVCpLi4urq+v63FJpCNwcnJyc3NzcnKSF+tEqvi0MwzT09PyzoH827/h1h1DXl5ePnDggPogQt3h\nmJ6evnz5MnlLCsstfC/1U/nxiCpGdidveZ+Hzz7tJJUPBgN1Q4s3J1Gf5k1MTLhvrNrCiTYZUgkv\nbMwns3za6c4n7Mmi3667/Izp6enp6Wmec+bMQX5iNjc3ZxQtkR31j3b1s48fU7nLkH2uHpZ2U+Vv\n3WEqSKq6FUvdYsyehSnvhS8vL/Ninfi0EytfGOSskjd+jd9ZqBXHkJu00OWKu0ifh3tldaryYZ6i\nLyti6bOFE20ypBJe2JgPaTcSP1+29/YkKePIwa2U48dOnn0TExP6l4ByTYf63wC5E8mfdlPlu3j6\n+Uiq6pqmdiGpyu28WB2pYuXLp5WVr10SHLI/CMcBwqkDpBocfMMFAAAAqARWPgAAAGmBlQ8AAEBa\n9PDpZbu7u9H+E0lN/OIXv2i7C/GCcBwgnDpAqsG5fft22Ap7uPK98sorqa18Aj4wJwjHAcKpA6Qa\nluB54tNOAAAAaYGVDwAAQFpg5QMAAJAWPbzP1y65BqyGgYIO4ZQLpyi8A7ZkHDn4m+RUbbqskedp\nzNntfYwNMjTdjacgG/lMDpuq7ciK7qSKZ7iEhHizGsMh4mlLQZcrLGxdQdeVcHjhTvj5eDKZRw7C\nzySX7Y2Ii/dUc9zbp5+k8fv5yNCGe914sgzZyJMPm6rxyNadKp5elk9bK5/Nm9UAjou7ejFsVkGX\nKyxsXUHXlXB44U74+XgymUcOnia5bG9EXLwn4d4+cpLG/8RqMjTixpOvyUaefH2pyvobSBVPL4sU\nmzerXVpU0OUKC1tX0HUlnBLqxxj8fEbtYm4OniY5sTciLt6TEG8fP0njN8mRoRE3nnxBNvLkw6ZK\n6u9iqgLfcAmFzZsVA20p6IoKC1tR0HUiHEfhaP18jmTcOfib5BRcvCch3j5+ksZvkiNDI248WYZs\nNCYfMFVSfxdTFVj5QnHp0qWFhYWFhYXDhw8fPnx4fn6+7R4JIcRoNDp48OCVK1fkxH38+LGUJu/u\n7p49e5a8JYUXFhbeffddvhdpQhUjux85ckQIcfXq1ZmZmd/+9reF+imE+P3vfy8b3d7ePnHihHv3\nt956q1gupkajDYcXbiCccty5c0d5wI3J+ORw9OjR73//+1988YV/u88//7xa3Y8dO6a2P336dH19\n/erVq1NTU7du3eIn6b59+5aeEecXMcjQdnd3FxcXP/zww0OHDp0/f15uJxuNyQdMldTfxVSFwDdc\nQpP7SXodRKig8xEWtqug60o4vHAD4ZSAaOF4MhMTE7k5eJrkVOfV3Tsi3lMmOeLtk6iTVL8SBjxt\nw6aqD4248bIsm5iYIBuNycvCoVLlcz6rOVV8wyWfBOVYCQ7ZH4TjAOHUAVINDr7hAgAAAFQCKx8A\nAIC0wMoHAAAgLXr49LIHDx5E+08kNfGzn/2s7S7EC8JxgHDqAKkG5+bNm2Gv6j1c+V5//fXUVj4B\nH5gThOMA4dQBUo0cfNoJAAAgLbDyAQAASAusfAAAANKih/f5WkGKrOTrqJRULSrobHIvY51J+fly\nBWYxhFMU3gFjYsah6WK5ciY59ZbsReo3euz0PhcYcCPYDqvbeqg2imfCvEKp2q5mjjBVE6PRSAgh\n56eaqDES9h/jY6CVByhIHZp8fk/zHYhQQWeTexnrbEVB12I4SukiRTDucIZMMtcJP58xMVuq6ull\nopRJTsLtmKR+3lz8fj6SqvG0Muoe9TT8U5UYr2buMMfHx2XT4+PjSmOkXlQHTy/Lp5WVTz74cXZ2\n1igarZsIFXQ2uZexzlYUdC2GI18rlR3B0ZPGwimNGpRxyGRo3DZXziSXWeyYpH7eXPx+PolK1Xha\n8dOHpOGfqqqHX83cYapI1VNSL1++rB74WR08vSxSNjc3d3Z2pN1KPsg8BlpU0NnkXsY6W1HQtRiO\n2KuyI5A6iWTOZ2gx+PmMiZGhcdtcOZOczY5J6ufNdcIkp6dqPK3I6cPT8E9VYryaucM8derU/fv3\nl5aWDh06NDU1df369ZWVleFwWH7YNYOVLwyLi4vr6+vcZRUDrSjobHIvW9NtKeja8vPpKjsCqZNI\n5kjhaP18EuJcJEMz2uZKmORsdkxSP2+uEyY5PVXbaaWfPsY0CqVqvJq5w3zvvfc2Nzdv3br12muv\nnTp1amVlZXt7OxJZmxGsfGG4du2aeq27rNqlRQWdTe4lIXW2oqBrMRyxV2XnDodI5poJpxz6oPiQ\nBRua0TZXwiRns2OS+nlznTDJ6akaTyty+hjTKJSq8WrmDvPo0aP79u378ssvz5w5Mzc3t729/fjx\n42gjFQLfcAmEMo35fIwenAgVdFzupUPqbEVB1244usrOHQ6XzDUQTgnIoHhiukmOhKw6Wc4kpyC3\nQm3iOrWXfiUMeObWl6rRmWfTPfIbw54XKH41c1gA9Ru6akEZDAYBb/Jl+IaLDwnKsRIcsj8IxwHC\nqQOkGhx8wwUAAACoBFY+AAAAaYGVDwAAQFr08Oll8PMBHYTjAOHUAVINDvx8+cDPBwgIxwHCqQOk\nGjn4tBMAAEBaYOUDAACQFlj5AAAApEUP7/O1hVtH1xZ1KOhsxVSL8qlFxOZFcHvauuvnCxIOKUym\nVlf8fGoUQnPCkXz4IfA0yek72nxyvEvGQ8x3jAFP3yEfOx9jUeWhQ/Xn3yjpfPEAaibsP8bHQFt+\nPoeOrm6aVNDZimV7lWlD5kLTyfW0ddTPFyQcUphPrU74+eRG4swbMk8hUQ8KP5McMdLZ7JikS1ym\nGLOfj3TeKOHLmEuPj9EzUkmu6s+z0eDB4ull+bSy8rl1dHXTpILOVizbq0zjLjSdXE9bR/18QcIh\nhfnU6oSfLzM58xyZD4uY5IiRbui0Y6ouGWWKZQfqIuxzO4fPnpMpt5BkyNj5GAvJ+XJVf56NBg8W\nTy+LFLeOri3qUNDZiom9yjTuQtPJ9bTlEqefL0g4pHCJqRWDn8/ozCP5cPWgp0mOGOkcdky9S1ym\nGLmfT++8zWFJxs7HWEjOl6v682w08mAFvuESCreOrl3qUNDxYgSjes3WtLtw5/x8FcMhuKdWtH4+\nmzNPaPkY1YOeJjndSOewY+pd4gcrcj8f8R0aHZZk7MYJWUjOl6v682k08mAFVr5QuHV0bVGHgs5W\njGBUrylyPW0d9fMFCYfAp1Yn/HxGSxzJh6sHhZ9JjhjpHHZMvUtcphi5n0/vPHdYSsjYjcJIfzmf\nj+rPp9HIgxUC33AJhFtHVzdNKuhsxVRP1G0JsVffpZPraRt2088XJBxSmE+tBsIpgU06qG4RTUxM\n8MyJetDTJEeMdEafHO8SlynqV8Lce2D+BEmVdJ5L+OQYydj5GAvJ+TxVf7mNBg8W33DJJ0E5VoJD\n9gfhOEA4dYBUg4NvuAAAAACVwMoHAAAgLbDyAQAASIsePr3sD3/4Q5z/QVIfv/zlL9vuQrwgHAcI\npw6QanBu374dtsIernwvv/xyaiufgA/MCcJxgHDqAKmGJXie+LQTAABAWmDlAwAAkBZY+QAAAKRF\nD+/ztUK0PqomFXRu9RrB3ZP++fmqhEMKd9rPR4ZGypTz8/G3tmId0sjZDqh7jEYlYRA/H9mRFCN9\niznYbwn7j/Ex0MoDFJTIQ5eTNUYMCrohU685hIXunmS98/NVCWfIJHbd9fPxA03KiFJ+PkfsenSd\n9vPZDKB8apGj4BmphPv5dFWkrVHeN/j5WqDFRwfpcrImiUFBR7bbihnrdPi9yO4d9fOFCmeoPTuR\n7x6/n48faFKmtJ9Pf6uKkeg67eezGUCNY9ePQkU/n66KtDXqUEiGAitfPm2tfPfu3VO/lTdM7pCn\np6dXV1fVn/hyvpK3pLB661nMtt1YLLcnuRf35eVlz7Or6+EoDhw44PiDOKsnnELop4C6+OqdJEPj\nZeR1dnZ2NvcyLf8uJ4JykieJjh+smP/my/bmKYSQf3vZLjJq7ORC5B+phAer/0ZlbJT3Lf6/+fAN\nl2AQmVY8NKmgU9uNb21NG5tQ9MbPVy4co8RO0S0/HxmasUwJP5/cwvMk0XVOI6fn6dY06mPnF6LS\nfj53SdUo71vkwQqB+3zhMN6zaQbbkJeXl9UfCpnWw+np6cuXL5O3pLDcwveyFSPbyVuCuydZlg0G\nA3VPSx+d8QO9iYkJW0M9CEf/3FJ+GNVAOOXQT4HLz5ienlZ/jpCh8TK6BMfxB8r09LSsZ/jsEzZj\nniQ6Ps0i/7RTz1P9ETZ89lGthI+dXIg8I5WQYOVG/jcfP3dI3/BpZwu0svLZ5GTNYBtykwo6st1t\nqnP3JOudn69iOERi10A4JfDx8/EDzcvILYX8fDxPWQ+JrtN+Pq5plGMkY+dHobqfT59XtkZJ34IH\ni5Uvnxa/4dIWCQ7ZH4TjAOHUAVINDu7zAQAAAJXAygcAACAtsPIBAABIix4+vezBgwepKUJ+/vOf\nt92FeEE4DhBOHSDV4Ny8eTPsVb2HK9/rr7+e2son4ANzgnAcIJw6QKqRg087AQAApAVWPgAAAGmB\nlQ8AAEBa9PA+X1u4jWtt0RUFHfx8/nV2ws83PT19/fp1vT/GTvLMy/n5jNo/mYC7OdKfsqMPj6ef\nj4+RJ1PIzydRQj5ZG59Fbo1fzMF+S9h/jI+BVh6gMGQStSZxiHg6oaCDn88RDp9anfDzyQ6Qp/WT\nMvy4i1J+PqP2L8sy0ode+vnIGPnU8oxUoQv5VG36w8wyNp8zpvGL39WAlS8MXKLWJI6Lu3ox7IiC\nDn6+fvj5hsPh7Ozs7Owsf5C0KsOPezk/H69HvdX70Fc/nz5GPrUK+fmyvUK+wWCgapubm9MbJa0Q\njV/8T6zGfb4wHD9+/OTJkydPnnz48OGVK1fa7s63rK+vyxe7u7tXrlz5+uuv1Y8+++wz8pYUVj/y\nLEa224oZ6yRvfYY2Go327dunmubelly6Ek6JqVU9nHJsbW1dvHjxxo0bQojNzc2dnZ2dnZ2zZ89u\nbW0Zy/Dj/tJLL83MzJw5c8bd0K1bt9bW1o4cObKxscGPnXpN+sCLPX78+OQzIvw/BD2rJ0+erKys\nCCE+//xzPR8yRj61PCNVyKrkazWLhBD3799Xr3kr+l4i+mAFvuESCrdErV3iV9DBz+douqN+vsXF\nxfX1de6T08sY0yvh57PNH9KHXvr5jDmTqVXIz6czMzOzf//+ycnJzc3NyclJ8lPbBBbRBysE7vMF\ngkvUmqTrCjr4+RzhdNTPp4Q15BNI4pwzCvOK+vl4PcY+9NLPR8bIp1YhP5/qvywpg5J3UvWPWI0T\nmMuMAoL7fPm0svJxiVqT9EBBJ+Dns4fTRT+fMr3pV0NShh/3cn4+Wz2kD73085Ex2lSFmZ+fT/Wf\nJEaWNOME1qdf8GCx8uWToBwrwSH7g3AcIJw6QKrBwTdcAAAAgEpg5QMAAJAWWPkAAACkRQ+fXra7\nuxvnf5DUx82bN9vuQrwgHAcIpw6QanBu374dtsIernyvvPJKaiufgA/MCcJxgHDqAKmGJXie+LQT\nAABAWmDlAwAAkBZY+QAAAKRFD+/ztYVNZNUu0Sro1C660CsdP5/aKEzKtC76+STEIWcctY82z18p\nx2sTfiJAUqDiwOuAhGn08ylsp1JFP5/xrWCXu+7FG/Yf42OgLT+fUWTVDBEq6IhBjfdNF3ql5ueT\nG+XzqPiznXLrjNPPl2UZccgNLdLKXG2eKKKUKycCrMnMJwlSIQnT5ufL8k6lQmFmzLRH3krI5a6B\nePH0snxaWflsIqtmiFBBRwxqvG+60Cs1P1/2zLtmDCe3zmj9fMQhZ5NWDvO0eYWUcsNSIsCanlUt\nCZIqCdPm58vyTqUqfj7+VkIudw3Ei6eXRYpNZNUuLSroiEGN900XeqXm5xuNRu+//77aSPCs00Fb\nfj7ikLOZBXO1eYWUcuVEgPEL5EiYNj+fyDuVqvj5+FsJudx1MV6sfGFwi6zapS0FnW5Qc/cwNT/f\n/Pz89vb25OTkxsbGxsbGuXPn/Jsm26Py8xGHnM0smKvNE0WUcuVEgPEL5EiYNj8foWKYnpDLXRfj\nxcoXht3d3cXFxQ8//PDQoUPnz59vuzvfMhqNDh48eOXKFXmqPH78WP5SvLu7e/bsWfKWFF5YWHj3\n3Xf5XrZiZPuRI0eEEFevXp2Zmfntb3/r7idpQgjx+9//Xm7Z3t4+ceKEe/e33nqrW+FcunRpYWFh\nYWHh8OHDhw8fnp+fd6RB9m0mnHI8//zzapU9duzY06dP19fXr169OjU1devWLVXs2rVr6vWxY8f4\n0RdCHD169Pvf//4XX3yR2yipTb2+c+eO/INPmCbYvn37lp4R4/cvWJjkrW2vimF6Qi53XYwX9/nC\nwEVWTRKhgo4Y1GzdHlrEacNe+/nUduOtl9w6GwinHMQhx82Cntq8Qkq5ciJA/RoY/JwN5efTw7T5\n+VSLQcIktfG3sjZyuWsgXnzDJZ8E5VgJDtkfhOMA4dQBUg0OvuECAAAAVAIrHwAAgLTAygcAACAt\nevj0svv378f5HyT18cknn7TdhXhBOA4QTh0g1eCsrq6Gvar3cOWbnp5ObeUT8IE5QTgOEE4dINXI\nwaedAAAA0gIrHwAAgLTo4aedrWBThLROkyIe49i534R0T/60FRFPi+G4FU7unkRrKfJRFOVmXkip\n43neOQw+qkyEMh1jnrYDWoelyGiAkpBWSFcjD1YIPMOlMm5FSDM0KeKx6Yf42I1+E4X+U950Ry1F\nnuHYajMWbkXhVAIfRRHZWEWp43neuYvV5Cqqw1I0tHvQ6rMUGQ1QvBVuUIKlqAUaXvncipBmaFLE\nY9MPGU0ljpNN/ylvuqOWIs9wbLUZC7eicCqBj6KIbKyi1PE87zxlWGGpw1Lk8KDVZykaWgxQpBVu\nUIKlqP9UtO3USh0iHpt+iI/d6DdR6D9tRcTTYjju8bp74jO0VixFPooisrGKUsfzvHMXi1mmQ/J0\neNDqsxTZDFCkFW5QijlYCVa+kLhtO20RXMSj64fcvp5y/eTN8cLRWor8w7GN16cnenN8YyuWIh9F\nEdkYSqnjOfe6JdMheXp60MJaimwGKNIKNyjFHKwEK19IjIqQFqlDxEP0Q7ZiVfopOmsp8gyHj1fH\n3ZNmwimBj6KIbAyl1PGce92S6ZA8PT1oYS1FNgMUaYUblGIO9lvCfngaA80/KN2hCGmsA8btdYh4\nbPoh49iJ7oR3W92cIE0Pu2kp8gzHVpuxcCsKpxL4KIrIxopKHR81j7uYfiX0uQfmSR2WIu5Ba8BS\nZDRA8Va4QSl4sPiGSz4JKkISHLI/CMcBwqkDpBocfMMFAAAAqARWPgAAAGmBlQ8AAEBaYOUDAACQ\nFj18bif8fEAH4ThAOHWAVIMDP18+8PMBAsJxgHDqAKlGDj7tBAAAkBZY+QAAAKQFVr4wkEcjNvOk\nRB/W1taWlpbU02bl84Rsb0lhRZViud0zvh2NRmqL2rikITdubW1V+VipyXDIAMlePnWqnzYTTglk\ni+4JYMxHDUSVL/q4UVLecVBUE6PRSCWpXkSFnqd8rR9fYzFhmVpVguVt6d3Qm+tQtniGS1WIhc4t\npauJmBV0XCcmcefWMz9fVsTjmJtkV/x8fAIMTdI++ewrORBRUCbHa8hMAeqNjo+Py8zHx8eVbUe9\nCEIdfj6bKo8Us02tEsEaL2WkG6S5+rLF08vyadHPx982Q4QKOodOTOLOrWd+vqyIxzE3yU74+YwT\ngEv7lpeXh8/IisvkeA2ZU3Aokf1Rjw+VV/Cq49eow883tKjyHFpEfWqVCNZ4KSPd4KdJTdni6WXR\nQSx0bildw7SooHPoxCQVc+uWn08U8TgWTZITg5/P2G3i5xuNRu+//77KVhSXyfEaBAuQNHrq1Cn5\nj0+HDh2ampq6fv36ysrKcDgsNegaIXnaVHmkmG1qFQ1WWE5J0g3SXFeyFbjPlwKtKOg8dWI+9MDP\nR3D75AolGa2fz9ht4uebn5/f3t6enJzc2NjY2Ng4d+6cKCiTM9ZAAiSNvvfee5ubm7du3XrttddO\nnTq1srKyvb09Pz8fKodQkDxtqjxSzDG1fIK1zVgF6QZprivZCqx8/aZFBZ2nTsxGz/x8BLdPLjfJ\nTvj5jBOA+PkuXbq0sLCwsLBw+PDhw4cPy0tkIZmcsQYSIGn06NGj+/bt+/LLL8+cOTM3N7e9vf34\n8eMIHXIkT5sqjxRzTC2fYG0zVkG6QZrrSrZC4BsugVq03W5prAPG7S0q6LhOzNhtW27Dfvn5SOfd\nHsfcJBsIpwQ+Pjku7VMjksVKyOR4DTxA0qj8/E3uOBgMwt7ky+rx89lUebbYydQqF6w+o2QNpBtG\neWQd2eIbLvkkKMdKcMj+IBwHCKcOkGpw8A0XAAAAoBJY+QAAAKQFVj4AAABpgZUPAABAWvTQUvTo\n0aPUFCGrq6ttdyFeEI4DhFMHSDU4n376adgKe7jyvfrqq6mtfAI+MCcIxwHCqQOkGpbgeeLTTgAA\nAGmBlQ8AAEBa9PDTzlZYW1uTD+lZW1u7e/fu+fPnjx492nanhGD9kR8a2N7aOq8X29raun79utx+\n+vRp/dFE7rZs3SM1CCHeeOON0WgknxOmtoi9n3jIpmVnSn8S0pVwZLWqcDPhFIV3wJiYvpHHJWVv\nYu/QHNgi8m+O9Ln08IPjmScpdvfuXbKXKhYwWJ6kcXrzbkRE2H+Mj4EW/XxDi8WtbppU0Nk8Ye62\neN+4/Uu3rHXUz1dHOETA1kw4JVAOIOnNMaoKSQ48LuGtkXNH5NmcnG/8eFUkSFUkT9sM5MX0t6pY\n2GBJkkOmXQweLJ5elk+Lfj79MuSeXmFpUkE3tHjC3G3xvhH7F7GsddTPV0c4RMCWxernk8jjmGWZ\nUVVIcuBx+Wvk3BF5Nuf+taA0YZ+GOvS4tqhixrdZDcHaLH3k/A0Fnl4WHbrFymZxa4s6FHQ2T5i7\nLd43PTejZc1NnH6+OsIhAjafobXi5xNCbG1tXbx48caNG0IIo6qQ5MDj8tfI+USU29zjx49PPiPC\nb2PqeTquLXox/lYSNliSJDEgiuiDFfiGSx0oi1vbHfmWsAo6IugiQi9bW26MljVFh/x8dYRDBGzk\np/H4+WS7MzMz6oaQrirUi6kcjM65Qn4+iTsiR3NjY2Nnn3H69OmSw64NkqewXFtIMb6XJGCwJEli\nQBTRByuw8oWFWNxapw4FHRF0qWLuttz95Ja1jvr56giHCNhErH4+IcSdO3fUnxpEVSg3khyMzrlC\nfj4Jj8izuX379i09I55fVRV6no5ri16Mv1UEDJYkSQyIIvpghcA3XAK1KD/dtlncGuiAcXutCjp+\nI8HRlq3bpBJ1n2DYcT9fwHCIgK2ZcEqwvLysbi5mmshNqQonJiZIDjbnXOankXNEJOvJbU6/EgY8\nYUP5+fQ8+QxUfj69GHmrCBsst/QRA2LwYPENl3wSlGMlOGR/EI4DhFMHSDU4+IYLAAAAUAmsfAAA\nANICKx8AAIC06OHTy7766qs4/4OkPuT/3AAjCMcBwqkDpBqc1dXVsFf1Hq58R44cSW3lE7CiOEE4\nDhBOHSDVyMGnnQAAANICKx8AAIC0wMoHAAAgLXp4n68V+u3nU1W5x1hFQUf27ZCfr6/hlIC43HL9\nfPpGNYpyGjnjW9FlP59geZZT5UlKB1tu2kcerBB4ellldJ2V0UnWALUq6DI/B6G7Ld43d51d8fM1\nEw6Rn8Xp5yMuN97tzHSCEE2jKKWR428lwy77+Uie/qo8Y7FywTouaO4pCj9fC7To5zM6yRqgVgVd\n5ucgdLfF++ausyt+vtyB+LTF+6bX6ZCf1RdOCYjLjXc7M50gRNNYTiPH36pW9OaGnfLzkTw9VXm2\nGVguWMcFzXOKhgJPL4sOXWdldJK1SBAFnfBzEFZR0JXwGkbi5/McSJVwuPwsl1b8fMTlZuw2P0H0\nkYqyGjn+VtJpPx/J01OVZ5uB5YJ1XNDcUzTmYCVY+QJjc5K1SEUFnbvOIAo6si/ZHrOfz11nkHC4\n/EwnHj8fcbnZup17gpTQyLnpqJ+P5OmpypMbjadSuWB9Lmjw86WO0UnWIkEUdO46gyjouHusK34+\nd51BwuHyszj9fMTlxrst/E6QEho5G53285E8PVV5Do1fiWA9L2jw80VBi34+7iRrrAPG7UEUdKoJ\n9Zm+sVgVBR2vc9gRP18D4XD5WQPhlIC43Hi3JyYmjCeI3u1yGjlbPV338+l5eqrybDOwXLBGySIv\nBj9fFCQox0pwyP4gHAcIpw6QanDwDRcAAACgElj5AAAApAVWPgAAAGnRw6eX7e7uxvkfJPXxi1/8\nou0uxAvCcYBw6gCpBuf27dthK+zhyvfKK6+ktvIJ+MCcIBwHCKcOkGpYgueJTzsBAACkBVY+AAAA\naYGVDwAAQFpg5QsDeTRiYw9LzGVtbW1paUk+7Eo803TZ3pLCvCp3MeN2dw48N/liNBqpLWrjkobc\nuLW11eINFdV0wMPd3ftD/OgY54N7BvpHSiq3zcnc5kifuwtmYzHC/mN8DLTo55PI5wwFfBhSLjEo\n6HLVawS3Xy1OBR1BeDvPDhw44F9n5X4VIGA4ygQkfTpGtZt7BmbekZLKhyYXoE9zMfv5itKD2egA\nz3CJjn/1r/7VwYMH1dvRaPT+++8fP368xS7p/NM//dPDhw+PHz/+7/7dv/vNb34j3544ceLjjz8m\nb0lhvRIyRlsxtX1xcZHvRSA/dReWrK+v/+AHP5iamlpfX19aWvrJT36ys7Pz8OHDqakp9Tdik4yN\njRF1y9ra2smTJ0lnTp48+ejRo3Pnzp07d+7tt98WQmxtbZ08eVK+Vm/139OXlpZGo5HcfubMGdtf\n4VEhjQHvvvvuzMzM1atXBZsPQojcGcgjtaFXvrGxIWfCzMyMPidzm3vppZfWn9H1v2+M0fEJqWaj\nEMJzQsrZqLZ3ZUK6wcpXFd1TJZc9pchqnSYVdLnqNYKPX81BKwo6Aneevfnmm0KIixcvGi8NH3/8\nsVzd5Urw05/+VF6ApG9BSeNGo9GPf/zjCxcunDhx4uHDhzs7O9evX29iPJXZ2tq6ePHijRs3hEXt\n5p6BwlsjRyq3KQxzm4tfI+ePMbqKE1Jos1Fu79aEdICVLyTz8/Pb29uTk5MbGxsbGxtyGrVOYwq6\nmtyE8SjoOLrzbDQajY2NcYPa+vr6Sy+99NFHH01MTMjL6/e+9z35o/v378vfptfX11dWVu7fvy+E\n+Nu//dtXX31VFvvd734nunO75cKFCzMzM0ePHpVv+XzwmYGeGjm9cpsLMLe5+DVyhSDRGSekmo1C\niNwJKbTZKDo4IR1g5QvJpUuXFhYWFhYWDh8+fPjw4fn5+Xb706SCLqybME4FHUd3nl24cOHrr7+W\n3f7TP/1T2y7yDyO1nMu9hBB/93d/J6/af//3f/+P//iPW1tbH3300crKysbGho9KOwbu3Lkj/+AT\nlvngnoGyjI9GjlRudAH6NNcBjVwRSHTVJ6TQZqMQonMT0kXY24Yx0KKfT5F7kzl4B4zbm1TQ5arX\njN22+dXiVNARuPNMdltZyhSDwUB+N0eVl7Hre8mjIE9JKZObmJiQA1xeXq5pCGH9fOPj4+qtUe3m\nnoGZt0aOVG50AWZZltucfiWMzc9XFGN0xgkpZyPZxTgh9dkoy9c9IW3Az5dPgnKsBIfsD8JxgHDq\nAKkGB9/tBAAAACqBlQ8AAEBaYOUDAACQFj20FD148KAHX7otxM9+9rO2uxAvCMcBwqkDpBqcmzdv\nhr2q93Dle/3111Nb+UQv/sOmPhCOA4RTB0g1cvBpJwAAgLTAygcAACAtsPIBAABIix7e52uFtbW1\nN954Y2trSz3L9fTp0zE8D2ltbe3u3bvnz5+XT1OUtx9sb0lhBSmmajYOUG2XafDafAqPRiP5dDT5\nQE5ZRr93IuOVe5W+p9JWOHwgpJi76WbCKYFtXCQNXkwOUG2XjxMTewdow3bSkdDIBMs9BFFBOk/i\nEqbh8MnsH6mwpypRlRhPGdtecRH2H+NjoEU/nzTGBdd95RLEz2cUqmVMcpbZxXskCpvtz6dwA36+\nFsMhKjtSMtebGK28kI/LmAYppgaonnMmvFVzmeWkI8Y+PsFqMvNJwlZIOs/jylikpcWHeqO2S5ny\njzbmO8zw9DIfGl751PzIsmw4HM7Ozs7OzuoXxwZwXNzVi+FwODY2pt7Kh17qb2dnZ/XCqhJSLNs7\nZB19u7qozc7OGp/y5y5svLhne5/bqZ4SORwOHQ8SjDAcyfLysvEaROok/cwaCacK+rhsaejFBoOB\n3DJ89nxI+QzJzO8ybTzp9AyH2hLLJ1gdhE2VdJ7HpVCR8mlZKNLMfimTTRhPmazOVPH0sujQxXKb\nm5s7OztySwzyxkJ+PqNQTTDJmbC79PTtT548kZaTzz//3Phk90KFOdX9fC2GI/aq7Aj+3kQbLcoL\nybhsaejFVFeFENLT5GnpkxhPOmLs4xOsQ2Y+0nkel0SPtLT4UGFMlfhHO+07xMoXksXFRel3Joa2\ndvH38+nOMyXeM0rUFDaN32AwePjwoWcU7sK1+vlaCUcwlZ27ab2fvFfGytuSFzrGZSs2MzOzf//+\nycnJzc3NyclJWcDT0icsJx0x9vEJ1iEzH+m8MS6xN9Iq4kOJMVXiH33xxRfJXh1KFStfSK5du6Ze\nHzt2rMWeSAr5+YjzTIn3jBI1hU3j9/zzz6sLbm4UvHADfr4WwxF7VXYEtzdRxC0vdIzLVmx3d3dx\ncfHDDz88dOjQ+fPn5UYfS5/EeNIRYx+fYB0y85HOG+MSeyMtLT5UGFMl/tFvvvmmw77DsB+exkCL\nfj6lDfP5JD1sB4zbC/n5jIK9jEnOVIvGMart8jb4+Pi4NIHZum0rPKzfz9duOLrKjpDrTWwgnHIY\nx6X3St4HIsXUeEmxLM/SJ+EnnRQB6sY+PsH0a2DwszV4qnrnjXHZIi0qPlQYU1U/lff5GvMdZviG\niw8JyrESHLI/CMcBwqkDpBocfMMFAAAAqARWPgAAAGmBlQ8AAEBa9PDpZfDzAR2E4wDh1AFSDQ78\nfPnAzwcICMcBwqkDpBo5+LQTAABAWmDlAwAAkBZY+QAAAKRFD+/ztUIhKV2TdEVBR94m7uczVqLe\nxunns1n3eDLuzD01cno9DpmcUcvXIZ+cj/Iwt0y5VPlbXljWxvWKxv7ERdh/jI+Bdv18bildTXRd\nQcebSNnPxyshb+P085FBGRNzZ555a+RIPTaZHDkfG/PJ1ZdqZppguWXKpTrc6zgkhZWlb8h8gXWk\niqeX5dOiny9XSlcTXVfQOURfpMIU/Hy8MH9r7EwMfj41KGNi7swzb40cqWdokcmR87Exn1x9qWb2\nCeYoUy5V4jjkbUm4L7COVPH0suio6Jmrla4o6HgTufTYz8cL2+q00ZafTx+UMTF35sJbI0fqsXkx\nyfnYUZ+cj/LQXaZcqsRxqIoRSx/3BXYiVax8ISkkpWuM+BV07iZS8/MVIio/HxmUnphezJa5xF8j\np+qxeTHJ+cib64RPzmeq5JYpkSpxHKoCxNI3Pj5OfIGdSBUrX0gKSekaoCsKOt5Eyn6+XKL18+mD\nIonJje7MZRkfjRypx+bFJOcjb64TPjmfqZJbpkSqxHGoihFL3/79+4kvsBOp4j5fmBaH3lK6mjpg\n3N4VBR1vYpiwn89Yif62gXBKQAbFE5uYmHBnnnlr5Eg9NpmczWxXt0+uvlRV/fpIPbWIWcFUieMw\nYzfw1B1WsdcXWEeq+IZLPgnKsRIcsj8IxwHCqQOkGhx8wwUAAACoBFY+AAAAaYGVDwAAQFr08Oll\nf/jDH6L9J5Ka+OUvf9l2F+IF4ThAOHWAVINz+/btsBX2cOV7+eWXU1v5BHxgThCOA4RTB0g1LMHz\nxKedAAAA0gIrHwAAgLTAygcAACAtenifrxUKedeapCsKOtI0/Hyi7XCK4unnkxC1W0U/H39LipEQ\ndHEd6XOJgdeKj59PoUILYj0Udtuoz4nD+xwXYf8xPgZa9PMpc4fNu1YTXVfQkX5m8PPtLUw0aZ3w\n8/FjqlBqN56qCGqSGzJ1HDk6/fDzSVSqAa2HxiNI0m7Geoinl+XTop9P4vCu1UTXFXSknxn8fFph\nrknrhJ+PH1O9gISnGtYkZ1TH6WV64+fTUw1lPbTZRknazVgP8fSy6CjkXWuYrijoSD99hpaOn8+m\nSXMQg5/PeEyJ2o2nGtYkx9Vx5Ch0wiSX6+cjqYayHtpsoyTtjloPsfIFJpR3LSBdUdCpfvJeGStP\nxM9n06SpXhkrj8HPJ9gxJWq3c+fOcYdfQJPczMwMUccROmGSy50qJNUXX3yRlymRqs02StLuqPUQ\n33AJzJ07d37961+33YtvGY1GP/zhD3/yk5/IS4/0kx09enR3d/cHP/jBj370I/3tkSNHVldXr169\n+tlnn0kF3Xe+8x2+F2lCFeP4R0H6KZ4p6I4ePbq9vZ2rI3/rrbc+/PBDn4YcjUYbjtSkiWd/eopG\nwimHPih+TIUQly5dki+kUU/+iaBSlT+SJrmVlRWHepBUziOS7O7uLi4unj59+p133pHqOII0yVUY\ncRPkThWS6oMHD/icLJGqtBvK17r1kKTNT4FOpIr7fGFaHHp712rqgHF7VxR0pJ8Z/HxaYa5JayCc\nEpBB8WOq3/6Rt4iMDj9ZIIhJjqvjsr0p6VdCd1CFaNjPp7aru6cihPWQ20ZVqnrazVgP8Q2XfBKU\nYyU4ZH8QjgOEUwdINTj4hgsAAABQCax8AAAA0gIrHwAAgLTo4Xc7Hzx40IFvFgXl5z//edtdiBeE\n4wDh1AFSDc7NmzfDXtV7uPK9/vrrqa18Aj4wJwjHAcKpA6QaOfi0EwAAQFpg5QMAAJAWWPkAAACk\nRQ/v87WCTaXWOk0q6DyVaWQXvRLlD+ufn69QOO6OddrPl5u5p0lO1aYPP/fodM4k5+PnkwdaLxMk\n1dxwiGRRlY8/VSHw9LLKuFVqzRCDgo60ZatNwStRgrGsd36+QuG4O9ZMOCVQlhxptzGOkWwsbZLL\n9ubM65G4j2nWFz+fPNZqFKFS5U3rqLN1yCSI8PO1Q4t+PpsnrG5iUNCRtmy1KUglumAs652fr1A4\n7o5lHfHzGcdINpY2yWX2806/RruPadYXP5+cUbOzs2o5V9urpMqbJhslRgli2YFawdPLoqOiSq1W\nmlTQkbZstSn0SohgzIdu+fkKhePumM/QYvDzGcdINpY2yYm9Odsich9T0RGTXK6fb3Nzc2dnR27f\n2toKlSpvWkLOVi5B7ESqWPlC4laptUWTCjrdx8bVaza4to30iu/SRT9foXBsHeO94hsj8fMZx6hv\nNA7N3ySnsEWUe0w7YZLL9fMtLi6ur68rkV6oVG1Nk7N1fHycSBA7kSpWvpBIc9XVq1enpqZ0T1hb\njEajgwcPXrlyRf7WL01aQojd3d2zZ8+St0eOHBFCXL16dWZmRiro3n33Xb4XaUIVI22R2tz9vHTp\n0sLCwsLCwuHDhw8fPjw/Py8VdEKI7e1th1FM8tZbbxXPJt5w3B0Tz/x8tYZTjjt37qi/D4xjJBuN\n6UmT3BdffOHfru0ouI+peGaSk0T6RYy9qRqRWj7JsWPHQqVqa5qcrfv3719cXPzwww8PHTokJYid\nSBX3+cK0qO5tEE9YYx0wbm9SQUfastVGus3vhw376OcrFI67Y82EUwJikjO698jG0iY51Xl13hnr\ncR/TrC9+PpWqO42sYKq8aXIDT920FnsliHWkim+45JOgHCvBIfuDcBwgnDpAqsHBN1wAAACASmDl\nAwAAkBZY+QAAAKRFD59etru7G+0/kdTEzZs32+5CvCAcBwinDpBqcG7fvh22wh6ufK+88kpqK5+A\nD8wJwnGAcOoAqYYleJ74tBMAAEBaYOUDAACQFlj5AAAApEUP7/O1AvHzxfMpf+sKOlsU7qZ77+ez\n+eTILkReKN/G6ecz9iFXQ2j0wBX1ydkOgdEOaIvLf5jNIw+leyq6BZBFI+XOP2MrnfMd/v+E/cf4\nGGjXz0dUVc0QoYLOHQWpc8i8hj3z8+V66QikEvI2Tj+f6s/QqejjIyJ7ZUV8cplFzsdnFMmwJjOf\nJGyF8/PzuXJHMl4yt7OCkWbM+adacZsj60sVTy/Lp0U/H1dVNUOECjp3FI6eDPvo58v10hFIJfyt\nsTMx+PmyPEWfxO2B8/fJZRY5n3FGGTOsg7Cpyl8IhsPh7Ozs8vKyMVUyXjK3s4KRZsz5pzbq1Tbm\nO8zw9LII0Y1WXFXVLi0q6NxRkDpLeA275efL9dIRSCXGOh205ecTHoo+idsD5++TExY5H59RpMVO\nOOQkT548WVlZEUJ8/vnnZ86cMaZKxkvmtigYqWDOP7kx1xzZoVSx8oVkZmaGqKpioBUFnTsKUqfb\na9gPP1+ul64cUfn5hJ+iL3cvUcQnZwwz15TZCYecZDAYPHz4UOn3hClV43h1H6QoqOgjzj/9Rw5z\nZIdSxcoXkt3dXaKqapcWFXTuKEid3GvYMz+fj5fOn2j9fMJD0Ze7l8TfJ2cMM9eU2Q2HnBBCiOef\nf1797nLs2DFjqmS8ZG5LCin6iPNPvsg1R3YoVdznC9OiurEh9qqqGuuAcXuLCjp3FLwnxGs47Jef\nL9dLZ+y27R5eA+GUw0fRx3vr8MD5+OSMOjqjKVPPR78GBj9bg6cqRzc9PZ1ZUiXjJXM7Kxip3op+\nZzTXHFlfqviGSz4JyrESHLI/CMcBwqkDpBocfMMFAAAAqARWPgAAAGmBlQ8AAEBa9PDpZffv34/8\nX0mC88knn7TdhXhBOA4QTh0g1eCsrq6Gvar3cOWbnp5ObeUT8IE5QTgOEE4dINXIwaedAAAA0gIr\nHwAAgLTo4aedDVPCPtMkdYh4fNQztre2Osnb7lqKgofDRx2npaiQoojYgkrLdFQ9xONj61IXrTq2\nTpLJ4569PsG6vU7GMl3M81vC/ntgDDT5b6TDPG1HMzQp4vFUz/C3Ou6eZJ21FNURjnr6vpL4xGkp\nIv0cMlGORB84P+7CW6aj10M8PrYuNWbVqdX9lJkmT+4JlRssP168lVzZFixFbdLwyqdeDD3sMzXR\npIjHXz1j9PUY63ToTkgNkVuK6ghHvtYlPjFbilQ/SbyqgD5wPlJ/mY5eD/H42LrUmFWnVvdTZpo8\nuSdUbrD8ePFWcmVbsBSlQq62o13qEPH4q2ccbh13T3yGFqelqI5wBJP45NKWpUjvJxflSPSB85H6\ny3T0eojHx9al7lp1yATgkyf3hMoNlh8v3kqubKsreQp8wyUIDm1HDAQX8RiLFSK3J6Q5vjFaS1Hw\ncIRJ4qM3xze2ZSni/SSiHILxuBeS6Ui4x8fYpe5adRwTQOJzQvkE6z5eIk+21ZU8BVa+iuRqO9ql\nDhGPrVgh3D0RnbUU1RGOYBKfaC1Fej+NohyC8XwpJNOREI+PrUvdtepwixMh94QSecH6HK9c2VZX\n8hQC33CpRq62oxmaFPHYiqme2O486bh7knXWUlRHOFzi00A4JSD9dIhyuNVLFwzJMj4yHd1zpHt8\nVD2kS41ZdWp1P6km9Nt+uSdUbrD8ePFWcmVbNeWZ4RsuPiSoCElwyP4gHAcIpw6QanDwDRcAAACg\nElj5AAAApAVWPgAAAGmBlQ8AAEBa9PC5nfDzAR2E4wDh1AFSDQ78fPnAzwcICMcBwqkDpBo5+LQT\nAABAWmDlAwAAkBY9/LSzYXykVi3SpIKObHeb6tx19s/PZ5snwiRL66Kfjxjy+Hh9bHnl5Hxciulu\nSzU0Go2EEDJMlWpUeMr5SPilg/W0jfbB0hf2H+NjoEU/X+b0rtVHDAo60pbDVOdTZ8/8fHyeZM+e\nucWnShf9fMSQNxwOZev6Y/zIQELJ+YxSTHdb4+PjsuT4+LhS7agX1anPepiZrjAk/NLBus8I/2J1\nWPrw9LJ8WvTzZU7vWn3EoKAjbTlMdT519szPx+eJ1K35hCNfR+7nI4Y8fQhkgA5bXjk5n0OKaWtr\nOBzOzc3Jbk9MTNy7d+/y5cvqmZ/Vqc96mJmuMI7wCwXrPiP8i9Vh6cPTy+LCR2rVIk0q6EhbDlOd\nf50OuuXnI22NRqP3339fbSR00c9HDHnHjx+XkraHDx/qObhteeXkfDa5o6OtU6dOyf99OnTo0NTU\n1PXr11dWVobDYamh106unI+EXzpYT9tobrFOWPqw8gUgV2rVLk0q6PQo9GKkBned/Ed8UF3086m2\n5ufnt7e3JycnNzY2NjY2zp075246fj8fMeRtb28LIU6dOvXkyZO7d+/qHXPY8kQpOZ9N7uho6733\n3tvc3Lx169Zrr7126tSplZWV7e3t+fn5Qu02Rq6cj4RfMVhP26ijWCcsfVj5KuEjtWqRJhV0pC1S\njHTMXafonZ+PtHXp0qWFhYWFhYXDhw8fPnyYXHO76OcjhrynT5+ur69fvXp1amrq1q1bqpjblidK\nyflsUkxHW0ePHt23b9+XX3555syZubm57e3tx48fR/uba66cj4RfOlhP22husW5Y+sJ+eBoDLfr5\nVAfcdymCE4OCjrTlNtXl1jnsl5/POE8yy32XLvr5iCFvdnZW5iDvomVZNjExkWvLKyfnM9aT25b8\nbFP+dDAYBLzJl9VpPVT167f9ePjlgs21jcp6covp60uoKyG+4ZJPgnKsBIfsD8JxgHDqAKkGB99w\nAQAAACqBlQ8AAEBaYOUDAACQFlj5AAAApEUPn9v56NGjaP99siZWV1fb7kK8IBwHCKcOkGpwPv30\n07AV9nDle/XVV1Nb+QR8YE4QjgOEUwdINSzB88SnnQAAANICKx8AAIC06OGnnQ1TSErXPEEUdKoq\nmw7N2JbaKGJV0EUbjrvOOP18niY5faPsob5LaUWfMV5ev9Fjx/scFUbxITmsuTO5RKqOcHJ1gLYd\nIyLsP8bHQIt+vqFJw9YAtSroMg8dGm9LboxZQRdtOLl1RujnU50ZOk1yZKPspNxL9kSUUvQNTTpA\nXj/x2GX1mOSyoKly8aF8qz8ejww/lPiQH1Bjl+Dni4UW/XzkbWPdqFVBl/np0PjYI1fQRRtObp0R\n+vkUbpMc2TgcDmdnZ2dnZ9VvCdUVffpepH7iscvqMcllQVMlfR4MBnL78JliMGPDDyU+lOgH1Ngl\n+PlShKiquK6vXYIo6ISfDq1zCrpow/Gs00Hzfj5JrkmObNzc3NzZ2ZFb5PP+yyn6bDpAUj/x2Iku\nmORIn9VhFULcv39fviDDDyU+FJazMlcHGH+qAt9wCQLx88Wm66uooCOQvbquoIswHHedvFd8Y/N+\nPtWu2yRHWFxcXF9fV2I5ubGEos+mAyT1E4+d6IJJjvR5ZmZm//79k5OTm5ubk5OTsgwZfijxobAc\n0FwdYPypCqx8FSGqqth0fUEUdASyV3cVdNGG465TROnnk+Sa5AjXrl1Tr48dOyZflFD02XSApH7i\nsRNdMMmRPu/u7i4uLn744YeHDh06f/683E6GH0p8KCwHNFcHGH+qQuAbLtUgqiqbhq1ualXQqSaG\nFh2asS213XhrIQYFXbTh5NbZQDgl8DHJkY0qVV6mqKKP6wB5/cRjl9VjkstCp2p07+mJkeGHEh/y\nAyrrydUB1pEqvuGST4JyrASH7A/CcYBw6gCpBgffcAEAAAAqgZUPAABAWmDlAwAAkBY9fHrZV199\nFe0/kdSE/PcaYAThOEA4dYBUg7O6uhr2qt7Dle/IkSOprXwCVhQnCMcBwqkDpBo5+LQTAABAWmDl\nAwAAkBZY+QAAAKRFD+/zNUyCfj5bMb02rkazdc9YZ3f9fL0Jpyi8A47E1NC46a2cn0+9Ne6ltnfR\nJNeWn88xS92TuROp4hkulRju9a45LG610qSCbmhxEJLauHqN4K6zo36+msIh8rlO+PlsURA/Hykj\nSpnkJDbfodrejEkua8PP5xbmlUvVNktzJzP8fO3Qop/PYXGrlSYVdKROVYbUNmTqNYK7zo76+eoI\nh8vnOuHns0WhD40L80qb5Gy+Q317Mya5rHE/n3smZ2VTtc3S3MkMP1//Id41h8WtFepQ0NkchKQ2\nrl4j+NTpIE4/Xx3h2ORzDmLw89mi0IfGhXnlTHI23yHZ3kWTnI+fzz2TRdlUbbM0dzLHn6rAN1yC\noAv5dItb2/36lrAKOlKnKkZqM6rXPPvJe8ULx+nnI3UGCccmn1O94rvE4+dzuyq5ME+UMsnZfIdk\n+4svvkh2jN8k5+Pnk9hmsqREqrZZmjuZ409VYOWrCPGuEYtb272rRUFH6lTFSG1G9ZpnP0Vn/Xx1\nhMPlc53w8/m4KrkwT5Qyydl8h2T7N9980zmTnI+fzz2TZZkSqdpmae5kjj9VIfANl2oQ75rb4lYf\nTSrobKo5W22O+wqOOofd9PPVEQ6XzzUQTgmIzo1Hwf18XJhXziSn4LdC9e3NmOSyxv187pmclU3V\nZk/Mncx1pIpvuOSToBwrwSH7g3AcIJw6QKrBwTdcAAAAgEpg5QMAAJAWWPkAAACkRQ+fXra7uxvt\nP5HUxC9+8Yu2uxAvCMcBwqkDpBqc27dvh62whyvfK6+8ktrKJ+ADc4JwHCCcOkCqYQmeJz7tBAAA\nkBZY+QAAAKQFVj4AAABp0cP7fA1jlJB5erAaoC0FXW5t7jr77ecrFA4fdVf8fGTIErfP0t8kZzvv\nyC4+h1jvc+nh10eTfr5CtlGVdhdTxTNcKjE0SchsnrD6iFBBN2RWOYJeJ2+6336+QuEoyY4U1jQT\nTglIP7klLmOuQZ6D8DPJ8cPBdX28WI/9fHqMpf18JC7POd9Mqnh6WT4t+vkyuyesViJU0HGrHEGv\nkzfdbz9foXAkynuXdcTPxy1xGXMN8hw8TXLGCcN3IcX66udzpFrIz0fi8pzzzaSKp5fFBTFj2Txh\nbdGigi7XKqfXWcJr2Gk/X6FwxF7vnQ8x+Pm4JU4w1yDPwdMkxw8HScxYrK9+PhJjaT8fictzzncx\nVYFvuARBmbFsnrB2aUVB57bKcRxew/75+YqGw713eq+M5Vv38xktcWTgxhw8TXKeIky9GO9S/CY5\nHz8fibGKn4+k6hNyF1MVWPkqQsxYNk9YJN1rUkHHrXIOuNew336+QuGIvd470RE/n9ESRwZuzMHH\nJOcpwiTFeJfiN8n5+PlIjKX9fCQuz5C7mKoQ+IZLNWw+Nvfn6cGJUEHHrXLGbg/3msBU08Ne+/kK\nhUO8d82EUwLST6Mljgyc5+BpkjMeDn34sh5SrK9+Pp4qT14WLpQqD1m/gaf7IxtIFd9wySdBOVaC\nQ/YH4ThAOHWAVIODb7gAAAAAlcDKBwAAIC2w8gEAAEiLHj697MGDB9H+E0lN/OxnP2u7C/GCcBwg\nnDpAqsG5efNm2Kt6D1e+119/PbWVT8AH5gThOEA4dYBUIwefdgIAAEgLrHwAAADSAisfAACAtOjh\nfb6G0RVW0qQlt0cipmpSQacX83R0pePn04sVDYcY2rri53NEoXoe1iRnG2x3TXKeqYby86nahBDT\n09Puq5njIJI+Fx93/YT9x/gYaNHPJ6Vo8tFlTXYjBgUdLyZRVjmCu86e+fmqhEMMbc2EUwIyKEcU\nSmAZyiQ3NFnrJM2b5LI6UzXOQ/dMzrxTlaij476aOQ4i/Hzt0KKfbzgczs7Ozs7Okotj3cSgoMvy\nrHIEd5098/NlFcIhhrasI34+WxS6wDKUSc5orZM0b5LL6kzVOA/dMznzTjXbe3SG9quZ+yDCz9d/\niI9tc3NzZ2dHyqvk88vbpUkFnSholfOs00a3/HyiQjjE0OYztBj8fMYoiMAylEnOaK2T9MAkp6dq\nnIfumSy8UyVHx3Y1yz2InUgVK18AlI9tcXFxfX1dybTa7te3NKOg4ziscu46ea+MlXfIz2fsv2c4\nxNDGe2WsvHU/n4QcUyKwfPHFF3klJUxyRmsdp6MmOZKqzZlnm8kSn1TJ0fnud79rvJrlHsROpIqV\nrxLEx3bt2jX1o2PHjrXXr29pUkHHIVY5/36K3vn5OP7hEEOb6Iifjx9TwQSW33zzTRCTnNFax+mo\nSU5P1ejMc89kWcYnVXJ0dnZ21I/0q1nuQexEqrjPVwniY1NGK/eH6cGJQUFHinGrnLHbtjqH/fLz\nVQmHGNqaCacEZFA8Cv32j7qNJEKY5IzWOlW4YZNcVmeqRmeeeyZn3qkq5NHhVzNyA892EOtIFd9w\nySdBOVaCQ/YH4ThAOHWAVIODb7gAAAAAlcDKBwAAIC2w8gEAAEiLHj69DH4+oINwHCCcOkCqwYGf\nLx/4+QAB4ThAOHWAVCMHn3YCAABIC6x8AAAA0gIrHwAAgLTo4X2+hiFmLKJSa50mFXT69lxHl7Fp\ntVf//HyFwtHr5NLHTvv5gpvkfGJXfeucSc4zVXLZCZKqzTaa26X4UxUCTy+rxpD5+WxOslqJQUFH\ntiuJiVFBR5oeMr9az/x8hcLJmJ+PaNI64eczJkY2VjHJkdjlYNUDtFSX9HnVAz+fcYKRy06oVG1+\nvtwuwc/XDi36+bhKrRliUNAZt9sUdKRp7lfrmZ+vUDi8TqJJ64