"""
data_x = list(in_data.keys())
- data_y = list(in_data.values())
+ data_y = [float(item) / 1e6 for item in in_data.values()]
hover_text = list()
xaxis = list()
anomaly_classifications = dict()
- # Create the header:
+ # Create the table header:
csv_tables = dict()
for job_name in builds_dict:
if csv_tables.get(job_name, None) is None:
csv_tables[job_name] = list()
- header = u"Build Number:," + u",".join(builds_dict[job_name]) + u'\n'
+ header = f"Build Number:,{u','.join(builds_dict[job_name])}\n"
csv_tables[job_name].append(header)
build_dates = [x[0] for x in build_info[job_name].values()]
- header = u"Build Date:," + u",".join(build_dates) + u'\n'
+ header = f"Build Date:,{u','.join(build_dates)}\n"
csv_tables[job_name].append(header)
versions = [x[1] for x in build_info[job_name].values()]
- header = u"Version:," + u",".join(versions) + u'\n'
+ header = f"Version:,{u','.join(versions)}\n"
csv_tables[job_name].append(header)
for chart in spec.cpta[u"plots"]:
# Write the tables:
for job_name, csv_table in csv_tables.items():
- file_name = spec.cpta[u"output-file"] + u"-" + job_name + u"-trending"
+ file_name = f"{spec.cpta[u'output-file']}/{job_name}-trending"
with open(f"{file_name}.csv", u"w") as file_handler:
file_handler.writelines(csv_table)