From 8d424becf2afd5916d0065425c9d36165b832a8c Mon Sep 17 00:00:00 2001
From: ParticularlyPythonicBS <anil@anilr.dev>
Date: Tue, 17 Mar 2026 11:30:46 -0400
Subject: [PATCH 1/2]  updating all dependencies to latest version

---
 uv.lock | 1450 ++++++++++++++++++++++++++++---------------------------
 1 file changed, 751 insertions(+), 699 deletions(-)

diff --git a/uv.lock b/uv.lock
index 03000af9..c2339a72 100644
--- a/uv.lock
+++ b/uv.lock
@@ -31,13 +31,22 @@ wheels = [
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 ]
 
 [[package]]

From 38fa269dc7c1ed1a38e54385e4e0ddfeffe21b03 Mon Sep 17 00:00:00 2001
From: ParticularlyPythonicBS <anil@anilr.dev>
Date: Fri, 20 Mar 2026 13:03:57 -0400
Subject: [PATCH 2/2]  sparse_iterkeys -> sparse_keys

---
 stubs/pyomo/core/base/param.pyi            |  2 +-
 temoa/_internal/table_data_puller.py       | 16 +++++-----
 temoa/components/capacity.py               | 18 +++++------
 temoa/components/commodities.py            | 16 +++++-----
 temoa/components/costs.py                  | 22 ++++++-------
 temoa/components/emissions.py              |  4 +--
 temoa/components/geography.py              |  2 +-
 temoa/components/limits.py                 | 30 +++++++++---------
 temoa/components/reserves.py               |  2 +-
 temoa/components/technology.py             | 36 +++++++++++-----------
 temoa/components/time.py                   |  8 ++---
 temoa/model_checking/network_model_data.py |  4 +--
 temoa/model_checking/pricing_check.py      | 19 ++++++------
 temoa/model_checking/validators.py         |  2 +-
 tests/test_full_runs.py                    |  2 +-
 15 files changed, 91 insertions(+), 92 deletions(-)

diff --git a/stubs/pyomo/core/base/param.pyi b/stubs/pyomo/core/base/param.pyi
index 8f60ebf1..c8d310a7 100644
--- a/stubs/pyomo/core/base/param.pyi
+++ b/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): ...
diff --git a/temoa/_internal/table_data_puller.py b/temoa/_internal/table_data_puller.py
index 4dff3ea0..4c5cf993 100644
--- a/temoa/_internal/table_data_puller.py
+++ b/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]:
diff --git a/temoa/components/capacity.py b/temoa/components/capacity.py
index e9c61314..e6f6cd22 100644
--- a/temoa/components/capacity.py
+++ b/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)
 
diff --git a/temoa/components/commodities.py b/temoa/components/commodities.py
index f75e4e6b..7ff4bcd2 100644
--- a/temoa/components/commodities.py
+++ b/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
diff --git a/temoa/components/costs.py b/temoa/components/costs.py
index cd22729c..f7ffe18d 100644
--- a/temoa/components/costs.py
+++ b/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
 
diff --git a/temoa/components/emissions.py b/temoa/components/emissions.py
index 03e9a9c4..657df327 100644
--- a/temoa/components/emissions.py
+++ b/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]
diff --git a/temoa/components/geography.py b/temoa/components/geography.py
index 96538c40..45e8f788 100644
--- a/temoa/components/geography.py
+++ b/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
 
