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PythonTour.ipynb
{
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"<font color='blue'>A Python Tour</font>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<font color='darkred'><b>Dr. Jos\u00e9 Manuel MAGALLANES</b></font> \n",
"Researcher at Center for Social Complexity, George Mason University (jmagalla@gmu.edu) \n",
"Professor at Department of Social Sciences, Pontificia Universidad Catolica del Peru (jmagallanes@pucp.edu)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I will present some tools that Python has to organise data that is on the web. Python is very efficient for web scrapping, particularly making use of pair of libraries, which I will call first:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from bs4 import BeautifulSoup as BS\n",
"import urllib2"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 80
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first step to get the data is to give the **[link](http://en.wikipedia.org/wiki/List_of_border_wars)** where the data is:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#URL\n",
"WhereInWWW='http://en.wikipedia.org/wiki/List_of_border_wars'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 96
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From here, we need to get the html from that url:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# get HTML\n",
"identification = { 'User-Agent' : 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)'}\n",
"requestToServer = urllib2.Request(WhereInWWW,headers=identification)\n",
"responseFromServerInHTML = urllib2.urlopen(requestToServer)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 97
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I have made a *request* to the server, giving my **identification** (so that the probability that I get served *improves*), and when I get a *response*, I use it in *BS* (beautiful soup) like this:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#now BS wil be in charge!\n",
"soup = BS(responseFromServerInHTML,'lxml') #lxml is not the default parser. Wanna omitting it?.\n",
"# print (soup.prettify()) # uncomment to see the WHOLE result."
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 98
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see what we have so far:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### getting title, first try\n",
"print soup.find_all('h1') #there should be one HEADER level 1, right?"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[<h1 class=\"firstHeading\" id=\"firstHeading\" lang=\"en\"><span dir=\"auto\">List of border conflicts</span></h1>]\n"
]
}
],
"prompt_number": 99
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I got the html in a bracket!...so it is a list with one element:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### getting title, second try (going into the \"span\" tag)\n",
"print soup.find_all('h1')[0].span #notice I use the index 0 to acces the first element of the list!"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<span dir=\"auto\">List of border conflicts</span>\n"
]
}
],
"prompt_number": 100
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### getting title, third try\n",
"print soup.find_all('h1')[0].span.string #the text in the tag \"span\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"List of border conflicts\n"
]
}
],
"prompt_number": 101
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"### getting title, altenative to third try\n",
"print soup.find_all('h1')[0].span.get_text()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"List of border conflicts\n"
]
}
],
"prompt_number": 102
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**KEEP IN MIND** that they **string** and **get_text()** do not always give you the same result (see details [here](http://omz-software.com/pythonista/docs/ios/beautifulsoup_ref.html)):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"someHtml = \"<title>Normal text and <b>text in bold<b></title>\"\n",
"soup = BS(someHtml)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 140
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print soup.get_text()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Normal text and text in bold\n"
]
}
],
"prompt_number": 141
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print soup.string"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"None\n"
]
}
],
"prompt_number": 142
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Different from the R [example](http://rpubs.com/jmagallanes/dataGathering) wher we just got a table, we are navigating the whole html of the wikipage. This flexibility will allow us to do interesting things. But let's continue getting familiar with BS:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# get HTML\n",
"identification = { 'User-Agent' : 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)'}\n",
"requestToServer = urllib2.Request(WhereInWWW,headers=identification)\n",
"responseFromServerInHTML = urllib2.urlopen(requestToServer)\n",
"soup = BS(responseFromServerInHTML,'lxml') \n",
"\n",
"#subtitles try 1\n",
"for item in soup.find_all('h2'):\n",
" print item.span"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"None\n",
"<span class=\"mw-headline\" id=\"19th_Century\">19th Century</span>\n",
"<span class=\"mw-headline\" id=\"20th_Century_before_World_War_II\">20th Century before World War II</span>\n",
"<span class=\"mw-headline\" id=\"1945-2000\">1945-2000</span>\n",
"<span class=\"mw-headline\" id=\"21st_Century\">21st Century</span>\n",
"<span class=\"mw-headline\" id=\"See_also\">See also</span>\n",
"<span class=\"mw-headline\" id=\"References\">References</span>\n",
"None\n"
]
}
],
"prompt_number": 144
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"###subtitles try 2\n",
"for subtitle in soup.find_all('h2'):\n",
" print subtitle.span.string"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'NoneType' object has no attribute 'string'",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-147-7b7fdeb5bebc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m###subtitles try 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0msubtitle\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msoup\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfind_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'h2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0msubtitle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'string'"
]
}
],
"prompt_number": 147
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Why did I get this error? (*see above for the answer!*)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"###subtitles try 3\n",
"for subtitle in soup.find_all('h2'): \n",
" try:\n",
" print subtitle.span.string\n",
" except AttributeError:\n",
" print \"no span tag in this subtitle!\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"no span tag in this subtitle!\n",
"19th Century\n",
"20th Century before World War II\n",
"1945-2000\n",
"21st Century\n",
"See also\n",
"References\n",
"no span tag in this subtitle!\n"
]
}
],
"prompt_number": 149
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that there were a couple of subtitles that have no **span** tag. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"###alternative to try 4\n",
"for subtitle in soup.find_all('h2'): \n",
" try:\n",
" print subtitle.span.string\n",
" except AttributeError:\n",
" continue # cleaner results but lose information of who lacked the span tag!"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"19th Century\n",
"20th Century before World War II\n",
"1945-2000\n",
"21st Century\n",
"See also\n",
"References\n"
]
}
],
"prompt_number": 150
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is time to get the tables, as we did in the R [example](http://rpubs.com/jmagallanes/dataGathering):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"allTables = soup.findAll('table', {'class': 'sortable wikitable'})#explore the page source to check tag and attribute!"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 159
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"##see contents try 1\n",
"for eachTable in allTables:\n",
" allRows=eachTable.find_all(\"tr\")\n",
" for eachRow in allRows:\n",
" print eachRow.get_text()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Start\n",
"Finish\n",
"Conflict\n",
"Combatants\n",
"Fatalities\n",
"\n",
"\n",
"1846\n",
"1848\n",
"Mexican\u2013American War\n",
"\u00a0United States - \u00a0Mexico\n",
"~29,000\n",
"\n",
"\n",
"1879\n",
"1883\n",
"War of the Pacific\n",
"\u00a0Chile - \u00a0Bolivia & \u00a0Peru\n",
"13,000+\n",
"\n",
"\n",
"Start\n",
"Finish\n",
"Conflict\n",
"Combatants\n",
"Fatalities\n",
"\n",
"\n",
"1910\n",
"1918\n",
"Border War\n",
"\u00a0United States - \u00a0Mexico\n",
"100+\n",
"\n",
"\n",
"1932\n",
"1935\n",
"Chaco War\n",
"\u00a0Bolivia - \u00a0Paraguay\n",
"~100,000\n",
"\n",
"\n",
"1938\n",
"1938\n",
"Battle of Lake Khasan\n",
"\u00a0Soviet Union \u2013 \u00a0Japan\n",
"~1,300\n",
"\n",
"\n",
"1939\n",
"1939\n",
"Slovak\u2013Hungarian War\n",
"\u00a0Slovakia \u2013 \u00a0Hungary\n",
"66\n",
"\n",
"\n",
"1939\n",
"1939\n",
"Battle of Khalkhin Gol\n",
"\u00a0Soviet Union and \u00a0Mongolia \u2013 \u00a0Japan and \u00a0Manchukuo\n",
"~16,000\n",
"\n",
"\n",
"Start\n",
"Finish\n",
"Conflict\n",
"Combatants\n",
"Fatalities\n",
"\n",
"\n",
"1947\n",
"1948\n",
"Indo-Pakistani War of 1947\n",
"\u00a0Pakistan \u2013 \u00a0India\n",
"~3,000\n",
"\n",
"\n",
"1950\n",
"Present (CF signed in 1953)\n",
"Korean War\n",
"\u00a0South Korea - \u00a0North Korea\n",
"2 419 010+\n",
"\n",
"\n",
"1962\n",
"1962\n",
"Sino-Indian War\n",
"\u00a0PRC \u2013 \u00a0India\n",
"~4,000\n",
"\n",
"\n",
"1965\n",
"1965\n",
"Indo-Pakistani War of 1965\n",
"\u00a0Pakistan \u2013 \u00a0India\n",
"~6,800\n",
"\n",
"\n",
"1966\n",
"1989\n",
"South African Border War\n",
"\u00a0Angola \u2013 \u00a0South Africa\n",
"Unknown\n",
"\n",
"\n",
"1967\n",
"1967\n",
"Chola incident\n",
"\u00a0India \u2013 \u00a0People's Republic of China\n",
"~5\n",
"\n",
"\n",
"1969\n",
"1969\n",
"Sino-Soviet border conflict\n",
"\u00a0PRC \u2013 \u00a0Soviet Union\n",
"Unknown\n",
"\n",
"\n",
"1971\n",
"1971\n",
"Indo-Pakistani War of 1971\n",
"\u00a0Pakistan \u2013 \u00a0India\n",
"~4,000+\n",
"\n",
"\n",
"1979\n",
"1990\n",
"Sino-Vietnamese conflicts 1979-1990\n",
"\u00a0Vietnam \u2013 \u00a0China\n",
"Unclear\n",
"\n",
"\n",
"1980\n",
"1988\n",
"Iran\u2013Iraq War\n",
"\u00a0Iran \u2013 \u00a0Iraq\n",
"1-2 million[1]\n",
"\n",
"\n",
"1981\n",
"1981\n",
"Paquisha War\n",
"\u00a0Ecuador \u2013 \u00a0Peru\n",
"~10\n",
"\n",
"\n",
"1982\n",
"1982\n",
"1982 Ethiopian\u2013Somali Border War\n",
"\u00a0Ethiopia \u2013 \u00a0Somalia\n",
"Unknown\n",
"\n",
"\n",
"1984\n",
"1987\n",
"Siachen Conflict\n",
"\u00a0Pakistan \u2013 \u00a0India\n",
"~2,400.\n",
"\n",
"\n",
"1985\n",
"1985\n",
"Agacher Strip War\n",
"\u00a0Burkina Faso \u2013 \u00a0Mali\n",
"179\n",
"\n",
"\n",
"1987\n",
"1987\n",
"1987 Sino-Indian skirmish\n",
"\u00a0India \u2013 \u00a0People's Republic of China\n",
"Unknown\n",
"\n",
"\n",
"1987\n",
"1988\n",
"Thai\u2013Laotian Border War\n",
"\u00a0Thailand \u2013 \u00a0Laos\n",
"~1,000\n",
"\n",
"\n",
"1989\n",
"1991\n",
"Mauritania\u2013Senegal Border War\n",
"\u00a0Mauritania \u2013 \u00a0Senegal\n",
"Unknown\n",
"\n",
"\n",
"1995\n",
"1995\n",
"Cenepa War\n",
"\u00a0Ecuador \u2013 \u00a0Peru\n",
"~450\n",
"\n",
"\n",
"1998\n",
"2000\n",
"Eritrean-Ethiopian War\n",
"\u00a0Ethiopia \u2013 \u00a0Eritrea\n",
"~70,000\n",
"\n",
"\n",
"1999\n",
"1999\n",
"Kargil War\n",
"\u00a0India \u2013 \u00a0Pakistan\n",
"~4,500\n",
"\n",
"\n",
"Start\n",
"Finish\n",
"Conflict\n",
"Combatants\n",
"Fatalities\n",
"\n",
"\n",
"2000\n",
"2006\n",
"2000-2006 Shebaa Farms conflict\n",
"\u00a0Israel \u2013 \u00a0Lebanon\n",
"30\n",
"\n",
"\n",
"2001\n",
"2001\n",
"2001 Indian\u2013Bangladeshi border conflict\n",
"\u00a0India \u2013 \u00a0Bangladesh\n",
"20\n",
"\n",
"\n",
"2008\n",
"2008\n",
"2008 Djiboutian\u2013Eritrean border conflict\n",
"\u00a0Djibouti \u2013 \u00a0Eritrea\n",
"144\n",
"\n",
"\n",
"2008\n",
"2012\n",
"Cambodian\u2013Thai border dispute\n",
"\u00a0Cambodia \u2013 \u00a0Thailand\n",
"42\n",
"\n",
"\n",
"2012\n",
"2012\n",
"2012 South Sudan\u2013Sudan border conflict\n",
"\u00a0Sudan \u2013 \u00a0South Sudan\n",
"262-322\n",
"\n",
"\n",
"2012\n",
"Ongoing\n",
"2012 Syrian\u2013Turkish border clashes\n",
"\u00a0Turkey - \u00a0Syria\n",
"20\n",
"\n"
]
}
],
"prompt_number": 162
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In fact, you can see the contents of the table, but the column titles appear again and again, something I don't need."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"##see contents try 2\n",
"for eachTable in allTables:\n",
" allRows=eachTable.find_all(\"tr\") # tr stands for table rows\n",
" for eachRow in range(1,len(allRows)): #when I visit a table, I jump the first row\n",
" allCells = allRows[eachRow].find_all(\"td\") # td stands for table data, organized in cells.\n",
" start=allCells[0].get_text(),\n",
" end=allCells[1].get_text(),\n",
" name=allCells[2].get_text(),\n",
" countries=allCells[3].get_text(),\n",
" fatalities=allCells[4].get_text()\n",
" print start,end,name,countries,fatalities"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(u'1846',) (u'1848',) (u'Mexican\\u2013American War',) (u'\\xa0United States - \\xa0Mexico',) ~29,000\n",
"(u'1879',) (u'1883',) (u'War of the Pacific',) (u'\\xa0Chile - \\xa0Bolivia & \\xa0Peru',) 13,000+\n",
"(u'1910',) (u'1918',) (u'Border War',) (u'\\xa0United States - \\xa0Mexico',) 100+\n",
"(u'1932',) (u'1935',) (u'Chaco War',) (u'\\xa0Bolivia - \\xa0Paraguay',) ~100,000\n",
"(u'1938',) (u'1938',) (u'Battle of Lake Khasan',) (u'\\xa0Soviet Union \\u2013 \\xa0Japan',) ~1,300\n",
"(u'1939',) (u'1939',) (u'Slovak\\u2013Hungarian War',) (u'\\xa0Slovakia \\u2013 \\xa0Hungary',) 66\n",
