From 69875ef9a0515a49808cd76fbdfd202b15d3690a Mon Sep 17 00:00:00 2001 From: Braden H Butler Date: Tue, 13 Sep 2022 19:17:03 +0000 Subject: [PATCH 1/2] miniproject1 --- bbutle11.ipynb | 382 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 382 insertions(+) create mode 100644 bbutle11.ipynb diff --git a/bbutle11.ipynb b/bbutle11.ipynb new file mode 100644 index 0000000..59b489b --- /dev/null +++ b/bbutle11.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Written text as operational data\n", + "\n", + "Written text is one type of data\n", + "\n", + "### Why people write?\n", + "\n", + " - To communicate: their thoughts, feelings, urgency, needs, information\n", + "\n", + "### Why people communicate?\n", + "\n", + "1. To express emotions\n", + "1. To share information\n", + "1. To enable or elicit an action\n", + "1. ...\n", + "\n", + "### We will use written text for the purpose other than \n", + "1. To experience emotion\n", + "1. To learn something the author intended us to learn\n", + "1. To do what the author intended us to do\n", + "\n", + "### Instead, we will use written text to recognize who wrote it\n", + " - By calculating and comparing word frequencies in written documents\n", + " \n", + "See, for example, likely fictional story https://medium.com/@amuse/how-the-nsa-caught-satoshi-nakamoto-868affcef595" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 1. Dictionaries in python (associative arrays)\n", + "\n", + "Plot the frequency distribution of words on a web page." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "class=\"menu-item\t54\n", + "\t38\n", + "\t35\n", + "
  • \t28\n", + "\t21\n", + "\t21\n" + ] + } + ], + "source": [ + "import requests, re\n", + "# re is a module for regular expressions: to detect various combinations of characters\n", + "import operator\n", + "\n", + "# Start from a simple document\n", + "r = requests .get('http://eecs.utk.edu')\n", + "\n", + "# What comes back includes headers and other HTTP stuff, get just the body of the response\n", + "t = r.text\n", + "\n", + "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n", + "wds = re.split('\\s+',t)\n", + "\n", + "# now populate a dictionary (wf)\n", + "wf = {}\n", + "for w in wds:\n", + " if w in wf: wf [w] = wf [w] + 1\n", + " else: wf[w] = 1\n", + "\n", + "# dictionaries can not be sorted, so lets get a sorted *list* \n", + "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n", + "\n", + "# lets just have no more than 15 words \n", + "ml = min(len(wfs),15)\n", + "for i in range(1,ml,1):\n", + " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 2\n", + "\n", + "Lots of markup in the output, lets remove it --- \n", + "\n", + "use BeautifulSoup and nltk modules and practice some regular expressions." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import requests, re, nltk\n", + "from bs4 import BeautifulSoup\n", + "from nltk import clean_html\n", + "from collections import Counter\n", + "import operator\n", + "\n", + "# we may not care about the usage of stop words\n", + "stop_words = nltk.corpus.stopwords.words('english') + [\n", + " 'ut', '\\'re','.', ',', '--', '\\'s', '?', ')', '(', ':', '\\'',\n", + " '\\\"', '-', '}', '{', '&', '|', u'\\u2014' ]\n", + "\n", + "# We most likely would like to remove html markup\n", + "def cleanHtml (html):\n", + " from bs4 import BeautifulSoup\n", + " soup = BeautifulSoup(html, 'html.parser')\n", + " return soup .get_text()\n", + "\n", + "# We also want to remove special characters, quotes, etc. from each word\n", + "def cleanWord (w):\n", + " # r in r'[.,\"\\']' tells to treat \\ as a regular character \n", + " # but we need to escape ' with \\'\n", + " # any character between the brackets [] is to be removed \n", + " wn = re.sub('[,\"\\.\\'&\\|:@>*;/=]', \"\", w)\n", + " # get rid of numbers\n", + " return re.sub('^[0-9\\.]*$', \"\", wn)\n", + " \n", + "# define a function to get text/clean/calculate frequency\n", + "def get_wf (URL):\n", + " # first get the web page\n", + " r = requests .get(URL)\n", + " \n", + " # Now clean\n", + " # remove html markup\n", + " t = cleanHtml (r .text) .lower()\n", + " \n", + " # split string into an array of words using any sequence of spaces \"\\s+\" \n", + " wds = re .split('\\s+',t)\n", + " \n", + " # remove periods, commas, etc stuck to the edges of words\n", + " for i in range(len(wds)):\n", + " wds [i] = cleanWord (wds [i])\n", + " \n", + " # If satisfied with results, lets go to the next step: calculate frequencies\n", + " # We can write a loop to create a dictionary, but \n", + " # there is a special function for everything in python\n", + " # in particular for counting frequencies (like function table() in R)\n", + " wf = Counter (wds)\n", + " \n", + " # Remove stop words from the dictionary wf\n", + " for k in stop_words:\n", + " wf. pop(k, None)\n", + " \n", + " #how many regular words in the document?\n", + " tw = 0\n", + " for w in wf:\n", + " tw += wf[w] \n", + " \n", + " \n", + " # Get ordered list\n", + " wfs = sorted (wf .items(), key = operator.itemgetter(1), reverse=True)\n", + " ml = min(len(wfs),15)\n", + "\n", + " #Reverse the list because barh plots items from the bottom\n", + " return (wfs [ 0:ml ] [::-1], tw)\n", + " \n", + "# Now populate two lists \n", + "(wf_ee, tw_ee) = get_wf('http://www.gutenberg.org/ebooks/1342.txt.utf-8')\n", + "(wf_bu, tw_bu) = get_wf('http://www.gutenberg.org/ebooks/76.txt.utf-8')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AOHwz8mL809Q/Bz6PY9nT8X0hFwDrwl6XOOdKgEuAjvgnsR8N1rUjSj5j8A8C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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plot the results: are there striking differences in language?\n", + "import numpy as np\n", + "import pylab\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "def plotTwoLists (wf_ee, wf_bu, title):\n", + " f = plt.figure (figsize=(10, 6))\n", + " # this is painfully tedious....