Python HTML.list方法代码示例

# 需要导入模块: import HTML [as 别名]
# 或者: from HTML import list [as 别名]
def RunStrategies():
    tipList = []  
    for name, strategy in STRATEGY_FUNCS.items():
        logging.info('Running strategy: %s\n'%(name))
        tip = strategy()
        if tip != '':
            tipList.append('<pre>\n' + tip + '\n</pre>')
    return HTML.list(tipList)

# 需要导入模块: import HTML [as 别名]
# 或者: from HTML import list [as 别名]
 def create_diamond_html_pages(self):
     diamonds = self.get_list_of_diamonds(self.results)
     diamondLinkList = []
     for diamond in diamonds:
         print 'Create Page for diamond: "%s"' % diamond
         results = filter(lambda x: self.results[x].get('RunInfo', 'dia') == diamond, self.results)
         results = {key: self.results[key] for key in results}
         fileName = '%s/results_%s.html' % (self.config.get('HTML', 'outputDir'), diamond)
         diamondLinkList.append(HTML.link(diamond, 'results_%s.html' % diamond))
         self.create_html_overview_table(results, fileName)
     htmlcode = HTML.list(diamondLinkList)
     fileName = '%s/results_diamonds.html' % self.config.get('HTML', 'outputDir')
     if self.verbosity:
         print 'save diamond file to: "%s"' % fileName
     utilities.save_html_code(fileName, htmlcode)
     pass

# 需要导入模块: import HTML [as 别名]
# 或者: from HTML import list [as 别名]
def create_diamond_html_pages(newResults, mapping):
    diamonds = map(lambda x: x[mapping['dia']], newResults)
    diamonds = sorted(list(set(diamonds)))
    diamondLinkList = []

    for diamond in diamonds:
        results = filter(lambda x: x[mapping['dia']] == diamond, newResults)
        fileName = 'results_%s.html' % diamond
        diamondLinkList.append(HTML.link(diamond, fileName))
        create_html_overview_table(results, fileName, mapping)
        print diamond
    htmlcode = HTML.list(diamondLinkList)
    fileName = 'results_diamonds.html'
    with open(fileName, "w") as f:
        f.write('%s' % htmlcode)
    pass

# 需要导入模块: import HTML [as 别名]
# 或者: from HTML import list [as 别名]
for current_node in as_pydot_graph.get_nodes():
	#print current_node.get('as_cc')
	if current_node.get('as_cc') == cc_input:
		current_node.set('style', 'filled')
		current_node.set('shape', 'box')
		#current_node.set('fillcolor', '.7 .3 1.0')
		current_node.set('fillcolor', 'orange')
		current_node.set('URL',"http://bgp.he.net/AS%s" % current_node.get_name())
		current_node.set('label','AS %s' % current_node.get_name()+'\n'+current_node.get('as_name'))
		as_dot_cluster.add_node(current_node)
	else:
		current_node.set('style', 'filled')
		current_node.set('shape', 'ellipse')
		#current_node.set('fillcolor', '.4 .3 1.0')
		current_node.set('fillcolor', 'green')
		current_node.set('URL',"http://bgp.he.net/AS%s" % current_node.get_name())
		current_node.set('label','AS %s' % current_node.get_name()+'\n'+current_node.get('as_name'))

as_pydot_graph.add_subgraph(as_dot_cluster)		
as_pydot_graph.write_dot(figure_folder+output_dot_filename)
as_pydot_graph.write_png(figure_folder+output_png_filename)
#html_file.write('<h1>IPv%s AS graph</h1>\n' %ip_input)
html_file.write('<h2>Graph statistics</h2>\n')
htmlcode =  HTML.list(['Number of nodes: %s' %filtered_graph.number_of_nodes(), 'Number of edges: %s' %filtered_graph.size()])
html_file.write(htmlcode)
html_file.write('<h2>IPv%s AS graph</h2>\n' %ip_input)
html_file.write('<p>This graph was last updated on %s based on RIS raw data %s</p>\n' %(time.strftime('%X %x %Z'), date_input))
html_file.write('<img src=''%s'' alt="IPv%s AS graph" width="90%%" class="center" />' %(rel_figure_folder+output_png_filename, ip_input))
html_file.close()