Sfz5gY2VjFJGcT7+l7kXnVAz+f\ncSKRy06oVPnE8+wS/Hz9h5ixSqjUaqVJBR3f7lDQkaYdfjUb3fLzFQqH1FlC+hiDn8+YGNlY2iQn\n9kZ0/PhxaYN7+PChHjuZVx01yeVaD8llJ1Sqjonn7lInUsXKFwBlxnKr1NqiSQWd7mNzKOhI026/\nWm/8fJ7hENzSx5j9fEaTnL6xtEmOsL29LYQ4derUkydP7t69q7aTedUPP59g1kNy2QmVqmPiubvU\niVSx8lWCmLG4Sq1dmlTQcR+bQ0FHmuZ+tZ75+QqFQ+DSx074+YwmObKxtEmO8PTp0/X19atXr05N\nTd26dUttJ/OqB34+o/WQXHZCpeqwjbq71IlUcZ+vEsSMxVVqzRCDgo5H4VDQ2Xqi3zPok5+vUDik\nTq5JayCcEviY5GxevXImOV28J6udmJjQ6yHzqgd+PqP1kFx2QqVq8/PldqmOVPENl3wSlGMlOGR/\nEI4DhFMHSDU4+IYLAAAAUAmsfAAAANICKx8AAIC06OHTy/7whz9E+08kNfHLX/6y7S7EC8JxgHDq\nAKkG5/bt22Er7OHK9/LLL6e28gn4wJwgHAcIpw6QaliC54lPOwEAAKQFVj4AAABpgZUPAABAWvTw\nPl/DlPCuNUlbCjrB1Gi27hnr7LefrxPhlIBY4oxjdIcMPx/HmCoZaSupuk8cSbSp4hkulRgW9K7V\nRJwKOqLcI7jr7L2fr1A4RD4Xp5+PWOKMY3SHnFXw87lj766fj6fKTYTNp6r3oZlU8fSyfFr088nX\nDu9aTUSooOPKPYK7zn77+QqFw+Vzcfr5iCXOOEZ3yFkFk5yqR9+rB34+h3tPjbThVEkfmkkVTy+L\ni6LetYZpUUGXq9zzqdNBp/18hcKxyecctOLnI5Y44xjdIYuyJjlb7D3w85FUjZOh4VRJH7qYqsA3\nXIJQzrvWGK0o6NzKPXedvFe8cKf9fIXCscnnVK/4Lq34+YglzjjG3JBFKZOchM+fHvj5SKrGydBw\nqqQPXUxVYOWrSBXvWgO0qKDjyj3/foq++/kKhcPlc3H6+YglzjhGd8iyTAmTnNFaJ3rh5yOpGk2E\nDadK+tDFVIXAN1yqUcK7VgcRKui4cs/YbVudw177+QqFw+VzDYRTApslTnVgYmLCHXJW1iRntNbx\nPnTUz0dS5SbC5lPV+9BMqviGSz4JyrESHLI/CMcBwqkDpBocfMMFAAAAqARWPgAAAGmBlQ8AAEBa\n9PDpZQ8ePIj2n0hq4uc//3nbXYgXhOMA4dQBUg3OzZs3w17Ve7jyvf7666mtfAI+MCcIxwHCqQOk\nGjn4tBMAAEBaYOUDAACQFlj5AAAApEUP7/M1jNG7ZrO4NU8hBZ3aReyVeNmKkUcTlVAVqkpIExH6\n+dQuonI4/K2tex3y8/EORGiSMx5xvc+lh18T8iDK145UczWEhVLVq9ULE1Og3opqYjQaCSHk/FQT\nNUbC/mN8DLTr5+P2rAYIoqCTu8inJenPHOLFdImXra3hM2yqQr0S3kRsfr7g4bjniV4JVwPG6ecj\nR3xosru1a5LjsXfCz6fMQfK5YnzmDPM0hP6pKvg0JqZA0sr4+Lhsa3x8XHmL1Ivq4Oll+bTo5zPa\nsxogiIIue2YWJJ03+rf4AElb8rVDVWhT0BHFF2moLT9fFjSc3HmiV8LVgHH6+STqiBvnQzwmOZJk\nWML+PjE7Ozs7O6ukr2q7GmmuhtA/VQWfxg5ToNwuLYzqeZ6XL1+WzxoNAp5eFhdEYVVCpVYrhRR0\no9Ho/fffV7souH9Ll3jZ2hJ5qkK9Et5ELg37+cKGkztP9EocakAbrfj5xN4jbrS7tWuS47HHb5Lb\n3Nzc2dmRg93a2jLOnFwNoX+qCj6NiSmQtHLq1Kn79+8vLS0dOnRoamrq+vXrKysrw+Gw+IgbAitf\nAJTCyq1SawtPBd38/Pz29vbk5OTGxsbGxsa+ffukW86o+1LYFHSiiKrQ3UQMfr6w4RSdJ7oakP+I\nl2/FzydMR5zY3do1yfHY4zfJLS4urq+vu/18uRpCUSFVBTEFklbee++9zc3NW7duvfbaa6dOnVpZ\nWdne3p6fn6/SYq1g5asEUVgZ7VktUkhBd+nSpYWFhYWFhcOHDx8+fPiv//qvpVvOqPtS2BR0ooiq\nkDcRm58vbDiF5glRA4pY/Xxi7xE32t3aNcnx2OM3yV27dk29tvn5cjWEolSqBGIKJK0cPXp03759\nX3755ZkzZ+bm5ra3tx8/fhxnpN8S9sPTGGjRz8ftWc0QREGn9iKf7xuL6beUjG35qAp1BR1pYhiZ\nny9sOD7zRFXC1YANhFMCcsSNdrfWTXIkdv1K6HkDzIeAqaqjr84Uh5/PdjYVSlUfhSo8MTFh8y/q\n56xaUAaDQcCbfBm+4eJDgnKsBIfsD8JxgHDqAKkGB99wAQAAACqBlQ8AAEBaYOUDAACQFj18etnu\n7m6c/5pTHzdv3my7C/GCcBwgnDpAqsG5fft22Ap7uPK98sorqa18Aj4wJwjHAcKpA6QaluB54tNO\nAAAAaYGVDwAAQFpg5QMAAJAWPbzP1zBGAVg8n/JHq6AjdbaioGs9HFvPYwinHEThZhtmrjavqKUv\nVwTYOTOfhPj5pqenia5PLxbKekiw7WWTa3Yj27D/GB8D7fr5dFFWY93onIKO1DlkfrXe+/kc8ySG\ncMpBFG62YQ6ZNo8MUBTxyXF/YWbKsBkzn6Q+Px95q8o4ZnJWys+n4HM+y5Nr1pEtnl6WT4t+PiLK\naqwbnVPQcbkXKdxvP597nsQQTjmIws02TDI3+AAL+eS4vzBzigBJhnVQn5+PvJW4zXlZKT+fxDjn\nM2+5ZkDw9LK4IGIwIspqp08a0SroSJ02v5qDTvv53PMkhnDKQRRutmGSucEHWMgnZ/QXkgy7aOaT\nED8feSvLuM15opSfTwhhm/MiT67ZiWyx8gVAicGIKKvtfn1LhAo6Y53crybpn5/PPU/iCacoROFm\nG6ZxbpABFvLJcX8hybCLZj4J8fORt7KM25wnKeHnI3P+3LlzxmK8uU5ki5WvEkQMRkRZbfcuXgUd\nqZP71frt53PPkxjCKQdRuNmGSeaG0a7n75Pj/kLBMuyimU9C/HzkrXzhNufJ7SX8fGTO2zSzvLlu\nZBv2w9MYaNfPJzRRVmN0TkFH6uR+tWHf/XyOeRJDOOWwKdxUH4zaPJtdL/PzyXF/oVEE2IyZT1Kf\nn4+8zfzMeeX8fArjzVGHXLOObPENl3wSlGMlOGR/EI4DhFMHSDU4+IYLAAAAUAmsfAAAANICKx8A\nAIC06OHTy+7fvx/tP5HUxCeffNJ2F+IF4ThAOHWAVIOzuroa9qrew5Vveno6tZVPwAfmBOE4QDh1\ngFQjB592AgAASAusfAAAANICK19V1tbWlpaW1DP05JML1NvWcXfP2Fv+vCtbsUJtEchPyb6j0Yh3\nZklDbtza2qrysVK04ZBK5DAbDqcEpJ9yyKQMyYHHorb7PHRNtqiPOrcV1cRoNFJJqhcRYkxVnzn8\n0POZrPb1fJSd8dg5TpDOpYr/ZK8EMaQY1TYNEIOIp1AUuRKZnlmKis4TvRLioGkmnBL4KIqGzE9E\njrso6NMxWnuGFieUfDs+Pi4zHx8fV54B9SIItabK5V9KpyClDdUtRcZj5z5B6k4Vz3DJp+GVT76Q\nU4oLO5ohBhFPoSj8JTKkoY5aiorOE70S4qDJYrUU+SiKyIHmx72oT2dosvaQI8vfyv6oh5ldvnxZ\nPvcrFPWl6pB/yfmZmeZk0VRzjx0/QepOFc9wiQtiSOHCjnZpUsRTKIpciUwu3bIUFZ0neiXEQeMz\ntFYsRT6KInKg+XEv6tMxWnvIkSVvT506Jf/x6dChQ1NTU9evX19ZWRkOh1XHXw8kVduZsrW1dfHi\nxRs3bgjTnCyaqvHYuU+QbqUqcJ+vOrohxe2saYvGRDz+UeRKZHT6YSkqPU+Ig4b8NB5LkY+iiBxo\n43Ev5NMxWnskRHuk3r733nubm5u3bt167bXXTp06tbKysr29bRMRtA5J1XamyMCPHj0qLHOyUKrG\nY+c+QbqVqsDKVxFiSHE7a5qnSRFPoShyJTI9sxRVmSfEQSNitRT5KIrIgTaqrAr5dIzWHnJkyduj\nR4/u27fvyy+/PHPmzNzc3Pb29uPHj6OV6ZBUbfKvO3fuyD/4hGVOFkrVeOzcJ0i3UhUC33CpBjGk\n2NQ2dRODiKdQFLkSmWG/LEUl5omqhDhomgmnBD6KInKg+XEv6tMxWnvIkTVqntSlbzAYhL3Jl9Wc\nKk9seXlZ3dbNQliKbMfOfYLUmiq+4ZJPgoqQBIfsD8JxgHDqAKkGB99wAQAAACqBlQ8AAEBaYOUD\nAACQFlj5AAAApEUPLUXw8wEdhOMA4dQBUg0O/Hz5wM8HCAjHAcKpA6QaOfi0EwAAQFpg5QMAAJAW\nPfy0s2HW1tbu3r17/vx5+dA8tTGSJ/eQ7skPYWxv1S5CCL3/pJhxyLzY1tbW9evXeTHSPdkQqXM0\nGslnTemd0T9BOn369BtvvCGbKP3JUrThuCdVM+EUhXeAB2jrpJ65LMNzNmKbPxJZuWpOf9Cl3kN9\nS+Fh1w+JSI6UHNbcmVwuVdssJc0ZW5dEmyqe4VIJ4l3LLHq2umlSQUfqtBXjVjmCHhSPsaN+viDh\n8ErIpIrTz6ccQDZLHC/DcxDeJjk9E1vsxN5HWs+yTB5NVSBUFO1aD6v4+dxiSAk5W4dMGVhHqnh6\nWT4t+vkyi56tbppU0JE6bcW4VY6gB2WM0dhK5H6+IOHwSsikitPPJ3FY4ngZnoO/SU7PxBb70GTv\nU61nWpJhicd6WNTP5xZDShyySXLyBgRPL4sL4l0TFj1bW9ShoCN12orlWuX0oHiMucTp5wsSDq+k\n6KRqxc8n8ixxvAzPwd8kp2dii53b+/TWhRCPHz8++Yw4v41ZwnpYxc/nI4YkZytXBsafqsA3XKqj\ne9fa7ouZOhR0qk6loCPF3FY5jiPGzvn5goRD9HI24vHzCQ9LHCljzKGQSU6HJ8btfXrrQoixsbGz\nzzh9+nSJRuumhPWwup/P2LT+I/1s5crA+FMVWPkqQrxrbXeHUoeCjtSpFHSkGLfKOeAxdtTPFyQc\nUgknTj+f8LDEkTLGHAqZ5CS2xLi9T29dCLFv376lZ8T5RYwS1sPqfj5j02o7OVu5MjD+VIXAN1yq\nQbxrqgPuD9OD06SCjtvOjMW4Vc7YbVkDj3HYTT9fkHCMlegDbyCcEvhY4kgZnkMhk5waL09M1kPs\nfaT1LMv0K2HAc7Z16yFPXhYulKrt6HDZJFEG1pEqvuGST4JyrASH7A/CcYBw6gCpBgffcAEAAAAq\ngZUPAABAWmDlAwAAkBZY+QAAAKRFD5/b+ejRo2j/fbImVldX2+5CvCAcBwinDpBqcD799NOwFfZw\n5Xv11VdTW/kEfGBOEI4DhFMHSDUswfPEp50AAADSAisfAACAtOjhp50N41ZVtU6TCroSqkLy04YV\ndC2G454n7qbj9PMJi0mOj0JtJP48KcwrZJKztWILUG9iNBoJIWSSKtIIMUryjPmo06ein0+vkLsA\nhcdBVCXh52uOFv18Q4snrG5iUNDx7W5VIflp8wq6FsNxz5PcpuP083GTHNG2ZSwH4s/LipjkjBXK\njTxAfXaNj4/LjePj48qwo14Eob5U5UY+D/UBVvHz6QxNLsCM6QD5QYSfrx1a9PPpl6GAjwHMJQYF\nHd/uVhVy5xx5a2ylo34+st09T3KbjtPPR3RuXNuWmXIg/jx/k5yxQlWJ2igDJDI/abZTj5q8fPmy\n4+myJagv1cwyD/UBVvHz6RhdgBnTAfKDCD9f/yGqKpsnrC2aVNAVtcqRnzavoGsxHPc88WzaQSt+\nPqJz49o2wXLg/jx/k5yxQvmaC+r02XXq1Kn79+8vLS0dOnRoamrq+vXrKysrw+Gw/MjrhKRqmwz6\nAKv4+XSMLkDBdID8IMLPlwRcLOdpVmuMZhR0ZHvA/vfAz0e2G996Nn3u3Dn9p/H4+YjOjWvbFGrg\n3J8nSpnkSJI2NaDkvffe29zcvHXr1muvvXbq1KmVlZXt7e35+flCLTYGSdU9GSSh/HxGF6BgOkB+\nEOHn6z9EVZVrVmuYJhV0YcfeMz8f2e7Oyt30/Px8nH4+onPj2jbBBs79eaKgSc6YpE0NqOrft2/f\nl19+eebMmbm5ue3t7cePH8fzeyqBpMonA98llJ/P6AIUTAfIDyL8fO3Qop/PpmermxgUdLlWOWO3\nbbephv3y85Ht7nmS23QD4ZSAm+SItm1iYoIMnPjzsoImucxi5jMGSGaXuvQNBoOwN/myOlNVqMmg\nElMDrOjnUxhdgBk7svwg6utLqMsgvuGST4JyrASH7A/CcYBw6gCpBgffcAEAAAAqgZUPAABAWmDl\nAwAAkBY9fHrZV199Fe0/kdSE/I8fYAThOEA4dYBUg7O6uhr2qt7Dle/IkSOprXwCVhQnCMcBwqkD\npBo5+LQTAABAWmDlAwAAkBZY+QAAAKRFD+/zNQz8fLZiqjYfPx+ps39+vurhqGFG6+fjfTCOWuRF\nXdQnxw12uT3phEPOx3foM9IScj6b6k8vkHvu6L0qPPi6CfuP8THQop+PvG2sG00q6GxjdEvROPpP\nh8yy1lE/X63hKE1anH6+TNPWSLeOMcOMpVHRJ2c02OX2pA6HnCJIhT6+w8xjpIXCzPJUf6pRvTMN\nxIunl+XTop+PvG2sG00q6GxjdEvROESZRursqJ+vjnC4Ji1OP59CBpWZRi3JjbqQT44b7Hx6UodD\nThEkVR/focIx0qJyPrfqT0I600C8eHpZXBA/H3nbdu9qUdDZxuiWonH0n5bwGsbp56sjHJsmzUEr\nfj7J1tbWxYsXb9y4IUyjlrijFgV9csRg59mT+B1yPr5DiXukReV8btWfhHSmi/Fi5asK8fNxXV/r\nBFfQ6WNUCjq3FK1QP3lzvHC0fr7g4dg0aao5vksrfj7V9MzMjLzZYxu1O2qJv0+OGOw8exK/Q87f\nd5ibeQk5n8R2BElnuhgvVr5KED8fedt272pR0JExKgWdW4pWqJ+is36+OsLhmrQ4/XySO3fuyD8+\nhH3U7qhlGX+fHDHYefYkfoecj+9Qkpt5CTmfozbBFH1djBf3+SpB/HzkbWPdaFJBZxtjrhTN2G11\nO0HvZ9ZZP18d4XBNWgPhlGN5eVndX8zsorjcqAv55LjBbmJiIrcn+jXQ5+5XIYKk6uM7zLwzz4rI\n+XJVf6QzDcSLb7jkk6AcK8Eh+4NwHCCcOkCqwcE3XAAAAIBKYOUDAACQFlj5AAAApEUPn162u7sb\n53+Q1McvfvGLtrsQLwjHAcKpA6QanNu3b4etsIcr3yuvvJLayifgA3OCcBwgnDpAqmEJnic+7QQA\nAJAWWPkAAACkBVY+AAAAadHD+3wNY5RmFVJh1UqTCjp9u6egq10/X0XKmc886+woulJOvpbbyRwI\n6OeztZLbhKohUoFcQTAbixH2H+NjoEU/n9zILW51