Reorganize source tree to use single autotools instance - vpp

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author	Damjan Marion <damarion@cisco.com>	2016-12-19 23:05:39 +0100
committer	Damjan Marion <damarion@cisco.com>	2016-12-28 12:25:14 +0100
commit	7cd468a3d7dee7d6c92f69a0bb7061ae208ec727 (patch)
tree	5de62f8dbd3a752f5a676ca600e43d2652d1ff1a /src/vnet/bfd/bfd_protocol.h
parent	696f1adec0df3b8f161862566dd9c86174302658 (diff)

Reorganize source tree to use single autotools instance

Change-Id: I7b51f88292e057c6443b12224486f2d0c9f8ae23 Signed-off-by: Damjan Marion <damarion@cisco.com>

Diffstat (limited to 'src/vnet/bfd/bfd_protocol.h')

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src/vnet/bfd/bfd_protocol.h

154

1 files changed, 154 insertions, 0 deletions

# Copyright (c) 2018 Cisco and/or its affiliates. # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at: # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Algorithms to generate tables. """ import logging import csv import prettytable import pandas as pd from string import replace from collections import OrderedDict from numpy import nan, isnan from xml.etree import ElementTree as ET from errors import PresentationError from utils import mean, stdev, relative_change, remove_outliers, split_outliers def generate_tables(spec, data): """Generate all tables specified in the specification file. :param spec: Specification read from the specification file. :param data: Data to process. :type spec: Specification :type data: InputData """ logging.info("Generating the tables ...") for table in spec.tables: try: eval(table["algorithm"])(table, data) except NameError: logging.error("The algorithm '{0}' is not defined.". format(table["algorithm"])) logging.info("Done.") def table_details(table, input_data): """Generate the table(s) with algorithm: table_detailed_test_results 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 = list() for column in table["columns"]: header.append('"{0}"'.format(str(column["title"]).replace('"', '""'))) # Generate the data for the table according to the model in the table # specification job = table["data"].keys()[0] build = str(table["data"][job][0]) try: suites = input_data.suites(job, build) except KeyError: logging.error(" No data available. The table will not be generated.") return for suite_longname, suite in suites.iteritems(): # Generate data suite_name = suite["name"] table_lst = list() for test in data[job][build].keys(): if data[job][build][test]["parent"] in suite_name: row_lst = list() for column in table["columns"]: try: col_data = str(data[job][build][test][column["data"]. split(" ")[1]]).replace('"', '""') if column["data"].split(" ")[1] in ("vat-history", "show-run"): col_data = replace(col_data, " |br| ", "", maxreplace=1) col_data = " |prein| {0} |preout| ".\ format(col_data[:-5]) row_lst.append('"{0}"'.format(col_data)) except KeyError: row_lst.append("No data") table_lst.append(row_lst) # Write the data to file if table_lst: file_name = "{0}_{1}{2}".format(table["output-file"], suite_name, table["output-file-ext"]) logging.info(" Writing file: '{}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(",".join(header) + "\n") for item in table_lst: file_handler.write(",".join(item) + "\n") logging.info(" Done.") def table_merged_details(table, input_data): """Generate the table(s) with algorithm: table_merged_details 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) data = input_data.merge_data(data) data.sort_index(inplace=True) suites = input_data.filter_data(table, data_set="suites") suites = input_data.merge_data(suites) # Prepare the header of the tables header = list() for column in table["columns"]: header.append('"{0}"'.format(str(column["title"]).replace('"', '""'))) for _, suite in suites.iteritems(): # Generate data suite_name = suite["name"] table_lst = list() for test in data.keys(): if data[test]["parent"] in suite_name: row_lst = list() for column in table["columns"]: try: col_data = str(data[test][column["data"]. split(" ")[1]]).replace('"', '""') if column["data"].split(" ")[1] in ("vat-history", "show-run"): col_data = replace(col_data, " |br| ", "", maxreplace=1) col_data = " |prein| {0} |preout| ".\ format(col_data[:-5]) row_lst.append('"{0}"'.format(col_data)) except KeyError: row_lst.append("No data") table_lst.append(row_lst) # Write the data to file if table_lst: file_name = "{0}_{1}{2}".format(table["output-file"], suite_name, table["output-file-ext"]) logging.info(" Writing file: '{}'".format(file_name)) with open(file_name, "w") as file_handler: file_handler.write(",".join(header) + "\n") for item in table_lst: file_handler.write(",".join(item) + "\n") 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 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. :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, continue_on_error=True) # Prepare the header of the tables try: header = ["Test case", ] history = table.get("history", None) if history: for item in history: header.extend( ["{0} Throughput [Mpps]".format(item["title"]), "{0} Stdev [Mpps]".format(item["title"])]) header.extend( ["{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["throughput"]["value"]) 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["throughput"]["value"]) except KeyError: pass except TypeError: tbl_dict.pop(tst_name, 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: 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"], None) is None: tbl_dict[tst_name]["history"][item["title"]] = \ list() try: tbl_dict[tst_name]["history"][item["title"]].