SINDBAD

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Sindbad.Types Module
julia
Types Module

The Types module consolidates and organizes all the types used in the SINDBAD framework into a central location. This ensures a single line for type definitions, promoting consistency and reusability across all SINDBAD packages. It also provides helper functions and utilities for working with these types.

Provided Types and Their Purpose

1. SindbadTypes

  • Purpose: Abstract type serving as the base for all Julia types in the SINDBAD framework.

  • Use: Provides a unified hierarchy for SINDBAD-specific types.

2. ModelTypes

  • Purpose: Defines types for models in SINDBAD.

  • Use: Represents various model/processes.

3. TimeTypes

  • Purpose: Defines types for handling time-related operations.

  • Use: Manages temporal aggregation of data on the go.

4. SpinupTypes

  • Purpose: Defines types for spinup processes in SINDBAD.

  • Use: Handles methods for initialization and equilibrium states for models.

5. LandTypes

  • Purpose: Defines types for collecting variable from land and saving them.

  • Use: Builds land and array for model execution.

6. ArrayTypes

  • Purpose: Defines types for array structures used in SINDBAD.

  • Use: Provides specialized array types for efficient data handling in model simulation and output.

7. InputTypes

  • Purpose: Defines types for input data and configurations.

  • Use: Manages input flows and forcing data.

8. ExperimentTypes

  • Purpose: Defines types for experiments conducted in SINDBAD.

  • Use: Represents experimental setups, configurations, and results.

9. OptimizationTypes

  • Purpose: Defines types for optimization-related functions and methods in SINDBAD.

  • Use: Separates methods for optimization methods, cost functions, methods, etc.

10. MetricsTypes

  • Purpose: Defines types for metrics used to evaluate model performance in SINDBAD.

  • Use: Represents performance metrics and cost evaluation.

11. MLTypes

  • Purpose: Defines types for machine learning components in SINDBAD.

  • Use: Supports machine learning workflows and data structures.

12. LongTuple

  • Purpose: Provides definitions and methods for working with longTuple type.

  • Use: Facilitates operations on tuples with many elements to break them down into smaller tuples.

13. TypesFunctions

  • Purpose: Provides helper functions related to SINDBAD types.

  • Use: Includes utilities for introspection, type manipulation, and documentation.

Key Functionality

purpose(T::Type)

  • Description: Returns a string describing the purpose of a type in the SINDBAD framework.

  • Use: Provides a descriptive string for each type, explaining its role or functionality.

  • Example:

julia
purpose(::Type{BayesOptKMaternARD5}) = "Bayesian Optimization using Matern 5/2 kernel with Automatic Relevance Determination from BayesOpt.jl"

Notes

  • The Types module serves as the backbone for type definitions in SINDBAD, ensuring modularity and extensibility.

  • Each type is documented with its purpose, making it easier for developers to understand and extend the framework.

Exported ​

Sindbad.Types.ArrayView Type
julia
ArrayView{T,N,S<:AbstractArray{<:Any,N}}

Fields:

  • s::S: The underlying array being viewed.

  • groupname::Symbol: The name of the group containing the array.

  • arrayname::Symbol: The name of the array being accessed.

Sindbad.Types.GroupView Type
julia
GroupView{S}

Fields:

  • groupname::Symbol: The name of the group being accessed.

  • s::S: The underlying data structure containing the group.

Sindbad.Types.LandWrapper Type
julia
LandWrapper{S}

Fields:

  • s::S: The underlying NamedTuple or data structure being wrapped.
Sindbad.Types.LongTuple Type
julia
LongTuple{NSPLIT,T}

A data structure that represents a tuple split into smaller chunks for better memory management and performance.

