1 # Copyright (c) 2017 Cisco and/or its affiliates.
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at:
6 # http://www.apache.org/licenses/LICENSE-2.0
8 # Unless required by applicable law or agreed to in writing, software
9 # distributed under the License is distributed on an "AS IS" BASIS,
10 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
11 # See the License for the specific language governing permissions and
12 # limitations under the License.
14 """General purpose utilities.
22 from os import walk, makedirs, environ
23 from os.path import join, isdir
24 from shutil import copy, Error
27 from errors import PresentationError
31 """Calculate mean value from the items.
33 :param items: Mean value is calculated from these items.
39 return float(sum(items)) / len(items)
43 """Calculate stdev from the items.
45 :param items: Stdev is calculated from these items.
52 variance = [(x - avg) ** 2 for x in items]
53 stddev = sqrt(mean(variance))
57 def relative_change(nr1, nr2):
58 """Compute relative change of two values.
60 :param nr1: The first number.
61 :param nr2: The second number.
64 :returns: Relative change of nr1.
68 return float(((nr2 - nr1) / nr1) * 100)
71 def remove_outliers(input_data, outlier_const):
74 :param input_data: Data from which the outliers will be removed.
75 :param outlier_const: Outlier constant.
76 :type input_data: list
77 :type outlier_const: float
78 :returns: The input list without outliers.
82 data = np.array(input_data)
83 upper_quartile = np.percentile(data, 75)
84 lower_quartile = np.percentile(data, 25)
85 iqr = (upper_quartile - lower_quartile) * outlier_const
86 quartile_set = (lower_quartile - iqr, upper_quartile + iqr)
88 for y in data.tolist():
89 if quartile_set[0] <= y <= quartile_set[1]:
94 def find_outliers(input_data, outlier_const=1.5):
95 """Go through the input data and generate two pandas series:
96 - input data without outliers
98 The function uses IQR to detect outliers.
100 :param input_data: Data to be examined for outliers.
101 :param outlier_const: Outlier constant.
102 :type input_data: pandas.Series
103 :type outlier_const: float
104 :returns: Tuple: input data with outliers removed; Outliers.
105 :rtype: tuple (trimmed_data, outliers)
108 upper_quartile = input_data.quantile(q=0.75)
109 lower_quartile = input_data.quantile(q=0.25)
110 iqr = (upper_quartile - lower_quartile) * outlier_const
111 low = lower_quartile - iqr
112 high = upper_quartile + iqr
113 trimmed_data = pd.Series()
114 outliers = pd.Series()
115 for item in input_data.items():
116 item_pd = pd.Series([item[1], ], index=[item[0], ])
117 if low <= item[1] <= high:
118 trimmed_data = trimmed_data.append(item_pd)
120 trimmed_data = trimmed_data.append(pd.Series([np.nan, ],
122 outliers = outliers.append(item_pd)
124 return trimmed_data, outliers
127 def get_files(path, extension=None, full_path=True):
128 """Generates the list of files to process.
130 :param path: Path to files.
131 :param extension: Extension of files to process. If it is the empty string,
132 all files will be processed.
133 :param full_path: If True, the files with full path are generated.
136 :type full_path: bool
137 :returns: List of files to process.
142 for root, _, files in walk(path):
143 for filename in files:
145 if filename.endswith(extension):
147 file_list.append(join(root, filename))
149 file_list.append(filename)
151 file_list.append(join(root, filename))
156 def get_rst_title_char(level):
157 """Return character used for the given title level in rst files.
159 :param level: Level of the title.
161 :returns: Character used for the given title level in rst files.
164 chars = ('=', '-', '`', "'", '.', '~', '*', '+', '^')
165 if level < len(chars):
171 def execute_command(cmd):
172 """Execute the command in a subprocess and log the stdout and stderr.
174 :param cmd: Command to execute.
176 :returns: Return code of the executed command.
181 proc = subprocess.Popen(
183 stdout=subprocess.PIPE,
184 stderr=subprocess.PIPE,
188 stdout, stderr = proc.communicate()
193 if proc.returncode != 0:
194 logging.error(" Command execution failed.")
195 return proc.returncode, stdout, stderr
198 def get_last_successful_build_number(jenkins_url, job_name):
199 """Get the number of the last successful build of the given job.
201 :param jenkins_url: Jenkins URL.
202 :param job_name: Job name.
203 :type jenkins_url: str
205 :returns: The build number as a string.
209 url = "{}/{}/lastSuccessfulBuild/buildNumber".format(jenkins_url, job_name)
210 cmd = "wget -qO- {url}".format(url=url)
212 return execute_command(cmd)
215 def get_last_completed_build_number(jenkins_url, job_name):
216 """Get the number of the last completed build of the given job.
218 :param jenkins_url: Jenkins URL.
219 :param job_name: Job name.
220 :type jenkins_url: str
222 :returns: The build number as a string.
226 url = "{}/{}/lastCompletedBuild/buildNumber".format(jenkins_url, job_name)
227 cmd = "wget -qO- {url}".format(url=url)
229 return execute_command(cmd)
232 def archive_input_data(spec):
233 """Archive the report.
235 :param spec: Specification read from the specification file.
236 :type spec: Specification
237 :raises PresentationError: If it is not possible to archive the input data.
240 logging.info(" Archiving the input data files ...")
243 extension = spec.debug["input-format"]
245 extension = spec.input["file-format"]
246 data_files = get_files(spec.environment["paths"]["DIR[WORKING,DATA]"],
248 dst = spec.environment["paths"]["DIR[STATIC,ARCH]"]
249 logging.info(" Destination: {0}".format(dst))
255 for data_file in data_files:
256 logging.info(" Copying the file: {0} ...".format(data_file))
259 except (Error, OSError) as err:
260 raise PresentationError("Not possible to archive the input data.",
263 logging.info(" Done.")