Update dependencies and fix breakages from pyomo 6.10

stubs/pyomo/core/base/param.pyi

@@ -66,7 +66,7 @@ class Param(IndexedComponent, IndexedComponent_NDArrayMixin):
     def sparse_keys(self): ...
     def sparse_values(self): ...
     def sparse_items(self): ...
-    def sparse_iterkeys(self): ...
+    def sparse_keys(self): ...
     def sparse_itervalues(self): ...
     def sparse_iteritems(self): ...
     def extract_values(self): ...

temoa/_internal/table_data_puller.py

@@ -216,7 +216,7 @@ def poll_flow_results(model: TemoaModel, epsilon: float = 1e-5) -> dict[FI, dict
                 res[fi][FlowType.OUT] -= flow
 
     # construction flows
-    for r, i, t, v in model.construction_input.sparse_iterkeys():
+    for r, i, t, v in model.construction_input.sparse_keys():
         annual = (
             value(model.construction_input[r, i, t, v])
             * value(model.v_new_capacity[r, t, v])
@@ -231,7 +231,7 @@ def poll_flow_results(model: TemoaModel, epsilon: float = 1e-5) -> dict[FI, dict
                 res[fi][FlowType.IN] = flow
 
     # end of life flows
-    for r, t, v, o in model.end_of_life_output.sparse_iterkeys():
+    for r, t, v, o in model.end_of_life_output.sparse_keys():
         if (r, t, v) not in model.retirement_periods:
             continue
         for p in model.retirement_periods[r, t, v]:
@@ -335,7 +335,7 @@ def poll_cost_results(
     entries: dict[tuple[Region, Period, Technology, Vintage], dict[CostType, float]] = defaultdict(
         dict
     )
-    for r, t, v in model.cost_invest.sparse_iterkeys():  # Returns only non-zero values
+    for r, t, v in model.cost_invest.sparse_keys():  # Returns only non-zero values
         # gather details...
         cap = value(model.v_new_capacity[r, t, v])
         if abs(cap) < epsilon:
@@ -395,7 +395,7 @@ def poll_cost_results(
                 {CostType.D_INVEST: model_loan_cost, CostType.INVEST: undiscounted_cost}
             )
 
-    for r, p, t, v in model.cost_fixed.sparse_iterkeys():
+    for r, p, t, v in model.cost_fixed.sparse_keys():
         cap = value(model.v_capacity[r, p, t, v])
         if abs(cap) < epsilon:
             continue
@@ -436,7 +436,7 @@ def poll_cost_results(
                 }
             )
 
-    for r, p, t, v in model.cost_variable.sparse_iterkeys():
+    for r, p, t, v in model.cost_variable.sparse_keys():
         if t not in model.tech_annual:
             activity = sum(
                 value(model.v_flow_out[r, p, S_s, S_d, S_i, t, v, S_o])
@@ -622,7 +622,7 @@ def poll_emissions(
 
     base = [
         (r, p, e, i, t, v, o)
-        for (r, e, i, t, v, o) in model.emission_activity.sparse_iterkeys()
+        for (r, e, i, t, v, o) in model.emission_activity.sparse_keys()
         for p in model.time_optimize
         if (r, p, t, v) in model.process_inputs
     ]
@@ -683,7 +683,7 @@ def poll_emissions(
 
     # iterate through embodied flows
     embodied_flows: dict[EI, float] = defaultdict(float)
-    for r, e, t, v in model.emission_embodied.sparse_iterkeys():
+    for r, e, t, v in model.emission_embodied.sparse_keys():
         embodied_flows[EI(r, v, t, v, e)] += value(
             model.v_new_capacity[r, t, v]
             * model.emission_embodied[r, e, t, v]
@@ -731,7 +731,7 @@ def poll_emissions(
 
     # iterate through end of life flows
     eol_flows: dict[EI, float] = defaultdict(float)
-    for r, e, t, v in model.emission_end_of_life.sparse_iterkeys():
+    for r, e, t, v in model.emission_end_of_life.sparse_keys():
         if (r, t, v) not in model.retirement_periods:
             continue
         for p in model.retirement_periods[r, t, v]:

temoa/components/capacity.py

@@ -48,7 +48,7 @@ def check_capacity_factor_process(model: TemoaModel) -> None:
             count_rptv[r, p, t, v] = 0
 