Fields

  • data::T: The underlying tuple data

  • n::Val{NSPLIT}: The number of splits as a value type

Type Parameters

  • NSPLIT: The number of elements in each split

  • T: The type of the underlying tuple

Sindbad.Types.SindbadTypes Type

SindbadTypes

Abstract type for all Julia types in SINDBAD

Type Hierarchy

SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • ArrayTypes: Abstract type for all array types in SINDBAD

    • ModelArrayType: Abstract type for internal model array types in SINDBAD

      • ModelArrayArray: Use standard Julia arrays for model variables

      • ModelArrayStaticArray: Use StaticArrays for model variables

      • ModelArrayView: Use array views for model variables

    • OutputArrayType: Abstract type for output array types in SINDBAD

      • OutputArray: Use standard Julia arrays for output

      • OutputMArray: Use MArray for output

      • OutputSizedArray: Use SizedArray for output

      • OutputYAXArray: Use YAXArray for output

  • ExperimentTypes: Abstract type for model run flags and experimental setup and simulations in SINDBAD

    • OutputStrategy: Abstract type for model output strategies in SINDBAD

      • DoNotOutputAll: Disable output of all model variables

      • DoNotSaveSingleFile: Save output variables in separate files

      • DoOutputAll: Enable output of all model variables

      • DoSaveSingleFile: Save all output variables in a single file

    • ParallelizationPackage: Abstract type for using different parallelization packages in SINDBAD

      • QbmapParallelization: Use Qbmap for parallelization

      • ThreadsParallelization: Use Julia threads for parallelization

    • RunFlag: Abstract type for model run configuration flags in SINDBAD

      • DoCalcCost: Enable cost calculation between model output and observations

      • DoDebugModel: Enable model debugging mode

      • DoFilterNanPixels: Enable filtering of NaN values in spatial data

      • DoInlineUpdate: Enable inline updates of model state

      • DoNotCalcCost: Disable cost calculation between model output and observations

      • DoNotDebugModel: Disable model debugging mode

      • DoNotFilterNanPixels: Disable filtering of NaN values in spatial data

      • DoNotInlineUpdate: Disable inline updates of model state

      • DoNotRunForward: Disable forward model run

      • DoNotRunOptimization: Disable model parameter optimization

      • DoNotSaveInfo: Disable saving of model information

      • DoNotSpinupTEM: Disable terrestrial ecosystem model spinup

      • DoNotStoreSpinup: Disable storing of spinup results

      • DoNotUseForwardDiff: Disable forward mode automatic differentiation

      • DoRunForward: Enable forward model run

      • DoRunOptimization: Enable model parameter optimization

      • DoSaveInfo: Enable saving of model information

      • DoSpinupTEM: Enable terrestrial ecosystem model spinup

      • DoStoreSpinup: Enable storing of spinup results

      • DoUseForwardDiff: Enable forward mode automatic differentiation

  • InputTypes: Abstract type for input data and processing related options in SINDBAD

    • DataFormatBackend: Abstract type for input data backends in SINDBAD

      • BackendNetcdf: Use NetCDF format for input data

      • BackendZarr: Use Zarr format for input data

    • ForcingTime: Abstract type for forcing variable types in SINDBAD

      • ForcingWithTime: Forcing variable with time dimension

      • ForcingWithoutTime: Forcing variable without time dimension

    • InputArrayBackend: Abstract type for input data array types in SINDBAD

      • InputArray: Use standard Julia arrays for input data

      • InputKeyedArray: Use keyed arrays for input data

      • InputNamedDimsArray: Use named dimension arrays for input data

      • InputYaxArray: Use YAXArray for input data

    • SpatialSubsetter: Abstract type for spatial subsetting methods in SINDBAD

      • SpaceID: Use site ID (all caps) for spatial subsetting

      • SpaceId: Use site ID (capitalized) for spatial subsetting

      • Spaceid: Use site ID for spatial subsetting

      • Spacelat: Use latitude for spatial subsetting

      • Spacelatitude: Use full latitude for spatial subsetting

      • Spacelon: Use longitude for spatial subsetting

      • Spacelongitude: Use full longitude for spatial subsetting

      • Spacesite: Use site location for spatial subsetting

  • LandTypes: Abstract type for land related types that are typically used in preparing objects for model runs in SINDBAD

    • LandWrapperType: Abstract type for land wrapper types in SINDBAD

      • GroupView: Represents a group of data within a LandWrapper, allowing access to specific groups of variables.

