{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Glitching an Image\n",
    "\n",
    "Glitching can be achieved in many different ways, including by opening an image file in a text editor and making random changes to the data, or by opening an image file in something like [Audacity](https://www.audacityteam.org/) and running sound filters on the data. \n",
    "\n",
    "The code below is by Mathieu Guimond-Morganti, and can be found on Github [here](https://github.com/guimondmm/prism-sort-glitch). His script allows you to take an input picture file (jpg) and specify parameters by which the data in that file will be manipulated. It works by isolating rows and columns of the data and doing a broken sort of the data (see also [this](http://datamoshing.com/2016/06/16/how-to-glitch-images-using-pixel-sorting/)). \n",
    "\n",
    "Run the code below to see it glitch this photograph of the Euphronios Krater:\n",
    "\n",
    "![the Euphronios Krater](pic01.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 1\n",
      ".......... Done!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Prism Sort glitch algorithm v0.1.3.\n",
    "\n",
    "Copyright 2016 Mathieu Guimond-Morganti\n",
    "\n",
    "This program is licensed under the Creative Commons Attribution-ShareAlike 4.0\n",
    "International License. To view a copy of this license, visit\n",
    "http://creativecommons.org/licenses/by-sa/4.0/.\n",
    "\n",
    "Usage:  %(interpreter)s %(scriptfile)s inputfile [options]\n",
    "-a, --angle=NUM     : rotates the glitch effect by this many degrees\n",
    "                      (default: 0, i.e. vertical)\n",
    "-b, --blocks=NUM    : number of blocks (default: 9)\n",
    "                      affects the overlapping of the glitch effect\n",
    "                      (higher = more intense)\n",
    "-d, --dither        : makes result more noisy, and less blocky\n",
    "-f, --fuzzyedges    : in combination with a rotation, will leave a fuzzy black\n",
    "                      border around the image\n",
    "-h, --help          : displays this help message\n",
    "-H, --horizontal    : processes the image horizontally (same as -a 90)\n",
    "-i, --intensity=NUM : intensity (recommended: -2~2; default: 0)\n",
    "                      will not go lower than (3 - number of blocks)\n",
    "-I, --interpol=NUM  : rotation interpolation (default: 0 = bicubic, sharp)\n",
    "                      (1 = bilinear, smooth; 2 = nearest, aliased)\n",
    "-J, --jpeg=NUM      : saves as JPEG at the specified quality\n",
    "                      (recommended: 75~95)\n",
    "-n, --numoutput=NUM : number of output files to be generated (default: 1)\n",
    "                      the output files are in the format:\n",
    "                      <originalfilename>_out<number>.<ext>\n",
    "                      files are overwritten without warning!\n",
    "-P, --png           : saves as PNG (default)\n",
    "-r, --resize=NUM    : resize factor (e.g. 2 divides side by sqrt(2); optional)\n",
    "-s, --sound         : play alert sound when done (silent by default)\n",
    "-V, --vertical      : processes the image vertically (default; same as -a 0)\n",
    "\"\"\"\n",
    "\n",
    "# Images glitched using this algorithm should be licensed under CC-BY-SA 4.0\n",
    "# as well, with proper attribution, as I believe they constitute\n",
    "# \"adapted material\" as covered under the terms of the license.\n",
    "# The reason for this humble request is to encourage people to further\n",
    "# experiment with this algorithm or its variants, as well as remix\n",
    "# other people's artwork.\n",
    "#\n",
    "# I don't want this project to be solely \"open source\";\n",
    "# I want it to be \"open art\" as well.\n",
    "# If no one credits the use of this algorithm, fewer people will use it.\n",
    "# And a world without glitch art would be sad :'(\n",
    "#\n",
    "# Finally, if you feel generous or you want to get in touch,\n",
    "# my email is guimondmm at gmail dot com\n",
    "\n",
    "\n",
    "from __future__ import print_function  # prevent Python 2 crash, unsupported!\n",
    "from random import random, randrange\n",
    "from math import sqrt, radians as rad, cos, sin\n",
    "from platform import system\n",
    "import sys\n",
    "import getopt\n",
    "import subprocess\n",
    "\n",
    "try:\n",
    "    BOLD = \"\\x1B[0;1m\"  # ANSI escape code for bold or bright text\n",
    "    NORM = \"\\x1B[0m\"  # ANSI escape code to reset text to normal\n",
    "    # verify which version of Python is running the script\n",
    "    assert(sys.version_info.major >= 3)\n",
    "except AssertionError:\n",
    "    print(BOLD+\"\\nThis script requires Python 3.\"+NORM)\n",
    "    print(\"Install it (if needed), then try the command: \"+BOLD,\n",
    "          \"py -3\" if system() == \"Windows\" else \"python3\",\n",
    "          __file__+NORM+\"\\n\")\n",
    "    sys.exit(69)\n",
    "\n",
    "try:\n",
    "    # check for prerequisites: Python 3.x, the Pillow module,\n",
    "    # and its dependencies ($ pip3 install Pillow)\n",
    "    # http://pillow.readthedocs.org/en/3.1.x/installation.html\n",
    "    from PIL import Image, ImageOps\n",
    "except ImportError:\n",
    "    # try to install Pillow automatically\n",
    "    print(\"\\nThis Python 3 script requires the \"+BOLD+\"Pillow\"+NORM+\" module \"\n",
    "          \"and its dependencies.\\nPlease wait...\", end=\"\")\n",
    "    try:\n",
    "        # install is the output of the command in bytes\n",
    "        install = subprocess.Popen([\"pip3\", \"install\", \"Pillow\"],\n",
    "                                   stdout=subprocess.PIPE,\n",
    "                                   stderr=subprocess.PIPE).communicate()[0]\n",
    "    except FileNotFoundError as e:  # if pip3 is not in PATH\n",
    "        install = bytes(str(e), \"ascii\")\n",
    "    if b'Successfully installed' in install:\n",
    "        print(\"\\r\"+BOLD+\"Pillow was automatically installed.\"+NORM,\n",
    "              \"\\nPlease try running the script again.\\n\")\n",
    "    else:  # manual instructions\n",
    "        print(\"\\r\"+BOLD+\"You need to install Pillow manually.\"+NORM)\n",
    "        print(\"Try the command: \"+BOLD, \"pip3 install Pillow\"+NORM)\n",
    "        print(\"or visit\"+BOLD,\n",
    "              \"http://pillow.readthedocs.org/en/3.1.x/installation.html\"\n",
    "              \"\\n\"+NORM)\n",
    "    sys.exit(69)\n",
    "\n",
    "\n",
    "# default parameters\n",
    "IMAGE_WIDTH, IMAGE_HEIGHT = None, None  # will be initialized later\n",
    "RESIZE_FACT = 1  # each dimension will be resized by sqrt(RESIZE_FACT)\n",
    "BLOCKS = 9  # size of a block is each dimension of the picture divided by this\n",
    "NUM_OUTPUT = 1  # how many different images to output\n",
    "DITHER = False  # randomizes the glitch so it looks noisier but less blocky\n",
    "INTENSITY = 0  # tweaks the probability of line corruption\n",
    "ROTATION = 0  # defaults to vertical\n",
    "JPEG = None  # save as .jpg if 0 < JPEG < 100\n",
    "FUZZY_EDGES = False  # if True, don't crop the output as much\n",
    "INTERPOLATION = 0  # for rotation; 0 = bicubic, 1 = bilinear, 2 = nearest\n",
    "BELL = ''  # silent by default\n",
    "L = []  # empty list\n",
    "\n",
    "\n",
    "def printHelp():\n",
    "    \"\"\"Print the script's usage message, formatted from docstring, and exit.\"\"\"\n",
    "    print(__doc__ % {'interpreter': 'py -3' if system() == 'Windows'\n",
    "                                    else 'python3',\n",
    "                     'scriptfile': __file__})\n",
    "    sys.exit(64)\n",
    "\n",
    "\n",
    "def openImage(file, resize=1):\n",
    "    \"\"\"Open an image file, and resize it as needed.\n",
    "\n",
    "    Args:\n",
    "        file (str):  the relative path of the image\n",
    "        resize (int):  a resize factor for the image's dimensions (default: 1)\n",
    "    Returns:\n",
