if "dpdk" in job_name:
hover_text.append(hover_str.format(
date=date,
- value=int(in_data[idx].avg),
+ value=int(in_data[idx]),
sut="dpdk",
build=build_info[job_name][str(idx)][1].rsplit('~', 1)[0],
period="weekly",
elif "vpp" in job_name:
hover_text.append(hover_str.format(
date=date,
- value=int(in_data[idx].avg),
+ value=int(in_data[idx]),
sut="vpp",
build=build_info[job_name][str(idx)][1].rsplit('~', 1)[0],
period="daily",
trace_samples = plgo.Scatter(
x=xaxis,
- y=[y.avg for y in data_y],
+ y=[y for y in data_y], # Was: y.avg
mode='markers',
line={
"width": 1
tst_lst = list()
for bld in builds_dict[job_name]:
itm = tst_data.get(int(bld), '')
- if not isinstance(itm, str):
- itm = itm.avg
+ # CSIT-1180: Itm will be list, compute stats.
tst_lst.append(str(itm))
csv_tbl.append("{0},".format(tst_name) + ",".join(tst_lst) + '\n')
if classification == "regression" or \
classification == "outlier":
result = "FAIL"
+ file_name = "{0}-progressions-{1}.txt".\
+ format(spec.cpta["output-file"], job_name)
+ with open(file_name, 'w') as txt_file:
+ for test_name, classification in job_data.iteritems():
+ if classification == "progression":
+ txt_file.write(test_name + '\n')
else:
result = "FAIL"