Merge branch 'patch-2' of https://github.com/prateekiiest/climate

ocw/metrics.py

@@ -89,6 +89,28 @@ def run(self, ref_dataset, target_dataset):
         :rtype: :class:`numpy.ndarray`
         '''
         return calc_bias(target_dataset.values, ref_dataset.values)
+
+
+class AbsoluteBias(BinaryMetric):
+    '''Calculate the absolute bias between a reference and target dataset.'''
+
+    def run(self, ref_dataset, target_dataset):
+        '''Calculate the absolute bias between a reference and target dataset.
+
+        .. note::
+           Overrides BinaryMetric.run()
+
+        :param ref_dataset: The reference dataset to use in this metric run.
+        :type ref_dataset: :class:`dataset.Dataset`
+
+        :param target_dataset: The target dataset to evaluate against the
+            reference dataset in this metric run.
+        :type target_dataset: :class:`dataset.Dataset`
+
+        :returns: The absolute difference between the reference and target datasets.
+        :rtype: :class:`numpy.ndarray`
+        '''
+        return calc_absbias(target_dataset.values, ref_dataset.values)
 
 
 class SpatialPatternTaylorDiagram(BinaryMetric):
@@ -281,6 +303,29 @@ def calc_bias(target_array, reference_array, average_over_time=False):
         return ma.average(bias, axis=0)
     else:
         return bias
+
+
+def calc_absbias(target_array, reference_array, average_over_time=False):
+    ''' Calculate absolute difference between two arrays
+
+    :param target_array: an array to be evaluated, as model output
+    :type target_array: :class:'numpy.ma.core.MaskedArray'
+
+    :param reference_array: an array of reference dataset
+    :type reference_array: :class:'numpy.ma.core.MaskedArray'
+
+    :param average_over_time: if True, calculated bias is averaged for the axis=0
+    :type average_over_time: 'bool'
+
+    :returns: Absolute Biases array of the target dataset
+    :rtype: :class:'numpy.ma.core.MaskedArray'
+    '''
+
+    bias = abs(target_array - reference_array)
+    if average_over_time:
+        return ma.average(bias, axis=0)
+    else:
+        return bias
 
 
 def calc_stddev(array, axis=None):

ocw/tests/test_metrics.py

@@ -66,6 +66,46 @@ def test_function_run(self):
         expected_result.fill(-300)
         np.testing.assert_array_equal(self.bias.run(
             self.target_dataset, self.reference_dataset), expected_result)
+
+
+class TestAbsoluteBias(unittest.TestCase):
+    '''Test the metrics.Bias metric.'''
+
+    def setUp(self):
+        self.bias = metrics.AbsoluteBias()
+        # Initialize reference dataset
+        self.reference_lat = np.array([10, 12, 14, 16, 18])
+        self.reference_lon = np.array([100, 102, 104, 106, 108])
+        self.reference_time = np.array(
+            [dt.datetime(2000, x, 1) for x in range(1, 13)])
+        flat_array = np.array(range(300))
+        self.reference_value = flat_array.reshape(12, 5, 5)
+        self.reference_variable = 'prec'
+        self.reference_dataset = Dataset(self.reference_lat,
+                                         self.reference_lon,
+                                         self.reference_time,
+                                         self.reference_value,
+                                         self.reference_variable)
+        # Initialize target dataset
+        self.target_lat = np.array([1, 2, 4, 6, 8])
+        self.target_lon = np.array([10, 12, 14, 16, 18])
+        self.target_time = np.array(
+            [dt.datetime(2001, x, 1) for x in range(1, 13)])
+        flat_array = np.array(range(300, 600))
+        self.target_value = flat_array.reshape(12, 5, 5)
+        self.target_variable = 'tasmax'
+        self.target_dataset = Dataset(self.target_lat,
+                                      self.target_lon,
+                                      self.target_time,
+                                      self.target_value,
+                                      self.target_variable)
+
+    def test_function_run(self):
+        '''Test bias function between reference dataset and target dataset.'''
+        expected_result = np.zeros((12, 5, 5), dtype=np.int)
+        expected_result.fill(300)
+        np.testing.assert_array_equal(self.bias.run(
+            self.target_dataset, self.reference_dataset), expected_result)
 
 
 class TestSpatialPatternTaylorDiagram(unittest.TestCase):