     # Check for bad values and count up the good ones
-    for r, p, _s, _d, t, v in model.capacity_factor_process.sparse_iterkeys():
+    for r, p, _s, _d, t, v in model.capacity_factor_process.sparse_keys():
         if v not in model.process_vintages[r, p, t]:
             msg = f'Invalid process {p, v} for {r, t} in capacity_factor_process table'
             logger.error(msg)
@@ -85,7 +85,7 @@ def create_capacity_factors(model: TemoaModel) -> None:
     capacity_factor_process = model.capacity_factor_process
 
     # Step 1
-    processes = {(r, t, v) for r, i, t, v, o in model.efficiency.sparse_iterkeys()}
+    processes = {(r, t, v) for r, i, t, v, o in model.efficiency.sparse_keys()}
 
     all_cfs = {
         (r, p, s, d, t, v)
@@ -95,7 +95,7 @@ def create_capacity_factors(model: TemoaModel) -> None:
     }
 
     # Step 2
-    unspecified_cfs = all_cfs.difference(capacity_factor_process.sparse_iterkeys())
+    unspecified_cfs = all_cfs.difference(capacity_factor_process.sparse_keys())
 
     # Step 3
 
@@ -234,7 +234,7 @@ def capacity_factor_process_indices(
     model: TemoaModel,
 ) -> set[tuple[Region, Season, TimeOfDay, Technology, Vintage]]:
     indices: set[tuple[Region, Season, TimeOfDay, Technology, Vintage]] = set()
-    for r, _i, t, v, _o in model.efficiency.sparse_iterkeys():
+    for r, _i, t, v, _o in model.efficiency.sparse_keys():
         for p in model.time_optimize:
             for s in model.time_season[p]:
                 for d in model.time_of_day:
@@ -604,8 +604,8 @@ def create_capacity_and_retirement_sets(model: TemoaModel) -> None:
 
     logger.debug('Creating capacity, retirement, and construction/EOL sets.')
     # Calculate retirement periods based on lifetime and survival curves
-    unique_rtv = {(r, t, v) for r, _i, t, v, _o in model.efficiency.sparse_iterkeys()} | set(
-        model.existing_capacity.sparse_iterkeys()
+    unique_rtv = {(r, t, v) for r, _i, t, v, _o in model.efficiency.sparse_keys()} | set(
+        model.existing_capacity.sparse_keys()
     )
     for r, t, v in unique_rtv:
         if t in model.tech_uncap:
@@ -637,19 +637,19 @@ def create_capacity_and_retirement_sets(model: TemoaModel) -> None:
                 model.retirement_periods.setdefault((r, t, v), set()).add(p)
 
     # Link construction materials to technologies
-    for r, i, t, v in model.construction_input.sparse_iterkeys():
+    for r, i, t, v in model.construction_input.sparse_keys():
         model.capacity_consumption_techs.setdefault((r, v, i), set()).add(t)
         model.used_techs.add(t)
 
     # Link end-of-life materials to retiring technologies
-    for r, t, v, o in model.end_of_life_output.sparse_iterkeys():
+    for r, t, v, o in model.end_of_life_output.sparse_keys():
         if (r, t, v) in model.retirement_periods:
             for p in model.retirement_periods[r, t, v]:
                 model.retirement_production_processes.setdefault((r, p, o), set()).add((t, v))
                 model.used_techs.add(t)
 
     # Link end-of-life emissions to retiring technologies
-    for r, _e, t, v in model.emission_end_of_life.sparse_iterkeys():
+    for r, _e, t, v in model.emission_end_of_life.sparse_keys():
         if (r, t, v) in model.retirement_periods:
             model.used_techs.add(t)
 

temoa/components/commodities.py

@@ -697,7 +697,7 @@ def create_technology_and_commodity_sets(model: TemoaModel) -> None:
         - M.tech_demand: Technologies that directly satisfy an end-use demand.
     """
     logger.debug('Creating technology and commodity subsets.')
-    for _r, _i, t, _v, o in model.efficiency.sparse_iterkeys():
+    for _r, _i, t, _v, o in model.efficiency.sparse_keys():
         if t in model.tech_flex and o not in model.commodity_flex:
             model.commodity_flex.add(o)
 
@@ -729,7 +729,7 @@ def create_demands(model: TemoaModel) -> None:
     demand_specific_distributon_dem = iget(4)
 
