import logging
import csv
-import pandas as pd
from string import replace
from collections import OrderedDict
from xml.etree import ElementTree as ET
from errors import PresentationError
-from utils import mean, stdev, relative_change, remove_outliers,\
- split_outliers, classify_anomalies, convert_csv_to_pretty_txt
+from utils import mean, stdev, relative_change, classify_anomalies, \
+ convert_csv_to_pretty_txt
def generate_tables(spec, data):
"""Generate the table(s) with algorithm: table_performance_improvements
specified in the specification file.
+ # FIXME: Not used now.
+
:param table: Table to generate.
:param input_data: Data to process.
:type table: pandas.Series
def _read_csv_template(file_name):
"""Read the template from a .csv file.
+ # FIXME: Not used now.
+
:param file_name: Name / full path / relative path of the file to read.
:type file_name: str
:returns: Data from the template as list (lines) of lists (items on line).
try:
header = ["Test case", ]
+ if table["include-tests"] == "MRR":
+ hdr_param = "Receive Rate"
+ else:
+ hdr_param = "Throughput"
+
history = table.get("history", None)
if history:
for item in history:
header.extend(
- ["{0} Throughput [Mpps]".format(item["title"]),
+ ["{0} {1} [Mpps]".format(item["title"], hdr_param),
"{0} Stdev [Mpps]".format(item["title"])])
header.extend(
- ["{0} Throughput [Mpps]".format(table["reference"]["title"]),
+ ["{0} {1} [Mpps]".format(table["reference"]["title"], hdr_param),
"{0} Stdev [Mpps]".format(table["reference"]["title"]),
- "{0} Throughput [Mpps]".format(table["compare"]["title"]),
+ "{0} {1} [Mpps]".format(table["compare"]["title"], hdr_param),
"{0} Stdev [Mpps]".format(table["compare"]["title"]),
"Change [%]"])
header_str = ",".join(header) + "\n"
for job, builds in table["reference"]["data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].iteritems():
- if tbl_dict.get(tst_name, None) is None:
+ tst_name_mod = tst_name.replace("-ndrpdrdisc", "").\
+ replace("-ndrpdr", "").replace("-pdrdisc", "").\
+ replace("-ndrdisc", "").replace("-pdr", "").\
+ replace("-ndr", "")
+ if tbl_dict.get(tst_name_mod, None) is None:
name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
"-".join(tst_data["name"].
- split("-")[1:]))
- tbl_dict[tst_name] = {"name": name,
- "ref-data": list(),
- "cmp-data": list()}
+ split("-")[:-1]))
+ tbl_dict[tst_name_mod] = {"name": name,
+ "ref-data": list(),
+ "cmp-data": list()}
try:
- tbl_dict[tst_name]["ref-data"].\
- append(tst_data["throughput"]["value"])
+ # TODO: Re-work when NDRPDRDISC tests are not used
+ if table["include-tests"] == "MRR":
+ tbl_dict[tst_name_mod]["ref-data"]. \
+ append(tst_data["result"]["receive-rate"].avg)
+ elif table["include-tests"] == "PDR":
+ if tst_data["type"] == "PDR":
+ tbl_dict[tst_name_mod]["ref-data"]. \
+ append(tst_data["throughput"]["value"])
+ elif tst_data["type"] == "NDRPDR":
+ tbl_dict[tst_name_mod]["ref-data"].append(
+ tst_data["throughput"]["PDR"]["LOWER"])
+ elif table["include-tests"] == "NDR":
+ if tst_data["type"] == "NDR":
+ tbl_dict[tst_name_mod]["ref-data"]. \
+ append(tst_data["throughput"]["value"])
+ elif tst_data["type"] == "NDRPDR":
+ tbl_dict[tst_name_mod]["ref-data"].append(
+ tst_data["throughput"]["NDR"]["LOWER"])
+ else:
+ continue
except TypeError:
pass # No data in output.xml for this test
for job, builds in table["compare"]["data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].iteritems():
+ tst_name_mod = tst_name.replace("-ndrpdrdisc", ""). \
+ replace("-ndrpdr", "").replace("-pdrdisc", ""). \
