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-rw-r--r--examples/har_extractor.py11
1 files changed, 7 insertions, 4 deletions
diff --git a/examples/har_extractor.py b/examples/har_extractor.py
index 6806989d..d6b50c21 100644
--- a/examples/har_extractor.py
+++ b/examples/har_extractor.py
@@ -3,6 +3,8 @@
https://github.com/JustusW/harparser to generate a HAR log object.
"""
import six
+import sys
+import pytz
from harparser import HAR
from datetime import datetime
@@ -52,15 +54,15 @@ class _HARLog(HAR.log):
return self.__page_list__
-def start(context, argv):
+def start(context):
"""
On start we create a HARLog instance. You will have to adapt this to
suit your actual needs of HAR generation. As it will probably be
necessary to cluster logs by IPs or reset them from time to time.
"""
context.dump_file = None
- if len(argv) > 1:
- context.dump_file = argv[1]
+ if len(sys.argv) > 1:
+ context.dump_file = sys.argv[1]
else:
raise ValueError(
'Usage: -s "har_extractor.py filename" '
@@ -119,7 +121,7 @@ def response(context, flow):
full_time = sum(v for v in timings.values() if v > -1)
started_date_time = datetime.utcfromtimestamp(
- flow.request.timestamp_start).isoformat()
+ flow.request.timestamp_start).replace(tzinfo=pytz.timezone("UTC")).isoformat()
request_query_string = [{"name": k, "value": v}
for k, v in flow.request.query or {}]
@@ -173,6 +175,7 @@ def response(context, flow):
"startedDateTime": entry['startedDateTime'],
"id": page_id,
"title": flow.request.url,
+ "pageTimings": {}
})
)
context.HARLog.set_page_ref(flow.request.url, page_id)