E4OCjrSlhD5StsL71rqfrzrC23x24MAB/zor\n96sAYcMhSjl5gGQ4ekPB/Xy8ldwm4vfzFaUHs9EBnl6WT4t+vsxicaubGBR0pC35WmnDOK37+arD\nzWerq6u8MzL5ubm5ubk5mbP8zUD9QiDfqou+7PPy8rLcPjs7W98zYMOGQ5Ry8sXs7CyxNIf18xlb\nyW0ifj9fUYyh8QmpZmOWZbkTUp+NanutE9IGnl4WF8THZrO4tUWTCjru2NO1YZzW/XzV4eazN998\nUwhx8eJF+eh6wscff3zw4EEhxOLiohDipz/96blz54QQ0rdw9uxZuddoNPrxj3984cKFEydOPHz4\ncGdnR32aFzlEKbe5ubmzsyMPtB5IWD+fsZXcJuIXyBXFGFrFCSm02Si3d2tCOsDKVxXdx0YsbnIa\ntU4zCjrSltirDSvUT94cL9yigk5HN5+NRqOxsTEuiltfX3/ppZc++uijiYkJeZH93ve+J390//79\n0Wgky6ysrEjx7N/+7d+++uqrstjvfvc70Z1/DiNKucXFxfX1dR5IWD+fsZXcJuIXyJWAhGackGo2\nCiFyJ6TQZqPo4IR0gJWvEsTHRixu8/Pz7XavSQUdd+zp2rBC/RRxK+h0dPPZhQsXvv76a9ntP/3T\nP7XtIv8UVsu53EsI8Xd/93fSOPr3f//3//iP/7i1tfXRRx+trKxsbGz4C7XbhSjlrl27pn6km/PC\n+vmMreQ20QGBXHFIaNUnpNBmoxCicxPSRdgPT2OgRT+f2h7Jfb4mFXSkLaINs3V72J6frzrcfCa7\nrQxqisFgIL+bo8rLPPW9ZLzylBwOh3NzcxMTE3KAjruYFQnu59OVcmqqqANUh5/P2EpuE/o1MPip\n2sp9PmNoxgkpZyPZxTgh9dkoy9c9IW3gGy75JCjHSnDI/iAcBwinDpBqcPANFwAAAKASWPkAAACk\nBVY+AAAAadHDp5c9ePCgB1+6LcTPfvaztrsQLwjHAcKpA6QanJs3b4a9qvdw5Xv99ddTW/lEL/7D\npj4QjgOEUwdINXLwaScAAIC0wMoHAAAgLbDyAQAASIse3udrGF0s5xCStUVbfj5b5bx78PMZ6+wo\nup9PPDusfEQB/Xy2VuDnC1hnDwn7j/Ex0KKfzyYkq5sY/Hxku7FyHeLnk6Y9/vwq/VFqQ/j5QlOr\nn09NP/Kgu7B+PmMr8PM5SkY7Gx3g6WX5tOjnswnJ6iYGPx/Zzisn6H4+vfAQfr6++PkGg4Hcrh78\nKAnr5zO2Aj+f3Ag/nw3c56sEkdXZhGRt0aSfj2znRjSC7uc7fvy4NKU9fPgQfr7e+PnUARJCSAGT\nJKyfz9gK/HwS+PlsYOWrii6rswnJ2qUxP5++3Shds7G9vS2EOHXq1JMnT6SphzTHd4GfL0KIn29m\nZmb//v2Tk5Obm5uTk5OqWFg/n7EV+PkE/HxOsPJVgsjqbEKytmjSz0e2G6VrNp4+fbq+vn716tWp\nqalbt24J+Pl64efb3d1dXFz88MMPDx06dP78eVUsrJ/P2Ar8fAJ+PjdhPzyNgRb9fFwV1gwx+PnI\ndlvlpNuyodnZWbmvEokN4efrvp9PzYFhnX4+Yyvw88nXxgkJP1+Gb7j0gwSH7A/CcYBw6gCpBgff\ncAEAAAAqgZUPAABAWmDlAwAAkBY9fHoZ/HxAB+E4QDh1gFSDAz9fPvDzAQLCcYBw6gCpRg4+7QQA\nAJAWWPkAAACkBVY+AAAAadHD+3wNQ8RyxE/WOkH8fGq7vpG8Jds9VYW8Ttl0M36+tsIRefPELUSM\nWV7o6efTi/GeF1XNOUyQqgbjsdYbLTHYZjBGyqelcaOaG+XUfbbDR+rvZLZh/zE+Btr18+l+ssa6\nUaufL9vr0uNvjcVyVYW8Er3pBvx87YbjmCek6WF35IWefj5eTCIlR1lBP5/DBKmOF5dKdsXPR7Iy\n2jH5DCGTsFCeCtvhy/LkmnVki6eX5dOin4/4yRrrRq1+vmyvS4+/NRYb5qkKSSWk6Qb8fC2G454n\njp40Fk45PP18xuHLeOXrQn4+mwlSP15cKtkVPx/JymjHNM4QUsA/T4Xt8GXecs2A4OllcUGkdMRP\n1nLnAvn5xF6XHn9r3J6rKtQLO5q2Ud3P12I47nlCmu6QvNDTz8eHL3UBN27ckAUK+fmMJkhyvLhU\nsit+PpKV0Y7JZwiZhIXyVNgOn8iTa3YiW6x8VdGldMRP1nbXvqWin8+N7ufTKaQqJE1LPaaiVj9f\nK+G45wlpukPyQk8/Hx++LKzfo/L38xn1fnxG6eep6I6fj2dFBiLyZojEP0+F7fAReOudyBYrXyWI\nlI74yVrtmhCB/HxulJ+PUEhVSJqen59vwM/XYjjueUKa7pC80NPPx4d/584d9QefxN/PZ9T7keMl\n/xZU56nojp+PZEUuOHI7nyEc/zwVtsNH4K13I9uwH57GQIt+PuIna4xa/XyqCdstJWMxH1Uhr0Q1\nPazfz9diOO55wnvSFXmhp5+PFFteXlZ3JfViWUE/H/8GUPbseHGppH4N9L/15UnAVElWfCBSRkhm\nSLZ3MhTKU2E7fKR+3nod2eIbLvkkKMdKcMj+IBwHCKcOkGpw8A0XAAAAoBJY+QAAAKQFVj4AAABp\n0cOnl/3hD3+I9p9IauKXv/xl212IF4TjAOHUAVINzu3bt8NW2MOV7+WXX05t5RPwgTlBOA4QTh0g\n1bAEzxOfdgIAAEgLrHwAAADSAisfAACAtOjhfb6G0d1UEYqpuqKgI/vCz+doOlo/n6ecT+xVx4m9\nmRcyyeXKIH0OsXHHGPAx8/GxV4xUIoPNzUfV2a1ghcDTy6pBdFnKiqJMY83QdQUdrxN+PkfTcfr5\nPOV8eg58pMLbJOcjg3Qf4qw2S1+QqnieRIOniuljrxKphLj3HBc0NScbCBZPL8unRT+ffK2bxpqh\n6wo6YxN6z/WRqreJ+Pl403H6+TzlfDa1mxypv0nORwbpPsRZbZa+IKmSPLkGT73Vx14lUgmfwMYL\nmj4nGwgWTy+LC67LIqaxdumKgs5Rp410/HytyAtL4Cnn03Pg08DfJOcjg3QfYhG3SY7kadM0krFX\niVRCJrDxgkbmZLeClWDlqwrRZXHTWOvEr6Bzk7ifr0V5YSE85Xw6xmlQwiTnlkHaDrGI2yRH8rRJ\n+MjYQ0WqMF7QyJx88cUXyV4xByvBylcJrsviprEW6YqCju8OP5+t6WbkhSXwlPPpGKdBCZOcTQbp\nPsQibpMcydMm4SNjDxWpwnhBI3Pym2++6VCw3xL2w9MYaNfPR0xjzdB1BR2vcwg/n73pBsIpgaec\nT++nQ63nY5JzyCBlPe5DnNVm6QuSKs+TaPDkGMnYK0aq+q8mqk2dKJFzsoFg8Q2XfBKUYyU4ZH8Q\njgOEUwdINTj4hgsAAABQCax8AAAA0gIrHwAAgLTo4dPLHjx4EOd/kNTHz3/+87a7EC8IxwHCqQOk\nGpybN2+Gvar3cOV7/fXXU1v5BHxgThCOA4RTB0g1cvBpJwAAgLTAygcAACAtsPIBAABIix7e52sY\nIs0yOrRaJFoFnbtwv/18ufayGOSFJfDx85EypWVyPl5Mvr1bGjkfP59CJcZndVHlob677Sx2X/ci\nD1YIPL2sGtzPZ3Ro1U3nFHS5hfvt51N6F6P2LAZ5YQl8/HykTGmZnKcXk2zvpZ9PomYmmdVZQT8f\nCdZ2FvP8STH4+VqgRT+fzaFVN51T0OUW7refT2LzOMYgLyyBj5/P4ZwrJJMr5MVU23vp58v2zkwy\nq7OCfj4SrO0sJsV4o/Dz9Rzi57M5tNoiWgVd0cKcTvv5hNPjGIO8sAQ+fj5SprRMzt+LqW/vlkbO\n089HZiaZ1aKgn48EazsxSTHeaMzBSrDyVUX389kcWu0SoYKuUOH++fmE0+MYj7ywED5+PlKmikzO\n04upb++ln8+ob1SzWpYp5OfTg3WcmCR/0mjMwUqw8lWC+PlsDq22iFZBl1u4334+4fQ4xiAvLIGP\nn4+UKS2T8/di6tt76ecjM/P111/XZ7XE389HgrWdxaQYOZVE3MF+S9gPT2OgRT8fd2g1Q+cUdLmF\nh3338zk8jjHIC0vg4+ezlSkqk8v1Ysp6yPZe+vkUcmaSWZ0V9PPxYMmJSaSAshhvNHiw+IZLPgnK\nsRIcsj8IxwHCqQOkGhx8wwUAAACoBFY+AAAAaYGVDwAAQFr08Ollu7u7cf4HSX3cvHmz7S7EC8Jx\ngHDqAKkG5/bt22Er7OHK98orr6S28gn4wJwgHAcIpw6QaliC54lPOwEAAKQFVj4AAABpgZUPAABA\nWvTwPl/DGD1V8XzK36Kfz60TI02Twv328/Gmbd1TY5Rb5Kjj9PPZlGzGxGyewhIyOWGS0ukF9POR\nvI1ZI+efZ+40Lu3n43PP1ij8fO3Trp+PO8kaIE4FnUMnRpomMWYJ+PncHkciL1SWIjmWOP18Slij\nS/J4Ym5PoSgik1OV8/mjuqSfj/z0jNnP55knGXtp5aFqVD8p+NwzFuPzGX6+FmjRz2d0kjVAhAo6\nt6qQNM11a/328+V6HPXCw+FwdnZ2dnZWXdPj9PNJdEmeMTG3p9BfJqdXbtT1ZVlGzkd+esbs55Pk\n5ulQhBZSHqq99Nr43DMW4/MZfr6eQzxVRidZi7SooHOrCknTXLeWS6f9fLkeR73w5ubmzs6O3CJV\nA7m04ucTe2V4tsTcnkJPmRyp3DZ/yPnIT8/INXI+eZKxl1YeSshJYZt7pBifz5EHK/ANl+roniqj\nk6x1WlHQuVWF3JTGdV/6j3j9nfbzFfI4Li4urq+vtyIvLIouwzN649y7SHxkcrxy4/wh5yM/PSPX\nyHnmqY+9ivJQoU4K99xTxfh8jjxYgW+4VOTIkSOrq6tXr1797LPPfvvb30on2enTp9955x3lJGuR\n0Wj0wx/+8Cc/+Ym8wyz9ZEePHt3d3f3BD37wox/9SH+rfhO/du2aEOIv/uIv/vIv/zK3iYWFhe98\n5zt8u9SJiWd/0pGfkp6QGMUzBd3Ro0e3t7dzdeRvvfXWhx9+mNtVQrThEK5du6Z+a9blhbWGU447\nd+78+te/lq8vXbokX8jE5ufnc3eRSJncysqKQz1IKpd/3OjzR0LOx//yX/4LOT2lRq7kaOvHJ09y\n7pBpLMv4RCohJwWfe8ZifD5HHqwQ+IZLNbifT+x1kjVDhAo6t6qQNE1izPru5/PxOKrCKhz95kqc\nfj6jdJDf+3F4CgvJ5FTlfP7Iesj5yE9P/UoY8JwN5efzydN2CSqqPJSQk4LPPVkbP3fIfA4eLL7h\nkk+CcqwEh+wPwnGAcOoAqQYH33ABAAAAKoGVDwAAQFpg5QMAAJAWPfxu5/3792P/WlFoPvnkk7a7\nEC8IxwHCqQOkGpzV1dWwV/UernzT09OprXwCPjAnCMcBwqkDpBo5+LQTAABAWmDlAwAAkBY9/LSz\nFZQBxKi2aZEmRTxucYmte7pkp8eWoirhkLedsxRxuw3xBFVR6ggh9CfAESeO+5jGLNPhfbPNmdwx\nFopUkqt/UsWM52/MwX5L2H8PjIHm/41UqUOMapsGiEHEQ9qyiWOMdfKe9MxSVCUc/rYrliIexXCv\nJ6iiUkcdDt60JPeYdshSRKaQKpY7xkKRSvRgbTot9/kLS1ELNLzy6eoQrghphhhEPKQtmzjGWCfv\nSc8sRVXCMb417h6bpYhHQTxBVZQ6/HDoTUtyj2mHLEVkCqkCuWMsZCnK7Bc0x7RvIFg8wyUuiDqE\nK0LapUkRD2krVzykV1Iit25ZiqqEY6vTQQyWImHqNvEElVbq8MNBmpa4j6mIXqajD4pMIVUmd4yF\nLEUkWIdOy33+Rh6swDdcKkLUIS+++GLbPTLQpIhHtSWc4iGCUa2i6I2lqFw4bqK1FBkhnqDSSh2u\n7DE27T6mInqZDh+UPoUkuWMURSxFJNj/9b/+l/DQaXUuWIFvuFSEqEMePHjAFSEt0qSIh7TFxUMO\nuFqlZ5aiKuFwOmEpMkK0QXfu3Cmn1OHKnjfffJM37T6mInqZjp4nmUKK3DGKIpYiEuz/+T//x0en\nxc/fyIMVAt9wCYS6fyNMapu6iUHEY/ObKHGMsdvDvfoY1ZNhvyxFVcLhbxsIpwRGq47epYmJCZs2\nqJxSJ3t2OGy2o9xjql8JfZrzpA5LEZlCmfcYi0YqURc0oh/Sb+A5zt/gweIbLvkkqAhJcMj+IBwH\nCKcOkGpw8A0XAAAAoBJY+QAAAKQFVj4AAABpgZUPAABAWvTwvxrg5wM6CMcBwqkDpBoc+PnygZ8P\nEBCOA4RTB0g1cvBpJwAAgLTAygcAACAtevhpZysQ9xXXs7VFHQq6csouW/eU1FAibV7d9fN5hmN8\n61lnnH4+IYRs0cdBqHpOdikhkzO2olcrX8vtp0+fVnbA0WgkhJBJqkgjxDEW/YLjnsklFH2CHVCF\nWzbZATmfwNPLQqBr24x6trppUkE3tHjCsjxlF4HUKVF+tY76+TzD4W/d4ZA64/Tzzc/PezoIVYxk\nl6y4TM7YCqlWRiErvHz58vj4uCw5Pj6u9DrqRRDqS5WMRZYZ7rXoBVH08abVdpL5kDn86rAe4ull\n+bTo58ssera6aVJBR+q0FctVFfKgiNrN2Erkfj7PcIzD968zTj+fEqjOzs4uLy/bHIR6jGSXrLhM\nztgKqVa+mJ2dlcvAcDicm5uTxeTjKC9fvjw9PR0ohiyrM1UyFgmx6FVX9BmbVttJ5tzhV4f1EE8v\niwvuCSuqUquVOhR0Nk+YKKjcI0EZ/WoO4vTzeYbD35ar00Yrfr4nT56srKwIIT7//PMzZ84YHYQk\nRrKLKCiTE0IYWyHVbm5u7uzsyEi3trZOnTol//Hp0KFDU1NT169fX1lZGQ6HgWIIjHsssgyx6FVU\n9NmaVttJ5tzhF7+cT+AbLhXhnrC2e2SgDgWdqpMo6GyV5+JQu3XOz5cbTpV+ku3x+PkGg8HDhw/X\n19dnZmbkWssdhCRGvosoIpOT8FZItYuLi+vr6+rte++9t7m5eevWrddee+3UqVMrKyvb29vz8/NB\nwwiGeyyyzPb2ttAsehUVfbam9R/pmZPWRRfkfAIrX0UuXbq0sLCwsLBw+PDhw4cPx3b+jEajgwcP\nXrlyRU5cqdESQuzu7p49e5a8JWP567/+63fffTe3zoWFBWMxUnluV+/cuaP/wScVdEKI7e3tXKnY\nW2+9lVs/p8VwqvRTNBJOCZ5//nm1xB47duzIkSNCiKtXr87MzCgHIYnxueee03eRL6RM7osvvvBp\n1NgK6YlUzam3R48e3bdv35dffnnmzJm5ubnt7e3Hjx9H+i2MvLHIF0+fPl1fX7969erU1NStW7eM\np16hVI1Nq+0kc9K6eCbnk0QbLO7zhUH/AJ3r2eqmSQUd94QZi/moClVh7lcbdtPP5xmO8a1/nQ2E\nUwL5vZXx8XF5z4w7CPXbPzJGsktWXCZnbMXWE/2Oqbr0DQaDsDf5skZSVWOR1kPdohdK0Wc7OiRz\n7vDT15dQV0J8wyWfBOVYCQ7ZH4TjAOHUAVINDr7hAgAAAFQCKx8AAIC0wMoHAAAgLbDyAQAASIse\nPrfz0aNH0f77ZE2srq623YV4QTgOEE4dINXgfPrpp2Er7OHK9+qrr6a28gn4wJwgHAcIpw6QaliC\n54lPOwEAAKQFVj4AAABp0cNPO1tBia/cerbmaVFB5y6mN52gn08Ws/WcNN0VPx/vg83RGNYnZ9PI\n6TUYD67e1eLDrR0yLuMwczMvJ+fLnc9kAncv3rD/GB8DLfr5hnY9W61EqKAbMmuXDmmaaGuyvvv5\nVDH1zC13OJ3w86n+qONoczS6k88K+uRsGrlMO168iToccoogFXIzn3GYuZkXCjPzm89kAjcQL55e\nlk+Lfj79MhTqgXU+RKig49YuHaO9Lx0/32AwUMWkK06HNM3rjNPPp1DH0eZodCefFfTJ2TRy+vHi\nTdThkFMESZWMyzZMiSPzonI+n/lMJnAD8eLpZXFBfGMlVGq10qKCjlu7dLhCLCk/n+q5EOL+/fuk\nQtJ0V/x8Ev042hyN7uRFQZ+cUSNHjhdvIn6HHBmXzZYn8jIvKufzmc9kAncxXtznq8T8/Pyf/Mmf\nTE5OPn78WAhx7ty5jz76yKZSawt/BZ0+ln379v3Lf/kvjdd6sVdBNzU1xYttb29PTU2dOnXqpz/9\n6d27d0kaXCFWws938eJF+brQDQzbQJoMZ2ZmZv/+/fv27Xv69Omf//mfk58a/WpF/XxBwimBfhzd\njkZb8pIbN2782Z/92fHjx3NblBo5WeGFCxekKIccrxdffJHsJR1y8nWcDjkyLuMwJbmZ+4dphM89\nMoF5o/HHi5WvEpcuXZIvpDfr9ddfP3jw4E9+8pNIlr3RaPTDH/5Q9UeKu44ePbq7u/uDH/zgRz/6\nkf5WzVQ5lr/4i7/4y7/8y9w6FxYWvvOd7/Bi0tolhPjss8/0s1RCeiKEuHPnzq9//WtVQCrojh49\nur29nav3e+uttz788EOvROwDaTKc3d3dxcXF06dPv/POO+fPnyc/JT0hdYpGwimNfhz5UZa4k5dl\npE9uZWUlV0AoNXKyKqWRIyfmgwcPSBPSIRdu3OEh47pz5w4fpiQ3c/8wCXzuScgEvnPnTufixX2+\nMKjbCSpYn4/UQxGhgo5bu3RI06n5+VTlxp6Tprvi58uYZ9EminMnnxX0yTkkf9mz48Wb0K+BwU/V\nIKmScdmG6Zl5VkTO55jPsjYygRuIF99wySdBOVaCQ/YH4ThAOHWAVIODb7gAAAAAlcDKBwAAIC2w\n8gEAAEiLHn63E5YioINwHCCcOkCqwQluKRpke7+HAwAAAPQbfNoJAAAgLbDyAQAASAusfAAAANIC\nKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8A\nAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0\nwMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoH\nAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAgLbDyAQAASAusfAAAANICKx8AAIC0wMoHAAAg\nLbDyAQAASAusfAA0xGg02r9//2AwmJycHI1GQoi1tbWBRtsdBCAVsPIB0BDvvPPO9773vSzL/v2/\n//fvvPOOEOKDDz6Ynp7OntF2BwFIhbG2OwBAKvzxj39Ur8fGxoQQv/rVr4bDYXs9AiBRsPIB0CjP\nPfdclmXz8/NCiN/85jePHj0aDAbj4+P/8A//cOHChbZ7B0ASDPAZCwANc+bMmc8///yf/umf1Jal\npaUf//jHOzs77XUKgITAygdACwwG9NTjWwAANYFvuADQEM8999za2poQ4ty5cxMTE/qWpaUluQUA\n0AC4zwdAQ/zN3/zNm2+++fXXX4+Njf3X//pfjVsAAA2AD1gAAACkBT7tBAAAkBZY+QAAAKQFVj4A\nAABpgZUPAABAWmDlAwAAkBZY+QAAAKQFVj4AAABpgZUPAABAWmDlAwAAkBZY+QAAAKQFVj4AAABp\ngZUPAABAWmDlAwAAkBZY+QAAAKQFVj4AAABpgZUPAABAWmDlAwAAkBZY+QAAAKQFVj4AAABpgZUP\nAABAWmDlAwAAkBZY+QAAAKQFVj4AAABpgZUPAABAWmDlAwAAkBZY+QAAAKQFVj4AAABpgZUPAABA\nWmDlAwAAkBZY+QAAAKQFVj4AAABpgZUPAABAWmDlAwAAkBZY+QAAAKQFVj4AAABpgZUPAABAWmDl\nAwAAkBb/HyDex+69KJnqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<pdfplumber.display.PageImage at 0x7f3ef8589710>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = p0.to_image()\n",
    "im"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another very useful tool is the visual debugger that shows what rows and columns are selected and if they are parsed correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAANKCAIAAAAhjVxYAACxRUlEQVR4nO39b3Bd1Z3nC/8k2TLC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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<pdfplumber.display.PageImage at 0x7f3ef8589710>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.debug_tablefinder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can be seen that because there is some text before and after the table, the last column is split in half, following the text width."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the simplest (but not flexible) solutions is to crop the page before processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cropped = p0.crop((0, 50, p0.width, p0.height-100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAK0CAIAAACoYi+FAACWZ0lEQVR4nO29b2gd17nv/9iKlbqx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\n2jXrndb8/wMnIrnrOnsZEgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<pdfplumber.display.PageImage at 0x7f3ed95ff780>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cropped.to_image().debug_tablefinder()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we only have the table itself, and all cells are separated correctly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't even need to specify [`table_settings`](https://github.com/jsvine/pdfplumber#table-extraction-settings) to extract the table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "table = cropped.extract_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The returned result is a list of lists containing rows of string data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['Polar low ID', 'Start time', 'End time', 'Start position', 'End position'],\n",
       " ['1',\n",
       "  '2002-01-12 01UTC',\n",
       "  '2002-01-12 16UTC',\n",
       "  '74.54N  28.01E',\n",
       "  '72.74N   46.36E'],\n",
       " ['2',\n",
       "  '2002-01-19 01UTC',\n",
       "  '2002-01-19  06UTC',\n",
       "  '71.00N   46.07E',\n",
       "  '69.21N   49.22E']]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table[:3]  # the first three rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Converting to `DataFrame`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can easily convert it to `pandas.Dataframe`. For the sake of brevity, we only convert the first 10 rows here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(table[1:11], columns=table[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Polar low ID</th>\n",
       "      <th>Start time</th>\n",
       "      <th>End time</th>\n",
       "      <th>Start position</th>\n",
       "      <th>End position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2002-01-12 01UTC</td>\n",
       "      <td>2002-01-12 16UTC</td>\n",
       "      <td>74.54N  28.01E</td>\n",
       "      <td>72.74N   46.36E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2002-01-19 01UTC</td>\n",
       "      <td>2002-01-19  06UTC</td>\n",
       "      <td>71.00N   46.07E</td>\n",
       "      <td>69.21N   49.22E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2002-01-22 10UTC</td>\n",
       "      <td>2002-01-22  12UTC</td>\n",
       "      <td>74.72 N  28.06E</td>\n",
       "      <td>74.77N   25.95E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2002-01-23  12UTC</td>\n",
       "      <td>2002-01-23  13UTC</td>\n",
       "      <td>70.23N   16.84E</td>\n",
       "      <td>69.90N   16.04E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2002-01-26  03UTC</td>\n",
       "      <td>2002-01-27  08UTC</td>\n",
       "      <td>72.09N   14.99E</td>\n",
       "      <td>76.34N    5.99W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2002-02-19  09UTC</td>\n",
       "      <td>2002-02-19  16UTC</td>\n",
       "      <td>74.28N   35.70E</td>\n",
       "      <td>73.50N    34.50E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2002-02-22  00UTC</td>\n",
       "      <td>2002-02-22  08UTC</td>\n",
       "      <td>74.04N   32.80E</td>\n",
       "      <td>76.27N    31.26E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2002-02-22  00UTC</td>\n",
       "      <td>2002-02-22  08UTC</td>\n",
       "      <td>75.70N   30.50E</td>\n",
       "      <td>76.52N    19.21E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2002-02-23  10UTC</td>\n",
       "      <td>2002-02-24  02UTC</td>\n",
       "      <td>68.50N    5.70E</td>\n",
       "      <td>66.10N    11.90E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2002-03-01  12UTC</td>\n",
       "      <td>2002-03-02  00UTC</td>\n",
       "      <td>68.80N   10.10E</td>\n",
       "      <td>69.10N    15.06E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Polar low ID         Start time           End time   Start position  \\\n",
       "0            1   2002-01-12 01UTC   2002-01-12 16UTC   74.54N  28.01E   \n",
       "1            2   2002-01-19 01UTC  2002-01-19  06UTC  71.00N   46.07E   \n",