+ replace("-ndrdisc", "").replace("-pdr", ""). \
+ replace("-ndr", "")
try:
- tbl_dict[tst_name]["cmp-data"].\
- append(tst_data["throughput"]["value"])
+ # TODO: Re-work when NDRPDRDISC tests are not used
+ if table["include-tests"] == "MRR":
+ tbl_dict[tst_name_mod]["cmp-data"]. \
+ append(tst_data["result"]["receive-rate"].avg)
+ elif table["include-tests"] == "PDR":
+ if tst_data["type"] == "PDR":
+ tbl_dict[tst_name_mod]["cmp-data"]. \
+ append(tst_data["throughput"]["value"])
+ elif tst_data["type"] == "NDRPDR":
+ tbl_dict[tst_name_mod]["cmp-data"].append(
+ tst_data["throughput"]["PDR"]["LOWER"])
+ elif table["include-tests"] == "NDR":
+ if tst_data["type"] == "NDR":
+ tbl_dict[tst_name_mod]["cmp-data"]. \
+ append(tst_data["throughput"]["value"])
+ elif tst_data["type"] == "NDRPDR":
+ tbl_dict[tst_name_mod]["cmp-data"].append(
+ tst_data["throughput"]["NDR"]["LOWER"])
+ else:
+ continue
except KeyError:
pass
except TypeError:
- tbl_dict.pop(tst_name, None)
+ tbl_dict.pop(tst_name_mod, None)
if history:
for item in history:
for job, builds in item["data"].items():
for build in builds:
for tst_name, tst_data in data[job][str(build)].iteritems():
- if tbl_dict.get(tst_name, None) is None:
+ tst_name_mod = tst_name.replace("-ndrpdrdisc", ""). \
+ replace("-ndrpdr", "").replace("-pdrdisc", ""). \
+ replace("-ndrdisc", "").replace("-pdr", ""). \
+ replace("-ndr", "")
+ if tbl_dict.get(tst_name_mod, None) is None:
continue
- if tbl_dict[tst_name].get("history", None) is None:
- tbl_dict[tst_name]["history"] = OrderedDict()
- if tbl_dict[tst_name]["history"].get(item["title"],
+ if tbl_dict[tst_name_mod].get("history", None) is None:
+ tbl_dict[tst_name_mod]["history"] = OrderedDict()
+ if tbl_dict[tst_name_mod]["history"].get(item["title"],
None) is None:
- tbl_dict[tst_name]["history"][item["title"]] = \
+ tbl_dict[tst_name_mod]["history"][item["title"]] = \
list()
try:
- tbl_dict[tst_name]["history"][item["title"]].\
- append(tst_data["throughput"]["value"])
+ # TODO: Re-work when NDRPDRDISC tests are not used
+ if table["include-tests"] == "MRR":
+ tbl_dict[tst_name_mod]["history"][item["title"
+ ]].append(tst_data["result"]["receive-rate"].
+ avg)
+ elif table["include-tests"] == "PDR":
+ if tst_data["type"] == "PDR":
+ tbl_dict[tst_name_mod]["history"][
+ item["title"]].\
+ append(tst_data["throughput"]["value"])
+ elif tst_data["type"] == "NDRPDR":
+ tbl_dict[tst_name_mod]["history"][item[
+ "title"]].append(tst_data["throughput"][
+ "PDR"]["LOWER"])
+ elif table["include-tests"] == "NDR":
+ if tst_data["type"] == "NDR":
+ tbl_dict[tst_name_mod]["history"][
+ item["title"]].\
+ append(tst_data["throughput"]["value"])
+ elif tst_data["type"] == "NDRPDR":
+ tbl_dict[tst_name_mod]["history"][item[
+ "title"]].append(tst_data["throughput"][
+ "NDR"]["LOWER"])
+ else:
+ continue
except (TypeError, KeyError):
pass
if tbl_dict[tst_name].get("history", None) is not None:
for hist_data in tbl_dict[tst_name]["history"].values():
if hist_data:
- data_t = remove_outliers(
- hist_data, outlier_const=table["outlier-const"])
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
- else:
- item.extend([None, None])
+ item.append(round(mean(hist_data) / 1000000, 2))
+ item.append(round(stdev(hist_data) / 1000000, 2))
else:
item.extend([None, None])
else:
item.extend([None, None])
- if tbl_dict[tst_name]["ref-data"]:
- data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- outlier_const=table["outlier-const"])
- # TODO: Specify window size.
