address greg's comment

Author: ernestoarbitrio

src/cr/cube/matrix/measure.py

@@ -806,7 +806,6 @@ def _subtotal_columns(self):
                 self._weighted_cube_counts.counts,
                 default_value,
                 self._dimensions,
-                diff_rows_nan=True,
             )
 
     @lazyproperty
@@ -823,7 +822,6 @@ def _subtotal_rows(self):
                 self._weighted_cube_counts.counts,
                 default_value,
                 self._dimensions,
-                diff_cols_nan=True,
             )
 
     @lazyproperty
@@ -1906,7 +1904,6 @@ def _inserted_rows(self):
             self._weighted_cube_counts.counts,
             default_value,
             self._dimensions,
-            diff_cols_nan=True,
         )
 
     @lazyproperty
@@ -1917,7 +1914,6 @@ def _inserted_columns(self):
             self._weighted_cube_counts.counts,
             default_value,
             self._dimensions,
-            diff_rows_nan=True,
         )
 
 

src/cr/cube/matrix/subtotals.py

@@ -371,7 +371,7 @@ def _subtotal_row(self, subtotal):
         return addend_sum - subtrahend_sum
 
 
-class WaveDiffSubtotal(_BaseSubtotals):
+class WaveDiffSubtotal:
     """Subtotal "blocks" created by adding and subtracting terms for wave differences.
 
     This class handles a special case for wave differences when a CAT_DATE variable is
@@ -381,54 +381,60 @@ class WaveDiffSubtotal(_BaseSubtotals):
     percentages level: (count1/base1) - (count2/base2).
     """
 
-    def __init__(
-        self,
-        base_values,
-        counts,
-        default_insertions,
-        dimensions,
-        diff_cols_nan=False,
-        diff_rows_nan=False,
-    ):
-        super(WaveDiffSubtotal, self).__init__(base_values, dimensions)
+    def __init__(self, base_values, counts, default_insertions, dimensions):
+        self._base_values = base_values
         self._counts = counts
         self._default_insertions = default_insertions
-        self._diff_cols_nan = diff_cols_nan
-        self._diff_rows_nan = diff_rows_nan
+        self._dimensions = dimensions
 
     @classmethod
-    def subtotal_rows(
-        cls,
-        base_values,
-        counts,
-        default_insertions,
-        dimensions,
-        diff_cols_nan=False,
-        diff_rows_nan=False,
-    ):
+    def subtotal_columns(cls, base_values, counts, default_insertions, dimensions):
+        """Return (n_column_subtotals, n_base_rows) ndarray of subtotal columns."""
+        return cls(
+            base_values, counts, default_insertions, dimensions
+        )._subtotal_columns
+
+    @classmethod
+    def subtotal_rows(cls, base_values, counts, default_insertions, dimensions):
         """Return (n_row_subtotals, n_base_cols) ndarray of subtotal rows.
         Keyword arguments:
         `diff_cols_nan` -- Overrides subtotal differences in the columns direction eg
         for column bases (default False)
         `diff_rows_nan` -- Overrides subtotal differences in the rows direction eg for
         row bases (default False)
         """
-        return cls(
-            base_values,
-            counts,
-            default_insertions,
-            dimensions,
-            diff_cols_nan,
-            diff_rows_nan,
-        )._subtotal_rows
+        return cls(base_values, counts, default_insertions, dimensions)._subtotal_rows
+
+    @lazyproperty
+    def _column_subtotals(self):
+        """Sequence of _Subtotal object for each subtotal in columns-dimension."""
+        return self._dimensions[-1].subtotals
+
+    def _multiple_subtrahends_or_addends(self, subtotal):
+        """Returns true if the subtotal has multiple addend or subtrahend terms."""
+        return any(subtotal.subtrahend_idxs) and (
+            len(subtotal.subtrahend_idxs) > 1 or len(subtotal.addend_idxs) > 1
+        )
+
+    def _nan_subtotals(self, axis):
+        """Generate an array filled with NaN values.
+
+        Matches the size of the specified axis of the base values.
+        """
+        return np.full(self._base_values.shape[axis], np.nan)
+
+    @lazyproperty
+    def _row_subtotals(self):
+        """Sequence of _Subtotal object for each subtotal in rows-dimension."""
+        return self._dimensions[0].subtotals
 
