pyforestry.norway.adapters package#

Submodules#

pyforestry.norway.adapters.allen_2020 module#

Simulation-facing facade for the Allen et al. (2020) Norway spruce model.

class pyforestry.norway.adapters.allen_2020.Allen2020Config(h40_m: float = 17.0, dominant_height_m: float = 12.0, start_total_age_years: float = 40.0)[source]#

Bases: object

Configuration for the simulation-facing Allen (2020) growth adapter.

dominant_height_m: float = 12.0#

h40_m: float = 17.0#

start_total_age_years: float = 40.0#

class pyforestry.norway.adapters.allen_2020.Allen2020GrowthModel(config: Allen2020Config | None = None)[source]#

Bases: GrowthModel

Aggregate-inventory growth adapter using Allen et al. (2020).

Projects an even-aged Norway spruce stand one step at a time (unthinned): dominant height, then surviving stems, then basal area, then volume.

build_context(stand: Stand, *, config: Allen2020Config | None = None, **kwargs) → SimulationContext[source]#: Build and seed a simulation context for Allen projections.

property component_id: str#: Stable identifier for the Allen 2020 model.

requirements() → Requirements[source]#: Declare aggregate inventory requirement for the adapter.

property source: SourceReference#: Bibliographic provenance.

update_step(ctx: SimulationContext, dt: float) → None[source]#: Advance one step using Allen dominant-height, survival, BA and volume.

class pyforestry.norway.adapters.allen_2020.Allen2020Model[source]#

Bases: object

Callable facade over the Allen (2020) Norway spruce kernels.

static dominant_height(dominant_height_m: float, age1: AgeMeasurement, age2: AgeMeasurement) → float[source]#: Project dominant height (m).

static site_index(dominant_height_m: float, age: AgeMeasurement) → SiteIndexValue[source]#: Return site index (dominant height at base age 40 yr).

static stand_volume(basal_area2: StandBasalArea | float, dominant_height2: float, age2: AgeMeasurement) → StandVolume[source]#: Return stand volume (m3/ha).

static stem_survival(stems1: Stems | float, age1: AgeMeasurement, age2: AgeMeasurement, si_h40: SiteIndexValue | float, *, thinning_quotient: float = 1.0) → Stems[source]#: Project surviving stems per hectare.

pyforestry.norway.adapters.bollandsas_2008 module#

Thin API facade and runtime adapter for Bollandsas et al. (2008).

class pyforestry.norway.adapters.bollandsas_2008.Bollandsas2008AdapterConfig(site_index_by_species: Mapping[str, float], latitude_deg: float, n_classes: int = 15, class_width_mm: float = 50.0)[source]#

Bases: object

Configuration for the Bollandsas simulation adapter.

class_width_mm: float = 50.0#

n_classes: int = 15#

class pyforestry.norway.adapters.bollandsas_2008.Bollandsas2008GrowthModel(config: Bollandsas2008AdapterConfig)[source]#

Bases: GrowthModel

Tree-list adapter around the Bollandsas diameter-class model.

build_context(stand: Stand, *, config: Bollandsas2008AdapterConfig | None = None, **kwargs) → SimulationContext[source]#: Build context and seed class-state payload for simulation updates.

property component_id: str#: Stable identifier for the Bollandsas 2008 model.

requirements() → Requirements[source]#

Declare the diameter-class representation this matrix model steps.

Bollandsås 2008 is a transition-matrix model over fixed diameter classes: its state is a per-species class vector, so the context should hold that representation rather than a tree list it never reads back. Publishing class vectors through set_aggregate_metrics while the context stood in tree_list mode meant every refresh rebuilt the metrics from the untouched plots and discarded the step entirely.

property source: SourceReference#: Bibliographic provenance.

update_step(ctx: SimulationContext, dt: float) → None[source]#: Advance one or more 5-year Bollandsas transitions.

pyforestry.norway.adapters.kuehne_2022 module#

Thin API facade for Kuehne (2022) Norway pine trajectories.

class pyforestry.norway.adapters.kuehne_2022.KuehnePineAdapterConfig(dominant_height_m: float, start_total_age_years: float)[source]#

Bases: object

Configuration for the Kuehne pine trajectory adapter.

class pyforestry.norway.adapters.kuehne_2022.KuehnePineGrowthModel(config: KuehnePineAdapterConfig)[source]#

Bases: GrowthModel

Aggregate-safe adapter that updates dominant height trajectories.

build_context(stand: Stand, *, config: KuehnePineAdapterConfig | None = None, **kwargs) → SimulationContext[source]#: Build context and seed Kuehne trajectory state.

property component_id: str#: Stable identifier for the Kuehne 2022 pine model.

requirements() → Requirements[source]#: Declare aggregate-mode compatibility.

property source: SourceReference#: Bibliographic provenance.

update_step(ctx: SimulationContext, dt: float) → None[source]#

Advance the stand: dominant height, then stem density, basal area and volume.

Projects one unthinned period using the Kuehne (2022) component equations. Site index at base age 40 (SI40, required by the stem-density and volume equations) is derived from the dominant-height trajectory.

pyforestry.norway.adapters.maleki_2022 module#

Thin model facade for Maleki et al. (2022) Norway stand equations.

class pyforestry.norway.adapters.maleki_2022.Maleki2022Config(species: Maleki2022Species = Maleki2022Species.NORWAY_SPRUCE, h40_m: float = 14.0, dominant_height_m: float = 14.0, start_total_age_years: float = 40.0)[source]#

Bases: object

Configuration for the simulation-facing Maleki growth adapter.

dominant_height_m: float = 14.0#

h40_m: float = 14.0#

start_total_age_years: float = 40.0#

class pyforestry.norway.adapters.maleki_2022.Maleki2022GrowthModel(config: Maleki2022Config | None = None)[source]#

Bases: GrowthModel

Aggregate-inventory growth adapter using Maleki et al. (2022).

build_context(stand: Stand, *, config: Maleki2022Config | None = None, **kwargs) → SimulationContext[source]#: Build and seed simulation context for Maleki projections.

property component_id: str#: Stable identifier for the Maleki 2022 model.

requirements() → Requirements[source]#: Declare aggregate inventory requirement for the adapter.

property source: SourceReference#: Bibliographic provenance.

update_step(ctx: SimulationContext, dt: float) → None[source]#: Advance one step using Maleki stem, basal-area, and height equations.

class pyforestry.norway.adapters.maleki_2022.Maleki2022ModelNorway[source]#

Bases: object

Compatibility model facade matching the Norway asset shape.

broadleaves = _MalekiSpeciesFacade(species=<Maleki2022Species.BROADLEAVES: 'broadleaves'>)#

norway_spruce = _MalekiSpeciesFacade(species=<Maleki2022Species.NORWAY_SPRUCE: 'norway_spruce'>)#

picea_abies = _MalekiSpeciesFacade(species=<Maleki2022Species.NORWAY_SPRUCE: 'norway_spruce'>)#

pinus_sylvestris = _MalekiSpeciesFacade(species=<Maleki2022Species.SCOTS_PINE: 'scots_pine'>)#

scots_pine = _MalekiSpeciesFacade(species=<Maleki2022Species.SCOTS_PINE: 'scots_pine'>)#

Module contents#

Runtime bindings for Norwegian equations — glue, not science.

Same contract as pyforestry.sweden.adapters: each module binds equation kernels from a domain package to the simulation runtime and carries no scientific coefficient literals of its own. Norway ships no whole growth-and- yield system yet, so there is no norway/systems package beside this one; when one arrives it goes there, not here.