X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Futils.py;h=c350fae13543ddd09dcc744e8803b9b80b17a3c5;hp=8365bfad5c0adf220c17017fbcb749125e6c1d43;hb=fce7b4b339f7a79b80143bbd796460720489d694;hpb=9821b058c2f4901a9b4d66667018da214513ab28 diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py index 8365bfad5c..c350fae135 100644 --- a/resources/tools/presentation/utils.py +++ b/resources/tools/presentation/utils.py @@ -1,4 +1,4 @@ -# Copyright (c) 2017 Cisco and/or its affiliates. +# 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: @@ -14,17 +14,21 @@ """General purpose utilities. """ +import multiprocessing import subprocess import numpy as np -import pandas as pd import logging +import csv +import prettytable from os import walk, makedirs, environ from os.path import join, isdir -from shutil import copy, Error -from math import sqrt +from shutil import move, Error +from datetime import datetime +from pandas import Series from errors import PresentationError +from jumpavg.BitCountingClassifier import BitCountingClassifier def mean(items): @@ -47,11 +51,7 @@ def stdev(items): :returns: Stdev. :rtype: float """ - - avg = mean(items) - variance = [(x - avg) ** 2 for x in items] - stddev = sqrt(mean(variance)) - return stddev + return Series.std(Series(items)) def relative_change(nr1, nr2): @@ -68,68 +68,12 @@ def relative_change(nr1, nr2): return float(((nr2 - nr1) / nr1) * 100) -def remove_outliers(input_data, outlier_const): - """ - - :param input_data: Data from which the outliers will be removed. - :param outlier_const: Outlier constant. - :type input_data: list - :type outlier_const: float - :returns: The input list without outliers. - :rtype: list - """ - - data = np.array(input_data) - upper_quartile = np.percentile(data, 75) - lower_quartile = np.percentile(data, 25) - iqr = (upper_quartile - lower_quartile) * outlier_const - quartile_set = (lower_quartile - iqr, upper_quartile + iqr) - result_lst = list() - for y in data.tolist(): - if quartile_set[0] <= y <= quartile_set[1]: - result_lst.append(y) - return result_lst - - -def find_outliers(input_data, outlier_const=1.5): - """Go through the input data and generate two pandas series: - - input data without outliers - - outliers. - The function uses IQR to detect outliers. - - :param input_data: Data to be examined for outliers. - :param outlier_const: Outlier constant. - :type input_data: pandas.Series - :type outlier_const: float - :returns: Tuple: input data with outliers removed; Outliers. - :rtype: tuple (trimmed_data, outliers) - """ - - upper_quartile = input_data.quantile(q=0.75) - lower_quartile = input_data.quantile(q=0.25) - iqr = (upper_quartile - lower_quartile) * outlier_const - low = lower_quartile - iqr - high = upper_quartile + iqr - trimmed_data = pd.Series() - outliers = pd.Series() - for item in input_data.items(): - item_pd = pd.Series([item[1], ], index=[item[0], ]) - if low <= item[1] <= high: - trimmed_data = trimmed_data.append(item_pd) - else: - trimmed_data = trimmed_data.append(pd.Series([np.nan, ], - index=[item[0], ])) - outliers = outliers.append(item_pd) - - return trimmed_data, outliers - - def get_files(path, extension=None, full_path=True): """Generates the list of files to process. :param path: Path to files. :param extension: Extension of files to process. If it is the empty string, - all files will be processed. + all files will be processed. :param full_path: If True, the files with full path are generated. :type path: str :type extension: str @@ -173,8 +117,8 @@ def execute_command(cmd): :param cmd: Command to execute. :type cmd: str - :returns: Return code of the executed command. - :rtype: int + :returns: Return code of the executed command, stdout and stderr. + :rtype: tuple(int, str, str) """ env = environ.copy() @@ -187,8 +131,10 @@ def execute_command(cmd): stdout, stderr = proc.communicate() - logging.info(stdout) - logging.info(stderr) + if stdout: + logging.info(stdout) + if stderr: + logging.info(stderr) if proc.returncode != 0: logging.error(" Command execution failed.") @@ -229,6 +175,29 @@ def get_last_completed_build_number(jenkins_url, job_name): return execute_command(cmd) +def get_build_timestamp(jenkins_url, job_name, build_nr): + """Get the timestamp of the build of the given job. + + :param jenkins_url: Jenkins URL. + :param job_name: Job name. + :param