X-Git-Url: https://gerrit.fd.io/r/gitweb?a=blobdiff_plain;f=resources%2Ftools%2Fpresentation%2Fnew%2Futils.py;h=a2aa0dc0717a925b685bba71482bf040b20f462e;hb=2d001ed910d3835848fccb7bb96a98a5270698fe;hp=a688928cda22d96142f3d43ac5a744a41de7f7d9;hpb=0e8d8a59fd6b8477b17a9222a5cfb0d94d24ff22;p=csit.git

diff --git a/resources/tools/presentation/new/utils.py b/resources/tools/presentation/new/utils.py
index a688928cda..a2aa0dc071 100644
--- a/resources/tools/presentation/new/utils.py
+++ b/resources/tools/presentation/new/utils.py
@@ -17,8 +17,9 @@
 import multiprocessing
 import subprocess
 import numpy as np
-import pandas as pd
 import logging
+import csv
+import prettytable
 
 from os import walk, makedirs, environ
 from os.path import join, isdir
@@ -225,7 +226,7 @@ def classify_anomalies(data):
     bare_data = [0.0 if np.isnan(sample) else sample
                  for _, sample in data.iteritems()]
     # TODO: Put analogous iterator into jumpavg library.
-    groups = BitCountingClassifier.classify(bare_data)
+    groups = BitCountingClassifier().classify(bare_data)
     groups.reverse()  # Just to use .pop() for FIFO.
     classification = []
     avgs = []
@@ -239,7 +240,7 @@ def classify_anomalies(data):
             continue
         if values_left < 1 or active_group is None:
             values_left = 0
-            while values_left < 1:  # To ignore empty groups.
+            while values_left < 1:  # Ignore empty groups (should not happen).
                 active_group = groups.pop()
                 values_left = len(active_group.values)
             avg = active_group.metadata.avg
@@ -253,6 +254,29 @@ def classify_anomalies(data):
     return classification, avgs
 
 
+def convert_csv_to_pretty_txt(csv_file, txt_file):
+    """Convert the given csv table to pretty text table.
+
+    :param csv_file: The path to the input csv file.
+    :param txt_file: The path to the output pretty text file.
+    :type csv_file: str
+    :type txt_file: str
+    """
+
+    txt_table = None
+    with open(csv_file, 'rb') as csv_file:
+        csv_content = csv.reader(csv_file, delimiter=',', quotechar='"')
+        for row in csv_content:
+            if txt_table is None:
+                txt_table = prettytable.PrettyTable(row)
+            else:
+                txt_table.add_row(row)
+        txt_table.align["Test case"] = "l"
+    if txt_table:
+        with open(txt_file, "w") as txt_file:
+            txt_file.write(str(txt_table))
+
+
 class Worker(multiprocessing.Process):
     """Worker class used to process tasks in separate parallel processes.
     """