-# 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:
"""General purpose utilities.
"""
+import multiprocessing
import subprocess
import numpy as np
import pandas as pd
from os import walk, makedirs, environ
from os.path import join, isdir
-from shutil import copy, Error
+from shutil import move, Error
from math import sqrt
from errors import PresentationError
iqr = (upper_quartile - lower_quartile) * outlier_const
quartile_set = (lower_quartile - iqr, upper_quartile + iqr)
result_lst = list()
- for y in data.tolist():
+ for y in input_list:
if quartile_set[0] <= y <= quartile_set[1]:
result_lst.append(y)
return result_lst
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.")
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]"]
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.")
+
+
+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)