SINDBAD

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Sindbad.Types Module
julia
Types

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

Purpose

This module serves as the backbone for type definitions in SINDBAD, ensuring modularity and extensibility. It provides a unified hierarchy for SINDBAD-specific types and includes utilities for introspection, type manipulation, and documentation.

Dependencies

External (third-party)

  • InteractiveUtils: Interactive exploration and debugging helpers.

  • Base.Docs: Documentation utilities for type introspection.

Included Files

  • LandTypes.jl: Types for land variables and land/array structures used during model execution.

  • ArrayTypes.jl: Specialized array types for efficient data handling.

  • InputTypes.jl: Types for input data/configuration (forcing/observation metadata and wiring).

  • SimulationTypes.jl: Types representing simulation setup/configuration and results.

  • ParameterOptimizationTypes.jl: Types for optimization workflows (algorithms, options, cost hooks).

  • MachineLearningTypes.jl: Types supporting machine-learning workflows and data structures.

Notes

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

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

  • The SindbadTypes abstract type serves as the base for all Julia types in the SINDBAD framework.

Examples

  1. Querying type purpose:
julia
using Sindbad.Types
purpose(BayesOptKMaternARD5)  # Returns the purpose string for the type
  1. Working with SINDBAD types:
julia
using Sindbad.Types
# All SINDBAD types are available through this module
source

Functions ​

requires_package ​

Sindbad.Types.requires_package Function
julia
requires_package(::Type{<:SindbadTypes})

Name of the external package whose Sindbad extension (ext/Sindbad<Package>Ext) provides the real implementation for a given dispatch type, e.g. requires_package(CMAEvolutionStrategyCMAES) == "CMAEvolutionStrategy". Returns nothing for types that need no external package (implemented directly in Sindbad, or not yet implemented for any backend). Used by generic dispatch-fallback methods (e.g. optimizer, mlOptimizer, mlModel, activationFunction, getPullback, updateMLModel) to tell users exactly which package to using when a selected backend type has no method loaded yet, instead of implying the method doesn't exist at all.

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Types ​

ActivationType ​

Sindbad.Types.ActivationType Type

ActivationType

Abstract type for activation functions used inMachine Learningmodels

Type Hierarchy

ActivationType <: MachineLearningTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

  • FluxSoftplus: Use Flux.jl Softplus activation function

  • FluxTanh: Use Flux.jl Tanh activation function

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ArrayTypes ​

Sindbad.Types.ArrayTypes Type

ArrayTypes

Abstract type for all array types in SINDBAD

Type Hierarchy

ArrayTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

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ArrayView ​

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.

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BayesOptKMaternARD5 ​

Sindbad.Types.BayesOptKMaternARD5 Type

BayesOptKMaternARD5

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

Type Hierarchy

BayesOptKMaternARD5 <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

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CMAEvolutionStrategyCMAES ​

Sindbad.Types.CMAEvolutionStrategyCMAES Type

CMAEvolutionStrategyCMAES

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

Type Hierarchy

CMAEvolutionStrategyCMAES <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

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CalcFoldFromSplit ​

Sindbad.Types.CalcFoldFromSplit Type

CalcFoldFromSplit

Use a split of the data to calculate the folds for cross-validation. The default way 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.

Type Hierarchy

CalcFoldFromSplit <: MachineLearningTrainingType <: MachineLearningTypes <: SindbadTypes <: Any

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ClearanceA0 ​

Sindbad.Types.ClearanceA0 Type

ClearanceA0

cut the vegetation carbon pools down to c_remain, as EtaScaleA0H does without the scaling by ηH

Type Hierarchy

ClearanceA0 <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

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ConcatData ​

Sindbad.Types.ConcatData Type

ConcatData

Concatenate data arrays for aggregation

Type Hierarchy

ConcatData <: SpatialDataAggr <: ParameterOptimizationTypes <: SindbadTypes <: Any

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CostMethod ​

Sindbad.Types.CostMethod Type

CostMethod

Abstract type for cost calculation methods in SINDBAD

Type Hierarchy

CostMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

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CostModelObs ​

Sindbad.Types.CostModelObs Type

CostModelObs

cost calculation between model output and observations

Type Hierarchy

CostModelObs <: CostMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

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CostModelObsLandTS ​

Sindbad.Types.CostModelObsLandTS Type

CostModelObsLandTS

cost calculation between land model output and time series observations

Type Hierarchy

CostModelObsLandTS <: CostMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

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CostModelObsMT ​

Sindbad.Types.CostModelObsMT Type

CostModelObsMT

multi-threaded cost calculation between model output and observations

Type Hierarchy

CostModelObsMT <: CostMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

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CostModelObsPriors ​

Sindbad.Types.CostModelObsPriors Type

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

Type Hierarchy

CostModelObsPriors <: CostMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

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CustomSigmoid ​

Sindbad.Types.CustomSigmoid Type

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.