"(u'1939',) (u'1939',) (u'Battle of Khalkhin Gol',) (u'\\xa0Soviet Union and \\xa0Mongolia \\u2013 \\xa0Japan and \\xa0Manchukuo',) ~16,000\n",
"(u'1947',) (u'1948',) (u'Indo-Pakistani War of 1947',) (u'\\xa0Pakistan \\u2013 \\xa0India',) ~3,000\n",
"(u'1950',) (u'Present (CF signed in 1953)',) (u'Korean War',) (u'\\xa0South Korea - \\xa0North Korea',) 2 419 010+\n",
"(u'1962',) (u'1962',) (u'Sino-Indian War',) (u'\\xa0PRC \\u2013 \\xa0India',) ~4,000\n",
"(u'1965',) (u'1965',) (u'Indo-Pakistani War of 1965',) (u'\\xa0Pakistan \\u2013 \\xa0India',) ~6,800\n",
"(u'1966',) (u'1989',) (u'South African Border War',) (u'\\xa0Angola \\u2013 \\xa0South Africa',) Unknown\n",
"(u'1967',) (u'1967',) (u'Chola incident',) (u\"\\xa0India \\u2013 \\xa0People's Republic of China\",) ~5\n",
"(u'1969',) (u'1969',) (u'Sino-Soviet border conflict',) (u'\\xa0PRC \\u2013 \\xa0Soviet Union',) Unknown\n",
"(u'1971',) (u'1971',) (u'Indo-Pakistani War of 1971',) (u'\\xa0Pakistan \\u2013 \\xa0India',) ~4,000+\n",
"(u'1979',) (u'1990',) (u'Sino-Vietnamese conflicts 1979-1990',) (u'\\xa0Vietnam \\u2013 \\xa0China',) Unclear\n",
"(u'1980',) (u'1988',) (u'Iran\\u2013Iraq War',) (u'\\xa0Iran \\u2013 \\xa0Iraq',) 1-2 million[1]\n",
"(u'1981',) (u'1981',) (u'Paquisha War',) (u'\\xa0Ecuador \\u2013 \\xa0Peru',) ~10\n",
"(u'1982',) (u'1982',) (u'1982 Ethiopian\\u2013Somali Border War',) (u'\\xa0Ethiopia \\u2013 \\xa0Somalia',) Unknown\n",
"(u'1984',) (u'1987',) (u'Siachen Conflict',) (u'\\xa0Pakistan \\u2013 \\xa0India',) ~2,400.\n",
"(u'1985',) (u'1985',) (u'Agacher Strip War',) (u'\\xa0Burkina Faso \\u2013 \\xa0Mali',) 179\n",
"(u'1987',) (u'1987',) (u'1987 Sino-Indian skirmish',) (u\"\\xa0India \\u2013 \\xa0People's Republic of China\",) Unknown\n",
"(u'1987',) (u'1988',) (u'Thai\\u2013Laotian Border War',) (u'\\xa0Thailand \\u2013 \\xa0Laos',) ~1,000\n",
"(u'1989',) (u'1991',) (u'Mauritania\\u2013Senegal Border War',) (u'\\xa0Mauritania \\u2013 \\xa0Senegal',) Unknown\n",
"(u'1995',) (u'1995',) (u'Cenepa War',) (u'\\xa0Ecuador \\u2013 \\xa0Peru',) ~450\n",
"(u'1998',) (u'2000',) (u'Eritrean-Ethiopian War',) (u'\\xa0Ethiopia \\u2013 \\xa0Eritrea',) ~70,000\n",
"(u'1999',) (u'1999',) (u'Kargil War',) (u'\\xa0India \\u2013 \\xa0Pakistan',) ~4,500\n",
"(u'2000',) (u'2006',) (u'2000-2006 Shebaa Farms conflict',) (u'\\xa0Israel \\u2013 \\xa0Lebanon',) 30\n",
"(u'2001',) (u'2001',) (u'2001 Indian\\u2013Bangladeshi border conflict',) (u'\\xa0India \\u2013 \\xa0Bangladesh',) 20\n",
"(u'2008',) (u'2008',) (u'2008 Djiboutian\\u2013Eritrean border conflict',) (u'\\xa0Djibouti \\u2013 \\xa0Eritrea',) 144\n",
"(u'2008',) (u'2012',) (u'Cambodian\\u2013Thai border dispute',) (u'\\xa0Cambodia \\u2013 \\xa0Thailand',) 42\n",
"(u'2012',) (u'2012',) (u'2012 South Sudan\\u2013Sudan border conflict',) (u'\\xa0Sudan \\u2013 \\xa0South Sudan',) 262-322\n",
"(u'2012',) (u'Ongoing',) (u'2012 Syrian\\u2013Turkish border clashes',) (u'\\xa0Turkey - \\xa0Syria',) 20\n"
]
}
],
"prompt_number": 164
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"##see contents alternative\n",
"print 'START','END','NAME','\\t\\t\\t\\t\\t\\tFATALITIES'\n",
"for eachTable in allTables:\n",
" allRows=eachTable.find_all(\"tr\")\n",
" \n",
" for eachRow in range(1,len(allRows)):\n",
" allCells = allRows[eachRow].find_all(\"td\")\n",
" print allCells[0].string,\n",
" print allCells[1].string,\n",
" print allCells[2].string,\n",
" print [country.string for country in allCells[3].find_all('a')],\n",
" print allCells[4].string"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"START END NAME \t\t\t\t\t\tFATALITIES\n",
"1846 1848 Mexican\u2013American War [u'United States', u'Mexico'] ~29,000\n",
"1879 1883 War of the Pacific [u'Chile', u'Bolivia', u'Peru'] 13,000+\n",
"1910 1918 Border War [u'United States', u'Mexico'] 100+\n",
"1932 1935 Chaco War [u'Bolivia', u'Paraguay'] ~100,000\n",
"1938 1938 Battle of Lake Khasan [u'Soviet Union', u'Japan'] ~1,300\n",
"1939 1939 Slovak\u2013Hungarian War [u'Slovakia', u'Hungary'] 66\n",
"1939 1939 Battle of Khalkhin Gol [u'Soviet Union', u'Mongolia', u'Japan', u'Manchukuo'] ~16,000\n",
"1947 1948 Indo-Pakistani War of 1947 [u'Pakistan', u'India'] ~3,000\n",
"1950 Present (CF signed in 1953) Korean War [u'South Korea', u'North Korea'] 2 419 010+\n",
"1962 1962 Sino-Indian War [u'PRC', u'India'] ~4,000\n",
"1965 1965 Indo-Pakistani War of 1965 [u'Pakistan', u'India'] ~6,800\n",
"1966 1989 South African Border War [u'Angola', u'South Africa'] Unknown\n",
"1967 1967 Chola incident [u'India', u\"People's Republic of China\"] ~5\n",
"1969 1969 Sino-Soviet border conflict [u'PRC', u'Soviet Union'] Unknown\n",
"1971 1971 Indo-Pakistani War of 1971 [u'Pakistan', u'India'] ~4,000+\n",
"1979 1990 Sino-Vietnamese conflicts 1979-1990 [u'Vietnam', u'China'] Unclear\n",
"1980 1988 Iran\u2013Iraq War [u'Iran', u'Iraq'] None\n",
"1981 1981 Paquisha War [u'Ecuador', u'Peru'] ~10\n",
"1982 1982 1982 Ethiopian\u2013Somali Border War [u'Ethiopia', u'Somalia'] Unknown\n",
"1984 1987 Siachen Conflict [u'Pakistan', u'India'] ~2,400.\n",
"1985 1985 Agacher Strip War [u'Burkina Faso', u'Mali'] 179\n",
"1987 1987 1987 Sino-Indian skirmish [u'India', u\"People's Republic of China\"] Unknown\n",
"1987 1988 Thai\u2013Laotian Border War [u'Thailand', u'Laos'] ~1,000\n",
"1989 1991 Mauritania\u2013Senegal Border War [u'Mauritania', u'Senegal'] Unknown\n",
"1995 1995 Cenepa War [u'Ecuador', u'Peru'] ~450\n",
"1998 2000 Eritrean-Ethiopian War [u'Ethiopia', u'Eritrea'] ~70,000\n",
"1999 1999 Kargil War [u'India', u'Pakistan'] ~4,500\n",
"2000 2006 2000-2006 Shebaa Farms conflict [u'Israel', u'Lebanon'] 30\n",
"2001 2001 2001 Indian\u2013Bangladeshi border conflict [u'India', u'Bangladesh'] 20\n",
"2008 2008 2008 Djiboutian\u2013Eritrean border conflict [u'Djibouti', u'Eritrea'] 144\n",
"2008 2012 Cambodian\u2013Thai border dispute [u'Cambodia', u'Thailand'] 42\n",
"2012 2012 2012 South Sudan\u2013Sudan border conflict [u'Sudan', u'South Sudan'] 262-322\n",
"2012 Ongoing 2012 Syrian\u2013Turkish border clashes [u'Turkey', u'Syria'] 20\n"
]
}
],
"prompt_number": 165
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we are dealing with this one page to do the exercise, it would be better to save it locally:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os\n",
"\n",
"# set working directory:\n",
"os.chdir(\"/Users/josemagallanes/tutorialSBP2014\") #customize this please!\n",
"\n",
"WhereInWWW='http://en.wikipedia.org/wiki/List_of_border_wars'\n",
"identification = { 'User-Agent' : 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)'}\n",
"requestToServer = urllib2.Request(WhereInWWW,headers=identification)\n",
"responseFromServerInHTML = urllib2.urlopen(requestToServer)\n",
"\n",
"data = responseFromServerInHTML.read()\n",
"saveHtml = open('warsList.html','w')\n",
"saveHtml.write(data)\n",
"saveHtml.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 168
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"from now on, I will stop bothering wikipedia:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"openHtml = open(\"warsList.html\",\"r\") #using data stored in my computer\n",
"soup = BS(openHtml,'lxml')\n",
"openHtml.close()\n",
"\n",
"allTables = soup.findAll('table', {'class': 'sortable wikitable'})\n",
"##see contents try 2\n",
"for eachTable in allTables:\n",
" allRows=eachTable.find_all(\"tr\") # tr stands for table rows\n",
" for eachRow in range(1,len(allRows)): #when I visit a table, I jump the first row\n",
" allCells = allRows[eachRow].find_all(\"td\") # td stands for table data, organized in cells.\n",
" start=allCells[0].get_text(),\n",
" end=allCells[1].get_text(),\n",
" name=allCells[2].get_text(),\n",
" countries=[country.string for country in allCells[3].find_all('a')]\n",
" fatalities=allCells[4].get_text()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 169
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could save the output to a file, but we are interested in something different than we did in R. This time I want to use the fact that I am inside an html file that also has links to **other pages**. In particular, you can that see in the wikipedia page that the countries have links to their own wikipage. I want to get each of them :"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"allTables = soup.findAll('table', {'class': 'sortable wikitable'})\n",
"for eachTable in allTables:\n",
" allRows=eachTable.find_all(\"tr\") # tr stands for table rows\n",
" for eachRow in range(1,len(allRows)): #when I visit a table, I jump the first row\n",
" allCells = allRows[eachRow].find_all(\"td\") # td stands for table data, organized in cells.\n",
" start=allCells[0].get_text()\n",
" end=allCells[1].get_text()\n",
" name=allCells[2].get_text()\n",
" fatalities=allCells[4].get_text()\n",
" countries=[country.get_text() for country in allCells[3].find_all('a')]\n",
" \n",
" ##start - code added\n",
" linksEachCountry=['http://en.wikipedia.org'+country.get('href') for country in allCells[3].find_all('a')]\n",
" print linksEachCountry\n",
" ##end - code added"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['http://en.wikipedia.org/wiki/United_States', 'http://en.wikipedia.org/wiki/Mexico']\n",
"['http://en.wikipedia.org/wiki/Chile', 'http://en.wikipedia.org/wiki/Bolivia', 'http://en.wikipedia.org/wiki/Peru']\n",
"['http://en.wikipedia.org/wiki/United_States', 'http://en.wikipedia.org/wiki/Mexico']\n",
"['http://en.wikipedia.org/wiki/Bolivia', 'http://en.wikipedia.org/wiki/Paraguay']\n",
"['http://en.wikipedia.org/wiki/Soviet_Union', 'http://en.wikipedia.org/wiki/Japan']\n",
"['http://en.wikipedia.org/wiki/Slovakia', 'http://en.wikipedia.org/wiki/Hungary']\n",
"['http://en.wikipedia.org/wiki/Soviet_Union', 'http://en.wikipedia.org/wiki/Mongolia', 'http://en.wikipedia.org/wiki/Japan', 'http://en.wikipedia.org/wiki/Manchukuo']\n",
"['http://en.wikipedia.org/wiki/Pakistan', 'http://en.wikipedia.org/wiki/India']\n",
"['http://en.wikipedia.org/wiki/South_Korea', 'http://en.wikipedia.org/wiki/North_Korea']\n",
"['http://en.wikipedia.org/wiki/China', 'http://en.wikipedia.org/wiki/India']\n",
"['http://en.wikipedia.org/wiki/Pakistan', 'http://en.wikipedia.org/wiki/India']\n",
"['http://en.wikipedia.org/wiki/Angola', 'http://en.wikipedia.org/wiki/South_Africa']\n",
"['http://en.wikipedia.org/wiki/India', 'http://en.wikipedia.org/wiki/China']\n",
"['http://en.wikipedia.org/wiki/China', 'http://en.wikipedia.org/wiki/Soviet_Union']\n",
"['http://en.wikipedia.org/wiki/Pakistan', 'http://en.wikipedia.org/wiki/India']\n",
"['http://en.wikipedia.org/wiki/Vietnam', 'http://en.wikipedia.org/wiki/China']\n",
"['http://en.wikipedia.org/wiki/Iran', 'http://en.wikipedia.org/wiki/Iraq']\n",
"['http://en.wikipedia.org/wiki/Ecuador', 'http://en.wikipedia.org/wiki/Peru']\n",
"['http://en.wikipedia.org/wiki/Ethiopia', 'http://en.wikipedia.org/wiki/Somalia']\n",
"['http://en.wikipedia.org/wiki/Pakistan', 'http://en.wikipedia.org/wiki/India']\n",
"['http://en.wikipedia.org/wiki/Burkina_Faso', 'http://en.wikipedia.org/wiki/Mali']\n",
"['http://en.wikipedia.org/wiki/India', 'http://en.wikipedia.org/wiki/China']\n",
"['http://en.wikipedia.org/wiki/Thailand', 'http://en.wikipedia.org/wiki/Laos']\n",
"['http://en.wikipedia.org/wiki/Mauritania', 'http://en.wikipedia.org/wiki/Senegal']\n",
"['http://en.wikipedia.org/wiki/Ecuador', 'http://en.wikipedia.org/wiki/Peru']\n",
"['http://en.wikipedia.org/wiki/Ethiopia', 'http://en.wikipedia.org/wiki/Eritrea']\n",
"['http://en.wikipedia.org/wiki/India', 'http://en.wikipedia.org/wiki/Pakistan']\n",
"['http://en.wikipedia.org/wiki/Israel', 'http://en.wikipedia.org/wiki/Lebanon']\n",
"['http://en.wikipedia.org/wiki/India', 'http://en.wikipedia.org/wiki/Bangladesh']\n",
"['http://en.wikipedia.org/wiki/Djibouti', 'http://en.wikipedia.org/wiki/Eritrea']\n",
"['http://en.wikipedia.org/wiki/Cambodia', 'http://en.wikipedia.org/wiki/Thailand']\n",
"['http://en.wikipedia.org/wiki/Sudan', 'http://en.wikipedia.org/wiki/South_Sudan']\n",
"['http://en.wikipedia.org/wiki/Turkey', 'http://en.wikipedia.org/wiki/Syria']\n"
]
}
],
"prompt_number": 170
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Above, we are getting at least two links in a list. As you can see, I needed to concatenate *http://en.wikipedia.org* to the link retrieved. Now, I want to get a particular information from that wikipage. In this case I will get the geographical coordinates of the country:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for eachTable in allTables:\n",
" allRows=eachTable.find_all(\"tr\") # tr stands for table rows\n",
" for eachRow in range(1,len(allRows)): #when I visit a table, I jump the first row\n",
" allCells = allRows[eachRow].find_all(\"td\") # td stands for table data, organized in cells.\n",
" start=allCells[0].get_text()\n",
" end=allCells[1].get_text()\n",
" name=allCells[2].get_text()\n",
" fatalities=allCells[4].get_text()\n",
" countries=[country.get_text() for country in allCells[3].find_all('a')]\n",
" linksEachCountry=['http://en.wikipedia.org'+country.get('href') for country in allCells[3].find_all('a')] \n",
" \n",
" #start -coded added\n",
" for link in linksEachCountry:\n",
" if link=='http://en.wikipedia.org/wiki/Soviet_Union': #notice this!\n",
" link='http://en.wikipedia.org/wiki/Russia'\n",
" externalWiki= urllib2.urlopen(link)\n",
" subSoup = BS(externalWiki)\n",
" coords=subSoup.find_all(\"span\",{'class': 'geo'})\n",
" print coords\n",
" ##end - code added"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[<span class=\"geo\">38.883; -77.017</span>, <span class=\"geo\">40.717; -74.000</span>]\n",
