def index(self):
h = HTML()
h.h1("Hello world")
h.h2("And")
h.h3("Bye")
indexString= str(h)
return indexString
def _create_page(self, link_prefix=""):
h = HTML('html', '')
h.h1("Cloud: %s" % self.cloud_name)
head = h.head()
head.link(rel="stylesheet",
type="text/css",
href="%sardana.css" % link_prefix)
heading = h.h2()
heading.a("Control Plane View",
href="%sControl_Planes.html" % link_prefix)
heading.text(SP * 10, escape=False)
heading.a("Region View", href="%sRegions.html" % link_prefix)
heading.text(SP * 10, escape=False)
heading.a("Service View", href="%sServices.html" % link_prefix)
heading.text(SP * 10, escape=False)
heading.a("Network View", href="%sNetworks.html" % link_prefix)
heading.text(SP * 10, escape=False)
heading.a("Server View", href="%sServer_View.html" % link_prefix)
heading.text(SP * 10, escape=False)
heading.a("Server Groups View",
href="%sServer_Groups.html" % link_prefix)
heading.text(SP * 10, escape=False)
h.br
return h
def write_html(classes, digest_filename):
time = ctime().split()
del time[
-2] # Don't really need the time, we want to highlight only the date.
h = HTML()
h.h1("Digest for " + " ".join(time))
for cls in classes:
h.h2(cls + ":")
unordered_list = h.ul
for item in classes[cls]:
unordered_list.li(item)
f = open(digest_filename, "w")
f.write(
'<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">'.
encode('utf-8'))
f.write(h.__unicode__().encode('utf-8'))
f.close()
def export(user1, user2, score, runtime):
h = HTML()
insertSources(h)
h.text("<div class=\"container\">", escape=False)
h.h1('Matched profile analytics', align='center')
h.p('Common interests: ' + str(score))
h.p('Runtime: ' + str(runtime))
t = h.table(klass='table table-bordered table-hover table-striped')
t.text('<thead>', escape=False)
r = t.tr
r.th('Name')
r.th(user1.name)
r.th(user2.name)
t.text('</thead>', escape=False)
common, unique1, unique2 = InterestUtil.compareInterests(
user1.interests, user2.interests)
for interest in common:
r = t.tr(klass="success")
r.td(interest.name)
r.td(interest.value)
r.td(interest.value)
for interest in unique1:
r = t.tr(klass="")
r.td(interest.name)
r.td(interest.value)
r.td('-')
for interest in unique2:
r = t.tr(klass="")
r.td(interest.name)
r.td('-')
r.td(interest.value)
h.text("</div>", escape=False)
file = open('output.html', 'w')
file.write(str(h))
__author__ = 'chris'
from html import HTML
import subprocess
import xml.etree.ElementTree as ET
tree = ET.parse('test.xml')
root = tree.getroot()
H = HTML('html')
H.h1("Speed in milliseconds")
rrd_dump = subprocess.check_output(
["rrdtool", "dump", "test.rrd", "test.xml"])
for ds in root.findall('ds'):
# name = ds.find('name').text
last = ds.find('last_ds').text
ms = int(last)
def colour(x):
if x < 2000:
return ('green')
else:
return ('red')
fontc = colour(ms)
def construct_report():
"""
construct an html report of all requests for the current day
report includes number of request(all, successful, failed) as well as a table of failed requests
"""
html = HTML()
html.h1('Daily Report')
report = {'status': 'OK', 'html': None}
current_date = date.today()
date_string = "\'{0}-{1}-{2}\'".format(current_date.year, current_date.month, current_date.day)
try:
con_mgr = Manager()
db_config_stream = pkg_resources.resource_stream('PIR', 'etc/dbconfig.yaml')
engine = con_mgr.get_connection(db_config_stream, security_level=ConnectionLevel.UPDATE, sql_echo=True)
# # query db to find number of total transactions for current day
num_transactions = engine.execute(
"SELECT COUNT(*) FROM pir_transactions WHERE trans_time::date = {0}".format(date_string)).fetchone()
# query db to get all failed requests for current day
failed_requests = engine.execute(
"SELECT * FROM pir_transactions WHERE trans_time::date = {0} AND result_code <> '200'".format(
date_string)).fetchall()
# query db to get all request that returned in over 5 seconds
long_requests = engine.execute(
"SELECT * FROM pir_transactions WHERE trans_time::date = {0} AND return_seconds > 5".format(
date_string)).fetchall()
pro_test_requests = engine.execute(
"SELECT COUNT(*) FROM pir_transactions WHERE trans_time::date = {0} AND remote_ip IN "
"('10.16.153.171', '10.16.153.105')".format(date_string)).fetchone()
pro_acceptance_requests = engine.execute(
"SELECT COUNT(*) FROM pir_transactions WHERE trans_time::date = {0} AND remote_ip IN "
"('10.20.201.2', '10.20.201.32')".format(date_string)).fetchone()
pro_production_requests = engine.execute(
"SELECT COUNT(*) FROM pir_transactions WHERE trans_time::date = {0} AND remote_ip LIKE '10.65.1%'".format(
date_string)).fetchone()
pro_dev_requests = engine.execute(
"SELECT COUNT(*) FROM pir_transactions WHERE trans_time::date = {0} AND remote_ip LIKE '172.16.132%'".format(
date_string)).fetchone()
# construct summary table
html.h2('Summary')
summary_table = html.table(cellpadding='10')