Type Hierarchy

CustomSigmoid <: ActivationType <: MachineLearningTypes <: SindbadTypes <: Any

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DataAggrOrder ​

Sindbad.Types.DataAggrOrder Type

DataAggrOrder

Abstract type for data aggregation order in SINDBAD

Type Hierarchy

DataAggrOrder <: ParameterOptimizationTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

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

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

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DoAggrObs ​

Sindbad.Types.DoAggrObs Type

DoAggrObs

Apply aggregation to observations

Type Hierarchy

DoAggrObs <: ParameterOptimizationTypes <: SindbadTypes <: Any

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DoCalcCost ​

Sindbad.Types.DoCalcCost Type

DoCalcCost

Enable cost calculation between model output and observations

Type Hierarchy

DoCalcCost <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoFilterNanPixels ​

Sindbad.Types.DoFilterNanPixels Type

DoFilterNanPixels

Enable filtering of NaN values in spatial data

Type Hierarchy

DoFilterNanPixels <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoNotAggrObs ​

Sindbad.Types.DoNotAggrObs Type

DoNotAggrObs

Do not apply aggregation to observations

Type Hierarchy

DoNotAggrObs <: ParameterOptimizationTypes <: SindbadTypes <: Any

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DoNotCalcCost ​

Sindbad.Types.DoNotCalcCost Type

DoNotCalcCost

Disable cost calculation between model output and observations

Type Hierarchy

DoNotCalcCost <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoNotFilterNanPixels ​

Sindbad.Types.DoNotFilterNanPixels Type

DoNotFilterNanPixels

Disable filtering of NaN values in spatial data

Type Hierarchy

DoNotFilterNanPixels <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoNotOutputAll ​

Sindbad.Types.DoNotOutputAll Type

DoNotOutputAll

Disable output of all model variables

Type Hierarchy

DoNotOutputAll <: OutputStrategy <: SimulationTypes <: SindbadTypes <: Any

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DoNotRunForward ​

Sindbad.Types.DoNotRunForward Type

DoNotRunForward

Disable forward model run

Type Hierarchy

DoNotRunForward <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoNotRunLazy ​

Sindbad.Types.DoNotRunLazy Type

DoNotRunLazy

Disable lazy run mode for running experiements based on Diskarrays and YAXArrays

Type Hierarchy

DoNotRunLazy <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoNotRunOptimization ​

Sindbad.Types.DoNotRunOptimization Type

DoNotRunOptimization

Disable model parameter optimization

Type Hierarchy

DoNotRunOptimization <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoNotSaveInfo ​

Sindbad.Types.DoNotSaveInfo Type

DoNotSaveInfo

Disable saving of model information

Type Hierarchy

DoNotSaveInfo <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoNotSaveSingleFile ​

Sindbad.Types.DoNotSaveSingleFile Type

DoNotSaveSingleFile

Save output variables in separate files

Type Hierarchy

DoNotSaveSingleFile <: OutputStrategy <: SimulationTypes <: SindbadTypes <: Any

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DoNotSpatialWeight ​

Sindbad.Types.DoNotSpatialWeight Type

DoNotSpatialWeight

Do not apply spatial weighting to metrics

Type Hierarchy

DoNotSpatialWeight <: ParameterOptimizationTypes <: SindbadTypes <: Any

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DoNotSpinupTEM ​

Sindbad.Types.DoNotSpinupTEM Type

DoNotSpinupTEM

Disable terrestrial ecosystem model spinup

Type Hierarchy

DoNotSpinupTEM <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoNotStoreSpinup ​

Sindbad.Types.DoNotStoreSpinup Type

DoNotStoreSpinup

Disable storing of spinup results

Type Hierarchy

DoNotStoreSpinup <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoOutputAll ​

Sindbad.Types.DoOutputAll Type

DoOutputAll

Enable output of all model variables

Type Hierarchy

DoOutputAll <: OutputStrategy <: SimulationTypes <: SindbadTypes <: Any

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DoRunForward ​

Sindbad.Types.DoRunForward Type

DoRunForward

Enable forward model run

Type Hierarchy

DoRunForward <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoRunLazy ​

Sindbad.Types.DoRunLazy Type

DoRunLazy

Enable lazy run mode for running experiements based on Diskarrays and YAXArrays

Type Hierarchy

DoRunLazy <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoRunOptimization ​

Sindbad.Types.DoRunOptimization Type

DoRunOptimization

Enable model parameter optimization

Type Hierarchy

DoRunOptimization <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoSaveInfo ​

Sindbad.Types.DoSaveInfo Type

DoSaveInfo

Enable saving of model information

Type Hierarchy

DoSaveInfo <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoSaveSingleFile ​

