Create separate functions for broadcasting and distributing timeslices

Author: tsmbland

src/muse/constraints.py

@@ -446,7 +446,7 @@ def max_production(
     from xarray import ones_like, zeros_like
 
     from muse.commodities import is_enduse
-    from muse.timeslices import convert_timeslice
+    from muse.timeslices import distribute_timeslice
 
     if year is None:
         year = int(market.year.min())
@@ -465,7 +465,7 @@ def max_production(
         .sel(**kwargs)
         .drop_vars("technology")
     )
-    capacity = convert_timeslice(techs.fixed_outputs) * techs.utilization_factor
+    capacity = distribute_timeslice(techs.fixed_outputs) * techs.utilization_factor
     if "asset" not in capacity.dims and "asset" in search_space.dims:
         capacity = capacity.expand_dims(asset=search_space.asset)
     production = ones_like(capacity)
@@ -724,7 +724,7 @@ def minimum_service(
     from xarray import ones_like, zeros_like
 
     from muse.commodities import is_enduse
-    from muse.timeslices import convert_timeslice
+    from muse.timeslices import distribute_timeslice
 
     if "minimum_service_factor" not in technologies.data_vars:
         return None
@@ -747,7 +747,7 @@ def minimum_service(
         .sel(**kwargs)
         .drop_vars("technology")
     )
-    capacity = convert_timeslice(techs.fixed_outputs) * techs.minimum_service_factor
+    capacity = distribute_timeslice(techs.fixed_outputs) * techs.minimum_service_factor
     if "asset" not in capacity.dims:
         capacity = capacity.expand_dims(asset=search_space.asset)
     production = ones_like(capacity)
@@ -808,7 +808,7 @@ def lp_costs(technologies: xr.Dataset, costs: xr.DataArray) -> xr.Dataset:
     from xarray import zeros_like
 
     from muse.commodities import is_enduse
-    from muse.timeslices import convert_timeslice
+    from muse.timeslices import distribute_timeslice
 
     assert "year" not in technologies.dims
 
@@ -821,7 +821,7 @@ def lp_costs(technologies: xr.Dataset, costs: xr.DataArray) -> xr.Dataset:
         selection["region"] = costs.region
     fouts = technologies.fixed_outputs.sel(selection).rename(technology="replacement")
 
-    production = zeros_like(costs * convert_timeslice(fouts))
+    production = zeros_like(costs * distribute_timeslice(fouts))
     for dim in production.dims:
         if isinstance(production.get_index(dim), pd.MultiIndex):
             production = drop_timeslice(production)

src/muse/costs.py

@@ -13,7 +13,7 @@
 
 from muse.commodities import is_enduse, is_fuel, is_material, is_pollutant
 from muse.quantities import consumption
-from muse.timeslices import convert_timeslice
+from muse.timeslices import distribute_timeslice
 from muse.utilities import filter_input
 
 
@@ -96,7 +96,7 @@ def net_present_value(
     raw_revenues = (production * prices_non_env * rates).sum(("commodity", "year"))
 
     # Cost of installed capacity
-    installed_capacity_costs = convert_timeslice(
+    installed_capacity_costs = distribute_timeslice(
         techs.cap_par * (capacity**techs.cap_exp),
     )
 
@@ -118,7 +118,7 @@ def net_present_value(
     material_costs = (production * prices_material * rates).sum(("commodity", "year"))
 
     # Fixed and Variable costs
-    fixed_costs = convert_timeslice(
+    fixed_costs = distribute_timeslice(
         techs.fix_par * (capacity**techs.fix_exp),
     )
     variable_costs = techs.var_par * (
@@ -256,7 +256,7 @@ def lifetime_levelized_cost_of_energy(
     fuels = is_fuel(technologies.comm_usage)
 
     # Cost of installed capacity
-    installed_capacity_costs = convert_timeslice(
+    installed_capacity_costs = distribute_timeslice(
         techs.cap_par * (capacity**techs.cap_exp),
     )
 
@@ -278,7 +278,7 @@ def lifetime_levelized_cost_of_energy(
     material_costs = (production * prices_material * rates).sum(("commodity", "year"))
 
