import logging
import csv
-import pandas as pd
+import re
from string import replace
from collections import OrderedDict
from numpy import nan, isnan
from xml.etree import ElementTree as ET
+from datetime import datetime as dt
+from datetime import timedelta
-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
+
+
+REGEX_NIC = re.compile(r'\d*ge\dp\d\D*\d*')
def generate_tables(spec, data):
logging.info(" Done.")
-def table_performance_improvements(table, input_data):
- """Generate the table(s) with algorithm: table_performance_improvements
- specified in the specification file.
-
- :param table: Table to generate.
- :param input_data: Data to process.
- :type table: pandas.Series
- :type input_data: InputData
- """
-
- def _write_line_to_file(file_handler, data):
- """Write a line to the .csv file.
-
- :param file_handler: File handler for the csv file. It must be open for
- writing text.
- :param data: Item to be written to the file.
- :type file_handler: BinaryIO
- :type data: list
- """
-
- line_lst = list()
- for item in data:
- if isinstance(item["data"], str):
- # 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:
- line_lst.append("")
- file_handler.write(",".join(line_lst) + "\n")
-
- logging.info(" Generating the table {0} ...".
- format(table.get("title", "")))
-
- # Read the template
- file_name = table.get("template", None)
- if file_name:
- try:
- tmpl = _read_csv_template(file_name)
- except PresentationError:
- logging.error(" The template '{0}' does not exist. Skipping the "
- "table.".format(file_name))
- return None
- else:
- logging.error("The template is not defined. Skipping the table.")
- return None
-
- # 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)
-
- # Prepare the header of the tables
- header = list()
- for column in table["columns"]:
- header.append(column["title"])
-
- # Generate the data for the table according to the model in the table
- # specification
- tbl_lst = list()
- for tmpl_item in tmpl:
- tbl_item = list()
- for column in table["columns"]:
- cmd = column["data"].split(" ")[0]
- args = column["data"].split(" ")[1:]
- if cmd == "template":
- try:
- val = float(tmpl_item[int(args[0])])
- except ValueError:
- val = tmpl_item[int(args[0])]
- tbl_item.append({"data": val})
- elif cmd == "data":
- jobs = args[0:-1]
- operation = args[-1]
- data_lst = list()
- for job in jobs:
- for build in data[job]:
- try:
- data_lst.append(float(build[tmpl_item[0]]
- ["throughput"]["value"]))
- except (KeyError, TypeError):
- # No data, ignore
- continue
- if data_lst:
- tbl_item.append({"data": (eval(operation)(data_lst)) /
- 1000000})
- else:
- tbl_item.append({"data": None})
- elif cmd == "operation":
- operation = args[0]
- try:
- nr1 = float(tbl_item[int(args[1])]["data"])
- nr2 = float(tbl_item[int(args[2])]["data"])
- if nr1 and nr2:
- tbl_item.append({"data": eval(operation)(nr1, nr2)})
- else:
- tbl_item.append({"data": None})
- except (IndexError, ValueError, TypeError):
- logging.error("No data for {0}".format(tbl_item[0]["data"]))
- tbl_item.append({"data": None})
- continue
- else:
- logging.error("Not supported command {0}. Skipping the table.".
- format(cmd))
- return None
- tbl_lst.append(tbl_item)
-
- # Sort the table according to the relative change
- tbl_lst.sort(key=lambda rel: rel[-1]["data"], reverse=True)
-
- # Create the tables and write them to the files
- file_names = [
- "{0}_ndr_top{1}".format(table["output-file"], table["output-file-ext"]),
- "{0}_pdr_top{1}".format(table["output-file"], table["output-file-ext"]),
- "{0}_ndr_low{1}".format(table["output-file"], table["output-file-ext"]),
- "{0}_pdr_low{1}".format(table["output-file"], table["output-file-ext"])
- ]
-
- for file_name in file_names:
- logging.info(" Writing the file '{0}'".format(file_name))
- with open(file_name, "w") as file_handler:
- file_handler.write(",".join(header) + "\n")
- for item in tbl_lst:
- if isinstance(item[-1]["data"], float):
- rel_change = round(item[-1]["data"], 1)
- else:
- rel_change = item[-1]["data"]
- if "ndr_top" in file_name \
- 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[0]["data"] \
- and rel_change >= 10.0:
- _write_line_to_file(file_handler, item)
- elif "ndr_low" in file_name \
- 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[0]["data"] \
- and rel_change < 10.0:
- _write_line_to_file(file_handler, item)
-
- logging.info(" Done.")
-
-
-def _read_csv_template(file_name):
- """Read the template from a .csv file.
-
- :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).
- :rtype: list
- :raises: PresentationError if it is not possible to read the file.
- """
-
- try:
- with open(file_name, 'r') as csv_file:
- tmpl_data = list()
- for line in csv_file:
- tmpl_data.append(line[:-1].split(","))
- return tmpl_data
- except IOError as err:
- raise PresentationError(str(err), level="ERROR")
-
-
def table_performance_comparison(table, input_data):
"""Generate the table(s) with algorithm: table_performance_comparison
specified in the specification file.