      • LandWrapper: Wraps the nested fields of a NamedTuple output of SINDBAD land into a nested structure of views that can be easily accessed with dot notation.

    • PreAlloc: Abstract type for preallocated land helpers types in prepTEM of SINDBAD

      • PreAllocArray: use a preallocated array for model output

      • PreAllocArrayAll: use a preallocated array to output all land variables

      • PreAllocArrayFD: use a preallocated array for finite difference (FD) hybrid experiments

      • PreAllocArrayMT: use arrays of nThreads size for land model output for replicates of multiple threads

      • PreAllocStacked: save output as a stacked vector of land using map over temporal dimension

      • PreAllocTimeseries: save land output as a preallocated vector for time series of land

      • PreAllocYAXArray: use YAX arrays for model output

  • MLTypes: Abstract type for types in machine learning related methods in SINDBAD

    • ActivationType: Abstract type for activation functions used in ML models

      • CustomSigmoid: Use a custom sigmoid activation function. In this case, the k_σ parameter in ml_model sections of the settings is used to control the steepness of the sigmoid function.

      • FluxRelu: Use Flux.jl ReLU activation function

      • FluxSigmoid: Use Flux.jl Sigmoid activation function

      • FluxTanh: Use Flux.jl Tanh activation function

    • MLGradType: Abstract type for automatic differentiation or finite differences for gradient calculations

      • EnzymeGrad: Use Enzyme.jl for automatic differentiation

      • FiniteDiffGrad: Use FiniteDiff.jl for finite difference calculations

      • FiniteDifferencesGrad: Use FiniteDifferences.jl for finite difference calculations

      • ForwardDiffGrad: Use ForwardDiff.jl for automatic differentiation

      • PolyesterForwardDiffGrad: Use PolyesterForwardDiff.jl for automatic differentiation

      • ZygoteGrad: Use Zygote.jl for automatic differentiation

    • MLModelType: Abstract type for machine learning models used in SINDBAD

      • FluxDenseNN: simple dense neural network model implemented in Flux.jl

    • MLOptimizerType: Abstract type for optimizers used for training ML models in SINDBAD

      • OptimisersAdam: Use Optimisers.jl Adam optimizer for training ML models in SINDBAD

      • OptimisersDescent: Use Optimisers.jl Descent optimizer for training ML models in SINDBAD

    • MLTrainingType: Abstract type for training a hybrid algorithm in SINDBAD

      • CalcFoldFromSplit: Use a split of the data to calculate the folds for cross-validation. The default wat to calculate the folds is by splitting the data into k-folds. In this case, the split is done on the go based on the values given in ml_training.split_ratios and n_folds.

      • LoadFoldFromFile: Use precalculated data to load the folds for cross-validation. In this case, the data path has to be set under ml_training.fold_path and ml_training.which_fold. The data has to be in the format of a jld2 file with the following structure: /folds/0, /folds/1, /folds/2, ... /folds/n_folds. Each fold has to be a tuple of the form (train_indices, test_indices).

      • LossModelObsML: Loss function using metrics between the predicted model and observation as defined in optimization.json

      • MixedGradient: Use a mixed gradient approach for training using gradient from multiple methods and combining them with pullback from zygote

  • MetricTypes: Abstract type for performance metrics and cost calculation methods in SINDBAD

    • DataAggrOrder: Abstract type for data aggregation order in SINDBAD

      • SpaceTime: Aggregate data first over space, then over time

      • TimeSpace: Aggregate data first over time, then over space

    • PerfMetric: Abstract type for performance metrics in SINDBAD

      • MSE: Mean Squared Error: Measures the average squared difference between predicted and observed values

      • NAME1R: Normalized Absolute Mean Error with 1/R scaling: Measures the absolute difference between means normalized by the range of observations