diff --git a/temoa/components/limits.py b/temoa/components/limits.py
index 3f55985a..09d8fef1 100644
--- a/temoa/components/limits.py
+++ b/temoa/components/limits.py
@@ -136,7 +136,7 @@ def limit_tech_output_split_average_constraint_indices(
 def limit_growth_capacity_indices(model: TemoaModel) -> set[tuple[Region, Period, Technology, str]]:
     indices = {
         (r, p, t, op)
-        for r, t, op in model.limit_growth_capacity.sparse_iterkeys()
+        for r, t, op in model.limit_growth_capacity.sparse_keys()
         for p in model.time_optimize
     }
     return indices
@@ -147,7 +147,7 @@ def limit_degrowth_capacity_indices(
 ) -> set[tuple[Region, Period, Technology, str]]:
     indices = {
         (r, p, t, op)
-        for r, t, op in model.limit_degrowth_capacity.sparse_iterkeys()
+        for r, t, op in model.limit_degrowth_capacity.sparse_keys()
         for p in model.time_optimize
     }
     return indices
@@ -158,7 +158,7 @@ def limit_growth_new_capacity_indices(
 ) -> set[tuple[Region, Period, Technology, str]]:
     indices = {
         (r, p, t, op)
-        for r, t, op in model.limit_growth_new_capacity.sparse_iterkeys()
+        for r, t, op in model.limit_growth_new_capacity.sparse_keys()
         for p in model.time_optimize
     }
     return indices
@@ -169,7 +169,7 @@ def limit_degrowth_new_capacity_indices(
 ) -> set[tuple[Region, Period, Technology, str]]:
     indices = {
         (r, p, t, op)
-        for r, t, op in model.limit_degrowth_new_capacity.sparse_iterkeys()
+        for r, t, op in model.limit_degrowth_new_capacity.sparse_keys()
         for p in model.time_optimize
     }
     return indices
@@ -180,7 +180,7 @@ def limit_growth_new_capacity_delta_indices(
 ) -> set[tuple[Region, Period, Technology, str]]:
     indices = {
         (r, p, t, op)
-        for r, t, op in model.limit_growth_new_capacity_delta.sparse_iterkeys()
+        for r, t, op in model.limit_growth_new_capacity_delta.sparse_keys()
         for p in model.time_optimize
     }
     return indices
@@ -191,7 +191,7 @@ def limit_degrowth_new_capacity_delta_indices(
 ) -> set[tuple[Region, Period, Technology, str]]:
     indices = {
         (r, p, t, op)
-        for r, t, op in model.limit_degrowth_new_capacity_delta.sparse_iterkeys()
+        for r, t, op in model.limit_degrowth_new_capacity_delta.sparse_keys()
         for p in model.time_optimize
     }
     return indices
@@ -843,7 +843,7 @@ def limit_emission_constraint(
         model.v_flow_out[reg, p, S_s, S_d, S_i, S_t, S_v, S_o]
         * value(model.emission_activity[reg, e, S_i, S_t, S_v, S_o])
         for reg in regions
-        for tmp_r, tmp_e, S_i, S_t, S_v, S_o in model.emission_activity.sparse_iterkeys()
+        for tmp_r, tmp_e, S_i, S_t, S_v, S_o in model.emission_activity.sparse_keys()
         if tmp_e == e and tmp_r == reg and S_t not in model.tech_annual
         # EmissionsActivity not indexed by p, so make sure (r,p,t,v) combos valid
         if (reg, p, S_t, S_v) in model.process_inputs
@@ -855,7 +855,7 @@ def limit_emission_constraint(
         model.v_flow_out_annual[reg, p, S_i, S_t, S_v, S_o]
         * value(model.emission_activity[reg, e, S_i, S_t, S_v, S_o])
         for reg in regions
-        for tmp_r, tmp_e, S_i, S_t, S_v, S_o in model.emission_activity.sparse_iterkeys()
+        for tmp_r, tmp_e, S_i, S_t, S_v, S_o in model.emission_activity.sparse_keys()
         if tmp_e == e and tmp_r == reg and S_t in model.tech_annual
         # EmissionsActivity not indexed by p, so make sure (r,p,t,v) combos valid
         if (reg, p, S_t, S_v) in model.process_inputs
@@ -866,14 +866,14 @@ def limit_emission_constraint(
         * value(model.emission_embodied[reg, e, t, v])
         / value(model.period_length[v])
         for reg in regions
-        for (S_r, S_e, t, v) in model.emission_embodied.sparse_iterkeys()
+        for (S_r, S_e, t, v) in model.emission_embodied.sparse_keys()
         if v == p and S_r == reg and S_e == e
     )
 