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
- else:
- item.extend([None, None])
+ data_t = tbl_dict[tst_name]["ref-data"]
+ if data_t:
+ 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"],
- outlier_const=table["outlier-const"])
- # TODO: Specify window size.
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
- else:
- item.extend([None, None])
+ data_t = tbl_dict[tst_name]["cmp-data"]
+ if data_t:
+ item.append(round(mean(data_t) / 1000000, 2))
+ item.append(round(stdev(data_t) / 1000000, 2))
else:
item.extend([None, None])
if item[-4] is not None and item[-2] is not None and item[-4] != 0:
# Sort the table according to the relative change
tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
- # Generate tables:
- # All tests in csv:
- tbl_names = ["{0}-ndr-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-ndr-2t2c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-ndr-4t4c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-pdr-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-pdr-2t2c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-pdr-4t4c-full{1}".format(table["output-file"],
- table["output-file-ext"])
- ]
- for file_name in tbl_names:
- logging.info(" Writing file: '{0}'".format(file_name))
- with open(file_name, "w") as file_handler:
- file_handler.write(header_str)
- for test in tbl_lst:
- if (file_name.split("-")[-3] in test[0] and # NDR vs PDR
- file_name.split("-")[-2] in test[0]): # cores
- test[0] = "-".join(test[0].split("-")[:-1])
- file_handler.write(",".join([str(item) for item in test]) +
- "\n")
-
- # All tests in txt:
- tbl_names_txt = ["{0}-ndr-1t1c-full.txt".format(table["output-file"]),
- "{0}-ndr-2t2c-full.txt".format(table["output-file"]),
- "{0}-ndr-4t4c-full.txt".format(table["output-file"]),
- "{0}-pdr-1t1c-full.txt".format(table["output-file"]),
- "{0}-pdr-2t2c-full.txt".format(table["output-file"]),
- "{0}-pdr-4t4c-full.txt".format(table["output-file"])
- ]
-
- for i, txt_name in enumerate(tbl_names_txt):
- logging.info(" Writing file: '{0}'".format(txt_name))
- convert_csv_to_pretty_txt(tbl_names[i], txt_name)
-
- # Selected tests in csv:
- input_file = "{0}-ndr-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"])
- with open(input_file, "r") as in_file:
- lines = list()
- for line in in_file:
- lines.append(line)
-
- output_file = "{0}-ndr-1t1c-top{1}".format(table["output-file"],
- table["output-file-ext"])
- logging.info(" Writing file: '{0}'".format(output_file))
- with open(output_file, "w") as out_file:
- out_file.write(header_str)
- for i, line in enumerate(lines[1:]):
- if i == table["nr-of-tests-shown"]:
- break
- out_file.write(line)
-
- output_file = "{0}-ndr-1t1c-bottom{1}".format(table["output-file"],
- table["output-file-ext"])
- logging.info(" Writing file: '{0}'".format(output_file))
- with open(output_file, "w") as out_file:
- out_file.write(header_str)
- for i, line in enumerate(lines[-1:0:-1]):
- if i == table["nr-of-tests-shown"]:
- break
- out_file.write(line)
-
- input_file = "{0}-pdr-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"])
- with open(input_file, "r") as in_file:
- lines = list()
- for line in in_file:
- lines.append(line)
-
- output_file = "{0}-pdr-1t1c-top{1}".format(table["output-file"],
- table["output-file-ext"])
- logging.info(" Writing file: '{0}'".format(output_file))
- with open(output_file, "w") as out_file:
- out_file.write(header_str)
- for i, line in enumerate(lines[1:]):
- if i == table["nr-of-tests-shown"]:
- break
- out_file.write(line)
-
- output_file = "{0}-pdr-1t1c-bottom{1}".format(table["output-file"],
- table["output-file-ext"])
- logging.info(" Writing file: '{0}'".format(output_file))
- with open(output_file, "w") as out_file:
- out_file.write(header_str)
- for i, line in enumerate(lines[-1:0:-1]):
- if i == table["nr-of-tests-shown"]:
- break
- out_file.write(line)
-
-
-def table_performance_comparison_mrr(table, input_data):
- """Generate the table(s) with algorithm: table_performance_comparison_mrr
- specified in the specification file.