      • NMAE1R: Normalized Mean Absolute Error with 1/R scaling: Measures the average absolute error normalized by the range of observations

      • NNSE: Normalized Nash-Sutcliffe Efficiency: Measures model performance relative to the mean of observations, normalized to [0,1] range

      • NNSEInv: Inverse Normalized Nash-Sutcliffe Efficiency: Inverse of NNSE for minimization problems, normalized to [0,1] range

      • NNSEσ: Normalized Nash-Sutcliffe Efficiency with uncertainty: Incorporates observation uncertainty in the normalized performance measure

      • NNSEσInv: Inverse Normalized Nash-Sutcliffe Efficiency with uncertainty: Inverse of NNSEσ for minimization problems

      • NPcor: Normalized Pearson Correlation: Measures linear correlation between predictions and observations, normalized to [0,1] range

      • NPcorInv: Inverse Normalized Pearson Correlation: Inverse of NPcor for minimization problems

      • NSE: Nash-Sutcliffe Efficiency: Measures model performance relative to the mean of observations

      • NSEInv: Inverse Nash-Sutcliffe Efficiency: Inverse of NSE for minimization problems

      • NSEσ: Nash-Sutcliffe Efficiency with uncertainty: Incorporates observation uncertainty in the performance measure

      • NSEσInv: Inverse Nash-Sutcliffe Efficiency with uncertainty: Inverse of NSEσ for minimization problems

      • NScor: Normalized Spearman Correlation: Measures monotonic relationship between predictions and observations, normalized to [0,1] range

      • NScorInv: Inverse Normalized Spearman Correlation: Inverse of NScor for minimization problems

      • Pcor: Pearson Correlation: Measures linear correlation between predictions and observations

      • Pcor2: Squared Pearson Correlation: Measures the strength of linear relationship between predictions and observations

      • Pcor2Inv: Inverse Squared Pearson Correlation: Inverse of Pcor2 for minimization problems

      • PcorInv: Inverse Pearson Correlation: Inverse of Pcor for minimization problems

      • Scor: Spearman Correlation: Measures monotonic relationship between predictions and observations

      • Scor2: Squared Spearman Correlation: Measures the strength of monotonic relationship between predictions and observations

      • Scor2Inv: Inverse Squared Spearman Correlation: Inverse of Scor2 for minimization problems

      • ScorInv: Inverse Spearman Correlation: Inverse of Scor for minimization problems

    • SpatialDataAggr: Abstract type for spatial data aggregation methods in SINDBAD

    • SpatialMetricAggr: Abstract type for spatial metric aggregation methods in SINDBAD

      • MetricMaximum: Take maximum value across spatial dimensions

      • MetricMinimum: Take minimum value across spatial dimensions

      • MetricSpatial: Apply spatial aggregation to metrics

      • MetricSum: Sum values across spatial dimensions

  • ModelTypes: Abstract type for model types in SINDBAD

    • DoCatchModelErrors: Enable error catching during model execution

    • DoNotCatchModelErrors: Disable error catching during model execution

    • LandEcosystem: Abstract type for all SINDBAD land ecosystem models/approaches

  • OptimizationTypes: Abstract type for optimization related functions and methods in SINDBAD

    • CostMethod: Abstract type for cost calculation methods in SINDBAD

      • CostModelObs: cost calculation between model output and observations

      • CostModelObsLandTS: cost calculation between land model output and time series observations

      • CostModelObsMT: multi-threaded cost calculation between model output and observations

      • CostModelObsPriors: cost calculation between model output, observations, and priors. NOTE THAT THIS METHOD IS JUST A PLACEHOLDER AND DOES NOT CALCULATE PRIOR COST PROPERLY YET

    • GSAMethod: Abstract type for global sensitivity analysis methods in SINDBAD

      • GSAMorris: Morris method for global sensitivity analysis

      • GSASobol: Sobol method for global sensitivity analysis

      • GSASobolDM: Sobol method with derivative-based measures for global sensitivity analysis