"[<span class=\"geo\">19.433; -99.133</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">-33.433; -70.667</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">-16.712; -64.666</span>, <span class=\"geo\">-17.800; -63.167</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">-12.0433; -77.0283</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">38.883; -77.017</span>, <span class=\"geo\">40.717; -74.000</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">19.433; -99.133</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">-16.712; -64.666</span>, <span class=\"geo\">-17.800; -63.167</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">-25.267; -57.667</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">60; 90</span>, <span class=\"geo\">55.750; 37.617</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">35.683; 139.767</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">48.150; 17.117</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">47.433; 19.250</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">60; 90</span>, <span class=\"geo\">55.750; 37.617</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">47.917; 106.883</span>, <span class=\"geo\">46; 105</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">35.683; 139.767</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">43.883; 125.317</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">33.667; 73.167</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">28.6133; 77.2083</span>, <span class=\"geo\">21; 78</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">37.550; 126.967</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">40.000; 127.000</span>, <span class=\"geo\">39.033; 125.750</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">39.917; 116.383</span>, <span class=\"geo\">35; 103</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">28.6133; 77.2083</span>, <span class=\"geo\">21; 78</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">33.667; 73.167</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">28.6133; 77.2083</span>, <span class=\"geo\">21; 78</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">-8.833; 13.333</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">-30; 25</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">28.6133; 77.2083</span>, <span class=\"geo\">21; 78</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">39.917; 116.383</span>, <span class=\"geo\">35; 103</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">39.917; 116.383</span>, <span class=\"geo\">35; 103</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">60; 90</span>, <span class=\"geo\">55.750; 37.617</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">33.667; 73.167</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">28.6133; 77.2083</span>, <span class=\"geo\">21; 78</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">21.033; 105.850</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">39.917; 116.383</span>, <span class=\"geo\">35; 103</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">32; 53</span>, <span class=\"geo\">35.683; 51.417</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">33.333; 44.433</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">-0.150; -78.350</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">-12.0433; -77.0283</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">9.0300; 38.7400</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">2.033; 45.350</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">33.667; 73.167</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">28.6133; 77.2083</span>, <span class=\"geo\">21; 78</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">12.333; -1.667</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">12.650; -8.000</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">28.6133; 77.2083</span>, <span class=\"geo\">21; 78</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">39.917; 116.383</span>, <span class=\"geo\">35; 103</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">13.750; 100.483</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">17.967; 102.600</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">18.150; -15.967</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">14.667; -17.417</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">-0.150; -78.350</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">-12.0433; -77.0283</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">9.0300; 38.7400</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">15.333; 38.917</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">28.6133; 77.2083</span>, <span class=\"geo\">21; 78</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">33.667; 73.167</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">31; 35</span>, <span class=\"geo\">31.783; 35.217</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">33.900; 35.533</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">28.6133; 77.2083</span>, <span class=\"geo\">21; 78</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">23.700; 90.350</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">11.600; 43.167</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">15.333; 38.917</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">11.550; 104.917</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">13.750; 100.483</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">15.633; 32.533</span>, <span class=\"geo\">15.63306; 32.53306</span>, <span class=\"geo\">15; 32</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">4.850; 31.600</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">39.917; 32.833</span>, <span class=\"geo\">41.017; 28.950</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[<span class=\"geo\">33.500; 36.300</span>]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 172
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, some countries have two pairs of coordinates (?)...That is because some wikipages give you the coordinate of the country, and the coordinate of the biggest city. For our purposes we will keep just one. Since the coordinates are offered in a list, I will select the first element of that list:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for eachTable in allTables:\n",
" allRows=eachTable.find_all(\"tr\") # tr stands for table rows\n",
" for eachRow in range(1,len(allRows)): #when I visit a table, I jump the first row\n",
" allCells = allRows[eachRow].find_all(\"td\") # td stands for table data, organized in cells.\n",
" start=allCells[0].get_text()\n",
" end=allCells[1].get_text()\n",
" name=allCells[2].get_text()\n",
" fatalities=allCells[4].get_text()\n",
" countries=[country.string for country in allCells[3].find_all('a')]\n",
" linksEachCountry=['http://en.wikipedia.org'+country.get('href') for country in allCells[3].find_all('a')] \n",
" for link in linksEachCountry:\n",
" if link=='http://en.wikipedia.org/wiki/Soviet_Union': #notice this!\n",
" link='http://en.wikipedia.org/wiki/Russia'\n",
" externalWiki= urllib2.urlopen(link)\n",
" subSoup = BS(externalWiki)\n",
" \n",
" #start - coded modified\n",
" coords=subSoup.find_all(\"span\",{'class': 'geo'})[0].string\n",
" ##end - code modified\n",
" \n",
" print coords, #comma prints elements in same line"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"38.883; -77.017 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"19.433; -99.133 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-33.433; -70.667 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-16.712; -64.666 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-12.0433; -77.0283 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"38.883; -77.017 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"19.433; -99.133 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-16.712; -64.666 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-25.267; -57.667 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"60; 90 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"35.683; 139.767 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"48.150; 17.117 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"47.433; 19.250 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"60; 90 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"47.917; 106.883 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"35.683; 139.767 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"43.883; 125.317 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"33.667; 73.167 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"28.6133; 77.2083 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"37.550; 126.967 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"40.000; 127.000 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"39.917; 116.383 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"28.6133; 77.2083 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"33.667; 73.167 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"28.6133; 77.2083 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-8.833; 13.333 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-30; 25 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"28.6133; 77.2083 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"39.917; 116.383 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"39.917; 116.383 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"60; 90 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"33.667; 73.167 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"28.6133; 77.2083 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"21.033; 105.850 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"39.917; 116.383 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"32; 53 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"33.333; 44.433 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-0.150; -78.350 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-12.0433; -77.0283 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"9.0300; 38.7400 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2.033; 45.350 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"33.667; 73.167 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"28.6133; 77.2083 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"12.333; -1.667 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"12.650; -8.000 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"28.6133; 77.2083 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"39.917; 116.383 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"13.750; 100.483 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"17.967; 102.600 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"18.150; -15.967 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"14.667; -17.417 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-0.150; -78.350 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"-12.0433; -77.0283 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"9.0300; 38.7400 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"15.333; 38.917 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"28.6133; 77.2083 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"33.667; 73.167 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"31; 35 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"33.900; 35.533 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"28.6133; 77.2083 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"23.700; 90.350 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"11.600; 43.167 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"15.333; 38.917 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"11.550; 104.917 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"13.750; 100.483 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"15.633; 32.533 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4.850; 31.600 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"39.917; 32.833 "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"33.500; 36.300\n"
]
}
],
"prompt_number": 174
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The element is a text (\"number; number\")...we need to get each number:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for eachTable in allTables:\n",
" allRows=eachTable.find_all(\"tr\") # tr stands for table rows\n",
" for eachRow in range(1,len(allRows)): #when I visit a table, I jump the first row\n",
" allCells = allRows[eachRow].find_all(\"td\") # td stands for table data, organized in cells.\n",
" start=allCells[0].get_text()\n",
" end=allCells[1].get_text()\n",
" name=allCells[2].get_text()\n",
" fatalities=allCells[4].get_text()\n",
" countries=[country.string for country in allCells[3].find_all('a')]\n",
" linksEachCountry=['http://en.wikipedia.org'+country.get('href') for country in allCells[3].find_all('a')] \n",
" for link in linksEachCountry:\n",
" if link=='http://en.wikipedia.org/wiki/Soviet_Union': #notice this!\n",
" link='http://en.wikipedia.org/wiki/Russia'\n",
" externalWiki= urllib2.urlopen(link)\n",
" subSoup = BS(externalWiki)\n",
" coords=subSoup.find_all(\"span\",{'class': 'geo'})[0].string\n",
" \n",
" #start - coded added\n",
" lati,longi=coords.string.split('; ') #using \";\" to divide the element in two!\n",
" print \"LAT:\",lati,\"LON:\",longi\n",
