def test_create_asset_timeline(har_data):
"""
Tests the asset timeline function by making sure that it inserts one object
correctly.
"""
init_data = har_data('humanssuck.net.har')
har_parser = HarParser(init_data)
entry = har_data('single_entry.har')
# Get the datetime object of the start time and total load time
time_key = dateutil.parser.parse(entry['startedDateTime'])
load_time = int(entry['time'])
asset_timeline = har_parser.create_asset_timeline([entry])
# The number of entries in the timeline should match the load time
assert len(asset_timeline) == load_time
for t in range(1, load_time):
assert time_key in asset_timeline
assert len(asset_timeline[time_key]) == 1
# Compare the dicts
for key, value in iteritems(entry):
assert asset_timeline[time_key][0][key] == entry[key]
time_key = time_key + datetime.timedelta(milliseconds=1)
def test_create_asset_timeline(har_data):
"""
Tests the asset timeline function by making sure that it inserts one object
correctly.
"""
init_data = har_data('humanssuck.net.har')
har_parser = HarParser(init_data)
entry = har_data('single_entry.har')
# Get the datetime object of the start time and total load time
time_key = dateutil.parser.parse(entry['startedDateTime'])
load_time = int(entry['time'])
asset_timeline = har_parser.create_asset_timeline([entry])
# The number of entries in the timeline should match the load time
assert len(asset_timeline) == load_time
for t in range(1, load_time):
assert time_key in asset_timeline
assert len(asset_timeline[time_key]) == 1
# Compare the dicts
for key, value in entry.iteritems():
assert asset_timeline[time_key][0][key] == entry[key]
time_key = time_key + datetime.timedelta(milliseconds=1)
har_parser = HarParser(json.loads(f.read()))
userRequestPages = [] ### User Requested Pages ###
entries = [] ### CREATE A TIMELINE OF ALL THE ENTRIES ###
for page in har_parser.pages:
userRequestPages.append((page.url, page.image_load_time,page.page_size, page.image_size))
#userRequestPages.append((page.url,0,0,0))
for entry in page.entries:
entries.append(entry)
#User Requested pages
userRequestPagesDF = pd.DataFrame(userRequestPages,columns=['url','loadtime','page_size','image_size'])
userRequestPagesDF.to_csv(outPathFile+'_userRequestPages.tsv',sep='\t',header=True,index=False)
timeline = har_parser.create_asset_timeline(entries)
webRequestURL = []
for key, value in timeline.items():
for data in value:
webRequestURL.append((data['request']['url'],data['time'])) # ,data['request']['queryString']
domain = set()
domainURL = []
domainDqpKeyValue = []
df = pd.DataFrame(webRequestURL,columns=['url','time'])
df = df.groupby('url')['time'].sum().reset_index()
def cleanDqp(dqp):
return re.sub('=+','=',re.sub('&+','&',dqp)).strip()