       "2            3   2002-01-22 10UTC  2002-01-22  12UTC  74.72 N  28.06E   \n",
       "3            4  2002-01-23  12UTC  2002-01-23  13UTC  70.23N   16.84E   \n",
       "4            5  2002-01-26  03UTC  2002-01-27  08UTC  72.09N   14.99E   \n",
       "5            6  2002-02-19  09UTC  2002-02-19  16UTC  74.28N   35.70E   \n",
       "6            7  2002-02-22  00UTC  2002-02-22  08UTC  74.04N   32.80E   \n",
       "7            8  2002-02-22  00UTC  2002-02-22  08UTC  75.70N   30.50E   \n",
       "8            9  2002-02-23  10UTC  2002-02-24  02UTC  68.50N    5.70E   \n",
       "9           10  2002-03-01  12UTC  2002-03-02  00UTC  68.80N   10.10E   \n",
       "\n",
       "       End position  \n",
       "0   72.74N   46.36E  \n",
       "1   69.21N   49.22E  \n",
       "2   74.77N   25.95E  \n",
       "3   69.90N   16.04E  \n",
       "4   76.34N    5.99W  \n",
       "5  73.50N    34.50E  \n",
       "6  76.27N    31.26E  \n",
       "7  76.52N    19.21E  \n",
       "8  66.10N    11.90E  \n",
       "9  69.10N    15.06E  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also convert columns with strings representing dates and times to columns of datetime-like objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df[['Start time', 'End time']] = df[['Start time', 'End time']].apply(pd.to_datetime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we already have an index ('Polar low ID'), we can reset it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.set_index('Polar low ID', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Start time</th>\n",
       "      <th>End time</th>\n",
       "      <th>Start position</th>\n",
       "      <th>End position</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polar low ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2002-01-12 01:00:00</td>\n",
       "      <td>2002-01-12 16:00:00</td>\n",
       "      <td>74.54N  28.01E</td>\n",
       "      <td>72.74N   46.36E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2002-01-19 01:00:00</td>\n",
       "      <td>2002-01-19 06:00:00</td>\n",
       "      <td>71.00N   46.07E</td>\n",
       "      <td>69.21N   49.22E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002-01-22 10:00:00</td>\n",
       "      <td>2002-01-22 12:00:00</td>\n",
       "      <td>74.72 N  28.06E</td>\n",
       "      <td>74.77N   25.95E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002-01-23 12:00:00</td>\n",
       "      <td>2002-01-23 13:00:00</td>\n",
       "      <td>70.23N   16.84E</td>\n",
       "      <td>69.90N   16.04E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2002-01-26 03:00:00</td>\n",
       "      <td>2002-01-27 08:00:00</td>\n",
       "      <td>72.09N   14.99E</td>\n",
       "      <td>76.34N    5.99W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2002-02-19 09:00:00</td>\n",
       "      <td>2002-02-19 16:00:00</td>\n",
       "      <td>74.28N   35.70E</td>\n",
       "      <td>73.50N    34.50E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2002-02-22 00:00:00</td>\n",
       "      <td>2002-02-22 08:00:00</td>\n",
       "      <td>74.04N   32.80E</td>\n",
       "      <td>76.27N    31.26E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2002-02-22 00:00:00</td>\n",
       "      <td>2002-02-22 08:00:00</td>\n",
       "      <td>75.70N   30.50E</td>\n",
       "      <td>76.52N    19.21E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2002-02-23 10:00:00</td>\n",
       "      <td>2002-02-24 02:00:00</td>\n",
       "      <td>68.50N    5.70E</td>\n",
       "      <td>66.10N    11.90E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2002-03-01 12:00:00</td>\n",
       "      <td>2002-03-02 00:00:00</td>\n",
       "      <td>68.80N   10.10E</td>\n",
       "      <td>69.10N    15.06E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Start time            End time   Start position  \\\n",
       "Polar low ID                                                            \n",
       "1            2002-01-12 01:00:00 2002-01-12 16:00:00   74.54N  28.01E   \n",
       "2            2002-01-19 01:00:00 2002-01-19 06:00:00  71.00N   46.07E   \n",
       "3            2002-01-22 10:00:00 2002-01-22 12:00:00  74.72 N  28.06E   \n",
       "4            2002-01-23 12:00:00 2002-01-23 13:00:00  70.23N   16.84E   \n",
       "5            2002-01-26 03:00:00 2002-01-27 08:00:00  72.09N   14.99E   \n",
       "6            2002-02-19 09:00:00 2002-02-19 16:00:00  74.28N   35.70E   \n",
       "7            2002-02-22 00:00:00 2002-02-22 08:00:00  74.04N   32.80E   \n",
       "8            2002-02-22 00:00:00 2002-02-22 08:00:00  75.70N   30.50E   \n",
       "9            2002-02-23 10:00:00 2002-02-24 02:00:00  68.50N    5.70E   \n",
       "10           2002-03-01 12:00:00 2002-03-02 00:00:00  68.80N   10.10E   \n",
       "\n",
       "                  End position  \n",
       "Polar low ID                    \n",
       "1              72.74N   46.36E  \n",
       "2              69.21N   49.22E  \n",
       "3              74.77N   25.95E  \n",
       "4              69.90N   16.04E  \n",
       "5              76.34N    5.99W  \n",
       "6             73.50N    34.50E  \n",
       "7             76.27N    31.26E  \n",
       "8             76.52N    19.21E  \n",
       "9             66.10N    11.90E  \n",
       "10            69.10N    15.06E  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also write a function to convert polar low positions (like \"74.54N 28.01E\") to numerical data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def geostr2coord(arg):\n",
    "    \"\"\"\n",
    "    Function to convert geographical coordinates in traditional notation to longitude and latitude\n",
    "    \n",
    "    [WIP]\n",
    "    \"\"\"\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then apply it to the relevant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for colname in ('Start position', 'End position'):\n",
    "    df[colname] = df[colname].apply(geostr2coord)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Tabula-py](https://github.com/chezou/tabula-py)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another promising library for PDF parsing is `tabula-py`, which is actually just Python bindings to a Java library with the same name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So the caveat is that this is not a pure Python package, and you have to install Java to make it work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nevertheless, let's give it a try."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tabula"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read data from the page 3 of the second PDF document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doc = tabula.read_pdf(moore2016_file,\n",
    "                      pages=3, multiple_tables=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Site</td>\n",
       "      <td>ID</td>\n",
       "      <td>Vicinity</td>\n",
       "      <td>Data</td>\n",
       "      <td>Latitude</td>\n",
       "      <td>Longitude</td>\n",
       "      <td>Elevation (m)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ittoqqortoormiit</td>\n",
       "      <td>04339</td>\n",
       "      <td>Scoresby Sund</td>\n",
       "      <td>Surface, Upper-Air</td>\n",
       "      <td>70.48◦N</td>\n",
       "      <td>21.95◦W</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aputiteeq</td>\n",
       "      <td>04351</td>\n",
       "      <td>Kangerdlugssuaq Fjord</td>\n",
       "      <td>None</td>\n",
       "      <td>67.78◦N</td>\n",
       "      <td>32.30◦W</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tasiilaq</td>\n",