\ append(tst_data["throughput"]["value"]) except (TypeError, KeyError): pass tbl_lst = list() for tst_name in tbl_dict.keys(): item = [tbl_dict[tst_name]["name"], ] if history: 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]) 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]) 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[-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 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): txt_table = None logging.info(" Writing file: '{0}'".format(txt_name)) with open(tbl_names[i], '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_name, "w") as txt_file: txt_file.write(str(txt_table)) # 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 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"]) ] for i, txt_name in enumerate(tbl_names_txt): txt_table = None logging.info(" Writing file: '{0}'".format(txt_name)) with open(tbl_names[i], '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_name, "w") as txt_file: txt_file.write(str(txt_table)) 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, continue_on_error=True) # Prepare the header of the tables header = ["Test Case", "Trend [Mpps]", "Short-Term Change [%]", "Long-Term Change [%]", "Regressions [#]", "Progressions [#]", "Outliers [#]" ] 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 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()} try: tbl_dict[tst_name]["data"][str(build)] = \ tst_data["result"]["throughput"] 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"]) > 2: 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() stdev_t = data_t.rolling(window=win_size, min_periods=2).std() median_first_idx = median_t.size - long_win_size try: max_median = max( [x for x in median_t.values[median_first_idx:-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 # Classification list: classification_lst = list() for build_nr, value in data_t.iteritems(): if isnan(median_t[build_nr]) \ or isnan(stdev_t[build_nr]) \ or isnan(value): classification_lst.append("outlier") elif value < (median_t[build_nr] - 3 * stdev_t[build_nr]): classification_lst.append("regression") elif value > (median_t[build_nr] + 3 * stdev_t[build_nr]): classification_lst.append("progression") else: classification_lst.append("normal") if isnan(last_median_t) or isnan(median_t_14) or median_t_14 == 0.0: rel_change_last = nan else: rel_change_last = round( ((last_median_t - median_t_14) / median_t_14) * 100, 2) if isnan(max_median) or isnan(last_median_t) or max_median == 0.0: rel_change_long = nan else: rel_change_long = round( ((last_median_t - max_median) / max_median) * 100, 2) tbl_lst.append( [tbl_dict[tst_name]["name"], '-' if isnan(last_median_t) else round(last_median_t / 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")]) tbl_lst.sort(key=lambda rel: rel[0]) tbl_sorted = list() 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) file_name = "{0}{1}".format(table["output-file"], table["output-file-ext"]) 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_sorted: file_handler.write(",".join([str(item) for item in test]) + '\n') txt_file_name = "{0}.txt".format(table["output-file"]) txt_table = None logging.info(" Writing file: '{0}'".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)) def table_performance_trending_dashboard_html(table, input_data): """Generate the table(s) with algorithm: table_performance_trending_dashboard_html 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", ""))) try: with open(table["input-file"], 'rb') as csv_file: csv_content = csv.reader(csv_file, delimiter=',', quotechar='"') csv_lst = [item for item in csv_content] except KeyError: logging.warning("The input file is not defined.") return except csv.Error as err: logging.warning("Not possible to process the file '{0}'.\n{1}". format(table["input-file"], err)) return # Table: dashboard = ET.Element("table", attrib=dict(width="100%", border='0')) # Table header: tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor="#7eade7")) for idx, item in enumerate(csv_lst[0]): alignment = "left" if idx == 0 else "center" th = ET.SubElement(tr, "th", attrib=dict(align=alignment)) th.text = item # 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] tr = ET.SubElement(dashboard, "tr", attrib=dict(bgcolor=background)) # Columns: for c_idx, item in enumerate(row): alignment = "left" if c_idx == 0 else "center" td = ET.SubElement(tr, "td", attrib=dict(align=alignment)) # Name: url = "../trending/" file_name = "" anchor = "#" feature = "" if c_idx == 0: if "memif" in item: file_name = "container_memif.html" elif "vhost" in item: if "l2xcbase" in item or "l2bdbasemaclrn" in item: file_name = "vm_vhost_l2.html" elif "ip4base" in item: file_name = "vm_vhost_ip4.html" elif "ipsec" in item: file_name = "ipsec.html" elif "ethip4lispip" in item or "ethip4vxlan" in item: file_name = "ip4_tunnels.html" elif "ip4base" in item or "ip4scale" in item: file_name = "ip4.html" if "iacl" in item or "snat" in item or "cop" in item: feature = "-features" elif "ip6base" in item or "ip6scale" in item: file_name = "ip6.html" elif "l2xcbase" in item or "l2xcscale" in item \ or "l2bdbasemaclrn" in item or "l2bdscale" in item \ or "l2dbbasemaclrn" in item or "l2dbscale" in item: file_name = "l2.html" if "iacl" in item: feature = "-features" if "x520" in item: anchor += "x520-" elif "x710" in item: anchor += "x710-" elif "xl710" in item: anchor += "xl710-" if "64b" in item: anchor += "64b-" elif "78b" in item: anchor += "78b" elif "imix" in item: anchor += "imix-" elif "9000b" in item: anchor += "9000b-" elif "1518" in item: anchor += "1518b-" if "1t1c" in item: anchor += "1t1c" elif "2t2c" in item: anchor += "2t2c" elif "4t4c" in item: anchor += "4t4c" url = url + file_name + anchor + feature ref = ET.SubElement(td, "a", attrib=dict(href=url)) ref.text = item if c_idx > 0: td.text = item try: with open(table["output-file"], 'w') as html_file: logging.info(" Writing file: '{0}'". format(table["output-file"])) html_file.write(".. raw:: html\n\n\t") html_file.write(ET.tostring(dashboard)) html_file.write("\n\t<p><br><br></p>\n") except KeyError: logging.warning("The output file is not defined.") return


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