    • OptimizationMethod: Abstract type for optimization methods in SINDBAD

      • BayesOptKMaternARD5: Bayesian Optimization using Matern 5/2 kernel with Automatic Relevance Determination from BayesOpt.jl

      • CMAEvolutionStrategyCMAES: Covariance Matrix Adaptation Evolution Strategy (CMA-ES) from CMAEvolutionStrategy.jl

      • EvolutionaryCMAES: Evolutionary version of CMA-ES optimization from Evolutionary.jl

      • OptimBFGS: Broyden-Fletcher-Goldfarb-Shanno (BFGS) from Optim.jl

      • OptimLBFGS: Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) from Optim.jl

      • OptimizationBBOadaptive: Black Box Optimization with adaptive parameters from Optimization.jl

      • OptimizationBBOxnes: Black Box Optimization using Natural Evolution Strategy (xNES) from Optimization.jl

      • OptimizationBFGS: BFGS optimization with box constraints from Optimization.jl

      • OptimizationFminboxGradientDescent: Gradient descent optimization with box constraints from Optimization.jl

      • OptimizationFminboxGradientDescentFD: Gradient descent optimization with box constraints using forward differentiation from Optimization.jl

      • OptimizationGCMAESDef: Global CMA-ES optimization with default settings from Optimization.jl

      • OptimizationGCMAESFD: Global CMA-ES optimization using forward differentiation from Optimization.jl

      • OptimizationMultistartOptimization: Multi-start optimization to find global optimum from Optimization.jl

      • OptimizationNelderMead: Nelder-Mead simplex optimization method from Optimization.jl

      • OptimizationQuadDirect: Quadratic Direct optimization method from Optimization.jl

    • ParameterScaling: Abstract type for parameter scaling methods in SINDBAD

      • ScaleBounds: Scale parameters relative to their bounds

      • ScaleDefault: Scale parameters relative to default values

      • ScaleNone: No parameter scaling applied

  • SpinupTypes: Abstract type for model spinup related functions and methods in SINDBAD

    • SpinupMode: Abstract type for model spinup modes in SINDBAD

      • AllForwardModels: Use all forward models for spinup

      • EtaScaleA0H: scale carbon pools using diagnostic scalars for ηH and c_remain

      • EtaScaleA0HCWD: scale carbon pools of CWD (cLitSlow) using ηH and set vegetation pools to c_remain

      • EtaScaleAH: scale carbon pools using diagnostic scalars for ηH and ηA

      • EtaScaleAHCWD: scale carbon pools of CWD (cLitSlow) using ηH and scale vegetation pools by ηA

      • NlsolveFixedpointTrustregionCEco: use a fixed-point nonlinear solver with trust region for carbon pools (cEco)

      • NlsolveFixedpointTrustregionCEcoTWS: use a fixed-point nonlinear solver with trust region for both cEco and TWS

      • NlsolveFixedpointTrustregionTWS: use a fixed-point nonlinearsolver with trust region for Total Water Storage (TWS)

      • ODEAutoTsit5Rodas5: use the AutoVern7(Rodas5) method from DifferentialEquations.jl for solving ODEs

      • ODEDP5: use the DP5 method from DifferentialEquations.jl for solving ODEs

      • ODETsit5: use the Tsit5 method from DifferentialEquations.jl for solving ODEs

      • SSPDynamicSSTsit5: use the SteadyState solver with DynamicSS and Tsit5 methods

      • SSPSSRootfind: use the SteadyState solver with SSRootfind method

      • SelSpinupModels: run only the models selected for spinup in the model structure

      • Spinup_TWS: Spinup spinup_mode for Total Water Storage (TWS)

      • Spinup_cEco: Spinup spinup_mode for cEco

      • Spinup_cEco_TWS: Spinup spinup_mode for cEco and TWS

    • SpinupSequence: Basic Spinup sequence without time aggregation

    • SpinupSequenceWithAggregator: Spinup sequence with time aggregation for corresponding forcingtime series

  • TimeTypes: Abstract type for implementing time subset and aggregation types in SINDBAD