    "        Image, int, int:  an Image object, its width, its height\n",
    "    \"\"\"\n",
    "    try:\n",
    "        image = Image.open(\"pic01.jpg\")\n",
    "    except FileNotFoundError:\n",
    "        print(\"No such file: '\"+file+\"'. Please double-check the command-line \"\n",
    "              \"syntax.\")\n",
    "        sys.exit(66)\n",
    "    # because don't want to manipulate JPEGs directly:\n",
    "    image = image.convert('RGB')\n",
    "    if resize != 1:  # makes image smaller for speed\n",
    "        image = image.resize((int(image.size[0]/sqrt(resize)),\n",
    "                              int(image.size[1]/sqrt(resize))),\n",
    "                             Image.LANCZOS)\n",
    "    width, height = int(image.size[0]), int(image.size[1])\n",
    "    return image, width, height\n",
    "\n",
    "\n",
    "def probability(p):\n",
    "    \"\"\"Determine if a certain event occurs.\n",
    "\n",
    "    Args:\n",
    "        p (float):  the probability of that event\n",
    "    Returns:\n",
    "        bool.\n",
    "    \"\"\"\n",
    "    return True if random() < p else False\n",
    "\n",
    "\n",
    "def partialSort(numList):\n",
    "    \"\"\"Sort a list partially and randomly.\n",
    "\n",
    "    Args:\n",
    "        numList (list):  the list to sort\n",
    "    Returns:\n",
    "        list:  the partially sorted list\n",
    "    \"\"\"\n",
    "    ls = list(numList)\n",
    "    a = randrange(len(ls) - 1)  # lower bound\n",
    "    b = randrange(a + 1, len(ls))  # upper bound\n",
    "    if probability(0.5):\n",
    "        if probability(0.95):\n",
    "            ls = ls[0:a] + sorted(ls[a:b]) + ls[b:]  # middle\n",
    "        else:\n",
    "            ls = sorted(ls[0:b]) + ls[b:]  # beginning\n",
    "    else:\n",
    "        if probability(0.95):\n",
    "            ls = ls[0:a] + list(reversed(sorted(ls[a:b]))) + ls[b:]  # middle\n",
    "        else:\n",
    "            ls = ls[0:a] + list(reversed(sorted(ls[a:])))  # end\n",
    "    return ls\n",
    "\n",
    "\n",
    "def progress(counter=0, total=None, done=False):\n",
    "    \"\"\"Display a rudimentary progression counter.\n",
    "\n",
    "    Args:\n",
    "        counter (int):  keeps track of progression (default: 0)\n",
    "        total (int):  the maximum number of iterations (default: None)\n",
    "        done (bool):  whether a task is done or not (default: False)\n",
    "    \"\"\"\n",
    "    if done:\n",
    "        print('.' * 10, \"Done!\", end=\"\")\n",
    "        print(\"\" if not L\n",
    "              else \" \\U0001F60A\\x1B[0m\"\n",
    "              if system() == \"\\x44\\x61\\x72\\x77\\x69\\x6E\"\n",
    "              else \" \\x3A\\x29\\x1B[0m\")\n",
    "    elif total is not None:\n",
    "        print(\"\" if not L else \"\\x1B[\"+str(31+(counter-1) % 6)+\";1m\", end=\"\")\n",
    "        print(counter, \"/\", total)  # fraction\n",
    "    else:\n",
    "        out = int((counter/2)/(IMAGE_WIDTH if IMAGE_WIDTH >= IMAGE_HEIGHT\n",
    "                               else IMAGE_HEIGHT) * 10)\n",
    "        print(out * '.' + (10 - out) * ' ', end=\"\\r\")  # animated dots\n",
    "\n",
    "\n",
    "def pixSort(image, startW=0, startH=0,\n",
    "            endW=IMAGE_WIDTH, endH=IMAGE_HEIGHT, p=0.8):\n",
    "    \"\"\"Glitch a region of an image using a purposefully broken pixel sort.\n",
    "\n",
    "    Args:\n",
    "        image (Image):  an Image object\n",
    "        startW (int):  starting W-coordinate of the region (default: 0)\n",
    "        startH (int):  starting H-coordinate of the region (default: 0)\n",
    "        endW (int): ending W-coordinate of the region (default: IMAGE_WIDTH)\n",
    "        endH (int): ending H-coordinate of the region (default: IMAGE_HEIGHT)\n",
    "        p (float): the probability of a line being glitched (default: 0.8)\n",
    "    Returns:\n",
    "        Image: the glitched Image object\n",
    "    \"\"\"\n",
    "    for y in range(startH, endH):  # for each line of the pic\n",
    "        progress(y)\n",
    "        if probability(p):\n",
    "            line = []\n",
    "            for x in range(startW, endW):\n",
    "                try:\n",
    "                    # make list of every pixel RGB value on the line as tuple\n",
    "                    line.append(image.getpixel((y, x)))\n",
    "                except IndexError:  # if out of bounds of the picture\n",
    "                    break\n",
    "            # backup of the line before sort, to unglitch a channel later\n",
    "            originalLine = list(line)\n",
    "            # if we broke out of the previous loop we might get out of bounds\n",
    "            try:\n",
    "                line = partialSort(line)\n",
    "            except ValueError:\n",
    "                pass  # My code is bad, and I should feel bad.\n",
    "            # restore one of the original channels at random (looks colourful)\n",
    "            if probability(p * 0.75):\n",
    "                colour = randrange(3)  # 0 = R, 1 = G, 2 = B\n",
    "                for px in range(len(line)):\n",
    "                    line[px] = ((originalLine[px][0]\n",
    "                                 if colour == 0 else line[px][0]),\n",
    "                                (originalLine[px][1]\n",
    "                                 if colour == 1 else line[px][1]),\n",
    "                                (originalLine[px][2]\n",
    "                                 if colour == 2 else line[px][2]))\n",
    "            # make the actual changes to the image object\n",
    "            for x in range(len(line)):\n",
    "                try:\n",
    "                    image.putpixel((y + (randrange(1, 3)\n",
    "                                         if probability(0.1) and DITHER\n",
    "                                         else 1),\n",
    "                                    startW + x),\n",
    "                                   (line[x]))\n",
    "                except IndexError:  # out of bounds of the picture\n",
    "                    break\n",
    "    return image\n",
    "\n",
    "\n",
    "def glitch(image, blocks=9, rotation=0):\n",
    "    \"\"\"Glitch an Image object at a specific angle and intensity.\n",
    "\n",
    "    Args:\n",
    "        image (Image): an Image object\n",
    "        blocks (int): affects size/intensity of overlapping blocks (default: 9)\n",
    "        rotation (int): an angle of rotation (default: 0)\n",
    "    Returns:\n",
    "        Image: the glitched Image object\n",
    "    \"\"\"\n",
    "    # the script works with overlapping rectangles of the following size:\n",
    "    wBlock, hBlock = int(IMAGE_WIDTH/blocks), int(IMAGE_HEIGHT/blocks)\n",
    "\n",
    "    # rotate the picture\n",
    "    if rotation != 0:\n",
    "        image = image.rotate(rotation,\n",
    "                             resample=(Image.BILINEAR\n",
    "                                       if INTERPOLATION == 1\n",
    "                                       else Image.NEAREST\n",
    "                                       if INTERPOLATION == 2\n",
    "                                       else Image.BICUBIC),\n",
    "                             expand=True)\n",
    "    if rotation == 0 or abs(rotation) == 90:  # adds a black border\n",
    "        image = ImageOps.expand(image,\n",
    "                                border=(wBlock if wBlock >= hBlock\n",
    "                                        else hBlock),\n",
    "                                fill=0)\n",
    "\n",
    "    # glitch loop\n",
    "    currentHeight = 0\n",
    "    # the loops continue a bit outside the original image's bounds\n",
    "    # in order to produce the distinctive \"fuzzy edges\" look,\n",
    "    # hence the lengthy conditionals.\n",
    "    while (currentHeight + hBlock * 2/3 <=\n",
    "           (IMAGE_HEIGHT if IMAGE_HEIGHT >= IMAGE_WIDTH\n",
    "            else IMAGE_WIDTH)*2):\n",
    "        currentWidth = 0\n",
