def xor(self, ctx, return_type, a, atype, b, btype):
assert atype == return_type
assert atype == btype
if z3.is_bool(a) or z3.is_bool(b):
a = util.as_bool(a)
b = util.as_bool(b)
assert return_type.size() == 1
return z3.Xor(a, b)
else:
return a ^ b
def lambda_handler(event, context):
try:
dt = datetime.utcnow()
log_stream = LOG_STREAM.format(dt.year, dt.month, dt.day)
result = {}
with Logger(logstream=log_stream, buffersize=20, context=context,
debug=util.as_bool(os.getenv(configuration.ENV_TRACE, False))) as logger:
logger.info("InstanceScheduler, version %version%")
logger.debug("Event is {}", util.safe_json(event, indent=3))
for handler_type in [SchedulerRequestHandler,
SchedulerSetupHandler,
ScheduleResourceHandler,
AdminCliRequestHandler,
CloudWatchEventHandler]:
if handler_type.is_handling_request(event):
start = time()
handler = handler_type(event, context)
logger.info("Handler is {}".format(handler_type.__name__))
try:
result = handler.handle_request()
except Exception as e:
logger.error("Error handling request {} by handler {}: ({})\n{}", json.dumps(event), handler_type.__name__,
e, traceback.format_exc())
execution_time = round(float((time() - start)), 3)
logger.info("Handling took {} seconds", execution_time)
return result
logger.debug("Request was not handled, no handler was able to handle this type of request {}", json.dumps(event))
finally:
configuration.unload_scheduler_configuration()
def __init__(self, parent, defn):
super(Account, self).__init__(parent, 'accounts/{id}', defn)
self.suspended = as_bool(self.suspended)
self.password_expired = as_bool(self.password_expired)
import matplotlib
import matplotlib.pyplot as plt
bm_file_path, _, _, _ = get_paths()
bm_results_file_path = append_to_filename(bm_file_path, "_results")
# Read results file into rows field.
rows = []
with open(bm_results_file_path) as f:
# Skip CSV header.
next(f)
for line in f:
line = parse_csv_line(line)
line[1] = as_bool(line[1])
line[2] = as_bool(line[2])
for i in range(3, len(line)):
line[i] = float(line[i])
rows.append(line)
# Create basic figure.
fig, axes = plt.subplots(figsize=(6, 3))
if action.startswith("hist_"):
hist_data = action[5:] # Strip hist_ at beginning.
hist_title = ""
# Contains indices of column containing measurements for
# when JS is on (index 0) and JS is off (index 1).
where JS was enabled and one where JS was disabled.
"""
from util import get_paths, parse_csv_line, append_to_filename, as_bool
from util import benchmark_columns as columns
import os
bm_file_path, _, _, _ = get_paths()
bm_js_file_path = append_to_filename(bm_file_path, "_js")
bm_no_js_file_path = append_to_filename(bm_file_path, "_no_js")
with open(bm_file_path, "r") as f:
out_js = open(bm_js_file_path, "w")
out_no_js = open(bm_no_js_file_path, "w")
# Get CSV header and write them to both output files.
csv_header = next(f)
out_js.write(csv_header)
out_no_js.write(csv_header)
for line in f:
# Write to respective file if the jsenabled column
# is either true or false.
if as_bool(parse_csv_line(line)[columns["jsEnabled"]]):
out_js.write(line)
else:
out_no_js.write(line)
out_js.close()
out_no_js.close()
def __init__(self, parent, defn):
super(Account, self).__init__(parent, 'accounts/{id}', defn)
self.suspended = as_bool(self.suspended)
self.password_expired = as_bool(self.password_expired)
# Skip CSV header
next(f)
results = {}
pages_scanned = 0
for line in f:
# Scan line for both JS and no JS.
row_js = parse_csv_line(line)
row_no_js = parse_csv_line(next(f))
noscript_tags = []
url = urlparse(row_js[columns["url"]])
# Check if page with JS enabled sent any noscript tags.
if as_bool(row_js[columns["noscript"]]):
file_path = os.path.join(noscript_dir_path, row_js[columns["dataFileName"]] + ".html")
noscript_tags.extend(get_noscript_tags(file_path))
# Check if page with JS disabled sent any noscript tags.
if as_bool(row_no_js[columns["noscript"]]):
file_path = os.path.join(noscript_dir_path, row_no_js[columns["dataFileName"]] + ".html")
noscript_tags.extend(get_noscript_tags(file_path))
if len(noscript_tags) > 0:
print("Processing {}".format(url.hostname))
r = process_noscript_tags(noscript_tags, url)
pages_scanned += 1
# Only keep unique results
break
js_row = parse_csv_line(js_line)
nojs_row = parse_csv_line(nojs_line)
# 6 = index first median col.
for i in range(6, len(js_row)):
# Parse values into floats.
js_row[i] = float(js_row[i])
nojs_row[i] = float(nojs_row[i])
out_row = [
# col 1: url
js_row[columns["url"]],
# col 2: noscript exists?
as_bool(js_row[columns["noscript"]])
or as_bool(nojs_row[columns["noscript"]]),
# col 3: script exists?
(int(js_row[columns["scriptCount"]]) > 0)
or (int(nojs_row[columns["scriptCount"]]) > 0),
# col 4: median load (js on)
median(js_row[6:11]),
# col 5: median domload (js on)
median(js_row[11:16]),
# col 6: median idle (js on)
median(js_row[16:21]),
# col 7: median load (js off)
median(nojs_row[6:11]),
# col 8: median domload (js off)
median(nojs_row[11:16]),
# col 9: median idle (js off)