    • TimeAggregation: Abstract type for time aggregation methods in SINDBAD

      • TimeAllYears: aggregation/slicing to include all years

      • TimeArray: use array-based time aggregation

      • TimeDay: aggregation to daily time steps

      • TimeDayAnomaly: aggregation to daily anomalies

      • TimeDayIAV: aggregation to daily IAV

      • TimeDayMSC: aggregation to daily MSC

      • TimeDayMSCAnomaly: aggregation to daily MSC anomalies

      • TimeDiff: aggregation to time differences, e.g. monthly anomalies

      • TimeFirstYear: aggregation/slicing of the first year

      • TimeHour: aggregation to hourly time steps

      • TimeHourAnomaly: aggregation to hourly anomalies

      • TimeHourDayMean: aggregation to mean of hourly data over days

      • TimeIndexed: aggregation using time indices, e.g., TimeFirstYear

      • TimeMean: aggregation to mean over all time steps

      • TimeMonth: aggregation to monthly time steps

      • TimeMonthAnomaly: aggregation to monthly anomalies

      • TimeMonthIAV: aggregation to monthly IAV

      • TimeMonthMSC: aggregation to monthly MSC

      • TimeMonthMSCAnomaly: aggregation to monthly MSC anomalies

      • TimeNoDiff: aggregation without time differences

      • TimeRandomYear: aggregation/slicing of a random year

      • TimeShuffleYears: aggregation/slicing/selection of shuffled years

      • TimeSizedArray: aggregation to a sized array

      • TimeYear: aggregation to yearly time steps

      • TimeYearAnomaly: aggregation to yearly anomalies

    • TimeAggregator: define a type for temporal aggregation of an array

Sindbad.Types.TimeAggregator Type
julia
TimeAggregator{I, aggr_func}

define a type for temporal aggregation of an array

Fields:

  • indices::I: indices to be collected for aggregation

  • aggr_func::aggr_func: a function to use for aggregation, defaults to mean

Sindbad.Types.TimeAggregatorViewInstance Type
julia
TimeAggregatorViewInstance{T, N, D, P, AV <: TimeAggregator}

Fields:

  • parent::P: the parent data

  • agg::AV: a view of the parent data

  • dim::Val{D}: a val instance of the type that stores the dimension to be aggregated on

Sindbad.Types.getSindbadDefinitions Method
julia
getSindbadDefinitions(sindbad_module, what_to_get; internal_only=true)

Returns all defined (and optionally internal) objects in the SINDBAD framework.

Arguments

  • sindbad_module: The module to search for defined things

  • what_to_get: The type of things to get (e.g., Type, Function)

  • internal_only: Whether to only include internal definitions (default: true)

Returns

  • An array of all defined things in the SINDBAD framework

Example

julia
# Get all defined types in the SINDBAD framework
defined_types = getSindbadDefinitions(Sindbad, Type)
Sindbad.Types.getTypeDocString Method
julia
getTypeDocString(T::Type)

Generate a docstring for a type in a formatted way.

Description

This function generates a formatted docstring for a type, including its purpose and type hierarchy.

Arguments

  • T: The type for which the docstring is to be generated

Returns

  • A string containing the formatted docstring for the type.
Sindbad.Types.loopWriteTypeDocString Method
julia
loopWriteTypeDocString(o_file, T)

Write a docstring for a type to a file.

Description

This function writes a docstring for a type to a file.

Arguments

  • o_file: The file to write the docstring to

  • T: The type for which the docstring is to be generated

Returns

  • o_file: The file with the docstring written to it
Sindbad.Types.methodsOf Function
julia
methodsOf(T::Type; ds="", is_subtype=false, bullet=" - ")
methodsOf(M::Module; the_type=Type, internal_only=true)

Display subtypes and their purposes for a type or module in a formatted way.

Description

This function provides a hierarchical display of subtypes and their purposes for a given type or module. For types, it shows a tree-like structure of subtypes and their purposes. For modules, it shows all defined types and their subtypes.