# 需要导入模块: import HTML [as 别名]
# 或者: from HTML import list [as 别名]
	def printDataHtml(self, filename, countStats = True, filterDataBoolean = False):
		if( self.size() == 0):
			print "no Data!"
			return 0
		f = open(filename,'w')
		#adds the unfiltered data
		f.write('computed stats for ' + self.pathname + '<p> un-filtered Statistics <p>')
		stringValue = '<p> number of points %s <p>' % ( str(self.size(dataBoolean = True)))
		f.write(stringValue)
		
		dataLabel = ['min','max','stdev','mean']
		#add a dummy line to the header, this will be the other top
		headers = np.hstack(('-',self.header_num())).tolist()
		#print "headers", headers
		
		#get the statistical data
		
		#min/max
		min,max =self.splitRange(dataBoolean = True)
		#turn into numpy matrices
		min = np.matrix(min)
		max = np.matrix(max)
		#stdev values
		stdevValues = np.matrix(self.stdev_num())
		#mean values
		meanValues = np.matrix(self.mean_num())
		#combined all of the data so that they are columns
		data = np.hstack((min.T,max.T,stdevValues.T,meanValues.T))
		#add the labels of what each column is ( max,min,stdev,mean)
		data = np.vstack((dataLabel, data))
		
		#add the headers which are the rows
		data = np.hstack((np.matrix(headers).T, data))
		#turn this numpy matrix to a list
		value = data.tolist()
		#turn this list to an html table string
		htmlTable = HTML.table(value)
		#write out this string to our file
		f.write(htmlTable)
		
		#if we have filteres applied print out that stats
		if( len(self.filters) != 0):
			#print out the filters
			filterStrings = []
			
			#create a list of all the filters, printed out as a string
			for i in range(len(self.filters)):
				filterStrings.append(self.getFilterString(self.filters[i], i))
			# this will create an HTML list of all of the filters
			htmlcode = HTML.list(filterStrings)
			f.write('<p>Filters<p>')
			f.write(htmlcode)
			f.write('<p>')
			
			#adds the filtered data
			f.write('<p> computed stasts for filtered data <p>')
			
			#number of points in the filtered data set
			
			stringValue = '<p> number of points %s <p>' % ( str(self.size(dataBoolean = False)))
			f.write(stringValue)
			
			
			# this will make the statistical table for the filtered data, this is the same process as above for the full
			# data and compare comments for information
			
			#get filtered data
			dataLabel = ['min','max','stdev','mean']
			#add a dummy line to the header, this will be the other top
			headers = np.hstack(('-',self.header_num())).tolist()
			#get statistical data
			min,max =self.splitRange(dataBoolean = False)
			min = np.matrix(min)
			max = np.matrix(max)
			stdevValues = np.matrix(self.stdev_numFiltered())
			meanValues = np.matrix(self.mean_numFiltered())
			
			#compile data into one large matrix
			data = np.hstack((min.T,max.T,stdevValues.T,meanValues.T))
			
			data = np.vstack((dataLabel, data))
			
			data = np.hstack((np.matrix(headers).T, data))
			
			value = data.tolist()
			#write out table
			htmlTable = HTML.table(value)
			f.write(htmlTable)
			
			
		#print out the count stats if the boolean is true and
		# there are counts data
		if( countStats == True and len(self.counts) != 0):
			f.write('<p> Counted occurances within the data set <p>')
			tableValues = []
			tableValues.append(['Condition1', 'Condition2', 'counts'])
			#for each count, make it a row in our table
			for i  in range (len(self.counts)):
				row = []
				countDic = self.counts[i]
#.........这里部分代码省略.........