     # Step 1: Check if any demand commodities are going unused
-    used_dems = {dem for r, p, dem in model.demand.sparse_iterkeys()}
+    used_dems = {dem for r, p, dem in model.demand.sparse_keys()}
     unused_dems = sorted(model.commodity_demand.difference(used_dems))
     if unused_dems:
         for dem in unused_dems:
@@ -741,8 +741,8 @@ def create_demands(model: TemoaModel) -> None:
     #          makes sense to just use segment_fraction directly
     # Step 2: Build the demand default distribution (= segment_fraction)
     # DDD = M.DemandDefaultDistribution  # Shorter, for us lazy programmer types
-    # unset_defaults = set(M.segment_fraction.sparse_iterkeys())
-    # unset_defaults.difference_update(DDD.sparse_iterkeys())
+    # unset_defaults = set(M.segment_fraction.sparse_keys())
+    # unset_defaults.difference_update(DDD.sparse_keys())
     # if unset_defaults:
     # Some hackery because Pyomo thinks that this Param is constructed.
     # However, in our view, it is not yet, because we're specifically
@@ -760,7 +760,7 @@ def create_demands(model: TemoaModel) -> None:
     #     # errors associated with the specification of demand shares by time slice,
     #     # but we check to make sure it is within the specified tolerance.
 
-    #     key_padding = max(map(get_str_padding, DDD.sparse_iterkeys()))
+    #     key_padding = max(map(get_str_padding, DDD.sparse_keys()))
 
     #     fmt = '%%-%ds = %%s' % key_padding
     #     # Works out to something like "%-25s = %s"
@@ -784,7 +784,7 @@ def create_demands(model: TemoaModel) -> None:
     demands_specified = set(
         map(
             demand_specific_distributon_dem,
-            (i for i in demand_specific_distribution.sparse_iterkeys()),
+            (i for i in demand_specific_distribution.sparse_keys()),
         )
     )
     unset_demand_distributions = used_dems.difference(
@@ -811,12 +811,12 @@ def create_demands(model: TemoaModel) -> None:
     #         Also check that all keys are made...  The demand distro should be supported
     #         by the full set of (r, p, dem) keys because it is an equality constraint
     #         and we need to ensure even the zeros are passed in
-    used_rp_dems = {(r, p, dem) for r, p, dem in model.demand.sparse_iterkeys()}
+    used_rp_dems = {(r, p, dem) for r, p, dem in model.demand.sparse_keys()}
     for r, p, dem in used_rp_dems:
         expected_key_length = len(model.time_season[p]) * len(model.time_of_day)
         keys = [
             k
-            for k in demand_specific_distribution.sparse_iterkeys()
+            for k in demand_specific_distribution.sparse_keys()
             if demand_specific_distribution_region(k) == r
             and demand_specific_distribution_period(k) == p
             and demand_specific_distributon_dem(k) == dem

temoa/components/costs.py

@@ -120,7 +120,7 @@ def lifetime_loan_process_indices(model: TemoaModel) -> set[tuple[Region, Techno
     because in myopic mode, previously optimized vintages remain active in later
     windows and their data must be accepted by the param's index set.
     """
-    indices = {(r, t, v) for r, i, t, v, o in model.efficiency.sparse_iterkeys()}
+    indices = {(r, t, v) for r, i, t, v, o in model.efficiency.sparse_keys()}
 
     return indices
 
@@ -335,7 +335,7 @@ def period_cost_rule(model: TemoaModel, p: int) -> float | Expression:
             global_discount_rate,
             vintage=S_v,
         )
-        for r, S_t, S_v in model.cost_invest.sparse_iterkeys()
+        for r, S_t, S_v in model.cost_invest.sparse_keys()
         if S_v == p and not model.is_survival_curve_process[r, S_t, S_v]
     )
     loan_costs += quicksum(
@@ -352,7 +352,7 @@ def period_cost_rule(model: TemoaModel, p: int) -> float | Expression:
             p_e,
             global_discount_rate,
         )
-        for r, S_t, S_v in model.cost_invest.sparse_iterkeys()
+        for r, S_t, S_v in model.cost_invest.sparse_keys()
         if S_v == p and model.is_survival_curve_process[r, S_t, S_v]
     )
 
@@ -365,7 +365,7 @@ def period_cost_rule(model: TemoaModel, p: int) -> float | Expression:
             p_0,
             p=p,
         )
-        for r, S_p, S_t, S_v in model.cost_fixed.sparse_iterkeys()
+        for r, S_p, S_t, S_v in model.cost_fixed.sparse_keys()
         if S_p == p
     )
 