\n", + " f .suptitle (title, fontsize=20)\n", + " ax = f.add_subplot(111)\n", + " ax .spines ['top'] .set_color ('none')\n", + " ax .spines ['bottom'] .set_color ('none')\n", + " ax .spines ['left'] .set_color ('none')\n", + " ax .spines ['right'] .set_color ('none')\n", + " ax .tick_params (labelcolor='w', top='off', bottom='off', left='off', right='off', labelsize=20)\n", + "\n", + " # Create two subplots, this is the first one\n", + " ax1 = f .add_subplot (121)\n", + " plt .subplots_adjust (wspace=.5)\n", + "\n", + " pos = np .arange (len(wf_ee)+1) \n", + " ax1 .tick_params (axis='both', which='major', labelsize=14)\n", + " pylab .yticks (pos, [ x [0] for x in wf_ee ])\n", + " ax1 .barh (range(len(wf_ee)), [ x [1] for x in wf_ee ], align='center')\n", + "\n", + " ax2 = f .add_subplot (122)\n", + " ax2 .tick_params (axis='both', which='major', labelsize=14)\n", + " pos = np .arange (len(wf_bu)+1) \n", + " pylab .yticks (pos, [ x [0] for x in wf_bu ])\n", + " ax2 .barh (range (len(wf_bu)), [ x [1] for x in wf_bu ], align='center')\n", + "\n", + "plotTwoLists (wf_ee, wf_bu, 'Difference between Pride and Prejudice and Huck Finn')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "and\t2836\n", + "of\t2676\n", + "to\t2646\n", + "a\t2217\n", + "in\t1422\n", + "his\t1205\n", + "he\t928\n", + "that\t920\n", + "was\t823\n", + "for\t798\n", + "with\t797\n", + "as\t672\n", + "I\t505\n", + "you\t497\n" + ] + } + ], + "source": [ + "#In case Project gutenberg is blocked you can download text to your laptop and copy to the docker container via scp\n", + "#Assuming the file name you copy is pg4680.txt here is how you change the script\n", + "# Please note the option errors='replace'\n", + "# without it python invariably runs into unicode errors\n", + "f = open ('pg4680.txt', 'r', encoding=\"ascii\", errors='replace')\n", + " \n", + "# What comes back includes headers and other HTTP stuff, get just the body of the response\n", + "t = f.read()\n", + "\n", + "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n", + "wds = re.split('\\s+',t)\n", + "\n", + "# now populate a dictionary (wf)\n", + "wf = {}\n", + "for w in wds:\n", + " if w in wf: wf [w] = wf [w] + 1\n", + " else: wf [w] = 1\n", + "\n", + "# dictionaries can not be sorted, so lets get a sorted *list* \n", + "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n", + "\n", + "# lets just have no more than 15 words \n", + "ml = min(len(wfs),15)\n", + "for i in range(1,ml,1):\n", + " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment 1\n", + "\n", + "1. Compare word frequencies between two works of a single author.\n", + "1. Compare word frequencies between works of two authors.\n", + "1. Are there some words preferred by one author but used less frequently by another author?\n", + "\n", + "Extra credit\n", + "\n", + "1. The frequency of a specific word, e.g., \"would\" should follow a binomial distribution (each regular word in a document is a trial and with probability p that word is \"would\". The estimate for p is N(\"would\")/N(regular word)). Do these binomial distributions for your chosen word differ significantly between books of the same author or between authors? \n", + "\n", + "Project Gutenberg is a good source of for fiction and non-fiction.\n", + "\n", + "E.g below are two most popular books from Project Gutenberg:\n", + "- Pride and Prejudice at http://www.gutenberg.org/ebooks/1342.txt.utf-8\n", + "- Adventures of Huckleberry Finn at http://www.gutenberg.org/ebooks/76.txt.utf-8" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import requests, re, nltk\n", + "#In case your text is not on Project Gutenberg but at some other URL\n", + "#http://www.fullbooks.com/Our-World-or-The-Slaveholders-Daughter2.html\n", + "# that contains 12 parts\n", + "t = \"\"\n", + "for i in range(2,13):\n", + " r = requests .get('http://www.fullbooks.com/Our-World-or-The-Slaveholders-Daughter' + str(i) + '.html')\n", + " t = t + r.text" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1323653" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(t)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 4dc56c2abec1e517e7a2997663d4f908416faa1d Mon Sep 17 00:00:00 2001 From: Braden H Butler Date: Thu, 15 Sep 2022 19:22:15 +0000 Subject: [PATCH 2/2] miniproject1 --- bbutle11.ipynb | 154 ++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 133 insertions(+), 21 deletions(-) diff --git a/bbutle11.ipynb b/bbutle11.ipynb index 59b489b..52abdc8 100644 --- a/bbutle11.ipynb +++ b/bbutle11.ipynb @@ -312,55 +312,167 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, + "execution_count": 28, + "metadata": {}, "outputs": [], "source": [ "import requests, re, nltk\n", - "#In case your text is not on Project Gutenberg but at some other URL\n", - "#http://www.fullbooks.com/Our-World-or-The-Slaveholders-Daughter2.html\n", - "# that contains 12 parts\n", - "t = \"\"\n", - "for i in range(2,13):\n", - " r = requests .get('http://www.fullbooks.com/Our-World-or-The-Slaveholders-Daughter' + str(i) + '.html')\n", - " t = t + r.text" + "from bs4 import BeautifulSoup\n", + "from nltk import clean_html\n", + "from collections import Counter\n", + "import operator\n", + "\n", + "# we may not care about the usage of stop words\n", + "stop_words = nltk.corpus.stopwords.words('english') + [\n", + " 'ut', '\\'re','.', ',', '--', '\\'s', '?', ')', '(', ':', '\\'',\n", + " '\\\"', '-', '}', '{', '&', '|', u'\\u2014' ]\n", + "\n", + "# We most likely would like to remove html markup\n", + "def cleanHtml (html):\n", + " from bs4 import BeautifulSoup\n", + " soup = BeautifulSoup(html, 'html.parser')\n", + " return soup .get_text()\n", + "\n", + "# We also want to remove special characters, quotes, etc. from each word\n", + "def cleanWord (w):\n", + " # r in r'[.,\"\\']' tells to treat \\ as a regular character \n", + " # but we need to escape ' with \\'\n", + " # any character between the brackets [] is to be removed \n", + " wn = re.sub('[,\"\\.\\'&\\|:@>*;/=]', \"\", w)\n", + " # get rid of numbers\n", + " return re.sub('^[0-9\\.]*$', \"\", wn)\n", + " \n", + "# define a function to get text/clean/calculate frequency\n", + "def get_wf (URL):\n", + " # first get the web page\n", + " r = requests .get(URL)\n", + " \n", + " # Now clean\n", + " # remove html markup\n", + " t = cleanHtml (r .text) .lower()\n", + " \n", + " # split string into an array of words using any sequence of spaces \"\\s+\" \n", + " wds = re .split('\\s+',t)\n", + " \n", + " # remove periods, commas, etc stuck to the edges of words\n", + " for i in range(len(wds)):\n", + " wds [i] = cleanWord (wds [i])\n", + " \n", + " # If satisfied with results, lets go to the next step: calculate frequencies\n", + " # We can write a loop to create a dictionary, but \n", + " # there is a special function for everything in python\n", + " # in particular for counting frequencies (like function table() in R)\n", + " wf = Counter (wds)\n", + " \n", + " # Remove stop words from the dictionary wf\n", + " for k in stop_words:\n", + " wf. pop(k, None)\n", + " \n", + " #how many regular words in the document?\n", + " tw = 0\n", + " for w in wf:\n", + " tw += wf[w] \n", + " \n", + " \n", + " # Get ordered list\n", + " wfs = sorted (wf .items(), key = operator.itemgetter(1), reverse=True)\n", + " ml = min(len(wfs),15)\n", + "\n", + " #Reverse the list because barh plots items from the bottom\n", + " return (wfs [ 0:ml ] [::-1], tw)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "#Plot the results: are there striking differences in language?\n", + "import numpy as np\n", + "import pylab\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "def plotTwoLists (wf_ee, wf_bu, title):\n", + " f = plt.figure (figsize=(10, 6))\n", + " # this is painfully tedious....