     # Fixed and Variable costs
-    fixed_costs = convert_timeslice(
+    fixed_costs = distribute_timeslice(
         techs.fix_par * (capacity**techs.fix_exp),
     )
     variable_costs = (
@@ -364,11 +364,11 @@ def annual_levelized_cost_of_energy(
     rates = techs.interest_rate / (1 - (1 + techs.interest_rate) ** (-life))
 
     annualized_capital_costs = (
-        convert_timeslice(techs.cap_par * rates) / techs.utilization_factor
+        distribute_timeslice(techs.cap_par * rates) / techs.utilization_factor
     )
 
     o_and_e_costs = (
-        convert_timeslice(techs.fix_par + techs.var_par) / techs.utilization_factor
+        distribute_timeslice(techs.fix_par + techs.var_par) / techs.utilization_factor
     )
 
     fuel_costs = (techs.fixed_inputs * prices).sum("commodity")

src/muse/demand_share.py

@@ -145,7 +145,7 @@ def new_and_retro(
            A_{a, s}^r = w_s\sum_i A_a^{r, i}
 
        with :math:`w_s` a weight associated with each timeslice and determined via
-       :py:func:`muse.timeslices.convert_timeslice`.
+       :py:func:`muse.timeslices.distribute_timeslice`.
 
     #. An intermediate quantity, the :py:func:`unmet demand
        <muse.demand_share.unmet_demand>` :math:`U` is defined from

src/muse/objectives.py

@@ -383,12 +383,12 @@ def lifetime_levelized_cost_of_energy(
     due to a zero utilisation factor.
     """
     from muse.costs import lifetime_levelized_cost_of_energy as LCOE
-    from muse.timeslices import convert_timeslice
+    from muse.timeslices import distribute_timeslice
 
     capacity = capacity_to_service_demand(technologies, demand)
     production = (
         capacity
-        * convert_timeslice(technologies.fixed_outputs)
+        * distribute_timeslice(technologies.fixed_outputs)
         * technologies.utilization_factor
     )
 
@@ -416,12 +416,12 @@ def net_present_value(
     See :py:func:`muse.costs.net_present_value` for more details.
     """
     from muse.costs import net_present_value as NPV
-    from muse.timeslices import convert_timeslice
+    from muse.timeslices import distribute_timeslice
 
     capacity = capacity_to_service_demand(technologies, demand)
     production = (
         capacity
-        * convert_timeslice(technologies.fixed_outputs)
+        * distribute_timeslice(technologies.fixed_outputs)
         * technologies.utilization_factor
     )
 
@@ -448,12 +448,12 @@ def net_present_cost(
     See :py:func:`muse.costs.net_present_cost` for more details.
     """
     from muse.costs import net_present_cost as NPC
-    from muse.timeslices import convert_timeslice
+    from muse.timeslices import distribute_timeslice
 
     capacity = capacity_to_service_demand(technologies, demand)
     production = (
         capacity
-        * convert_timeslice(technologies.fixed_outputs)
+        * distribute_timeslice(technologies.fixed_outputs)
         * technologies.utilization_factor
     )
 
@@ -480,12 +480,12 @@ def equivalent_annual_cost(
     See :py:func:`muse.costs.equivalent_annual_cost` for more details.
     """
     from muse.costs import equivalent_annual_cost as EAC
-    from muse.timeslices import convert_timeslice
+    from muse.timeslices import distribute_timeslice
 
     capacity = capacity_to_service_demand(technologies, demand)
     production = (
         capacity
-        * convert_timeslice(technologies.fixed_outputs)
+        * distribute_timeslice(technologies.fixed_outputs)
         * technologies.utilization_factor
     )
 

src/muse/outputs/mca.py

@@ -35,7 +35,7 @@ def quantity(
 from muse.outputs.sector import market_quantity
 from muse.registration import registrator
 from muse.sectors import AbstractSector
-from muse.timeslices import convert_timeslice, drop_timeslice
+from muse.timeslices import distribute_timeslice, drop_timeslice
 from muse.utilities import multiindex_to_coords
 
