"""General purpose utilities.
"""
-from os import walk
-from os.path import join
+import subprocess
+import numpy as np
+import pandas as pd
+import logging
+
+from os import walk, makedirs, environ
+from os.path import join, isdir
+from shutil import copy, Error
from math import sqrt
+from errors import PresentationError
+
def mean(items):
"""Calculate mean value from the items.
:rtype: float
"""
- return float((nr2 - nr1) / nr1 * 100)
+ return float(((nr2 - nr1) / nr1) * 100)
+
+
+def remove_outliers(input_list, outlier_const=1.5, window=14):
+ """Return list with outliers removed, using split_outliers.
+
+ :param input_list: Data from which the outliers will be removed.
+ :param outlier_const: Outlier constant.
+ :param window: How many preceding values to take into account.
+ :type input_list: list of floats
+ :type outlier_const: float
+ :type window: int
+ :returns: The input list without outliers.
+ :rtype: list of floats
+ """
+
+ data = np.array(input_list)
+ 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
+
+ # input_series = pd.Series()
+ # for index, value in enumerate(input_list):
+ # item_pd = pd.Series([value, ], index=[index, ])
+ # input_series.append(item_pd)
+ # output_series, _ = split_outliers(input_series, outlier_const=outlier_const,
+ # window=window)
+ # output_list = [y for x, y in output_series.items() if not np.isnan(y)]
+ #
+ # return output_list
+
+
+def split_outliers(input_series, outlier_const=1.5, window=14):
+ """Go through the input data and generate two pandas series:
+ - input data with outliers replaced by NAN
+ - outliers.
+ The function uses IQR to detect outliers.
+
+ :param input_series: Data to be examined for outliers.
+ :param outlier_const: Outlier constant.
+ :param window: How many preceding values to take into account.
+ :type input_series: pandas.Series
+ :type outlier_const: float
+ :type window: int
+ :returns: Input data with NAN outliers and Outliers.
+ :rtype: (pandas.Series, pandas.Series)
+ """
+
+ list_data = list(input_series.items())
+ head_size = min(window, len(list_data))
+ head_list = list_data[:head_size]
+ trimmed_data = pd.Series()
+ outliers = pd.Series()
+ for item_x, item_y in head_list:
+ item_pd = pd.Series([item_y, ], index=[item_x, ])
+ trimmed_data = trimmed_data.append(item_pd)
+ for index, (item_x, item_y) in list(enumerate(list_data))[head_size:]:
+ y_rolling_list = [y for (x, y) in list_data[index - head_size:index]]
+ y_rolling_array = np.array(y_rolling_list)
+ q1 = np.percentile(y_rolling_array, 25)
+ q3 = np.percentile(y_rolling_array, 75)
+ iqr = (q3 - q1) * outlier_const
+ low, high = q1 - iqr, q3 + iqr
+ item_pd = pd.Series([item_y, ], index=[item_x, ])
+ if low <= item_y <= high:
+ trimmed_data = trimmed_data.append(item_pd)
+ else:
+ outliers = outliers.append(item_pd)
+ nan_pd = pd.Series([np.nan, ], index=[item_x, ])
+ trimmed_data = trimmed_data.append(nan_pd)
+
+ return trimmed_data, outliers
def get_files(path, extension=None, full_path=True):
return chars[level]
else:
return chars[-1]
+
+
+def execute_command(cmd):
+ """Execute the command in a subprocess and log the stdout and stderr.
+
+ :param cmd: Command to execute.
+ :type cmd: str
+ :returns: Return code of the executed command.
+ :rtype: int
+ """
+
+ env = environ.copy()
+ proc = subprocess.Popen(
+ [cmd],
+ stdout=subprocess.PIPE,
+ stderr=subprocess.PIPE,
+ shell=True,
+ env=env)
+
+ stdout, stderr = proc.communicate()
+
+ logging.info(stdout)
+ logging.info(stderr)
+
+ if proc.returncode != 0:
+ logging.error(" Command execution failed.")
+ return proc.returncode, stdout, stderr
+
+
+def get_last_successful_build_number(jenkins_url, job_name):
+ """Get the number of the last successful build of the given job.
+
+ :param jenkins_url: Jenkins URL.
+ :param job_name: Job name.
+ :type jenkins_url: str
+ :type job_name: str
+ :returns: The build number as a string.
+ :rtype: str
+ """
+
+ url = "{}/{}/lastSuccessfulBuild/buildNumber".format(jenkins_url, job_name)
+ cmd = "wget -qO- {url}".format(url=url)
+
+ return execute_command(cmd)
+
+
+def get_last_completed_build_number(jenkins_url, job_name):
+ """Get the number of the last completed build of the given job.
+
+ :param jenkins_url: Jenkins URL.
+ :param job_name: Job name.
+ :type jenkins_url: str
+ :type job_name: str
+ :returns: The build number as a string.
+ :rtype: str
+ """
+
+ url = "{}/{}/lastCompletedBuild/buildNumber".format(jenkins_url, job_name)
+ cmd = "wget -qO- {url}".format(url=url)
+
+ return execute_command(cmd)
+
+
+def archive_input_data(spec):
+ """Archive the report.
+
+ :param spec: Specification read from the specification file.
+ :type spec: Specification
+ :raises PresentationError: If it is not possible to archive the input data.
+ """
+
+ logging.info(" Archiving the input data files ...")
+
+ if spec.is_debug:
+ extension = spec.debug["input-format"]
+ else:
+ extension = spec.input["file-format"]
+ data_files = get_files(spec.environment["paths"]["DIR[WORKING,DATA]"],
+ extension=extension)
+ dst = spec.environment["paths"]["DIR[STATIC,ARCH]"]
+ logging.info(" Destination: {0}".format(dst))
+
+ try:
+ if not isdir(dst):
+ makedirs(dst)
+
+ for data_file in data_files:
+ logging.info(" Copying the file: {0} ...".format(data_file))
+ copy(data_file, dst)
+
+ except (Error, OSError) as err:
+ raise PresentationError("Not possible to archive the input data.",
+ str(err))
+
+ logging.info(" Done.")
Code Review / csit.git / blobdiff