Report: Configure 1901.1.13

[csit.git] / resources / tools / presentation / utils.py

diff --git a/resources/tools/presentation/utils.py b/resources/tools/presentation/utils.py
index a2aa0dc..3fdec85 100644 (file)
--- a/resources/tools/presentation/utils.py
+++ b/resources/tools/presentation/utils.py
@@ -25,6 +25,7 @@ from os import walk, makedirs, environ
 from os.path import join, isdir
 from shutil import move, Error
 from math import sqrt
+from datetime import datetime
 
 from errors import PresentationError
 from jumpavg.BitCountingClassifier import BitCountingClassifier
@@ -120,8 +121,8 @@ 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()
@@ -178,6 +179,29 @@ 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.
 
@@ -217,13 +241,13 @@ def classify_anomalies(data):
     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: pandas.Series
+    :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) else sample
+    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)
@@ -234,9 +258,9 @@ def classify_anomalies(data):
     values_left = 0
     avg = 0.0
     for _, sample in data.iteritems():
-        if np.isnan(sample):
+        if np.isnan(sample.avg):
             classification.append("outlier")
-            avgs.append(sample)
+            avgs.append(sample.avg)
             continue
         if values_left < 1 or active_group is None:
             values_left = 0