    "        while (currentWidth + wBlock * 2/3 <=\n",
    "               (IMAGE_WIDTH if IMAGE_WIDTH >= IMAGE_HEIGHT\n",
    "                else IMAGE_HEIGHT)*2):\n",
    "            image = pixSort(image,\n",
    "                            currentHeight,\n",
    "                            currentWidth,\n",
    "                            currentHeight + hBlock,\n",
    "                            currentWidth + wBlock,\n",
    "                            # I pulled the following formula out of my a**,\n",
    "                            # but empirically it seemed to yield the best\n",
    "                            # p values for blocks between 5 and 11.\n",
    "                            p=(1 - (blocks**2 / (blocks + 1)**2) **\n",
    "                               randrange(blocks - 4 + INTENSITY,\n",
    "                                         blocks - 1 + INTENSITY)))\n",
    "            currentWidth += int(wBlock * 2/3) if blocks > 1 else wBlock\n",
    "        currentHeight += int(hBlock * 2/3) if blocks > 1 else hBlock\n",
    "\n",
    "    # undo the rotation\n",
    "    if rotation != 0:\n",
    "        image = image.rotate(-rotation,\n",
    "                             resample=(Image.BILINEAR\n",
    "                                       if INTERPOLATION == 1\n",
    "                                       else Image.NEAREST\n",
    "                                       if INTERPOLATION == 2\n",
    "                                       else Image.BICUBIC),\n",
    "                             expand=True)\n",
    "\n",
    "    return image\n",
    "\n",
    "\n",
    "def main():\n",
    "    \"\"\"Main loop; Open and save the picture file.\"\"\"\n",
    "    try:\n",
    "        iteration = 0\n",
    "        while iteration < NUM_OUTPUT:\n",
    "            progress(iteration + 1, total=NUM_OUTPUT)\n",
    "\n",
    "            # opening the file and glitching it\n",
    "            im = openImage(FILENAME, RESIZE_FACT)[0]\n",
    "            im = glitch(im, BLOCKS, ROTATION)\n",
    "\n",
    "            # cropping the image to original size, except if fuzzy edges,\n",
    "            # in which case a black border is left around the picture.\n",
    "            trig = (abs(sin(rad(ROTATION)))\n",
    "                    if abs(sin(rad(ROTATION))) > abs(cos(rad(ROTATION)))\n",
    "                    else abs(cos(rad(ROTATION))))  # for fuzzy edges\n",
    "            left = int((im.width -  # the current image width\n",
    "                        IMAGE_WIDTH -  # the original image width\n",
    "                        (0 if not FUZZY_EDGES\n",
    "                         else IMAGE_WIDTH/BLOCKS*trig))/2)  # less crop\n",
    "            top = int((im.height -\n",
    "                       IMAGE_HEIGHT -\n",
    "                       (0 if not FUZZY_EDGES\n",
    "                        else IMAGE_HEIGHT/BLOCKS*trig))/2)\n",
    "            right = int(IMAGE_WIDTH +\n",
    "                        (im.width -\n",
    "                         IMAGE_WIDTH +\n",
    "                         (0 if not FUZZY_EDGES\n",
    "                          else IMAGE_WIDTH/BLOCKS*trig))/2)\n",
    "            bottom = int(IMAGE_HEIGHT +\n",
    "                         (im.height -\n",
    "                          IMAGE_HEIGHT +\n",
    "                          (0 if not FUZZY_EDGES\n",
    "                           else IMAGE_HEIGHT/BLOCKS*trig))/2)\n",
    "            # Pillow uses (left, top, right, bottom) coordinates,\n",
    "            # which define a rectangle region to keep.\n",
    "            im = im.crop(box=(left, top, right, bottom))\n",
    "\n",
    "            # saving the output\n",
    "            if JPEG is None:\n",
    "                im.save(FILENAME.split('.')[0]+'_out'+str(iteration)+\".png\")\n",
    "            else:\n",
    "                im.save(FILENAME.split('.')[0]+'_out'+str(iteration)+\".jpg\",\n",
    "                        optimize=True,\n",
    "                        quality=JPEG,\n",
    "                        subsampling=0)  # see Pillow doc for jpeg options\n",
    "\n",
    "            iteration += 1\n",
    "            progress(done=True)\n",
    "        print(BELL)  # blank line if silent\n",
    "\n",
    "    except KeyboardInterrupt:\n",
    "        print(\"\\nCancelled.\"+NORM if not L\n",
    "              else \"\\n\\x1B[0m\\U0001F308\"\n",
    "              if system() == \"\\x44\\x61\\x72\\x77\\x69\\x6E\"\n",
    "              else \"\\n\\x3A\\x27\\x28\\x1B[0m\")\n",
    "        sys.exit(70)\n",
    "\n",
    "# reading command-line parameters and launching the main loop\n",
    "if __name__ == \"__main__\":\n",
    "    try:\n",
    "        if sys.argv[1] in ('-h', '--help'):\n",
    "            printHelp()\n",
    "        FILENAME = sys.argv[1]\n",
    "        opts, args = getopt.getopt(sys.argv[2:],  # list of valid flags\n",
    "                                   \"hHVdPfsi:r:b:n:J:a:I:\",\n",
    "                                   [\"interpol=\", \"fuzzyedges\", \"vertical\",\n",
    "                                    \"help\", \"blocks=\", \"numoutput=\", \"resize=\",\n",
    "                                    \"dither\", \"intensity=\", \"horizontal\",\n",
    "                                    \"angle=\", \"jpeg=\", \"sound\",\n",
    "                                    \"png\", \"\\x67\\x61\\x79\"])\n",
    "    # IndexError if FILENAME unspecified\n",
    "    except (IndexError, getopt.GetoptError):\n",
    "        printHelp()\n",
    "    for opt, arg in opts:\n",
    "        try:\n",
    "            if opt in ('-h', '--help'):  # usage message\n",
    "                printHelp()\n",
    "            elif opt in ('-b', '--blocks'):  # affects size & num of glitches\n",
    "                BLOCKS = int(arg)\n",
    "            elif opt in ('-n', '--numoutput'):  # number of files generated\n",
    "                NUM_OUTPUT = int(arg)\n",
    "            elif opt in ('-r', '--resize'):  # resize each side by sqrt(arg)\n",
    "                RESIZE_FACT = int(arg)\n",
    "            elif opt in ('-d', '--dither'):  # effect more noisy, less blocky\n",
    "                DITHER = True\n",
    "            elif opt in ('-i', '--intensity'):  # intensity affects probability\n",
    "                INTENSITY = int(arg)\n",
    "            elif opt in ('-H', '--horizontal'):  # rotate by 90 degrees\n",
    "                ROTATION = 90\n",
    "            elif opt in ('-V', '--vertical'):  # useless by default\n",
    "                ROTATION = 0\n",
    "            elif opt in ('\\x2D\\x2D\\x67\\x61\\x79'):  # puts some joy in your life\n",
    "                L.append('')\n",
    "            elif opt in ('-J', '--jpeg'):  # output in jpeg with given quality\n",
    "                JPEG = int(arg)\n",
    "            elif opt in ('-P', '--png'):  # useless by default\n",
    "                JPEG = None\n",
    "            elif opt in ('-a', '--angle'):  # rotation by this angle\n",
    "                ROTATION = int(arg)\n",
    "            elif opt in ('-f', '--fuzzyedges'):  # leaves a black frame\n",
    "                FUZZY_EDGES = True\n",
    "            elif opt in ('-I', '--interpol'):  # resizing always uses Lanczos!\n",
    "                INTERPOLATION = int(arg)\n",
    "            elif opt in ('-s', '--sound'):  # ding!\n",
    "                BELL = '\\a'\n",
    "        except ValueError:\n",
    "            printHelp()\n",
    "    # failsafe for intensity, otherwise yields poor results\n",
    "    INTENSITY = INTENSITY if BLOCKS + INTENSITY >= 3 else 3 - BLOCKS\n",
