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nhra_gt.domain.params_generated

Auto-generated parameter container for NHRA simulation.

This file is automatically generated from the parameter registry CSV. DO NOT EDIT MANUALLY.

Classes

ParamsGenerated

Auto-generated parameter container from registry CSV.

Source code in src/nhra_gt/domain/params_generated.py 
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@struct.dataclass
class ParamsGenerated:
    """Auto-generated parameter container from registry CSV."""

    nep_to_cost_ratio_metro: float = 0.9  # Model parameter (unitless)
    nep_to_cost_ratio_regional: float = 0.83  # Model parameter (unitless)
    nep_to_cost_ratio_remote: float = 0.75  # Model parameter (unitless)
    rurality_weight: float = 0.35  # Model parameter (unitless)
    remote_weight: float = 0.07  # Model parameter (unitless)
    nominal_cth_share_target: float = 0.45  # Model parameter (fraction)
    effective_cth_share_base: float = 0.38  # Model parameter (fraction)
    cap_growth: float = 0.065  # Model parameter (fraction/year)
    has_cumulative_cap: bool = False  # Model parameter (unitless)
    use_equilibrium_bargaining: bool = False  # Model parameter (unitless)
    use_stage_game_equilibria: bool = True  # Model parameter (unitless)
    equilibrium_selection_rule: str = struct.field(
        default="payoff_dominant", pytree_node=False
    )  # Model parameter (unitless)
    nep_per_nwau_start: int = 1  # Model parameter (unitless)
    nep_annual_growth: float = 0.03  # Model parameter (fraction/year)
    representative_nwau: int = 1  # Model parameter (unitless)
    input_cost_per_nwau_start: int = 1  # Model parameter (unitless)
    input_cost_annual_growth: float = 0.04  # Model parameter (fraction/year)
    demand_base: int = 1  # Model parameter (unitless)
    avoidable_ed_share: float = 0.18  # Model parameter (fraction)
    discharge_delay_base: int = 1  # Model parameter (unitless)
    bed_capacity_index: int = 1  # Model parameter (unitless)
    cost_shifting_intensity: float = 0.35  # Model parameter (unitless)
    fragmentation_index: int = 1  # Model parameter (unitless)
    audit_pressure: float = 0.5  # Model parameter (unitless)
    admin_burden_weight: float = 0.25  # Model parameter (unitless)
    occupancy_base: float = 0.88  # Model parameter (unitless)
    offload_base_min: int = 18  # Model parameter (unitless)
    within4_base: float = 0.53  # Model parameter (unitless)
    rr_beta_pressure: float = 0.35  # Model parameter (unitless)
    rr_beta_offload: float = 0.015  # Model parameter (unitless)
    offload_threshold_min: int = 20  # Model parameter (unitless)
    tau: float = 0.25  # Model parameter (unitless)
    bargaining_cost: float = 0.12  # Model parameter (unitless)
    political_salience: float = 0.3  # Model parameter (unitless)
    use_quantal_response: bool = False  # Model parameter (unitless)
    qre_lambda: int = 4  # Model parameter (unitless)
    use_burden_feedback: bool = False  # Model parameter (unitless)
    burden_to_throughput_beta: float = 0.06  # Model parameter (unitless)
    noise_sd: float = 0.03  # Model parameter (unitless)
    capacity_lag: float = 0.15  # Model parameter (unitless)
    orchestration_mode: str = struct.field(
        default="simultaneous", pytree_node=False
    )  # Model parameter (unitless)
    isolated_game: Any = struct.field(default=None, pytree_node=False)  # Model parameter (unitless)
    cap_rule_type: str = struct.field(
        default="hard", pytree_node=False
    )  # Model parameter (unitless)
    audit_rule_type: str = struct.field(
        default="proportional", pytree_node=False
    )  # Model parameter (unitless)
    adjustment_cost_beta: int = 5  # Model parameter (unitless)
    cannibalization_beta: float = 0.1  # Model parameter (unitless)
    block_funding_base: float = 0.15  # Model parameter (fraction)
    shifting_friction: float = 0.05  # Model parameter (unitless)
    signal_lag_months: int = 1  # Model parameter (months)
    claims_lag_months: int = 3  # Model parameter (months)
    gp_out_of_pocket: int = 40  # Model parameter (NZD)
    gp_wait_time_min: int = 15  # Model parameter (minutes)
    patient_time_value_hour: int = 25  # Model parameter (NZD/hour)
    expansion_lag: float = 0.1  # Model parameter (unitless)
    contraction_lag: float = 0.2  # Model parameter (unitless)
    use_sequential_bargaining: bool = False  # Model parameter (unitless)
    discount_rate: float = 0.9  # Model parameter (unitless)