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 [%]"])
+ "Delta [%]"])
header_str = ",".join(header) + "\n"
except (AttributeError, KeyError) as err:
logging.error("The model is invalid, missing parameter: {0}".
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", "").\
+ replace("1t1c", "1c").replace("2t1c", "1c").\
+ replace("2t2c", "2c").replace("4t2c", "2c").\
+ replace("4t4c", "4c").replace("8t4c", "4c")
+ if "across topologies" in table["title"].lower():
+ tst_name_mod = tst_name_mod.replace("2n1l-", "")
+ 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]))
+ if "across testbeds" in table["title"].lower() or \
+ "across topologies" in table["title"].lower():
+ name = name.\
+ replace("1t1c", "1c").replace("2t1c", "1c").\
+ replace("2t2c", "2c").replace("4t2c", "2c").\
+ replace("4t4c", "4c").replace("8t4c", "4c")
+ 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", "").\
+ replace("1t1c", "1c").replace("2t1c", "1c").\
+ replace("2t2c", "2c").replace("4t2c", "2c").\
+ replace("4t4c", "4c").replace("8t4c", "4c")
+ if "across topologies" in table["title"].lower():
+ tst_name_mod = tst_name_mod.replace("2n1l-", "")
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", "").\
+ replace("1t1c", "1c").replace("2t1c", "1c").\
+ replace("2t2c", "2c").replace("4t2c", "2c").\
+ replace("4t4c", "4c").replace("8t4c", "4c")
+ if "across topologies" in table["title"].lower():
+ tst_name_mod = tst_name_mod.replace("2n1l-", "")
+ 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 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")
- # 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"])
- ]
-
- 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"
if tst_name.lower() in table["ignore-list"]:
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:]))
- tbl_dict[tst_name] = {"name": name,
- "data": OrderedDict()}
+ groups = re.search(REGEX_NIC, tst_data["parent"])
+ if not groups:
+ continue
+ nic = groups.group(0)
+ tbl_dict[tst_name] = {
+ "name": "{0}-{1}".format(nic, tst_data["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"])
convert_csv_to_pretty_txt(file_name, txt_file_name)
-def _generate_url(base, test_name):
+def _generate_url(base, testbed, test_name):
"""Generate URL to a trending plot from the name of the test case.
:param base: The base part of URL common to all test cases.
+ :param testbed: The testbed used for testing.
:param test_name: The name of the test case.
:type base: str
+ :type testbed: str
:type test_name: str
:returns: The URL to the plot with the trending data for the given test
case.
url = base
file_name = ""
- anchor = "#"
+ anchor = ".html#"
feature = ""
if "lbdpdk" in test_name or "lbvpp" in test_name:
- file_name = "link_bonding.html"
+ file_name = "link_bonding"
+
+ elif "114b" in test_name and "vhost" in test_name:
+ file_name = "vts"
elif "testpmd" in test_name or "l3fwd" in test_name:
- file_name = "dpdk.html"
+ file_name = "dpdk"
elif "memif" in test_name:
- file_name = "container_memif.html"
+ file_name = "container_memif"
+ feature = "-base"
elif "srv6" in test_name:
- file_name = "srv6.html"
+ file_name = "srv6"
elif "vhost" in test_name:
if "l2xcbase" in test_name or "l2bdbasemaclrn" in test_name:
- file_name = "vm_vhost_l2.html"
+ file_name = "vm_vhost_l2"
+ if "114b" in test_name:
+ feature = ""
+ elif "l2xcbase" in test_name:
+ feature = "-base-l2xc"
+ elif "l2bdbasemaclrn" in test_name:
+ feature = "-base-l2bd"
+ else:
+ feature = "-base"
elif "ip4base" in test_name:
- file_name = "vm_vhost_ip4.html"
+ file_name = "vm_vhost_ip4"
+ feature = "-base"
elif "ipsec" in test_name:
- file_name = "ipsec.html"
+ file_name = "ipsec"
+ feature = "-base-scale"
elif "ethip4lispip" in test_name or "ethip4vxlan" in test_name:
- file_name = "ip4_tunnels.html"
+ file_name = "ip4_tunnels"
+ feature = "-base"
elif "ip4base" in test_name or "ip4scale" in test_name:
- file_name = "ip4.html"
- if "iacl" in test_name or "snat" in test_name or "cop" in test_name:
+ file_name = "ip4"
+ if "xl710" in test_name:
+ feature = "-base-scale-features"
+ elif "iacl" in test_name:
+ feature = "-features-iacl"
+ elif "oacl" in test_name:
+ feature = "-features-oacl"
+ elif "snat" in test_name or "cop" in test_name:
feature = "-features"
+ else:
+ feature = "-base-scale"
elif "ip6base" in test_name or "ip6scale" in test_name:
- file_name = "ip6.html"
+ file_name = "ip6"
+ feature = "-base-scale"