@@ -378,7 +378,7 @@ def period_cost_rule(model: TemoaModel, p: int) -> float | Expression:
             p_0,
             p,
         )
-        for r, S_p, S_t, S_v in model.cost_variable.sparse_iterkeys()
+        for r, S_p, S_t, S_v in model.cost_variable.sparse_keys()
         if S_p == p and S_t not in model.tech_annual
         for S_i in model.process_inputs[r, S_p, S_t, S_v]
         for S_o in model.process_outputs_by_input[r, S_p, S_t, S_v, S_i]
@@ -395,7 +395,7 @@ def period_cost_rule(model: TemoaModel, p: int) -> float | Expression:
             p_0,
             p,
         )
-        for r, S_p, S_t, S_v in model.cost_variable.sparse_iterkeys()
+        for r, S_p, S_t, S_v in model.cost_variable.sparse_keys()
         if S_p == p and S_t in model.tech_annual
         for S_i in model.process_inputs[r, S_p, S_t, S_v]
         for S_o in model.process_outputs_by_input[r, S_p, S_t, S_v, S_i]
@@ -417,7 +417,7 @@ def period_cost_rule(model: TemoaModel, p: int) -> float | Expression:
 
     base = [
         (r, p, e, i, t, v, o)
-        for (r, e, i, t, v, o) in model.emission_activity.sparse_iterkeys()
+        for (r, e, i, t, v, o) in model.emission_activity.sparse_keys()
         if (r, p, e) in model.cost_emission  # tightest filter first
         and (r, p, t, v) in model.process_inputs
     ]
@@ -484,7 +484,7 @@ def period_cost_rule(model: TemoaModel, p: int) -> float | Expression:
             p_0=p_0,
             p=p,
         )
-        for (r, e, t, v) in model.emission_embodied.sparse_iterkeys()
+        for (r, e, t, v) in model.emission_embodied.sparse_keys()
         if (r, p, e) in model.cost_emission
         if v == p
     )
@@ -502,7 +502,7 @@ def period_cost_rule(model: TemoaModel, p: int) -> float | Expression:
             p_0=p_0,
             p=p,
         )
-        for (r, e, t, v) in model.emission_end_of_life.sparse_iterkeys()
+        for (r, e, t, v) in model.emission_end_of_life.sparse_keys()
         if (r, p, e) in model.cost_emission
         if (r, t, v) in model.retirement_periods and p in model.retirement_periods[r, t, v]
     )
@@ -687,8 +687,8 @@ def create_costs(model: TemoaModel) -> None:
     var_indices = set(model.cost_variable_rptv)
 
     # Step 2
-    unspecified_fixed_prices = fixed_indices.difference(cost_fixed.sparse_iterkeys())
-    unspecified_var_prices = var_indices.difference(cost_variable.sparse_iterkeys())
+    unspecified_fixed_prices = fixed_indices.difference(cost_fixed.sparse_keys())
+    unspecified_var_prices = var_indices.difference(cost_variable.sparse_keys())
 
     # Step 3
 

temoa/components/emissions.py

@@ -40,7 +40,7 @@ def emission_activity_indices(
 ) -> set[tuple[Region, Commodity, Commodity, Technology, Vintage, Commodity]]:
     indices = {
         (r, e, i, t, v, o)
-        for r, i, t, v, o in model.efficiency.sparse_iterkeys()
+        for r, i, t, v, o in model.efficiency.sparse_keys()
         for e in model.commodity_emissions
         if r in model.regions  # omit any exchange/groups
     }
@@ -53,7 +53,7 @@ def linked_tech_constraint_indices(
 ) -> set[tuple[Region, Period, Season, TimeOfDay, Technology, Vintage, Commodity]]:
     linkedtech_indices = {
         (r, p, s, d, t, v, e)
-        for r, t, e in model.linked_techs.sparse_iterkeys()
+        for r, t, e in model.linked_techs.sparse_keys()
         for p in model.time_optimize
         if (r, p, t) in model.process_vintages
         for v in model.process_vintages[r, p, t]

temoa/components/geography.py

@@ -129,7 +129,7 @@ def create_geography_sets(model: TemoaModel) -> None:
           of (region_from, t, v, i) tuples.
     """
     logger.debug('Creating geography-related sets for exchange technologies.')
-    for r, i, t, v, o in model.efficiency.sparse_iterkeys():
+    for r, i, t, v, o in model.efficiency.sparse_keys():
         if t not in model.tech_exchange:
             continue