X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Futils.py;h=0a9d985a88743fc5ab8af1b8484e991b8d61b061;hb=fcea4eb3f63218e77cc594ff4322dc40d72fd632;hp=ab86bafdd7c4574046010f02571576afc07f86f3;hpb=8c0d241b8b4b4289be8333b7fd1d5ce0726414d6;p=csit.git

diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py
index ab86bafdd7..0a9d985a88 100644
--- a/resources/tools/presentation/utils.py
+++ b/resources/tools/presentation/utils.py
@@ -14,6 +14,7 @@
 """General purpose utilities.
 """
 
+import multiprocessing
 import subprocess
 import numpy as np
 import pandas as pd
@@ -271,3 +272,85 @@ def archive_input_data(spec):
                                 str(err))
 
     logging.info("    Done.")
+
+
+def classify_anomalies(data, window):
+    """Evaluates if the sample value is an outlier, regression, normal or
+    progression compared to the previous data within the window.
+    We use the intervals defined as:
+    - regress: less than trimmed moving median - 3 * stdev
+    - normal: between trimmed moving median - 3 * stdev and median + 3 * stdev
+    - progress: more than trimmed moving median + 3 * stdev
+    where stdev is trimmed moving standard deviation.
+
+    :param data: Full data set with the outliers replaced by nan.
+    :param window: Window size used to calculate moving average and moving
+        stdev.
+    :type data: pandas.Series
+    :type window: int
+    :returns: Evaluated results.
+    :rtype: list
+    """
+
+    if data.size < 3:
+        return None
+
+    win_size = data.size if data.size < window else window
+    tmm = data.rolling(window=win_size, min_periods=2).median()
+    tmstd = data.rolling(window=win_size, min_periods=2).std()
+
+    classification = ["normal", ]
+    first = True
+    for build, value in data.iteritems():
+        if first:
+            first = False
+            continue
+        if np.isnan(value) or np.isnan(tmm[build]) or np.isnan(tmstd[build]):
+            classification.append("outlier")
+        elif value < (tmm[build] - 3 * tmstd[build]):
+            classification.append("regression")
+        elif value > (tmm[build] + 3 * tmstd[build]):
+            classification.append("progression")
+        else:
+            classification.append("normal")
+    return classification
+
+
+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)