elif "l2xcbase" in test_name or "l2xcscale" in test_name \
or "l2bdbasemaclrn" in test_name or "l2bdscale" in test_name \
or "l2dbbasemaclrn" in test_name or "l2dbscale" in test_name:
- file_name = "l2.html"
- if "iacl" in test_name:
- feature = "-features"
+ file_name = "l2"
+ if "macip" in test_name:
+ feature = "-features-macip"
+ elif "iacl" in test_name:
+ feature = "-features-iacl"
+ elif "oacl" in test_name:
+ feature = "-features-oacl"
+ else:
+ feature = "-base-scale"
if "x520" in test_name:
- anchor += "x520-"
+ nic = "x520-"
elif "x710" in test_name:
- anchor += "x710-"
+ nic = "x710-"
elif "xl710" in test_name:
- anchor += "xl710-"
+ nic = "xl710-"
+ elif "xxv710" in test_name:
+ nic = "xxv710-"
+ else:
+ nic = ""
+ anchor += nic
if "64b" in test_name:
- anchor += "64b-"
+ framesize = "64b"
elif "78b" in test_name:
- anchor += "78b-"
+ framesize = "78b"
elif "imix" in test_name:
- anchor += "imix-"
+ framesize = "imix"
elif "9000b" in test_name:
- anchor += "9000b-"
- elif "1518" in test_name:
- anchor += "1518b-"
+ framesize = "9000b"
+ elif "1518b" in test_name:
+ framesize = "1518b"
+ elif "114b" in test_name:
+ framesize = "114b"
+ else:
+ framesize = ""
+ anchor += framesize + '-'
if "1t1c" in test_name:
anchor += "1t1c"
anchor += "2t2c"
elif "4t4c" in test_name:
anchor += "4t4c"
+ elif "2t1c" in test_name:
+ anchor += "2t1c"
+ elif "4t2c" in test_name:
+ anchor += "4t2c"
+ elif "8t4c" in test_name:
+ anchor += "8t4c"
- return url + file_name + anchor + feature
+ return url + file_name + '-' + testbed + '-' + nic + framesize + feature + \
+ anchor + feature
def table_performance_trending_dashboard_html(table, input_data):
:param table: Table to generate.
:param input_data: Data to process.
- :type table: pandas.Series
+ :type table: dict
:type input_data: InputData
"""
+ testbed = table.get("testbed", None)
+ if testbed is None:
+ logging.error("The testbed is not defined for the table '{0}'.".
+ format(table.get("title", "")))
+ return
+
logging.info(" Generating the table {0} ...".
format(table.get("title", "")))
# 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]
td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
# Name:
if c_idx == 0:
- url = _generate_url("../trending/", item)
+ url = _generate_url("../trending/", testbed, item)
ref = ET.SubElement(td, "a", attrib=dict(href=url))
ref.text = item
else:
# 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
+
+ now = dt.utcnow()
+ timeperiod = timedelta(int(table.get("window", 7)))
+
tbl_dict = dict()
for job, builds in table["data"].items():
for build in builds:
if tst_name.lower() in table["ignore-list"]:
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:]))
- tbl_dict[tst_name] = {"name": name,
- "data": OrderedDict()}
+ groups = re.search(REGEX_NIC, tst_data["parent"])
+ if not groups:
+ continue
+ nic = groups.group(0)
+ tbl_dict[tst_name] = {
+ "name": "{0}-{1}".format(nic, tst_data["name"]),
+ "data": OrderedDict()}
try:
- tbl_dict[tst_name]["data"][build] = (
- tst_data["status"],
- input_data.metadata(job, build).get("generated", ""),
- input_data.metadata(job, build).get("version", ""),
- build)
+ generated = input_data.metadata(job, build).\
+ get("generated", "")
+ if not generated:
+ continue
+ then = dt.strptime(generated, "%Y%m%d %H:%M")
+ if (now - then) <= timeperiod:
+ tbl_dict[tst_name]["data"][build] = (
+ tst_data["status"],
+ generated,
+ input_data.metadata(job, build).get("version", ""),
+ build)
except (TypeError, KeyError):
pass # No data in output.xml for this test
tbl_lst = list()
for tst_data in tbl_dict.values():
- win_size = min(len(tst_data["data"]), table["window"])
fails_nr = 0
- for val in tst_data["data"].values()[-win_size:]:
+ for val in tst_data["data"].values():
if val[0] == "FAIL":
fails_nr += 1
fails_last_date = val[1]
:type input_data: InputData
"""
+ testbed = table.get("testbed", None)
+ if testbed is None:
+ logging.error("The testbed is not defined for the table '{0}'.".
+ format(table.get("title", "")))
+ return
+
logging.info(" Generating the table {0} ...".
format(table.get("title", "")))
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:
td = ET.SubElement(tr, "td", attrib=dict(align=alignment))
# Name:
if c_idx == 0:
- url = _generate_url("../trending/", item)
+ url = _generate_url("../trending/", testbed, item)
ref = ET.SubElement(td, "a", attrib=dict(href=url))
ref.text = item
else:
Code Review / csit.git / blobdiff