round(last_avg / 1000000, 2),
'-' if isnan(rel_change_last) else rel_change_last,
'-' if isnan(rel_change_long) else rel_change_long,
- classification_lst[-long_win_size:].count("regression"),
- classification_lst[-long_win_size:].count("progression")])
+ classification_lst[-win_size:].count("regression"),
+ classification_lst[-win_size:].count("progression")])
tbl_lst.sort(key=lambda rel: rel[0])
with open(txt_file_name, "w") as txt_file:
txt_file.write(str(txt_table))
+
def table_performance_trending_dashboard_html(table, input_data):
"""Generate the table(s) with algorithm:
table_performance_trending_dashboard_html specified in the specification