     retirement_emissions = quicksum(
         model.v_annual_retirement[reg, p, t, v] * value(model.emission_end_of_life[reg, e, t, v])
         for reg in regions
-        for (S_r, S_e, t, v) in model.emission_end_of_life.sparse_iterkeys()
+        for (S_r, S_e, t, v) in model.emission_end_of_life.sparse_keys()
         if (reg, t, v) in model.retirement_periods and p in model.retirement_periods[reg, t, v]
         if S_r == reg and S_e == e
     )
@@ -996,7 +996,7 @@ def limit_growth_capacity(
         capacity_prev = sum(
             value(model.existing_capacity[_r, _t, _v])
             * min(1.0, (_v + value(model.lifetime_process[_r, _t, _v]) - p_prev) / (p - p_prev))
-            for _r, _t, _v in model.existing_capacity.sparse_iterkeys()
+            for _r, _t, _v in model.existing_capacity.sparse_keys()
             if _r in regions
             and _t in techs
             and _v + value(model.lifetime_process[_r, _t, _v]) > p_prev
@@ -1115,7 +1115,7 @@ def limit_growth_new_capacity(
         p_prev = model.time_exist.last()
         new_cap_prev = sum(
             value(model.existing_capacity[_r, _t, _v])
-            for _r, _t, _v in model.existing_capacity.sparse_iterkeys()
+            for _r, _t, _v in model.existing_capacity.sparse_keys()
             if _r in regions and _t in techs and _v == p_prev
         )
     else:
@@ -1241,13 +1241,13 @@ def limit_growth_new_capacity_delta(
         p_prev = model.time_exist.last()
         new_cap_prev = sum(
             value(model.existing_capacity[_r, _t, _v])
-            for _r, _t, _v in model.existing_capacity.sparse_iterkeys()
+            for _r, _t, _v in model.existing_capacity.sparse_keys()
             if _r in regions and _t in techs and _v == p_prev
         )
         p_prev2 = model.time_exist.prev(p_prev)
         new_cap_prev2 = sum(
             value(model.existing_capacity[_r, _t, _v])
-            for _r, _t, _v in model.existing_capacity.sparse_iterkeys()
+            for _r, _t, _v in model.existing_capacity.sparse_keys()
             if _r in regions and _t in techs and _v == p_prev2
         )
     else:
@@ -1259,7 +1259,7 @@ def limit_growth_new_capacity_delta(
             p_prev2 = model.time_exist.last()
             new_cap_prev2 = sum(
                 value(model.existing_capacity[_r, _t, _v])
-                for _r, _t, _v in model.existing_capacity.sparse_iterkeys()
+                for _r, _t, _v in model.existing_capacity.sparse_keys()
                 if _r in regions and _t in techs and _v == p_prev2
             )
         else:
diff --git a/temoa/components/reserves.py b/temoa/components/reserves.py
index 2b60c9bb..fad8968d 100644
--- a/temoa/components/reserves.py
+++ b/temoa/components/reserves.py
@@ -32,7 +32,7 @@
 def reserve_margin_indices(model: TemoaModel) -> set[tuple[Region, Period, Season, TimeOfDay]]:
     indices = {
         (r, p, s, d)
-        for r in model.planning_reserve_margin.sparse_iterkeys()
+        for r in model.planning_reserve_margin.sparse_keys()
         for p in model.time_optimize
         if (r, p) in model.process_reserve_periods
         for s in model.time_season[p]
diff --git a/temoa/components/technology.py b/temoa/components/technology.py
index 1bf34ea1..6ba1fb8d 100644
--- a/temoa/components/technology.py
+++ b/temoa/components/technology.py
@@ -60,8 +60,8 @@ def lifetime_process_indices(model: TemoaModel) -> set[tuple[Region, Technology,
     Based on the efficiency parameter's indices, this function returns the set of
     process indices that may be specified in the lifetime_process parameter.
     """
-    indices = {(r, t, v) for r, i, t, v, o in model.efficiency.sparse_iterkeys()}
-    indices = indices | set(model.existing_capacity.sparse_iterkeys())
+    indices = {(r, t, v) for r, i, t, v, o in set(model.efficiency.sparse_keys())}
+    indices = indices | set(model.existing_capacity.sparse_keys())
 
     return indices
 
@@ -146,9 +146,9 @@ def populate_core_dictionaries(model: TemoaModel) -> None:
     """
     logger.debug('Populating core sparse dictionaries from efficiency parameter.')
     first_period = min(model.time_future)
-    exist_indices = model.existing_capacity.sparse_keys()
+    exist_indices = set(model.existing_capacity.sparse_keys())
 
-    for r, i, t, v, o in model.efficiency.sparse_iterkeys():
+    for r, i, t, v, o in set(model.efficiency.sparse_keys()):
         # A. Basic data validation and warnings
         process = (r, t, v)
         lifetime = value(model.lifetime_process[process])
@@ -226,13 +226,13 @@ def populate_core_dictionaries(model: TemoaModel) -> None:
 def create_survival_curve(model: TemoaModel) -> None:
     rtv_interpolated = set()  # so we only need one warning
 
