resolves #68

chainladder/core/dunders.py

@@ -30,10 +30,12 @@ def _validate_arithmetic(self, other):
                     other.odims = obj.odims
                 elif len(obj.odims) == 1 and len(other.odims) > 1:
                     obj.odims = other.odims
+                    obj.valuation = other.valuation
                 if len(other.ddims) == 1 and len(obj.ddims) > 1:
                     other.ddims = obj.ddims
                 elif len(obj.ddims) == 1 and len(other.ddims) > 1:
                     obj.ddims = other.ddims
+                    obj.valuation = other.valuation
                 other = other.values
             if not is_broadcastable:
                 # If broadcasting doesn't work, union axes similar to pandas
@@ -85,7 +87,6 @@ def _validate_arithmetic(self, other):
 
     def _arithmetic_cleanup(self, obj, other):
         ''' Common functionality AFTER arithmetic operations '''
-
         obj.values = obj.values * obj._expand_dims(obj._nan_triangle())
         obj.values[obj.values == 0] = np.nan
         return obj

chainladder/core/tests/test_triangle.py

@@ -327,3 +327,11 @@ def test_df_period_input():
 def test_trend_on_vector():
     raa = cl.load_sample('raa').latest_diagonal
     assert raa.trend(.05, axis=2).to_frame().astype(int).iloc[0,0]==29216
+
+def latest_diagonal_val_to_dev():
+    raa = cl.load_sample('raa')
+    assert raa.latest_diagonal.val_to_dev()==raa[raa.valuation==raa.valuation_date]
+
+def vector_division():
+    raa = cl.load_sample('raa')
+    raa.latest_diagonal/raa

chainladder/core/triangle.py

@@ -357,23 +357,22 @@ def val_to_dev(self, inplace=False):
     def _val_dev_chg(self):
         xp = cp.get_array_module(self.values)
         obj = copy.deepcopy(self)
-        if self.shape[-1] == 1:
-            return obj
         x = np.nan_to_num(obj.values)
         val_mtrx = \
             (np.array(obj.valuation.year).reshape(obj.shape[-2:], order='f') -
              np.array(pd.DatetimeIndex(obj.odims).year)[..., None])*12 + \
             (np.array(obj.valuation.month).reshape(obj.shape[-2:], order='f') -
              np.array(pd.DatetimeIndex(obj.odims).month)[..., None]) + 1
         rng = np.sort(np.unique(val_mtrx.flatten()[val_mtrx.flatten()>0]))
-        x = [np.sum((val_mtrx==item)*x,-1, keepdims=True)
-               for item in np.array(rng)]
+        x = [np.sum((val_mtrx==item)*x, -1, keepdims=True)
+             for item in np.array(rng)]
         x = np.concatenate(x, -1)
         obj.values = x
+        obj.values[obj.values == 0] = xp.nan
         obj.ddims = np.array([item for item in rng])
         obj.valuation = obj._valuation_triangle()
         obj._set_slicers()
-        return obj
+        return obj.dropna()
 
 
     def grain(self, grain='', trailing=False, inplace=False):
@@ -460,7 +459,7 @@ def grain(self, grain='', trailing=False, inplace=False):
         if not self.is_cumulative:
             obj = obj.cum_to_incr()
         if self.is_val_tri:
-            obj = obj.dev_to_val()
+            obj = obj.dev_to_val().dropna()
         if inplace:
             self = obj
             return self