" ##end - code added"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"LAT: 38.883 LON: -77.017\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 19.433 LON: -99.133\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -33.433 LON: -70.667\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -16.712 LON: -64.666\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -12.0433 LON: -77.0283\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 38.883 LON: -77.017\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 19.433 LON: -99.133\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -16.712 LON: -64.666\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -25.267 LON: -57.667\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 60 LON: 90\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 35.683 LON: 139.767\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 48.150 LON: 17.117\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 47.433 LON: 19.250\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 60 LON: 90\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 47.917 LON: 106.883\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 35.683 LON: 139.767\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 43.883 LON: 125.317\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 33.667 LON: 73.167\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 28.6133 LON: 77.2083\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 37.550 LON: 126.967\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 40.000 LON: 127.000\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.917 LON: 116.383\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 28.6133 LON: 77.2083\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 33.667 LON: 73.167\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 28.6133 LON: 77.2083\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -8.833 LON: 13.333\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -30 LON: 25\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 28.6133 LON: 77.2083\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.917 LON: 116.383\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.917 LON: 116.383\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 60 LON: 90\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 33.667 LON: 73.167\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 28.6133 LON: 77.2083\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 21.033 LON: 105.850\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.917 LON: 116.383\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 32 LON: 53\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 33.333 LON: 44.433\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -0.150 LON: -78.350\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -12.0433 LON: -77.0283\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 9.0300 LON: 38.7400\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 2.033 LON: 45.350\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 33.667 LON: 73.167\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 28.6133 LON: 77.2083\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 12.333 LON: -1.667\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 12.650 LON: -8.000\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 28.6133 LON: 77.2083\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.917 LON: 116.383\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 13.750 LON: 100.483\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 17.967 LON: 102.600\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 18.150 LON: -15.967\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 14.667 LON: -17.417\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -0.150 LON: -78.350\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" -12.0433 LON: -77.0283\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 9.0300 LON: 38.7400\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 15.333 LON: 38.917\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 28.6133 LON: 77.2083\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 33.667 LON: 73.167\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 31 LON: 35\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 33.900 LON: 35.533\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 28.6133 LON: 77.2083\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 23.700 LON: 90.350\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 11.600 LON: 43.167\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 15.333 LON: 38.917\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 11.550 LON: 104.917\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 13.750 LON: 100.483\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 15.633 LON: 32.533\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 4.850 LON: 31.600\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 39.917 LON: 32.833\n",
"LAT:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" 33.500 LON: 36.300\n"
]
}
],
"prompt_number": 175
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we need just to make sure we can related each coordinate to the country (and the rest of the information):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for eachTable in allTables:\n",
" allRows=eachTable.find_all(\"tr\") # tr stands for table rows\n",
" for eachRow in range(1,len(allRows)): #when I visit a table, I jump the first row\n",
" allCells = allRows[eachRow].find_all(\"td\") # td stands for table data, organized in cells.\n",
" start=allCells[0].get_text()\n",
" end=allCells[1].get_text()\n",
" name=allCells[2].get_text()\n",
" fatalities=allCells[4].get_text()\n",
" countries=[country.get_text() for country in allCells[3].find_all('a')]\n",
" linksEachCountry=['http://en.wikipedia.org'+country.get('href') for country in allCells[3].find_all('a')] \n",
" \n",
" coords=[] #line added\n",
" \n",
" for link in linksEachCountry:\n",
" if link=='http://en.wikipedia.org/wiki/Soviet_Union': #notice this!\n",
" link='http://en.wikipedia.org/wiki/Russia'\n",
" externalWiki= urllib2.urlopen(link)\n",
" subSoup = BS(externalWiki)\n",
" lati,longi=subSoup.find_all(\"span\",{'class': 'geo'})[0].string.split('; ') #modified\n",
" \n",
" #start - coded added\n",
" coords.append((float(lati),float(longi))) \n",
" countryInfo=zip(countries,coords) \n",
" print [name,countryInfo] \n",
" ##end - code added "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[u'Mexican\\u2013American War', [(u'United States', (38.883, -77.017)), (u'Mexico', (19.433, -99.133))]]\n",
"[u'War of the Pacific', [(u'Chile', (-33.433, -70.667)), (u'Bolivia', (-16.712, -64.666)), (u'Peru', (-12.0433, -77.0283))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Border War', [(u'United States', (38.883, -77.017)), (u'Mexico', (19.433, -99.133))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Chaco War', [(u'Bolivia', (-16.712, -64.666)), (u'Paraguay', (-25.267, -57.667))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Battle of Lake Khasan', [(u'Soviet Union', (60.0, 90.0)), (u'Japan', (35.683, 139.767))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Slovak\\u2013Hungarian War', [(u'Slovakia', (48.15, 17.117)), (u'Hungary', (47.433, 19.25))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Battle of Khalkhin Gol', [(u'Soviet Union', (60.0, 90.0)), (u'Mongolia', (47.917, 106.883)), (u'Japan', (35.683, 139.767)), (u'Manchukuo', (43.883, 125.317))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Indo-Pakistani War of 1947', [(u'Pakistan', (33.667, 73.167)), (u'India', (28.6133, 77.2083))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Korean War', [(u'South Korea', (37.55, 126.967)), (u'North Korea', (40.0, 127.0))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Sino-Indian War', [(u'PRC', (39.917, 116.383)), (u'India', (28.6133, 77.2083))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Indo-Pakistani War of 1965', [(u'Pakistan', (33.667, 73.167)), (u'India', (28.6133, 77.2083))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'South African Border War', [(u'Angola', (-8.833, 13.333)), (u'South Africa', (-30.0, 25.0))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Chola incident', [(u'India', (28.6133, 77.2083)), (u\"People's Republic of China\", (39.917, 116.383))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Sino-Soviet border conflict', [(u'PRC', (39.917, 116.383)), (u'Soviet Union', (60.0, 90.0))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Indo-Pakistani War of 1971', [(u'Pakistan', (33.667, 73.167)), (u'India', (28.6133, 77.2083))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Sino-Vietnamese conflicts 1979-1990', [(u'Vietnam', (21.033, 105.85)), (u'China', (39.917, 116.383))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Iran\\u2013Iraq War', [(u'Iran', (32.0, 53.0)), (u'Iraq', (33.333, 44.433))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Paquisha War', [(u'Ecuador', (-0.15, -78.35)), (u'Peru', (-12.0433, -77.0283))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'1982 Ethiopian\\u2013Somali Border War', [(u'Ethiopia', (9.03, 38.74)), (u'Somalia', (2.033, 45.35))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Siachen Conflict', [(u'Pakistan', (33.667, 73.167)), (u'India', (28.6133, 77.2083))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Agacher Strip War', [(u'Burkina Faso', (12.333, -1.667)), (u'Mali', (12.65, -8.0))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'1987 Sino-Indian skirmish', [(u'India', (28.6133, 77.2083)), (u\"People's Republic of China\", (39.917, 116.383))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Thai\\u2013Laotian Border War', [(u'Thailand', (13.75, 100.483)), (u'Laos', (17.967, 102.6))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Mauritania\\u2013Senegal Border War', [(u'Mauritania', (18.15, -15.967)), (u'Senegal', (14.667, -17.417))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Cenepa War', [(u'Ecuador', (-0.15, -78.35)), (u'Peru', (-12.0433, -77.0283))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Eritrean-Ethiopian War', [(u'Ethiopia', (9.03, 38.74)), (u'Eritrea', (15.333, 38.917))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Kargil War', [(u'India', (28.6133, 77.2083)), (u'Pakistan', (33.667, 73.167))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'2000-2006 Shebaa Farms conflict', [(u'Israel', (31.0, 35.0)), (u'Lebanon', (33.9, 35.533))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'2001 Indian\\u2013Bangladeshi border conflict', [(u'India', (28.6133, 77.2083)), (u'Bangladesh', (23.7, 90.35))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'2008 Djiboutian\\u2013Eritrean border conflict', [(u'Djibouti', (11.6, 43.167)), (u'Eritrea', (15.333, 38.917))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'Cambodian\\u2013Thai border dispute', [(u'Cambodia', (11.55, 104.917)), (u'Thailand', (13.75, 100.483))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'2012 South Sudan\\u2013Sudan border conflict', [(u'Sudan', (15.633, 32.533)), (u'South Sudan', (4.85, 31.6))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"[u'2012 Syrian\\u2013Turkish border clashes', [(u'Turkey', (39.917, 32.833)), (u'Syria', (33.5, 36.3))]]"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 178
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you see, I use the zip function in Python, which eases our job. See an example below if you are not familiar with it:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"coordinates=[(1,2),(4,9),(61,3.0)]\n",
"countryNames=['USA','PRC','UAE']\n",
"zip(countryNames,coordinates)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 177,
"text": [
"[('USA', (1, 2)), ('PRC', (4, 9)), ('UAE', (61, 3.0))]"
]
}
],
"prompt_number": 177
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"LET'S MAKE A NETWORK"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With everything we have done so far, we are just a step away to build a network. First, let's get the libraries needed:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from itertools import combinations\n",
"import networkx as nx"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 181
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#empty graph creation\n",
"countriesGraph=nx.Graph() #new code"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 182
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#getting data for nodes and edges\n",
"allTables = soup.findAll('table', {'class': 'sortable wikitable'})\n",
"for eachTable in allTables:\n",
" allRows=eachTable.find_all(\"tr\")\n",
" for eachRow in range(1,len(allRows)):\n",
" allCells = allRows[eachRow].find_all(\"td\")\n",
" start=allCells[0].get_text()\n",
" end=allCells[1].get_text()\n",
" name=allCells[2].get_text()\n",
" countries=[country.get_text() for country in allCells[3].find_all('a')]\n",
" \n",
" ##start-code added\n",
" \n",
" #some cleaning\n",
" countries=['China' if country==\"PRC\" or country==\"People's Republic of China\" else country for country in countries]\n",
" \n",
" #adding nodes (vertices)\n",
" countriesGraph.add_nodes_from(countries)\n",
" \n",
" #adding edges\n",
" countriesGraph.add_edges_from(combinations (countries,2))\n",
" \n",
" ##start-code added"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 183
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print countriesGraph.nodes()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[u'Turkey', u'Bangladesh', u'Eritrea', u'Sudan', u'Lebanon', u'Mali', u'Mongolia', u'Ethiopia', u'Slovakia', u'Somalia', u'Peru', u'Laos', u'Bolivia', u'Burkina Faso', u'Ecuador', u'Manchukuo', u'Israel', u'Iran', u'Djibouti', 'China', u'Chile', u'Thailand', u'Iraq', u'Mauritania', u'North Korea', u'Vietnam', u'Cambodia', u'Japan', u'Syria', u'Soviet Union', u'Pakistan', u'Angola', u'Mexico', u'South Africa', u'India', u'Senegal', u'Paraguay', u'Hungary', u'South Korea', u'United States', u'South Sudan']\n"
]
}
],
"prompt_number": 184
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print countriesGraph.edges()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[(u'Turkey', u'Syria'), (u'Bangladesh', u'India'), (u'Eritrea', u'Ethiopia'), (u'Eritrea', u'Djibouti'), (u'Sudan', u'South Sudan'), (u'Lebanon', u'Israel'), (u'Mali', u'Burkina Faso'), (u'Mongolia', u'Manchukuo'), (u'Mongolia', u'Japan'), (u'Mongolia', u'Soviet Union'), (u'Ethiopia', u'Somalia'), (u'Slovakia', u'Hungary'), (u'Peru', u'Bolivia'), (u'Peru', u'Ecuador'), (u'Peru', u'Chile'), (u'Laos', u'Thailand'), (u'Bolivia', u'Paraguay'), (u'Bolivia', u'Chile'), (u'Manchukuo', u'Japan'), (u'Manchukuo', u'Soviet Union'), (u'Iran', u'Iraq'), ('China', u'Vietnam'), ('China', u'India'), ('China', u'Soviet Union'), (u'Thailand', u'Cambodia'), (u'Mauritania', u'Senegal'), (u'North Korea', u'South Korea'), (u'Japan', u'Soviet Union'), (u'Pakistan', u'India'), (u'Angola', u'South Africa'), (u'Mexico', u'United States')]\n"
]
}
],
"prompt_number": 185
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's do some plotting:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plot\n",