build_nr: Build number. + :type jenkins_url: str + :type job_name: str + :type build_nr: int + :returns: The timestamp. + :rtype: datetime.datetime + """ + + url = "{jenkins_url}/{job_name}/{build_nr}".format(jenkins_url=jenkins_url, + job_name=job_name, + build_nr=build_nr) + cmd = "wget -qO- {url}".format(url=url) + + timestamp = execute_command(cmd) + + return datetime.fromtimestamp(timestamp/1000) + + def archive_input_data(spec): """Archive the report. @@ -239,10 +208,7 @@ def archive_input_data(spec): logging.info(" Archiving the input data files ...") - if spec.is_debug: - extension = spec.debug["input-format"] - else: - extension = spec.input["file-format"] + extension = spec.input["file-format"] data_files = get_files(spec.environment["paths"]["DIR[WORKING,DATA]"], extension=extension) dst = spec.environment["paths"]["DIR[STATIC,ARCH]"] @@ -253,11 +219,119 @@ def archive_input_data(spec): makedirs(dst) for data_file in data_files: - logging.info(" Copying the file: {0} ...".format(data_file)) - copy(data_file, dst) + logging.info(" Moving the file: {0} ...".format(data_file)) + move(data_file, dst) except (Error, OSError) as err: raise PresentationError("Not possible to archive the input data.", str(err)) logging.info(" Done.") + + +def classify_anomalies(data): + """Process the data and return anomalies and trending values. + + Gather data into groups with average as trend value. + Decorate values within groups to be normal, + the first value of changed average as a regression, or a progression. + + :param data: Full data set with unavailable samples replaced by nan. + :type data: OrderedDict + :returns: Classification and trend values + :rtype: 2-tuple, list of strings and list of floats + """ + # Nan mean something went wrong. + # Use 0.0 to cause that being reported as a severe regression. + bare_data = [0.0 if np.isnan(sample.avg) else sample + for _, sample in data.iteritems()] + # TODO: Put analogous iterator into jumpavg library. + groups = BitCountingClassifier().classify(bare_data) + groups.reverse() # Just to use .pop() for FIFO. + classification = [] + avgs = [] + active_group = None + values_left = 0 + avg = 0.0 + for _, sample in data.iteritems(): + if np.isnan(sample.avg): + classification.append("outlier") + avgs.append(sample.avg) + continue + if values_left < 1 or active_group is None: + values_left = 0 + while values_left < 1: # Ignore empty groups (should not happen). + active_group = groups.pop() + values_left = len(active_group.values) + avg = active_group.metadata.avg + classification.append(active_group.metadata.classification) + avgs.append(avg) + values_left -= 1 + continue + classification.append("normal") + avgs.append(avg) + values_left -= 1 + return classification, avgs + + +def convert_csv_to_pretty_txt(csv_file, txt_file): + """Convert the given csv table to pretty text table. + + :param csv_file: The path to the input csv file. + :param txt_file: The path to the output pretty text file. + :type csv_file: str + :type txt_file: str + """ + + txt_table = None + with open(csv_file, '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" + if txt_table: + with open(txt_file, "w") as txt_file: + txt_file.write(str(txt_table)) + + +class Worker(multiprocessing.Process): + """Worker class used to process tasks in separate parallel processes. + """ + + def __init__(self, work_queue, data_queue, func): + """Initialization. + + :param work_queue: Queue with items to process. + :param data_queue: Shared memory between processes. Queue which keeps + the result data. This data is then read by the main process and used + in further processing. + :param func: Function which is executed by the worker. + :type work_queue: multiprocessing.JoinableQueue + :type data_queue: multiprocessing.Manager().Queue() + :type func: Callable object + """ + super(Worker, self).__init__() + self._work_queue = work_queue + self._data_queue = data_queue + self._func = func + + def run(self): + """Method representing the process's activity. + """ + + while True: + try: + self.process(self._work_queue.get()) + finally: + self._work_queue.task_done() + + def process(self, item_to_process): + """Method executed by the runner. + + :param item_to_process: Data to be processed by the function. + :type item_to_process: tuple + """ + self._func(self.pid, self._data_queue, *item_to_process)