Colton dev branch

fiscalsim_us/variables/gov/hhs/medicaid/eligibility/categories/older_child/is_older_child_for_medicaid.py

@@ -1,6 +1,6 @@
+import numpy as np
 from fiscalsim_us.model_api import *
 
-
 class is_older_child_for_medicaid(Variable):
     value_type = bool
     entity = Person
@@ -17,4 +17,22 @@
         is_older_child = ma.age_range.calc(age)
         state = person.household("state_code_str", period)
         income_limit = ma.income_limit[state]
-        return is_older_child & (income < income_limit)
+
+        try:
+            result = is_older_child & (income < income_limit)
+            return result
+        except Exception as e:
+            print(f"Calculation failed: {str(e)}")
+            print(f"is_older_child: type={type(is_older_child)}, value={is_older_child}")
+            print(f"income: type={type(income)}, value={income}")
+            print(f"income_limit: type={type(income_limit)}, value={income_limit}")
+
+            # Convert to numpy arrays and ensure correct types
+            is_older_child = np.asarray(is_older_child if is_older_child is not None else False).astype(bool)
+            income = np.asarray(income if income is not None else 0).astype(float)
+            income_limit = np.asarray(income_limit if income_limit is not None else 0).astype(float)
+
+            # Attempt calculation again
+            result = is_older_child & (income < income_limit)
+            print("Calculation succeeded after type conversion")
+            return result

fiscalsim_us/variables/gov/irs/credits/premium_tax_credit/is_ptc_eligible.py

@@ -1,6 +1,6 @@
+import numpy as np
 from fiscalsim_us.model_api import *
 
-
 class is_ptc_eligible(Variable):
     value_type = bool
     entity = TaxUnit
@@ -15,4 +15,9 @@ def formula(tax_unit, period, parameters):
         on_marketplace = (
             add(tax_unit, period, ["has_marketplace_health_coverage"]) > 0
         )
-        return on_marketplace & eligibility.calc(income_level)
+        income_eligible = eligibility.calc(income_level)
+
+        on_marketplace = np.array(on_marketplace, dtype=bool)
+        income_eligible = np.array(income_eligible, dtype=bool)
+
+        return on_marketplace & income_eligible

fiscalsim_us/variables/gov/states/co/ssa/oap/co_oap_eligible.py

@@ -1,4 +1,5 @@
 from fiscalsim_us.model_api import *
+import numpy as np
 
 
 class co_oap_eligible(Variable):
@@ -12,10 +13,23 @@ def formula(person, period, parameters):
         assets = person("ssi_countable_resources", period)
         joint_claim = person("ssi_claim_is_joint", period)
         p = parameters(period).gov.states.co.ssa.oap
-        asset_limit = where(
-            joint_claim, p.resources.couple, p.resources.single
+
+        assets = np.array(assets)
+        joint_claim = np.array(joint_claim, dtype=bool)
+
+        asset_limit = np.where(
+            joint_claim,
+            p.resources.couple,
+            p.resources.single
         )
+
         below_asset_limit = assets <= asset_limit
+
         age = person("age", period)
         in_age_range = p.age_range.calc(age)
+
+        in_age_range = np.array(in_age_range, dtype=bool)
+
+        below_asset_limit = below_asset_limit.astype(bool)
+
         return below_asset_limit & in_age_range

fiscalsim_us/variables/gov/states/co/ssa/state_suplement/co_state_supplement_eligible.py

@@ -1,5 +1,5 @@
 from fiscalsim_us.model_api import *
-
+import numpy as np
 
 class co_state_supplement_eligible(Variable):
     value_type = bool
@@ -12,8 +12,19 @@ def formula(person, period, parameters):
         ssi_eligible = person("is_ssi_eligible_individual", period)
         is_disabled = person("is_ssi_disabled", period)
         is_blind = person("is_blind", period)
-        disabled_or_blind = is_disabled | is_blind
         age = person("age", period)
         p = parameters(period).gov.states.co.ssa.state_supplement
+
+        # Convert to boolean arrays and handle potential None or NaN values
+        ssi_eligible = np.array(ssi_eligible).astype(bool)
+        is_disabled = np.array(is_disabled).astype(bool)
+        is_blind = np.array(is_blind).astype(bool)
+
+        disabled_or_blind = is_disabled | is_blind
         in_age_range = p.age_range.calc(age)
-        return disabled_or_blind & ssi_eligible & in_age_range
+
+        # Convert in_age_range to boolean array
+        in_age_range = np.array(in_age_range).astype(bool)
+
+        # Use bitwise & for the final operation
+        return disabled_or_blind & ssi_eligible & in_age_range