data = input_data.filter_data(table, continue_on_error=True)
# Prepare the header of the tables
- header = ["Test Case",
+ header = [" Test Case",
"Trend [Mpps]",
- "Short-Term Change [%]",
- "Long-Term Change [%]",
- "Regressions [#]",
- "Progressions [#]",
- "Outliers [#]"
+ " Short-Term Change [%]",
+ " Long-Term Change [%]",
+ " Regressions [#]",
+ " Progressions [#]",
+ " Outliers [#]"
]
header_str = ",".join(header) + "\n"
stdev_t = data_t.rolling(window=win_size, min_periods=2).std()
median_first_idx = pd_data.size - long_win_size
try:
- max_median = max([x for x in median_t.values[median_first_idx:]
- if not isnan(x)])
+ max_median = max(
+ [x for x in median_t.values[median_first_idx:-win_size]
+ if not isnan(x)])
except ValueError:
max_median = nan
try:
# Test name:
name = tbl_dict[tst_name]["name"]
- logging.info("{}".format(name))
- logging.info("pd_data : {}".format(pd_data))
- logging.info("data_t : {}".format(data_t))
- logging.info("median_t : {}".format(median_t))
- logging.info("last_median_t : {}".format(last_median_t))
- logging.info("median_t_14 : {}".format(median_t_14))
- logging.info("max_median : {}".format(max_median))
-
# Classification list:
classification_lst = list()
for build_nr, value in pd_data.iteritems():