Adding compare_matrices utility

avocado/utils/data_structures.py

@@ -23,6 +23,7 @@ avocado core code or plugins.
 
 import sys
 import math
+from itertools import izip
 
 
 def ordered_list_unique(object_list):
@@ -52,6 +53,73 @@ def geometric_mean(values):
     return math.exp(sum([math.log(number) for number in values]) / no_values)
 
 
+def compare_matrices(matrix1, matrix2, threshold=0.05):
+    """
+    Compare 2 matrices nxm and return a matrix nxm with comparison data and
+    stats. When the first columns match, they are considered as header and
+    included in the results intact.
+
+    :param matrix1: Reference Matrix of floats; first column could be header.
+    :param matrix2: Matrix that will be compared; first column could be header
+    :param threshold: Any difference greater than this percent threshold will
+                      be reported.
+    :retrun: Matrix with the difference in comparison, number of improvements,
+             number of regressions, total number of comparisons.
+    """
+    improvements = 0
+    regressions = 0
+    same = 0
+    new_matrix = []
+
+    for line1, line2 in zip(matrix1, matrix2):
+        new_line = []
+        elements = izip(line1, line2)
+        try:
+            element1, element2 = elements.next()
+        except StopIteration:             # no data in this row
+            new_matrix.append(new_line)
+            continue
+        if element1 == element2:          # this column contains header
+            new_line.append(element1)
+            try:
+                element1, element2 = elements.next()
+            except StopIteration:
+                new_matrix.append(new_line)
+                continue
+        while True:
+            try:
+                ratio = float(element2) / float(element1)
+            except ZeroDivisionError:  # For 0s, allow exact match or error
+                if float(element2) == 0:
+                    new_line.append(".")
+                    same += 1
+                else:
+                    new_line.append("Error %s/%s" % (element2, element1))
+                    improvements += 1
+                try:
+                    element1, element2 = elements.next()
+                except StopIteration:
+                    break
+                continue
+            if ratio < (1 - threshold):     # handling regression
+                regressions += 1
+                new_line.append(100 * ratio - 100)
+            elif ratio > (1 + threshold):   # handling improvements
+                improvements += 1
+                new_line.append("+" + str(100 * ratio - 100))
+            else:
+                same += 1
+                new_line.append(".")
+            try:
+                element1, element2 = elements.next()
+            except StopIteration:
+                break
+        new_matrix.append(new_line)
+
+    total = improvements + regressions + same
+    return (new_matrix, improvements, regressions, total)
+
+
 class Borg:
 
     """

selftests/unit/test_data_structures.py

@@ -29,6 +29,18 @@ class TestDataStructures(unittest.TestCase):
         self.assertEqual(data_structures.geometric_mean(xrange(1, 180)),
                          67.1555819421869)
 
+    def test_compare_matrices(self):
+        """
+        Verify the correct value is produced when comparing matrices.
+        """
+        # Note that first row contains header in first column, while the
+        # second contains only values (for testing purposes)
+        matrix1 = [["header", 51.7, 60], [1, 0, 0]]
+        matrix2 = [["header", 57.2, 54], [2, 51, 0]]
+        self.assertEqual(data_structures.compare_matrices(matrix1, matrix2),
+                         ([["header", '+10.6382978723', -10.0], ['+100.0',
+                          'Error 51/0', '.']], 3, 1, 5))
+
     def test_lazy_property(self):
         """
         Verify the value is initialized lazily with the correct value