Arguments

  • T::Type: The type whose subtypes should be displayed

  • M::Module: The module whose types should be displayed

  • ds::String: Delimiter string between entries (default: newline)

  • is_subtype::Bool: Whether to include nested subtypes (default: false)

  • bullet::String: Bullet point for each entry (default: " - ")

  • the_type::Type: Type of objects to display in module (default: Type)

  • internal_only::Bool: Whether to only show internal definitions (default: true)

Returns

  • A formatted string showing the hierarchy of subtypes and their purposes

Examples

julia
# Display subtypes of a type
methodsOf(LandEcosystem)

# Display with custom formatting
methodsOf(LandEcosystem; ds=", ", bullet=" * ")

# Display including nested subtypes
methodsOf(LandEcosystem; is_subtype=true)

# Display types in a module
methodsOf(Sindbad)

# Display specific types in a module
methodsOf(Sindbad; the_type=Function)

Extended help

The output format for types is:

## TypeName
Purpose of the type

## Available methods/subtypes:
 - subtype1: purpose
 - subtype2: purpose
    - nested_subtype1: purpose
    - nested_subtype2: purpose

If no subtypes exist, it will show " - None".

Sindbad.Types.purpose Function
julia
purpose(T::Type)

Returns a string describing the purpose of a type in the SINDBAD framework.

Description

  • This is a base function that should be extended by each package for their specific types.

  • When in SINDBAD models, purpose is a descriptive string that explains the role or functionality of the model or approach within the SINDBAD framework. If the purpose is not defined for a specific model or approach, it provides guidance on how to define it.

  • When in SINDBAD lib, purpose is a descriptive string that explains the dispatch on the type for the specific function. For instance, metricTypes.jl has a purpose for the types of metrics that can be computed.

Arguments

  • T::Type: The type whose purpose should be described

Returns

  • A string describing the purpose of the type

Example

julia
# Define the purpose for a specific model
purpose(::Type{BayesOptKMaternARD5}) = "Bayesian Optimization using Matern 5/2 kernel with Automatic Relevance Determination from BayesOpt.jl"

# Retrieve the purpose
println(purpose(BayesOptKMaternARD5))  # Output: "Bayesian Optimization using Matern 5/2 kernel with Automatic Relevance Determination from BayesOpt.jl"
Sindbad.Types.showMethodsOf Method
julia
showMethodsOf(T)

Display the subtypes and their purposes of a type in a formatted way.

Description

This function displays the hierarchical structure of subtypes and their purposes for a given type. It uses methodsOf internally to generate the formatted output and prints it to the console.

Arguments

  • T: The type whose subtypes and purposes should be displayed

Returns

  • nothing

Examples

julia
# Display subtypes of LandEcosystem
showMethodsOf(LandEcosystem)

# Display subtypes of a specific model type
showMethodsOf(ambientCO2)

Extended help

The output format is the same as methodsOf, showing:

## TypeName
Purpose of the type

## Available methods/subtypes:
 - subtype1: purpose
 - subtype2: purpose
    - nested_subtype1: purpose
    - nested_subtype2: purpose

This function is a convenience wrapper around methodsOf that automatically prints the output to the console.

Sindbad.Types.writeTypeDocString Method
julia
writeTypeDocString(o_file, T)

Write a docstring for a type to a file.

Description

This function writes a docstring for a type to a file.

Arguments

  • o_file: The file to write the docstring to

  • T: The type for which the docstring is to be generated

Returns

  • o_file: The file with the docstring written to it

Internal ​

Base.getproperty Method
julia
Base.getproperty(g::GroupView, aggr_func::Symbol)

Accesses a specific array within a group of data in a GroupView.

Returns:

An ArrayView object for the specified array.

Base.propertynames Method
julia
Base.propertynames(o::GroupView)

Returns the property names of a group in a GroupView.

Base.show Method
julia
Base.show(io::IO, gv::GroupView)

Displays a summary of the contents of a GroupView.

© Copyright 2025 SINDBAD Development Team