       "      <td>04360</td>\n",
       "      <td>Sermilik Fjord</td>\n",
       "      <td>Surface, Upper-Air</td>\n",
       "      <td>65.60◦N</td>\n",
       "      <td>37.63◦W</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ikermiit</td>\n",
       "      <td>04373</td>\n",
       "      <td>Køge Bugt Fjord</td>\n",
       "      <td>Surface</td>\n",
       "      <td>64.78◦N</td>\n",
       "      <td>40.30◦W</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ikermiuarsuk</td>\n",
       "      <td>04382</td>\n",
       "      <td>North of Cape Farewell</td>\n",
       "      <td>Surface</td>\n",
       "      <td>61.93◦N</td>\n",
       "      <td>42.07◦W</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ikerasassuaq</td>\n",
       "      <td>04390</td>\n",
       "      <td>Cape Farewell</td>\n",
       "      <td>Surface</td>\n",
       "      <td>60.03◦N</td>\n",
       "      <td>43.12◦W</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Narsarsuaq</td>\n",
       "      <td>04270</td>\n",
       "      <td>Cape Farewell</td>\n",
       "      <td>Upper-Air</td>\n",
       "      <td>61.15◦N</td>\n",
       "      <td>45.43◦W</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0      1                       2                   3  \\\n",
       "0              Site     ID                Vicinity                Data   \n",
       "1  Ittoqqortoormiit  04339           Scoresby Sund  Surface, Upper-Air   \n",
       "2         Aputiteeq  04351   Kangerdlugssuaq Fjord                None   \n",
       "3          Tasiilaq  04360          Sermilik Fjord  Surface, Upper-Air   \n",
       "4          Ikermiit  04373         Køge Bugt Fjord             Surface   \n",
       "5      Ikermiuarsuk  04382  North of Cape Farewell             Surface   \n",
       "6      Ikerasassuaq  04390           Cape Farewell             Surface   \n",
       "7        Narsarsuaq  04270           Cape Farewell           Upper-Air   \n",
       "\n",
       "          4          5              6  \n",
       "0  Latitude  Longitude  Elevation (m)  \n",
       "1   70.48◦N    21.95◦W             65  \n",
       "2   67.78◦N    32.30◦W             13  \n",
       "3   65.60◦N    37.63◦W             36  \n",
       "4   64.78◦N    40.30◦W             85  \n",
       "5   61.93◦N    42.07◦W             39  \n",
       "6   60.03◦N    43.12◦W             88  \n",
       "7   61.15◦N    45.43◦W             65  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the package conveniently converts extracted tables to `pandas.DataFrame`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But what about the second table?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "doc = tabula.read_pdf(moore2016_file,\n",
    "                      pages=3, multiple_tables=True, area=(500, 0, 850, 500))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even with `multiple_tables=True`, it could not extract two tables from the same page. Therefore, to get the second table (at the bottom of the page), we used a \"dirty\" solution again - we cropped half of the page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Flight</td>\n",
       "      <td>Date</td>\n",
       "      <td>Science aim</td>\n",
       "      <td>Area of operation</td>\n",
       "      <td>Take-off UTC</td>\n",
       "      <td>Landing UTC</td>\n",
       "      <td>Dropsondes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B268</td>\n",
       "      <td>21 February</td>\n",
       "      <td>Easterly tip jet</td>\n",
       "      <td>Cape Farewell</td>\n",
       "      <td>1048</td>\n",
       "      <td>1627</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B271</td>\n",
       "      <td>25 February</td>\n",
       "      <td>Polar low interacting with Greenland</td>\n",
       "      <td>Iceland Sea</td>\n",
       "      <td>1035</td>\n",
       "      <td>1625</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B274</td>\n",
       "      <td>2 March</td>\n",
       "      <td>Barrier winds</td>\n",
       "      <td>Denmark Strait</td>\n",
       "      <td>1107</td>\n",
       "      <td>1455</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B276</td>\n",
       "      <td>5 March</td>\n",
       "      <td>Barrier winds</td>\n",
       "      <td>Denmark Strait</td>\n",
       "      <td>1120</td>\n",
       "      <td>1706</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B277</td>\n",
       "      <td>6 March</td>\n",
       "      <td>Barrier winds</td>\n",
       "      <td>Denmark Strait</td>\n",
       "      <td>1027</td>\n",
       "      <td>1600</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B278</td>\n",
       "      <td>9 March</td>\n",
       "      <td>Barrier winds and air–sea interaction</td>\n",
       "      <td>Denmark Strait</td>\n",
       "      <td>1031</td>\n",
       "      <td>1511</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        0            1                                      2  \\\n",
       "0  Flight         Date                            Science aim   \n",
       "1    B268  21 February                       Easterly tip jet   \n",
       "2    B271  25 February   Polar low interacting with Greenland   \n",
       "3    B274      2 March                          Barrier winds   \n",
       "4    B276      5 March                          Barrier winds   \n",
       "5    B277      6 March                          Barrier winds   \n",
       "6    B278      9 March  Barrier winds and air–sea interaction   \n",
       "\n",
       "                   3             4            5           6  \n",
       "0  Area of operation  Take-off UTC  Landing UTC  Dropsondes  \n",
       "1      Cape Farewell          1048         1627          12  \n",
       "2        Iceland Sea          1035         1625          16  \n",
       "3     Denmark Strait          1107         1455           9  \n",
       "4     Denmark Strait          1120         1706           8  \n",
       "5     Denmark Strait          1027         1600          17  \n",
       "6     Denmark Strait          1031         1511           6  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tables still need a bit of cleaning, including the correct column names, of course."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some online converters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PDFTables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "https://pdftables.com/ - pay-per-page service focused on tabular data extraction from the folks at ScraperWiki"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "API: https://github.com/pdftables/python-pdftables-api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PDF to XLS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "http://pdftoxls.com/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* https://pythonbytes.fm/episodes/show/26/how-have-you-automated-your-life-or-cli-with-python\n",
    "* https://www.propublica.org/nerds/item/heart-of-nerd-darkness-why-dollars-for-docs-was-so-difficult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <small>\n",
       "    <p> This post was written as an IPython (Jupyter) notebook. You can view or download it using\n",
       "    <a href=\"http://nbviewer.ipython.org/github/ueapy/ueapy.github.io/blob/src/content/notebooks/2017-06-30-pdf-scraping.ipynb\">nbviewer</a>.</p>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HTML(html)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:datasci]",
   "language": "python",
   "name": "conda-env-datasci-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}