\n", + " f .suptitle (title, fontsize=20)\n", + " ax = f.add_subplot(111)\n", + " ax .spines ['top'] .set_color ('none')\n", + " ax .spines ['bottom'] .set_color ('none')\n", + " ax .spines ['left'] .set_color ('none')\n", + " ax .spines ['right'] .set_color ('none')\n", + " ax .tick_params (labelcolor='w', top='off', bottom='off', left='off', right='off', labelsize=20)\n", + "\n", + " # Create two subplots, this is the first one\n", + " ax1 = f .add_subplot (121)\n", + " plt .subplots_adjust (wspace=.5)\n", + "\n", + " pos = np .arange (len(wf_ee)) \n", + " ax1 .tick_params (axis='both', which='major', labelsize=15)\n", + " pylab .yticks (pos, [ x [0] for x in wf_ee ])\n", + " ax1 .barh (range(len(wf_ee)), [ x [1] for x in wf_ee ], align='center')\n", + "\n", + " ax2 = f .add_subplot (122)\n", + " ax2 .tick_params (axis='both', which='major', labelsize=15)\n", + " pos = np .arange (len(wf_bu)) \n", + " pylab .yticks (pos, [ x [0] for x in wf_bu ])\n", + " ax2 .barh (range (len(wf_bu)), [ x [1] for x in wf_bu ], align='center')" + ] + }, + { + "cell_type": "code", + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n", 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QkvP8Svz9aOcA1wWvrWb2JnB1PsFYHiXYFmFr8ylyK3nH8c532wVitcFlYW0+6bHO5bJo85bjh1icDlwE1DSzc51z24tRBlCsNqmwbb+CXQxOzawavj3rgr/X+WX8Z/2WIMutxD5+YEebEWlrEaZVi0gr1vkfnK898T20A/E9nAAbzOwZ4PpiBJN5BJ+pvwG3mVlb/INol+Jv7SuKws6R/NrWsIb42LKwL3u18VddYqoUwSlwOH5dVzjnFpTRMsIH8YHOua+KOW+sb67h8i53zj1c8moVaG3wnt83ykjh+jzonLuqlJbfyMyqxAhQm0UtM/Lves659aW0/PeC92Pwl+BT2BGAvod/4OZAdjxQ936M+kx0zp1cxOWVxToUm5k1x69nO3yPaazAFHwvC+zo9Y8W7g2IHj94J865P81sCbCbmTV3zkX3gMUq62f8Zdi9zayqcy66ZzDWPKVW52IK79sTnXOvFZbZzKriezWW4x+UXBY1/ZAYs4WPmab4YDAyfxX8h0L0F81w0BGrra8fI63Y53lw/o4CRgVfeg7HP+B0KrCfme3nnNuc3/wl3BbFFV6vpvlMb5ZPejyVSXvhnFtkZkfi24AhQA0z+3uM86u0RB6382JMz2+fFMeJ+MB0jHPunMgJQXtX1r3ixTr/AZxza/Bf7K40/yMoRwEX4B++rY//ErGr5uKD0y7FmKewc6SwKyLr8Pd0pxeSr0AV/heiCmNmKUB4ANqxZbio8K9EHZGg5cXyCf7D68jgkkhR8pZmfari7yuM1j14/yIirSTbYxvkfnDH8im+4Twa34OajR8uB3YEqcfi7+f7yjkXORj19/jgvlvwrb0oymOfFsjMWuIv27QDLiwgMAUfHC4E2prZXjGm9wve34sxLZZwvr5FKcs5l4O/7SON2Nss1vJLu85FVdx92wj/AfRxjGCsNv5Wkmjh8+HwGNO6ETsAXRO87x5jWkaMtF06z51zK51zE5xzp+G3cWv8A6EFKcm2KG69NhA8WGRmrWNk6b6ryygDZdZeOOeW44OhL/FfJF4xs+qlvZxAvsdtcEk71rFZXOFfuJsQY9pRpVB+YXZpXznnfnLOPYmv60Z8sF0awrdXFCfW22l7mVk94CD8vd/fFTL/HKCBme1XjGXuJKmD0+Bb/Ev4hmchOy7ZloWn8cHKrWa207cUM0spzs+tOedC+CEcTjazobHymFnHYB1LxDn3O377NAceCAL5yPJrBwclzrmV+HvLMszs5lgBn5m1zicgKMjdwS/6hMtIx99AD36bhj2Cv0TzYHCpInrZ1c0sumEIXzLYI9aCgx6fmfiG7VT8gzebg2m/4h9guBwfHL0XNe9W/FP6zfH3btWMUafmZtZhF9eh1AQfBDPxAcNQ51xWQfmDe9H+E/x7X+TxYf4nDo/A3zuV5x4ly/8nP8Nl3WhmDSLytwKG4YdbeTpqnseC9zuC23PC83TGjzLwO/7p612qcymYjA+Mh5nZsbEymNkhZhb+RbqV+MvWBwcBWDhPNfxwbo1iFPFs8H5j+LwM5qlO/m1b+H72c4IeyvA8u+MvKeZR3PPczFLN7LAYeaqx41aqwn6JpiTboiSexn/m3Rt1XOyFH1s50ZRpexF82e6JP0YGAJMiz7FSNBZ/KfzS4LgDcn/97W5KcFtSDAuC9+6RiWa2NzsumZelYp3/ZrZXULdoDfC3H2TvaoWCNjbcizyjGLP+3cw6RaUNx1/Of7GgqyCBB4P3J8ysRYx61TKzboVVImku60d8EKaw4+dLD8fffPsJcFZUz1epcs79YWYD8U+qzQnuYZyHvxy8O/4m4Yb4p8SL6kx8UPSkmV2G76Jfi7/Z+QB8j8Qh+Ma9pC4JyrkQ6G5mU/FDSeyFH5LlBHYc2JfgL4vehj+AP8Lfn9ICf5N0Z/yl8F+LuOxl+BPxGzN7DX8Pz0B8wPeoc25mOKNz7vsgSH8KmGdmb+MvzVbDB59H4AOVdhHlT8cHnROC+9+ygd+cc89F5TkO/3BB5D2l4WnnRvwd7Xb8Zf8LgePN7D38ZdUm+O10GL7X/ttdWIfSNAN/b9dnQKsYwSP4y2ILIv7/F377DATmBsf1Hvjtugkf5Ebfrxb+4M9zmdA597GZ/Qs/6PlXZjYef34Owgcyl8a47eYl4ORg+V+Y2ev482gQ/kPt/BiXPEtS58IMCILoWKY558aa2cn4sfymmH/Y7stgebvjz4298cf2JufcdjN7GH9/5tdmNjnYFj2CbfF+8Hcu59wHZpYFZOKPn1fxwcvx+Etp4fGPI+eZa2YzgSOBT4JjtGkwz1Ri91oV5zyvCXxkZj/hj6vf8G1cryDva865AntaSrItSmgkPgg7Bfg8aOvqA6fhv7SdUArLKDXl0V4459aY2TH48WX74Y/dE4rwgGxxlvGzmd2C/wL1PzN7GX+89sLv3//hP892xev4nvGrzKwjvrd2D3w7MIV8OihKi3NuS3HOf/znxgQz+xTfE7kU/2Dqifj9W9yAOrJ9Cj8QdTy+rfyUHV/Yi+ItYJb5hxuX4eOow/FfAK4rbGbn3HQzuw7/xePH4LP3V/w9pnvie2Y/IvYVtDwFVegXOw+fsBk/1uhn+N/u7kv+v9Qzg1IaSipieiv8N94f8V3g6/GXgJ8DBkTlHRPUuVUB5dXBj8/3Gb67PzvY0VPwH1K1IvIOCcobUsC2mhEjvRY+iPoKf+JswAdUo4j6yVf8h8Yl+Mut64LtvRAfvF1BPr+ckd82xn8bCw9EvRl/ohb0C1Edg+32W5B/Nf4G+MeBnlF5q+AbxF/YMc7tjBjlhY+dzlHTwoO8byGf4VrwT4OGHzBYjQ/swwNt30DsX14pzjrke7wVtr+LcK7EenWPMV8aPlAJ/xLM7/ifBs3vV4wm4m+paFtAvT/FDyy+Ad+Lme9wWPgv0VfiB8fOxl+qfhM4tIB5ilXnAsoZXoRtNioifxP8D2l8gz+XNgZ1GA/8HxHD9wTrdRX+XMvG33P5HL4BH0OMtgEf+F+Jb1M24z/URuPPow34p9ej16E+vi1cGczzDb7taEU+wydRxPMc/0F6Lf4DbSG+zfsdf2nvQor4C2bF3RYF1b2g8waoi//yEv5Fuu+BqynHX4gq5DiLdf4Vub0oYNndyaf9jzhfwuN3fhRsp/A8w4uybSPO7ZhtEr6d/CLiGHke/4XnG2BtMbZ1eFD4Q6PSd8f3+i8JjqF5wbFZNda6F7LNC1qPmNulOOc/Pni8ix2/iLYZP1bwW0C/YmyL8LkR/VqP75T7B7F/HavA7RGs/5fBdvwdf9Uh1o+nFHQsHI7/oYal+M/F34My/wVkFLZuFhQiIrLLgkt1vwPvOX/foZQDM2uD71V7yTl3RrzrI1IUZlYX3yv/pXOuSA++mdkP+Ace27gijAQhFVNS33MqIuVuf/ylpKIOWyLFYGbNYtwbnoa/ygG+11okoZhZY4t6cDS4B3ok/jaQIh235h+yaYO/CvBLIdmlAkuae05FJP6cc1+Td+BrKV1X4AfunoG/H6wZfrSJlvhLgq/ErWYi+TsFP+7mu/iB+tPx90G3xV/q/XdBM5vZEPzzFafh25f7XAnGZpWKQ8GpiEjF8Q7+YYre+A/4rfjL+Q/j733VfVqSiObi72U9kh0D1v8K3Anc65wr7On0IUBX/DjG1zvnivOAj1RAuudURERERBKG7jkVERERkYSh4FREREREEoaCUxERERFJGApORURERCRhKDgVERERkYSh4FREREREEoaCUxERERFJGApORURERCRhKDgVERERkYSh4FREREREEoaCUxERERFJGApORURERCRhKDgVERERkYSh4FREREREEoaCUxERERFJGApORURERCRhKDgVERERkYSRrMHpQODfwIfAesABz5ewrJbAU8BSYDOwABgFNNjVSoqIiIhIXlXjXYEychNwILARWAy0K2E5rYGPgSbAZOB7oAtwOdAXOAz4Y1crKyIiIiJesvacXgm0BeoCF+1COY/iA9PLgAHAdUBP4EFgX+DOXaqliIiIiOSRrMHp+8CP+Mv5JdUa6I2/jD86atqtwJ/A34FahRVkZpm7UA+JM+2/ik37r+LSvqvYtP8qrnjvu2QNTktDj+B9GrA9atoGYBaQBnQrQlk6QSs27b+KTfuv4tK+q9i0/youBacJat/gfX4+038M3tuWQ11EREREKoVkfSCqNNQL3tflMz2cXj/WxKBLPBOgYcOGB2dkZOzKLQYSRwcffDDafxVXrVq1YNdu8ZE4adiwoc69CkxtZ4W22cxCEf9nOeeyymvhCk7LSLATswAyMjJcKBQqZA4RKQsZGRnxroKUUKtWrVDbKVL+zOwb51zcGk9d1s9fuGe0Xj7Tw+lry74qIiIiIpWDgtP8/RC853dPaZvgPb97UkVERESkmBSc5u/94L03O2+nOvgB+DcBc8qzUiIiIiLJTMEpVMP/glTrqPSf8cNItQKGRU0bgR/f9Dn8eKciIiIiUgqS9YGoAcELoFnwfggwJvh7FXBN8PduwHfAb/hANNLF+J8vfRg4OsjXFT8G6nzgxlKut4iIiEillqzB6UHA4Ki0vYMX+ED0Ggr3M5AB3Ab0BY4FlgEP4XtP15RCXUVEREQkkKzB6fDgVRQLACtg+iLgnF2rjoiIiIgUhe45FREREZGEoeBURERERBKGglMRERERSRgKTkVEREQkYSg4FREREZGEkaxP6yeUr5eso9V1U+JdDZGkseCe/vGugpQDtZ0ipauitJ3qORURERGRhKHgVEREREQSRtIGp2bW28yuKOG8Q8zMmVntUq6WiIiIiBQgaYNToDdwRQnnnQIcAmwqtdqIiIiISKH0QFQMzrnfgd/jXQ8RERGRyiYuPadmdqSZvW9mG81snZnNMLNOZtbczJ4ys1/MLNvM5pvZHWZWPWLeVsEl9zPN7Dkz22BmK83s1og8w4GrgT2DvM7MxgTTDjGz18xsmZn9aWZfmtlZUfXLc1k/YpmnmdnjQZ0Xm9kIM0vm3mcRERGRclXuPadm1h14B3gfGAz8CRwG7AZsBVYDVwFrgLbAcKAxcEFUUfcDbwADgSOBW81slXNuNPBfoA3QEzgpyB/uCd0TmAX8B8gJlv20mW13zr1YSPXvA14Nlnk0cAswDxhX9C0gIiIiIvmJx2X9u4H/AX2ccy5Iezti+jXhP8xsFj54fcrMLnXO/RWRb55zLhywTjWzJsANZvaYc26xmS0DNjvn5kQu3Dn3UkT5BswEWgLnA4UFpzOdc1cHf79jZn2Bk4kRnJpZJpAJUKVu40KKFRERgKysLLKysgDYtmldnGsjIvFQrpekzawW0BV4JiIwjZxuZnaFmX1rZtnAFuAFIBXYIyr7xKj/JwAt8IFmQXVoYGYPm9lvQflb8EFk2yKswrSo/7/Nb3nOuSznXIZzLqNKWr0iFC0iIpmZmYRCIUKhEGo7RSqn8r5fsgFgwLJ8pl8BPIAPPE8EugDDgmk1ovKuzOf/5oXUYQwwCH9bQG+gM/BUjPJjWRv1/19FnE9EREREiqC8L+uvAbaTfwB5KjDeOXdjOMHMOuSTt0k+/+cX+GJmNYDjgGHOuf9EpOuhJhEREZEEUK5BmXPuT2AucHZwv2e0msDmqLSzYuSDHQ86hZ2MD0wXB//H6tVMxa9z7jLMrA5wQqGVFxEREZEyF48Hoq4D3gXeMrMs/ANPhwAh/FP8l5nZXOBnfGC6Tz7l7Gdmj+Ofnj8SOBe43Dm3PZj+PdDUzIYA3wCrnHMLzOxT4BYzW4/vxb0OWAfULfU1FREREZFiKffL2c65mUAvIA14HngZOArf43kb/on5O4L3v4DL8inqWnxA+Sp+mKnbgUcipo/D3196H/ApfkgqgDOBX4BngYeC+Z8thVUTERERkV0Ul1+Ics59gO/tjOWcGGmxbgFY55w7o4Bl5MQqyzn3E36M0mjDI/KMwQe24f8XxKqDc25IfssXERERkeLTz5eWg4671SN0T/94V0NEpEJR2ylSOekpdRERERFJGBWu5zS/S+wiIiIiUvGp51REREREEkaF6zmtiL5eso5W102JdzVEysQC3RMoZURt5w46z6QyUc+piIiIiCQMBaciIiIikjAqXHBqZplmNiBG+gIzeyDi/zFmFirXyomIJInu3bszcODAUimrVatWXHPNNbn/DxkyhIyMjFIpW0SST0W85zQT/3Okk6LSTwL+KPfaiIiIiEipqYjBaUzOuS/iXQcRERER2TW7dFnfzFqZmTOz08zscTNbZ2aLzWyEmaVE5GtnZi+Z2SIz22Rm88zsiqg8Q4KyakctI/dyvZnNAA4GBgd5nZkNic6XT12Hm9mqGOnOzC6J+P8EM/vMzP40szVmNtfMjir5VhIRSUzz5s2jb9++pKenU6tWLdq3b8/o0aPz5Bk7diz77LMPdevWpV+/fixevDjP9Ouuu46OHTtSu3ZtWrZsyVlnncXy5cvLczVEJMmUVs/pfcCrwED879bfAswDxgXTdwN+AF4ANgAHASOAmsDdxVjOxcFyfgFuD9J+3rWq72BmrYHxwEPAP4Aa+GA4vbSWISKSKI4//njat2/P888/T2pqKj/88APr16/PnT537lyWLl3KyJEjyc7O5vLLLyczM5M333wzN8/KlSu54YYbaNGiBb///jsjR46kZ8+efPPNN6SkVLjHGkQkAZRWcDrTOXd18Pc7ZtYXOJkgOHXOTQemA5iZAR8BacD5FCM4dc59a2Z/Ar875+aUUt0jdQI2OOf+EZH2Zn6ZRUQqqlWrVvHrr78yefJkOnbsCMDRRx+dJ8/69euZMmUKDRo0AGD58uVceeWVZGdnU7NmTQCeeuqp3Pzbtm3jkEMOoWXLlnz00UcceeSR5bQ2IpJMSutr7bSo/78FWob/MbMawaX+n4DNwBbgTmAvM0uk+16/BuqZ2TNm1tvMapW0oGBUgZCZhbZtWleKVRQR2XXp6ensvvvuXHjhhbz88susXLlypzydO3fODUwBOnToAMCSJUty09566y0OPfRQ6tWrR9WqVWnZ0jf98+fPL1G9srKyyMjIICMjA7WdIpVTaQWna6P+/wt/STzsXuAaIAs4FugM3BFMq0GCcM79AJwI7I3vMV1lZmPNrHEJyspyzmU45zKqpNUr7aqKiOySlJQUpk2bRrNmzRg6dCjNmjXjiCOO4IsvdjxbWr9+/TzzVK9eHYCcnBwAPv30U0444QRatmzJc889x+zZs5kzZ06ePMWVmZlJKBQiFAqhtlOkciqvG4JOBf7tnLvPOfeucy4EbI3KE27JqkelN6B05ESXbWY7le2cm+KcOwJoCJwLHAP8u5TqICKSMNq1a8err77K2rVreffdd8nJyaF///5s3769SPNPnDiRxo0b8/LLL3PCCSfQrVs3mjVrVsa1FpFkV17BaU385XwAzKwKcHpUnvAjoO0j8nUF6kbli+6VLarFQB0z2y0irXd+mZ1z65xzY4GJQIcSLE9EpEKoVq0aPXv25KqrrmLZsmWsXbu2SPNlZ2dTrVo1/KME3gsvvFBGtRSRyqK87vd8BxgW3HO6GhgGpEbl+QRYAjxsZjfjn5C/Flgfle97oI+Z9cEPuv+rc64og++/DWQDT5nZSGAv4MLIDGZ2AXBIkHcp0Abf6/tsEddTRKRC+Oqrr7jmmmsYNGgQe++9N2vWrOHee+/lwAMPJD29aAOU9OrVi1GjRnHFFVdw/PHH8/HHH/P888+Xcc1FJNmVV8/ppcCHwGjgKfwvPOV5St859xf+V56244dzuhq4CFgTVdYdwHf4kQA+BY4vSgWcc6uAU/APak0C/g84MyrbV0Bj4F/4h7xuAp4A/lmUZYiIVBTNmjWjadOm3HnnnfTr14+LL76Y9u3b89prrxW5jGOPPZZ7772XV199lRNOOIEPPviAN954owxrLSKVgTnn4l2HpJfavI1rPnhUvKshUiYW3NM/3lUoUEZGBqFQKN7VkBJIbd4GtZ1eop9nklzM7DPnXEa8lq8RkkVEREQkYRR6z2kh45Bud84V7bHOCi74qdWCgvltLp9u6I671SOkb70iIsWitlOkciqw59TMWuEHzM/v9VS+MyefWyh4WxwVv6qJiIiIJIfCek6X4gfMz8+qUqxLossCCrrT/4fyqoiIiIhIsiowOA2eoNeTBIBzbik+WBcRERGRMpJIv2uftL5eso5W102JdzUkDvSErUjJVda2U+2GVHZ6Wl9EREREEoaCUxER2SXTpk1j1KhR8a6GiCQJBaciIrJLFJyKSGlK5uC0JX6oq6XAZmABMApoUMxyDgcmB/PnAAuBN4G+pVNNEREREQlL1uC0NfAZcA7wye233/5W69atG6Wmpl7evHnzlQ0aNPhX+McFzGyImTkz62hm75jZn2b2vZmdDFwEfAgcDXx4//33v7HPPvvUqlGjRr9mzZq91b9//5lmVi1uaykisgu6d+/OwIED86TNmDEDM+Obb75hwYIFmBnjxo3jggsuoF69erRs2ZJbb72V7dv9768MHz6ckSNH8ttvv2FmmBlDhgzJLW/cuHF07NiR1NRUdt99d2688Ua2bt1anqspIhVMsj6t/yjQBLjMzH4ApgLPjh49us7GjRtPuvHGGy8H0oALI+YZix/L9H7gUuClhQsXZu+xxx45wMFmdiDwIvD4cccd91nfvn3/c+ONNx5RrVq1+4Ary3HdRETK1bXXXsspp5zC+PHjmT59Orfddhv77bcfp512Gueddx4//vgj7733HhMnTgSgcePGgL/cP2jQIM4++2zuv/9+vvrqK26++Wb++OMP/vOf/8RzlUQkgSVjcNoa6I2/DD8a+BiY4ZwbDNQBem/btq36DTfccL6Z3REx34POuacAzOwzYMWbb75Z98ILL/zKzOYD04BnnXMXB/kvr1mzZsdzzz33QjO7wzn3Rzmtn4hIuTryyCMZOXIkAL169eLtt99mwoQJnHbaabRs2ZLmzZuTmppKt27d8sx3yy230L17d5555hkA+vb1d0Ndf/313HTTTbRs2bJ8V0REKoRkvKzfI3ifZmYG/A14JUjbAMw688wzq+HX/ZCI+aaF/wgCzZW//PLLJqDtlVde2QvYAxhnZlWXLVvWfsuWLW0OO+ywb4EawP7RlTCzTDMLmVlo26Z1pb2OIiLlpnfv3nn+79ChA4sXLy5wnm3btvH5559z6qmn5kkfNGgQ27dvZ/bs2THny8rKIiMjg4yMDNR2ilROyRic7hu8zwcaAdWAFRHTf2zatGn47/SI9LVR5fw1c+bM6UDKgAEDJgZpbwJbWrRo8W316tVrtGvXrkOQvnt0JZxzWc65DOdcRpW0eiVfGxGROKtfv36e/6tXr05OTk6B86xatYotW7YQ0d4C5P6/evXqmPNlZmYSCoUIhUKo7RSpnJIxOA23ZuuAVcAW/P2nYetWrMiNVWO3joG5c+fOB3rWr19/Hfhv9J9++imzZ8/+46mnnrovNTW1C9AZeCt63sie011bHRGRslGjRg3++uuvPGlr1qwplbIbNWpEtWrVWLlyZZ70cPubnp4eazYRSQyNwjFM8Mosz4UnY3Cayzm3Df/Ufp7rSuPGjcPMHBD7ulLg0EMPbQ+826FDh/erVq264rLLLrsvIyOjfbdu3aaec8451+bk5PzDOReKdb9pZM9paa6TiEhpadmyJd9//32etGnTpuWTO3+xelKrVKnCwQcfzCuvvJInfdy4caSkpHDIIYcgIglrVTiGCV5Z5bnwZAxOwzcphXtQbwV6mNnTZtbnoosuOurmm2+mS5cuHznn8r1pqlq1alW7devWF5hXtWrVv2/duvXynJycK81sWLVq1cZOmjRpflZW1qnNmjWbY2ZpZb1SIiKl7aSTTuLHH3/kyiuv5N133+XGG2/k7bffLnY57dq1Y8WKFYwZM4ZQKMSCBQsAGDFiBO+//z7nnHMOU6dO5YEHHuDmm2/m/PPP18NQIpKvZAxOfwje2wI456YBpwMZwOvjx4/PuPrqq5k6dertBRVSs2bNmmaWAnwAbHfOvQycCBy0devWcaeffnqrRx99lIyMjA3AXwWVJSKSiPr3789dd93F+PHjOemkk/jtt9946KGHil3OaaedxpAhQ7j22mvp3Lkzw4cPB/yDVC+99BKhUIjjjz+eUaNGcfXVV/PII4+U8pqISDIx51y861DaWgM/4YeSag1sj5hWB1gGGP4+1D8LKOdq4AHgOeDsGNOfA/4PuAz4d0EVSm3exjUfPKpIlZfksuCe/vGuQqWXkZFBKKRbvyui1OZtqIxtp9oNiTcz+yyetyUmY8/pz/hhoVoBw6KmjQBq4QPLyMC0XfCK9GHwPhA4IGraQUG6A97b1QqLiIiIiJeMg/ADXIwffP9h/E+Pfgd0xY+BOh+4MSr/d8G7RaR9AjyN/wnUT4GJwG/4oHcAUB0YBcwrrDIdd6tHSN+ERUSKRW2nSOWUrMHpz/h7TG8D+gLH4i/nP4TvPS3qWCnnAjOBIUAf/G0B64GPgCeAl0qz0iIiIiKVXbIGpwCL8L2eRWH5pDtgTPASERERkTKWjPecioiIiEgFlcw9pwnj6yXraHXdlHhXQ8qZnrgV2TXJ2HaqXRApnHpORURERCRhKDgVEZFcQ4YMISOj7IY3HDhwIN27dy+z8kWk4lNwKiIiIiIJQ8GpiIiUquzs7HhXQUQqsAoRnJrZGDMLmVkvM/vKzP40s4/MbL+IPClmdp2Z/WRmm81svpkNjipnhpmNN7NzzOxXM9toZs+ZWaqZdTGzT4K0GWa2R9S8jczsGTP7w8w2BXni9tNeIiLl4csvv+Too48mLS2NBg0acNZZZ7FixYrc6QsWLMDMeOGFFzj77LOpX78+xx9/PACLFi3i2GOPpWbNmrRq1Yr//ve/8VoNEalAKtLT+nsA9wN3Atn4371/2cw6Oucc/vftB+MH3v8c6AU8ZWZ/OOfeiCinG9AIuDQo88GgvK7AffifNX0YyMIP4B82CdgHuAZYBfwDeN/MOjnnfiqLFRYRiafff/+d7t270759e8aOHcvGjRu57rrr6NWrF6FQiOrVq+fmveaaazj55JN55ZVXqFKlCs45TjzxRFatWsWTTz5JjRo1uPXWW1m9ejVt2rSJ41qJSKKrSMFpOnCYc+5H8D2l+J8U3dfMtgIXAec4554J8r9rZs2BW4HI4LQ2cKJzbl1QTnfgfOAo59zMIK0FMNrM0pxzm8ysL3AY0N0590GQ5z1gAT5IvaDM1lpEJE5GjhwJwNSpU6lbty4Abdq0oVu3brz66qucccYZuXm7devG6NGjc/9/8803+eKLL5gzZw5du3YF4OCDD6Z169YKTkWkQBXisn5gQTgwDXwbvLcEjga2AxPNrGr4BUwHDjKzKhHzhcKBaeAn4C/8T5JGpgG0CN67ACvDgSmAc+5PfNB7eKzKmllmcCtCaNumdbGyiIgktE8++YTevXvnBqYAXbt2pVWrVnz00Ud58vbv33+neZs2bZobmALsueeeHHzwwQUuMysri4yMDDIyMlDbKVI5VaTgdG3U/38F7zXwl+mrAOuALRGvMfje4eaFlLPBObc9n7IJ5l8Zo04r8D26O3HOZTnnMpxzGVXS6sVcIRGRRLZs2TKaNm26U3rTpk1ZvXr1TmmRli9fTpMmTXaaN1ZapMzMTEKhEKFQCLWdIpVTRbqsX5DVwFb8pfftMabHCiyLYxkQq0VtGixbRCTpNG/enJUrd24+V6xYsVMPqJnl+b9Zs2Yx5125ciU1a9Ys3YqKSFKpSD2nBXkP33NazzkXivH6q7ACCjEXaGJmR4YTzCwN6E/e2wFERJJG165dmTp1Khs2bMhN+/TTT1mwYAGHHx7zjqZcnTt3ZsWKFcydOzc3beHChXz++edlVl8RSQ5JEZw6534A/gO8ZGb/NLOjzay/mV1rZrs8dolzbirwMX50gMFmdhzwJlATP4KAiEjSueqqqwDo06cPkydP5oUXXuDkk0+mY8eOnHLKKQXOe+yxx3LggQdy6qmn8uKLLzJhwgT69+9f6GV9EZGkCE4Dw4DbgbPxgeMYfM/mzFIqfwDwDjAKeAUwoKeGkRKRZNW4cWPef/99atSowRlnnMGwYcM44ogjeOedd/IMIxWLmfHaa6/RoUMHhg4dypVXXskll1zCIYccUk61F5GKyvwQoVKWUpu3cc0Hj4p3NaScLbinf+GZpMxlZGQQCoXiXQ0pgdTmbUi2tlPtglQEZvaZcy5uPzSUTD2nIiIiIlLBJcvT+gmt4271COnbsohIsajtFKmc1HMqIiIiIglDwamIiIiIJAwFpyIiIiKSMHTPaTn4esk6Wl03Jd7VkHKgJ3FFSk8itp06x0XKnnpORURERCRhKDiNwcyGm9mqIuQLmdmYcqiSiEipGzduHGPGjMmT1r17dwYOHBifComIoOA0P/8F+sS7EiIiZSlWcCoiEm+65zQG59xiYHG86yEiIiJS2SRlz6mZ7Wdmb5vZajP708y+M7NhwbT+ZvaOma00s/VmNsfMekfNv9NlfTPb38xmmVlOUN4J5blOIiKlaciQIbz66qt88MEHmBlmxvDhw3Onjx07ln322Ye6devSr18/Fi/O+3191apVDB48mIYNG5KWlkb37t13+plYM+ORRx7JkzZ8+HAaNWpUZuslIhVfsvacvg58B/wfsBnYF6gbTNsrmP4AsB3oB7xlZkc652bFKszMagJTgVXAmUBNYBRQG/imzNZCRKSM3HzzzSxcuJC1a9fy6KOPAtCyZUtmzJjB3LlzWbp0KSNHjiQ7O5vLL7+czMxM3nzzzdz5BwwYwE8//cQDDzxAo0aNuP/+++nRowdffPEF++yzT7xWS0SSQNIFp2bWCB+Anuic+zpInh6e7px7JCJvCvA+sB9wLhAzOAXOAZoAXYNL/pjZAuCj0q6/iEh5aN26Nenp6Wzfvp1u3brlmbZ+/XqmTJlCgwYNAFi+fDlXXnkl2dnZ1KxZk7fffptZs2YxY8YMjjrqKAB69uxJq1atuP/++3n88cfLfX1EJHkk42X91cAi4D9mNsjMmkRONLOWZvaMmS0BtgJbgN5A2wLK7AJ8Fg5MAYJe1pX5zWBmmcHT/KFtm9btwuqIiJSvzp075wamAB06dABgyZIlAHzyySc0adIkNzAFqFWrFscddxwffbRr39mzsrLIyMggIyMDtZ0ilVPSBafOue34YHM58BSw3Mw+NLNOQU/pa8ChwC1AD6Az8BZQo4BimxE7EM03OHXOZTnnMpxzGVXS6pVsZURE4qB+/fp5/q9evToAOTk5ACxbtowmTZpEz0bTpk1ZvXr1Li07MzOTUChEKBRCbadI5ZR0l/UBnHPfA6eYWTXgCOBeYArQHegE9HPOvR3OH9xTWpDlQLsY6Tu3ziIiSa558+asXLnzd/MVK1aQnp6e+39qaip//fVXnjxr1qwp8/qJSMWWdD2nkZxzW5xz7wH/ApoHL/APSQFgZnsChxVS1KfAwWbWMmK+w1BwKiIVWPXq1XN7Q4uja9eurFy5kpkzZ+ambdq0iSlTpnD44YfnprVs2ZLvvvsu9//t27czffp0REQKknQ9p2Z2AP5J/JeBX4AGwD+B/wFz8OOXjjSzm4E6wAhgSSHFPg3cBEwxs+H4p/Vvxz+9LyJSIbVr147JkyczadIkWrZsSYsWLYo0X58+fTj00EMZNGgQ99xzDw0bNuSBBx4gOzubf/zjH7n5TjrpJEaPHk2nTp3Ye++9+e9//8v69evLanVEJEkkXXCKvwS/ArgRaAGsxT+R/0/n3GYzOxkYDYzHB6p34i/3759fgc65TWbWB/gP8BKwALgaH7CKiFRIF198MV988QVDhw5lzZo13HrrrUWed9KkSVx99dVcccUV5OTk0KVLF9577708w0jdeuutrFy5kptuuonq1atzySWXsN9++zF69OiyWB0RSRLmnIt3HZJeavM2rvngUfGuhpSDBff0j3cVJEpGRsZOg8NLxZDavA2J1nbqHJfKwMw+c85lxGv5SX3PqYiIiIhULMl4WT/hdNytHiF92xYRKRa1nSKVk3pORURERCRhKDgVERERkYSh4FREREREEobuOS0HXy9ZR6vrpsS7GlIK9KSuSPmJd9up810kPtRzKiIiIiIJQ8FpDGY2xswKHRjRzFYFvxglIpK0xo0bx5gxY/Kkde/enYEDB8anQiKS1BSciohIgWIFpyIiZUXBqYiIiIgkjAoXnJrZkWb2vpltNLN1ZjbDzDoF0w4ys+lmtsnM1pjZC2bWNGLe7mbmzGz/qDJnmNn4Iiz3f2aWY2afmdmhZbOGIiKJY8iQIbz66qt88MEHmBlmxvDhw3Onjx07ln322Ye6devSr18/Fi9enDutS5cuDBkyJGaZnTp1Kofai0hFVKGCUzPrDkwHtgCDgUHAh8BuZtYYmAGkAWcClwJHAe+YWfVdXG4L4C1gNTAQeBx4IViWiEjSuvnmm+nRowedOnVi9uzZzJ49m/POOw+AuXPn8sgjjzBy5EiysrL4/PPPyczMzJ333HPPZfz48WzcuDE3bePGjYwfP56hQ4eW+7qISMVQ0YaSuhv4H9DHOeeCtLcBzOye4P8+zrn1QdqPwBzgFODFXVjuFUAO0N85tyko+0/g+V0oU0Qk4bVu3Zr09HS2b99Ot27d8kxbv349U6ZMoUGDBgAsX76cK6+8kuzsbGrWrMkZZ5zBVVddxSuvvMI555wD+PtXt2zZwplnnlnu6yIiFUOF6Tk1s1pAV+CZiMA0UhdgWjgwBXDOzQUWAIfv4uK7AO+EA9PAxELqm2lmITMLbdu0bhcXLyKSeDp37pwbmAJ06NABgCVLlgBQt25dBg4cmOdhqjFjxnDCCSfQsGHDmGVmZWWRkZFBRkYGajtFKqcKE5wCDQADluUzvTmwIuL/lsBTBx988G6DBg26EFgwZsyYS0qy4Bo1aux5yimndAIWA5uBFc65t6pWrZqT3zzOuSznXIZzLqNKWr2SLFZEJKHVr18/z//Vq/s7qHJydjSN5557Lh9++CG//PILP//8Mx9++GGBl/QzMzMJhUKEQiHUdopUThXpsv4aYDs+CI1lGdAk+Ls18DHQZPHixZsaNmz4NbBu3333PQXgsMMOaxw1bwNgVT7lXtK1a9dWwF/AS8ASIH3Dhg0HbN26tUZJV0ZEpDI48sgjadOmDWPGjME5R4sWLejdu3e8qyUiCazC9Jw65/4E5gJnm5nFyDIX6GNmdYBHgSZPP/30AytWrEibNm3aA0DPmjVrPgHQr1+/G8MzmdnuQLt8FtsbeHivvfZaMGHChI1mdhFwA3Bh3bp1R5fe2omIJK7q1avn6Q0trqFDh/LMM8/w7LPPcvbZZ1OlSpVSrJ2IJJsKE5wGrgMOBN4ys5PNrI+ZDTez44B/AdSsWXPG5MmTez/55JO/Dx069HTga+BVgAMPPPDqv/3tb9v/+9//9mzSpMnpZnYKMAn/FH4s9wPZKSkpxznnagJvmNlxZpYJ3AFkl+naiogkgHbt2vH1118zadIkQqEQS5cuLdb8gwcPZunSpSxcuDD3wSgRkfxUqODUOTcT6IUfwul54GX8cFGLnXO/Az0aN26cdsYZZ3DppZfWww8z1cs591dQxIYnn3xy1p577ml//PHH08BdwG3AD9HLSktLqwEcAEx78sknv73ssstuaNq0absqVapMqlu37rXVq1f/O7Apej4RkWRz8cUX07t3b4YOHUrnzp3Jysoq1vzNmjWja9euHHbYYbRt27aMaikiyaIi3XMKgHPuA+DIfKZ9AbyBv0x/AzAyOs9BBx301YwZM44ArgIeC5InR5UzBDgHeApYCcx46KGHjnzooYfCWVrjbx3oBvy0a2skIpLYGjVqxMSJBQ5QAkD37t2JNZjK6tWr+eyzz3jkkUfKonoikmQqVM9pEYUf78xvDJJwev1Cygk/XHUu0AroH5TdFt9r2xGYAsQc4D9yKKki1VpEJMls2LCBuXPncskll1CnTh3OOOOMeFdJRIqmUTiGCV6Zhc9Seipcz2k5CgfuVYDTgdnB/+uBs/G9sxnkM8C/cy4LyALIyMhwoXv6l3V9RUQSymeffUaPHj3Yc889efbZZ0lLK96P6nXcrR5qO0XiYpVzLiNeC0/G4DTcM5rfAHnh9LWFlBOevpwdgWmYw98KkIEfoH9Xfn1KRCQp5XeZX0SkIMl4WT/8cFN+d923Cd7nF7GctflMXxO81yxatURERESkMMkYnL4fvPdm5/WrAxyGf8p+TiHlzAH+xN9vWivG9P2D919LVEsRERER2UkyXtb/GZiGD06HAf+OmDYCH2g+jg88w8KD8H8fkbYJeBK4DD+m6VX4y/ngH4YaAmwFxhdWoa+XrKPVdVOKuRoSDwt0f5tIwohX26l2QCS+kjE4BbgY//OlDwNHA98BXYEe+Mv5N0bl/y54j/7lqZvxw1ZdARwCzAKaAicDNYL0n0u78iIiIiKVVTJe1gcfMGYAY/BB6dX4sUkfwo9N+kdBM5vZmGAIqPXAEfjB+tOBS4DjgI/wP5V6s5kNL5tVEBFJPm+88QZmxoIFC+JdFRFJUMnacwqwCD+QflFE95hG2ojvaY3ubRURERGRUpasPaciIiIiUgElbHBqZj3MzJlZi4i02Wa2zczqR6R9bWZ3Bn8fZGbTzWyTma0xsxfMrGlE3u5BmftHLWuGmRX4YJOZHWlm/zOzHDP7zMwOLbWVFRFJMI888gi77747tWrVYsCAAUyfPh0zY8aMGQBs2rSJyy67jGbNmlGjRg06d+7MtGnT8pThnGP48OE0adKEOnXqcPbZZ7N+/fo4rI2IVCQJG5wCc4Et+Hs+MbM04GDgL/xwUJhZOrAf8KGZNQZmAGnAmcClwFHAO2YW8ydGiyoIkN8CVgMD8U/7vxAsS0QkqUycOJFLL72UE044gYkTJ3LAAQdw7rnn5slz/vnn8/TTT3PjjTcyceJEdt99d/r3789HH32Um+fhhx/mtttuIzMzk/Hjx1OzZk2uvfba8l4dEalgEvaeU+fcJjP7DB+cvox/kGkdMD1ImwIcjh/e6WPghmDWPs659QBm9iN+vNKYPzFaDFcAOUB/59ymoOw/ged3oUwRkYR01113ceyxxzJ69GgAevfuzapVq3jssccA+O6773jxxRd5+umnGTx4MAB9+vThgAMO4Pbbb2fq1Kls27aNe++9lwsuuIA77rgjN0+vXr1YsmRJfFZMRCqERO45BZhJ0HOKH9LpI+CDqLT/BcFoF2BaODAFcM7NBRbgg9hd0QV4JxyYBiYWNIOZZZpZyMxC2zatKyiriEjC2Lp1K1988QUnnHBCnvTI/z/99FOcc5x66qm5aSkpKZx66qm5PaeLFi1i2bJlnHjiiXnKOfnkkwtcflZWFhkZGWRkZKC2U6RySvTg9ENg/+Ae0yOC/z8EMsysRkQaQHNgRYwyVuCHgdoVzYCVkQlBoLoxvxmcc1nOuQznXEaVtHq7uHgRkfKxatUqtm3bRuPGjfOkR/6/bNkyateuTVpa3jubmjZtyqZNm9i8eTPLly8HoEmTJnnyRP8fLTMzk1AoRCgUQm2nSOWU6MHprOC9O/6y/kxgHj4oPBr4GzuC02VArFavKf5eUfCX5gGi70FtUEg9lkeXHdwDW7uQ+UREKpRGjRpRpUoVfv/99zzpkf83b96cjRs3smnTpjx5VqxYQVpaGqmpqTRr1gyAlSvzfK/f6X8RkWgJHZw659YA3wBXAtuAL5xzDn95/1r8PbPh4HQufmD8OuH5zawz0CrID7A4eG8fkWd3dvx8aX4+BXoFAWnYSSVYJRGRhFa1alU6derE5MmT86S/9tpruX937twZM2P8+B2DnDjnGD9+PIcf7u+i2n333WnWrNlO5UyYMKEMay8iySBhH4iK8CEwDJjqnNsWkXY/8KNzLnwp/1/ARcBUM7sX36t5D/A18CqAc25x8MtPt5vZJnxwfgM7elbzMyqowxtm9i+gBXA9kF0qaygikkCuv/56TjnlFC655BJOOOEEZs2axZQp/jfuU1JSaN++PWeccQaXXHIJGzZsoHXr1jzxxBN8//33uQ9NValShWuvvZZrrrmGRo0accQRR/Dqq6/y3XffFbRoEZHE7jkNhHtGZ8ZIyx2zxDn3O9ADf+n+RWB0kK+Xc+6viHnPABbin7S/C7gN+KGgCjjnlgDHAo3wge7FwP8BmwqaT0SkIjr55JN5+OGHmTRpEgMGDODTTz/lgQceAKBu3boAPPHEEwwePJjbbruNE088kd9++4033ngjt+cU4IorruCGG27gP//5D6eccgobN27kvvvui8s6iUjFYf4quZSl1OZtXPPBo+JdDSmCBff0j3cVpJRlZGQQCoXiXY0K74477uDOO+9k9erV1KxZs1yWmdq8DfFoO9UOSGVnZp855zLitfyKcFlfRETK0e+//87dd99Njx49SEtL48MPP+Tee+/l3HPPLbfAVEQqLwWn5aDjbvUI6Zu4iFQQ1atX5/vvv+fZZ59l3bp1NG/enMsvv5zbb7+9XOuhtlOkclJwKiIiedSrV48333wz3tUQkUqqIjwQJSIiIiKVhIJTEREREUkYuqxfDr5eso5W102JdzWkEHpCVySxlGfbqfNfJHGo51REREREEoaCUxERKRXDhw+nUaNGhebLyMhgyJAhZV8hEamQFJyKiEipOO+885g6dWq8qyEiFZzuORURkVLRsmVLWrZsGe9qiEgFV2l6Ts1sPzN728xWm9mfZvadmQ2LmH6imYXMLMfMlpvZfWZWLWJ6OzN7ycwWmdkmM5tnZleYWaXZhiKS3ObNm0ffvn1JT0+nVq1atG/fntGjRwMwZcoUevXqRZMmTahbty7dunVj2rRpeeaPdVn/m2++4bDDDqNGjRq0b9+e1157rdzWR0QqpsrUc/o68B3wf8BmYF+gLoCZnQa8CDwO3AC0Bu7GB+/XBPPvBvwAvABsAA4CRgA1g7wiIhXa8ccfT/v27Xn++edJTU3lhx9+YP369QD8+uuvHH/88VxzzTWkpKTw1ltv0a9fP2bOnMlhhx0Ws7zs7Gz69OlDo0aNGDt2LNnZ2VxxxRVs3LiR/fffvzxXTUQqkEoRnJpZI2Av4ETn3NdB8vRgmgH3A8865y6OmGczMNrM7nbO/eGcmx41z0dAGnA+Ck5FpIJbtWoVv/76K5MnT6Zjx44AHH300bnTL7nkkty/t2/fTo8ePZg3bx5PPvlkvsHp008/zcqVK5k7d27u5f5WrVpx+OGHl+GaiEhFV1kuSa8GFgH/MbNBZtYkYlpbYA9gnJlVDb+A94AawP4AZlbDzEaY2U/4ntctwJ3AXkH+PMwsM7hNILRt07qyXTsRkV2Unp7O7rvvzoUXXsjLL7/MypUr80xfvHgxgwcPZrfddqNq1apUq1aNadOmMX/+/HzL/OSTTzj44IPz3Id62GGH0aRJk3znycrKIiMjg4yMDNR2ilROlSI4dc5tB3oDy4GngOVm9qGZdQLCN0i9iQ84w69fg/Tdg/d78Zf4s4Bjgc7AHcG0GjGWmeWcy3DOZVRJq1f6KyUiUopSUlKYNm0azZo1Y+jQoTRr1owjjjiCL774gu3bt3PCCSfw8ccfc9ttt/H+++/z6aef0q9fP3JycvItc/ny5TED0YKC08zMTEKhEKFQCLWdIpVTpbisD+Cc+x44