-
- :param table: Table to generate.
- :param input_data: Data to process.
- :type table: pandas.Series
- :type input_data: InputData
- """
-
- logging.info(" Generating the table {0} ...".
- format(table.get("title", "")))
-
- # Transform the data
- logging.info(" Creating the data set for the {0} '{1}'.".
- format(table.get("type", ""), table.get("title", "")))
- data = input_data.filter_data(table, continue_on_error=True)
-
- # Prepare the header of the tables
- try:
- header = ["Test case",
- "{0} Throughput [Mpps]".format(table["reference"]["title"]),
- "{0} stdev [Mpps]".format(table["reference"]["title"]),
- "{0} Throughput [Mpps]".format(table["compare"]["title"]),
- "{0} stdev [Mpps]".format(table["compare"]["title"]),
- "Change [%]"]
- header_str = ",".join(header) + "\n"
- except (AttributeError, KeyError) as err:
- logging.error("The model is invalid, missing parameter: {0}".
- format(err))
- return
-
- # Prepare data to the table:
- tbl_dict = dict()
- for job, builds in table["reference"]["data"].items():
- for build in builds:
- for tst_name, tst_data in data[job][str(build)].iteritems():
- if tbl_dict.get(tst_name, None) is None:
- name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
- "-".join(tst_data["name"].
- split("-")[1:]))
- tbl_dict[tst_name] = {"name": name,
- "ref-data": list(),
- "cmp-data": list()}
- try:
- tbl_dict[tst_name]["ref-data"].\
- append(tst_data["result"]["throughput"])
- except TypeError:
- pass # No data in output.xml for this test
-
- for job, builds in table["compare"]["data"].items():
- for build in builds:
- for tst_name, tst_data in data[job][str(build)].iteritems():
- try:
- tbl_dict[tst_name]["cmp-data"].\
- append(tst_data["result"]["throughput"])
- except KeyError:
- pass
- except TypeError:
- tbl_dict.pop(tst_name, None)
-
- tbl_lst = list()
- for tst_name in tbl_dict.keys():
- item = [tbl_dict[tst_name]["name"], ]
- if tbl_dict[tst_name]["ref-data"]:
- data_t = remove_outliers(tbl_dict[tst_name]["ref-data"],
- outlier_const=table["outlier-const"])
- # TODO: Specify window size.
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
- else:
- item.extend([None, None])
- else:
- item.extend([None, None])
- if tbl_dict[tst_name]["cmp-data"]:
- data_t = remove_outliers(tbl_dict[tst_name]["cmp-data"],
- outlier_const=table["outlier-const"])
- # TODO: Specify window size.