-    for r, _, t, v, _ in model.efficiency.sparse_iterkeys():
+    for r, _, t, v, _ in model.efficiency.sparse_keys():
         model.is_survival_curve_process[r, t, v] = False  # by default
-    for r, t, v in model.existing_capacity.sparse_iterkeys():
+    for r, t, v in model.existing_capacity.sparse_keys():
         model.is_survival_curve_process[r, t, v] = False  # by default
 
     # Collect rptv indices into (r, t, v): p dictionary
-    for r, p, t, v in model.lifetime_survival_curve.sparse_iterkeys():
+    for r, p, t, v in model.lifetime_survival_curve.sparse_keys():
         if (r, t, v) not in model.survival_curve_periods:
             model.survival_curve_periods[r, t, v] = set()
         model.survival_curve_periods[r, t, v].add(p)
@@ -341,8 +341,8 @@ def check_efficiency_indices(model: TemoaModel) -> None:
     """
     # TODO:  This could be upgraded to scan for finer resolution
     #        by checking by REGION and PERIOD...  Each region/period is unique.
-    c_outputs = {o for r, i, t, v, o in model.efficiency.sparse_iterkeys()}
-    c_outputs = c_outputs | {o for r, t, v, o in model.end_of_life_output.sparse_iterkeys()}
+    c_outputs = {o for r, i, t, v, o in model.efficiency.sparse_keys()}
+    c_outputs = c_outputs | {o for r, t, v, o in model.end_of_life_output.sparse_keys()}
 
     diff = model.commodity_demand - c_outputs
     if diff:
@@ -356,8 +356,8 @@ def check_efficiency_indices(model: TemoaModel) -> None:
         logger.error(f_msg)
         raise ValueError(f_msg)
 
-    c_inputs = {i for r, i, t, v, o in model.efficiency.sparse_iterkeys()}
-    c_inputs = c_inputs | {i for r, i, t, v in model.construction_input.sparse_iterkeys()}
+    c_inputs = {i for r, i, t, v, o in model.efficiency.sparse_keys()}
+    c_inputs = c_inputs | {i for r, i, t, v in model.construction_input.sparse_keys()}
     c_carrier = c_inputs | c_outputs
 
     symdiff = c_carrier.symmetric_difference(model.commodity_carrier)
@@ -372,10 +372,10 @@ def check_efficiency_indices(model: TemoaModel) -> None:
         logger.error(f_msg)
         raise ValueError(f_msg)
 
-    techs = {t for r, i, t, v, o in model.efficiency.sparse_iterkeys()}
-    techs = techs | {t for r, t, v, o in model.end_of_life_output.sparse_iterkeys()}
-    techs = techs | {t for r, i, t, v in model.construction_input.sparse_iterkeys()}
-    techs = techs | {t for r, e, t, v in model.emission_end_of_life.sparse_iterkeys()}
+    techs = {t for r, i, t, v, o in model.efficiency.sparse_keys()}
+    techs = techs | {t for r, t, v, o in model.end_of_life_output.sparse_keys()}
+    techs = techs | {t for r, i, t, v in model.construction_input.sparse_keys()}
+    techs = techs | {t for r, e, t, v in model.emission_end_of_life.sparse_keys()}
 
     symdiff = techs.symmetric_difference(model.tech_production)
     if symdiff:
@@ -392,7 +392,7 @@ def check_efficiency_indices(model: TemoaModel) -> None:
 def check_efficiency_variable(model: TemoaModel) -> None:
     count_rpitvo = {}
     # Pull non-variable efficiency by default
-    for r, i, t, v, o in model.efficiency.sparse_iterkeys():
+    for r, i, t, v, o in model.efficiency.sparse_keys():
         if (r, t, v) not in model.process_periods:
             # Probably an existing vintage that retires in p0
             # Still want it for end of life flows
@@ -403,7 +403,7 @@ def check_efficiency_variable(model: TemoaModel) -> None:
 
     annual = set()
     # Check for bad values and count up the good ones
-    for r, p, _s, _d, i, t, v, o in model.efficiency_variable.sparse_iterkeys():
+    for r, p, _s, _d, i, t, v, o in model.efficiency_variable.sparse_keys():
         if p not in model.process_periods[r, t, v]:
             msg = f'Invalid period {p} for process {r, t, v} in efficiency_variable table'
             logger.error(msg)
@@ -443,7 +443,7 @@ def check_existing_capacity(model: TemoaModel) -> None:
     """
     Check that all existing capacities are properly accounted for in the model.
     """
-    for r, t, v in model.existing_capacity.sparse_iterkeys():
+    for r, t, v in model.existing_capacity.sparse_keys():
         cap = value(model.existing_capacity[r, t, v])
         if cap <= 0:
             msg = (
diff --git a/temoa/components/time.py b/temoa/components/time.py
index 6df7e523..df04f748 100644
--- a/temoa/components/time.py
+++ b/temoa/components/time.py
@@ -95,7 +95,7 @@ def validate_segment_fraction(model: TemoaModel) -> None:
             (p, s, d) for s in model.time_season[p] for d in model.time_of_day
         }
         keys: set[tuple[int, str, str]] = {
-            (_p, s, d) for _p, s, d in model.segment_fraction.sparse_iterkeys() if _p == p
+            (_p, s, d) for _p, s, d in model.segment_fraction.sparse_keys() if _p == p
         }
 
