+def table_nics_comparison(table, input_data):
+ """Generate the table(s) with algorithm: table_nics_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
+ 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", ]
+
+ if table["include-tests"] == "MRR":
+ hdr_param = "Receive Rate"
+ else:
+ hdr_param = "Throughput"
+
+ header.extend(
+ ["{0} {1} [Mpps]".format(table["reference"]["title"], hdr_param),
+ "{0} Stdev [Mpps]".format(table["reference"]["title"]),
+ "{0} {1} [Mpps]".format(table["compare"]["title"], hdr_param),
+ "{0} Stdev [Mpps]".format(table["compare"]["title"]),
+ "Delta [%]"])
+ 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["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", "").\
+ replace("1t1c", "1c").replace("2t1c", "1c").\
+ replace("2t2c", "2c").replace("4t2c", "2c").\
+ replace("4t4c", "4c").replace("8t4c", "4c")
+ tst_name_mod = re.sub(REGEX_NIC, "", tst_name_mod)
+ if tbl_dict.get(tst_name_mod, None) is None:
+ name = "-".join(tst_data["name"].split("-")[:-1])
+ tbl_dict[tst_name_mod] = {"name": name,
+ "ref-data": list(),
+ "cmp-data": list()}
+ try:
+ if table["include-tests"] == "MRR":
+ result = tst_data["result"]["receive-rate"].avg
+ elif table["include-tests"] == "PDR":
+ result = tst_data["throughput"]["PDR"]["LOWER"]
+ elif table["include-tests"] == "NDR":
+ result = tst_data["throughput"]["NDR"]["LOWER"]
+ else:
+ result = None
+
+ if result:
+ if table["reference"]["nic"] in tst_data["tags"]:
+ tbl_dict[tst_name_mod]["ref-data"].append(result)
+ elif table["compare"]["nic"] in tst_data["tags"]:
+ tbl_dict[tst_name_mod]["cmp-data"].append(result)
+ except (TypeError, KeyError) as err:
+ logging.debug("No data for {0}".format(tst_name))
+ logging.debug(repr(err))
+ # No data in output.xml for this test
+
+ tbl_lst = list()
+ for tst_name in tbl_dict.keys():
+ item = [tbl_dict[tst_name]["name"], ]
+ 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])
+ 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:
+ item.append(int(relative_change(float(item[-4]), float(item[-2]))))
+ if len(item) == len(header):
+ tbl_lst.append(item)
+
+ # Sort the table according to the relative change
+ tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
+
+ # 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")
+
+ convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
+
+
+def table_soak_vs_ndr(table, input_data):
+ """Generate the table(s) with algorithm: table_soak_vs_ndr
+ 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 table
+ 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"]),
+ "Delta [%]"]
+ header_str = ",".join(header) + "\n"
+ except (AttributeError, KeyError) as err:
+ logging.error("The model is invalid, missing parameter: {0}".
+ format(err))
+ return
+
+ # Create a list of available SOAK test results:
+ tbl_dict = dict()
+ for job, builds in table["compare"]["data"].items():
+ for build in builds:
+ for tst_name, tst_data in data[job][str(build)].iteritems():
+ if tst_data["type"] == "SOAK":
+ tst_name_mod = tst_name.replace("-soak", "")
+ if tbl_dict.get(tst_name_mod, None) is None:
+ tbl_dict[tst_name_mod] = {
+ "name": tst_name_mod,
+ "ref-data": list(),
+ "cmp-data": list()
+ }
+ try:
+ tbl_dict[tst_name_mod]["cmp-data"].append(
+ tst_data["throughput"]["LOWER"])
+ except (KeyError, TypeError):
+ pass
+ tests_lst = tbl_dict.keys()
+
+ # Add corresponding NDR test results:
+ for job, builds in table["reference"]["data"].items():
+ for build in builds:
+ for tst_name, tst_data in data[job][str(build)].iteritems():
+ tst_name_mod = tst_name.replace("-ndrpdr", "").\
+ replace("-mrr", "")
+ if tst_name_mod in tests_lst:
+ try:
+ if tst_data["type"] in ("NDRPDR", "MRR", "BMRR"):
+ if table["include-tests"] == "MRR":
+ result = tst_data["result"]["receive-rate"].avg
+ elif table["include-tests"] == "PDR":
+ result = tst_data["throughput"]["PDR"]["LOWER"]
+ elif table["include-tests"] == "NDR":
+ result = tst_data["throughput"]["NDR"]["LOWER"]
+ else:
+ result = None
+ if result is not None:
+ tbl_dict[tst_name_mod]["ref-data"].append(
+ result)
+ except (KeyError, TypeError):
+ continue
+
+ tbl_lst = list()
+ for tst_name in tbl_dict.keys():
+ item = [tbl_dict[tst_name]["name"], ]
+ 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])
+ 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:
+ item.append(int(relative_change(float(item[-4]), float(item[-2]))))
+ if len(item) == len(header):
+ tbl_lst.append(item)
+
+ # Sort the table according to the relative change
+ tbl_lst.sort(key=lambda rel: rel[-1], reverse=True)
+
+ # 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")
+
+ convert_csv_to_pretty_txt(csv_file, "{0}.txt".format(table["output-file"]))
+
+
Code Review / csit.git / blobdiff