Add menu run functionality

Author: JMGilbert

dscim/menu/main_recipe.py

@@ -285,6 +285,7 @@ def damage_function():
         def scc():
             damage_function()
             self.global_consumption
+            self.global_consumption_no_pulse
             self.logger.info("Processing SCC calculation ...")
             if self.fit_type == "quantreg":
                 self.full_uncertainty_iqr
@@ -293,6 +294,8 @@ def scc():
                     self.stream_discount_factors
                     self.calculate_scc
                 self.uncollapsed_sccs
+                self.uncollapsed_marginal_damages
+                self.uncollapsed_discount_factors
 
         course_dict = {"damage_function": damage_function, "scc": scc}
 
@@ -928,6 +931,7 @@ def gmsl_max(self):
         return gmsl_max
 
     @cachedproperty
+    @save(name="global_consumption_no_pulse")
     def global_consumption_no_pulse(self):
         """Global consumption under FAIR control scenario."""
 
@@ -1306,6 +1310,41 @@ def stream_discount_factors(self):
             discounting_type=self.discounting_type,
             fair_aggregation=self.fair_aggregation,
         )
+    
+        @cachedproperty
+    @save("uncollapsed_discount_factors")
+    def uncollapsed_discount_factors(self):
+        pop = self.collapsed_pop.sum("region")
+        pop = pop.reindex(
+            year=range(pop.year.min().values, self.ext_end_year + 1),
+            method="ffill",
+        )
+        f = self.calculate_discount_factors(
+            self.global_consumption_no_pulse / pop
+        ).to_dataset(name="discount_factor")
+        for var in f.variables:
+            f[var].encoding.clear()
+
+        return f
+
+    @cachedproperty
+    @save("uncollapsed_marginal_damages")
+    def uncollapsed_marginal_damages(self):
+
+        md = (
+            (
+                (self.global_consumption_no_pulse - self.global_consumption_pulse)
+                * self.climate.conversion
+            )
+            .rename("marginal_damages")
+            .to_dataset()
+        )
+
+        for var in md.variables:
+            md[var].encoding.clear()
+
+        return md
+
 
     def ce(self, obj, dims):
         """Rechunk data appropriately and apply the certainty equivalence

dscim/utils/menu_runs.py

@@ -115,58 +115,11 @@ def run_ssps(
                 f"{save_path}/{menu_option}_{discount_type}_eta{menu_item.eta}_rho{menu_item.rho}_global_consumption.nc4"
             )
 
-        if marginal_damages:
-            md = (
-                menu_item.global_consumption_no_pulse
-                - menu_item.global_consumption_pulse
-            ) * menu_item.climate.conversion
-            md = md.rename("marginal_damages").to_dataset()
-            for var in md.variables:
-                md[var].encoding.clear()
-            md.chunk(
-                {
-                    "discount_type": 1,
-                    "weitzman_parameter": 1,
-                    "ssp": 1,
-                    "model": 1,
-                    "gas": 1,
-                    "year": 10,
-                }
-            ).to_zarr(
-                f"{save_path}/{menu_option}_{discount_type}_eta{menu_item.eta}_rho{menu_item.rho}_uncollapsed_marginal_damages.zarr",
-                consolidated=True,
-                mode="w",
-            )
-
-        if factors:
-
-            # holding population constant
-            # from 2100 to 2300 with 2099 values
-            pop = menu_item.collapsed_pop.sum("region")
-            pop = pop.reindex(
-                year=range(pop.year.min().values, menu_item.ext_end_year + 1),
-                method="ffill",
-            )
+        if marginal_damages == True:
+            md = menu_item.uncollapsed_marginal_damages
 
-            df = menu_item.calculate_discount_factors(
-                menu_item.global_consumption_no_pulse / pop
-            ).to_dataset(name="discount_factor")
-            for var in df.variables:
-                df[var].encoding.clear()
-            df.chunk(
-                {
-                    "discount_type": 1,
-                    "weitzman_parameter": 1,
-                    "ssp": 1,
-                    "model": 1,
-                    # "gas":1,
-                    "year": 10,
-                }
-            ).to_zarr(
-                f"{save_path}/{menu_option}_{discount_type}_eta{menu_item.eta}_rho{menu_item.rho}_uncollapsed_discount_factors.zarr",
-                consolidated=True,
-                mode="w",
-            )
+        if factors == True:
+            df = menu_item.uncollapsed_discount_factors
 
 
 def run_rff(
@@ -235,55 +188,8 @@ def run_rff(
                 f"{save_path}/{menu_option}_{discount_type}_eta{menu_item.eta}_rho{menu_item.rho}_global_consumption.nc4"
             )
 
-        if marginal_damages:
-            md = (
-                (
-                    (
-                        menu_item.global_consumption_no_pulse
-                        - menu_item.global_consumption_pulse
-                    )
-                    * menu_item.climate.conversion
-                )
-                .rename("marginal_damages")
-                .to_dataset()
-            )
-
-            for var in md.variables:
-                md[var].encoding.clear()
-
-            md.chunk(
-                {
-                    "discount_type": 1,
-                    "weitzman_parameter": 14,
-                    "runid": 10000,
-                    "gas": 1,
-                    "year": 10,
-                }
-            ).to_zarr(
-                f"{save_path}/{menu_option}_{discount_type}_eta{menu_item.eta}_rho{menu_item.rho}_uncollapsed_marginal_damages.zarr",
-                consolidated=True,
-                mode="w",
-            )
-        if factors:
-
-            f = menu_item.calculate_discount_factors(
-                menu_item.global_consumption_no_pulse / menu_item.pop
-            ).to_dataset(name="discount_factor")
-
-            for var in f.variables:
-                f[var].encoding.clear()
+        if marginal_damages == True:
+            md = menu_item.uncollapsed_marginal_damages
 
-            f.chunk(
-                {
-                    "discount_type": 1,
-                    "weitzman_parameter": 14,
-                    "runid": 10000,
-                    "gas": 1,
-                    "region": 1,
-                    "year": 10,
-                }
-            ).to_zarr(
-                f"{save_path}/{menu_option}_{discount_type}_eta{menu_item.eta}_rho{menu_item.rho}_uncollapsed_discount_factors.zarr",
-                consolidated=True,
-                mode="w",
-            )
+        if factors == True:
+            f = menu_item.uncollapsed_discount_factors