- if data_t:
- item.append(round(mean(data_t) / 1000000, 2))
- item.append(round(stdev(data_t) / 1000000, 2))
- else:
- item.extend([None, None])
- else:
- item.extend([None, None])
- if item[1] is not None and item[3] is not None and item[1] != 0:
- item.append(int(relative_change(float(item[1]), float(item[3]))))
- if len(item) == 6:
- tbl_lst.append(item)
-
- # Sort the table according to the relative change
- tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
-
- # Generate tables:
- # All tests in csv:
- tbl_names = ["{0}-1t1c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-2t2c-full{1}".format(table["output-file"],
- table["output-file-ext"]),
- "{0}-4t4c-full{1}".format(table["output-file"],
- table["output-file-ext"])
- ]
- for file_name in tbl_names:
- logging.info(" Writing file: '{0}'".format(file_name))
- with open(file_name, "w") as file_handler:
- file_handler.write(header_str)
- for test in tbl_lst:
- if file_name.split("-")[-2] in test[0]: # cores
- test[0] = "-".join(test[0].split("-")[:-1])
- file_handler.write(",".join([str(item) for item in test]) +
- "\n")
-
- # All tests in txt:
- tbl_names_txt = ["{0}-1t1c-full.txt".format(table["output-file"]),
- "{0}-2t2c-full.txt".format(table["output-file"]),
- "{0}-4t4c-full.txt".format(table["output-file"])
- ]
+ # Generate csv tables:
+ csv_file = "{0}.csv".format(table["output-file"])
+ with open(csv_file, "w") as file_handler:
+ file_handler.write(header_str)
+ for test in tbl_lst:
+ file_handler.write(",".join([str(item) for item in test]) + "\n")
- for i, txt_name in enumerate(tbl_names_txt):
- logging.info(" Writing file: '{0}'".format(txt_name))
- convert_csv_to_pretty_txt(tbl_names[i], txt_name)
+ convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
def table_performance_trending_dashboard(table, input_data):
- """Generate the table(s) with algorithm: table_performance_comparison
+ """Generate the table(s) with algorithm:
+ table_performance_trending_dashboard
specified in the specification file.
:param table: Table to generate.
"Short-Term Change [%]",
"Long-Term Change [%]",
"Regressions [#]",
- "Progressions [#]",
- "Outliers [#]"
+ "Progressions [#]"
]
header_str = ",".join(header) + "\n"
continue
if tbl_dict.get(tst_name, None) is None:
name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
- "-".join(tst_data["name"].
- split("-")[1:]))
+ tst_data["name"])
tbl_dict[tst_name] = {"name": name,
"data": OrderedDict()}
try:
- tbl_dict[tst_name]["data"][str(build)] = \
- tst_data["result"]["throughput"]
+ tbl_dict[tst_name]["data"][str(build)] = \
+ tst_data["result"]["receive-rate"]
except (TypeError, KeyError):
pass # No data in output.xml for this test
tbl_lst = list()
for tst_name in tbl_dict.keys():
- if len(tbl_dict[tst_name]["data"]) < 3:
+ data_t = tbl_dict[tst_name]["data"]
+ if len(data_t) < 2:
continue
- pd_data = pd.Series(tbl_dict[tst_name]["data"])
- data_t, _ = split_outliers(pd_data, outlier_const=1.5,
- window=table["window"])
- last_key = data_t.keys()[-1]
- win_size = min(data_t.size, table["window"])
- win_first_idx = data_t.size - win_size
- key_14 = data_t.keys()[win_first_idx]
- long_win_size = min(data_t.size, table["long-trend-window"])
- median_t = data_t.rolling(window=win_size, min_periods=2).median()
- median_first_idx = median_t.size - long_win_size
+ classification_lst, avgs = classify_anomalies(data_t)
+
+ win_size = min(len(data_t), table["window"])
+ long_win_size = min(len(data_t), table["long-trend-window"])
+
try:
- max_median = max(
- [x for x in median_t.values[median_first_idx:-win_size]
+ max_long_avg = max(