 OUTPUT_QUANTITY_SIGNATURE = Callable[
@@ -350,12 +350,10 @@ def sector_supply(sector: AbstractSector, market: xr.Dataset, **kwargs) -> pd.Da
             ]
             agent_market.loc[dict(commodity=excluded)] = 0
 
-            result = convert_timeslice(
-                supply(
-                    agent_market,
-                    capacity,
-                    technologies,
-                ),
+            result = supply(
+                agent_market,
+                capacity,
+                technologies,
             )
 
             if "year" in result.dims:
@@ -580,13 +578,12 @@ def sector_consumption(
             ]
             agent_market.loc[dict(commodity=excluded)] = 0
 
-            production = convert_timeslice(
-                supply(
-                    agent_market,
-                    capacity,
-                    technologies,
-                ),
+            production = supply(
+                agent_market,
+                capacity,
+                technologies,
             )
+
             prices = a.filter_input(market.prices, year=output_year)
             result = consumption(
                 technologies=technologies, production=production, prices=prices
@@ -720,12 +717,10 @@ def sector_fuel_costs(
                 year=output_year,
             ).fillna(0.0)
 
-            production = convert_timeslice(
-                supply(
-                    agent_market,
-                    capacity,
-                    technologies,
-                ),
+            production = supply(
+                agent_market,
+                capacity,
+                technologies,
             )
 
             prices = a.filter_input(market.prices, year=output_year)
@@ -776,7 +771,7 @@ def sector_capital_costs(
                 year=output_year,
                 technology=capacity.technology,
             )
-            data_agent = convert_timeslice(data.cap_par * (capacity**data.cap_exp))
+            data_agent = distribute_timeslice(data.cap_par * (capacity**data.cap_exp))
             data_agent["agent"] = a.name
             data_agent["category"] = a.category
             data_agent["sector"] = getattr(sector, "name", "unnamed")
@@ -833,13 +828,12 @@ def sector_emission_costs(
             i = (np.where(envs))[0][0]
             red_envs = envs[i].commodity.values
             prices = a.filter_input(market.prices, year=output_year, commodity=red_envs)
-            production = convert_timeslice(
-                supply(
-                    agent_market,
-                    capacity,
-                    technologies,
-                ),
+            production = supply(
+                agent_market,
+                capacity,
+                technologies,
             )
+
             total = production.sel(commodity=enduses).sum("commodity")
             data_agent = total * (allemissions * prices).sum("commodity")
             data_agent["agent"] = a.name
@@ -906,7 +900,7 @@ def sector_lcoe(sector: AbstractSector, market: xr.Dataset, **kwargs) -> pd.Data
             capacity = agent.filter_input(capacity_to_service_demand(demand, techs))
             production = (
                 capacity
-                * convert_timeslice(techs.fixed_outputs)
+                * distribute_timeslice(techs.fixed_outputs)
                 * techs.utilization_factor
             )
 
@@ -983,7 +977,7 @@ def sector_eac(sector: AbstractSector, market: xr.Dataset, **kwargs) -> pd.DataF
             capacity = agent.filter_input(capacity_to_service_demand(demand, techs))
             production = (
                 capacity
-                * convert_timeslice(techs.fixed_outputs)
+                * distribute_timeslice(techs.fixed_outputs)
                 * techs.utilization_factor
             )
 

src/muse/quantities.py

@@ -153,7 +153,7 @@ def gross_margin(
     - non-environmental commodities OUTPUTS are related to revenues.
     """
     from muse.commodities import is_enduse, is_pollutant
-    from muse.timeslices import convert_timeslice
+    from muse.timeslices import distribute_timeslice
     from muse.utilities import broadcast_techs
 
     tech = broadcast_techs(  # type: ignore
@@ -190,7 +190,7 @@ def gross_margin(
     enduses = is_enduse(technologies.comm_usage)
 
     # Variable costs depend on factors such as labour
-    variable_costs = convert_timeslice(
+    variable_costs = distribute_timeslice(
         var_par * ((fixed_outputs.sel(commodity=enduses)).sum("commodity")) ** var_exp,
     )
 