         if expected_keys != keys:
@@ -149,7 +149,7 @@ def validate_time_manual(model: TemoaModel) -> None:
     if model.time_sequencing.first() != 'manual':
         return
 
-    segment_fraction_psd: set[tuple[int, str, str]] = set(model.segment_fraction.sparse_iterkeys())
+    segment_fraction_psd: set[tuple[int, str, str]] = set(model.segment_fraction.sparse_keys())
     time_manual_psd: set[tuple[int, str, str]] = {
         (p, s, d) for p, s, d, s_next, d_next in model.time_manual
     }
@@ -361,7 +361,7 @@ def create_time_season_to_sequential(model: TemoaModel) -> None:
 
     sequential: dict[tuple[int, str], float] = {}
     prev_n: float = 0
-    for p, s_seq, s in model.time_season_sequential.sparse_iterkeys():
+    for p, s_seq, s in model.time_season_sequential.sparse_keys():
         num_days: float = value(model.time_season_sequential[p, s_seq, s])
         if (
             model.time_sequencing.first() == 'consecutive_days'
@@ -409,7 +409,7 @@ def create_time_season_to_sequential(model: TemoaModel) -> None:
             logger.error(msg)
             raise ValueError(msg)
 
-    for p, s in model.segment_fraction_per_season.sparse_iterkeys():
+    for p, s in model.segment_fraction_per_season.sparse_keys():
         if s not in model.time_season[p]:
             continue
 
diff --git a/temoa/model_checking/network_model_data.py b/temoa/model_checking/network_model_data.py
index 22e815ef..1ff4e448 100644
--- a/temoa/model_checking/network_model_data.py
+++ b/temoa/model_checking/network_model_data.py
@@ -170,7 +170,7 @@ def _build_from_model(
         raise NotImplementedError('Cannot build network data from model using a myopic_index')
 
     dem_com = defaultdict(set)
-    for r, p, d in model.demand.sparse_iterkeys():
+    for r, p, d in model.demand.sparse_keys():
         dem_com[r, p].add(d)
 
     techs: defaultdict[tuple[Region, Period], set[EdgeTuple]] = defaultdict(set)
@@ -183,7 +183,7 @@ def _build_from_model(
 
     linked_techs = {
         LinkedTechTuple(r, driver, emission, driven)
-        for r, driver, emission, driven in model.linked_techs.sparse_iterkeys()
+        for r, driver, emission, driven in model.linked_techs.sparse_keys()
     }
 
     res = NetworkModelData(
diff --git a/temoa/model_checking/pricing_check.py b/temoa/model_checking/pricing_check.py
index a7027f4e..03e9a493 100644
--- a/temoa/model_checking/pricing_check.py
+++ b/temoa/model_checking/pricing_check.py
@@ -40,11 +40,10 @@ def price_checker(model: TemoaModel) -> bool:
     warnings = False  # flag
     # some sets for x-checking
     registered_inv_costs = {
-        (region, tech, vintage) for (region, tech, vintage) in model.cost_invest.sparse_iterkeys()
+        (region, tech, vintage) for (region, tech, vintage) in model.cost_invest.sparse_keys()
     }
     efficiency_rtv = {
-        (region, tech, vintage)
-        for (region, _, tech, vintage, __) in model.efficiency.sparse_iterkeys()
+        (region, tech, vintage) for (region, _, tech, vintage, __) in model.efficiency.sparse_keys()
     }
     sorted_efficiency_rtv = sorted(efficiency_rtv, key=lambda rtv: (rtv[1], rtv[0], rtv[2]))
 
@@ -58,11 +57,11 @@ def price_checker(model: TemoaModel) -> bool:
     # var costs for the period = vintage year
     base_year_variable_cost_rtv = set()
 