+ [x for x in avgs[-long_win_size:-win_size]
if not isnan(x)])
except ValueError:
- max_median = nan
- try:
- last_median_t = median_t[last_key]
- except KeyError:
- last_median_t = nan
- try:
- median_t_14 = median_t[key_14]
- except KeyError:
- median_t_14 = nan
+ max_long_avg = nan
+ last_avg = avgs[-1]
+ avg_week_ago = avgs[max(-win_size, -len(avgs))]
- if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0:
+ if isnan(last_avg) or isnan(avg_week_ago) or avg_week_ago == 0.0:
rel_change_last = nan
else:
rel_change_last = round(
- ((last_median_t - median_t_14) / median_t_14) * 100, 2)
+ ((last_avg - avg_week_ago) / avg_week_ago) * 100, 2)
- if isnan(max_median) or isnan(last_median_t) or max_median == 0.0:
+ if isnan(max_long_avg) or isnan(last_avg) or max_long_avg == 0.0:
rel_change_long = nan
else:
rel_change_long = round(
- ((last_median_t - max_median) / max_median) * 100, 2)
-
- # Classification list:
- classification_lst = classify_anomalies(data_t, window=14)
+ ((last_avg - max_long_avg) / max_long_avg) * 100, 2)
if classification_lst:
if isnan(rel_change_last) and isnan(rel_change_long):
continue
tbl_lst.append(
[tbl_dict[tst_name]["name"],
- '-' if isnan(last_median_t) else
- round(last_median_t / 1000000, 2),
+ '-' if isnan(last_avg) else
+ 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[win_first_idx:].count("regression"),
- classification_lst[win_first_idx:].count("progression"),
- classification_lst[win_first_idx:].count("outlier")])
+ classification_lst[-win_size:].count("regression"),
+ classification_lst[-win_size:].count("progression")])
tbl_lst.sort(key=lambda rel: rel[0])
for nrr in range(table["window"], -1, -1):
tbl_reg = [item for item in tbl_lst if item[4] == nrr]
for nrp in range(table["window"], -1, -1):
- tbl_pro = [item for item in tbl_reg if item[5] == nrp]
- for nro in range(table["window"], -1, -1):
- tbl_out = [item for item in tbl_pro if item[6] == nro]
- tbl_out.sort(key=lambda rel: rel[2])
- tbl_sorted.extend(tbl_out)
+ tbl_out = [item for item in tbl_reg if item[5] == nrp]
+ tbl_out.sort(key=lambda rel: rel[2])
+ tbl_sorted.extend(tbl_out)
file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"])
# Rows:
colors = {"regression": ("#ffcccc", "#ff9999"),
"progression": ("#c6ecc6", "#9fdf9f"),
- "outlier": ("#e6e6e6", "#cccccc"),
"normal": ("#e9f1fb", "#d4e4f7")}
for r_idx, row in enumerate(csv_lst[1:]):
if int(row[4]):
color = "regression"
elif int(row[5]):
color = "progression"
- elif int(row[6]):
- color = "outlier"
else:
color = "normal"
background = colors[color][r_idx % 2]
# Prepare the header of the tables
header = ["Test Case",
- "Fails [#]",
- "Last Fail [Timestamp]",
- "Last Fail [VPP Build]",
- "Last Fail [CSIT Build]"]
+ "Failures [#]",
+ "Last Failure [Time]",
+ "Last Failure [VPP-Build-Id]",
+ "Last Failure [CSIT-Job-Build-Id]"]
# Generate the data for the table according to the model in the table
# specification
continue
if tbl_dict.get(tst_name, None) is None:
name = "{0}-{1}".format(tst_data["parent"].split("-")[0],
- "-".join(tst_data["name"].
- split("-")[1:]))
+ tst_data["name"])
tbl_dict[tst_name] = {"name": name,
"data": OrderedDict()}
try:
th.text = item
# Rows:
- colors = {"very-bad": ("#ffcccc", "#ff9999"),
- "bad": ("#e9f1fb", "#d4e4f7")}
+ colors = ("#e9f1fb", "#d4e4f7")
for r_idx, row in enumerate(csv_lst[1:]):
- if int(row[1]) > 7:
- color = "very-bad"
- else:
- color = "bad"
- background = colors[color][r_idx % 2]
+ background = colors[r_idx % 2]
tr = ET.SubElement(failed_tests, "tr", attrib=dict(bgcolor=background))
# Columns:
Code Review / csit.git / blobdiff