     @lazyproperty
     def _subtotal_rows(self):
         """(n_row_subtotals, n_cols) ndarray of subtotal rows."""
         subtotals = self._row_subtotals
-
+        n_cols = self._base_values.shape[1]
         if len(subtotals) == 0:
-            return np.empty((0, self._ncols))
+            return np.empty((0, n_cols))
 
         return np.vstack(
             [
@@ -437,45 +443,24 @@ def _subtotal_rows(self):
             ]
         )
 
-    @classmethod
-    def subtotal_columns(
-        cls,
-        base_values,
-        counts,
-        default_insertions,
-        dimensions,
-        diff_cols_nan=False,
-        diff_rows_nan=False,
-    ):
-        """Return (n_column_subtotals, n_base_rows) ndarray of subtotal columns."""
-        return cls(
-            base_values,
-            counts,
-            default_insertions,
-            dimensions,
-            diff_cols_nan,
-            diff_rows_nan,
-        )._subtotal_columns
-
     @lazyproperty
     def _subtotal_columns(self):
         """(n_rows, n_col_subtotals) matrix of subtotal columns."""
         subtotals = self._column_subtotals
+        n_rows = self._base_values.shape[0]
         if len(subtotals) == 0:
-            return np.empty((self._nrows, 0))
+            return np.empty((n_rows, 0))
         return np.hstack(
             [
-                self._subtotal_column(subtotal, default).reshape(self._nrows, 1)
+                self._subtotal_column(subtotal, default).reshape(n_rows, 1)
                 for subtotal, default in zip(subtotals, self._default_insertions.T)
             ]
         )
 
     def _subtotal_column(self, subtotal, default):
         """Return (n_rows,) ndarray of values for `subtotal` column."""
-        if (
-            self._dimensions[-1].dimension_type == DT.CAT_DATE
-            and len(subtotal.subtrahend_idxs) > 0
-            and len(subtotal.addend_idxs) > 0
+        if self._dimensions[-1].dimension_type == DT.CAT_DATE and any(
+            subtotal.subtrahend_idxs
         ):
             if self._multiple_subtrahends_or_addends(subtotal):
                 return self._nan_subtotals(axis=0)
@@ -496,10 +481,8 @@ def _subtotal_column(self, subtotal, default):
     def _subtotal_row(self, subtotal, default):
         """Return (n_cols,) ndarray of values for `subtotal` row."""
 
-        if (
-            self._dimensions[0].dimension_type == DT.CAT_DATE
-            and len(subtotal.subtrahend_idxs) > 0
-            and len(subtotal.addend_idxs) > 0
+        if self._dimensions[0].dimension_type == DT.CAT_DATE and any(
+            subtotal.subtrahend_idxs
         ):
             if self._multiple_subtrahends_or_addends(subtotal):
                 return self._nan_subtotals(axis=1)
@@ -517,19 +500,6 @@ def _subtotal_row(self, subtotal, default):
 
         return default
 
-    def _multiple_subtrahends_or_addends(self, subtotal):
-        """Returns true if the subtotal has multiple addend or subtrahend terms."""
-        return any(subtotal.subtrahend_idxs) and (
-            len(subtotal.subtrahend_idxs) > 1 or len(subtotal.addend_idxs) > 1
-        )
-
-    def _nan_subtotals(self, axis):
-        """Generate an array filled with NaN values.
-
-        Matches the size of the specified axis of the base values.
-        """
-        return np.full(self._base_values.shape[axis], np.nan)
-
 
 class OverlapSubtotals(SumSubtotals):
     """Subtotal blocks used exclusively for the "overlap" cube measure.