API documentation for the litter_model module

The litter_model module creates a LitterModel class as a child of the BaseModel class. At present a lot of the abstract methods of the parent class (e.g. spinup()) are overwritten using placeholder functions that don’t do anything. This will change as the Virtual Ecosystem model develops. The factory method from_config() exists in a more complete state, and unpacks a small number of parameters from our currently pretty minimal configuration dictionary. These parameters are then used to generate a class instance. If errors crop here when converting the information from the config dictionary to the required types (e.g. timedelta64) they are caught and then logged, and at the end of the unpacking an error is thrown. This error should be caught and handled by downstream functions so that all model configuration failures can be reported as one.

Classes:

LitterModel(data, core_components[, ...])

A class defining the litter model.

class virtual_ecosystem.models.litter.litter_model.LitterModel(data: Data, core_components: CoreComponents, model_constants: LitterConstants = LitterConstants(), static: bool = False)

A class defining the litter model.

This model can be configured based on the data object and a config dictionary. At present the underlying model this class wraps is quite simple (i.e. two litter pools), but this will get more complex as the Virtual Ecosystem develops.

Parameters:

• data – The data object to be used in the model.

• core_components – The core components used across models.

• model_constants – Set of constants for the litter model.

• static – Boolean flag indicating if the model should run in static mode.

Methods:

check_init_data()	Check the init data contains the required variables.
cleanup()	Placeholder function for litter model cleanup.
from_config(data, configuration, core_components)	Factory function to initialise the litter model from configuration.
spinup()	Placeholder function to spin up the litter model.
update(time_index, **kwargs)	Function to update the model.

Attributes:

core_constants	The core constants used in the model.
data	A Data instance providing access to the shared simulation data.
grid	The Grid details used in the model.
layer_structure	The LayerStructure details used in the model.
litter_chemistry	Litter chemistry object for tracking of litter pool chemistries.
model_constants	Set of constants for the litter model.
model_timing	The ModelTiming details used in the model.
model_update_bounds	Bounds on model update frequencies.
vars_populated_by_first_update	Variables that are initialised by the model during the first update.
vars_populated_by_init	Variables that are initialised by the model during the setup.
vars_required_for_init	Required variables for model initialisation.
vars_required_for_update	Variables that are required by the update method of the model.
vars_updated	Variables that are updated by the model.

check_init_data() → None

Check the init data contains the required variables.

This method is used to check that the set of variables defined in the vars_required_for_init class attribute are present in the Data instance used to create a new instance of the class.

Raises:

ValueError – If the Data instance does not contain all the required variables or if those variables do not map onto the required axes.

cleanup() → None

Placeholder function for litter model cleanup.

core_constants: CoreConstants

The core constants used in the model.

data: Data

A Data instance providing access to the shared simulation data.

classmethod from_config(data: Data, configuration: CompiledConfiguration, core_components: CoreComponents) → LitterModel

Factory function to initialise the litter model from configuration.

This function unpacks the relevant information from the configuration file, and then uses it to initialise the model. If any information from the config is invalid rather than returning an initialised model instance an error is raised.

Parameters:

• data – A Data instance.

• configuration – A validated Virtual Ecosystem model configuration object.

• core_components – The core components used across models.

grid: Grid

The Grid details used in the model.

layer_structure: LayerStructure

The LayerStructure details used in the model.

litter_chemistry: LitterChemistry

Litter chemistry object for tracking of litter pool chemistries.

model_constants: LitterConstants

Set of constants for the litter model.

model_timing: ModelTiming

The ModelTiming details used in the model.

model_update_bounds: tuple[Quantity, Quantity] = (<Quantity(30, 'minute')>, <Quantity(3, 'month')>)

Bounds on model update frequencies.

This class attribute defines two time intervals that define a lower and upper bound on the update frequency that can reasonably be used with a model. Models updated more often than the lower bound may fail to capture transient dynamics and models updated more slowly than the upper bound may fail to capture important temporal patterns.

spinup() → None

Placeholder function to spin up the litter model.

update(time_index: int, **kwargs: Any) → None

Function to update the model.

If the model is static, the inner update method, self._update will only run once, at most.

Parameters:

• time_index – The index representing the current time step in the data object.

• **kwargs – Further arguments to the update method.

vars_populated_by_first_update: tuple[str, ...] = ('litter_mineralisation_rate_cnp',)

Variables that are initialised by the model during the first update.

These are the variables that are initialised by the model and stored in the data object when running the update method for the first time. They will be available for other models to use in their update methods but not in the setup methods.

vars_populated_by_init: tuple[str, ...] = ()

Variables that are initialised by the model during the setup.

These are the variables that are initialised by the model and stored in the data object when running the setup method and that will be available for other models to use in their own setup or update methods.

vars_required_for_init: tuple[str, ...] = ('litter_pool_above_metabolic_cnp', 'litter_pool_above_structural_cnp', 'litter_pool_woody_cnp', 'litter_pool_below_metabolic_cnp', 'litter_pool_below_structural_cnp', 'lignin_above_structural', 'lignin_woody', 'lignin_below_structural')

Required variables for model initialisation.

This class property defines a set of variable names that must be present in the Data instance used to initialise an instance of this class. It is a tuple containing zero or more tuples, each providing a variable name and then a tuple of zero or more core axes that the variable must map onto.

For example: (('temperature', ('spatial', 'temporal')),)

vars_required_for_update: tuple[str, ...] = ('litter_pool_above_metabolic_cnp', 'litter_pool_above_structural_cnp', 'litter_pool_woody_cnp', 'litter_pool_below_metabolic_cnp', 'litter_pool_below_structural_cnp', 'lignin_above_structural', 'lignin_woody', 'lignin_below_structural', 'stem_turnover_cnp', 'root_turnover_cnp', 'foliage_turnover_cnp', 'stem_lignin', 'senesced_leaf_lignin', 'root_lignin', 'herbivory_waste_leaf_cnp', 'herbivory_waste_leaf_lignin', 'litter_consumed_above_metabolic_cnp', 'litter_consumed_above_structural_cnp', 'litter_consumed_woody_cnp', 'litter_consumed_below_metabolic_cnp', 'litter_consumed_below_structural_cnp', 'air_temperature', 'soil_temperature', 'matric_potential')

Variables that are required by the update method of the model.

These variables should have been initialised by another model or loaded from external sources, but in either case they will be available in the data object.

vars_updated: tuple[str, ...] = ('litter_pool_above_metabolic_cnp', 'litter_pool_above_structural_cnp', 'litter_pool_woody_cnp', 'litter_pool_below_metabolic_cnp', 'litter_pool_below_structural_cnp', 'lignin_above_structural', 'lignin_woody', 'lignin_below_structural', 'litter_mineralisation_rate_cnp')

Variables that are updated by the model.

At the moment, this tuple is used to decide which variables to output from the Data object, i.e. every variable updated by a model used in the specific simulation. It is also be used warn if multiple models will be updating the same variable and to verify that these variables are indeed initialised by another model, and therefore will be available.