JXjI6Qh8sDkF6BVkyQS+iDFrOEg9Ffi3c+6+8AQz00+KiEjSaNeuHa+++ipbtmzhww8/5J///Cf9+/dnxowZfPHFF7z11lv07ds3N392dnaB5TVr1ozvv/9+p/ToXlkRkUiVouc0knNui3PuPeBfQHNgGbAEaOWcC8V4/RHMWhN/OR8AM6sCnF7e9RcRKWvVqlWjZ8+eXHXVVSxbtoxly5YBkJqampvnt99+Y9asWQWW07lzZz777DMWL16cmzZr1iwFpyJSoErRc2pmBwAPAC8DvwANgH8C/3POrTazq4HnzKwu8BbwF7A3MAAY6JzbBLwDDAvuOV0NDANSo5clIlIRffXVV1xzzTUMGjSIvffemzVr1nDvvfdy4IEH0q1bN1q2bMnVV1/N7bffzoYNG7j11lvZbbfdCizznHPO4Y477qB///4MHz6c7Oxsbr755iL9ipSIVF6Vped0ObACuBEffD6KH1bqBADn3MvAifjhoV4BJgAXA5/jA1WAS4EPgdH4+1a/QU/pi0iSaNasGU2bNuXOO++kX79+XHzxxbnjkqampjJhwgSqVq3KwIEDufnmm7n++us56qijCiwzLS2NqVOnUqtWLU4//XRGjBjByJEj2XPPPctprUSkIjLnXLzrkPRSm7dxzQePinc1pBAL7tEtxMkoIyODUCgU72pICaQ2b0N5tZ06/0V2MLPPnHMZ8Vp+Zek5FREREZEKoFLccxpvHXerR0jfykVEikVtp0jlpJ5TEREREUkYCk5FREREJGEoOBURERGRhKF7TsvB10vW0eq6KfGuhhRCT+uKJJbybDt1/oskDvWcioiIiEjCUHAqIiIiIglDwWkJmdlxZubMrFW86yIiIiKSLBScioiIiEjCUHAqIiIiIgkjqYNTMxtjZiEz62VmX5nZn2b2kZntF5EnxcyuM7OfzGyzmc03s8FR5ZiZDTezlWa2wcyeBeqW+wqJiCSQN998k5SUFH799dc86b/++ispKSlMnjwZgMmTJ5ORkUGNGjVo1qwZ1157LVu2bIlHlUWkAkjq4DSwB3A/cCdwBtAEeNnMLJj+b+AmIAvoD0wEnjKz4yLKuAy4JcgzEMgG7iuX2ouIJKg+ffrQokULnnnmmTzpY8aMoUmTJvTv359x48Zx8skn06VLF1577TVuvfVWsrKyuP766+NUaxFJdJVhnNN04DDn3I/ge0rxAei+ZrYVuAg4xzkXbl3fNbPmwK3AG2ZWBfgn8Lhz7qYgz1QzewfYLb+FmlkmkAlQpW7jMlgtEZH4qlKlCkOGDOGZZ57h1ltvxcxwzvHMM8/wf//3f1SpUoV//OMfnH322Tz66KO586WmpjJs2DCuv/56GjZsmKfMrKwssrKyANi2aV25ro+IJIbK0HO6IByYBr4N3lsCRwPbgYlmVjX8AqYDBwWB6e5Ac2ByVLkTClqocy7LOZfhnMuoklavVFZERCTRDB06lN9++40ZM2YA8P777/Pbb79xzjnnMH/+fBYuXMhpp53G1q1bc189e/YkJyeHb775ZqfyMjMzCYVChEIh1HaKVE6VIThdG/X/X8F7DaARUAVYB2yJeI3B9yo3B5oF+VdGlRP9v4hIpbP33nvTvXt3nn76aQCefvppunTpwn777ceqVasAOPbYY6lWrVrua6+99gJg0aJFcau3iCSuynBZvyCrga3AYfge1Ggr2bGNmkRNi/5fRKRSOu+88zj//PO5++67mTBhAiNHjgQgPT0d8JfqO3XqtNN84SBVRCRSZQ9O38P3nNZzzr0TK4OZLQKWAycCb0dMOrnsqycikvhOPvlkhg0bxumnn8727ds5/fTTAdh3333ZbbfdWLBgAeeff36caykiFUWlDk6dcz+Y2X+Al8zsPiCEv9y/H9DWOXeec25bMO0BM1sFfAicArSPW8VFRBJIjRo1OOussxg9ejRnnHEG9evXByAlJYWRI0fy97//nfXr19OvXz+qV6/OL7/8wqRJkxg/fjxpaWnxrbyIJJxKHZwGhgHzgfOB24D1+IemnozIMwr/1P+FwBXAa8C1wAvlWE8RkYQ1YMAARo8ezdChQ/OkDxo0iLp163LXXXfx1FNPUaVKFfbee2+OO+44qlevHqfaikgiS+bgtKVzbjvQAtgMLAMmOedGAOExTnHOOXzwOSq/goI8NwPvAO8DZwK/RpYjIlKZTZs2jT333JOePXvuNK1fv37069cvDrUSkYooWYPT1sDH+IeWJgPfA12Ay4G++Aeg/ihmmXWAZ4BNQO3izNhxt3qE7ulfzMWJiCS+H374gW+//ZbHHnuMW2+9lZSU0hsERm2nSOWUrENJPYoPTC8DBgDXAT2BB4F98b8WVVwPAfWAu0uniiIiFd8FF1zAmWeeybHHHstll10W7+qISBJIxp7T1kBvYAEwOmrarfhfbfo7cDXwZxHLPBE4J5gvGbeZiEiJhAffFxEpLcnYc9ojeJ/GzmOXbgBmAWlAtyKW1wR4ApgEPF8K9RMRERGRfCRjL+C+wfv8fKb/iO9ZbYv/mdLCPIEP4i8sTiXMLBPfS0v1ZvvQ6ropxZldytkC3dcmknC+XrKuzNtOnfsiMTUys1DE/1nOuazyWngyBqfhH2Nel8/0cHr9IpQ1FDgBGASsKE4lgp2YBZDavI0rzrwiIiIicbTKOZcRr4Un42X90tIKP7zUK8C4uNZEREREpJJIxuA03DNaL5/p4fS1hZTzFJANXFwKdRIRSTjjxo1jzJgxedK6d+/OwIED41MhERGSMzj9IXhvm8/0NsF7fvekhv0N/zDU74CLeD0dTL8x+H9SSSsqIhJPsYJTEZF4S8Z7Tt8P3nvjg+/IJ/br4Afg3wTMKaScZ/FP9UdrAxwJfAl8BnyxC3UVERERkQjJ2HP6M34YqVbAsKhpI4BawHPkHeO0XZ06dT4xs/ERaZeZ2fNmdq6ZjQLOM7Of69Wr165GjRrUqVOnjZm1NLNXy25VRETKxpAhQ3j11Vf54IMPMDPMjOHDh+dOHzt2LPvssw9169alX79+LF68OM/8OTk5XHvttey+++6kpqZy4IEH8uabb+60nP/+97/st99+pKamsueee3LfffeV9aqJSAWXjMEp+PtEVwIP4y+73w28B1yJv5x/Y1T+7w4++ODOBRVoZmcDNxx77LFTp06dyvXXX/8W8BM+2BURqVBuvvlmevToQadOnZg9ezazZ8/mvPPOA2Du3Lk88sgjjBw5kqysLD7//HMyMzPzzD9w4EDGjBnDDTfcwOuvv07nzp054YQT+PLLL3Pz3H///Vx00UUMGDCAN954g4suuoibb76ZRx55pDxXVUQqmGS8rA++9zQDuA3oCxwLLMP/BOkIYE0JyuwCTHvxxRffA/5+1FFH/XDDDTfclF/myHFOq9RtXILFiYiUndatW5Oens727dvp1i3vb5KsX7+eKVOm0KBBAwCWL1/OlVdeSXZ2NjVr1mT69OlMmTKFGTNmcNRRRwHQu3dv5s+fz5133skrr7zC+vXrGTFiBDfddBO33norAL169WLTpk3ccccdXHTRRVSpUmWnemVlZZGV5YdT3LYpvxEBRSSZJWvPKcAi/E+ONgeqA3sCVxA7MLUPPvjgg0LK+xI41sz2MrOuZnZrQZmdc1nOuQznXEaVtPwGDhARSTydO3fODUwBOnToAMCSJUsAePfdd2nWrBmHHXYYW7duzX0dffTRhEJ+3O7Zs2fz559/cuqpp+bJ07NnT1asWLHTbQJhmZmZhEIhQqEQajtFKqdk7TktC0/hH6jKBG4B/jCz/wC3Oue2xbVmIiKlqH79+nn+r169OuDvMwVYtWoVy5cvp1q1ajvNG+4NXbVqFQD77bdfzGUsWrSIPffcs7SqLCJJRMHpDjn4HtZIuV0HzrntwIPAg2a2O3AWcCewGPhPeVVSRCTe0tPT2W233Zg0aVKBeQDeeOMNmjZtutP0fffdd6c0ERFQcBppMX6IqEi9Y2V0zi0C7jGzc4AOZV0xEZGyUL169dze0OI4+uijGTlyJLVr16Zdu3Yx8xxyyCHUrFmTpUuX0r+/fr9eRIpOwekOE4FzzexBYArQA/8wFQBm9jiwGj8+6rpgehvgn+VfVRGRXdeuXTsmT57MpEmTaNmyJS1atCjSfL169aJPnz706tWLf/7zn+y3336sX7+eL7/8kpycHO6++27q16/P8OHDufzyy/ntt9848sgj2b59O/Pnz+f9999n4sSJZbx2IlJRKTgNOOemmNkN+GGozgMmA5cH7wCzgfOBC4Aa+GGkznfOTSr/2oqI7LqLL76YL774gqFDh7JmzZrcp+oLY2ZMmDCBu+66i1GjRrFw4ULS09M56KCDuPTSS3PzXXvttbRo0YIHH3yQkSNHUqNGDdq2bcugQYPKapVEJAmYcy7edUh6qc3buOaDR8W7GlKABffosmOyysjIyH2CXCqW1OZtKOu2U+e+yM7M7DPnXEa8lq+e03LQcbd6hNQAiogUi9pOkcopmcc5FREREZEKRsGpiIiIiCQMBaciIiIikjB0z2k5+HrJOlpdNyXe1aiU9LCDSMVVmm2n2gKRikM9pyIiIiKSMBScioiIiEjCUHBaRGY2xMycmdUO/m8V/H9cvOsmIiIikiwUnBbdFOAQYFO8KyIiIiKSrPRAVBE5534Hfo93PURERESSWdx7Ts3sSDN738w2mtk6M5thZp2CaQeZ2XQz22Rma8zsBTNrGjFv+NL6aWb2eDD/YjMbYWYpEflamtk4M1tpZtlm9rOZ3R5VjyPM7INgWX+Y2RNmVidiep7L+iIiFU337t0ZOHBgnrQZM2ZgZnzzzTcsWLAAM2Ps2LH8/e9/p06dOjRp0oQRI0bsVNZ7771H165dqVGjBk2bNuXiiy9m48aNO5U7Y8YMTj31VGrXrs3ee+/No48+WubrKSIVW1yDUzPrDkwHtgCDgUHAh8BuZtYYmAGkAWcClwJHAe+YWfWoou4DNgIDgeeBW4K/w54FdgcygX7AnUBqRD0OA94FlgfzXQEcCzxdOmsqIlJx/OMf/yAtLY3x48dz/vnnM2LECEaPHp07fd68efTt25dGjRrx6quvMmLECMaOHbtT4Atw/vnnc+CBBzJx4kS6d+/OsGHD+OSTT8pzdUSkgon3Zf27gf8BfZxzLkh7G8DM7gn+7+OcWx+k/QjMAU4BXowoZ6Zz7urg73fMrC9wMjAuSOsCnOGcez34f0ZUPe4BPnbODQonmNkSYLqZ7e+c+6a4K2ZmmfhgmCp1Gxd3dhGRuNlvv/14/PHHAejTpw8rV67krrvu4qKLLiIlJYXbb7+dPffck9dee40qVaoAkJ6ezqBBg5g9ezaHHHJIbllnnHEGN910E+B7bl9//XUmTJhAly5dYi47KyuLrKwsALZtWleWqykiCSpuPadmVgvoCjwTEZhG6gJMCwemAM65ucAC4PCovNOi/v8WaBnx/5fA3cGl+T2i6pGGf9BpnJlVDb+Aj/A9ugcXd92CumY55zKccxlV0uqVpAgRkbg46aST8vx/8skns3TpUhYvXgzAJ598wkknnZQbmAKccsopVK1alY8++ijPvL179879u1q1arRp0ya3nFgyMzMJhUKEQiHUdopUTvG8rN8AMGBZPtObAytipK8A0qPS1kb9/xdQI+L/QUAIeBD4zcy+NLOjI+pRBXgUH4yGX5uBavjbAUREKo0mTZrE/H/ZsmW5702bNs2Tp0qVKjRs2JDVq1fnSa9fv36e/6tXr05OTk4p11hEkkk8L+uvAbbjg9BYlgFNYqQ3BT4rzoKcc0uAIcFDUl2A4cBrQS/qWsAFaW/GmH1pcZYlIpKoatSowV9//ZUnbc2aNTvlW7lyZcz/mzdvnvsenWfbtm388ccfpKdH9x2IiBRP3HpOnXN/AnOBs83MYmSZC/SJemK+M9AKf8m9JMvc7pybA4zAP2i1Z1CPOcC+zrlQjJeCUxFJCi1btuT777/PkzZtWvRdUTBx4sQ8/0+YMIHmzZvTsqW/W6pr165MnDiRbdu25cmzdetWDj88+q4rEZHiifcDUdfhn5J/y8yygD/x93+GgH8BFwFTzexeoDb+waWvgVeLugAzqwdMxT+xPx//lP7V+CfzvwuyXYt/+Gk7MB7YAOwB9AdudM7N37XVFBGJv5NOOoknn3ySK6+8kv79+/P+++/z9ttv75Rv3rx5XHDBBZxyyinMnDmTJ598koceeoiUFN+fcdNNN9GpUycGDBjARRddxOLFi/nnP/9Jnz598jwMJSJSEnEdSso5NxPohe/FfB54GT9c1OJg0PseQA7+yfzR+GGmejnn/opdYkw5+ID2cuA14Bn8rzz1ds5lB/X4CDgSaAw8B7yOD1gXEfu+VxGRCqd///7cddddjB8/npNOOonffvuNhx56aKd89913H+vXr+eUU07h8ccf5+abb+aSSy7Jnb7ffvvx1ltvsXLlSk4++WRuuukmzjjjDMaPH1+eqyMiScpiPygvpSm1eRvXfPCoeFejUlpwT/94V0HiLCMjg1AoFO9qVAgLFixgr7324vXXX+e4446Ld3VIbd6G0mo71RaIFJ2Zfeacy4jX8uN9Wb9S6LhbPUJqGEVEikVtp0jlFPefLxURERERCVPPqYiIANCqVSt0q5eIxJt6TkVEREQkYajntBx8vWQdra6bEu9qVAp66EEkeZRW26l2QaRiUc+piIiIiCQMBaciIiIikjAUnIqIiIhIwlBwKiIiIiIJQ8GpiIiIiCQMBaciIpXYkCFDyMjIYMqUKXTo0IG0tDT69+/P6tWr+emnn+jRowe1atUiIyODr776Kne+kSNH0rlzZ+rVq0fTpk05/vjj+emnn/KU3b17dwYOHMjYsWPZZ599qFu3Lv369WPx4sXlvZoiUoEoOBURqeQWLlzILbfcwh133