def index(self):
h = HTML()
h.h1("Hello world")
h.h2("And")
h.h3("Bye")
indexString= str(h)
return indexString
def buildFlotTitle(category, title, timelineStats, uid):
"""
Builds markup to render title for txn visualizations
:param category: Category of transactions visualized by this flot
:param title: Text title for this visualization
:param timelineStats: Timeline stats with delta series to be plotted
:param uid: Unique identifier to generate css selector id
"""
constituentNames = [
'{} -> {}'.format(deltaSeries.beginProbeName,
deltaSeries.endProbeName)
for deltaSeries in timelineStats.getTscDeltaSeriesCollection()
]
title = '{} {} charts {}'.format(
category, title, ' for constituent - ' if constituentNames else '')
element = HTML().div(klass=TIME_POINT_STATS_TITLE)
element.h3(title, style='display: inline')
if constituentNames:
elementId = '{}ConstituentSelector'.format(uid)
constituentSelector = element.select(id=elementId, klass=SELECTOR)
for i, constituentName in enumerate(constituentNames):
if i == len(constituentNames) - 1:
constituentSelector.option(constituentName,
selected='selected')
else:
constituentSelector.option(constituentName)
return element
def buildDiffTitle(lhs, rhs):
"""
Builds a title for the transaction diff table
"""
from xpedite.report.markup import TIME_POINT_STATS_TITLE
title = 'Transaction diff\ntxn #{} vs txn #{}'.format(lhs, rhs)
element = HTML().div(klass=TIME_POINT_STATS_TITLE)
element.h3(title)
return element
def html(self, n=None):
html = HTML()
if n: html.h3('Node #' + str(n))
if self.host:
html.b(self.host)
html.b('(' + self.ip + ')')
else:
html.b(self.ip)
p = html.p('')
if self.city: p += self.city + ', '
if self.regionName: p += self.regionName + ', '
elif self.region: p += self.region + ', '
if self.country: p += self.country
html.p
html.a(WHOIS_URL + self.ip)
return html
def buildReport(self, app, resultOrder, classifier, events, probes,
txnFilter, benchmarkPaths):
"""Builds a html report from contents of a profile info module"""
from xpedite.report.codeFormatter import CodeFormatter
highlightWrapBegin = '<div class="wy-nav-content">'
highlightWrapEnd = '</div>'
description = """
<div>This report provides an overview of profile info parameters used in recording this profile.</div>
<div>The report below comprises, the list of probes, performance counters, classifiers, and other attributes that control transaction generation.</div>
"""
report = HTML()
report += description
appInfoList = [
'App Name = {}'.format(app.name),
'App Host = {}'.format(app.ip),
'App Info = {}'.format(app.appInfoPath),
'xpedite Run = {}'.format(app.runId),
]
if resultOrder:
appInfoList.append('Result Order = {}'.format(resultOrder))
report += formatList(appInfoList)
report.h3('Probes')
report += self.buildProbeTable(probes)
if benchmarkPaths:
report.h3('Benchmarks')
report += formatList(benchmarkPaths)
if events:
report.h3('Performance Counters')
report += self.buildPmcTable(events)
if txnFilter:
report.h3('Transaction Filter')
report += CodeFormatter().format(txnFilter, 'highlight',
highlightWrapBegin,
highlightWrapEnd)
if classifier:
report.h3('Transaction Classifier')
report += CodeFormatter().format(classifier, 'highlight',
highlightWrapBegin,
highlightWrapEnd)
return report
def buildStatsTitle(category, benchmarkNames, transactionCount):
"""
Builds title markup for the stats table
:param category: Category of transactions in this profile
:param transactionCount: Number of transactions
:param benchmarkNames: Names of given benchmarks
"""
title = '{} latency statistics ({} transactions) {}'.format(
category, transactionCount,
' vs benchmark - ' if benchmarkNames else '')
element = HTML().div(klass=TIME_POINT_STATS_TITLE)
element.h3(title, style='display: inline')
if benchmarkNames:
bechmarkSelector = element.select(
onchange='onSelectBenchmark(this)', klass=SELECTOR)
for benchmarkName in benchmarkNames:
bechmarkSelector.option(benchmarkName)
return element
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])
h.h3("TP samples are")
for k in range(0, len(th_list)):
