from xml.etree import ElementTree as ET
from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers, find_outliers
+from utils import mean, stdev, relative_change, remove_outliers, split_outliers
def generate_tables(spec, data):
item = [tbl_dict[tst_name]["name"], ]
if tbl_dict[tst_name]["ref-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- table["outlier-const"])
+ outlier_constant=table["outlier-const"])
+ # TODO: Specify window size.
item.append(round(mean(data_t) / 1000000, 2))
item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
if tbl_dict[tst_name]["cmp-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- table["outlier-const"])
+ outlier_constant=table["outlier-const"])
+ # TODO: Specify window size.
item.append(round(mean(data_t) / 1000000, 2))
item.append(round(stdev(data_t) / 1000000, 2))
else:
item = [tbl_dict[tst_name]["name"], ]
if tbl_dict[tst_name]["ref-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- table["outlier-const"])
+ outlier_const=table["outlier-const"])
+ # TODO: Specify window size.
item.append(round(mean(data_t) / 1000000, 2))
item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
if tbl_dict[tst_name]["cmp-data"]:
data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- table["outlier-const"])
+ outlier_const=table["outlier-const"])
+ # TODO: Specify window size.
item.append(round(mean(data_t) / 1000000, 2))
item.append(round(stdev(data_t) / 1000000, 2))
else:
name = tbl_dict[tst_name]["name"]
median = pd_data.rolling(window=win_size, min_periods=2).median()
- trimmed_data, _ = find_outliers(pd_data, outlier_const=1.5)
+ trimmed_data, _ = split_outliers(pd_data, outlier_const=1.5,
+ window=win_size)
stdev_t = pd_data.rolling(window=win_size, min_periods=2).std()
rel_change_lst = [None, ]