num_transactions_row = summary_table.tr
num_transactions_row.td("Number of Requests")
num_transactions_row.td(str(num_transactions[0]))
success_row = summary_table.tr
success_row.td("Number of Successful Requests")
success_row.td(str(num_transactions[0] - len(failed_requests)))
failure_row = summary_table.tr
failure_row.td("Number of Failed Requests")
failure_row.td(str(len(failed_requests)))
long_row = summary_table.tr
long_row.td("Number of Requests Over 5 Seconds")
long_row.td(str(len(long_requests)))
# construct request origin table -- this is based off of given ip addresses in shared confluence page
html.h2('Prometrix Request Origin')
origin_table = html.table(cellpadding='10')
pro_dev_row = origin_table.tr
pro_dev_row.td("Dev")
pro_dev_row.td(str(pro_dev_requests[0]))
pro_test_row = origin_table.tr
pro_test_row.td("Test")
pro_test_row.td(str(pro_test_requests[0]))
pro_acceptance_row = origin_table.tr
pro_acceptance_row.td("Acceptance")
pro_acceptance_row.td(str(pro_acceptance_requests[0]))
pro_production_row = origin_table.tr
pro_production_row.td("Production")
pro_production_row.td(str(pro_production_requests[0]))
if long_requests:
html.h2("Requests Over 5 Seconds")
long_table = html.table(border="1")
header_row = long_table.tr
header_row.th('Transaction ID')
header_row.th('Geocode Status')
header_row.th('Address')
header_row.th('HTTP Result Code')
header_row.th('HTTP Result Message')
header_row.th('IP')
header_row.th('Transaction Date')
header_row.th('Seconds to Return')
for trans in long_requests:
address = trans['street'] + ', ' + trans['city'] + ', ' + trans['state'] + ' ' + trans['postal_code']
row = long_table.tr
row.td(trans['trans_id'])
row.td(str(trans['geocode_status']))
row.td(address)
row.td(trans['result_code'])
row.td(trans['result_message'])
row.td(trans['remote_ip'])
row.td(str(trans['trans_time']))
row.td(str(trans['return_seconds']))
if len(long_requests) > monitor_config['monitoring_configurations'][config_level]['long_threshold']:
report['status'] = 'ERROR'
else:
report['status'] = "WARNING"
# if we have failed requests, include them in report
if failed_requests:
html.h2('Failed Requests')
failed_table = html.table(border="1")
header_row = failed_table.tr
header_row.th('Transaction ID')
header_row.th('Geocode Status')
header_row.th('Address')
header_row.th('HTTP Result Code')
header_row.th('HTTP Result Message')
header_row.th('IP')
header_row.th('Transaction Date')
header_row.th('Seconds To Return')
for trans in failed_requests:
address_components = [trans['street'], trans['city'], trans['state'], trans['postal_code']]
address = ', '.join(filter(None, address_components))
row = failed_table.tr
row.td(trans['trans_id'])
row.td(str(trans['geocode_status']))
row.td(address)
row.td(trans['result_code'])
row.td(trans['result_message'])
row.td(trans['remote_ip'])
row.td(str(trans['trans_time']))
row.td(str(trans['return_seconds']))
# if there are enough failed requests, indicate an error
# if there is a failed request, indicate a warning
if len(failed_requests) > monitor_config['monitoring_configurations'][config_level]['failed_threshold']:
report['status'] = 'ERROR'
if report['status'] != 'ERROR':
report['status'] = 'WARNING'
except ManagerConnectionException as e:
logging.error("Could not connect to database server. Caching failed. {0}".format(e))
html.p('COULD NOT CONNECT TO DATABASE')
report['status'] = 'ERROR'
except SQLAlchemyError as e:
logging.error('Error: {0}'.format(e))
logging.error('Unable to retrieve desired information from database')
html.p('SQL ERROR: {0}'.format(e))
report['status'] = 'ERROR'
report['html'] = str(html)
return report
tn_new[th_index].append(chain)
line = input_file.readline()
tp_samples.append(tp_new)
fp_samples.append(fp_new)
tn_samples.append(tn_new)
fn_samples.append(fn_new)
return (tp_samples, fp_samples, tn_samples, fn_samples)
th_list = read_th_nums(th_list)
feature_importances = read_feature_importance(num_of_iters, feature_importances)
feature_names = read_feature_names(feature_names)
tp_samples, fp_samples, tn_samples, fn_samples = read_samples(num_of_iters, tp_samples, fp_samples, tn_samples, fn_samples, th_list)
recal, precision, f1_score = read_stats(num_of_iters, recal, precision, f1_score)
h.h1('While training on the problems 0-200', color = ('rgb(205, 12, 24)'))
for i in range(0, num_of_iters, 2):
h.h2('considering iteration number ' + str(i))
h.h3("Feature importances are:")
table_data = []
table_line = []
table_line.append("feature_name")
table_line.append("importance")
table_data.append(table_line)
h.p("feature_name \t\t\t importance")
for j in range(0, len(feature_names)):
table_line = []
table_line.append(feature_names[j])
table_line.append(feature_importances[i][j])
table_data.append(table_line)
h.p(feature_names[j] + '\t\t\t ' + feature_importances[i][j])