# 需要导入模块: import HTML [as 别名]
# 或者: from HTML import list [as 别名]
    header_row = ['Last name',   'First name',   'Age', 'Score'],
    col_width=['', '20%', '10%', '10%'],
    col_align=['left', 'center', 'right', 'char'],
    col_styles=['font-size: large', '', 'font-size: small', 'background-color:yellow'])
f.write(htmlcode + '<p>\n')
print htmlcode
print '-'*79



#=== LISTS ===================================================================

# 1) a HTML list (with bullets) may be built from a Python list of strings:

a_list = ['john', 'paul', 'jack']
htmlcode = HTML.list(a_list)
print htmlcode
f.write(htmlcode)
f.write('<p>')
print '-'*79


# 2) it is easy to change it into a numbered (ordered) list:

htmlcode = HTML.list(a_list, ordered=True)
print htmlcode
f.write(htmlcode)
f.write('<p>')
print '-'*79

# 需要导入模块: import HTML [as 别名]
# 或者: from HTML import list [as 别名]
for pref_list in prefix_per_asn_dict.values():
	total_country_nb_prefix = total_country_nb_prefix + len(pref_list)	

# Rounding the number of AS paths in global_international_as_counter
for as_name, as_path_nb in global_international_as_counter.items():
	global_international_as_counter[as_name] = float(as_path_nb)*100/total_nb_as_path

output_routing_report_file.write('### Routing report for %s based on RIS raw data %s\n### Samer Lahoud - July 2012\n' % (cc_input, date_input))
output_routing_report_file.write('\n### Summary routing report for %s\n' % cc_input)
output_routing_report_file.write('\n## Summary statistics\nTotal number of ASes: %s\nTotal number of prefixes: %s\nTotal number of AS paths: %s\n' %(len(asn_list), total_country_nb_prefix, total_nb_as_path)) 

#html_file.write('<h1>Routing report for Lebanon</h1>')
html_file.write('<h2>Global provider statistics</h2>')
html_file.write('<p>This report was last updated on %s based on RIS raw data %s</p>' %(time.strftime('%X %x %Z'), date_input))

htmlcode =  HTML.list(['Total number of ASes: %s' %len(asn_list), 'Total number of prefixes: %s' %total_country_nb_prefix, 'Total number of AS paths: %s' %total_nb_as_path])
html_file.write(htmlcode)

output_routing_report_file.write('\n## International transit provider distribution for %s\n' %cc_input)
all_transit_table = HTML.Table(header_row=['International transit provider', 'Percentage of AS paths'])

for all_global_inter_asn in global_international_as_counter.most_common():
	output_routing_report_file.write("%.2f %%\tvia\t AS%s\t%s\n" % (all_global_inter_asn[1], all_global_inter_asn[0], asn2name_list[all_global_inter_asn[0]]))
	all_transit_table.rows.append([asn2name_list[all_global_inter_asn[0]], "%.2f" %all_global_inter_asn[1]])

global_figname = plot_as_pie([elem[0] for elem in global_international_as_counter.most_common()], [elem[1] for elem in global_international_as_counter.most_common()], 'International transit provider distribution for %s' % cc_input)	

# html_file.write('<h4>Top 10 international transit providers</h4>')
# html_file.write('<img src=''%s'' alt="Top 10 international transit providers" width="450" height="450" />' %(rel_figure_folder+global_figname))
# htmlcode = str(top_transit_table)
# html_file.write(htmlcode)

# 需要导入模块: import HTML [as 别名]
# 或者: from HTML import list [as 别名]

#.........这里部分代码省略.........
		}

	# dict of colors for each result:
	result_colors = {
			'success':      'lime',
			'failure':      'red',
			'error':        'yellow',
		}

	t = HTML.Table(header_row=['Test', 'Result'])
	for test_id in sorted(test_results):
		# create the colored cell:
		color = result_colors[test_results[test_id]]
		colored_result = HTML.TableCell(test_results[test_id], bgcolor=color)
		# append the row with two cells:
		t.rows.append([test_id, colored_result])
	htmlcode = str(t)
	print htmlcode
	f.write(htmlcode)
	f.write('<p>')
	print '-'*79

	#-------------------------------------------------------------------------------

	# 7) sample table with column attributes and styles:
	table_data = [
			['Smith',       'John',         30,    4.5],
			['Carpenter',   'Jack',         47,    7],
			['Johnson',     'Paul',         62,    10.55],
		]
	htmlcode = HTML.table(table_data,
		header_row = ['Last name',   'First name',   'Age', 'Score'],
		col_width=['', '20%', '10%', '10%'],
		col_align=['left', 'center', 'right', 'char'],
		col_styles=['font-size: large', '', 'font-size: small', 'background-color:yellow'])
	f.write(htmlcode + '<p>\n')
	print htmlcode
	print '-'*79