EFWVhYff/wxmZmZnH766Zx++umMHz+erVu3cvrpp+cO0r948WIuueQSJk+ezBNPPMG2bds49NBDWbduXZ6y586dyyOPPMLIkSPJysri888/JzMzMx6rKSIVhMY5LSNmlglkAlSp2zjOtRERyd/q1auZPXs2rVu3BuCrr77i/vvv55lnnuHss88GwDlH//79+f7772nfvj0PPvhg7vzbtm2jV69eNGnShMmTJ+fOA7B+/XqmTJlCgwYNAFi+fDlXXnkl2dnZ1KxZc6e6ZGVlkZWV5cvdtG6n6SKS/NRzWkacc1nOuQznXEaVtHrxro6ISL5atWqVG5gC7LPPPgD07Nlzp7QlS5YAMGfOHHr16kXDhg2pWrUqaWlpbNy4kfnz5+cpu3PnzrmBKUCHDh3ylBMtMzOTUChEKBRCbadI5aTgVESkkqtfv36e/6tXr75TejgtJyeHhQsX0rt3b5xzPP7448yaNYtPP/2UJk2akJOTU6Syo/OJiITpsr6IiBTL22+/zaZNm5g8eTK1atUCYOvWraxevTrONRORZKCeUxERKZbs7GxSUlKoWnVH/8a4cePYunVrHGslIslCPaciIlIsPXv2ZNu2bZxzzjmce+65zJs3jwceeGCnS/giIiWhnlMRESmWjh07MmbMGObOnctxxx3H2LFjeeWVV6hXTw8wicius/CYdVJ2Upu3cc0Hj4p3NSqFBff0j3cVJMFkZGQQCoXiXQ0pgdTmbSiNtlPtgkjxmNlnzrmMeC1fl/XLQcfd6hFS4ygiUixqO0UqJ13WFxEREZGEoeBURERERBKGglMRERERSRjJfM9pS+A2oC/QEFgGTAJGAGuKMH8tYADQH/gbsDuwHfgBeBH4N/BXUSry9ZJ1tLpuSrEqLwXTAw4iya802k61FSIVT7IGp62Bj4EmwGTge6ALcDk+WD0M+KOQMo4AngdWA+/jA9sGwAnAA8DJwNGAfoNPREREpJQka3D6KD4wvQzfwxn2L+BK4E7gwkLKWA78H/AKeXtIrwFmAIcCw4CRpVJjEREREUmue07NbEzNmjW/BnoDC4DRUVluBf4E/o6/bF+QL4EX2PnS/QZg5MaNGzGzB8xsyC5WW0SkzAwZMoSMjLgNV7iT7t27M3DgwHhXQ0QSWNL1nNarV69O8Oc0/D2ikTYAs/DBazdgegkXs6WE84mIlKubb76Z7OzseFdDRKTIkqrnFKBGjRo1gj/n55Plx+C97S4sZuguzCsiUm5at27N/vvvX6J5nXPk5Oi2ehEpX0kXnKakpFQBmDNnDmb2lJn9YmbZZjbfzO7YuHHjhiBrfTNrZWbOzE4zs8fNbJ2ZLTazEWaWZ9uY2SlmNr9q1ap/HXnkkX0///zzHwqqh5llmlnIzPS7iSISN5GX9deuXct5551HixYtqFGjBnvssQfnn39+bt7hw4fTqFEjPvroIzp37kyNGjV45ZVX+PPPP7nkkkvYd999SUtLY6+99mLYsGGsX78+z7K2b9/OPffcwz777ENqaipt27blmWeeKdf1FZFS0SgcwwSvzPJceNJd1g/7+eefa+OftL8KP3RUW2D4iSeeuGL69J2u5t8HvAoMxD+BfwswDxgHYGZ/A15u3br1Jw8//HDrzz//fMMxxxyTVtDynXNZQBZAavM2rtRWTESkhK666io+/vhjHnzwQZo1a8aiRYuYOXNmnjybNm1i8ODBXHvttbRt25YWLVqwadMmtm3bxp133knjxo1ZtGgRd955J6eeeipTp07NnffSSy/lmWee4ZZbbuFvf/sb77zzDkOHDqVhw4Ycd9xx5b26IlJyq5xzcbtZPemC0+3bt28DOOusszaeddZZI8LpZjYL+HPmzJnP/vXXX1SvXn1txGwznXNXB3+/Y2Z98UNFjQvSrqtbt+6y+fPnZ6SkpKw49thje9x8880DgTvyq0fwLSMToErdxqW2fiIiJfXJJ58wbNgwBg0alJv2f//3f3nyZGdn869//YsTTzwxT/pjjz2W+/fWrVvZa6+9OPzww1m4cCF77LEHP/30E4899hhPP/00gwcPBuCYY45h2bJljBgxosjBaVZWFllZWQBs27SuROspIhVb0l3WzwlukNq+fXtbM7vCzL41s2z8Q0wvbN26tcrChQsh7z2p06KK+RY/iD8AtWrV6n7hhRe2SElJWQEchR+If0JB9XDOZTnnMpxzGVXS6u36iomI7KKDDjqI+++/n0cffZT582Pflm9m9OvXb6f05557jk6dOlG7dm2qVavG4YcfDpBbzvTp00lJSeGkk05i69atua+jjz6aL7/8km3bthWpjpmZmYRCIUKhEGo7RSqnpAtO161btwHgnnvuGYgfLH8icCLQpUGDBlcBbNq0KRuYEzHb2qhi/gLCD1adtXXr1sb16tVbhw9Mww9UrSyTFRARKSOPPPIIAwYM4LbbbmPfffelTZs2vPTSS3nyNGjQgOrVq+dJmzhxImeffTaHHHIIr7zyCnPmzGHixIkAuQ9MrVq1im3btlGvXj2qVauW+xoyZAhbt25l2bJl5bOSIlLhJV1wmpOTsxmY9sYbb6QfcMAB/3PO3eicm+ac+/Tiiy/uA/D777+/jh/vFIBjjjmmJdAuRnGDgWcbN2689d///vdLwC8R05qU4WqIiJS6+vXr8/DDD7N8+XL+97//0bVrV8466yy+/fbb3DxmttN8r7zyCl27duXRRx+lX79+dO3alQYNGuTJk56eTtWqVZk7dy6ffvrpTq8mTdRkikjRJN09p4GLN27c+H2nTp3+hv/Z0e+Arp988kkPgLvvvvvho48+Ojfz5ZdfHr6ZKrdVDsZLfQpI2XPPPRf++OOPp27fvn1lSoqP588444wjXnzxxXJZGRGR0nbAAQdw//3388ILL/D999/ToUOHfPNmZ2eTmpqaJ+2FF17I83/Pnj3Ztm0b69ato1evXmVSZxGpHJI1OP15xYoV/33xxRfP79SpU4927dr1f/rppzd/8cUX64G606dPL/Qu+9TU1OoEPcujRo1q1bVrVwYNGnTrueeeyzfffMOcOXMKKUFEJLEcfvjhnHTSSey///6YGU888QS1atWiS5cuBc7Xq1cvhg0bxp133knXrl158803iR71ZN999+XCCy/k9NNP59prryUjI4OcnBzmzZvH/Pnz+e9//1uWqyYiSSRZg1NWrlz5D6DGlVdeeSL+l6Em4HtRX4/Oe/zxxx/vnHsjav4/gL0AMjIy2L59+6njx4+/e/z48S2BEHAl8EnZroWISOk55JBDGDNmDAsWLKBKlSp06tSJt956i5YtWxY43wUXXMAvv/zCQw89RE5ODr169WLs2LF069YtT77Ro0fTtm1bnnjiCW655Rbq1q1Lhw4dOPfcc8tytUQkyZhzGoKzrKU2b+OaDx4V72oklQX39I93FaSCyMjIIBTSb2FURKnN27CrbafaCpHiM7PPNM5pkuu4Wz1CaiBFRIpFbadI5ZR0T+uLiIiISMWl4FREREREEoaCUxERERFJGLrntBx8vWQdra6bEu9qVEh6mEGk8trVtlPth0jFpJ5TEREREUkYCk5FREREJGEkfXBqZqeZ2ZB410NERERECpf0wSlwGjAk3pUQERERkcJVhuBURERERCqIpA5OzWwMcApwlJm54PWbmf1pZtUi8i01sz/MzIL/U8xsrZmdH5Gnp5nNNbMcM1thZo+aWe1yXykRkXIyb948+vbtS3p6OrVq1aJ9+/aMHj06d/ojjzxCmzZtSE1NZZ999uHBBx/MM//w4cNp1KgRX3zxBd26dSMtLY1OnTrx4YcflveqiEgFktTBKXA78D7wBXBI8DoLSAP+BmBmbYAmQB2gQzDfgUA94MMgz37A28AqfLB7K3AmML6c1kNEpNwdf/zxVKlSheeff57XXnuNSy+9lA0bNgDwxBNPcOmll3LCCSfw+uuvc+qpp3L11Vdzzz335Clj06ZNDB48mAsuuIBXX32V1NRUTj75ZDZt2hSPVRKRCiCpxzl1zv1sZquBFOfcnHC6mS0DjgDmBu//A/4K/p4XvP/unPs+mOVm4DfgBOfctqCM1cDLZnaIc2529LLNLBPIBKhSt3EZraGISNlYtWoVv/76K5MnT6Zjx44AHH300QBs376d4cOHM2TIEEaOHAlA7969WbduHXfffTdXXHEFNWrUACA7O5tRo0bRs2dPAJo3b06nTp2YOXMmffv23Wm5WVlZZGVlAbBt07oyX08RSTzJ3nOanw/xASjAkcDM4BWZ9lFE/i7AxHBgGngV2AocHmsBzrks51yGcy6jSlq90qy7iEiZS09PZ/fdd+fCCy/k5ZdfZuXKlbnTFi9ezNKlSzn11FPzzDNo0CDWr1/P119/nZtWvXp1unfvnvt/hw4dcsuIJTMzk1AoRCgUQm2nSOVUmYPTw4N7TI8I/o8MWA8P/g9rDqyILCAIVP8A0su8tiIi5SwlJYVp06bRrFkzhg4dSrNmzTjiiCP44osvWLZsGQBNmzbNM0/4/9WrV+em1alTh5SUHR811atXByAnJ6esV0FEKqjKHJymA72AvYL/ZwG7mVlvoCl5g9Nl+PtSc5lZFaAhsBoRkSTUrl07Xn31VdauXcu7775LTk4O/fv3p3nz5gB5elMBVqzw3+HT0/WdXURKrjIEp38BNaLSvgbWAjcC3zvnfnfOrQG+CdI24h+iCpsLnBQEpGEn4+/Zjbz8LyKSdKpVq0bPnj256qqrWLZsGbVr16ZFixa88sorefKNGzeOunXr5t6jKiJSEkn9QFTge+BEMxsALAaWOueWmtksoD/weETeD4FhwDtR95fegQ9WJ5nZY0BL4F5gaqyHoUREKrqvvvqKa665hkGDBrH33nuzZs0a7r33Xg488EDS09MZPnw4F1xwAQ0bNqRXr1588MEHPPbYY9x11125D0OJiJREZQhOHwU6AU8BDYARwHB8INof/yBUWDg4zdMb6pybZ2b9gLuACcB64EXg2jKuu4hIXDRr1oymTZty5513snTpUurXr0+PHj249957ATj//PPJycnhoYce4qGHHqJly5aMHDmSK6+8Ms41F5GKzpxz8a5D0ktt3sY1Hzwq3tWokBbc0z/eVZAKLiMjg1AoFO9qSAmkNm/DrrSdaj9ESsbMPnPOZcRr+ZWh5zTuOu5Wj5AaSRGRYlHbKVI5VYYHokRERESkglBwKiIiIiIJQ8GpiIiIiCQM3XNaDr5eso5W102JdzUqFD3IICK72naqHRGpmNRzKiIiIiIJQ8GpiIiIiCSMShOcmtkYMwsFfw8xM2dmtYP/WwX/HxffWoqIlJ8hQ4aQkbHrQxlmZWUxadKkndJbtWrFNddcs8vli0jlUmmC0yhTgEOATfGuiIhIRZdfcCoiUhKV8oEo59zvwO/xroeIiIiI5FUpe06jL+vnk6eHmW0ws7si0s4zs3lmttnMfjOza8unxiIiZeedd97hgAMOoFatWhx++OHMmzcvd9rIkSPp3Lkz9erVo2nTphx//PH89NNPudO7d+/OZ599xjPPPIOZYWaMGTMmT/kPPvggLVu2pEGDBpx++umsXbu2nNZMRCqiShmcFsbM+uAv/d/nnLshSPsH8BgwCTgu+Pt2M7skXvUUEdlVCxcu5B//+Ac33ngjL774IitXrmTQoEE45wBYvHgxl1xyCZMnT+aJJ55g27ZtHHrooaxbtw6ARx99lHbt2nHssccye/ZsZs+eTf/+O4ZwGjduHNOnTycrK4t7772XN954gxtuuCEu6yoiFUOlvKxfEDM7ARgH3OSceyBIqwvcCtzhnBsRZH3HzNKAm8zsMefctqhyMoFMgCp1G5db/UVEimP16tXMmjWLNm3aALB9+3ZOOukkfvjhB9q1a8eDDz6Ym3fbtm306tWLJk2aMHnyZM4++2w6dOhArVq1aNy4Md26ddup/GrVqjFp0iSqVvUfN99++y0vvfQSjz76aMz6ZGVlkZWV5Ze3aV1pr66IVADqOc3rFOAV4OpwYBo4BKgFvGJmVcMv4D2gKdAyuiDnXJZzLsM5l1ElrV551F1EpNhatWqVG5gCdOjQAfA9pgBz5syhV69eNGzYkKpVq5KWlsbGjRuZP39+kcrv0aNHbmAaLn/lypVs2bIlZv7MzExCoRChUAi1nSKVk4LTvE4AVgMTo9IbBe/zgC0Rr/eD9N3LpXYiIqWsfv36ef6vXr06ADk5OSxcuJDevXvjnOPxxx9n1qxZfPrppzRp0oScnJwSl++cY/PmzaVRfRFJQrqsn9elwFXANDM7yjn3R5C+Ong/DlgRY74fyqNyIiLl6e2332bTpk1MnjyZWrVqAbB161ZWr15dyJwiIiWn4DSv9UAf4ANgqpn1dM6tB2YD2UAL51zJf+hZRKQCyc7OJiUlJc9l+XHjxrF169Y8+apXr17knlQRkcIoOI3inPvDzHoBHwJvmFlf59xaMxsOPGRmewIz8bdEtAV6OOdOil+NRUTKRs+ePdm2bRvnnHMO5557LvPmzeOBBx7Y6VJ9u3btmDp1KlOnTqVhw4bstddeNGzYMD6VFpEKT/ecxuCcWwYcDbQCJphZdefcffin7/sBk4EXgbPwQayISNLp2LEjY8aMYe7cuRx33HGMHTuWV155hXr18j6odNNNN9G+fXtOO+00OnfuzOuvvx6nGotIMrDwWHZSdlKbt3HNB4+KdzUqlAX39C88k0gRZGRkEAqF4l0NKYHU5m3YlbZT7YhIyZjZZ865jHgtX5f1y0HH3eoRUiMpIlIsajtFKidd1hcRERGRhKHgVEREREQShoJTEREREUkYCk5FREREJGEoOBURERGRhKHgVEREREQShoJTEREREUkYCk5FREREJGEkc3DaEngKWApsBhYAo4AGxSwnPZhvQVDO0qDclqVSSxERERHJlay/ENUa+BhoAkwGvge6AJcDfYHDgD+KUE7DoJy2wHvAS0A74BygP3AI8Esp111ERESk0krWntNH8YHpZcAA4DqgJ/AgsC9wZxHLuQsfmP4LODooZwA+yG0SLEdERERESkkyBqetgd74y/Cjo6bdCvwJ/B2oVUg5tYN8fwLDo6Y9AvwG9AH23qXaioiIiEiuZAxOewTv04DtUdM2ALOANKBbIeV0A2oG+TdETdsOTI1anoiIiIjsomQMTvcN3ufnM/3H4L1tOZUjIiIiIkWUjA9E1Qve1+UzPZxevyzLMbNMIDP4d6uZ/a+Q5YnXCFgV70pEUZ0qrkb4BxvrxLsiUnyfffZZjpl9E+96JJCKdt6XR30r2jYpa6W1PfY3s1DE/1nOuaxSKLdIkjE4TQjBTswCMLM/nXMZca5ShWBmoUTbVqpTxRU0rvrgqri26TjfoaKd9+VR34q2TcpaaW2PeG/XZLysH+7RrJfP9HD62nIqR0RERESKKBmD0x+C9/zuBW0TvOd3L2lplyMiIiIiRZSMl/XfD95744PvyCf26+AH4N8EzCmknDlAdpC/Dnmf2E8Jyo9cXkEmFCGPeOV2T0sxqE4Vl7ZTxaa2M6+KdjyXR30r2jYpa6W1PeK6Xc05F8/ll5Wp+ODxMuDfEen/Aq4EHgcujEhvF7x/H1XO4/iHmv4FXB2RfhnwULCcvqVWaxEREZFKLlmD0+ifL/0O6Iofk3Q+cCh5f740vBEsqpzony/9BGgPnAisDMr5uUzWQERERKQSStbgFGB34DZ8z2ZDYBkwERgBrInKm19wCpCO/2WpAUBzfFD7FnALsLi0Ky0iIiJSmSXjA1Fhi4Bz8AFldWBP4Ap2DkzBB6WxAlOA1cDlwfzVg/KGUoTA1Mw6mNl0M9tkZkvN7DYzq1LcFamIzOxUM3vNzJaY2UYz+8zMzoiR73wz+9HMcoI8R8fIs5uZTTSzDWa2ysweMbO08lmT8hWs60Yzc2ZWOyLdzOwGM1tkZtlmNtPMDooxf1Ifc2ZW1cyuC46ZzWa22MwejMqjbVWBJcN+MbN9zOxxM/vKzLaZ2YwYeUrtOE30Yz4enweJ/NliZgPN7GMz+yOo3w9mdpOZVY/IU2mOj5icc3oV/9XSOfeUc26pc26zc26Bc26Uc65BOA/QAFgKvAv0wt/j+idwR0Q56cF8C4JylgbltkyAddylFzAbGAucBvQEHsD3UF8akecMYBtwM/6Wi2fxD6HtH5GnGvAN8DnQHzgLWAE8X1b7rpBXLefcWc65sc65751zfzrnNjjnQs65q51z1Xdxu40FlgfbqnZE+vXBtrkEOAZ4Ez9+Z7NiHnMV+lW3bt0J9evX3/TQQw+tfe+99/564oknfj/ttNM+cXnPvWJtq/PPP/+qRx99dHtaWpo75ZRTPoz3Oib5q8Dzr5jH8N+cPw8XB2WtcM594Jw7O97rib/1axHwCv62shkx8pTaOV1O7UOJ287Iz4PMzMzL/u///u8nwI0aNWqLc26hc+7N008//e7S+jyI02dLcY6PC4A7gJOC+v0zqN8jZbFPjznmmBeqVau29bbbbvvp7bff/rNfv36uTp06OSU8Pnb1M7Ro26isd0ISvlo73wg659wk59w9zrn3gv+/d841jDiw1gB1I3b+tfiRAuoG+X4I5pselDMp+H+Fc27vBFjXkh9Y0ChG2ljg14j/fwCeivg/Bfg6snGIaGT2ikg7DT8KQ5uy2HeFvPoG+f9wzo0PynjcObcsSJ/lnKtRwm12JL6n/hoiglOgBn7c3Vsi8tYCfo9sOIpwzMX9uNiVV8+ePc+pWrWqmzdvXr77r7jb6p///GcL59yvzrkN9957r6tWrdqWZNhWCfoq9PwrxjF8iXNum3NulXPuGefcXc65/zjnPnLOvRTvdQVSIv4eT1RwWprndDm1D7vUdkZ8HlwUzLOxR48ev6anp691zj3nnPuzbdu27tBDD/0schuW9PMgDp8tpXHM3IkfN91K+/ioXbv2thEjRjjnO1K+27hxYzg4Le7xURqfoUXbHuW9A5LgNTXYEZdGpf8rSP9PsFNnAnkaSWAPfNBxvPMBjXPOjYwq57Ig/e0EWNdSfQH/ADYHf+8dbIu+UXluAf6I+P9ZYE5UnupADhG9sKW57wp5HeR8z2l0D2kd59xnQTlXl2DbVAH+h7/1ZAh5g9Oewf/touZ5CohszAs75uJ+DOzK65hjjlneu3fvAvdfCbbVU8651c65G3777TeXLNsqQV+Fnn9FPIZ7O+e2B+XVibGcagmwrpH1jxWclto5XU7tQ2m0ndWcc2udc9nOuX0jPw9uu+22XoCbMmVKjnMuNaJ+xf48iNNnS2kcJ1cFgaCVxfFx+eWX93HOmXOuu3PO9e3b96cSHB+lcRwU6ZXM95yWhdb4IaoWAKOjpt2K7wL/O/4bTjuihqZyzi0ENtWpU+eAIN+fwPCoch4BfgP64E+yZHIIO360IL/hu74D0s2scUS+6O34F36UhHYUXXH2XUG+BF4A/opK3wCMDP7uXox6hV0IpMaoG/j13Ab8GJX+HXm3Qb7HHMXbVomo9fz585vuscce61NSUvY1s/XBfVETBgwY8BjB/qtbt+4BFHFbHX744Q5/X/plwNI99tiDoOe0om+rRFSk88/MinIM34+/3HkmecefDttSarUuO6V5Tpd1+1BabWc6/pcV5+N7NnM/D2655ZZqAB06dEgFakfMU5LPg/L+bCkxM6tiZmlmdji+HXrM+aiw1I+Phx566F12PPxN27Zt1xezrNI6DopEwWnx9Ajep5F3cH/wjeQsIA3ohr9/Y22MMtbss88++wE1g/zRjet2/Pipkcur8IKb0QewI4BrELyvjcq6Jmp6vtsxIk9RFGfflVT4Q3FrcWYys4bA7cBVzrlYH6wNgI3OuW1R6WuAtIib6EtrWyWiHsuXL+e5556r6Zw7CDgdH1gePHny5Oe3b98+C0jr0KFDR4q4rY4//vjjgEnA8+FMaWlpOVT8bZWIinr+pVPwMbw/cEBQzuqg3Gvw41AfTcX5TCvNc7qs24fSajtX4i9Ltz322GPPJuLzoEuXLu0Bateu/TV5h3ksyedBeX+27Io/g9eHwAf43mQoh+OjQYMGm4tZVnl8huaqKCdyotg3eM/vJ0vD33Ly+8lTABo3bpxeGuVUFGbWCn+/6WTn3Jg4VaNU9l0hhgbvbxdzvjvxl5fe3IVlJ7t9nXNs377dASc65950zr2M/6be5ZlnnskGaNKkSaOiFFalSpUqZmbk/TEOKTtFOv+CfVKQzsH7SmAGfvzp+/EPXL6Lv7Kxzy7UU3ZWWm2nA4b9+uuvKaFQaMwhhxyyKLi8/+ywYcPuAPjggw8G73p1K5RDgSPwX65OxF85TVTl8RmaS8Fp8dQL3tflMz2cXh//baNejDwNmjRp4qLyF1ROhWZm6fhxYX/DPw0ZFv4WG72NGkRNz3c7EntYsPwUZ9+VxCX4MXW/xN8XVCRmth8+qL3NzOqbWX38t0+AemZWE7+etWMM6dEA2BRcioLS21aJqF6DBg1o1KjRYudcZM/KR8Bfn376aU2AZs2aOQrfVkPT09PtzTfffBP/dG6uTZs21aDib6tEVKTzr0aNGtkUfAw3Cf4/F2iFf8q6Hv4D8XmgIzAFf+9gIivNc7qs24dSazvNbPr++++/bLfddtvy7rvv7gFcB/y9Xr162QDnnHNOdD1K8nlQ3p8tJeac+9z5h/j+hb+sf5GZtaYcjo81a9akFrOssv4MzUPBadn5nqj7Vsxsd/ylx9/jU6XyFYwX9wb+g+I459ymiMnhe1ui7+1pB6x2zv0ekS96O1bH348bfU9RvJwMjMIPAXUKxbvnrQ1+SJPZ+AZgDTvu51mM//nd7/EPTEX3CEXfI5TvMUfibKsSa9++fX6TLCUlxQHsv//+v1PwtmoFjGratOmqGTNm5LnMtWjRIrZs2VKNJNhWFVXTpk1/p+BjOPyZVQV/a8ebwHp8r83ZQAgfqJ5STlUuqdI8pytE+2Bmac2aNZvTtGnTPSZMmPB6Wlpa+2DZ7ffYY48PAR555JFnomYryedBRf1s+Tx434tyOD5+/PHHuiUoq9woOC2e8DeDWN8uItPX4nsL+5hZnYjpg4DsM84443/FKKdCMrOq+DH+2uCfmlwZOd059wv+8sCpEfOkBP+/FZH1LaCzme0ZkXYC/uGh4lw+L86+K44BwEv4y4zdgV+KOf9H+Ht5Il/3BtOOxV+y/Bj/ARy5rdKA49l5W8U85vD3M1Vk64477jhWrVrV0swiL90fCVQ77LDD/gQ488wzv6LgbfUUkL18+fL/ELWtXn75ZapVq7aVir+tElGRzr8DDzzwfxR8DK8N0pbjv9BFcvifqwbosov1LWuleU6Xdfuwy22nmVWtW7fum1u3bm0zefLkeXvttddp+IAnG/i+U6dOJ+25556bZ86ceSTBA6Ul/TyIw2dLaTkseP+VMj4+Nm3axKxZs1oWs6yy+gyNrbyHS6jgr/Oc93g+08PDLByN7wpfBryDH0A3E9iIH1fsmCDf1HzKCQ8zdW4CrHOJXkAW/sPiMvwN0pGv1CBPeJy5m/BB2RjyHyj5M3ywdgb+g6m4AyUXed8Vo8xTnXNbnHOLnHOlNi4eUUNJBWnX45+aHIZ/8GMKfkDmphF5Cjrm4n5M7OLrvHXr1rn09PQN+KDkePzT2ouAd1zec6+gbbXWOedWr17tmjVr5o455hj3zjvvuMcff9zVqlXL3XjjjUExblICrHMyvYp0/s2ZM+fEQo7hnkE53+VTzrBgeqkNaVOSF763aWDwmg3Mi/g/LchTaud0GbcPu9x2hj8PHnroIffyyy+PjfV5cNttt01JSUlxJ5988lu7+nlQzp8tJTk+3sY/yNcP/wT8iGBfvBSRp0yOj4suuuiqY489NjzOaXHKKovP0Py3UVnvhCR7tQ42/q/OuZSoaXWccxud/8WgWsHO7oC/YT872Om3A1Wcc7Wdc5uC/NHj9KUE5TtXgQfixw834fJ5tYrIdz7wE7AZf1ljpwMbaIl/qnoj/knO0QQNfFntuyK8znLObXXO/Vba+4nYwakBN+Iv9Wfjn+7sFGPe/I65uB8Tu/hq7Zxz33777SL8pdw/8bdAjLn44ot3j9x/hWyrh51z/3XO/XfGjBnj999//6XVqlXb2rBhw8033XST27JlyxfB9GEJsM7J9Cry+VfIMZwW5M12sc/Vx4Ll/DOe64u/faTA9q80z+kybh92ue0s4ufBc1lZWa5+/fq/l8bnQTl+tpTk+LgdHxhvxPc0fg5cClSLyFMmx0dKSsrmww8/3E2bNm1KMcsq7c/QgrdRWe+EJHwVdxDadsErupxKNwh/ArxKa98Ndv7XaX5xzu2ZAOtVWV6ltf9ivYYEZSRDL3Oivkpr/z0U5H/Q+UHFw+kdnQ9atzj/QRrv9U2mV2nsuy5B3k3OuQOiph0U7Lvtzrn9EmB9k/nVPdgP+fUQVwv2XaxzqNwG4TfnHFIsrfH3cDTB39/0HdAVf+lgPn5oiMinicMbOHqIlIZBOW3x31Q+Adrjh5NYGZTzc5msQeVVGvuuB37ImhT8/YuLYixnLf4BKSldpXXuxTIEeBo/rNdNpVNdiVJa+68u/h64g4C5+PEVm+IfTKyJ/5W1h8qg/pVZae27p/DjE/8FTMSP4tIKf+9+dXy7eWXpV7/SGxC8AJrhf+TnF3xvLPjbBa4J/m6Fv+81vG8iFfc4KLkEiOIr4mt359zTzv+e+l/OX9od5ZxrECNvWKxy0p3vBfgtKGeZ8z+n2DIB1jFZX7u674a4wi1IgPVM1ldpnXvRr/B+Vc9pxdh/tZ1zdzrn5jvnNjt/L/E053/aNN7rmKyv0th35vy5NsM5t8b5W6NWO+emO+dOT4B1TNbXcFewBRF5W8VIK+lxUOKXek5FREREJGFoKCkRERERSRgKTkVEREQkYSg4FREREZGEoeBURERERBKGglMRERERSRgKTkVEREQkYSg4FREREZGEoeBURERERBKGglMRERERSRj/D2naDqB5FX8RAAAAAElFTkSuQmCC\n", "text/plain": [ - "1323653" + "
    " ] }, - "execution_count": 23, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "len(t)" + "import requests, re, nltk\n", + "\n", + "ttlus = \"https://www.gutenberg.org/cache/epub/164/pg164.txt\"\n", + "jtcoe = \"https://www.gutenberg.org/cache/epub/3748/pg3748.txt\"\n", + "bible = \"https://www.gutenberg.org/cache/epub/10/pg10.txt\"\n", + "\n", + "(tmf, null_0) = get_wf(ttlus)\n", + "(wf, null_0) = get_wf(jtcoe)\n", + "(tsaf, null_0) = get_wf(bible)\n", + "\n", + "plotTwoLists(tmf, wf, 'Difference between 20,000 Leagues and Center of Earth')\n", + "plotTwoLists(tmf, tsaf, 'Difference between 20,000 Leagues and The King James Bible')" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -374,7 +486,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.8.10" } }, "nbformat": 4,