"import pandas as pd\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 187
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#centrality measures\n",
"nodeDegree=nx.degree_centrality(countriesGraph)\n",
"nodeBetweenness=nx.betweenness_centrality(countriesGraph)\n",
"nodeCloseness=nx.closeness_centrality(countriesGraph)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 188
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#dataFrame for Pandas\n",
"Centrality=[ [country, nodeDegree[country],nodeCloseness[country],nodeBetweenness[country]] for country in countriesGraph]\n",
"headers=['Country','Degree','Closeness','Betweenness']\n",
"DFCentrality=pd.DataFrame(Centrality,columns=headers)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 197
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot.figure(1)\n",
"plot.scatter(DFCentrality.Betweenness, DFCentrality.Closeness,s=DFCentrality.Degree*1000, alpha=0.6)\n",
"plot.title(\"scatterplot (size of dot represents degree of node)\")\n",
"plot.xlabel(\"betweenness\")\n",
"plot.ylabel(\"closeness\")\n",
"plot.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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mpKQgMTERUqkUjRs3hpGRkfC9eJGxsTHeeOMNREVFIScnB9u2bVP6D09N7/vzZf/+97/j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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x113e38f10>"
]
}
],
"prompt_number": 198
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"scatter= pd.scatter_matrix(DFCentrality, diagonal='kde')\n",
"plot.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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z54MBzJAUqqDXXvNh06ZVdO36AvXr19d3OBql0QRSu3ZtDh8+zNq1a9m0aROA\nyvf91C1l4unpyZEjR2jYsCEZGRnUqFFDMy+iCnjhhRf46ivVxqaMnb09jBwJS5dKs7IEQdf8/Lrg\n59dF32FohUZvYX3xxResW7eO8ePH88ILL3Dt2jX8/FT7hqtuKZOgoCBu3LjB4MGD+fDDDzX5MoRK\nZto0qUpvVpa+IxGEykUrpUzy8vKwtrbW9GnVIhYyGTdN99/w4VKxxfff19gphX8h/v6Ml94WEv7x\nxx8EBgbSokUL5b8nTZqkySYEoVw++AC+/hrUWJYkCEIZNJpAPvvsM0JDQ6lZsyYA7dq149ixY5ps\nQhDKxd0dmjeHX3/VdySCUHloNIGkpqbSqlUr5b8LCgqopsbOPs9biZ6ZmUlISAhBQUHKPbxTUlIY\nMGAAfn5+fPvtt5p5AVXEwYMHcXPrTfv2/bl165a+w9GZGTOkKb1yub4jEZ5n4sT3aNSoF++8M0vf\noQgq0GgC6dWrF7/99hsgTcX96KOPGDBggErH/ttK9BUrVtCvXz+WLVvGnDlzAPjpp5+YPHky4eHh\nhIWFUSA2gFDZf/6zjDt3RhAX586SJUv0HY7O+PtL+6eHhek7EqE0165d49dfL5OXt4gffzzLnTt3\n9B2SUAaNJpCpU6cSHR1NcXExffr0oWbNmkyZMkWlY/9tJXp0dDQ9evTA0dFROS3YxsaGtLQ08vPz\nyc/Px8LCQmOvo7i4mPDw44SFHaiU5QcaN65Jfv4eioujlPvQVwUyGcyZAx99JK5CDJGDgwPW1llk\nZ39PtWq5Kk3Nv379OjNnzmPt2vXIRafqnEbXgdSqVUu58ltdiYmJyg8zZ2dnLl26pHwsPT1dOavL\n1taW9PR0xo0bh4eHB9OnT2fu3LnP3da2PM6ePceqVVeRyWzJzz/M4MF9NXZuQ1CrVj1sbe9hYiLD\nyspK3+Ho1IAB0nqQDRtgxAh9RyM8yczMDD+/zly7Vgs3t5oqbX08ZcoirlzpgEwWQZMmjejWrZsO\nIhUe02gCyczMJCwsjMjISGXNF5lMxpo1a8o89t9WotetW1c5NTg7O5u6desydepUVqxYgY+PD337\n9mXIkCHUqVOnxDmfTGS+vr74+vqq+Yoq5zREMzNTbG1bIZPd1mjiNQYyGSxaBGPHwquvggYvXAUN\nqFWrNq1adaJatVMqPd/EBBQKOVXsbWwwNJpA3n77bWxsbOjevbtyY3hVP6D+bSW6u7s7R44cwcPD\nQ3kl8uiPplL7AAAgAElEQVTRIxwcHLCwsKBmzZoUFRU9c87yljLx8mrPhAmF5Obm071713Kdw5DN\nmzedunVXUadOE4YOHarvcHTOxweaNYNly0DFUm2CDlhZWfHBB0P44484PD2HKT9D/s2SJbNYteoX\nXnqpm7j60AONLiRs2bJlhTaFX7VqFevWreP1119nwoQJTJ06laVLl5KRkcGsWbNIS0tjwYIFtG7d\nmsuXLzNz5kxycnLw8/NTDq4/JhYyGTdt919cHHTtCjExoOIuu4IaxN+f8dLblrbz58/H0dGRESNG\n6P3eekXfwIWFhcjlcr2/jqpKFx9AH34If/0FGzdqtZkqqbz9J5fLycnJoXr16piIDe31Qm8JpHr1\n6uTm5mJmZoalpaUymCw9FCGqyAdQbGwso0Z9Sn6+gq++epOAgAANRyeURRcJJDcXWraEb76BvpVr\nnoTelaf/5HI5Y8a8x8mTSfj5vcDKlaEiieiB3kqZ5OTkIJfLKSwsJDs7m+zsbL0kj4rav/8A9+/3\nRqEYz6+/HtJ6e8XFxURGRnH69BkxFVGHqlWDtWvhzTfh9m19RyOkpqZy7FgK1tYLOHToKpmZmfoO\nSSiDxtP76dOnWbRoESDNpjpz5oymm9C6bt26Ur36AYqLf6R//05aby88PIJvvolj6dJLnDgRqfX2\nhP/XrZuUQEaPhuJifUdTtdWqVQtT00xu3FiEuXkOdnZ2+g5JKINGE8jChQv5+uuvWbt2LSDd0lKn\nmKI6pUwA1q1bR+/evTU+k8jLy4uIiO85fnwJwcEDNXru0uTm5iOT2QLVycurXFteGoNPPoGiIqng\noqA/JiYm+Pn50bfva/j4iBlVxkCj03h3797NyZMn8fT0BMDe3p5CFcufPlnKZMSIEfTu3Vu5xe3j\nUiYeHh5MmTKFbdu2ERERQUpKCvv27dPkS1B6XBBSF3r29CE//xCmpib4+lbOjWcMmZkZbNsGnTpB\n48bwzjv6jqhqsra2Zvr0QURGxtK586sarS4haIdGE4iTk1OJhBEXF6dyqYzSSpk8TiDR0dFMmzYN\na2trZWmRI0eOcP36dQICApgyZQp9jXgU1NramiFD+uk7jCqtVi3Yuxe6d5dKvourEf1o2bIFLVu2\n0HcYgoo0egsrJCSEfv36kZaWxtixY+nXrx+TJ09W6djExERlwnB2diY+Pl752NOlTNLS0oiOjqZZ\ns2Zs3bqVxYsXUyxuYAsV5OICERHw448QEgL54m6iIPwrjSYQf39/fvvtN5YuXcorr7xCbGysylva\nqlLKBCA7O5t69erh6upKYGAgtra2tGvXrsTYyGOP63LNnTuXo0ePVvwFasm5c+do334o3t7DSn0d\ngu40bAhRUXD/Pnh5gdjOxrB8881KmjcfxJAhbyvLJQn6o9EEcu3aNUxMTAgODsbV1ZXt27eXWmKk\nNJ6enhw9epSioiKOHTumHEeB/y9lcvv2beWVSPv27Tlz5gzFxcX8+eefyl0Qn/RkAlG/DpburF+/\niwcPgrl37xU2bNip73CqPDs72LRJqtw7ahT06gW7d4PYMUD/Vq48hI3NYs6fN+HcuXP6DqfK02gC\nGTRoEGZmZqSlpfHqq69y/Phxxo0bp9Kxjo6O+Pv70717d3x9fXFycmLq1KmAdGts586dTJw4UVnf\nqn///ty8eRN/f39effVVlermGKquXd0xMdmFmdk+unTxLPsAQetkMhg8GBIS4PXXpQq+9epB797S\nxlQrV0oD74cOweHD//9z5Ih0GywqCi5cgFu3xPRgTfL2bsL9+6HUqZPOSy+9pO9wqjyNrkRv164d\nf/zxB6GhoZiamvL+++/j5eXF2bNnNdWEyiqykjk3N5dFi5aSk5PHrFmTqVevnoajKyk/P58ffliH\nubk5Y8YMU67ir8oMsZbSvXtw8iRcviyVQMnMhL//LvkcuRwePZJ+Cgrgzh3pec2aQceO0qZWgYFg\nY6Of16Ar5e2/sLA97NhxjCFDuhMQEMBff/3F8eMX8PBoRps2rSkqKiImJgYXFxfs7e21ELmgt1Im\nffr0YfDgwXz99dccPHiQ+vXr06pVK73c16/IB9Dy5d+zaNFtTExq069fGkuXztdwdCXt3r2fjRuz\nUCiKGDPGgV69umu1PWNgiAmkvPLzITYWTp2C33+Xrk6GDIH335cSS2VUnv7LzMykc+eJyOWjMTH5\nidOnVzN79ncUFvZALg/nyy8nqrTJlFAxeitlsnLlSpKSkli0aBH169fnr7/+YuTIkZpsQidsbKyR\nyR4il/9NtWravxqwsrJAocgF8rC0FHPfKxsrK2jfHqZOhf37pdtiDRtK1YBHjxZlVB4zMzPDzExB\nYeE9LC3B1NQUKysz8vMfYGEh/VswLBq9Anns8V7G9evX1/SpVVaRb7BFRUWsXbuOnJyHvPHGGKpX\nr67R2EprLzLyNKamJnh7dxB/KFSuK5Dnyc6Gzz6DH36QBuwnT5Y2SKoMytt/Z8+e5cCBcPr08cfD\nw4O7d+/yxx+XeOklVxo3bqyFSIWn6e0WVnR0NG+99RYPHz4EpFImy5cvp127dmUeu3r1atauXcuo\nUaMYP3688veZmZnMnj2bu3fvsmDBAlq1aqV8bMCAAbRp04b585+9xVQVPoAqs6rUfwkJ0myvGjVg\nzRrp6sTYVaX+q2z0dgvrv//9L1988QWXLl3i0qVLfPHFFyxcuLDM454sY3Lo0CFSUlKUjz0uY7Js\n2bISm0bFxMSQn59f5bZkFSofNzc4cQI6d5ZudRnwkiVBKEGjCeSvv/4qsX7Dw8ODv/76q8zjSitj\n8lh0dDQ9evTA0dFRuZgQ4JtvvmHSpEla+5Yjl8tVXsMiCBVlZiYVdVy3Dl57DZYsgar6BV783RkP\njdbCGjp0KCNGjGDEiBEoFAo2btyoUqXcxMREZc0sZ2dnLl26pHystDImmZmZ1KtXr8yCh0/uie7r\n66vyYsL4+HhGjvyEvLxiliyZRPfuYlaUoBv+/tIsrYED4dw5ab3JP2//Sk8ulzNx4myOHImnd++W\nLF06X2woZeA0mkDeffdd9u7dS1hYGDKZjFGjRqm0m58qZUysra2VZUw++ugj5s+fT1xc3L+e98kE\noo6wsN9JT++JpWVD1q7dKxKIoFMuLtJ6kzfflGZq7dwJ/5SJq9RSU1M5fDiZBg3Ws3fvWB48eCDW\nehg4jaZ3c3Nz+vfvz8qVK/n+++/p16+fSiWZ1S1jkpSUxJgxY5g5cyYbNmwgIiJCky+DFi2ac//+\nSpKT59O6dSUY0RSMTrVqsH49DB0K3t5SQqns6tevj5XVbc6f74ut7X2dbqkglI9GrkBat2793Mdk\nMhkxMTH/evyTZUxef/11ZRmTpUuXEhISwqxZs1i5ciULFiwAUO4BcuzYMQ4fPkzXrl018TKUEhKu\nYmsbjImJPQkJ1zR6bkFQlUwmlZVv1Uq6pbVwITwxQbHSyczMpKCgLo0aTSYnZylZWVkiiRg4jUzj\nTUhIIDIyEl9f3xKD2jdv3qRhw4a8+OKLFW1CbRWZRrhjxw7ef38vcrktEyY4MXv2NA1HJ5RFTAMt\nKT4eBgyQxki++goMvfRbefovNzcXX9+xZGS4U7fuHxw//rPYVEoPdL4OJDAwkI8//piXX365xO/P\nnj3LvHnz2L17d0WbUFtFP4COHDlCdnY2gYGBmJlpdKhIUIFIIM/6+28YPhxyc2HLFqhTR98RPV95\n++/mzZucOHECHx8f5f5Agm7pPIG0bNmSy5cvl/qYMdbCArCzsyM/P5/U1FTqGPJfaiUlEkjpiovh\n449hwwZYuxa6GejW4eXtv4CAAA4dOsTAgQPZunWrFiITyqLzhYR5eXmkp6c/8/v09HTlqnRVrF69\nmm7duvHDDz+U+H1mZiYhISEEBQUpk9HKlSvp2LEjvXr1Ys+ePRV7AU8xNzcnO7srjx4Npm7dpho9\ntyBUhKmpNBaydCkMGwbTp8MTy6OM2i+//MKBAynI5VPZti2WEydO6DskoQwaSSA+Pj58+eWXz/x+\nyZIl+Pj4qHQOdVejBwQEEBkZSVhYGIsXL9bEy1CSFjJ5AF0AO42eWxA0oV8/iImB1FRo3Rq2bzf+\nhYfSrqH2QDBQi8OHD+s3IKFMGkkgX375JfHx8bi4uDBy5EhGjhyJi4sLV65cKTWxlEbd1eiPC6uZ\nm5uTm5uriZeh9PbbbwO/AF/RqpWYBSIYptq1YeNGWLFCKsbo6yttamWsiWTVqlXARWASEFOidJFg\nmDQyOlyrVi22b99OTk4Oe/bsQSaTsXz5crWq2Kq7Gv3xJk/Lly8nKChIEy9D6ZtvvuGbb77R6DkF\nQVv8/SE6Wlo38vbb0mZVU6ZAcDBouZC0xikUWfoOQVCDRqcXVa9enSFDhpTrWHVXowNERUWxf/9+\ndu4sfR/x8pYyEQRjY2YGY8ZIVX3DwqQS8e+8A337Qv/+0LMn1Kql7yiFysZg5qd6enqyfv163nzz\nTY4dO8bnn3+ufOzxanQPDw/llcitW7eYMWMGu3btem5F3vKWMhEEY2ViIiWM/v0hLU2a7rt2rbQA\nsW1b8PGBLl2k7XXF5n5CRWllQ6nyWrVqFevWreP1119nwoQJytXoGRkZzJo1i7S0NBYsWEDr1q2Z\nOHEi4eHhODo6ArB3716srKyU5xLTQI2b6D/NysuDiAjp58QJOHsWmjaVkknXrlJiadBAc+2J/jNe\nettQypCIN7BxE/2nXYWF0rjJiRNw/LiUWOrUkRKJjw/06SMN0peX6D/jJRII4g1s7ET/6ZZcDpcu\nwbFj0s9770GnTuU/n+g/4yUSCOINbOxE/xk30X/GS29b2gqCIAhVh14SyPNKlixZsoQGDRrg7u6u\nLFny888/07lzZxo2bEiHDh2IjY1FLpczf/58GjVqhLu7uyh5IAiCoAc6TyDPK1mSmprKqlWrWLFi\nBY0aNWLGjBkADB8+nMDAQJYtW4ZMJmPOnDlERUURGRnJ9OnTqVmzJvPmzdP1yxAEQajydJ5Anley\n5Pz581haWtKrVy8CAwO5e/cuAGZmZkRHR+Pt7Y21tTV5eXmcP38ea2trevfuTceOHTEzM+PBgwe6\nfimCIAhVms4TSGJiorLOv7OzM/Hx8crfFxYWYm1tjbOzM0VFRcoKv2fPnuWll15iypQp2NracvHi\nRTIzM3F2dsbZ2Rlzc3MSEhJ0/VIEQRCqNJ2vRH9eyRJXV1fMzc3Jy8sjKSkJMzMz6tatC4CXlxfL\nli0jMDAQe3t7unTpQmZmJklJSSQnJ/Po0aMSpU8A2rZt+9wV6oLh8/HxEf1nxDw9Rf8ZqxpqlCjQ\neQJ5XskST09PHj16xMGDB9m3bx8ODg4AFBQU4O7uTlRUFBYWFlhbW+Pp6cn+/fvZv38/UVFRmJub\nP/OiL168KKYRGjExDdS4yWQy6tVTsGqVVFZFMB7qJH6dJxBHR0f8/f3p3r07r7/+Ok5OTsqSJePG\njSMkJAQHBwfWrVvH1KlTqV27NgcPHuS7777DwcGBefPm0aJFC7y8vPjiiy+oXbs2S5cu1fXLEASh\nDLt2Sfu4m5tLK9uFykcsJBQMkug/4/a4/yIiYMgQafOrf+5ICwZOrERHfAAZO9F/xu3J/ps2DXJz\n4fvv9RyUoBKtJpCCggIsLS3LFZguiQ8g4yb6z7g92X+ZmeDmBqdOSf8rGDaNljIZNmwYWVlZFBcX\n4+3tjaurK2vWrKlwkIIgVA329tJOiV98oe9IBE0rM4FcuXIFOzs7duzYgaenJwkJCaxevbrCDT+v\nnElmZiYhISEEBQUpy5msXLmSjh070qtXL/bs2QPA0aNHcXFxwc/PjzFjxlQ4HkEQtOett2DzZsjI\n0HckgiaVmUCqVatGbm6ucqMnKysrsrOzK9To88qZAKxYsYJ+/fqxbNky5syZA0BAQACRkZGEhYWx\nePFi5XNHjhxJeHg4P/30U4XiEQRBuxwcpBlZT31fFIxcmQlkypQpeHh4YGtrS6dOnbhx44ZaC01K\n87xyJgDR0dH06NEDR0dH8vLyAGjcuDEA5ubm5ObmKp+7YcMG+vXrx+HDhysUT1WUn5/Po0eP9B2G\nYMAKCwspKCjQ2PlCQuDHH0EMbVUeZa4DGT58OMOHD1f+u3HjxoSHh1eo0cTERNz+GU1zdnbm0qVL\nysfS09OV+57b2tqSlpZGvXr1AFi+fDlBQUGAtDo9Pj6e9PR0goOD8fPzw8SkZD58ck90X19ffH19\nKxR3ZRETc4mlS3dTvboZH344RvnfVxAeS0lJYdGi9Tx6pOC994KVf68V8fLLUFwsbafboYMGghT0\nrswEsmTJEsaOHYudnR0zZ87kwoULzJ8/n5dffrncjT6vnAlA3bp1ycvLw9ramuzsbOWHW1RUFPv3\n72fnzp0A2NjYAFC/fn1l+fc2bdqUaOfJBCL8v+PHYzA370tGRhLx8fEigQjPiI2NIyurPRYWtkRF\nxWokgchkMHIk/PyzSCCVRZm3sFavXo2dnR2nTp3ijz/+YN68eXz88ccVatTT05OjR49SVFTEsWPH\n8PT0VD7m7u7OkSNHuH37tvJK5NatW8yYMYO1a9cql9k/HocpKCjg8uXLtGzZskIxVSXdurXh0aMw\nate+8kwNMUEAaNWqOXZ25zA1PcLLL7fS2HlHjoSNG0HcPa0kFGXw8PBQKBQKRUhIiGLr1q0KhUKh\naNeuXVmHlWnlypWKrl27Kr7//nuFQqFQTJkyRaFQKBT37t1TjB8/XtG/f39FTEyMsm03NzeFr6+v\nws/PT5GXl6dYtWqVokOHDgp/f3/Fhg0bnjm/Ci+tSsvLy1MUFhbqO4znEv2nfwUFBYr8/PxyHftv\n/eflpVAcPFjeqARtU+dvr8yFhLNmzeLUqVNkZmZy8eJFHj58iJ+fX4mBb0MkFqIZN9F/xu3f+m/R\nIkhKgmXLdByUoBKNr0T/66+/cHJywsLCgoyMDG7duvXMeIOhER9Axk30n3H7t/5LSABfX0hJARO9\nbKot/BuNrkQHaWbUl19+CcDDhw/Jz88vf3SCIFRpbm7S6vTTp/UdiVBRZSaQhQsXsmTJEtauXQtA\n9erVmTRpUoUaregqdLlcTmhoKL6+vvz2228VikUQBN0bNAi2b9d3FEJFlZlAdu/ezfr167GysgLA\n3t6ewsLCcjeoiVXoUVFRpKWlceDAAb799luKiorKHY8gCLoXFATiu5/xKzOBODk5lUgYcXFxFZoT\nrolV6OfPn6dnz55YWFjg5eXF5cuXyx2PIAi65+4OOTlw9aq+IxEqoswEEhISQr9+/UhLS2Ps2LH0\n69ePyZMnl7vBxMREnJycAGkVenx8vPKx0lahP/bkKvTExEScnZ1LPYdgeBQKBWlpaSXK0AiG4e+/\n/+b+/fs6b1cmk3Yp/OeutGCkylyJ7u/vT6dOndi7dy9yuZzly5crb2eVhyZWobu6upKUlETLli1J\nTk6mU6dOpbYlSpkYhv37w9m4MZqaNeV88skb2Nvb6zskAbh27RqhoVspLpbxzjt9aNOmtU7bDwyU\nNpmaOlWnzQoapNIsrGrVqtGlSxc6dOhAWloaSUlJ5W5QE6vQPT09OXLkCAUFBZw7d45WrUpfKTt3\n7lzlj0ge+nP6dAJ2dkFkZjoqvzwI+hcff438fA+gCzExur+X5O8vbTL18KHOmxY0pMwEsnHjRl58\n8UW6detG3759CQwMJDAwsNwNOjo64u/vT/fu3fH19cXJyYmp/3wFCQkJYefOnUycOFF59TB//nzu\n3r3LwIED8fPzo6CgAG9vb+zt7QkICGDSpEmYmpqWOx5B+/r370hBwQaaNcvC1dVV3+EI//D0bEv9\n+jHY2kbQrZtn2QdomJ2dVBNLFNM2XmUuJGzTpg2///67cszBWIiFaIZFoVAoryBVIfpPNx7/N1an\nb1Shav998QUkJsKKFRptXqgAdf72yhwDqV27Nra2thUOSqjaNP0BJWiGvvslMBB69pT2CBFvEeNT\nZgJ56aWX6NatGwMGDKBmzZqA9KabPn261oMTtCMzM5MtW/ZTo0Y1Bg3qg4WFhb5DEjTk2rVr7N59\nklatGtOjRze9J4iyNGsG5uYQGwutdTuGL2hAmWMgDg4ODBo0CDMzM3JycsjOzq7wlraCfm3ffpBT\np+qwe3c2Fy5c0Hc4ggYtW7aTuLjWrF8fTWpqqr7DKZNMJl2F/P67viMRyqPMKxBtbcq0evVq1q5d\ny6hRoxg/frzy95mZmcyePZu7d++yYMECWrVqxd69e5k+fTp16tQhIiICgKNHjzJmzBiaNGlC48aN\nxb7oaqhd247i4luYmmaJ25OVTN26tqSlXcXGppBq1arpOxyVvPIKLFwIs2bpOxJBXWUOol+/fp11\n69YRGRmpLKIok8k4cuRIuRtNTU3lnXfeYePGjYwYMYLFixcrFxcuXLiQNm3a4OHhwZQpU9i2bRsP\nHjygWrVq9OjRQ5lAjh07xqFDh5g/f37pL0wMwj5XUVERFy9exMbGhmbNmhnkbQ7Rf+WTk5NDbGws\nTk5Oyr8pfVCn//LywMEBbtyQiiwK+qXRQfQpU6bQsWNHPvnkE8zNzZUNVERp5Uwev9mjo6OZNm0a\n1tbWynImj8denrZhwwb++OMP3n33XXr06FGhmKoSMzOzEutvhMqjevXqFdpuWh+srcHHB/bvh2HD\n9B2NoI4yE0hSUhJhYWEabTQxMVFZT8vZ2ZlLly4pHyutnElpe3a3b9+e+Ph40tPTCQ4Oxs/PD5On\nNhcQK9EFwTg8HgcRCcS4lJlAhg8fzpw5cxg1ahS1atVS/r4i5SjKU87kaTY2NgDUr18fd3d3YmNj\nn9nkSlvjN4IgaNYrr8BHH0FxMYh1wcajzFlYK1as4Oeff8bf3x9PT0/lT0WoW86kNI9nghUUFHD5\n8mVatmxZoZgEQdCfRo3A0VFsMmVsykwgN27c4Pr168/8VIS65Uwel2+PjY2lV69eFBQUsGnTJry9\nvenbty8hISGinIkgGLnAQFGd19iUOQuroKCAnTt3cvz4cb777jsSExOJj4+nb9++uoqxXMQsHuMm\n+s+4laf/TpyAKVMgOlpLQQkqUafvykwgs2bNQqFQEBYWxuXLl3n48CGdOnXi4sWLGglWW8QHkHET\n/WfcytN/RUXSdN6YGGjYUEuBCWVSp+/KvIUVHh5OaGiostyFjY2N+MMWBEHjzMwgIEDcxjImZSaQ\nZs2a8ffffyv/HRUVhbu7u1aDEgShahJlTYxLmQlkypQpBAUFkZKSgp+fH+PGjVMOeJfX6tWr6dat\nGz/88EOJ32dmZhISEkJQUBCxsbEA7N27l+bNm9O1a1fl8+RyOaGhofj6+vLbb79VKBZBEAxH794Q\nHg4FBfqORFBFmQnEy8uL8PBw9u3bx+eff86VK1cqNI03NTWVffv2ER4ezqFDh0hJSVE+tmLFCvr1\n68eyZcuYM2cOAB07dnxmvCUqKoq0tDQOHDjAt99+S1FRUbnjEQTBcNSuDa1awbFj+o5EUIVKW9qe\nPn2agwcP4uXlRVJSEmfOnCl3g6WVMXksOjqaHj164OjoWKKMydPlxi9cuEDPnj2xsLDAy8uLy5cv\nlzseQRAMS9++IG4sGIcyE8jChQtZsmQJa9euBaRaO5MmTSp3g4mJicq6V87OzsTHxysfK62MSWkS\nEhKUOyQ+fY4nPbkn+tGjR8sdsyAIuhMcDDt2gFyu70iEspRZymT37t2cPHlSedvK3t6ewsLCcjeo\niTImrq6uJCUl0bJlS5KTk+nUqVOpzxOlTATB+Li5Qd26cOoUdOmi72iEf1PmFYiTk1OJhBEXF6cs\nhFgemihj4unpyZEjRygoKODcuXO0atWq3PEIgmB4goNh61Z9RyGUpcwEEhISQr9+/UhLS2Ps2LH0\n69ePyZMnl7tBTZQx8fb2xt7enoCAACZNmiTKmAhCJTN4MGzbJm5jGboyV6ID5ObmsnfvXuRyOX37\n9v3XqwNDIVYyGzfRf8ZNE/3XogWsWQNGtr2J0dPohlIfffQRPj4+9O7dW1lCXRAEQdsGD4YtW0QC\nMWRlXoGsWbOGiIgIoqKisLW1pWvXrnTt2pWgoCBdxVgu4huscRP9Z9w00X9xcdCjByQlSWVOBN3Q\naC2scePG8eOPPxIeHs6IESPYvHkzr7/+eoWDVGc1em5uLjNnziQgIIATJ04AcPToUVxcXPDz82PM\nmDEVjkcQBMPSvDk4O8PBg/qORHieMhPIG2+8QadOnXjrrbcoKipi27Zt3L9/v0KNqrsafefOnTg7\nO7NlyxYWLFigfO7IkSMJDw/np59+qlA8giAYptGj4Z8laIIBKjOBZGZmUlRURM2aNbG3t6dOnTqY\nm5tXqFF1V6NfuHABf39/7OzsqF27Ng8ePEAmk7Fhwwb69evH4cOHKxSPIAiG6bXXYO9eqOB3VkFL\nyryzuGPHDkBa/7Fv3z78/PwoLi4ucdWgrsTEROVaEmdnZy5duqR8rLTV6E+uPHdyciIhIYH27dsT\nHx9Peno6wcHB+Pn5YWKiUmUWQRCMhL099OoFmzbBxIn6jkZ4mkor0SMiIoiIiODBgwd07969RGXc\n8lB3NfrjlefNmzcnJSWFZs2aKWeE1a9fH3d3d2JjY2nTpk2Jdp5cie7r64uvr2+F4q4scnNzWb9+\nPfb29gwaNEgkXgNy584dtm7dSvPmzenRo4e+wzEIEybAe+9BSAjIZPqORnhSmQlk//79dO3alXff\nfRdHR0eNNOrp6cn69et58803OXbsGJ9//rnyscer0T08PJRXIo9Xnjds2JCMjAxq1KhBdnY2tra2\nFBQUcPnyZVq2bPlMO6KUSek+/vhztm41xcQkBplMRnBwsL5DEv4REjKXixdfwsLiJ7ZurfvMl6Kq\nyN9f2q3w2DEQ3wENS5lfPatVq8bQoUNLJI+ZM2dWqFF1V6MHBQVx48YNBg8ezIcffgjA5s2b8fb2\npg+YFH0AAB8RSURBVG/fvoSEhIjV6Gq4f/8hJib1USjq8ODBA32HIzzh77/zsLBoTHGxreibf8hk\n0l7p33yj70iEp5W5DsTd3Z3op3a59/Dw4MKFC1oNrKKMaR1BSkoKs2d/iZmZKaGhHzy3iKSm3Lx5\nk/nzl1Gnji1z576PlZWVVtsrD2Pqv3PnzrFgwY+8+KIDCxfOemb7AXXP9fXXG2jb1oX33ptstLcX\nNd1/OTnQuDGcPw8uLho7rVAKdfruuQlk+fLlLFu2jGvXrtG0aVPl77Oyshg6dGiJ206GyJg+gGbO\nnMfmzXWBQsaMyWfOnIpd4VUGxtR/ffpM4Nq1V3j0KJIvv3yZgQMH6jskvdNG/82eDQ8ewPLlGj2t\n8BSNlDIZPnw4ffr0YdasWYSGhipP6ODgYBS1sIyJo2NdFIp4ZDI5DRuK/eYNXVFREaampsj+GdF1\ndKzBn39exNz8jsbGCYVnvfceNGsmJZJGjfQdjQAqFlO8ffs2R44cYcSIEaSnp5OTk0OTJk10EV+5\nGdM32OvXrzNp0meYmpqwfPnHyinLVZmh9t/589EsX/47DRrYMmPGOGxtbcnJyWHnzp24uLjQRWxg\nAWiv/2bPltaErFih8VML/9BoKZOVK1cybNgwPv30UwAKCwsrXMqkomVM5HI5oaGh+Pr68lsl2Pvy\nzJlLNGgwljp1hnPhQqy+wxH+xd69Z6lW7TVu3nQgMTERkHbpfP3110Xy0IH33pPKvMfF6TsSAVRI\nIOvWrePAgQPKdRcNGzYkOzu73A1qooxJVFQUaWlpHDhwgG+//ZaioqJyx/M8VlZWmJiYKNeraFO7\nds3Iy9vDo0eHaNWq/Jt1qaq4uJhff/2Vg1WgyNC9e/ewsbGhcePGpT62Z88e7t27h1wu5/Dhw8ov\nLs/j7e3Gn38uBi7gosZobmpqKtevXyc5OZm9e/eSlZVV6vOKiopISEggMzMTgD179rBp0yaKi4tV\nbqsyq1MHPvoIpk4FA7xArXLKXAdSo0aNEjNBkpKSlHual0dpZUweny86Oppp06ZhbW1doozJ+PHj\nS5QxuXDhAj179sTCwgIvLy8uX75M27Ztyx3T06R72/6ALY0atUShKP2PXVOysnKQyewABTk5D7Xa\nFsCbb05jy5ZMTEzus3x5OsOHD9d6m/pSt64b8ApJSenY2dkpP7iLiooIDn6XlJRGNGiwni5dWrB5\ncxrm5hn8/PNUvL29Sz3f/v3HSUgwIykpmRs3bmBvb19mDPHx8YSG7iY3t5jo6KMUFbWlZcsd7Nq1\n8pnn/vzzdo4efYCd3QNeeMGMuXOPo1BYc+rUBZYsCa3Qf4vKYtIkWLUKtm+Xdi4U9KfMK5DRo0cz\nYsQIHjx4wKeffkrfvn0ZP358uRtMTExUJgxnZ2fi4+OVj6laxiQhIeG553jS3LlzlT9Hjx5VM9Ju\nQB+glprHqe/GjVTMzT0wMWlLUlKq1tuLiUlCJgukqOhlg5+OXXG1gQFAxxJXzllZWaSmFlKz5lju\n3JETHX0NC4s+FBa24M8//3zu2WJjU6hWLYiCgmbKW1hlSU29TUHBi+TlOXP/vg01aowmISEdeSnb\n7SUk3MbOzoesrBqcPn2e4uJOyGS9uHjxurovvNIyN4fvv4e334a7d/UdTdVW5hXIq6++ipeXF9u2\nbUMul/P7779XaJC3omVM3NzclOdo1aoVycnJdOrUqdS2yrsSXVr7shYwx9o6vVznUIefX0cSE7dh\nampCly6Dtd7evHkTmDTpK2rUsGDy5G+13p4+1a37N+npC4F83n77beXv7e3tefPNLmzd+iljx3rj\n69uB2bNXUr++HQMGPH/L5hkzXuOTT36icWN7+vTpo1IM7dt7Eh29hezsPBwdX+DChYVMnx5U6hqP\n11/355df9uPl5Yin52wuXfqA/Pxi5s79UO3XXpl17gzjxsEbb8Du3aLEid4o1JCTk6PO00t169Yt\nxZAhQxSPHj1SDBs2TJGcnKx87LPPPlOEhYUpUlNTFYMGDVIoFArFL7/8ovj2228Vf//9tyIgIECh\nUCgUJ0+eVLz//vuK/Px8Rc+ePRVFRUXPtKPmSxMMjOg/46aL/isoUCheflmhmDNH601VKer03XNv\nYcXGxjJ06FBGjx7NrVu36NixIw0bNsTLy4uIiIhyJyxNlDHx9vbG3t6egIAAJk2aJMqYCEIVZGEB\nO3fCTz/Bzz/rO5qq6bnrQAIDAxkzZgz37t1j7ty5LFv2f+2deVRUR9qHnwajoCh4cIkGFTMHdY6y\nC4iKIiLGEQkYRcVRNGZUMo4z4+fCURSiJmJiHGMMehLMRMMYlyFChhjHGMFxlwEhblEPLoiCUZRF\naBig6/ujQ0cEIvbtZgn1nHNPN91Vv3pvvXrfvrf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YmLS6+aEGZ+LEifTv359FixbRtWtX\nTp8+zZUrV9iwYYMMIBLJT8grTRNiamrK2LFjWbp0Ke+++y6g3WP6k08+YcyYMfj6+vLll1/qym/e\nvBkHBwdeeeUVgoKCiI+PB8DW1pbVq1fj4uLCP//5T1JTU5k5cyYeHh6Eh4dTXl4OwJUrVwgLC8PD\nw4M//vGP5OfnN/5JtwAeP35Meno669at0+0QOGTIELy9vXVlKioqeO+99xg8eDDBwcGcO3cOgNu3\nbzNu3DicnJxwdHQkKysLgMOHDzN58mQ8PT155513dDoWFhasWbOGgQMHEhISopvB/ejRI9566y2G\nDRvG5MmTycjIACAjI4PRo0fj5OSEi4sLJSUllJSUEBQUhLOzM/b29hw/frwxukkikYPoTYGFhUWN\nv4uLi4WZmZkoLi4WycnJYtGiRUKj0YjHjx8LZ2dnUV5eLs6ePSu8vLxEUVGRuHz5sjAzMxPx8fFC\nCCFsbW3F4sWLhUajEUIIMWrUKFFQUCCEEGLp0qVi9+7dQgghJkyYILKzs4UQQnz00UciOjq6sU65\nRbFnzx7x+9//vtbnN27cEIMGDRJCCJGYmCgmTpwo1Gq1OH78uPDw8BBCCLFq1SoRGxsrhNAORqrV\nalFSUiJ8fX2FWq0WVVVVYurUqeL06dNCCCFUKpWu/BtvvCF27NghhBAiMjJSJCQkCCGEOH/+vPjd\n734nhBAiNDRUHD58WAghRElJiaisrBSffvqpiIiIEEIIodFoRHFxsVH6RSJ5GjmI3gwQQuh2AIuP\nj+fQoUO67SSLioo4deoUx48fJygoiI4dOzJgwACGDBlSQyM0NBSVSkVaWhrnz5/X/Vr+3//+x+PH\nj/Hx8eHYsWMEBAQAUFVVha2tbaOdY0uiIWNRSUlJTJ8+HTMzM4YNG0ZJSQl5eXm4u7sTHh7OgwcP\nmD17Nt26dSM+Pp5Lly7h6ekJaPebTk5OxsPDgzZt2jB9+nQA3fIjM2fO5MsvvyQxMZHqLPuCggLU\najWenp6Eh4cTGhrKzJkzad++PU5OTqxfvx6VSsXs2bPp27ev0fpGInkSGUCaAYcOHaJLly5YWFig\n0WhYvnw5oaGhNco867FE9aCXRqNh0KBBtbYUzc3NxdraWveoRVI/48aNY8mSJb9Ypnrnv6cZP348\nrq6uxMXFMWzYMPbt24dGo8HPz4+///3vtcq3a9cOMzMzAF544QXKysoAbYBPSkqid+/eNcrPmzeP\nMWPGEBcXh4ODA2fOnMHZ2ZkzZ86wa9cuAgICWLduXaNsJtVUVCehaDQabGxsWL9+PYMGDaq3fGFh\nIbt27SIsLKwRrWwdyDGQJqR6EH3jxo26C1ZISAg7d+7k/v37AFy9epXS0lLGjh1LYmIixcXFXLly\nhTNnztSp6ebmxr179zh9+jQAJSUlXLt2jR49etC3b1/i4+MRQlBRUcGlS5ca50RbGBYWFri4uBAR\nEaHzQ2pqKrdu3dKV8ff3Z/fu3ZSVlXHy5EksLCx48cUXuX79Oi+++CKLFy9m9OjRXLp0iQkTJnDs\n2DEuX74MaBewy87O/kUbQkJC+PDDD3XjV9VjIFlZWbz88susWrWKAQMGkJWVRXZ2NhYWFoSFhTF9\n+nS+//57Y3RLs6F9+/acO3eOzMxMZs2axZo1a36x/KNHj4iJiWkk61oXMoA0AWq1GmdnZ377298S\nHh7OnDlzWLhwIQDDhg0jJCSEyZMnY29vT1hYGFVVVbrtPIcOHcqf//xnPDw86n1U8fnnn7N161Yc\nHBwYOnQoV65cASAmJobk5GScnJxwdnZWtFrqr53Y2FhycnJwd3dn0KBBrF69mpdeekn3eGvs2LG4\nu7szfPhwNm3axNatWwHYt28fgwYNws3NjdLSUoKDgzEzM+OTTz5h5cqVODg44OfnR15eHlDzcZlK\npdL9vWDBAiwtLRk+fDgDBw7ULc/+wQcfYG9vj7u7OwMGDGDo0KGkpKTg5OSEq6srqampzJ8/vzG7\nqskQQvDgwQPdHRxo+9/f3x8vLy9dn4WHh5OVlYWzszNLly5lwYIF/Otf/wIgKCiIOXPmANrtgSMi\nIoD6kx7qS1CxtbUlOjoaBwcH/P39uXHjBgBRUVGEhYUxatQoHBwc2L17t06rvjbCw8NxdXXFwcGB\nDz74AIBdu3bh6emJo6Mj06ZNM3hf6k2TjsBInovHjx8LIbSDuXZ2dk1sjUTSNJiamgonJyfRp08f\n0a1bN5GTkyOE0P6/CA4OFhUVFaK8vFyMHDlS3L17V9y8eVOX/CCEELt37xZLliwRQgjh5uYmPD09\nhRBCzJo1Sxw6dKhW0sO0adN0SQ/e3t51JqjY2tqKVatWCSGEWLt2rXjrrbeEENpkCHt7e/Ho0SOR\nnZ0tfvOb3wghRL1tZGVl1ZgFXlhYKIQQon///qKkpKTGZ80BOQbSghg/fjwPHz6kZ8+eul8mEklr\nw9zcXDeWFx8fz6RJkzh16hTx8fGcPXsWNzc3QPv49siRIwwbNqxGfS8vLzZt2sTly5cZOHAgBQUF\n5OXlcfr0abZs2cI333xTZ9JDmzZtuHDhQq0ElSlTpgAwc+ZMQJsMsXr1akB7V/nqq69iZWWFlZUV\npqam3Lt3j+PHj9fZRvVmXPPnz2fmzJkMHToUgMGDBzNt2jRmzJhBUFCQEXv3+ZABpAWRkpLS1CZI\nJM2KiRMnMmfOHEpKStBoNMyaNYvIyMgaZW7evFnj7549e1JQUMDBgwcZMWIEDx8+ZM+ePXTs2JEO\nHTrUm/SQmppaZ4JKNZ07dwZqJkMAWFlZ6d63bduWsrKyX0ysyMzMJCEhgbfeekuXYRcXF8fJkyeJ\ni4vjvffeq3cMtLGRYyASiaTFcuLECezs7OjQoQNTp04lPj5el6Bw584d7t+/T/fu3SkqKqpRb8iQ\nIWzatImRI0fi5eXFhg0b8PLyArQJEnUlPdSXoPJLiDoy9VQqVb2JFfn5+ZSUlPDaa68RFRVFeno6\nQghu3rzJ0KFD2bhxI7m5ubqxl6ZG3oFIJJIWRXUSikajoU+fPmzcuBGAXr16ERUVxfz588nJyaFj\nx47ExcXRtWtXpkyZgouLC2PGjGH9+vV4eXnx7bff8vLLL9OrVy8ePXqkCyDm5ua6pIerV6/Stm1b\nYmJi6N27N59//jmbN29m7ty5qFQq3n77bezs7GrY92QyxJPvn+TJxIon2zAzM2P27NloNBq6dOnC\nmjVrqKqqYsaMGRQWFtKxY0eioqJo166dkXu5Yci1sCQSiUSiF/IRlkQikUj0QgYQiUQikeiFDCAS\niUQi0QsZQCQSiUSiFzKASCQSiUQvZACRSCQSiV7IACKRSCQSvfh/qO9np10RoyMAAAAASUVORK5C\nYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10ccc6510>"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot.figure(4)\n",
"bp = DFCentrality.boxplot()\n",
"plot.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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DBw8exD333AMA6OzshE6n89p79GXu3KxYUlKCBx98EH5+fkhMTERbWxsaGxsR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\njTdmoE5GaqgAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x100758690>"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot.figure(5)\n",
"nx.draw(countriesGraph)\n",
"plot.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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8vNStW1e++uor8/mFCxdKyZIlc71YWHLkyBFZtmyZREdHy9KlS2X//v0qMtID\nJu+LnIhIRESEfPvtt/mWzxGCGzdulNTUVHHS6+VpkMmm53UQSF+0SGVGk+ZraciXV/hGgNhaWUlq\naqq8/PLLMnLkSHNbP/zwwx2arxK+CkuU8FXcE0ajUYb27y+NDAa5Wsgp3z0gJR0cZPXq1RIcHCxO\nTk7i4uIigHTr1k0aNmwogYGBMnHiRLly5YrExMRI1apVzW02bdpUhg8fLl5eXtLQzs5c7zULAVwT\nzUimDlrIwsEgniDt0cIX5vxwHgWp6Od3x7jya/PFF18070+YMEG6du0qIiKLFy+WkiVLyqFDh/7B\nO634K0JCQmT9+vWSmppq3lasWJFvKkaR20KwQ4cO8vvvv8vj4eFiML2UCUgH0wtcClpaxMC/EL6h\nINUrVRKRu1v0FyR8LV/mFP89lJ+vwkzTpk3x8PDIN+1eDgsXLiR2wQJWp6TgXsh66wArUlN5LiIC\no9HInDlzGGQKUHDu3DleeuklunfvzurVqyldujSdO3fm119/RUQAEBHmz5+Pq6srFXW3Y0W5AYFo\nAf8nAg3RUtV9CqxBS6q+Cmht0RcnICk1FRHhzTff5LHHHsPNze2ONkGL9JRDTjq6ZcuW8eKLL/L9\n999TrVq1Qt4BxT9F27ZtMRgM5m3Pnj35pmLMQafT0b17d1q3bs3F69dxsbOjrOncODR/5ADgIzRf\nbcvIZJb/ZwMnbGyo06ABoH13pk6dygcffMBTTz1Fx44def3113O1m5f8jin+QxSv7Fc8LJw5c0Yc\nHBykQoUKsmTJknzLGI1GqV2unKwrpMabdxus14terxdHR0fp3LmzWRtYtGiRVK9eXX777TfJzMyU\nmJiYXJpC06ZNZebMmRIaGiqBNjYFBvq33Nab1uxytHNrtHjLh0Eq+fsXqk3LacHo6GipVKmS+Pj4\nyIEDB/75D0Txj2M0GqVupUrymZXVPT/L/7OyknpVqqilBMV9ozRfBQBz5syhefPmREZG5or+1KdP\nH4YPH07Xrl3x9PTk5O+/U9XiuqPAs2hJ1t9ES14fZXF+FVp0omqAV0YGmRkZREVF8fHHH5vLnD9/\nnrS0NNq1a4eHhwfdu3fPpYEC2NraMn36dBKNRiIh39jBS4B4NO3X0bTlxFkOBfYCPwGVqlTh/Pnz\n5mhQJ0+eZOLEiXfUZ9mHAwcOcOrUKdatW5dLI1Y8uuh0OhasWsV7zs4svofrFgPvOzuzYOVKpb0q\n7hslfBWA+evQAAAgAElEQVSAJny7du3Ks88+y4YNG7h8+bL5XFRUFBEREbRr1oyA7Gy+NB0X4Akg\nHDiElt/1J25Pz8Wixd19A1iOlnfVCjhx4kSutvv27Yuvry+XL1+mbNmydO3aFYDk5ORc5erVq0el\nMmX4FS2kYF4BvBctdrM72hTiDMDFdO414GO0UI+V69Wjb9+++Pv7U758eSIjI+nbt+8dP6Q5+2vW\nrCE6OpqqVatSu/a9ZhJWPMyULVuW77duZbi7O29ZW3PpLmUvAm9ZW/Oqhwffb91KmTJliqqbin8j\nxa16K4qfbdu2ib29vdy8eVNERGrUqCFTp04VEZHevXtL+/btRUTk8Vq15C2QMNPU2y6TUUrOVFwq\niJ3FNPBLJuOnnPMxaC5AH330Ub5GKJY0atQol+tGDl9//bW0dXK6r2nvnSBlfX0LbDM/1q5dK97e\n3rJr1657va2KR4i4uDh5vkcPcbO3l+4Gg6wB2Wva1oB0MxjEzd5enu/RQ+Li4oq7u4p/AUrzVTB7\n9mxatmyJs7OWTO2ZZ57JNfWcM816KzkZP25nENpF7uxC9miZZHLYiTbdm0MoWjq6nHR+lmzfvp1u\n3boRHByMm5sbu3fv5tChQ3eUi4iI4IiDAwvucbovCRjs6MjrY8ZgVcjkDBs2bKBPnz6sWrWKevXq\n3VN7ikeLwMBAZs2bx+/nz1PvvfeYWqcO/UuXpn/p0kytU4ew997jzJ9/MmvePAIDA4u7u4p/AQ8m\n9YnikSU1NZXFixdjNBrx9fUFtOw9N27c4NChQ+h0OvPap7OTE6kW19ZDm8o11wUct9hviDYVnJOq\nbS/gBXh7e+fqg4gwcOBAXnzxRT777DM8PT157LHH7lj3BXBwcGDNpk00Cw9Hf+sWXQoxxmvAUwYD\nYV260H/gwEJcATExMURGRrJixQrq169fqGsUjz7u7u68PGwYLw8bVtxdycW1a9eIj48nOTkZZ2dn\ngoODcXJyKu5uKf4GSvP9j7NixQpsbGw4duwYBw8e5ODBgxw7doxGjRoxZ86cXGUr1KjBUQutsS6Q\nhubacxltndVoUb4jsBDYDJxCc9+w1+upUKFCrnozMjK4fPkyPj4+hcprW61aNX7Yto1hHh4MsLPj\nYAHlkoFZQD1HR0L79OHzqKhCGchs2rSJ7t27s3z5csLDw/+yvELxTyAibNu2jYj27Qnx9SWiYUNe\nat2aLg0aEOjtzYBevfjll1+Ku5uK+0QJ3/84c+bMoW/fvgQEBFCiRAlKlCiBj48PQ4cOZf78+WRn\nZ5sF1oCXXmK1ra3ZoMoK2IiW3LwGmmVxDcDVdL4pMBXN3/YpoDYgTk60bq153ubUa2dnxyeffML4\n8eMLnde2Zs2a7Dt2jMA33uBJd3caOTvzPjAdmAIM1usJsrdnbbNmTP/uO6Z8/jnW1tZ3rRMgNjaW\niIgIli1bRqNGjQp3ExWKB8zZs2epW7ky/du0IXztWv5IT+doUhK7b9zgWFISx9LSCF6wgA4NG9Kq\nUSOuXLlS3F1W3CM6yW9uT6EogPBq1Rhx5AhP5XPuBlACOA945nN+gL09waNG8daYMQ+0T1lZWaxe\nvZrdP/3E9UuXsDMY8AsOJqJbN4KCggpdz9atW+nSpQtLliyhadOmD7SPCkVhOX78OM3Cwxlx4wYv\nGY3cba4mCxil17Pax4ete/bg4+NTVN1U/F2K09pL8eixfPlyKWMwyCWTBfEGU5jHc2ixcUMLsDTe\nhJa/9sKFC8U9hHz58ccfxdvb+45YwQpFUdGkSROZMmWKhJQoUej0lDnbWBsbqVOpkqSkpNxX28HB\nweaY64qiQU07K+6Jp59+moghQ2hlMHABza+3LNr6ryPaGm9etgIRBgPfrlr1UL6Z79ixg86dO7Ng\nwQKeeOKJ4u6O4iFi//799O/fn8DAQDw9PWnUqBF79+792/WOGzeOyMjIXMd0Oh0x339Pq+vXea4Q\nE5JRQBu0MKtRWVnEnzzJ3Dx2GoVFp9OpgCFFjLJ2/o+QkJDA4sWLOf/HH6SnpOBWogT16tenTZs2\nhVoLteTdiROxs7cnbPJk3khN5XcRczALS84AM21tibazY+GKFQ/lVO5PP/3E008/zbx582jevHlx\nd0fxkNGvXz969+7N5MmT0ev1bNu2DTs7u3+kLaPRyPYff2TPXWKr53AQeBtYjWZLcRmYmJ3N55Mm\n0X/AACVIHwWKW/VW/LNs3rxZOrdqJe729vK8nZ18CDINZCxIXWdnCfbykgnvvSeJiYn3XPfWrVul\nS5s24m5vLwPs7OQTtBy7H4O0dXISLycneXXoUDlz5syDH9gD4OeffxZvb2+V1FyRLydOnBB7e3vJ\nzMwssMzKlSulefPmUrVqVZkxY4Y5V3NsbKwEBATkKpsztbt+/XrR6/Via2srTk5OUrNmTRERqVSp\nkpTV66UlSEmQYaYlnfymmUeBPJfnWDZIOUdH2bFjxx3TyGPHjpWePXua93fu3CktWrSQ4OBg+fTT\nT3OlZ9y1a5fUr19f3NzcpH79+vK///0v1z3Q6XQyb948qVGjhpQpU0amTJny92/2fxAlfP+lZGdn\ny6tDh0opg0Gmg9ws4Eu8G6S3vb0EeXnJ4cOH76uthIQEmTRxogx9/nnp27WrDBsyRL755pv7Xn8q\nCnbv3i3e3t6yZs2a4u6K4iGmTJky0rlzZ1m9erVcv34917nNmzdLUFCQxMTEyMmTJ6VZs2bmNIL5\nCV9LATdu3DiJjIzMdd7Hy0vcQTaCxIPUBfmqgO/tVhBX00v0HrT8wwLypk4nY0aPviPX8bhx48zC\n99q1a2IwGGT27NmSkJAgPXr0EFtbW3P5ffv2ya5duyQrK8ssyGNiYsx16XQ6eeKJJ+TYsWOyb98+\ncXZ2llOnTj2YG/4fQq35/gsREV7s35+fv/6avSkpvAA4F1C2LvBNWhq9ExOpWaMGx44du+f2/Pz8\neH3ECP43axZRixYx5bPP6N27Nw4ODn9nGP8Y+/bto127dkRFRfHkk08Wd3cUDzGxsbGUK1eOwYMH\nExAQwCuvvMLVq1cBzUe+R48eNG/enHLlyjFy5Ei+++67QtUrmuKT61hWZibhQDPAH+gExBRwfWNg\nGdr0c1OgMvANUEKEaxcv3rXtH374gdDQUHr16oWfnx/jx48nKyvLfL527drUq1cPa2trwsPD6dmz\nJytXrsxVx5AhQ6hYsSK1a9cmPDycmJiCeqooCCV8H3HyMwgZ/dZbbF+0iHUpKXgUsp7xQJQITz7+\nOGlpaf9kl4uVAwcO0LZtW7788kvat29f3N1RPOQEBgYyYcIE4uLi2LhxIzExMUybNg2AnTt3Ehp6\nO4BqaGgohw8fJikp6b7a0ul0hFjsl+R2KNf8aAZ8B1wFhqMlG7kE3Lh5k1u3brFixQqioqJYv359\nLuG6a9cuatSoYd4vXbo0rq6u5v2EhAQGDRpE9erVcXFxYerUqXeEerXM7OXr60tCwt16qsgPJXwf\ncfr160eVKlU4evQoCQkJjB49mjmzZjEjJSVfI6j8yPla9hahXHIyS5Ys+ae6W6wcPHiQNm3aMGPG\nDDp27Fjc3VE8YoSFhdGtWze2bNkCQMOGDXNZPu/du5dq1arh7OyMv78/V69eJTs7G9DimcfHx5vL\n2tjY3Jk2U6/n6n30Sw/0B0qjxVtftnQpXL3KrzNnsuPll3k/IoKPPvyQgwcOEB8fT1hYWK7IWKdP\nn+bGjRvm/ffee4/MzEzWrVvHjRs3GDZsGEajMW+zuVAGXveOEr6PMCdPnuT48eMMHToUFxcX7O3t\n0el0uKem0h44YlH2Epor0BVgCxAAzATKo70xbwECgcG3bjF94kQ+/PBDypYti6enJz169GDbtm1F\nObQHzqFDh2jVqhWfffYZnTp1Ku7uKB4BTpw4wZQpU0hISCA7O5v9+/czf/58evXqBUDHjh1ZuHAh\nmzdv5tSpU3z00Uc8/fTTAJQrVw4vLy+io6O5fPkyY8eOzSWgQkND+fXXX0lPTzcf8/Lx4adCWlLP\nBtahJQxJMu1fREvtuT0ri8FGIw6ZmUxKTua1mzfRZ2WRcewYNcqX50pionks58+f55133sHG5rbj\ny/nz5/Hw8MDT05MtW7bcEWY2L/lNoSv+GiV8H2HKly+Pv78/ERERrFmzhhs3bvDF5MkMSU4mAphn\nUXYh0JzbkacuAnvQQkN+YVHuSSDhzBlsbW3Zvn078fHx1KlTh+7duxfFkO6J7Oxsrly5woULF+46\nVX7kyBFatWrFJ598QpcuhUnFoFCAs7Mzu3btIiwsDA8PD4YPH0737t3N/rlNmzZl6tSpfPDBBzz1\n1FN07NiR119/3Xz9jBkz+Prrr6lXrx7Vq1cnICDAfK5JkyaUL1+eUqVKUadOHQA8PDxIsrVlj6mM\nDgqMbuWCFrY1GKgIbMCUqxot01h/wBuoAiwCBgH1jEa2p6Yy6fXXGfj880RHR9OgQQPq1auXq2/j\nxo3jl19+ISAggI8++oihQ4fmenHIL++10nzvg2I09lI8AOLi4mTkyJESGBgoTk5O4u7oKDtAfgYJ\nsrCODAVZYvo/FkQHEmdxPhYkwPT/M87OsnDhQnMbRqNRAgMDZe/evcU40tt92bFjh/R4+mmxt7ER\nNzs7KeHgIHprawktX16ioqLM7h4iIkePHhVfX19ZsGBBMfZaoSgcH77/vjxtb2+2Xi7slgFSD2RR\nIcr+DuLr4CDff/99cQ/3P43SfB9x8hqEJKWmshgIAxzQppOPA6eBDhbX+aBNM+eHa3Y227Zto1On\nTvj5+eHh4cGff/6Zb37douTgwYPULl+e3i1bErpiBQlZWVxLT+diaiop2dmMP3mS5S+/TFCJEkz7\n+GOOHTtGixYtmDRpEt26dSvWvisUeQkJCWHTpk25jrl5ehKj0zHGxobCTuRmA8+jfacLM69TCvhf\nairvjhhxT/1VPFhUhKt/EWFhYbgaDGy/dQuA3mhTzz7AM2iGGTnc7YO/bmXFD/PmMWfOHL755huc\nnZ0pVapUsa7rbNmyhWfbtWNycjI9uHO9xBpoC7S9dYvfgKfHjOHdsWOZMn06PXv2LPL+3gtnz57l\nl19+4fr16zg4OODv7094eDhWVurd+N9MftO1dnZ2VKlenRWXLnHt/HkmpKcX6CYIkAgMQMtZvQbt\ne1AYOgKvnDrFwYMHc1k+50d2dvY9R8FT/DXq2/0Ik59BSFp2NrVN53sCy4H5QK9C1inAL0YjGRkZ\n+Pr6YjQamTBhAufPn/8nhlAoBg8eTOsWLViUnEwkdz60VdHWrnMoB+xITSUgK4v4M2eKrJ/3QnZ2\nNuvWraNd06bUqVSJr3v3JnboUJb178/gtm0p5+fHx5MmqVRx/0FsbW3ZfuAAn6enE6DXM9TOjqNA\nH2A02nd0OmAAgoBNaHm1Lb8Dt9DWhAOAJ4APgccszr8KJKWk0KB+fQYOHMjBg7ezYo8bN45u3brx\nwgsv4Ovry4cffoijo6PZvxk0F8cSJUqYrbkV944Svo8w+RmEPNm+PcednABtWrk22oecNzNtfuYR\nOrQkCLaennz44YdERkZSo0YNMjIyiiS37fr162nSpAne3t6ULFmStm3bsmPHDjasXk2NrCwKSnlw\nBC3ogCWuwIaMDKZMnEhcXNw/2/F75OLFi4TXqMHorl3pvHUrcWlprLp5k9nJySxOSuJgUhLzL17k\n0LhxlA8KYsWKFcXdZcU/REGzSa6uruh0OtZt3ozHsGG0dHdnDvCRtTW2Oh1jHB1JAyKAU2geCwMs\nrh8HbDdtw4FPyf2dr4cWpKNcyZK4uroyZMiQXO0vW7aMypUrc+bMGV599VWaNm3K4sWLzefnzp1L\nt27dlEb8dyjuRWfFgyUjI0N83dzkiMm44jmQ0fdguPGMwSD/+/TTIu/3l19+Ke7u7jJixAg5deqU\n3Lx5U5YsWSKRkZHiaGMj3e7RACVne0mvl7dGjCjy8RTEn3/+KWV8fWWsjU2hjGr2gPg5OMjc2bOL\nu+uKB0xwcLA4OTmJm5ubeTMYDPLYY4+JiBbG8fTp0+bykZGR8uqrr0p6erq89NJLogO5bHpOMkGc\nQI6b9iujpfHMeY4iQRrlebZ+AyldooTcvHlTHB0d5fLlyyKixYEuXbp0rr4uWrRIGjZsKCIiWVlZ\nUrJkSdmzZ08R3al/J0rz/Zdha2vL0GHDeNlg4DiwChjyVxeZiAG22tgQ2auwk9QPhqSkJN544w1e\nf/11Jk6cSJkyZXB2dqZLly54OjlRy+TgPxQt6s+zgGUQzBBgs+n/cUA3i7InMzKY+fnnZJgyxfyT\n/stVq1blxx9/LPB8ZmYm7R5/nF6XLzMuK+uuSdJzqAPEpKYyfNAgtm/f/sD6qih+dDodK1eu5Nq1\na+Zt+vTpBWrD1tbWODg4oNfr0ev1GHQ6vEznbAAvtIhYN9G+H7Usrq2dp65vgL7AH4mJBAUFkZqa\nyuHDh83nw8LCcpXv2LEjv/76K2fPniUmJgZXV1ezi5Ti/lDC9xHk3LlzbN++nQ0bNvDTTz/dsS44\n4s03+cPbm5rAGDSDq79iO9DDYGDJ6tW5Qs0VBUeOHOH69et06NDhjnOrli2jmtHIMqAG2o+KK9p6\nVg55hdhyi7IBgGRksGvXLgDKli173/7LrVu3ZuzYsXccX7lyJb6+vhw6dIjGjfNOgOcu5xAfT2RW\nFlbA3WMG3aYy8JGyTv1PYCl4/fz8uHDhgnl///79ZgMtT09PMguowwXN9/eAxbH9Fv+fQ5uKbgU0\nrlWLuLg4HBwccrWddzrZ3t6eZ555hnnz5jFv3jxzoBHF/aOE7yNCVlYWK1asoGWDBtQuX5432rXj\n465deaVNG8oGBNCzUyd27txp/gJVqFoVf19fvjUYiKHgH/qLwHvW1nRydGT+ihV3FR7/FOfOncPZ\n2ZkqVarccS7x5k1cgApogQPc0da3Nt6lvrxlk7OyzC8oXbp0oWTJkjg4OPDKK6+g0+nYt29fofrZ\np08f5s2bd8fxuXPn0rNnz7+0Tv78ww958dYt88vCvdiOd0WLS33y5Ml7uErxKNOsWTOio6O5fv06\nUVFRHD9+3HwuNDSULHLPAFnSFpgM/IEWCWsTt19SL6M9exsMBp7u3Zs333wzV6StgujVqxfR0dGs\nWrXKHGhEcf8o4fsIcPjwYSoEBvJxr170/vln4tPS2HHjBjE3brDrxg1+T0ujzooV9GnZkqZ16tCn\nTx8yMjI4cvo0z02bxuulS1PR0ZGJOh3LgLVoLkjdDQYq2tvzR0QE2/bto0WLFsUyvsDAQJKSkjh6\n9Ogd53JeJiydIUqivTQU9EKRt2y6CF27dsXX1xd/f3/c3d2xs7PDzs6O+Ph4RowYwZtvvsmHH37I\n9OnTmTt3LitXriQ2NpZ9+/bx22+/ceHCBVq0aMGVK1dyTVVfu3aNtWvXEhkZeYff5vr16+nQoQMV\nKlRg5MiR/Hr0KE9x2zjMDU1L+RltGrARWoILf6A18JPFOBYCxrQ0qlerxlNPPcXq1avN57Zs2UJA\nQAAzZ86kVKlSVK5cmc2bN/Pjjz9St25dKlWqxIIFCwq6/YqHCEv3o5EjR3L9+nUqVqzI/v37iYiI\nMJeztbXFycmJGXp9vvWMBRoA4cDHaFPMObHea6MtzexITWXChAncunULDw8Pjh49itFoLDBiVcOG\nDbGysiI0NJTAwIKiBCgKi04KWmBQPBTs2rWL9s2b88mtW/xVmAgj8KaVFTOtrdlz9CjlypUDNAH2\n888/M++rrzh/9izpaWm4eXgQ9sQT9H7uOdzc3P7xcdyNpKQkQkJCeO211xg1alSucyHe3nRITOQa\nMNd07CxaEPkstLfHUkAUmkvFOLSAIpZlSwFr1qyhYsWKhIWFMXLkSMqXL09WVhYDBgygVatWVKlS\nhZs3b5q3pKSkXPs5W3p6Ora2tvj6+uLs7ExKSgqJiYm0bt2adevW0apVK6pWrUpcXBwxMTEMHDiQ\nUqVK8d5772Fz6hSHs7L4w9SnnP6DJnwHAm8BLwKfoGkrOWJ+HZr7yOSKFRk2dix9+/YlMTERg8HA\nli1baNmyJYMGDWLs2LHMmDGDzz//nPDwcCZNmkRcXBzt27fn2rVr2NraPqiPTVHMnDt3jpoVKrAj\nNZWKf1H2GTRhPBxN6+1kZcVmnY7Gjo64ZWeTamXFcRHSnZx44dVX6dO3Lx4ed+ZEa9asGT169KBv\n374PfkD/NYrP1kvxV5w5c0ZKurrK6gIsYZuQf7Ltd2xspHaFCg91Mvu8zJo1Szw9PWXUqFFy6tQp\nuXHjhixfvlyqVqokTXU66WkxvjOm8JjZpv0QC8vOsZCr7I/ab40kJSXJn3/+KQaDQXbt2iXXrl2T\n999/X2xtbSUqKqrQ/dyyZYu4urrKiRMn5ODBg1K1alUZMGCALFq0SDw9PWXgwIEyZswYqVChgjRt\n2lSeeeYZadWqlfj6+op7Af0XkGgQd4tj50FsQW5ZlDkKUtHPT0REGjVqJEuXLhURLXG7tbW1JCYm\niohIfHy86HQ6WbVqlbnf5cqVk9jY2Af3gSkeCqKjoqSUwSBn8vwGHAc5CJIGMg/EFeQwiBHkJZDy\nFpbSOZsRZCdIT4NBPAwGWblyZa62tm3bJgEBAZKWllZMo/13oYRvEePo6ChOTk7i5OQkOp1OHBwc\nzPt54w8P6tNH3rS2LtANpSlIVD7HjSCtHB3vSag8DKxfv14aN24snp6e4uPjI+3atZNZs2aJo5WV\n9MgjfK0KEL7jTG4VOWUH2diITqeT7OxsERH55JNPpHz58hIUFCRjx46Vxx9//J7vU9myZWXRokVy\n6tQpsbW1lUuXLomISEhIiGzatElEREqUKCEuLi5mFxIHBwexAbl4F+Gb1xVEB3La9P82kFYgttbW\n4urqKnq9XsaMGSMimvANCAgw9y8zM1N0Op388ccf5mONGjWS+fPn/63PR/Fw8unUqeLn4CDL0FyO\nclzUyoI4grQHWQlyCqQTSFg+gjfvthst/vP8uXNFRKRXr14SFBQky5YtK+bR/ntQwrcYsfyxzsuN\nGzfEzcFBEgoQrtl3Eb4Csg6kdrlyYjQai3hUD4aMjAyZOXOm+Pn5SYCLi8z9ix+L/LarIN729nLy\n5MkH2rfx48dL27ZtZdy4cdK+fXvzccvPMyIiQr799lvzuWXLlklLFxcRtIQWOpCsQgpfI5rf5vMg\nT9StKyKaMB09erSIKOH7XyY6OloaNWok69evlwZVq0qAwSDvWlvLHjQ/3sMgy0Ae0+nEBWQESHIh\nvz9HQLwdHGTp0qW5XmAt+eOPP8TJyemR/Z0pTpTB1UPCuHHjclkQTps6leupqZQ07TdFc69piWao\ncybP9X8C1dEsHAHKAqfPnsXPz4/+/fvz66+/ArBkyZI7/POmTJnCU0899WAHdJ8YjUYWLlxI5cqV\nWbp0KStWrGDJhg0Mc3CgcDbJGmlAJ4OBnn37mte+HxS9evUiJiaGr776it69e+dbJjIykkmTJrF9\n+3ays7MJDQ3lp/R0zqK5P5UA9uZ75Z1koFmo7rG3p9uAAURHR5tdpxT/Dfbv30///v0JDAzE09OT\nRo0asXfv7SeodevW7Dx8mFXbtxPXvTsDSpempbc3XQMCeNPXlzgR/gQmooWlLAxVgEmpqXz6wQcF\nlgkKCiIpKUmlFLwPlPB9SMj78P60ceMd/qufAy8DV9DyeOZwBk04v4QWszUbzXK2fnY2gwYN4rHH\nHqNVq1YAdOjQgTNnzuRyW5g7d26BQqSoEBHWrVtH7dq1mTp1KjNnziQmJoa6detSv359vlqwgDYO\nDvxQiLouAy0MBvxatuSjTz994H0NDg6mYcOGpKSk5OubDNCmTRvGjx/PZ599hre3N82aNaNS1ap8\nYWuLDi1Gbz80d6hd5J+7NWffDhgFHM3IYPTo0ezduzeX5Svkn2NV8e+hX79+VKlShaNHj5KQkMDY\nsWOxs7O7o1ytWrX4cs4c9p8+ze+XLnH03Dl8/fyYROGFriURwOEjR/5u9xX5Udyq938Zy2nKsWPH\nSs+ePc3nGlSpkmtdsClI3zzTQk1BhpvWPC3zeP4A0gJkAsiI4cNFRKRmzZqye/duEREZNGiQvPXW\nWyIicuTIEXF3d5eMjIwiHv1ttm/fLo899phUqlRJli9fXuAU1tatWyXQy0vCnZ1lHpoxieX92APy\nnIODuNnby6jhw/OdJitOTp48KV4ODnL2HqfPjSARdnbyxrBhxT0ERTFw4sQJsbe3l8zMzDvO5Uw7\nW5Z98cUXJTAwUF566SVZt26dlHRwkHkgdfI8V1NAOpj+XwNSE8QFpDnIbItyA2xsBDB/n5YuXSoh\nISFy9OhROXPmTK4p6a+//loqVaokrq6u0rFjx1xGf4rcKM33IcUmH5eQsDz7gpaxKADobHF8I5qL\nyjvAp59/jru7O6dOnTKHPuzdu7fZ73Pu3Ll07dq1WFxQDh06RLt27ejevTt9+/bl8OHDPP300wVq\nbY0bN+b3P/9kxNy5zK5fnxJ6PRWcnanm4oK/wcAz3t5UePttTsbF8cHkyQ9dSr5y5crx1vjxtDEY\nuPDXxQHtM37T1pbTZcow+t13/8nuKR5Sypcvj7+/PxEREaxZs4YbN24UWLZly5b4+Phw4MABfH19\n6dWrF62srHgaOIGWhCGHBUAP0/9OaL7/V4HX0MKz5pR9IisL0GanoqOjGTlyJJs2baJy5cp3tO/j\n48PatWu5fPkyERERdO3aleTk5L93A/6tFLf0/y9jqfnOmjVLWrRoYT7XOCzsDs03r3FVU5CZIB1B\nnrEo+z1Ia5D+dnYyderUfNuuUKGCbN26VYKCgmTHjh1FMt4cTp06Jd27dxcfHx+ZNm3afbsuJCYm\nyq+//iq//PKL/PHHH5KVlfWAe/rPMH70aCllMMhGk1ZbkMZ7DqSXvb2EVqwoFy9eLO5uK4qRuLg4\nGQZlvPsAACAASURBVDlypAQGBoqTk5O8/PLLcuXKlVya7/79+8XX1zfXda6urv9n76zDozq+P/zu\nxjfZhBAIARIStLgFKZo02LdoCsW1kODFWqS0uBOstKVQirtLkVKgeIsGp0hwQnACcZ3fH5MsuxHs\nR7DM+zx52HtH7txld8+dmXPOR7QyNxcCGYI3MumzdRGEHkRUOp+9NiAmJb3eJp8BRUBAgChatKgI\nDg429J9y5psS45A4hSnv19QgE+Pj48PBgwcJDAzkwoULhEZFpUo/mPIYwAJYBUQgNXsFUBM4BSxN\nTKRGjRpER0eze/dugoODDe3atm1Lz549sbS0pHLlyhlzUykICQmhe/fuVKxYkU8++YRLly7Ru3fv\nNPeuXgYnJyeKFClCqVKlyJMnzwcjbzZk5EgmzJtHb3d3itrZ8ZNGwwXgDnL//k+gsa0tJW1syNKu\nHXuOHsXZ2fndDlrxTnFzc2PcuHHcuHGDHTt2sH37dqZNm2aySnTgwAHKljWVUMiTJw/JgpqtkJnS\nQM56vwCsk47PAl8hU7M6AKuRvyEgHf4AJk+eTI8ePciVK1e649y/fz8tW7bE3d2dLFmycPjwYU6d\nOpVu/cyMMr7vCfny5WP48OE0a9aMli1bMiApib5xcvT0XGgskGICd5FOPFqkklGu3Llp1KgRefLk\nYfLkySQmPkvI2LZtW86ePUubNm0y4nZMePz4Md999x3FihVDp9Nx/vx5hg4dil6vz/Brv680bdaM\n01ev8tuWLfxTvz4NXFwoaWeHt5MTI4sVo86kSdy4d48fZ83C1tb2XQ9X8R5RsWJFWrZsya5du0zO\nV6lShcDAQJNzt2/fJiEpBWVNpDPiSWA50hgn8y1y+2oP8AS5jZX8sH896d+//vqL0aNHs3bt2jTH\nJYSgS5cueHl5ERgYSGhoKBUqVECItKYNCrXs/B4zesQI0dja+qV0X43/wkEUsbUVf/75Z7p9R0RE\nCL1eL4KCgjJs/BEREWLcuHEiW7Zsws/PT9y4cSPDrqVQfKycP39eTJ48Wdy6dUvEx8eLY8eOicKF\nC4vZs2eL+fPnmzhcubu7i7Fjx4r79++LCRMmCHd3d+Fka2tw8uuW5FCVA9MkLyVBTAMRiUzIoedZ\nsppqOp1hafnkyZPCxcXF4EhlvOwcHR0tsmfPLtauXSvCw8PF3LlzhYWFhSEeXWGKmvm+x/T99ltu\n5c/P9xYWL62AEwM01+n4tEEDateunW69adOmUatWLfLnz/9GxmpMbGwsM2bMoECBAgQGBrJ//35m\nz56tkrErFK+BXq/n0KFDVKxYkaxZs9KvXz9atWplyAtgvPT8559/EhwcTJkyZbh58ybbtm2jbbt2\n/GZuDsjZ7k5krmfjH//JwEogD3JpukvS+UvIJenka5QsWZJNmzbh7+/Ptm3bTK5vZWXFjz/+yMiR\nIylQoECaIXGKZyhhhfece/fuUadqVcrduMGEmBhSpzp/xlWgg05Hdi8vlm3YkK4Hs4eHB1mzZmX5\n8uUUKlTojY01OUHG0KFDKVCgAGPHjsXT0/ON9a9QKF6dCxcuUK1MGY5GRZHnFdoJoJWVFe7duzN+\nypSMGl6mRRnfD4CwsDB6+/uzbsMGfDUaukZFURLpLBEB7AVm2NlxUAi+7t2bIaNGvdUwGyEEmzdv\n5vvvv8fGxoZx48bx2WefvbXrKxSK5zM1IIA5w4ezMzKSHC9RPznEbWfBguw6fNjgdxAdHY0QAhsb\nmwwdb6bgXa55K16Ne/fuifFjxohCuXIJSzMzodVohM7CQlQoXFjMnTtXREREvPUx7d27V1SpUkUU\nLVpUrF+/XuV4VSjeQxITE8XwwYNFXp1O/P0KIW537twRu3fvFs3r1xc2FhbC0sxMWJqZCWtzc9Gk\nTh2xc+dO9Z1/TdTM9wNFCEFCQgLmSXs5b5sTJ04wePBg/vvvP0aMGEHr1q0/mFAfhSKzsmLZMkYO\nGoR49IjuERHUFgIHIAqZhGOWTsduIWjTpg11Gzfm265dSXz4kO4REbQSAkdk1EUocm94hp0d8Vmy\nMH3uXGrVqvUO7+zDQxlfxSsRFBTEkCFD2L17N4MHD6Zz586vHaerUCjePkII9u7dy6+TJnHsyBFC\nIyKwsbTEDLDPnZt9+/ezbetWenz1FXOioqhP+mGOAtgGdLCxIeDXX2n7jnPEf0go46t4KW7fvs3I\nkSNZvXo1ffr0oU+fPtjZ2b3rYSkUijdEbGwsVatWpVy5cqyZP5/tSb4lL8N54DMbG+auWcPnn3+e\nkcP8aFDG9yMlJCSEOb/9xql//+VJaCg6nY48hQrxVdeulC5d+qX7efToEePHj+f333/Hz8+PgQMH\n4uTklIEjVygUr4qHhwf37t3DzMyMwoUL87///Y8hQ4ZgmZRg42UJCgqiTKFCrBGC9AMV02Yv0NLR\nkWt3776TXPEfGirO9yPj6NGjNK9fn6J583Jr/HiabNtGn0OHaLNrF06//06DKlWoXKIEy5cvf27m\nmfDwcMaMGUOhQoV48uQJp0+fZuLEicrwKhTvIRqNhk2bNhEWFsbvv//OggUL0s1ElRZCCIQQXLx4\nkQLW1q9seAGqA/ni49m4ceNrtM6EvBM3L0WGsHD+fJHdxkZM12hEaDqejHEg1oMoYWsrOrdrl0qm\nLCYmRvz000/CxcVFNG/eXFy4cOEd3Y1CoXhZjEVahBCiS5cuol69eqJ+/foie/bsokCBAmLIkCEm\nAh1eXl5izJgxolatWoZsdyU/+UTkBOGQJNiyMcXvx2YQ5UEUArEChAbE5aQyLxD+IHzKlxdCpJY7\n7NWrl3BzcxPOzs6ic+fO4sSJE0IIIUJCQoROpxMPHz401D127JjInj37ByOW8jqome97wNOnT1m/\nfj1z5sxh9uzZrFmzhgcPHqSqp9fruXbtWpp9rFyxgu+6dWNXVBRfJ3kwpoU50AjYHxFB0OrVdO/Y\n0eA5vWjRIj755BM2b97Mli1bnpuEY8mSJdSpU+e17lehULx5RNJK1vHjx9myZQvt27enY8eO3Lhx\ngz///JPDhw8zffp0kza//PILvXv35tGjRzg7O/PfpUvsQOaAbgE0R+YSADgDtAeGIcU/FmHqiKUB\nygGHT5wgNDQ01fgqVKjAyZMnuXDhAg4ODvTo0QMAFxcXvL29WblypaHuokWLaNmy5ccdQfGurf/H\nyLFjx4Sfn59wdXUVWbNmFVWqVBFHjhxJVe/kyZOia4cOIouNjahjby++0ulER51O1NPrRRZra9Hu\nyy/FwYMHXxhHd+XKFZFNpxMnkp5Ad4FwfUH+Zy8QP4MoZWsrevfqJYoVKyaKFi0qsmfPnlFvi0Kh\nyCDc3d2FnZ2dcHR0FNWqVRPjxo1LNWvcvn27KF68uOHY29tbdOzY0XB869YtkVOnM/mdqApiddLr\nCUb5ngWInSlmvt5I2dO8dnYiKCgo1czXmKdPnwpbW1tx//59IYQQy5cvF1WqVBFCCBEfHy9cXFzS\n/M38mHg3QaIfOZ06daJ9+/ZMnjwZS0tL9u3bZxKOk5CQwLdff82K+fPpGhfH2fh4ckVFmfTxEJi3\ndi0tt26l6uef8/uSJek6T8ycPp32cXGUeoUxagAbYEJEBK1nzmTuqlXo9XratWv3yverUCjeLRqN\nhg0bNuDj42M4l5iYyODBg9m3bx+nT59GCEF4eDhCCEM+5ooVKxrqJyQkEJ+YSEvgH6S6URRSWrAJ\ncBioYnRNU/HCZ5gB8fHxqc7Pnz+fVatWcfjwYeLj44mKiuLUqVP4+PjQqFEjunXrxrVr1zh//jwO\nDg6UK1fu//OWvPeoZec3zMWLFzl//jw9e/bE3t4ea2tratWqRYkSJQC5NFTLy4v5s2aRJSoK5/h4\nsiS1/Rz4Jem1E/BtYiK2ERFc2LgR39q10Wq1XLlyBZAf7pUrV+Lt7c3k6dNxiosjFrlE9DlwG9AD\n9kid2PSoBThaWODs7GySoB1Aq9WyZMkSSpcuTYECBZg6daqhbP78+VSrVs3kvnv16kWePHno3bs3\nly5dMpR5e3szfvx46tSpQ86cOenXr1+ay1IKheLNsWrVKjZv3sy8efN48OABa9asMThWJWOcpCdL\nliw8iI6mGhCITKRRgWfSghUwlTg1FS+E3EAI8CAuDkdHR44ff1b75s2b9OvXj8GDB3P9+nVu3Lhh\nkqLS2tqapk2bsnjxYhYvXpwpJgHK+L5hChUqRO7cuWnRogWbNm3iyZMnJuWd2rfnwD//sCgxkQ1I\n0eqJSWXGYtcA54BbwO7YWDRHjph8aWbMmMGsWbNo0KABn9rY8BewALBF7sfkAsKAp4DLc8arBbpE\nRvL7Tz+lWT537lyWL1/OypUrGTZsGJcvX06zXu3atcmRIwfHjx8nZ86cqRSVZsyYwYABAzh69Cj7\n9+9nzZo1zxmVQqH4/3L79m2yZMlCtmzZuHjxIhMmTEhVx/g3xcrKCjMzM4KReePnAYeM6tZF/rZs\nBa4BprvHUAO5D2xtbk5gYCAbNmwwPNDfv38fIQQuLi6EhYUxePBgYmJiTNq3a9eOefPmsXHjRoNi\n08eMMr4ZQPbs2YmMjKR79+64urrSp08fHj16RFhYGEuWLsVfCOoDBYFBwLqkdr7ACeBm0vES5HKP\nDbA0MhKAO3fkPHbFihWMGjWKuLg4KsfE0BtYn9TuVQO3SwnB1QsX0izr0aMHhQsXpmzZslSuXJnt\n27enqnP8+HFiY2P5/vvvcXJyYtCgQcTFxRmefDUaDb6+vtSoUYPcuXPTuHHjNPtRKBRvjo4dO5I7\nd24KFSpE27Zt6dixY6rVLeNjKysr/Pz9+UmrpQBwFOl0lUxxYC7S4aoO0nET5OoaQGMgDngQEUGr\n5s3x8/MztC1btizdu3fHx8eH6tWrU7x48VQSo1WqVEGr1eLp6Zkp5EfVnm8GYG1tzZdffsmWLVs4\ndOgQHTt2xN7enpwuLuiEwFjvxxM4jZyl6oF6yNnvAGA58HtSvWTv5RVLllCqVCn+/fdf6tWrR3R0\nNMTH8xvpp4BLC13SNQHsgIiICMLDw9HpdCb1jBNy5MyZk+Dg4FR9HThwgLJlTXeAypUrx/79+ylT\npkyqflxcXNLsR6FQvB5Xr15Ndc7BwYGlS5eanGvZsqXh9a5du1K1mTp1KqsXL2ZneDhF07hO/aQ/\ngM1AMeBn4DIwHHgMPAC+iYlh/19/sXPnTkPbMWPGMGbMGMNxly5dSEmePHlo3bp12jf5kaFmvhlI\naGgoo0aN4vr16wQEBDB88GAqJyZyNKncG+iHNIRFgPHAF0jj+y9y37YV4JZUD2DpwoUEBgZiZ2dH\nzpw5sbS0RAN8iQwFAOnw8KLZb21gf9Lrp4C9Xs++fftemBou5ZMzyCfWwEDTHaBjx46Z7AkrFIr3\ni8DAQPz9/XFzc8PJyYmqVaty5swZxgYE4KvTcT+NNuuAGOR+7yTkDFgDxAINkQbYAZgZE0P0sWPM\nnzfvpcezf/9+Ll68qIyv4vW4cOECN2/e5PHjx8TFxeHt7U3u3LkZNGgQ9588wRFpXP9GehIuBRoA\ne4AdyA/1deTSjhcQhPQyTI76jQ8Pp0ePHlhaWnLhwgU8PT0pZG3NdeRsGaBUUv2Q54zTF9iZdP2d\nZmZYZ8vG6tWradKkSbptUjprJFOmTBksLS0ZN24cDx48YOLEiZibm5vMdtNqp1Ao3h2dOnWiWLFi\nnD17luDgYIYNG4aVlRX+XbvSslcvqul0pPTw+A1wBlojZ8DfIx2ztiH3hHsl1TMHBkdGMmPixJf6\n7rdv357WrVvz448/ZhqhFmV83zB6vZ6nT58yevRoChQowKZNm2jTpg3t27fHwdKSk8BUYCxyploa\nubScH2k8tyL3TnYCY5AOVDmBoUn9O9vasmDBAurWrUuZMmWwsbHhVEwMJ5HhASD3YAYg071lJW1v\nZ3dgFfJLMzkhgdjERCZPnkzVqlUNddLaH0o+Z/wa4M8//yQ4OJgyZcpw8+ZN/vzzz1Rt0+pHoVC8\nfZ4XlTF8+HCu3LpF4549KY6c0WqBBOTvUxDS8I5FPujPAArwzHG0KfI3qxNw9fp1E+fKDh060K9f\nP5o3b06OHDnw8/MjODiYBQsWcP36dRo3bvy23oJ3z7sJL/648fb2FnPmzBGJiYniu+++E1WrVhX2\n9vYCEFojIWtvEJONgtbvJQWthycdB4CoAcIRRJaksry2tuLcuXOiQ4cOYsiQIUIIIcaOGiXqmJu/\nMLFGWn/LQXxWrtw7fscUCsXbJn/+/KJJkybijz/+EKGhoYbzw4cPF23atBE/fPed6K3ViiFyF0u4\ngagIIisIWxDuIJonvTZOvjEv6TfsNojSIAoXLGjou3379sLe3l6sXbtW3L9/X9SvX1/88MMP7+L2\n3zlq5ptBCCFYuXKlIc7u/v37WJuby0+xUb2UcXPFkLPdQ8AU5Cw5BDiSVOd+bCw5cuQwXCMyMpKg\nq1fZHR9P9CuO8RbQX6ej/8iRr36DCoXig2bXrl0ULFgwVVSGSFomvnfzJoUTE+mI3Nf9Czm7jUXu\n/V5BOoWmTLbRgWcrdq2BK9eukZiYaCj38vLiiy++IFu2bLRu3TrTRj4o45uBGMfZBQUFkTVbNpNy\ngVxe3oz8IE9C7v8CBCM/wM5I45u87Fy+bFmyZs2KEMKwzBsTE8OI8eN5pNGw7SXHdh2ordPR47vv\nlP6mQpEJcXNzY9y4cdy4cYMdO3awfft2pk2bZtgSiouNNQmHKQQ4Ig1xLZ4Zj7KYTigmATWRW17D\ngdi4OK5fvw7ILScV+SBRxjeDMDMzSxVn1+PrrxHIvROQH+IeyBluNeAzpAMDSIcoH+SecAOgGfID\n3qZzZ0MM7YoVKxg9ejSLFy+mdOnSaM3MaGllRQ8rK86mM677wAStlk91OjqPGMHAH37ImDdAoVB8\nMFSsWJGWLVuya9cucufOzd27d8nq4sIDTFfnCiONhrEj1jGehTmmXLH7Jum8MHK6Esr5ElDGN0O4\ndu0aJUqUMMTZ3bt3j2PHjjF48GAqlSjBb9pnb3tO5Ow3GBiMnO2C/I+ZhfwAn0Ya7Pw5c1KhQgU+\n/fRTXF1dCQoKomnTpgghmD9/Ps2aNeP05ctk++YbamXJgrdezwhgGtJ5q5VOR0Fray40a8amvXvp\n8+23b+9NUSgU7w0XLlxgypQpBAcHk5CQQGBgIEuWLKF9+/bUqFGDgwcP4po3L0ttbQ0pbwEskLPa\nEUhHzsXIxEDJpFyxm2VubuJcqQzvM1SSjTfMjh07ePTokSG5REoCfv2VhjVrkjNa7tC+zEdxN9BV\np6NtixZ4e3szduxY/P39DR/q8ePHExQUxN69e7GxsWHEmDF8P2wYGzZs4PTJk1x98ACdvT3V8ubl\n5+bNyZo165u5WYVC8UGi1+s5dOgQU6ZMISwsjDJlytCqVSvatWuHpaUlw4cP5+eff+ZmdDQtAON0\nHDOQe7+lkUIL3XgWUeGLjKAojUwaFGNpiSb6mTdKWpEOmTby4d35en189O/fX3h5eYl169alKtu3\nb59oVq+eyGJlJXwtLUVWEB4gfnqOJ3I4iB9BZNPpRKlSpUTlypVFUFCQSb8bNmwQuXPnFrdu3Xpb\nt6lQKDIJ40aPFm2srV85ikKA6GZpKX4YOPBd38J7i0YItQ6QkQghGDtiBDMDAhgQFUW7JKH7a8B3\nyETlTYE2SDEEDXAPWG5pyWKtlgL58nHx9m0GDhxI//79TcSlz5w5w2effcamTZtMpMEUCoXiTfD4\n8WMqFC/OgJAQ/F/BVCzVaPjOyYkjZ8/i7OycgSP8cFHGN4MZ8cMPrJs2jS0REeRKo/wOMAfYgNTw\njQEiNRp8GjYkPDqa27dvs2jRIkqVkmq9Hh4ezJ07l1KlSlGwYEFKlizJ7t2739btKBSKTMalS5fw\nrliR/k+f0jsh4YU55H/TahlqZ8eOAwcoXrz4Wxnjh4hyuEoDDw8PrKysePjwocn5MmXKoNVquXHj\nxkv188cff7Bg6lS2pWN4Qcr9fY9MIXkZGXu7Tgi2btxI/vz5OXLkiMHwgtwfiY+P58svv8TPz08Z\nXoVCkaEULFiQf44fZ37evJSxs+M3IDxFnUikBGF5vZ6prq7sO3pUGd4XoIxvGmg0GvLly8eyZc/U\ndU+fPk1UVNQrOQdMGjaMgMhIcrzi9b2ANhYW5HBySjPP6S+//IJOp2PcuHGv2LNCoVC8Ou7u7gRe\nuMDENWvYUqMGeaysKGdtjZelJVUdHMhjbc3q6tUZvmwZZ65coWDBgu96yO89yvimQ5s2bVi4cKHh\neMGCBbRr187gKh8ZGcmvv/5KiRIlqF27Nn/88Yeh7vz58ylbtizHT5+mF/A/pEpRMveRuZddkCpG\n3wLG0tGBwJ3YWEaOGcPgwYO5ffu2oSwsLIwTJ06wdOlSRo0aZSI63bRpU3LmzImbmxv9+vXjypUr\nb+z9UCgUmRutVkvt2rVZv2MHZ65cocWoUTwpUoRxf/zBiUuX2LxnD/Xq1TPxS1GkjzK+6fDpp5/y\n9OlTzp8/T0JCAitWrKBNmzaAdKKaMGECq1atYs2aNQwaNIhevXqZLAGfPHkSz4QEzgCf8kxxCKRr\n/l1kfFwTZDxv8nw6EjnzbQB42thw+vRpWrVqBcDu3bt5/Pgxo0ePxsHBIdUsvF69egQFBXH48GEe\nPHjA0KFDUSgUijdNrly5aNasGffu3aNatWq4urq+6yF9cCjj+xzatm3LwoUL2b59O0WLFiV37tyG\nsvXr1zNo0CAKFSqEj48PrVu3Zt26dYZyrUZDfyFwBLogM79EAPFI6cBhyJlvG8A4IngbUALwA6rF\nxFCxYkWOHDlCYGAgLVq0wNnZ2TCOlL5yHTp0wNbWlpw5czJ06FC2bNliklNVoVAo3hRubm6EhYXx\n+PHjdz2UDxJlfNNBo9HQtm1blixZkmrJOSwsjNOnT+Pp6Wmo7+npyb59+wzHNpaWOCS9zok0uneB\n/4BEIJ/RtYwTkx8wOraPjyc6MpL8+fPzxRdfMHjwYKytrdMd86RJk6hZsyZZs2alfPnyhIaGGnKq\nKhQKxZtEo9FQpEgRzp07966H8kGijO9zyJMnD/ny5WPr1q0mOpN6vZ6SJUty9OhRw7mjR49SvXp1\nw7GZmRkRafSZnBvVeDc20Oh1VWSuVIBwc3Msra05d+4c1atX5+uvv053rMnZaqZOnUpISAhHjkgd\nJBVJplAoMopixYop4/uaqPSSL2DOnDmEhoZiY2NDfHy84XyjRo0ICAggb9683L59m2XLljF37lxD\nubVOx7HISGqnWPZNzo06ChiPzOt8AvBIKq8FtAPmAv9aWXFn0SJsbW2ZM2fOcz2tg4ODsbW1xdnZ\nmZCQELXfq1AoMpyiRYsq4/uaqJnvC8iXLx9lyz5bGE7OTTpgwAB8fX1p3LgxY8aMYcqUKXh5eRnq\n5Mydm1nW1iYKRsnMAJyAUsBqpOZl8hK1LfA3sBH4JzKSe/fusXfvXiwtLVONzThPqq+vLz4+PpQu\nXZoGDRrQvHnzzJszVaFQvBWKFi3K2bPpaagpnofKcJWBVCxalCH//Uf9F9Qrj0w12djoXC8LC+ab\nm7P3n39M9C8VCoXifSAiIoKffvqJySNGUMHTE3Nzc5xdXWnarh01a9ZEq1Vzu+ehjG8GsmLFCkZ0\n7MiByEgck87dBX4EogEr4BLwBzKfc/Ls9yjgrdEwacYMunbt+raHrVAoFOly69YtJo0Zw6KFC6mi\n1dI4PBwnpFPpDWCenR0Rej3d+vWje8+ez3USzcwo45vB9O3encMLFvB9ZCSLgS1Iua3jyDzOeYAn\nQF6gO1AIqKvVUqdZM5MMWwqFQvGuOXbsGA1r1aJVeDg94+JwT6OOAA4Co2xsiChalHV//aVkTNNA\nGd8MJi4ujqply3L9zBkGAh3AMAtOJgFplAOQzlcFypTh6NGjatlGoVC8N5w9e5bPPv2UWeHhPEEK\nwiQHV+qB0zxzHAUZUtnX0pJDhQrx96FDhpS4V65cYfbs2W938O8hyvhmIEII/Fq35vKGDawzWnpO\ntz4y+cYSFxf+PXlSSXEpFIoMxcPDg3v37mFmZkb27NmpX78+P/zwQ6rfnvj4eIq4uzM0JIS2QjAf\nU+ObHgJoa22NfYsWzJg3L2Nu4gNFTa0ykGmTJnFywwY2pzC8KeN8k9EAI4FWDx/SuE6dNGN0vb29\nmTNnTsYMWKFQZCo0Gg2bNm0iLCyMf//9l5s3b/LTTz+lqrdp0yayh4XR9hXnahpgSnQ0y5YvV5mw\nUpBpje+bkg1Mj9jYWCaOGcOCyEhsX7HtyLg4HgcFsXfv3lRlxuFFCoVC8abIkSMHzZs3Z/PmzYD0\nZk4Wj+nUoQOfhoWl2zZ5QnEImdHP2EQfAIiJYf7cuQwfPlyJwSSRaY3vm5INTI/169fzSUICxV5n\nbEC3iAhmBAT8v8ehUCgUzyN5hS04OJglS5bg7e0NwMSJE1m1ahU//fQT8ZGRrAV2v6CvishcBTuN\nzi0FWgjBnOnTU9XPzGIwmdb4wotlAzdv3kyZMmVwcHCgVq1aJnWvXbuGVqtl7dq1FClShJIlS7J4\n8WJD+a8TJlAxPBxfIAtQDgg2uva/SK/nAsBUo/PDkfKCbYXgrx07OHLkCFqtNk2BhJCQEEqWLMnk\nyZPZvXs3bm5uJuUeHh7s3Cm/BvHx8SxevJhPP/2USpUqsWTJEpOMXQqFIvMhhMDX1xdHR0fc3d25\nevUqP/zwAwAbNmxg0KBBaLVaSuh0tAHWPb87AFoCyVOaMGAr0Bu4EhKSqm5mFoPJ1Mb3ebKBAHZ2\ndixevJhHjx7x7bff0rNnT4KCgkz6WLlyJX/99ReTJk3Cz8+P6OhoAI6ePctqpDrRI2S6SBujdnOB\n5cBKpJNV8mJL8pzbAShpbc3ly5fTHPvVq1fx9vamV69efPPNN2nWMV6iXrRoEQEBAfz888/8eALM\nvQAAIABJREFU8ssvTJo0yeRhQaFQZD40Gg0bNmzg8ePHPH78mK5du1K0aFHCw8M5deoUnp6eRERE\nYAt48mIHK5DGdy0Qm/SvJ/AJEB0Xl8qPJTOLwWRq4wvPlw308vKiWLFimJmZUadOHRo1asSGDRtM\n2g8YMAA3Nzdq166Nh4cHe/bsASAiJoZuQH3km1wSMI5064EUWSgLVAb+Sjpv/NF0EIKnT5+mGvPZ\ns2fx8fFh5MiR+Pn5vdR9rl+/nh49elCuXDnKli1Ljx49TCQQFQpF5kav19OtWzfCwsI4ePCgQTzG\n3t6eMGTyn+ov6gQoCrgjZ7xLgVbIGbCtlZVJ+GRmF4PJ1Mb3ebKBII3cV199xSeffIKDgwOrV6/m\n1KlTJn0Yp37MmTMnwcHPFpfLkj7GCSNzArfTqBOh0WBjY2NyTgjBkiVLcHV1pUmTJi9xl5J//vnn\nuRKICoUic5L8mxceHs5vv/2GTqejSpUqBvEYrVbLqchIlgC+L9lnK2AacqbcFOl0VSRvXpPf18wu\nBpOpjS+kLxsohODbb7/F1dWVPXv28OTJE5o0afLST2X2Ot1L7Y+kxBWZgjIeuBAXx5MnT0zKNRoN\nI0aMwMnJiVatWhn2R3Lnzs2jR49ISJBSDg8ePODWrVuGdlWqVHmuBKJCocicNGjQAL1eT758+fj7\n779ZsGABNjY2BvEYf39/bO3tqQ14JbXRYCoWk9JFtSWwF6iBXPGbYWdH1/79lRiMEUpSkPRlA2/f\nvk22bNlwcHBg48aNbNy4EV/fl3v2+6JpUxYuXEhdIagFnEMa1rSSrAmeLTf7AP2A6YCzqyvr169P\nVd/CwoJVq1bh6+tLu3btWLRoEQULFiRbtmzMmzePRo0aMXz4cJMPcqNGjZg+fTrlypVDq9Uyc+bM\n5+oDKxSKj5+rV6+mW2Zra0vPnj3p2bMnu3btomfDhhAeDkD7pL9kElK0dTM6dw34VwhWtGiBTqcz\n1NFqtcyaNYtZs2Y96ychZU8fL5l+5gvpywZOnjyZlStXkidPHpYtW0aXLl1M2j3vKS1g0iQSzM2Z\ngpQP9EeKKUDqp0Tjp8h8SI/nHzQaHsXE4Ofnl+Z1LCwsWLt2LXfv3qVTp04IIfj111+ZO3cuFSpU\noGTJkri6uhrqt2nThr59+9K9e3e6detGnz59aN269Uu9PwqFInPj7e2Nnbs748xfbb4WC3TU6ejR\ns6eJ4VWo9JIZSue2bYlftYo5MTGpDO7z2Ax0dnTkSkgIVlZWGTU8hUKheGlCQkKoVKoUPR4+xCMx\nkcNAKFKdLRdyqTmvUf1IoJWNDZpq1Vi9ZQtmZmbvYNTvL2rmm4FMnjGDE+7uDLaw4GWfcHYDHXQ6\n1m7dqgyvQqF4b9BoNDRq2pQRQjAJyIbUIi8EhCS9rgdsABYCn9raoq9blxV//KEMbxqomW8Gc+/e\nPRr4+OB++TJDo6Mpnk69h8BsrZYpOh3LN2zAx8fnbQ5ToVAo0iVZSrBhZCQ9Y2LSzNwXCawAhgJW\nzs4EzJyJr69vpnKiehXUzDeDcXZ25u9Dhyj27bfUyZKF6no984FdwH7kU+JXNjYUsLbmv8aN2Xvk\niDK8CoXiveHkyZNUr1SJgo8f82s6hrc4Mg74K6A1EP7wIZvXrHmr4/zQUDPft0hcXBwbN25kxZw5\n3Lt9m9i4OBwdHfGqX5+Ofn5ky5btXQ9RoVBkYrZu3cr48eM5d+4cZmZmlCpVihOHDlH9yROsgUUv\n0ccI4Dxw0dYW/wkT6NqjR8YO+gNFGV+FQqFQMHv2bAYOHIi/vz+dO3fG2dmZgQMHsnX2bDrExxPE\nyxnf4cBlZBa/Ni4uXAwONslspZCod0ShUCgyOWFhYQwcOJD+/fszYcIE8ufPj52dHYd27GBGUu6D\nBKAn4AI0A/4zau8B/J2iz4qAQ0QECxYsYMCAAbi7u+Pv78+5c+cy/oY+AJTxVSgUikzOmTNnCA0N\npWHDhoZzx48f5/Ht29RBJgFaA5RCGl0HYKxR+7RcqjRAl7AwuiWJNZw5c4Zq1apRp06djLuRDwhl\nfBUKhSKTc/PmTfR6PcWKPXOnOn/+PBW1WoOR+ASZLMgR6ATseMm+zTUaOnTogF6vp127dmTLls0g\nopCZUcZXoVAoMjlubm6EhYVx9uxZw7mnT5+iN0q3W8qovgsyB/2LlHePIxXeHB0dDX9BQUHs3bv3\nDY7+w0TldlYoFIpMTvHixXF0dGTjxo2G2a9eryc8KTnG60bqlkFKCT5+/PjNDPQjQhnf94irV69y\n5coVwsPDsbe3p1ChQib6wgqFQpER6PV6JkyYwKBBgwgLC6NTp064uLiwJz6eXsj89K+DFZAgBAsX\nLqRBgwbY2Nhw8OBBChYsmOl/25Txfcckx/7OmDCBM2fOUMzSElshCNNoOB0TQ9XKlek+YAC1atVS\n7voKhSLD8PPzw9XVlXHjxvHbb79hbm5OhEZDIWQGvrQEYdLCWChmll7P1IkTuXDhAsOHDyc8PJyK\nFSsyY8aMjLqNDwYV5/sOOXPmDA1r1iR3RAQ9wsNpDFgalUcAS4Ff7OzQ5MzJxp07cXNzezeDVSgU\nmY4xo0ezdMQIXOLjDSIKOYFWQEPA4jltjwONnJy4cucO5q+ohpQZUMb3HXH06FHqfvYZU8PDeZGw\nnwCmmJkxLUsW9hw+TL58+d7GEBUKRSbl2LFjjB40iL379vFlTAyNkVrkscgEGnOAIKAzMBCwTtE+\nGqil09FoyBC+HTTobQ79g0EZ33fArVu3qFiiBL+EhuL7Cu1maLX8mCsXh8+cwcHBAQAPDw/mzp2L\nj48PY8eO5erVq8yePTtjBq5QKD561q5ZQ9d27RgRGUlbwC6demeQIgr3kDnqk/eFI4GWNjZY1ajB\n8g0b1HZZOqh35f9BYGAg/v7+uLm54eTkRNWqVTl69OgL200ZN46W4eGvZHgBuicmUvrhQ+b+/rvh\nnLFiyODBg5XhVSgUr822bdvo3rYtf0ZG0o30DS9IMYXVSCnB+kAY0ghXt7VF//nnLFq9Whne56De\nmf8HnTp1olixYpw9e5bg4GCGDRv2Qg3eyMhIFi5YQE+j+LlXoVdUFL9Onkxi4osi7BQKheLFeHh4\nsHPnTkJDQ2ndpAlroqIo+5JttcAUIDeQ18yMCcWL03fWLBatXq30yF+AMr6vycWLFzl//jw9e/bE\n3t4ea2tratWqRYkSJQDYuHGj4XjmzJlERkYC8PPPP/MwIoKjQGEgL7AMmbKtatLxj0bXuQz4IIWr\nSwL7AMunT9mxI3V+meHDh9O2bVvDcdOmTcmZMydubm7069ePK1euvPH3QaFQfNhoNBo0Gg0LFyyg\nphBUSTqf8LLtkQY4wcKC7QcP0rp1a6Xh+xIo4/uaJMfgtmjRgk2bNvHkyRND2a5du/j6668ZOHAg\na9euZfXq1UycOBGAQ3//jQZYh0xEPgyZsq0/8Aty2WYocNPoWt8DIcBiYBWQLyKCvbt3pxpTyg98\nvXr1CAoK4vDhwzx48IChQ4e+obtXKBQfE4mJiYwbPpxzkZEMA9yR0oBXMH34nwCEG7W7jnS6qgA4\nxMdTw8eHIUOGvN3Bf6Ao4/v/YNeuXRQsWJDu3bvj6upKnz59ePToEevXr6d169bUrFmTggULMmjQ\nINatWwfAk6RML98CuYA2SG/mOsj0bSWRaiA7k66RH6iBdOkvCXRDfiEe37mTajwpfec6dOiAra0t\nOXPmZOjQoWzZskUtVysUilRcuHABoqI4D8QBp4DByN8m44f/lcByo3ZNAHuk81XN+HgOHzmiZr0v\niTK+/w/c3NwYN24cN27cYMeOHWzfvp1p06bxzz//4Onpaajn6enJ6dOnCQsLw9xCRsYl50k1R7rw\nG+dNzQEEJ70OB3ojnRocgL7AbcDSOqVzf2omTZpEzZo1yZo1K+XLlyc0NJTr16//v+5ZoVB8fNy7\nd4/cGg3mSD1eB2T4UFoP/xuS2twFzgKjkTPjIYBWGd6XRhnfN0TFihVp2bIlu3btokqVKiZez0eP\nHqVEiRLo9XqcXFxeqr/kj/AvwAXkE2coMBX5ZJr9BanZDh06xJQpU5g6dSohISEGFREVWaZQKFIS\nExODhRCUwjTRT1oP/6eSyg4DBXgW46tDGd9XQRnf1+TChQtMmTKF4OBgEhISCAwMZMmSJbRv355G\njRqxbNky/v77b4KCgggICOCLL74AoMGXXyKQyznpYVx+Gynh5QwcAX5CxtE1bdbsueMLDg7G1tYW\nZ2dnQkJC1H6vQqFIF1tbW6KSZr7GpPXwn7xxVR6ZaCMq6TgUSFQP9y+NMr6viV6v59ChQ1SsWJGs\nWbPSr18/WrVqRbt27fD29mbq1KmMHTsWX19fGjVqRP/+/QE5QwbptZwexrlR+yI/3O7AN4AnYGlp\nScGCBVO3S/JaBPD19cXHx4fSpUvToEEDmjdvrvZiFApFmuTNm5fL8fGpJAJTPvz/bFTmAhRDOo3e\nJ2mPWBnfl0ZluHoH/PzTT2wYNIg/IyMxe4V2kcCntraMWrKERo0aZdTwFApFJiJv3rz8+uuv9PDz\nwzw4mAtGZdeAPsB+oAjQFrnHeyOp/CowBvgDiDc3p4yXF5UqVWLUqFFv7wY+UNTM9x3QuUsXKFWK\n7lZWLxSjTiYGaGVjQ6n//Y+GDRtm5PAUCkUm4erVqzT/4gvaNWuG9aNHWGK6JeYBrAceIFfrOvPM\n8ILMS/A7sAbIniOHEn55BZTxfQdYWlqyeutW/itWjOY2Ntx+Qf3LgJdGw52CBZmzdKlaPlYoFP8v\nhBAEjBtH+aJFifvlFw6EhXEqKgpLYPwr9vUQ6KTT8f2YMWrZ+RVQOk/vCAcHB/46cIBBffpQfOFC\namq1dIuIoDQyn+pT4B9ghp0dR4FmLVqwYt06zp07R+nSpbl69SqrVq3i7s2bxEZH45gjB1WrV6dm\nzZoqn6pCoXgu3/fvz6Zff+VEdDSuRuf/ACojDcO3pK/Zm0wIUE+nw7dzZ9q2b8/fu3erycFLovZ8\n3wOePn3K4kWL+H3aNC7fukV4TAz21tYUyZePrgMG0LRpU2xsbFi2bBl9+/alVP78HAsMpFliIvli\nY7FEPn1usLMjws6Obt98Qyd/f4PykUKhUCQzd/ZsJvTpwz+RkQYlImNuAfWQTlZfJ71O6ZsSAvxu\nZsYMS0t6DhjA4GHDlNF9RZTx/UCIj4+ne8eO7Fy+nO/j4miBjKszRgAHgek2Nhx1dGTL7t1pekUr\nFIrMhYeHB3PmzMHLy4t8Li6se/gQz+fUj0aGF81AGtrPkV7PEcA1nY59iYk0a9aM7v36UapUqef0\npEgPtT75AZCYmEj7Zs24sWYNJ+Li6Ig0vLsBY/cGDVAJWBYVRf87d/CuWJFr16691DWuXbuGVqs1\npJ+sW7cuixYtepO3oVAo3hHJYYibN2/GNTbWYHjTE0+wBtohH+bXAiWQ22HnNRqiypfn2p07zFqw\nQBne/wfK+GYQW7duxcvLi+zZs+Pi4kLdunU5cODAa/U1efx4rm7bxrrISPQv2aZzYiKDnjyhvo8P\nCQkvq0/yjC1btpgoJCkUig8bIQTff/MND8PCXkk8oQly2XkTcFYI9h848Fq/KQpTlPHNAGbPnk3r\n1q359NNPOXjwIJcuXaJjx46sWLHilfuKjY1lysSJzI6MxOYV236dmIjdgwds2bLlla+rUCg+LjQa\nDZevX+cqryaeoAFmAtORq22JCQksXbr0LY7840QZ3zdMWFgYAwcOpH///kyYMIH8+fOj1+v58ssv\nmT59OocPH6ZSpUo4OjpSqVIlfv75Z+Lj4w3ttVotixYtonTp0uTOnRt/f3/yxsXRD3BFZpOJT3HN\nn5HxeHWAQ0bnI4FPwsJo1aIFtWvX5o8//jCUCSFYunQpRYsWpXTp0uzdu9ekT29vb+bMmQPA5cuX\n8fHxIVu2bJQsWZIJEyYQHh6OQqH4cHjw4AHR8fGvJJ6QTDtkOsmCQHYLC/bte16OPsXLoEKN3jBn\nzpwhNDQ03UQY5ubm/Pjjj3h6enLo0CFatWpF4cKFqVmzpqHOokWLWL16NTdv3qSGjw9FkbF3rkDN\npL9qSXXvIhOcHwT+RH6J7gK2yOWj64BFfDzt27enV69e6PV6vL292bx5M0OGDGHBggXY29vTuXNn\nE29F41SVAN9//z3Vq1fnv//+46uvvsLJyQk/P7839K4pFIqMICYmhjVr1hASHEzH1q3RIPdvU4on\nfI8MbbyIzN2cNUU/pY1em2s0PE6SRlW8Pmrm+4a5efMmer2eYsWKpVletmxZKlSogJmZGZUrV6ZN\nmzZs2GD6nNmtWzcKFCjAZ599hlarpQpQHciHNLw7jerGI59iXYAOyCfXP5PK1iOXlcrb2ODk5ETr\n1q0NusJbtmyhdevWVK1alZIlS9K5c+d0A+Tz589PjRo1sLCwoGTJknTr1i3VmBUKxfvF5s2bcXd2\nZn7XrjjExbEuPh5HUq+cPU88ISWJwKOEBGxsXnUTTJESZXzfMG5uboSFhXH27Nk0y4ODg+natSsl\nS5bE3t6eqVOncurUKZM6xh6EQgjKGpUZa/2C9EDMZ3RcFjkLDgdOI4UY7BMTefr0KZ6enoblosOH\nD1O69LPn2TJlyqR7T+Hh4fTu3Zvy5cvj4OBA3759U41ZoVC8PyyYNw+/pk1Z9/Qpf4WFSbk/4FPg\nToq6zxNPSMk2wM7eHnt7+4wYdqZCGd83TPHixXF0dGTjxo1plo8ePZq4uDi2bNnCkydP6Nu3ryG8\nJy3MtFpinnO9cGT6yWSOIcON7JCz4KNAuFaLra0tR48epXr16gBUqFCB48ePG9oFBgame41ffvmF\nCxcusHLlSkJDQ5k6depzx6xQKN4df/31FwN79GB3VBSVjM5rkF7N95DxusmkVE7rTvqZrWbY2VG+\nalWVUOMNoPZ83zB6vZ4JEyYwaNAgwsLC6NSpE9mzZ2fnzp3s3r2b27dvU6hQIZycnNi9ezcLFy7E\n3d093f6sLS25HhVlOE65MGwGjETu7/4FnAFqJ5U1AiYCZ2NiePDgAcuWLWPu3LmAjOPt06cP//vf\n/9Dr9QbnqrS4ffs2jo6OODs7c+TIEX7++XnPxgqF4m3i4eHBvXv3MDMzo3Dhwly7eJHfo6L4xKjO\n1aR/fYA9wGQgWeHbA7lFZUznNNr+g1xVu75sGTpdyhQ/ildFzXwzAD8/PxYvXsyBAweoWLEihQoV\nYu7cubRs2ZLhw4dz4sQJXF1dCQgIoGfPnqkcnYzJkTs3u6ysnpVj+lSaE6gAVESGCfyFnPUCDEB6\nMj6KjaWrvz93g4NpUq8eRVxd+WfPHnr27Im/vz/t2rWjW7du6T7N9u3bl6ioKNzd3fnmm2/o3r27\nevJVKN4TNBoNmzZtIiwsjK5duxIaFkbkc+r/ilQimp90LEj9UJ+S00BjGxsWr16tDO8bQqWXfM+J\niIggj7MzgZGRpD8/Tp8qQC6NhlFC4IzMaHMDWGhpySKNhmpVqjB19mzy5cv3/I4UCsV7Sd68eZkz\nZw4+Pj40r1+f4M2b8UCuiM0EViBXw/oCRZPaVAAuAVmQeeGPIyMl5iT9C9KR8xJQF+hrY8NPc+fS\nvEWLt3NTmQA1833PsbW1pZOfH71tbNJNBZcea5GGdrEQFEaGD2RHOmH9GBvLjZgYKu/eTdWyZTl6\n9OgbHrlCoXhbJM+htuzYwTWgIVKdqChyK6oaMg9AMjpkjK8HciVtJFIz/C5SWOEEsEurZa2ZGbPL\nlmXd9u3K8L5h1Mz3AyA2Npb/VatGvpMnmRkTY7JR3wGZ33lUijZ/Am2RiTbOIr9k6bEe6Gpvz76j\nR9MVYqhbty4tW7ZUKScVivcMDw8PHj58iIWFBY8fP2YsUA4IQG5DJVMG+A2ZLMMbuSU1B7gPzEXu\nAWdBiipkz56drK6u5Mid2yQ5j+LNoWa+7yEeHh7odDr0ej16vR4nJycKlSnDIXd3smm17ObZHk3K\nPeBbwHdIo7wB6dXo8YLr+QKDw8Pp3KpVunVUrmeF4v1Eo9GwYcMGHjx4gJlGwwBgB7APGUKU/BcE\nJOex0yD9RECuhg0EcgHLgNIODvy+ciV1GzYkS5Ysb/NWMhXK+L6HGDtQJP/NnDmTPgMGkM3Dgx55\n8lDczo7JGg3XkVlpFgANraz4BBl+dBC57PSydE1M5PzZs+nGJysUivcbrVZLFp2OEKRXszfw2Ogv\nDBlKlEzKUJfcyPzOtxMScHJy4vjx48qxMgNRxvcD4b///qNnz55cu36dG48ecVOj4VLbtlx2ceFw\nrlwMzZWLzXFxVAQG8Wy2q0WqloBcgv4VmV6uNmC8mLQUsIiKon2bNuTKlYvmzZvz33//GcpVrmeF\n4v2nUcOGLNZqqYn0UF6INLzRSFEE4wQ9KfcbayB/HxJ0Oi5dusSePXvewogzL8r4vqek3IovUqQI\nM2fOpFKlSoSFhfH06VNmLljAZ3Xq8DA8nMFDh5LV0hId0sMxLSYAq4A1SAPdC/mFTOYOcPbMGU6e\nPEnZsmVN8k2nles5JCSExYsXs3LlSpYvN9ZBUSgU74Lu33zDTGtrQH63LyAdLPMgY3uNf1VSzmn9\ngTtmZtyPjGTlypV07twZRcahkmy8hwgh8PX1xdz82X9PQECAybExXl5elCpVirxWVrSJjmYaqR2w\nQO4BTwQKJf21BtYhl6dAPonFxseTJUsWBg4cyPTp0wkMDKRs2bIm/eTPn5/8+fMDmOR6VkILCsXb\n5+rVq4bXnp6e5HB3Z/l//9EaGJP0l5JdaZyLAR5aWHD9+nWyZk0praB406iZ73tIsgPF48ePDX/p\nGTaNRkPp0qWJjY3FEimwEJxGvTCkfqen0TlPpFNGMgUBS3Nz4uLiAJnv+d9//03Vl8r1rFC8v/Qb\nNowuGg2pv7npcw+oq9MxNiBAGd63hDK+HxBmZmZpKg8JIXB0dOTRc/It63mW6zmZo0i1pGQuAfGJ\niQbFkuPHj1OpknF2WInK9axQvJ/s3LmTr7/+mm7ffENDnY5VvDh71Umgsk5H67596daz51sYpQKU\n8X1vScvIenp6cunSJRPnpuR6hQoV4gEy1Cg9GiFj/y4i94OWIcOMkokHcmbPzoMHDwgICABIteQM\nKtezQvE+Mn/+fFq1asWqVasICAhg865djPTwoIitLT9qNIQa1Y0DVgM+dnbUsbfn++nTGTp69Dsa\neeZE7fm+pzRo0AAzMzPDce3atVmzZg2+vr4UK1aMqKgo7t27Z3CEsrKyoqO/PxunT0cT/0yx09ip\nYgAwD2iMzAk9BfAyKrcBCpcsSdGiRdFZWZEYHo69tTUWZmZEJyRgb2tLnTp16Nu3L3369MHd3Z0i\nRYrQvXt3RqsvrkLxRhBCsH//fg4fPsyT0FCsbWzIlSsXX3zxBQ4ODmnWHzZsGIsXL2bPnj0ULlwY\nkMplp65cYf/+/cwICGDg1q1YabWYazQ8iY2lcqlS9Bg0iC+++AJLS8u3fZuZHpXh6iPi6tWrlC9a\nlKvR0ehfse1IZC5XvVaLk6UlPWNiaCQETsgZ8TVgnpUVyzQaanz2GVN/+w1XV9c3ewMKRSYmPDyc\nRQsXMmPiROIePOB/cXE4xsYSrdVy0caGvxMSaNa0Kd2/+cag+R0TE4Ofnx8XL17kjz/+wNnZOd3+\nE5N0vePi4siSJQsWFhZv69YUaaCM70dGtw4duLFqFRsiI196WeMhkN/MjIiEBNYDn5P+fkQYMMXM\njDlZsrB1zx6KFSv2JoatUGRqLl26RF1vb0o8ecLXERF4kzoU6A4wx8yMny0t6fvDD/h17UqTJk1w\ndHRk8eLFSm3oA0MZ34+MuLg4fGvVwuzIEZZFRmJrVNYNmcXmB6SXsz+wHahiYcGtuDgKAedf8jqL\ngO+dnPgnSR7xVejWrRu5c+fmhx9+eKV2CsXHSFBQENXLl2fEkyf4v8TP8S3gcxsbHtjY0LJ9ewIC\nAky2qBQfBsr4fkQki2qbm5uTmJBAXHQ0vsC0xERypqh7DvjVyopFyOUofVwci5Bp6V6WkWZmBH72\nGeu3b0+3zvz585kzZw779u1Lt45CkVmJjo6mZIECfBMSQpdXiBi4C1SwsGDS4sWcPXeOy5cvs2jR\noowbqOKNo7ydPyKSc0I/ffqU3Xv20Parr9hsbU1ec3O87e1pqtfT2N6eCvb21HBwwLFvX/oOGkR9\nCwtex92iX0ICe/ft48aNG2/8XhSKD4WUQih6vZ5evXq9VNvVq1fj/uTJKxlegBzAb3FxjB08+DVG\nrHgfUN7OHynlypXj999/p1KlSnTp0oX206Yxf/58smfPTsDXXxMbG0vHjh2xFYLfIyP5FzkbHoTc\nA+4PdAKSXTJ2Az8hE3X4J/05Ag2FwN3dnYSEBLRa+Szn7e1N27ZtqVKlCl27diU+Ph69Xo+FhQWP\nHj2iQ4cOuLm5MWpUWnm4FIoPi+SHXh+fV1k3ksyYMIEBr5kXvRbQ884dbt16XoBh+sTHx6ebNU+R\n8aiZ70dOo0aN0Gg02NjYkC9fPooWLYqXlxcWFhbExcWR+OQJVZCB+D8jw4/WInU/5yb1cRX4AmiJ\nNMKBQN+ksmaxsamumRz+VLhwYWbNmmXIR/3o0SOTcoXiY2fXrl20adOGrFmzUqxYMU6cOAFIBaIt\nW7Zw88oV6iMlQIcktXkM1AeckVnnhiIzUCVzHxk2mAuwiYjg3717MSYwMJAOHTrg4eHBkCFDuH37\ntqHMw8ODGTNmULlyZbJkyaKS47xDlPH9yMmWLRuFCxfm5s2bgGnyjoSEBPKamRk0gZsBVYFSQFdg\nU1K9ZA/oL5EOW6OTyhKB9Fyt4uLiCA4O5u7du9LIp/iSK1cDxcdEWp/n48eP06JFC+rVq8fdu3fZ\nuHGjSerG8+fPU12rxRxTXW6BXHW6AfwJHAamG/XbDSn9dwL5nTwfFGR4mI2MjMTLy4uSIrQ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BY/+DEoiGuvvbbkcaV05zRt2pTBgwdz+eWX065dO+rXr18iZ/ny5cupX78+VapU4bXXXmP69OlY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\nXLUCEW4XAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x113f93e90>"
]
}
],
"prompt_number": 196
}
],
"metadata": {}
}
]
}
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