Changed

- Replace `sum(x[i,j] for j in range(J))` using `poi.quicksum(x.select(i,'*'))` based on PyOptInterface's features
- Replace `{(i,j):model.get_value(x[i,j]) for i in range(I) for j in range(J)}` using `x.map(model.get_value)` based on PyOptInterface's features

prepshot/_model/co2.py

@@ -88,10 +88,7 @@ def emission_calc_rule(self, y : int) -> poi.ConstraintIndex:
             A constraint of the model.
         """
         model = self.model
-        return poi.quicksum(
-            model.carbon_capacity[y, z]
-            for z in model.zone
-        )
+        return poi.quicksum(model.carbon_capacity.select(y, '*'))
 
     def emission_calc_by_zone_rule(
         self, y : int, z : str
@@ -111,10 +108,7 @@ def emission_calc_by_zone_rule(
             A constraint of the model.
         """
         model = self.model
-        return poi.quicksum(
-            model.carbon_breakdown[y, z, te]
-            for te in model.tech
-        )
+        return poi.quicksum(model.carbon_breakdown.select(y, z, '*'))
 
     def carbon_breakdown(
         self,
@@ -141,7 +135,4 @@ def carbon_breakdown(
         model = self.model
         ef = model.params['emission_factor'][te, y]
         dt = model.params['dt']
-        return poi.quicksum(
-            ef * model.gen[h, m, y, z, te] * dt
-            for h in model.hour for m in model.month
-        )
+        return ef * dt * poi.quicksum(model.gen.select('*', '*', y, z, te))

prepshot/_model/cost.py

@@ -105,8 +105,8 @@ def fuel_cost_breakdown(
         dt = model.params['dt']
         vf = model.params['var_factor'][y]
         w = model.params['weight']
-        return 1 / w * fp * sum(model.gen[h, m, y, z, te]
-            for h in model.hour for m in model.month) * dt * vf
+        return (1 / w * fp * dt * vf
+            * poi.quicksum(model.gen.select('*', '*', y, z, te)))
 
     def cost_var_line_breakdown(
         self, y : int, z : str, z1 : str
@@ -134,9 +134,8 @@ def cost_var_line_breakdown(
         dt = model.params['dt']
         vf = model.params['var_factor'][y]
         w = model.params['weight']
-        return 0.5 / w * lvc * dt * vf * \
-            sum(model.trans_export[h, m, y, z, z1]
-            for h in model.hour for m in model.month)
+        return (0.5 / w * lvc * dt * vf
+            * poi.quicksum(model.trans_export.select('*', '*', y, z, z1)))
 
     def cost_var_tech_breakdown(
         self, y : int, z : str, te : str
@@ -163,8 +162,8 @@ def cost_var_tech_breakdown(
         dt = model.params['dt']
         vf = model.params['var_factor'][y]
         w = model.params['weight']
-        return 1 / w * tvc * sum(model.gen[h, m, y, z, te]
-            for h in model.hour for m in model.month) * dt * vf
+        return (1 / w * tvc * dt * vf
+            * poi.quicksum(model.gen.select('*', '*', y, z, te)))
 
     def cost_fix_line_breakdown(
         self, y : int, z : str, z1 : str

prepshot/output_data.py

@@ -41,8 +41,9 @@ def create_data_array(
     index_tuple = cartesian_product(*coords.values())
     if len(dims) == 1:
         index_tuple = [i[0] for i in index_tuple]
+    data_values = data.map(model.get_value)
     data = np.array(
-        [model.get_value(data[tuple_]) for tuple_ in index_tuple]
+        [data_values[tuple_] for tuple_ in index_tuple]
     ).reshape([len(coord) for coord in coords.values()])
     return xr.DataArray(data=data,
                         dims=dims,