    "    IMAGE_WIDTH, IMAGE_HEIGHT = openImage(FILENAME, RESIZE_FACT)[1:]\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4Px4olGcBVccKqBtmtLl12jhAHZTK7A8VLc3TgkhSrwZMHlJImXhsw8CZ58iOlNtZmdTTtiw9Vakl2gmJUqEPqEbTi2gGDhT2A2ZnO9Ac8ea9VMhH4950+3gODSCkKICu+tTqESE3VIuMLjQ3Vfbt3iaSOrB49uT44DuWqFtLPPrqzmQ8FfJT41ytlWTjoCXePAMcCqnAex1y+NAwqfBxPePDH3mcb3emgDPNCYAqzc3eGjpzjqeUvExWgGm8RQCBjbC40xZtPvDurunMOLTlQstXdI8DZY7+WwXg9M4KoxvoOdpqGdNfyodF4H3RALsbW9oOlGfZ2GYhYImJi8dLk7t/b1W2fH6h2/6z42HgJ/AezQCO7LQVDJdv2XXXjldXJxvwHCQpyTviYgSIhZVZeRufPoA4cIz+eCl0yuUfmGqTRUfdh55UVdOIFwBxzGaV5vX2WOaqcpw5C2yyvIcpINInAmapChxxwtGqYCqmgGDGkfWCN8IeElhoUVXV1ihFv6fElEZoRh+LqVJEl7olhibHB1vaehbPmt77QKUj26qPVJrI+hpma26cu9Cb2rcc0Q4YffbPb3duekrYNrg9d1ormzOz3nzCoX2TNjp/arF318k9Tf9xGN/wsoUvb9t5DQGPo4/v98rZtD1k04Wepnh8LuOVUvcde/uBD7LBujQ8LwUwsMn6uaG/1NY1F9B7coWhwWMiaat+qCcOqKatEkwyCkRgtLXsLjrxFx4XFY5FgS+xeL+6B5iq2kt8fGgAaO22t/93rQ1ovn+6IdomywBFU0jYNGycqch2Tu5N/bNvjyEgK7bsPT/RaoSGxUIc6NfsQVHQq2oVSGeZGLWXjfWgkKwAmizgmMENAQxTx7R+WqTSQ45qwV65RgJd8ggyEJJmFLiB2WkN2tuQmJy8/eZc9woxMj4mJ4QYPjVnspZlyYc914HWeAm4yRmcdy2fq/Vpm4dWiN1klW3/fG7WP5vsa2ct89RZW4fFJMTSIvDYtp3rViz9o9+I8pd/vFz1Vvz0yTZydOSz5LNJcgZIZhdQwV7KHcYuAEeddULL4r2bDlXjsGRJvDO1TbTxu0PoBEqHbEYy7mjx84WRlrK98Qvf9Hb3wrX2IFtzGYrZZL8nlgBk1RbHNw417xnxpJK3zhZayvZDtsfU5U0AA0RmvyiVE9neTMpAzOftRTyeH2puQRfzQIdkKxDTxMuGBFC2scGFeDIBVIslwATBMDFNRMEQ0DHB3vk85IhGbPQ0TapqgFRExQbIMZhxCrnKQmZPh/qBWDgZPWS2L15UKeQ7OJyS6oEXt7jlMSEEaM4WL4xg5JZmm7fJZbTIimzztiuPOdK61GwUgEcYAd7XtlDVKWYMIFucemLDfwIviBVOZU/zkQD/oXX6gI2LyXXbgQpvmI4WvnN8CnjK2YzWFwJ8/vErwhvF6Tu2sGJ6eFtzDMNWb/Qe6gOer9iNl2A/+dR4eKrNcd9SSsBHPgjwk1X2SDWpWWB+0SZoXztlIdAh2FfvfnFC9hkE5sTmhySb1WgXC+N5AI2OvuUkto8etWRxVTMnlGlAUarAkNQKMDy0yLH4tEQiqiQBh6A93laserxBKBYFMpMZwMAUBMEUBUDH1E0j7Ch7G3uVAff89Dr/z2R3rZ6jf3etWmzLj2/6nTbyfrlz2VJPTZQbD+o85s/2/1rFwAvZ/NWvOoJJpZsZYN/75rjlzMws8kyOZJ//x8b6ytvOO6e+crMStF0VGoiaLjz6b5sl/uIJh659rpGLVxDOPa1SX9k/HRjJJdXAc3ZJXn9iiezj2NzbWjFaVmek6FuHbNwNR+PlKbpnNfuvlkolYMyRbM99p+2akEj7FCqqMaMnmBkLfEZ2OvBzahRitWzEoqWmvL+l/YNV9QHPNDxv1txAo+o0kMT+4BzJWDwlzO2zfv6yx+eNsr3ff9+/NtkIdBUdH0t5o3nysl5/s4OH9tMITl62vL7ywb6ApHDqOoB7dwkAsx15Ld56RR3D5MJEmz0sxsrFbfufnamZBZe8Zc1d/xypqRxT5LaOLn/Ny4o31Hdt5+IjPGlCq2I6Q5eOJaeqMwoKc8P25qIq4t4mKmpAUyJEQ0BVsymT4AjtAX28WOdy6EI+XwTmpW2M68tMAVScdRlDqNZSnR1aTMiNNz0VeymZdiuX7bXnePXy1QD7B4Acdtf7aOpIjR45bVPCwWkfQiwPYNXqRTb//07CJ+ueVPL0joP+ZqFoY3tut1w7K8Cp6wI6n70vAlyxcggg50iPyeyNNNaXA8QniA4B+0eP7HrLeqvuHHbUN9x7AGNX/MKV8Zr6u7YW1ncG5njjgMevdEqsiDi0thjZjH60bZ9DLAtSRQZu3WbbgD66wrOMFJ0tSc5jSuSLAYKaiMUBw3DEScclUfThBsLMPqRaVcfFCecnjn+SUEU2a0WYNYnoQ02JcKT5iEHPcLyh2XnFdr85zZvdcseCeZ02fTttnY9NeSmwrH9TzAKcfbxMpOkVT8V2bmfAWvb1F7Y3/JwfnXsU8KGgr8jDpmdCu4QeC+UmLbSYcpBjSn0/jVRsAHwvOk6pG5gut7TssjtssLi+5XzYH9nd2lIruLbO2jkWm/MZTy3C9e/wyr0jVHMOIUmWD+z1uKJdg7uaWlqX05JK2CutMO6pQ5JrbVvm1NRUZYhQOkA5wkoIWLLAZthdnAhQjpA887Zimrg44SrXHS2qUqFnQTeQnfbI+B07t0l5mt6e+M7lHmE8aa29U4zozUMtBSbjQDfeR35xat/7Iw7D/5LPyHJkYJv8888nARJ91zC6ecuMHupT1dqlacElf9kB1KDzFp/DrY451AEgWWghzmhL8sMpGQlGgWo5GYrYXNH+oLrFgoVzmB49QjZqV9SCeSvULePWontwLfuL2WTRm6GW2UxOuPQg3BL3dDfz0rGurpYyYnLIHuE7Rz3L+EUTaasg5QrZHMW4BHz1O7bd+Ltfu+r8C7nHiebRnXWuhXz4YM7Mc6Br+L0vdQ3AoU6JDhstUoa31nflWZLg61fvLvs0Xi8POjg72wBoy0FAdWruZckqm+kbcT4J4MmAueu9R88FaFrWQ3dYeKxxnyPNwlQtm3wYaBnwcHQDSxkA6F5+qL5lZQFAuM5Wv3b+XJJHAMNztKWOLmG6kZj20ijVjgOz88019TlakrFsqAywv8j8WIphj8VJCfQ6EhDjkuizTg/n4mVdPESxo8UmbIPj3ojpzsSNZXJTZT574ceAg0P23nr+he/KTXK+E7mSnbCxyvTrLa1tZfMDt9Z/zMT4lIy4e9CexyNXrqhvA0xUPQXG9SesvWWw78tHzXqpbwfwLDrwrW475OuOwR3dk3FaCsAQ3vredXbqvoz9zeuODcgyLhx1E499Uu1ub+GvLwEL58zgEpDr7jl/RuR4z+b96eDUXDpvOdAkR4DfO8bV95m9wCvhAO9ioYWFIhaEKmXgyOWz2LoFuO3+sbPfcqJ16QUaaMSBya09T+q1l05ZwEN5o9VSZR7BdjLP7/f03+kSj1ccjkRibDLrRiSMZNWCWTRpVbBFjLVLPPZrWrfZiO/dfs+subP3bHsJ2LbpJasyN7m3WGZhymbScxlR11XA1AMkWQZcFX3ggiiaaItm20qn7FSm4QcrELU1yFyz6RUBfpfPZjQDOBEJ+NXEQYC5EsnUgSTUMXff3l9dH7VJzfcf3Q3MqrPofvp07ju4ThiOWCJSIbxmUVdtG4CDxQPDsUYXANLNc6QRgJ8628s9Tc1AJV8GNOzV84GcDpzTG6T/FjL7mNqVnRHggQMyK5cB48/NErfaFrVJljXswPD+pyqR2m87PT2SK1aWzwM4+ApAWvaGKCqT8UUimVpVj9ozorQoZjiscjAk25+sZDzBbdJxCZs1bz4YFs245y7bNfLEE1c/+ywrl5qASsE0TVGUAb0+bkXAAAaCdtQwmoknRmsziNSpeALH3WFW82wzKQmGGEp6MzSnMrQPYUG/tq+z8RamC4WIo1ic1RECDvl6ctdWgI2f4pk9+ypFiUM6IJwo7hmufxKk88vDkf/bEdCstDrbmgRWJGkVWzhaKo8D0VkJ4D9OnFpka0jBXJeuNXzfSO4KR8gCXh6JA6tbD7ALQO0ufLfPxafdwEAUIJU9lFKOsKuF5c1yrVT8f/uMaT28UALIFQH6Ve/Vb17PX5/whBetlIsqApApERbLkUgE0sd12wN7KD0xS4od0ouAYXaevI79o01qtZrNjE9PTwPnvf8jVsvy2LazzsIwNUBENgzNsgB78gtga0h1A+jyLWlRrI5P6AaezjUVbxQXCiSEkkOQB5Ts7FzqYDij+EzDmiYswNzXKe/oH2v4gGi07fjj7Yff+mytIbcjCvDmX3JGT0RTIZwCzNwwDfnOQtffdwykuwLEyUXq+HBV6AJ4njDw9zmktmmAGVYBgzCg6bqimCrCLzfmiKX9z4mR/n3M+y5xRAEOzHX6Ea/tUCILYIgd2SZnZWujW4ZKCLVRomPl6NrFAJtyAJsmooRsHPpgkVfyHgd3XCw8XlCBVDPDA8WEKQKRYZvwz+qKHdKLs6QYMBgJqwJd3cW+vTuymcnpiYAkHxNMwNR1QEAyDMMwKlArgdqb/Sv33BROJNRiA09DUfI2HSHfWOtSahSJFHWwcPmHPtPgHsfdRDjUWL4A/nGGPayXvuJDrLOO4F9b/M3mrvDUFdmpYaC1mpnpmUf3eozDy8MoQgOVJVAxZKTXjtr9/x2E/FC6uQHjdU6dm+aPxvRIpLFINau9FTjrzQ0crvLZ2oefdtTRwM4DfUC0KTAXNkUSRVEtFv144ILhC7SqinXh6gBEjQZxi7rb8wQ9KoM1txqsNgXgFTmC4zRSygcaZZrsxy4M+5BD0wlSMTXjjVFrNUPnnMiwV3NejUfqwfC92MKREpLUiILj6+uHWck0uRmdcoFr/Ua+///g4nQ1KzVYLZt8jo8WaNouN+q6BnKlKjA81ID9WjA7Vq6qQCRkD7Vm5AGzkTuqjRyDmcmuVFoMZYCtVBj0hj89x+trk9g4ZEDXG2BVtQobngcbM+Tg+K9QhFcEE4gmFKCUB4gmAiTXKNi4P8un8kfT8P+EZ0s2ISl/YM7iR+YwckDo8sjDvUEnvx/OKf7dYdYKu5tCBREgVjuORkwkNrNiFIbPrrW5zFM6r2ikG50J3vKfBnxroU+XKw128HydJ15buqcl3V7f0oXp7Jz6StPUZdGOZLZqynoFqKplIKqn7WaYuMghmBRVNTcNkCaMDyEyBzz+qDXZKLcE6HU2/Qef/09sB6xIAG0xoQ3GCgE0Hy+atnJHLwJ2VICFG++Yx919QKXDJhh/3ECzq/7ZMkrQpTJ68RyAAyPygZEEnXQueFun18+/EyAA1aZE2ZGYBBM9rAFCnbY3W6zzxAzCrok6lrgVkQZKej+cs9HLxNI/r0HjyoYWpS2wg0ywBMhia36/J9oIcRnT8UQtGvUa08BBsQnQGzmvm6YpYgIVVXNr/FetgoCAixzJWDxXLCRjcSBHgakC8PcHNgLnnXOUe7MkNeAtAKPOG0gURfNIUFUc9kcIBRrMcTaQrOpN3rQsAvSPIhaB0cl5Vn28MlJ1RZiF0wzYc18ILwSUAyMAczojvz1QFCeBP/qGvcZE8NnVniwj6ilDEgBBqI0V1+vE+++eEvj5cF8DXdlKn829Bu5/AqD/VK9nW1sbkBm1qTOiB/Cyhf1A1uHAhxM2kxQpViuFICNipuXu6MPPPNu7cB1Qyjci51o1JEuA7BBU0zT3MSASwoccAcrR3NycLRWamx0l0aECoFQ1IOkzLEjpxsih13kfS5KiqaCogCYCNCUC/IQ2Ye8gyZg3ebmpKkBblJwG/O0Ze/hCHQs9zefEbsK2ES5e2Qssey4J8Nz+ChG1Ustg1rA66oteAyFZFWNhnEhMsIP54+GwLtYuu119/OaAhxDt1Oo64dDcholZAIiMQhAhDKXzc3+upbgXV0RVC7xaQAVMMtbPG6UI8MFjlh+5P3FwyrbQ6vYd4x2htt6F61KmBhx7ZF3/poqyKFm5G9zURaZpCj7PDT+5sSdmx45dwAFtACvxTE8FaKpWgVRnBfgzLwDvMry8QaOH+t30KXsGAuIDICuJShlerQAFUwGUoKdFf1tTeWT6/NOO2bt9575D2ZaF84FkuR+gUCYqAvkWe6wXFca89a+mbzzLZhpuMZJAb/5Y4OmdW8LhopLoBFRdyDtbj1xjXJRld1zC4Ui5WAakaBgQi3Z8c1UomyHZ5dFkU60asf4DSIpHxo2BJDDS4VudB3gTc+ZVbNXq+8J78IHcCaA96MeGzI/fma4Zt/TvExpNwG12dAuXogEVZyvMPl0F7nhx+7uOWCVUq/vzuaPm9WyyjNlChkRsWVflyKOPB/ZP2zTSs9yFSyJxCzkEJ6HZ/v5D85k11aJJkqTqppv6AcHJrVZWAaqFCjCHhewGkMbuB6TdCwGJ+4GBiCc1KIagiu5811odhJeOMpbBsc1A5iGVGqYSqoYpRoWB0cmO7q7h8Wxm9CCQ7pkLkICeuYA5aFvwJ8IBiUMTbQbk/KoA3LFvCCgJRrQSy0tVQK2aooMSWjB8NJHutjbHDJWQHg6Fw0CpVNIwiCFaa1A3DU1XnRCgkVBRCslbB2jxKcG0eTlALnkBIEalcx6dfWF745DMBkJyVguMQuq+CYCMZ07LKYalIHflLZGRD1L9tWOvMXMFIJtslYrTHRGlbMYGxzIdDj/9+/v+8b7TTxdME3AVWtaGnhBKqVirIFQsHbmbAEQQBAFBEATTNIXAZuokbylUNKCUzQOPYGvgW05fDzwy+RLQwnqg3xwGJoqE5Knlvb0RRw9dDB1FEJRjaQKqG4FlHXGgpBaAIx2qdUitmII8fmiorbstUaFa0YCEfMgerMlpYLBkf98b5vl8Ryam0XTglwxeGukF3r6oFfjlgbwBuVweiMVSYSvzmkQxGKtXwaCqVU1C6AXV3jKVigKqFkaRBMBQTVkUTd1+aUhPh0vZydmsV72pfUprrVS1sJf+ACOd+yQ+V8UgHgDrT0g99URAe5u15K6LeqexHZITfxiW9Qqwx7F2K4Q3Mdd0UFyVTUDJTehmtL0lrVUjG/sGj5pnN+5duI5ErGoYCKYqCwpSoYSSADDEeEYvdziJxTzeUxD6GEwLnaZpCoJvnN1tRdWQZH0yW2DjS+614y9/B/Cfm+8G3sFbgH8dPQYIIlTNgqZZd8cESX2hZhxoWmu55S0AFrWMAXsmrS46LUYLmkFLT+LIRQuPm9dVrQkPP6kb+OkT24Cvn8WqcAooVyMAL01ba+t8mmRxGliVPQSoagxIJZNAWI5awrUBhi9rVYtOCVMz9ZBAjFjWkQ+VqAGCZV0ATFGQBNFwllFvpT0+PTYWZu4cb+ye6q/IQjgV9Sa7lirItbLu5hcndKHNXyNVKwAPb4qQpJgDjkpGVszuBm7fAjAe6TqevjwVsImD8aYooD48aEq6EEI0xGMWeXotobD1qReHzpu3EFOoKoKCIeh2kgMsZzBnZ/TwwLR/1obmmsHMhN/49IcADh0uGOkwsOqUdcChSVuuSBueGHLJj/5S07hbTVuFn58RXGEhh/N8IgtcWvXW/Z/eZrMgc2sdMwByjdJjJl1zN3ngU9saK/GARHNjd+gxV5hSEkAkWwakmG1j6xxdTB2Tngl5jMW3gtjgwnvobFjfR3X7cQeAS54GmDT587FxgANl4J0jc1rP8FwwP66uA274Y+1D3nOOJyD87r5/XPjBD9W/aHZzgzFUtXrbigOWeYZQUOicAZrrvMg6NRHodzJMHrHGy4r01q5/WoULsvZYf8rR0GVFe2tfKJcA5NOsnztbNgJTEz5LlSwBc1vpRwdaJjzkmwlc39wwkWSrUuprrKcBpELRWyg+hamQdJjQyRJgxDRAfdfbrbrMPbupgzuP9VR52qO1SHACjxE+PZHzMoNt8XmODfHMcOQUYNIEaBEoVgcBygrQUXpJ29+MbNPCPcU+GrklmT7MkyMR5FprjnWlvkqgMmMLtWpFvc3sAeQDnVptqWXTszwDACHqLaBuxxQ3vcpeSZGDNgrGmm2CmSsdByDZ+9q94/uB8rSnjpONaSA34fBm1UAHkjSY+Jzofm05N0HSSM/8QT4HLEFQHE5Fdi13MQ3HAF3c72jA8joZHfj7p1a6d2sFb/29K1TrtpggRr6Mj/Ub4hm3XCF214tDwBlpgHwR3VTd3n2rq/Mre2RO6JycKgOds5sApc4xcnTSs2v3pNOi0QA5TDNAAmzuNUhNAnhQrVYB2usD/huA6S1LG/SQjM/sK1Y9RWbBGWHN2