from string import replace
from errors import PresentationError
-from utils import mean, stdev, relative_change
+from utils import mean, stdev, relative_change, find_outliers
def generate_tables(spec, data):
line_lst = list()
for item in data:
if isinstance(item["data"], str):
- line_lst.append(item["data"])
# Remove -?drdisc from the end
if item["data"].endswith("drdisc"):
item["data"] = item["data"][:-8]
+ line_lst.append(item["data"])
elif isinstance(item["data"], float):
line_lst.append("{:.1f}".format(item["data"]))
elif item["data"] is None:
else:
tbl_item.append({"data": None})
except (IndexError, ValueError, TypeError):
- logging.error("No data for {0}".format(tbl_item[1]["data"]))
+ logging.error("No data for {0}".format(tbl_item[0]["data"]))
tbl_item.append({"data": None})
continue
else:
else:
rel_change = item[-1]["data"]
if "ndr_top" in file_name \
- and "ndr" in item[1]["data"] \
+ and "ndr" in item[0]["data"] \
and rel_change >= 10.0:
_write_line_to_file(file_handler, item)
elif "pdr_top" in file_name \
- and "pdr" in item[1]["data"] \
+ and "pdr" in item[0]["data"] \
and rel_change >= 10.0:
_write_line_to_file(file_handler, item)
elif "ndr_low" in file_name \
- and "ndr" in item[1]["data"] \
+ and "ndr" in item[0]["data"] \
and rel_change < 10.0:
_write_line_to_file(file_handler, item)
elif "pdr_low" in file_name \
- and "pdr" in item[1]["data"] \
+ and "pdr" in item[0]["data"] \
and rel_change < 10.0:
_write_line_to_file(file_handler, item)
# Transform the data
data = input_data.filter_data(table)
- logging.info(data)
# Prepare the header of the tables
try:
append(tst_data["throughput"]["value"])
except TypeError:
pass # No data in output.xml for this test
- logging.info(pformat(tbl_dict))
for job, builds in table["compare"]["data"].items():
for build in builds:
for tst_name in tbl_dict.keys():
item = [tbl_dict[tst_name]["name"], ]
if tbl_dict[tst_name]["ref-data"]:
- item.append(round(mean(tbl_dict[tst_name]["ref-data"]) / 1000000,
- 2))
- item.append(round(stdev(tbl_dict[tst_name]["ref-data"]) / 1000000,
- 2))
+ data_t, _ = find_outliers(tbl_dict[tst_name]["ref-data"],
+ table["outlier-const"])
+ 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"]:
- item.append(round(mean(tbl_dict[tst_name]["cmp-data"]) / 1000000,
- 2))
- item.append(round(stdev(tbl_dict[tst_name]["cmp-data"]) / 1000000,
- 2))
+ data_t, _ = find_outliers(tbl_dict[tst_name]["cmp-data"],
+ table["outlier-const"])
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
if item[1] is not None and item[3] is not None:
txt_table = prettytable.PrettyTable(row)
else:
txt_table.add_row(row)
+ txt_table.align["Test case"] = "l"
with open(txt_name, "w") as txt_file:
txt_file.write(str(txt_table))