+
+
+def table_performance_trending_dashboard(table, input_data):
+ """Generate the table(s) with algorithm: table_performance_comparison
+ 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
+ data = input_data.filter_data(table)
+
+ # Prepare the header of the tables
+ header = ["Test case",
+ "Thput trend [Mpps]",
+ "Change [Mpps]",
+ "Change [%]",
+ "Anomaly"]
+ header_str = ",".join(header) + "\n"
+
+ # Prepare data to the table:
+ tbl_dict = dict()
+ for job, builds in table["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,
+ "data": list()}
+ try:
+ tbl_dict[tst_name]["data"]. \
+ append(tst_data["throughput"]["value"])
+ except TypeError:
+ 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"]) > 2:
+ pd_data = pd.Series(tbl_dict[tst_name]["data"])
+ win_size = pd_data.size \
+ if pd_data.size < table["window"] else table["window"]
+ # Test name:
+ name = tbl_dict[tst_name]["name"]
+ # Throughput trend:
+ trend = list(pd_data.rolling(window=win_size).median())[-2]
+ # Anomaly:
+ t_data, _ = find_outliers(pd_data)
+ last = list(t_data)[-1]
+ t_stdev = list(t_data.rolling(window=win_size, min_periods=2).
+ std())[-2]
+ if isnan(last):
+ anomaly = "outlier"
+ elif last < (trend - 3 * t_stdev):
+ anomaly = "regression"
+ elif last > (trend + 3 * t_stdev):
+ anomaly = "progression"
+ else:
+ anomaly = "normal"
+ # Change:
+ change = round(float(last - trend) / 1000000, 2)
+ # Relative change:
+ rel_change = int(relative_change(float(trend), float(last)))
+
+ tbl_lst.append([name,
+ round(float(last) / 1000000, 2),
+ change,
+ rel_change,
+ anomaly])
+
+ # Sort the table according to the relative change
+ tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
+
+ file_name = "{}.{}".format(table["output-file"], table["output-file-ext"])
+
+ logging.info(" Writing file: '{}'".format(file_name))
+ with open(file_name, "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')
+
+ txt_file_name = "{}.txt".format(table["output-file"])
+ txt_table = None
+ logging.info(" Writing file: '{}'".format(txt_file_name))
+ with open(file_name, 'rb') as csv_file:
+ csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+ for row in csv_content:
+ if txt_table is None:
+ txt_table = prettytable.PrettyTable(row)
+ else:
+ txt_table.add_row(row)
+ txt_table.align["Test case"] = "l"
+ with open(txt_file_name, "w") as txt_file:
+ txt_file.write(str(txt_table))
Code Review / csit.git / commitdiff