CLIMATE-341 - Port calcAnnualCycleMeans from metrics_kyo to ocw/utils

ocw/tests/test_utils.py

@@ -196,5 +196,26 @@ def test_total_mean(self):
         np.testing.assert_array_equal(
             utils.calc_climatology_year(self.test_dataset)[1], total_mean)
 
+
+class TestCalcClimatologyMonthly(unittest.TestCase):
+    ''' Tests the 'calc_climatology_monthly' method from ocw.utils.py '''
+
+    def setUp(self):
+        self.lats = np.array([10, 20, 30, 40, 50])
+        self.lons = np.array([20, 30, 40, 50, 60])
+        start_date = datetime.datetime(2000, 1, 1)
+        self.times = np.array([start_date + relativedelta(months=x)
+                               for x in range(36)])
+        self.values = np.array([1]*300 + [2]*300 + [0]*300).reshape(36, 5, 5)
+        self.variable = 'testdata'
+        self.dataset = Dataset(self.lats, self.lons, self.times,
+                               self.values, self.variable)
+
+    def test_calc_climatology_monthly(self):
+        expected_result = np.ones(300).reshape(12, 5, 5)
+        actual_result = utils.calc_climatology_monthly(self.dataset)
+        np.testing.assert_array_equal(actual_result, expected_result)
+
+
 if __name__ == '__main__':
     unittest.main()

ocw/utils.py

@@ -311,3 +311,27 @@ def calc_climatology_season(month_start, month_end, dataset):
     t_series = reshape_data[:, month_index].mean(axis=1)
     means = t_series.mean(axis=0)
     return t_series, means
+
+
+def calc_climatology_monthly(dataset):
+    ''' Calculate monthly mean values for a dataset.
+
+    :param dataset: Monthly binned Dataset object with the number of months
+        divisible by 12
+    :type dataset: ocw.dataset.Dataset object
+
+    :returns: Mean values for each month of the year
+    :rtype: A 3D numpy array of shape (12, num_lats, num_lons)
+
+    :raise ValueError: If the number of monthly bins is not divisible by 12
+    '''
+
+    if dataset.values.shape[0] % 12:
+        error = (
+            "The length of the time axis in the values array should be "
+            "divisible by 12."
+        )
+        raise ValueError(error)
+    else:
+        return reshape_monthly_to_annually(dataset).mean(axis=0)
+