eoMR2cQOtJWdVcttJi0t+rTV/UCf/6P92FFkkDKeepktwnEHY/DxgY0B9pI5kZy2SGfyB10bInO9jl+yrLsiP6C66sgiYBalIH7HPXiWxNxy3RT8REbxZfIpjzaKBd0fD/Md39VfN7teRbnfUnfEjHUigqgykBXMTwptDFVbmmOANFUGidFpx9mrw3MoOGLHwNK0yXADC5tQQDDwAhj6zl9SjALRsZGAeY0dseq/UBXiW20bTIB1isSMFSxUfLXT3q8bdTt3qDjkeZMpjkZZNwUm7RIz44Bv3i7/Z3L8kwW8kCz41IZrwJUaGCJ8IF99RDQ3JT0W/MnA+hUUQ2LRzNN068xFVAAQdCpKoDZEgEic+xOPrzEJjDn3bQVuOWiFqBN8syQxQZjmWIyK1Q9H+w8i4F3v3EEuBX17dI8f2szewDAnAPsOIofAMPVnVtLwL+1PXgspgf/vttbt+uPOWo6G3CQtpanaQamXhRBLpqGiE2AfepzC7IWQ/DYk8DRJ7KhLg5jDt63qqS3nrwFQDzJyn1jGCKwIN1gV9pnLe+lTf1Zu9+RHvubDCd0zPI5omI3ENbPAZrHbN7KSBONJIFKjcq4EXOqV+x1L4UDKsiTWnwczLDt8nnTCnr2xluEdidBmwC64TipCIIMmKZkkZJwrgxUXNdah2AUZ6eAy/4yCfz5o14U9f1vrg3AeiuT/JqoD01z1rvmGMBV9H1zS0AHb1huJScApKd2A2jzlraWgLue2wfMr/N17+vz+G6hTuyIWrZuM0A4DR1EUTBExGAKhh99zc4G9rf7HgRKZW18euIok1yxFaiU88CRa1cDBxxxHFjM3JRxCoCppvMA24b2AI/841Hg45dd8dyfb3cbH9UtAuyc3rjUVvg0Zx2bH1NAe0vL6GTG313xmQGg7Q32KjQUouE0YAYtYnrDoL2o4Fyl6OxLsfDkPXt8U+VYbqMHmE5kmk3BWi6qWiUacrcV0eklZW/9TR20eY5ISimVKijKHSfIwAefSwHGrR5VeFs8wDXHzEQUuDiger+KPkC4LZFm0oRs547EguVy9zLA3Ecxth1gbATQ8r0702Gm88c1JYBIIgW4+F+VbHqmlT02WZnT1VwoRPy5iw3LdbzORq8jyga+FIkyPpHXSmWv62Zzonl0lPFpA+hsjgHpdLq1mYU+r4K99Ns/MgvCFotYqgCjI2NArlQ+eMjDpG6LTq9qMwv2XM1xgs3bW1qAnHyaPvdVf1dleQoQKzZvNZ6hOSpiZwR6DZAcvkevIIcJVaiGwZg9lZwojDq9arH9AeIqRiVtOPKnaZqYppvMWXZZk5CHhdNOblCziqIoNNnc6B/OBqje6zGYBFG5VG2OMsU/9UjVy0ZnUgWEtukKC4HFC1unimPCUAmoRjCrMkBEIiT3FMsvVwQM7bGpDE7I2kJHWN7hrLfLv3iFXdXVCmRf3eHXbpmW9aA+G52EQATR4xRlQHZEo47uNuCVrbvamloP7qU5MQF0dy4DZEHUgjxvhgWdAzrAwtHQBIDRGgfSrWmgND216AjPHWb38B6AguqKGJ852h62nPxGoDz612IqYLVSnhsHtHPnWT8nEyyv5oCJ1xFQormigAwGZpyQ0Vk18vMzw3ubHF31ASdLmMrgwn2RCdv7QZMkTM/sJriJU2Lei+OhOGBo07liJpxYzvgm/9u/InhUPZawadUoo4yTiByMkKI6eI0wD5hX2Qacx1IgktEtn8btc/Wu9IKqS/ZzUwCpMFXt7zl9S16ny6ZGyogCdDpb/d5NDj0znQnOZICyJLu6dFcNatY77+kysoAZpBzNaVs27O7uBkbGp4GiQSjeCbS0tADxeNwfg55IkuXJ3gNHAKRbpVcA9FOLQFtzG6CGw8WcR4fnWyOcCO3aai/cqQ6bAqiWv2THGTVdFd40BaiCUNBFLK/CqACojdj/w4AiohqExAFNbWmKNIl1yfbFBLMnkyknR+B0Lo+J5vIcIcduYXqLw4hWyZVEuSmm6yoSTsZmC7SjPVeP0oOuG9EobZDnDuYzuhn6gIqt99QB0lKZPiAktKjFshWGJvSgR44AqIwRYqo6ZSgKe+xnhkIyEHW4l64Ftidz1PHmp1oFJnRddHwtbBKiKIYYVEkDUDECu7Rd/NWPvljfFNjb57ar5rMzxhP73HhfGypP/Ab4xMlHuDV37j6cZ83xLfbDO+IexSsFWfTe5rkHp/qHZuhhty855Mf/08Az9LzFiaf3q0Cr40k/1tyEACbx8gxaH8kArj+lSe9cy3Rj32kX3n6PE4fpkzGfqwD0hQMBQf9jjgO/XOgLxZO8wbl4IgwcO9LAxcuFBb1PXb7wbODfszv9e8efH3rqnWeun+muXLEIIDU6jGd4ZIZEAxJElwImpGf2ZMnnGvt5NDc1QBoLE7aUvEulA/sBIs6IlLPAjjSXSD2ANMum0rNne7LS9qAfVlVD02cnpMb+z5rnzkqL5qgmFc8Wun1P1Qr2Uh3ZR3PPtAg1VC/aVhhFTmw5UJvL9qsb+W6NIdIdc5/dzU5GnwvkAE5c0vF/h7r1kYxX45PVW8rKV5bzQCN50IXOFuVf5hZgVscqhr1pFUPhTmFG3XfO0loFHXMcq2ypihS21r8U9iiLIEFoEDAJ6eaMBD2aMACh7gQuM9fAEy6bSgDXb/UUeysttCg76z6SAsQQeiIJhJzUhtu2bAc6lswHzPFAqgW1dZE5vkeeoYef2eVxM2nXF9rn1aYKTKoaIDjIYQom5mHSl4AiAdOacecjtbGZkVa++kogFXBqPEDo8p0kZvAnNP4pVperEcXjWvxedksGSqpAoRBQP/ZpL8+T1wF9vAwcOy+kMYGVqqTDWwCSHDJ8P2thX6a+zsFBKYxesUbG9CnPBKBUTqBDId/IN90PXuZDp6YaaXDLwublQKvc59YcKk0ApERFjQCqkge6sjw/Og68rdOmxSXTAEZ37Qe0IB5UTUUzjYahe4Dsk+lTznkR/ok3TX2+Egamnf0x7ThXCcUZlmnCAJ7v69+bD8bgz52t9AMBT587zz/d//M9G7nzfLt8hKVhffZ56+e84eWVBQckX2bHaSftxb6xvQm1U5cxNG97PcDWLqmrT3u5i64uugC5LOSqI0C7l9rWABSt0t7SbKvt61bRvn199Z/oKBskGUexY2KYVbtDUhiMimVGEQTVmCHhsuU5YmYcWp8AKFdDgpP/yg+P9r8M3PymtW7NN162dalqaAowqs1Aqkw2MgWI4Q6pAtC8dCEwuHMvoCUX+p9ZkTUtuVCZIY5DHPJ4goHxTH2DVEtrdmICCDsGI9fzXG5pLDrragF4abBfcU94tKD/oLQYyoHKI+YEVGHCPRzhIMcWy/95p63JSmwrm6Y5XfEQThJtbJ7dMT/TVtUFJN8q7aJL3JnoWto1zHAPi4DBK28E5n/t63hqXhGY3zsbQbTRom4VNbSnuZoor7Wh51ytowQQVqkChmkKNEaOmgTtprVzJRQTs1SnwSylYsBkxvPHnHLUps2pZvfncyKtqQRwoFyYEwHYdnAcIGbluwv4niUnUnk9m5QaBxOkwx5VT7qHLbZ4Ezau6/ObWoB+R73huhLq1cbMsoQJZARyNbaN3t5eiVzUVnPNYg+w/o/PA7lJJdmiAqF5rP8jwDdPWf31J7cCrD/Xap+f+PuXlnX8cJcvGaizykOm2N02Z2icSnUEGNy/B+hhkdwV1nbSwyKZMJB99DH5rh/s/79/dT7+L3+/ZvWmM5kGuSEsqHHzsV/tXPOxXXpYcBBJEUEXy+QAAUGYgW6H1Rx4CWLs7K5CAjMv1e3b726rAI9lRpYvtHVHhyaqRy2IASUjB3y+2Vbr3FYdBFIollmxRQu7WfE+8oitHPvtR9cC5enimkWzs3IDhitlIm312NfNCYfeTgd0l4PogOE4eVuqejSjrf/Ft5//RrfZaqfBofgw8PG/RpNtIUkOsBSiQdJxuKrQDeQmy0BcFA7RCnTCIWgbHqqI4XWtiwGmNlvtr1q74Ev/LKZWeQ/ctd/Wty7oXCiZ5p5tDFT3i1W9q6sLkAmLaXu2LA/yT7+86YYLv1T8xFnrfvjPB+69byozlKQJUJKJg9tnRA65tROfZ6Fdaf2Lx+NAuQDWVuKAFIV8wSQOKFIJzLeceWb9o/95/z0ACTukwsplKapjhioLCMnglGUNHZA1494/2ButHqZkaMC0xTK3QCoJTPWFAMU0zSJAPBY2VA0wY2JC5Lr3c+Xv5oY1GShVUuiy1FCyqHJJr4ccnx00yuUqgO/AM6kwocdbGR0RHFOzabQCaql05knrZqcSvodVUbKAnCkDUdNIxOJaMKZLqCI6TLRGBYhH7f0uHg4BgkY8TFsqok8cWrtiHYBzZqfQ/zy75hcXDpay9kTGnICr4dyQkkjlFarZyrye2WI0AUiIVDQpKtpvg8enMmsefXzzhV9KtTQvXrJk+/ZiGR2I5KRyMiA89vl4+mUtJnU7gONgXMgDkWAc3srZS3bsA31nEwZQFAHGRhvEt2fzeYCIGrEYFxlAKRsZvS2CWg5yAkOlwrP92c8dtbDnKLsvz+zat3e0CjSVFUCZlySbA77Q0gZE5YqtwjaqkWgIKBe1RV2RSolFXeG4IQFT41toPzJUxxIpBoUQCcmXWEPYV9V0gITHpYZLpaqZIy6HoBqTgbAE0NzSdPIRvZruSchVsqgpQDZVQKmWw1pVEsL4tHNyCK3kIJnFxBglVE1MV8NiDJAqhKMs6EibQnnB+Atgi7g6HWZTON/5nKHPchPQir7jZBVT3hVDHSh2OJnti07Iggt/GRaB5F0PPJts2bNrd1XTVfJAvCmuBrfIWQG+yGKtAljuUA4LPf1jq06rBwda2nByUxDOCwRPWtyzZw+wiEXOw1JGdRrswDUhSjODZRI1/nsHp3NhifJ0Ze3StXbV0L5xSyCPqMC0phHz2MBOo2glLBGiclVXATHE0s6kXmVpZ8IiqZppGJIhVevYKgFdQM55lrBoOKpYQqw/pq1oKi0RhKQJYnTKug9YsXRBRCxMZj1tgUnU/jypCDTHw4X8lBX77/n0R4k7RzdaLsSCWCQSM8sxJaqCndJkeW+7mt+rr74AILIVkBgVD3VQOh12CXGbNZZ8/cwa5ieX8uuRdMLRypfqMh3KVRmoZEq33f7rro5OHNxafOK8rc8GArHmeS4lCEIHYAbPNQ78uO+3N6nlGQ9ZisbCwIF9ewFu+23N1VLz/PpbxlcvqK+0QHnRcwj4+n5Pj2RhxQ+PWO3WLI3vLQc3ptZwcjDmiezTBbOlGgIW9gYDjcL28q3JTfre+0cB1Bl1ejXw+wsaJ2M5DHxhszeM48RbLN+RQNbwAHxyfUsiMitfthV0oZIdXPOjh3YBnz/TO8lqKDz7hZECkGptHhueMfqy5JhhxRlkiBo4642nAVpDnsOCRfMX7tm/lxmgo7UFh1qwxJ4GfSxtd8Jh/sN7PeOqic1yaqmsnA2IEpWk72fIQw4rS1mfL8J9UVRSg0xtORGTNW/Cm8Rs3j7wfHVANnIUozWnwYuWI2OjzAANQQpJ7qn11cM7nDkgSAJQQAfaKRYq5MvlRLixHhmQtQlzcEJO2YnzRDFjnY0kVCsEz0nKv5JIzjOB8ZHRehFjjZOM4qmX7fHRtNdl2bCODLNi7VxlZpAOi+ai+Qtr73Ogf992wLS2Jd3WZLuHa7o4UVm4yr1FS9lET86m5iwKRLI/8LzHObd2eSlH89OjwPZdHi19U0+0hnSKmpCSvQUxbebCpTkAktbQyKMEK2OKAJC2sfOUw5xEaXVeDLtemNLrG2uxXAZEPQaIUjFfRiz05SMzUiCpaZXW5CG2lMlYazgZVgBRkoFr2QycMjdWzOqAidneVdv1KWeV5adsbjPWmm7wPuvMl2W1bkI2tjlJNwLIsX3r1uUrVzID9Pf3A4bFHE7aK1tqz1iFqkNLBJ9DkGnYSjA1UcIM6G7HNY/LmzjgMzlERKDY7KmtTKGnK7gv5EU5kvccwsrpcmFEAaaFROCDZkhV1RSPARTtDmz2Me1DjSxoF6/0niPMfICEHxRNx7EqKFNKKKIQWUG4gS7BAvngUCVFSBgBaFlbKpUsw2pq7kIAtYTjGl4qldWKCcxd5C3j5h4bS5x0gVQm7R0q2dnozCxLi+OT/K0oQGuYTWcBBJBjz76te/ZtZQaIRlLAdL4A0O1GeTQO97BB84bSDCqL9u3xDARr4k0nH2ebqp597klgqDNt31WqjqeD2dNhzWTAwFaKnnbvzg3At1YFw1KUdMNODZUUgDFHVnTIVrGf7naA6VggBbsUb3Z3k1DwDTPBZCICJMvlH7PqK8om0TqofWZnAyExqsmEJw2ARFlJt1k6h+VrVgDX/v1vQAoF2LOq1NLShg8hgKlBGylcCmdl3QCSsUY5PCL7AXyXZEkARAstnHPKA8gxU4YFCwInWb4uLocuxywCEMwd48+AmkzEirkJgAOjyXgCKJVsPYEQDYUqtSeclYOZvOUwmVAUJ6YLOP8vAPdclKnv0tse4h3zRIB5NgY86ITrxebauZWaioGwLtPsVUJuudF31kHTPqudvJUdRnO4ORnNTZaSM2jiAUPKx3SERCuAKQvhmD1Y1RFAqFY+//Yjxb+pwHX5QltrCBga8Z/waE/nuEO15URgE68F31nah4EAcgiCKQiCP5mQX2VmnZ49akUZRRv4fBsJRCdq79+t3cCnfB4lZiB5r170DfOalcs3W0baOR1TOzcDX+k9AmiL9vWNViN5xFg3IW9X0pv3AVXyoQLVeOJD1zx++2VrgTGh56t3PAB2ppePPODoeSseXipxPnnyemDa8ZKf3Vn9oFYA7pBdPU/nafM9tC5lvY2yRazF1KrWgEeVm+cDprLjtyzLlbZlDuzpXLzuiFDtvS6kWzqyYyqWL1z20J5i+bGntwGnXngqkAwrZiJNOA90d7XGks3Ar+74U6QuIZELbz7TTieiCwKw6fmg6XjZYuBjR7Tl8iVLoWIYhmEY1sy7eb5qFQOmaVpkQRTFmuBJK6rFOriaRANDjZjHcNKEL22dBiTJ0y26wU4ASH6FWzE3YR2ZzLa+zo42HB47Z+rN7UK8FDakxMvjHgN7fCQPhOgEQjl+JS4s6SYQLlXjSEA+C1Cek7biCcpmFkhMKUBII7t7AxBvsV1mlncsrE5rwPImzxw9ccCz3aRSnmqoatSiQsivO0/Ps4cCGTDUZSY7Ln37iY///X6KOy44tsEJHkBWHi4V1KqmM3kAoJx5pX86V1GBppYm4Li3XTDW1jv0toNAkybYJ2lIkjYzGVOcYJBSsYrPVGSDw2248o411/aMO9ShwRy77QQhICtZ9dPWLCYauY0kcN1a4hNZQGj2hc4EthW9d7ank9j4wia33H3C8cBocQSYmxDEihBTEgUltXnAM4KfukQBTArVOKFC/g5p5E2ZeYBsCJoVXgDAeFWxNHiK5R7RPAGIYMgJQKvYyNrZFNIKo0Bn3NvFR3xBGNGwJzqp07X+RFamBqVzCUCmz6p83xElYOWsZVcSXhGrzDvjWGOilNRyMbGBGc+crB7MZ0Xd2XSaxL0TRTEUBkRpPtA9C0Pf3z1rPrDl1W0Tk5NAMtlUnlmwTjXZwzs4OgDU5h10uA3BmWM/cpg0Qo4a/DJNK5WH6b9qHcLO3NfYtHr6xwHD8BBCDUocPelEuxOl1t8iA/2TGjA+MAhsVYeB+akQYaOqR0ZK2vZ+byAia9qAMtNhAzMyW4tN7uo/CMzujpdFE4gYUDVF4gwMAErPAsAoakCqQrJ3CXBou+2CpZb3MWcVQWqxXfd2sXlL0m65mvUImB/UkV3+nyeu2AU8uw0DbWpgU0d797RZzBZNYvX5gBBSXfHwxK5XHA3TsSfsHd91+splQPuef0lh9Gj7LlN8+cX7gZFwYmxsDFDVijSj9MP69bZH4NYdvwPaGjEZgiCIolhLVMDlnA/j6uTB9V+/uL7y0VwD5nssATCVt0f2M821u2yJHsDUGiYgPhz86Jv/d8zqgFz+zVXeavjJk57mbZsv4GyqUgJevWAJsPqBkhYWepyle/Mba/tf1hvz44mlxzas18dsIXginWqfqhWpXDAqUq6qAVLHjEmhgLvvfc4urW1wIocFKxfNram57vfPNGzpwrJVRwHtqdcV/1wDr/MeEbBos6bZG4fe1OBee100aV9bcyQwsOVFIJH2GpSZAIa2NMi2dng4ZvXiF1/ZDTzwvlXn/P5VwDjGE/QHfFMzlfZU1+Kyk4DMX7cAz3/2mONvG7zc8cla2FMrlb4QxNilERuPMnID7hswRLs+WcCY2T1TEqKGUAUGgzmAe3znTKdizWLIwfWZD+fqnKpV+Xd1NCAJps9+ZhEG1xbzX8HrQg5B8LYE0WHXTbOBhsMiRM2If/zjH4Hjl80F8hmvgWI5PsmvpS74Se0i+/plWzhmIWBQvP8jC4FrnvA0/U/5+nKkbwD1/S8BY73NQOqZzfec3Gq4GZI0exW+0fk4rTUwK7FBG1mGpBlEUNe5Qekw9Bm3/3Bpf3vzXGCvnvHX51rSbrmrWjji+Mb0yQ9CvnZRhaMN+uY/PrxcHAI627rrm70mvDZy3HD1pZWKiuOQ6/r7TNUEOQHQngfQE9L0yllAtVQF/GmP7SjdRumwA3D3kpoKsWzP6veff/XLx60CyjGP/C5yAgB3y6xJe3dtHi0BvzsqCZyttHUtkEWHr/jlffdahdPeatdsnwhkk1zjpHsXq42NkUo4bLkQ5CoHNakxdQEOxVeY5RFgvc+DBHipnPHK0BJ31sMLzzED7FxTOyzl6mh9swgexhwaOwSkUqfN9EyAiQZTyetBDlMUrKwKlpjj8i9TTrK5l/DFvDQDvJuv6moKy1TtU9sBEgrQ9ZpE7ulal+7vP29zFU/tAV4FXhzzeMavvMmOgrlyM9ef65Hrz9yzC6gsawXMUtOmHTnnCHoO3GUXdIeWHDMSUJ7vLUNHK9BhNo54MKOhsoX9RZSYjRyffN5A9Ta5dLz5mmMXGbkQsFsM8OSFcmAU2lzFYm5GIlSu4eqh5GgIv3jl593Ka6/7kVu2guNjMe9d+/trYymYQfnpIceN3/yYX6/x6W/83Cp85pu3/vTbH8cJrrS2lVxO1++8w2pwP15Yzlf5klVYfOmHgHA4AVR8C08m/60bHvnal97u78Rl111Z268PXAnM73rCreh3JQmRP+0D6DFs88ownBGzyzH40raTAE6cBCIXLwfKv94OlC97xxlKwU0w35qyiU3FUXFFO+oSk4xOALG2xh7905IkRxXAzFesAtZ6CHsiiSnHykMbt0+JwHe22b6Ac8Qm4IDTfwtuPM2J+Zv5zMdMrjYnyrRz7OizzzxVXwksWLgYKFc9rAqJUQC/e0OjAxGwkOOGb/yPaZrohiu6XPGtW6//+ket8me+eatpmmq5Yu0OVlInUaS72yaDhSu8HoskgNEPD8z59yNA6p3vAkb9cZSUZy3IV4Jb0i1XXldTc73yBBAx3+TWzA3fO1ZSyz7xSmuTx8c1IASvxsPLUl1ARCC8YwtAi0fnn88AvHF08I1/fS6hhe6+DGCZo4NxcwctDCbC/uB9+1nTA/yuVBgQ7AX6p22e489H1nUItAFCov1zD9vcQBQvxQigFitKV7KvMIGDEzhoMYLX7IIVy/LlMnDrji2fPOvNONrkfVUd+BsAn9smPbhjQ6UQ6OT3vv0tq/DXu+52K49c40UTHnXMiQsXzN25ZZNbY2+APnRBrvXat9xSXQdjwW82uP7rH/UfFCiKoiBLfroioVedxD03v+U2IM9Y4lSEJyJA75w2S5H47HO1aQb3QLpp6tNf+TKwvGPOdg4AN3zm+6GpAGWLtL8JOHfS06H90ZiOi6ST3iJWy1qIIiAKXPjLwoq5GUCSeP+JbQAHPaL4WEd0ej769ME3rDrm+G6pcADgPcc7I+LsQmo14DuzorsVUwFUVRUMG4OyhYNW4dUC76pm2tKdQKUYCcXthWjKAZV2tlwcH4uVX3wE+N/LP+e/dOXP7gbGW1qBUKEyKFaBplRbzpBwjgBvjwC8HYDcWr3yYF7wWbiu/rZHMDTffE35Dro7/qSTRwcHSiWPGnV3dwBksgBWmPjsWtN/pqrhpmCo0ZQLhum6kt/wjf8BZFlWA9TOQ47in95sFUo5+NOdAKJcPudU4G/X3QKsXBd48eLeczbcfS9w7iWXbeBe4KTsyg3tASXSpye/t4fvN9/mYYzYQnGCvM9Sn2ybJ1rznKQ3auZT7RQ6Y3Hal6kAz3o76/SyI5v2Y3RWrjwlJVVKw8MArVVbGHHVHVIwUnROu8JYHtCWNCuCCHSEygUnfXa4iG4UNaMACLLi4oSgBWKczMpUWI2dc9QpwLV/DuT9axeawE5zZRrFPhWgp23+WNWb/gW2h4AOjIkIuq6s8Ry8h/bzyL8ePGL1kUDfgX63Phz1EDQWTZTKFUGUFSfRd8TyrKwIB4SqneZntFazJxgOchiGUZvpWxL9+rFqtRoKphgUhLCHT9fcKZEA9Je46qirgDvabp/47V+B5pa2kqpngrl4Vp/S+eK+HUCzPvmicwbFMZM2o/hky0Egetv3mpGjV3qsePzbCBFBiM4FctN9gFEaIqkA6SzZJqVU6KQyvkjn1of53d5JVh8FbF4xBhh/fWYKjJ5TpyaJTrxUDQPMSs6znvyKw/8tj8aAN/znPwDnvenDYwOEDEBDiIoVQFW1aWd7TsikmtoLhSwQj6emi0ZMyhX15E+Pq5HSO8azQ5PzVwAHth6q5mxeNZQk29wFtFXKgBiJjxYngVUt3UUfev14kw58Dgno73j10ksuvuOuv3Y7S+b5+bS3tVo0w+UzjmDRbtXjrDOZbKWiKori5io6ODwKoCDoTvRn3RnvVgJoDwMsCuEZU+r0n5paK9pUdeczpMYmoFtvvx94J+9ya+7I/cMqfO5L72l4i/08tzRD/PpXb/uJ/2fcWWx5+f9F2/PLiw5r4Abg1DbbH/a7dz/vVl5y4ar2OMBlT3k48eP1AcXUXf216SjvfaEKaFpMlj086FFstU0p9k4awCv+H2M+9fSSntpNAdi01yacP/n+98ZG7PLBA3ZP3LxFB/cze8GM5rt6q+xMLTHNWnHf08orjW+Lx6NAzqeTCQn20imN7Qd+8luAz36+9saoQzJO+dufGz5ZjM4FGOnHOtbQMQRK4dee5nrItTTwjq4B1REjt8RagBxNwFXxhRUToGR64uvnnrLLOjowK7jRAHefsxJ4xwM77z7Hy2/zyd84T1j1Cg1gtf/H/MLE+gvs8hOPNTDmhRolkxW92RVUXQWamhDkCKBSnl+3+L3ftdmNg17qDcF7mdpovVZLXd3twC42ebfssdU4v/vNn4FomMuITy34QO29Tkc3vPeAv/qn/4CJK4Fo4X4AQbCMvYJr0CrOGNR1GPjXv167zRvfZGvrK7EWHOtzxcRK1RfyRb67LIOEBISdgwRfGbGH64YNBx8dpGfauGHDQfeudNxearOSzlqasj/qFgyCZ0D97wXnPvVXiDwFqNEGYYjnn39+faVnoMe0zkSIpphdEYA+lAH/Y9Ih/MjxmW/eGnjS/3605tHX1rl/iW4GSUuHaAal8GRHZnryjW8892k8dUW0394az3rPZcDddzPF3//xjlv8983jmJVfP9oqt4S/9/EJb77vmIDW64Dx8oMARYlYCCg6YnhUr90+gT4uqqmZdwLAJYs3sXst8O+9tUrJA3VxC0WHOnZoJjDNVDXadUgbtdDk7CW1tH3LBluroffa7FT83bbstvuWdM80Undud8Zrb7bbjtmrK05ts92Ji5zzzz+Wt8W33fPXvvPzcP0DwB2RBivzAue46aG+PYLvgASrYDg56A2FPtPhPTVa2hx6Y0j4keOGqy+tf4cfSnVciBQIcjERk/hOC6RcGhsfz2VL076TDUXHG+ruu+8BQhI5usqLA4/u48Vv86JVPvpt7/xZq2c4aVoCfA/Iv5ID6JjDSD9gSrZcKiRtT4XU+70H3kOtEVj4HcBkYpgtw0DB52ZmgVzneRlO29OfSNtYMp0ZjjbbKvy3LDpuh7MG/nbtJqApbL8012uH2jbdZfuIlIaGpG7mnNR+07q17vNP+MV9VuH0blv6Y2gnL40DE9hE67E2G10evulH4KS7KTUwCLt4IIpifQSDgWBxiaLg7hCSppRNN1wjW+Jw6vO6bUVucBahECjrRiC2XxRnz563+dUXCj6eo6fFTp/VE48DfS8zSGLZuhC79rLEHoIdcDVOZqMt+8FzD+91tm8rGUS0MEbnXCBWdrw+S7Y0UfLRQY3y9vd6P2/bnQutAJjcczQrngK2XvsL92rXGb8DWlMAW+/zbtv/+Ba7e9lXgWW0kyZesbFq96GDEjbH2hR+EOg4/d322yWn00ccY/2fN/xc+STCW0f/MSftPv9e57Tzrz3iMLALl9K2H1jiSKET4/ZG0/zuYwGefQHYNeHLyvSz2/n4h4HMpL0gI5GIa/FQVU/TKEj2M1VNBVLIWTBNc9vW3StWLiYZoR45PNcPvYG7hgWhsI0lmuogWhjKJQTB9O9bmn7ySac+8czTnb7DrSqlMYChvJFOAuluDE6dpxRp75DidtcX0r3hvhcevv5i4Kzr7vY9kcV2riSim0JAJSKjTwFv6LVJ6+azT7YK2d/x4/NtTu1Vhpff6ev/UbnoBwCmb+3nAwATD/3BvfrMe/qBi/4JkHvau+2aP22wvzU8H9hPfn62tTOkqxMHgP1qbBmnWg1eOf3dQHjcXuVDw/uBwUPCyhW2afTf1dE3bAXxzCcfeNp9/kuLFj2xsR94adDhxneMsmo+8IiTLer2ldG+gQlA3DWQm56g+kYg2nxfwVWb/vynhGJAc0tHKBwDytMHh2VZF0WglPVojLVeU82kmnoBAaFDSumGGgqFDCfXTQMf0hoqVLViy2SvZdU6wybcEVLyQBShVIJonHLJr6dDJJctaeWAliOTHweMb3dOfbUCLF2Ewb5NYypClP0e7/nw9Rf/fPsgcMRsT0n6bInljl64cncJMJxDkM93HJ+mn91pt1iAtnMcuOgDZ3+DHfjs4VHa590K0PfRMW5tBw4u9XwQ3/J0DOjMAIyWznDrr73Q3rnuPZS2Ck8XjUppW1ds8vMPlb923gp42KrvmkwBzLZXzuBGAYh/+qD0RwH4GUv44plX3smnj5y+8UGPQ+ro2f2x45qB3z7lY93mnMSOzd9wjsadO2d2qzoA7BqbkCST03LAyN0jxx5r0yQ2bWDt0Z/82CdBsAQWJZmUZdmab7/NxYJUC4IwB2jBzJtmNqOlmuLZTB1yWImUFEEKiC2is3uJuMfEmeUogFCy0qsJaKogUSogiIp/oxF5buPTiAGZasHsNcC+6zYv7pwNCJPkGCURIZFSDc/u9c2/bKRnKfDzT57rVn7sp7jnRxfm/8L/WG3nJqvQlDXf2mH3YbvY9estC/ntg6VgDv9zUkdPZgFKvx0jOxf41L+e6ZhlM9cdqgRIzQAP3nqVe9fiN59kFdY4CRde2vnvHw13G1PV75yw5ptPeh++2s+bxuN3fnr5R3mo6a7ZylHLgTFKL0y0CaPc+GDa7Ni1wvGZ6ptuG6hGgNZun1z9qnf0U3L+7Mrkg6FZAMpADtXkqPXAm6enTz75ZKC3t5dPf4KPfXJqciSUbC+IBiCDaooCISASSROEsSHmLhKBSYjIhqaoAmFNsUn4a/EcBsL3fg1w9UddlElZXtpOzvISQsoABGoEBYVwKJqZDEzMt274NvCJ73+hpRQHDJEs++JaJzDbFzxNBQZ3Aqt+6G1Ub1jCAof9GPvIaeew/wHn7IEfPWoTzJZFTdsdFftkomn1gp3bxa5qEDlaum3qov5nPcdvArRXjxw9ZKeeEooAHZ3whuZnHvQsUv/Ya28TR7fbMWSJeW96b7cwy9CiyZ3XHe91/gGzB5gYmwYoFG7/2xMnEUYeY+vjgErLg+VYrBWguPyYkQlbRp2th2PpFiAz6EtapERZtsYqmjC0Y2JWUwRQVVWrVvj+V4H1n//87N5eYGxshK993UqELQi2HrFULhqmZqV0Tvnc612ws5WALEimiqCQUJykpf52P/raJfU3u0GpbkhuyDr9taQ9/vJLp607MtDa//pSAfjxb+/isNC5uKkrvpJSLcVzobe1UcwWAF+bZfN6yTWN3TM/+KN/uuWWRbZIedJj9ru+cMzahncdc/Af9ZXbONsq7P1KEuA/BWD4yJAsJIGx0RljUoD77nr8MFctSM/tBPomxoB7N3v5J085JaD+Wspa4K9DG92ar//v/xaKnp7NOtyjtXsu0N5dm7H0oT/dNFMHEula1KmlHC+xpabm6O86ltWrPmT9t1MdxohG44VIUG067andxUgSEEQnmv6tb2nYp8z+zbFsllRjnwngLfLATJd+krVVahtv3QDMdxwyznas4S5CAJN7djw1fjIw2Wsv1h++2PjAsgdbGrh+pR1lQ195LsB/bgfu7jh/66FxID+DM5UF1y1cc5ir/4m/CvRpIrB17zCwcK4nvSvJOGCQAAr7d5iUgLed91a3weTkhH+RWw6k9gkQYu38ysqMKSENn8FPRMRFjhu++QlA1ypuOocjxx1q9hUntFq3TyQJx+IAVXHposXhqsdSiGEMxSw7u0uYUSCsSMAoA0gN1FNAa8eBoVdV1iytveAki7hjo5ex9Lq7/u5v8rVPXmYVnExotlD3oNOgJeYRpP2p9tWpncA0NimSa/V8NpzU1SCf8zB/tQrXbDoI0BwCeg+JZxx1BjDe/0RN+8jwyIfPsCXPiSHHjc0XibzeSVPclFkMTO4dBfb2TwJLmjz2a0FkMbCP3cCa5aePcBA4Y42nAC2VSsYMkqWVjEXwBR80CkSwwTB8zXA0Y4CmlgFBEGZjb6ims514R1g7BUmQgbI2rSSo+hKShKCqITgOjBadzWYmsLwap2tNDHaz9vNPW7O5Nm8IMG2LjodkD6s2X/0tf5NvnmcLIX97ahB4bIdLY2wsuf6jR7uN5385t77tacCiH8D5mwMvjLq6uN80iEz/J7ZTQeyBrQBrzgJaxibuvOVnwNvW1XqRGYXM7Y8A0D33Y4MrgbGeqm/SeSVjF6R0DzBLKOKgxbJ5Hk9rpkpAnAhgUsohA+O+5FupdEul4rlTRH2RqkIdKsiOW4JhiFRUwr5suD5vSJMYLnK459AITrCrE9eIGfcJL4YBlCuGJCmReFO5MB2J+7xvNCJhyo4+NFKK9rK0XLoLaCLC7IAO7cOOb8Mv3nZLZyP3100t9mecOejdWHzL6W752X88+omyTTHOXtTm/gWucqw5D17jOc/9LM3f5Tjws7RzNR1449m711oFzafecEF0tqrKVD/A9DQQa2/SmqKAUchYVx8dsM0lp6w4SbYOWar0jvVUgfbBUMzJkLWIg0/ttdOOtEV1JY46NQGsW7YYMGLeUtlbzgALaQP2ktGxPJK8BvlsJu7zgSqVKoAS9xqYPsLikgdRNAgriN4lPRwPO3ahwFn2FcsNtbnJi2B1UNNss+dGACsCThIVtIpKWAq36NUKTgSHaZ1P6yRlUBWmyTWlI0CYNhKBFCymY5U3fqcMLKo1agNS61qrsCrtIcerv7vGLQvVOdU+Wwz5+4sBY+yJTuHvEa4+xh64b784fV5ZAP4eMd+wJgk8ujkH8D9rbv8FwD29m95wBcBl3+oHTuDT/mfGnYHrec9qsA/xHnxpW5cgA5ecZ8tNL37T5iWN9mi1fSlAU1t/QQL6e4k5dHaQnoVOwGX24IRxElV5H2DEZEAseis+WdIAgejKM44bfeSuYaYIqq/j8bir+hRQZNm714l9dWwriNWKjzeKgi8DvaiWTSf/m4mOxcj86GuX5IsqkIzIusNzSJvsJ1aXVQBJkUXDDo9EdyKtnQgD62Bfo4IRMiIOws1dtuggO199eBh4fsNG4EKfHt09dPbgssXHrVx6YE8gPzzQ51CgVD45rzlGHZTaW8Ov2q4Vv25o5YYHPjgn22YzT4KTiP3WB2xt1c1XvhVgTJt25CHrBLqH5SXApJOieuFugH/yb+vnm+X5AOkFZPa9+NT2d593LiDlDODQnv6RtD1uA4vWTmwfAkhXPnrEyoP79laKhfUO/7YHL5Wf0bTAeIZHJv8NdLTVWtGWXfRZIEchWYhP5m09oeQzU4RDEb/eMppqAzutf7eT6dUCE/OJO39slTu72oB9hzx/mZAQihACcqlCkjgW5TBNU61kAT3SLDhqfPNIG6ckVQZ0VZNCIYdAibogCqIgGLod9SWagBhF103R8dSZzuVUomedeQaQWCVx0xY3Bh+806Kef/EVmUp7stbHdbZz2upzLz2WWGOvy/hyL1YlOjZxx4vTvO804LbVjZ3rs23dmzbZsskFx9qb8XHrV1o0Y3pMA9i+ZxBbID+4nWclXtr4N2ABNo984GyADvew1mERIK6RF0848XjiTYDS2QTc/8iDb5m3fOPR64Chu+40W+YBKLGxsTFDFJpa25RJNrX3ApIPOeSJe/Ve2kKdQCha64dhxYXEiRpRQ8zP7G4TBBMvPNpfLQhCIgkOZxo4wUk2VURAEg3D3VaUULRaqFx7+4PXfe2jgrvrOA7QFTsliqiXXYokglGtGolQqJIfB/KO0jCbz+GE8LeZGrBV3QTEYylkXXPCJYGCo+aX4kNbdu1Mx2uFrnXO3LR2t4272Ya3extQPHTgyo+uB71119Dctuw3D1IPS1pKrY5FO9RiD8RCff7CXoA7t+9k7pHcs9s9nqywic6jGKiGgDgPWZXpdsA7wCw2chLAyACz5y2ZvSCvVYF/73gFyLUlI2ee0LVjENjVlpwczQCcvq6qVqOJeCyebN+DOD/F/q0mXndje74ANK/4B86690OhVAKSxDV1QggJQIRoxZcbX/BlQzBR66Pm/SDJrrUWrDNSHTDVqokGCFMhg7IpIAOZ6dxYJg9MTmTaSFiOhVqzjQqDTlSgVqnObm8B9g6NALquH7NulVGNAjt32CJaqVIBFswDGB8PATHJBKKTccanpl31Q/fAX68GuGz6knlP/Ts3OaY3kLDsfme0av9um195w7lefOzCc98jP/4sAwMTS7onMuXZjeL+W0OhiXLVym49f9AminHTRrXb79nNhWnee8ybqxowZ3aZuYQhk2gBCtjxkpnfArQ7yDrnfb0A/c+iaq9u3TI4PgboIQW4+IKL4oOZWQf6gfckZudOWA2gGqFkWJZlTZIfXYYwMcrKE3UfcrRf9Gcg+cw8oG2eF1VgwXBOAzJMJ9SSKImAjtYwVTngEn6tOEGjjOaSZEcRWFmcZEXESQxfzmiidU4qmMQFi/hc/PaT/fevqtZGeT9dtL0rVi/x5ubV3Z6xSlQ8Ypgvlhc4JDOUnDH9XA20RhtrQc55W+fPr3/59TyhN9Q4MgfYvTe/0nfQ1rqlnQB/3tGwca6pq2G9BQerdgKWpuM2AFJCCjlZe+d1zC7lPI+9Qk96+56Akm310rmA0tELHKjznOoOexJHqqc27+DggM0wmeVJwJRqdx8LQsFDZuevObmmwcb7f+aWW1pTwMBEDpsvqAUZ0NRg/F1d9vuUk094cMxzkJoz2ztvRvAhx+RUziVmyWQDZX5DOOecN9TUPPgQwMP/frqnPhCtEUTFPTNdWjje43dLemn3CEColsuxYO3aw7koS9vsr1bb5gPdbciOhqacK6fDvu8dzPT4QqUBMZECVm59FhjnzYC50FsSsW4v1rk+m62iVwrVAqAICjMccvDfgiVG2JPtOOvEFaXgyD4y0F1zPnkdb9ej2yR7bGp6wjn9tatrkdvAp3glEYu6KVvCjc7vMeoc4YGHHnqypsb+fFPonefIfNP2DI/yJuqgaf+M6XwPKYGQm3YrWWAhSJC6FgPMyqn3HC5+OHW83c/pji5gx+59usEeCsBcNRJNeKqAU9o7d6cCeCbFmoD2wTSgIGHnyebC71wIPPGorfQbYyCuBrLQy2cfxW93WghhaSCNBo5X4B584/ys5zlM0zQM08qnLljqBJs7cTwIDdN0fC5loMld30YVapKjg5AJFR1PpMlMq7MaZB/uir4MjNls3kV8YeaTv+q/CzB8OWYFy31YEDoXOMixz1aATtAgY8nRYuONCXim6OHNM4Qv7ugGBxtcGN4NMGf9o++dMYcz8A0jbRUe6mgHtKJYloR2yyBlLhj2xYwvWzn+6MsBpbC0cAHw1LlHYp12APwDzhX54+fv2nJC/AT7M6NEJSWAoOa/NodFyohAThDxKV1qoFY4qUMOCzMMwwQE02JdBcD10jJN0y3LQMlwWF/DBAx5usblR3GihNeu9AZUEjwKIwiS4ajhBEFwo9UXLmyAHILQSOwUve76PgVEo6ttXk3b7fT7f56+cy7QatRaIF3o850WIJBVJwYBhKB1rbsHYN+Oiezh0kMkV9uOg1OR5wClOJ5Op5sIAz3l0uSk5wt92xNktMCj5vry8uxjJ5A/t41/GONv+rCybh+q51cguIEOb30joD24seCY1kJ6ETCMugQnvmPh3EFU65KG6Ri2i58gW4kwBFX131IOy64/n0wdM2KapuBHOkFwMdJzW0Xy5/syBQ+Z6rH19UD9XTbhNISHH7Z1VrzJ3k2UoMPw8O4B4Id7NgI7fGt1maM5G+3z2gvERizLixnGXcEunDV7+97nmRk+vu+nVqFzKARkstPpdHo+RwD7onsHBwPZDYyUZ6O5mb+NJzY+9ezO9ScuBSQigBSWuEAyM9dL/3iL8RbD7+wpWYpOXQfEN60V/mR7sszIbZgmzrbigl537LR9u6GDYYu7NbGMlar7CFsJ5t5qvcB0VGwAgmg41FJweAgT0fSlpvdTGlkJH3dMazgcBnS9EaIojThBfxIP97ECgons9v4J2/Ip+pKTACOUgH1Dw02ptL18vv9toPx1O03Fwvb2kGTTsO1MSJEusGMA9ZrIvo3Zs+bZzt/9DWKFiKg2niU7ZgEHJ3NqUVuHCmyolsVkm+Loyoxk6Zvf+brvVrVtzjJe3r5/YByI0APovfuA0sDF0gUZ9xhtERHRtCelZJM3TdOsnIimIeMk3sefV1jTACWZ9vfWRg5B2P687aEiuKEbojkwMoWj6nCzdpdCoagkALIiBUMiDVtzIggCpiA7iFwVBUCUJEE3nCM5TEH3IalfD+NCo7M3JQMjmNXVchIwBREQ/cywaAXfEKpLKV2zV5XNLmD+yuWVfNk++1Y2gXLRHtmQzNCkTaijhKfiGoDYKF9PuaJP2Jb0hqFzJYfkDk5OU9UT7bMQpUcZASpqPJVKqbZhjO//MBVrnQdu5ytj/UNCWdq+fS9wGsuA/jdFgJHlajQ9UtrvbYuiKNrb9GM2GRMEIaTpgFabV7iOkAQTJgD4mQTb6VPCPf5SkADBPS3QNARBNMG04mW9bUUJA3YGY/elelIUp613mKLgSBG63zPA9JcdEGjAUuvhiKgGZsUEE9E+j8jdnqz7BUzDtJNbAtCUbsM6S8sHml4EQummfQcHZ1k62IkSMOlgkZJMVqrTQNZobWdwqmoApJPhoBPBq+0qY+Y5icOJ39FYAtg3mjeqOiGpWtZUR1lpIE3kx9ocZ5G/3s37LzZQPIr43PMbRFmIJdJAlF8DkYH3A+XZgxIRl2kTEU13SpzkrbIsI4QAwTr/ti5OCZe+miY1V03TkzIs5NZN0AUhhKNHF0XRCtUzRURRMDAxnRQMzjBXASHsmvwNAGHaepmh6xbxAAwMyfIyEgxmQA4TMyD5WLuJWhaMALIbCAKGe8aHu8dZmGVC1JfsuKpXcVxRvCdIMvC+D3/wS1d+JVIlsfgoNm/ND3EwY+tVM+hVUwYEUYVlu4b3AcxaWJNgKybNo7pnxZG2C8iODdTDeG5DtoQi6LmyRlktllU38VIilS5XVM3ZjjduVN7/P2EcHVKOA+ggoZiWV/ZngM7t08C+dDmeWZAXNRc/7PHDi102DCNMCShbKDKThtQiGzWUw5JvDQG/s49psziiYJEizdJhJBUJa9pMvfE76uG7n60N/Z4hdroWkq/3yJtamPasGRwmG6vdGWfr2bJlZ82lfz7qhTom43TTtU9zAkOOnjf5woz5Q1340WfWznTp/ocyOFnhm5tbY9FExvEm7EmcDdx0y8XA9s2eW9HePf31zzlOu/cFJ7hexFaVmoLn5KCJ9TcBiDPnpayHsfG6bGDY5/1IgiogVNQALT2cwifwiCCqyoaHnfbZNgRdvB2YyYPttd/owwg5JIfDtVj28crlwM/CNwOVnL1VzXWSUXn3+ghPQphuZXqfqxvty7V0zJgF0IWDW72c5aXCX/yXdFUDZFECTN2QJaHkpMEwqufjoMXyNZ4P6ZZXG6jthchnSiVbv9KOrZOcDntbkizWya7WW16z9w5E4iKNFoJFogwTkdqQpdeNHEEMNUUPOXRHmhfqNDOGIBkGWectrVFxoj7idgZQwuYX+JRVvkG+1dpQ/KAa4zgbjXGq/dj1xaNrmj1835/e7CgY7p9oSTLWrDrG93h1qr2xEt0PR53lOdBv/FcgJrvXuBMwrKN3lErEnG52JKkf3BL9xc03Pvt0GHj2ac/DtMd3iI4LbfnbTzzlszWVLS1eJj4efopG8C+zAeVQG0h+hKIRoZFeVRFLgClKOqIYJFCvFzlqwL+v+Q5gk2pmXjR1XcOKqGlJoRarrbHGFqN6+NgXP6U5/h/l75bDYQX4DFe6DVR1FDBFHTDH7a3dMmpbcArvAaKRbxtRm1DH9MwArZ2CI6Tm4zBj6j4Xnv6rN6ZtFwQiLfp/XwVkQQQkJVz1BfyVi/lVi+ccefrZNU8Lp9N26ZDHaJuzzu2p1Uz7M+mgvbnxzqre3QARlEZUxjR0QRBE0ablVcfxSnaM+yK6IYcAV9X8/7it1HA8VZANXUCodW4WJFVGQAXUoqlLOsVaAjATaKVi1XUGS6Wy2Syg4iWCyopZ4A3GicA/t9hLM7vQ2zXz9AHTeXVHwR6t1pAIqIbJrBTAK8PqhIccI7MBbuZI4Pt4nmltK88CjgiPA9pO28x01AKAL2rPAEVVBZSw7j/NKNLRuWrd0eGWOquhu4B8O5qAgqkC6LqXFdS3Jcv3NQ59iDbKYNwQNE1zBBPnnXVgoYV7TtL/O3KYDnqahiCImKYgYDZgMQws5lcP66ouIL1eBuqBBx490clwWioVFDsMyycWnW0Ayn1hwHQsAEMTnt24QgyIt3ZqWVv3kMtmgZKoMFYGqJaVppTbvnMAYKedl6UDSJAHBrbuARI9YWDHoG377UkBRMKJqq7HIxJQhVAg26spRmPVYObQb/GD78S/b/+Y8iUf7uiyh04QvDH0rzTJCwv1gyw/YivKXgtLVE0HDMN+qjWdYWKiWMaRNy3blitH/xfIEXAxUjBNexQN0xRUTEQpaDmzwJTsIFtVFzBFdebgfT9opri/z87suVdA16q6rsuynHUOYDbPNSYWZXEGcKXD8Z14skfGS+wEKr+azjbZPIeil2VZSUkyVirE7pbJqmc4VZoA9qPjoEWeBDA7UgLyzYuAuSXbi1MLA1T1bEiSprI5oL2jLR6Pv//i99lvz0wKkVpFWghZczmCpjJhW/Ela6qdvtEw3N3E9Cm/hbdZZQ+V7bfcXbLQIhWakbMeHgNIRfKVUoPoEPd0FVVVa85xe73IUWOKk23jjXXJ/gYFqdooZsbWxJuiaZpC/c7aEATBMPT9HAJEOPXUkyORiKIopnsUyD9E48MC0H5uEjjzCHthhRMeGX+SfwOHRkbjTlpPua2zJ1qORSL02wu6mvNtQ0mAfgpACAFooQAkWhYB+b2vApHOXmCykB8dAxgZGQFkWQKi4VA0Ir/8nG0xXrpooSiGavQRApIccsbcmOWliBIl0+K4JQldN4kAouBTJduxa77HSQUs5ZiqAsbMZw1YJyYYjsrXmi7rQQaGafdBMAxDFgMY9l8wpG5si6/G+isIAoYsSSDU7YyGYEsxpiBiGvYtogToekhENSQFXZVrBtE0dUmyxm6hyZo1y6z9ch9OmoZPo05VAWFRExB2bAr5A33uQ7IUgclpXZ9jM6SD44VpcTosK1TtJdLS6g1HYgKgigksdpLpzG+VhqytQZEAK4GwIEtW8EA8EgXaO1qAZDysSObYQdv/zygVY+0tGIHF2kmrl9fRM2iCLgqyZGu+QyGHTfYx77bm0De+poYvMVy5PCNnbS1H3clbZxnFFew8cbJqAIJomqYpBCWK/45yhAK9s+UUEVMQME0zCyG5ls0xDWxqYRqmKAkWhhlWWETVRBB0FalWoyZANZcNkQJ2wQnZaQspDd/SeeavLwOXXngRsP+QHZug+LI/DzEFvO0NJw06uW/27R+cEEmmQi5b+O5m74H5VoBJCsB8x8qzf0LPZTNgn/beEZeA5nDUOgdxzZrVQN/+3YBeKaeTLUmHML/68oYTLljS/1IgaGI5y7UVFUBmnPAcz+lO1m1WQ5JMJ/tvYKnV5643W4F83v7eRLJ2x6mBbCmwcEXXBUwQcNiGSqWi+A6b/e+8za79zLtmuOJNbuj1aV2rDb0W4VNf+gwQDgd30G//sKbZq5e9FXjsL555/ZgVdjT6bb+4lTrY6DtO+JhYYH086zgSWzB7/WvoTH+57M1u+Vf3vwTMWvHah93fo9jRlPf+z43AwUFbT9q7zo7olGY+zuf/Ae796z1uORaGc8/3X93/4x8C+Zkz8Fvw3+k5ojWEodfWJpX7PFcrqdHe97Fg9BjwY+OGhq+44+ZbgEuv+GSgtu4cxEfTzwL4IqFf+bmtoh4aazC73ZPDu9YcZZXft8JTfF3OboKuQ+rk4eLlgaHxjFtum78AeOnV2oiselAKNkaqF1eAqKPvMcv22w1p3sw312nqzO8e/nX5vKcpTjSQY0wgMTMPa0Fj5PjLdZ8CLrqyNpeDWEPcHHVH7LUO8TbrnK3NGfQd07lG6Hx1nbvbVK2tZ3DEccOZNbvmEpv3j3R0px23qJ2TfvydKwaRY+SFQNhmDWw459SXwx4B3z9+ACiZM66xURshaXomgTIIDI8NA13tto+75qQQiKxqzKrfcsMPL/tcXa3+hYaNL3TYlKtX1hxJUFkDf/it/WmOsfY1aHzjr9I0rWF9rY7LXUPB+o9c8YmaG/U65OB7jbcVo5FPMgSXjvgpY/K2mhbj47bawKjXtUwfbC3HR3psx/EtvhPhRLJhR1f40dGVwJaewxHb3eNSX8hTXRzKFnDObm0IiqP1DscKrD4ah2YoTgzBeGwI2PQUZ62rS0IBwBlnnoNeF+HhOucqkQud1FRH8HKK5VZ5zbVH3cdf38oFzg0VqEotTrDMuMVnvIbt9L/bVmqDFlxFqVhT/doWW5MZtth6gQcQPxX4aZh6nf2gqckWSQrFqZpLqKKgltJO7x+d8ALvoozIDuZtyY4D5bDn27fw1PXAp32k5cknlPEmDxV0KQyoekObI5zW1uS4QkeFDOXNQGfbpVx7HV+07QCltioQb/PNNwC3/NjWlV326S9Q76ftbjRqOeX07gDL5+DmMlndje9ct2oVfi+JxzidEYDSa83Sf4kcNWK7bgIdGKPB7caoIVc//Rmf/HjNo1qbGtO0yWkTnKwjrqni28Hv+OpPP3ZC7Tb8g9/abcxKpeYSiahYKYRetkOzjlvrparaRmSVw5WXu7KA7jtueO8TT0VWrtnt80+7v+nphbKnrKwWS0AiFiVjL8ofcNaX02Ap4F8ofMvpZnixym4F+Nc9f2PxIu75m1XffcSxgDTOni2eWR+LYFggh/a8eriwrjkOZm5hXRe2MtBQS0eyynAtR4ICHw512Cbl3A4AYfo1FJICcNMX3medPwsIgmmaZjIRBXL5ukz4jlmr08oqPDgOmOjnX3mlaVARS4CkybWbyM23cPllh++HCz/53s9wUMdiS3/Eddx4pb/N53P8NPxzIDedXd2T7TgwCRw88tznnnoOGBkcyoyPAYbicGKTw11i20ibzWpkVYA5c+YAfU5KFmDZrvmvLNh94doL3ZpdWzdL49kL8byCPlf412mL3QwPjJaEfHZS9i2NhagjYYBOFFYrwBXbVeDf647mld2AubSdL1656M925lP3KPQX8wFSetnnvuyWb7n+sENmANzJOe/lAevgnss+P3Hf3Z85lzPvMx4CitMTvPvtx3N/tt92nX/54esBXWjMPACYMhblkARRlBylrW/n8HMYUZRSBME5j97eOHQdMGgzDUMQkQzJvlRDFC6/7DZuvoTLgV9yo1X3ManWQm3BlVd/Erj+uz9ya5qIcEWw55D4aRQYPHAwo+4XxyaBC9/9nqf//TiQGR8LCxKwr9/22Gbu/J7u9taX7YBec+4soG/fLkDyxXaXZUWeiGzY5K3gsFEJt6VeduPr4QNtHzygemmoImZIk6VIWLTNZjCC0lmxCiqv8F2FG5crwBUvb0DtATanUtzyiz1dmT/d3gd8+0OnAvEjeOH6gNf7C+95u1t2s4w2BhHs8wPffhl3Aagndnd3A+HxMFCRJPoPZnlvaq6dlMbubCjkHoVSq38wdextRdB13daOh0IhyfGyiUY8rr6EGi0jxOxtyGb6etqACbIGBoatV1GFBsTKQL+Nm/G5EM+g5nDaG1akjQB8hE9hBFr/CkKv/Bs4ad2cVwbXpo/OAp+58uqRTAlIJuz5llQnVLWzbZNhrnQJoRIGLJI7z3H5BA4yJnZqId+51KYUq1SK2eDZb5OKt211zWqrHsobhmYbZgC4ogDwZVbAk1+uwIsq8GWwZOZvEwEOlMS4VAL0tkVAajHZ3LMzjcZw8HjQV14ZYnV3esvEHKrA+39gVW86g7V7OBpY9NMNR36iBPLknw4BYcOgYh7gz+dG3sNv/gkYoUFArMqu+7NYy1ILWMhhOadbyCHLsiQJlrTiP7MjiUzCCeA3TU1VM4Wi+xgLV2SbgzEsXYUHl1926c1XWDTjUpcIBFnMGjBlAfjV9TcBl/LJGsoxfSPR8WHSSal/X644vzK1aQNrJw5ukg0TKBZKUWvXcwX8cBgQFBtrD43ncAS5iE/lqy+ZkvY1T7V5cx+PySEplsOYOOaAW6m+6DFqr+7ea2jVRDyKZu/8UTJfsm15B8mfVf9pOoeAZGgJ4kGgMH4QiI7bZ3w2BN8JXQCiGKI/n02GrT5ZCRMf4e2LHB5/D0cvirwI6jHHHAPs2LiJ0W1VVojm7XzowwA37wGQdU9yqGE+rXgn9+cvr/6wVQiHlWKpCnyM38zUXeDn8getwsWXXOqvv5s7p3/RKOTDgQ/hMafKFTMqSH74/R+75S9+5fOHeSDQukr62dmfrK//2uOHGrZPOv4+oyNgpaQF4PE3NwOX77Vpz2S5QfdaVtdanodfDkgrudEC0LmgYWrUTM3vt1641i3/9pYZEhQ50NEBkEqGgdt4unEja1fcUQWoc597PXD7jTch+LIJEpRE6iOl6iEate+tMfkImIePkL0Dj658hMtnauY3WNcqz+sgFr92aayBfmKe0VgXnk/aOg/r6Az3eCczpgFzl6zIlQ1wspsFofCKzcr8Kj7fKny2JZC81U4rGWo4CrUWkAUlj7GzjjCuAf9cGG0SMGeu34GoTiG9BcAoZ0VJQnkN5eQMIFkp2zzkUBznMlmWPvGD373mA5Rr7PbFYsDwKBH98BUfOsyNv+Fnh7nqwlVXe8lAr5UDH3nN8trGPWt0oe6Mpveh/OaMhYAZPEt8EZtPe8Rm1NMiIktKRVtmGc2mgJxpJCMzuhaUh0Z+seh44LETbTn1iuZT/A3+548vAsWzGsnq99WuumRonlvW9dozgfDHtMHAwbMAJ1/h2cA3nExlHkTtVxi6Lvrca2ZSbNaDItguKY0px+sCp32pFJB4lfoUDj4ISaLt/iE22AVmhG9d6/8Vi9deF5Z+UZx+R01lmD2J2MlAvjjtrx/l+GvearMjX74Pg13XvNUWVtUQwFvijYLhHLj/A62PCRFgQ9bmVxYbAaZAbG8GfjXRyMhUraVkhdHi57FZ0bnzeutuCMD4+Ktu+YDNJtvTf8kGbrNm82QAUZIMv8chSMxkSalFGs245r0fXsrhkeP/8E476qk1s1Au2sxMoRDgMELUTZ0PDIMPU6sQe00wg9tcelFtgwUXHpTvmVtTGWFOLl8GkrEmf72JeXzIFmTCMSB5fMj+hP9UAU6bW6u93Z3xRnkoLW84qAI5xyM/nxvzN06LnUA+N0I91CIwSr+XdGD16lUNbvHBs88+4JYHgpe8WMpZtruhKMv4gt0RAnuW63Vl1hmGwkLiX/8ap35b0cDNYyn5aEA9ExKJRq2FY7n+upD2BSP9/wX+LQb4/bdrG5y0nFd/IVxDwH//KtYvkjYDwehJ9rLskBNKHlcADmF/wu0bBGBWe21+scVpHeA5AShMZXPLdCDTYmevmz8nsM+FdwwD84+oCahrDB/8zzOurrfr7Q0yJ/thYvzDbjlubyg2Z/ONZc4ZgXOVY6897gXtIUDzGRmkgE+67BEMoYo/bh6QxImhCQLnrVj1SIIo3n19rR1w/DO1HW25UbDelslk/PVzmONilSAItoexg3CGgSJ6OHeYvaxvwEuo9cGPfgFobes2TZOvfnrBhbWNL/jeO971T65ivb9yKQdGP3h8/ZNNSrMdLkQOA8x2+JW+abkpkUiIROJx17E76Qb3qvuBB4Q1V1hosccJhxkJSKFxRQXe9afAGdtAezN7d9eynLGU8CMnqe7IihXAKWdb6nkZcPSJ9jRd9jmLwgnAsYwC33DOcvjG0XDyQwBPyxES1n1BhKg1jDkFCex0+jbIUsRI4kcOi/ER0CVZrp+zeunFOjMWqAa3lXijbaVB3JtF8aRamuaClQglEYsDpWI+OzU5MZ6RJIW7/vo+uIrHr7n6NOAqHgdYPqtyr/wty4kwYY/IHzA+8n8NYoFCzL/lI/ZybZYARjT7lkrxkKZ0dMVnGVpJKqoWNc25avi3fRB4uf8lds8FTMUelWsfCig3T1zWBkTCnSQDuedy0N2bCXxjdloyCu4RwK1tzd5gBc7Yc9kCGRTLiVCnA4/zgIgXghN5bR8uw6wXcxwQTPuwem9unKx4siRJYt3T59xc+4i0E5xzqBDYVqI08C35fwCLu7YEuezUZKq5pVJWTUNl50FhBdcyJnzvL8BeqsDCr7zQ1Fmrj5cRRlu8D5zt6B2q7D/9drv8q0sBTv8lrF0NtNFRUOXPPDAeCYX1qmrx2uv/x9boUNwAjGX18nMvhU9ZU3nSVrSn/ccIwck9MeDBaQV9VBTTVuXUNKLEZD7AGrekmo5Zvu4+5+fpVsC+KOKta2tBu/kRJATDqtzIF4Ezsb/kzKd5aJndbDarnBv9mmU/5fASLtTnB5Rl2Rp8b+xER2suWzEGrwWplC2yH9gT4O1jxF6f4GMJtDMqSpPxBE6M8sJ5vcBz23eLIpQy/BGw94u7gZ4nueXY0BKHTc3bG+0nYPYsm3solr28DSFWye32JPWWwdJ26HEgYxwSi4UpsVsyTElWtKgAvHDvHfadp7QCbWrz1Kc6m2/arHd3DsUA0mIFaHHGbGU0AzyUXYyYZtJWfYshDJ2W1gBrDKh5b0ty8gHbdwCGN6MiIAlGDREv+zIhf2PHdVZhDq/Bu1gJtQAX1ZwZt6KMBF9wAQD3/fSqmgeE5ABOKZIE6I2CM+vh9PMumPliLeYdHpk2bfYC59/61g8cpmV3p40KndQa9IFMU61Oq7eRXPWttQDPLw5ovp960Yt+jigznoiemcwCTmJOrvrLP/1XP/vmc+pvceG0FQ3k2E9d5zdEGIBk6/tfV4SHqR3Oq02YwWHj/r/8xCr4KEfd6/w1b+L7j8hXA4L2upRuNf4+AoLrKfjf6lMMn2W5pelwXrjlkK32LtJgUHTdU6E+v3UXcOaxJ9Q364gBvP+z3/RXPr7+jW7ZXNr4yDCgYqUCcBQ/yoWn+q92FgOr4hcP+H9xzRfOnOmxDlhO/GGYwSXqtUAwa+4KrlLHYnrWWfYa8yNHLXYINWkxLBZS/u80sl4OGo9KBfBG7jxOH61xeAyAX7X37nde9E4uAJbc9I5dn7rbKrhX54s2TuQaZWLMmrUpezT3YHB/hwF459mBAOiYG7v2yL+nmk+bqauTU1Wgq8vm9dqFII1pmZEHBMLxBldnNl3/dytMEGVBSptOQjMHgsjhZLS6/05bbeOTVnwHXNtVwUOpTdUEpBkyYNb2pi654P970l2fvf6dF14wZWWZuImpC+2CC3Od2R/zeWm40Ct75rHdwwCjQfbQgpP/BtAZ3Jckx/8UKE9NeBfeGXBm7nrSBKqmbfu49LTATpH4z3ZmhuliXWckwf90Z6perxYcvPk2rUiq2vXvm0rJ8ylUf/Sl4Bt9OOE66MrBbA2GwVmX8a9bXl9GHwfsCFtMH5bUfcJh3aD9O9RUbvxhnvzaW34PHHfqx2paxpx8qQ23laGp2rdEIh4KbGCf/9JJawMONgMDnkJy1I8Qfx4AXuRVy0T6sYveDAwP2wt04URgaa5sbYCyLij16Yt0U/ahgq2IEqwcf3UoIjQgPIKesQoOoxjYlM0Z8GzpSlt99/8BILjdv44Yj9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's take a look at it\n",
    "from IPython.display import Image\n",
    "Image(filename='-f_out0.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This line:\n",
    "\n",
    "```\n",
    "    try:\n",
    "        image = Image.open(\"pic01.jpg\")\n",
    "```\n",
    "\n",
    "originally read like this:\n",
    "\n",
    "```\n",
    "    try:\n",
    "        image = Image.open(file)\n",
    "```\n",
    "\n",
    "In which case, this code can be its own command line program - we call it from the terminal (which we access from a notebook using `!` and we feed it the filename of the image we want to mess around with). Try it now- run the following codeblock, and then download the results to see what you've got!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 5\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".......... Done!\n",
      "2 / 5\n",
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      "3 / 5\n",
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      "\n"
     ]
    }
   ],
   "source": [
    "!python prismsort.py pic01.jpg -n 5 -r 3 -a 45 -i -2 --fuzzyedges --jpeg 95"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This creates 5 randomly glitched versions of that photo that are 1/3rd the size of the original, with a diagonal pattern at a 45° angle, and a slightly reduced intensity, with fuzzy (feathered) edges, and saves them in JPEG at 95% quality.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's take a look at them; the filenames range from pic01_out0.jpg to pic01_out4.jpg.\n",
    "# rerun or copy the line below with the different file names to display each picture\n",
    "Image(filename='pic01_out0.jpg')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The parameters that you can call are:\n",
    "\n",
    "```\n",
    "-a, --angle=NUM     : rotates the glitch effect by this many degrees\n",
    "                      (default: 0, i.e. vertical)\n",
    "-b, --blocks=NUM    : number of blocks (default: 9)\n",
    "                      affects the overlapping of the glitch effect\n",
    "                      (higher = more intense)\n",
    "-d, --dither        : makes result more noisy, and less blocky\n",
    "-f, --fuzzyedges    : in combination with a rotation, will leave a fuzzy black\n",
    "                      border around the image\n",
    "-h, --help          : displays this help message\n",
    "-H, --horizontal    : processes the image horizontally (same as -a 90)\n",
    "-i, --intensity=NUM : intensity (recommended: -2~2; default: 0)\n",
    "                      will not go lower than (3 - number of blocks)\n",
    "-I, --interpol=NUM  : rotation interpolation (default: 0 = bicubic, sharp)\n",
    "                      (1 = bilinear, smooth; 2 = nearest, aliased)\n",
    "-J, --jpeg=NUM      : saves as JPEG at the specified quality\n",
    "                      (recommended: 75~95)\n",
    "-n, --numoutput=NUM : number of output files to be generated (default: 1)\n",
    "                      the output files are in the format:\n",
    "                      <originalfilename>_out<number>.<ext>\n",
    "                      files are overwritten without warning!\n",
    "-P, --png           : saves as PNG (default)\n",
    "-r, --resize=NUM    : resize factor (e.g. 2 divides side by sqrt(2); optional)\n",
    "-s, --sound         : play alert sound when done (silent by default)\n",
    "-V, --vertical      : processes the image vertically (default; same as -a 0) \n",
    "\n",
    "```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now get glitching!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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