	#=== LISTS ===================================================================

	# 1) a HTML list (with bullets) may be built from a Python list of strings:

	a_list = ['john', 'paul', 'jack']
	htmlcode = HTML.list(a_list)
	print htmlcode
	f.write(htmlcode)
	f.write('<p>')
	print '-'*79


	# 2) it is easy to change it into a numbered (ordered) list:

	htmlcode = HTML.list(a_list, ordered=True)
	print htmlcode
	f.write(htmlcode)
	f.write('<p>')
	print '-'*79


	# 3) Lines of a list may also be added one by one, when using the List class:

	html_list = HTML.List()
	for i in range(1,10):
		html_list.lines.append('square(%d) = %d' % (i, i*i))
	htmlcode = str(html_list)
	print htmlcode
	f.write(htmlcode)
	f.write('<p>')
	print '-'*79


	# 4) To save memory, a large list may be built from a generator:

	def gen_lines(i):
		'lines generator'
		for x in range(1,i):
			yield 'square(%d) = %d' % (x, x*x)
	htmlcode = HTML.list(gen_lines(10))
	print htmlcode
	f.write(htmlcode)
	f.write('<p>')
	print '-'*79


	#=== LINKS ===================================================================

	# How to create a link:

	htmlcode = HTML.link('Decalage website', 'http://www.decalage.info')
	print htmlcode
	f.write(htmlcode)
	f.write('<p>')
	print '-'*79


	f.close()
	print '\nOpen the file %s in a browser to see the result.' % HTMLFILE

# 需要导入模块: import HTML [as 别名]
# 或者: from HTML import list [as 别名]
	# set a nice graphic status for prefix visibility
	if int(seen_nb_RRC) == 0: 
		prefix_status = '<img src=%s alt="NOK"/>' %(rel_figure_folder+'nok_icon.png')
	elif int(seen_nb_RRC) == int(total_nb_RRC): 
		prefix_status = '<img src=%s alt="OK"/>' %(rel_figure_folder+'ok_icon.png')
	else: 
		prefix_status = '<img src=%s alt="MID_OK"/>' %(rel_figure_folder+'mid_ok_icon.png')

	# put everything in a table row in the output files
	output_table.rows.append([prefix_status, ipv6_prefix, prefix_allocation_type[ipv6_prefix], RRC_visibility, str(prefix_asn), first_seen_time, last_seen_time, str(prefix_holder), prefix_descr])
	txt_report_file.write('%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n' %(ipv6_prefix, prefix_allocation_type[ipv6_prefix], RRC_visibility, prefix_asn, first_seen_time, last_seen_time, prefix_holder, prefix_descr))
 
	if seen_nb_RRC > 0:
		cc_announced_prefix_nb = cc_announced_prefix_nb +1

# put global statistics and insert html headers
htmltable = str(output_table)
#html_file.write('<h1>IPv6 report for %s</h1>' %country_name)
html_file.write('<h2>Prefix statistics</h2>')
htmlcode =  HTML.list(['Total number of allocated prefixes: %s' %len(cc_allocated_ipv6_prefix_list), 'Number of announced prefixes: %s' %cc_announced_prefix_nb])
html_file.write(htmlcode)
html_file.write('<h2>Allocation and visibility report</h2>')
html_file.write('<p>This report was last updated on %s</p>' %time.strftime('%X %x %Z'))	
html_file.write(htmltable)
html_file.close()

# put global statistics in txt file
txt_report_file.write('###\nSummary IPv6 report for %s\n###\n' % cc_input)
txt_report_file.write('%s\t%s\t%s\n' %('Country Code', 'Total Number of Allocated Prefixes', 'Number of Announced Prefixes'))
txt_report_file.write('%s\t%s\t%s\n' %(cc_input, len(cc_allocated_ipv6_prefix_list), cc_announced_prefix_nb))

# 需要导入模块: import HTML [as 别名]
# 或者: from HTML import list [as 别名]
def write_note(title, data):  # TODO: Improve this
    output = "<h2>%s</h2>" % title
    output += HTML.list(data)
    write_to_file(output, 'a')