-    for r, p, t, v in model.cost_fixed.sparse_iterkeys():
+    for r, p, t, v in model.cost_fixed.sparse_keys():
         fixed_costs[r, t, v].add(p)
         if p == v:
             base_year_fixed_cost_rtv.add((r, t, v))
-    for r, p, t, v in model.cost_variable.sparse_iterkeys():
+    for r, p, t, v in model.cost_variable.sparse_keys():
         var_costs[r, t, v].add(p)
         if p == v:
             base_year_variable_cost_rtv.add((r, t, v))
@@ -273,11 +272,11 @@ def check_tech_uncap(model: TemoaModel) -> bool:
     logger.debug('starting price check #4:  uncapacitated techs')
     efficiency_rtv = {
         (region, tech, vintage)
-        for (region, _, tech, vintage, __) in model.efficiency.sparse_iterkeys()
+        for (region, _, tech, vintage, __) in model.efficiency.sparse_keys()
         if tech in model.tech_uncap
     }
 
-    fixed_cost_periods = {(r, t, v): p for r, p, t, v in model.cost_fixed.sparse_iterkeys()}
+    fixed_cost_periods = {(r, t, v): p for r, p, t, v in model.cost_fixed.sparse_keys()}
     rtv_with_fixed_cost = efficiency_rtv & set(fixed_cost_periods.keys())
     if rtv_with_fixed_cost:
         logger.error(
@@ -288,7 +287,7 @@ def check_tech_uncap(model: TemoaModel) -> bool:
         for rtv in rtv_with_fixed_cost:
             logger.error('%s: %s', rtv, fixed_cost_periods[rtv])
 
-    rtv_with_invest_cost = efficiency_rtv & set(model.cost_invest.sparse_iterkeys())
+    rtv_with_invest_cost = efficiency_rtv & set(model.cost_invest.sparse_keys())
     if rtv_with_invest_cost:
         logger.error(
             'The following technologies are labeled as unlimited capacity, but have an INVEST cost'
@@ -298,7 +297,7 @@ def check_tech_uncap(model: TemoaModel) -> bool:
 
     var_cost_periods = defaultdict(set)
     # by starting from the cost side, we will naturally omit anything with NO var costs at all.
-    for r, p, t, v in model.cost_variable.sparse_iterkeys():
+    for r, p, t, v in model.cost_variable.sparse_keys():
         if (r, t, v) in efficiency_rtv:
             var_cost_periods[(r, t, v)].add(p)
     # use it to check for all/none var costs in viable periods
@@ -333,7 +332,7 @@ def check_tech_uncap(model: TemoaModel) -> bool:
     capacity_params = (model.existing_capacity,)
     bad_cap_entries = False
     for param in capacity_params:
-        bad_entries = {(r, t, v) for r, t, v in param.sparse_iterkeys() if t in model.tech_uncap}
+        bad_entries = {(r, t, v) for r, t, v in param.sparse_keys() if t in model.tech_uncap}
         if bad_entries:
             for entry in bad_entries:
                 logger.error(
diff --git a/temoa/model_checking/validators.py b/temoa/model_checking/validators.py
index d94406dd..6545f9a0 100644
--- a/temoa/model_checking/validators.py
+++ b/temoa/model_checking/validators.py
@@ -340,7 +340,7 @@ def validate_efficiency(
 
 
 def validate_reserve_margin(model: TemoaModel) -> None:
-    for r in model.planning_reserve_margin.sparse_iterkeys():
+    for r in model.planning_reserve_margin.sparse_keys():
         if all((r, p) not in model.process_reserve_periods for p in model.time_optimize):
             logger.warning(
                 'Planning reserve margin provided but there are no reserve technologies serving '
diff --git a/tests/test_full_runs.py b/tests/test_full_runs.py
index 9cb39cec..1ef95429 100644
--- a/tests/test_full_runs.py
+++ b/tests/test_full_runs.py
@@ -76,7 +76,7 @@ def test_against_legacy_outputs(
     efficiency_param: pyo.Param = mdl.efficiency
     # check the set membership
     assert (
-        len(tuple(efficiency_param.sparse_iterkeys())) == expected_vals[ExpectedVals.EFF_INDEX_SIZE]
+        len(tuple(efficiency_param.sparse_keys())) == expected_vals[ExpectedVals.EFF_INDEX_SIZE]
     ), 'should match legacy numbers'
 
     # check the size of the domain.  NOTE:  The build of the domain here may be "expensive" for