@@ -340,7 +340,7 @@ def maximum_production(technologies: xr.Dataset, capacity: xr.DataArray, **filte
         filters and the set of technologies in `capacity`.
     """
     from muse.commodities import is_enduse
-    from muse.timeslices import convert_timeslice
+    from muse.timeslices import distribute_timeslice
     from muse.utilities import broadcast_techs, filter_input
 
     capa = filter_input(
@@ -352,7 +352,9 @@ def maximum_production(technologies: xr.Dataset, capacity: xr.DataArray, **filte
     ftechs = filter_input(
         btechs, **{k: v for k, v in filters.items() if k in btechs.dims}
     )
-    result = capa * convert_timeslice(ftechs.fixed_outputs) * ftechs.utilization_factor
+    result = (
+        capa * distribute_timeslice(ftechs.fixed_outputs) * ftechs.utilization_factor
+    )
     return result.where(is_enduse(result.comm_usage), 0)
 
 
@@ -543,7 +545,7 @@ def minimum_production(technologies: xr.Dataset, capacity: xr.DataArray, **filte
         the filters and the set of technologies in `capacity`.
     """
     from muse.commodities import is_enduse
-    from muse.timeslices import convert_timeslice
+    from muse.timeslices import distribute_timeslice
     from muse.utilities import broadcast_techs, filter_input
 
     capa = filter_input(
@@ -564,7 +566,9 @@ def minimum_production(technologies: xr.Dataset, capacity: xr.DataArray, **filte
         btechs, **{k: v for k, v in filters.items() if k in btechs.dims}
     )
     result = (
-        capa * convert_timeslice(ftechs.fixed_outputs) * ftechs.minimum_service_factor
+        capa
+        * distribute_timeslice(ftechs.fixed_outputs)
+        * ftechs.minimum_service_factor
     )
     return result.where(is_enduse(result.comm_usage), 0)
 
@@ -574,10 +578,10 @@ def capacity_to_service_demand(
     technologies: xr.Dataset,
 ) -> xr.DataArray:
     """Minimum capacity required to fulfill the demand."""
-    from muse.timeslices import convert_timeslice
+    from muse.timeslices import distribute_timeslice
 
     timeslice_outputs = (
-        convert_timeslice(technologies.fixed_outputs.sel(commodity=demand.commodity))
+        distribute_timeslice(technologies.fixed_outputs.sel(commodity=demand.commodity))
         * technologies.utilization_factor
     )
     capa_to_service_demand = demand / timeslice_outputs

src/muse/readers/csv.py

@@ -136,7 +136,7 @@ def to_agent_share(name):
 
 def read_technodata_timeslices(filename: Union[str, Path]) -> xr.Dataset:
     from muse.readers import camel_to_snake
-    from muse.timeslices import convert_timeslice
+    from muse.timeslices import TIMESLICE
 
     csv = pd.read_csv(filename, float_precision="high", low_memory=False)
     csv = csv.rename(columns=camel_to_snake)
@@ -170,7 +170,7 @@ def read_technodata_timeslices(filename: Union[str, Path]) -> xr.Dataset:
         if item not in ["technology", "region", "year"]
     ]
     result = result.stack(timeslice=timeslice_levels)
-    result = convert_timeslice(result)
+    result = result.sel(timeslice=TIMESLICE.timeslice)
     # sorts timeslices into the correct order
     return result
 
@@ -607,7 +607,7 @@ def read_initial_market(
     """Read projections, import and export csv files."""
     from logging import getLogger
 
-    from muse.timeslices import TIMESLICE, convert_timeslice
+    from muse.timeslices import TIMESLICE, distribute_timeslice
 
     # Projections must always be present
     if isinstance(projections, (str, Path)):
@@ -630,8 +630,8 @@ def read_initial_market(
         getLogger(__name__).info("Base year import not provided. Set to zero.")
         base_year_import = xr.zeros_like(projections)
 
-    base_year_export = convert_timeslice(base_year_export)
-    base_year_import = convert_timeslice(base_year_import)
+    base_year_export = distribute_timeslice(base_year_export)
+    base_year_import = distribute_timeslice(base_year_import)
     base_year_export.name = "exports"
     base_year_import.name = "imports"