Merge pull request #355 from NCAR/dev

Dev

Author: allibco

ldcpy/calcs.py

@@ -345,40 +345,46 @@ def magnitude_range(self) -> xr.DataArray:
         The range of the dataset
         """
         if not self._is_memoized('_magnitude_range'):
+            mag_min = self.min_abs_nonzero
+            mag_max = self.max_abs
+            mag_range = np.log10(mag_max) - np.log10(mag_min)
+            # print('RANGE = ', mag_range)
+            self._magnitude_range = mag_range
+
             # Get the range in exponent space
-            def max_agg(ds):
-                return ds.max(skipna=True)
+            # def max_agg(ds):
+            #    return ds.max(skipna=True)
 
-            def min_agg(ds):
-                return ds.min(skipna=True)
+            # def min_agg(ds):
+            #    return ds.min(skipna=True)
 
             # avoid divde by zero warning
-            a_d = abs(self._ds.copy())
+            # a_d = abs(self._ds.copy())
             # print('a_d = ', a_d.data)
-            log_ds = np.log10(a_d, where=a_d.data > 0)
+            # log_ds = np.log10(a_d, where=a_d.data > 0)
             # print('log_ds = ', log_ds.data)
             # print('data: min abs nonzero = ', self.min_abs_nonzero)
             # print('data: max value = ', self.max_val)
-            if len(self._not_agg_dims) == 0:
-                my_max = max_agg(log_ds)
-                my_min = min_agg(log_ds)
-            else:
-                stack = log_ds.stack(multi_index=tuple(self._not_agg_dims))
-                my_max = stack.groupby('multi_index').map(max_agg)
-                my_min = stack.groupby('multi_index').map(min_agg)
+            # if len(self._not_agg_dims) == 0:
+            #    my_max = max_agg(log_ds)
+            #    my_min = min_agg(log_ds)
+            # else:
+            #    stack = log_ds.stack(multi_index=tuple(self._not_agg_dims))
+            #    my_max = stack.groupby('multi_index').map(max_agg)
+            #    my_min = stack.groupby('multi_index').map(min_agg)
             # print('max exp= ', my_max)
             # print('min exp = ', my_min)
-            if (
-                np.isinf(my_max).any()
-                or np.isinf(my_min).any()
-                or np.isnan(my_max).any()
-                or np.isnan(my_min).any()
-            ):
-                self._magnitude_range = -1
-                return self._magnitude_range
-            else:
+            # if (
+            #    np.isinf(my_max).any()
+            #    or np.isinf(my_min).any()
+            #    or np.isnan(my_max).any()
+            #    or np.isnan(my_min).any()
+            # ):
+            #    self._magnitude_range = -1
+            #    return self._magnitude_range
+            # else:
 
-                self._magnitude_range = my_max - my_min
+            # self._magnitude_range = my_max - my_min
 
         return self._magnitude_range
 
@@ -974,7 +980,7 @@ def quantile_value(self) -> xr.DataArray:
     @property
     def max_abs(self) -> xr.DataArray:
         if not self._is_memoized('_max_abs'):
-            self._max_abs = abs(self._ds).max(dim=self._agg_dims)
+            self._max_abs = abs(self._ds).max(dim=self._agg_dims, skipna=True)
             self._max_abs.attrs = self._ds.attrs
             if hasattr(self._ds, 'units'):
                 self._max_abs.attrs['units'] = f'{self._ds.units}'
@@ -1005,7 +1011,7 @@ def min_abs_nonzero(self) -> xr.DataArray:
     @property
     def max_val(self) -> xr.DataArray:
         if not self._is_memoized('_max_val'):
-            self._max_val = self._ds.max(dim=self._agg_dims)
+            self._max_val = self._ds.max(dim=self._agg_dims, skipna=True)
             self._max_val.attrs = self._ds.attrs
             if hasattr(self._ds, 'units'):
                 self._max_val.attrs['units'] = f'{self._ds.units}'
@@ -1015,7 +1021,7 @@ def max_val(self) -> xr.DataArray:
     @property
     def min_val(self) -> xr.DataArray:
         if not self._is_memoized('_min_val'):
-            self._min_val = self._ds.min(dim=self._agg_dims)
+            self._min_val = self._ds.min(dim=self._agg_dims, skipna=True)
             self._min_val.attrs = self._ds.attrs
             if hasattr(self._ds, 'units'):
                 self._min_val.attrs['units'] = f'{self._ds.units}'

tests/test_calcs.py

@@ -163,7 +163,7 @@ def test_mean(self):
 
     def test_magnitude_range(self):
         value = float(test_overall_calcs.magnitude_range.values)
-        print('VALUE = ', value)
+        # print('VALUE = ', value)
         self.assertTrue(np.isclose(value, 2, rtol=1e-04))
 
     def test_mean_abs(self):