Sindbad.Types.DoSaveSingleFile Type

DoSaveSingleFile

Save all output variables in a single file

Type Hierarchy

DoSaveSingleFile <: OutputStrategy <: SimulationTypes <: SindbadTypes <: Any

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DoSpatialWeight ​

Sindbad.Types.DoSpatialWeight Type

DoSpatialWeight

Apply spatial weighting to metrics

Type Hierarchy

DoSpatialWeight <: ParameterOptimizationTypes <: SindbadTypes <: Any

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DoSpinupTEM ​

Sindbad.Types.DoSpinupTEM Type

DoSpinupTEM

Enable terrestrial ecosystem model spinup

Type Hierarchy

DoSpinupTEM <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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DoStoreSpinup ​

Sindbad.Types.DoStoreSpinup Type

DoStoreSpinup

Enable storing of spinup results

Type Hierarchy

DoStoreSpinup <: RunFlag <: SimulationTypes <: SindbadTypes <: Any

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EnzymeGrad ​

Sindbad.Types.EnzymeGrad Type

EnzymeGrad

Use Enzyme.jl for automatic differentiation

Type Hierarchy

EnzymeGrad <: MachineLearningGradType <: MachineLearningTypes <: SindbadTypes <: Any

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EtaScaleA0H ​

Sindbad.Types.EtaScaleA0H Type

EtaScaleA0H

scale carbon pools using diagnostic scalars for ηH and c_remain

Type Hierarchy

EtaScaleA0H <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

EtaScaleA0HCWD ​

Sindbad.Types.EtaScaleA0HCWD Type

EtaScaleA0HCWD

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

Type Hierarchy

EtaScaleA0HCWD <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

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EtaScaleAH ​

Sindbad.Types.EtaScaleAH Type

EtaScaleAH

scale carbon pools using diagnostic scalars for ηH and ηA

Type Hierarchy

EtaScaleAH <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

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EtaScaleAHCWD ​

Sindbad.Types.EtaScaleAHCWD Type

EtaScaleAHCWD

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

Type Hierarchy

EtaScaleAHCWD <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

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EtaScaleH ​

Sindbad.Types.EtaScaleH Type

EtaScaleH

scale only the heterotrophic carbon pools using diagnostic scalars for ηH

Type Hierarchy

EtaScaleH <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

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EvolutionaryCMAES ​

Sindbad.Types.EvolutionaryCMAES Type

EvolutionaryCMAES

Evolutionary version of CMA-ES optimization from Evolutionary.jl

Type Hierarchy

EvolutionaryCMAES <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

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FiniteDiffGrad ​

Sindbad.Types.FiniteDiffGrad Type

FiniteDiffGrad

Use FiniteDiff.jl for finite difference calculations

Type Hierarchy

FiniteDiffGrad <: MachineLearningGradType <: MachineLearningTypes <: SindbadTypes <: Any

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FiniteDifferencesGrad ​

Sindbad.Types.FiniteDifferencesGrad Type

FiniteDifferencesGrad

Use FiniteDifferences.jl for finite difference calculations

Type Hierarchy

FiniteDifferencesGrad <: MachineLearningGradType <: MachineLearningTypes <: SindbadTypes <: Any

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FluxDenseNN ​

Sindbad.Types.FluxDenseNN Type

FluxDenseNN

simple dense neural network model implemented in Flux.jl

Type Hierarchy

FluxDenseNN <: MachineLearningModelType <: MachineLearningTypes <: SindbadTypes <: Any

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FluxRelu ​

Sindbad.Types.FluxRelu Type

FluxRelu

Use Flux.jl ReLU activation function

Type Hierarchy

FluxRelu <: ActivationType <: MachineLearningTypes <: SindbadTypes <: Any

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FluxSigmoid ​

Sindbad.Types.FluxSigmoid Type

FluxSigmoid

Use Flux.jl Sigmoid activation function

Type Hierarchy

FluxSigmoid <: ActivationType <: MachineLearningTypes <: SindbadTypes <: Any

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FluxSoftplus ​

Sindbad.Types.FluxSoftplus Type

FluxSoftplus

Use Flux.jl Softplus activation function

Type Hierarchy

FluxSoftplus <: ActivationType <: MachineLearningTypes <: SindbadTypes <: Any

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FluxTanh ​

Sindbad.Types.FluxTanh Type

FluxTanh

Use Flux.jl Tanh activation function

Type Hierarchy

FluxTanh <: ActivationType <: MachineLearningTypes <: SindbadTypes <: Any

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FluxnetParameterLearningWROASTED ​

Sindbad.Types.FluxnetParameterLearningWROASTED Type

FluxnetParameterLearningWROASTED

Experiment type for learning spatial variability of WROASTED model parameters from FLUXNET data using a machine learning model in SINDBAD.MachineLearning

Type Hierarchy

FluxnetParameterLearningWROASTED <: MachineLearningExperimentType <: MachineLearningTypes <: SindbadTypes <: Any

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ForcingWithTime ​

Sindbad.Types.ForcingWithTime Type

ForcingWithTime

Forcing variable with time dimension

Type Hierarchy

ForcingWithTime <: ForcingTime <: InputTypes <: SindbadTypes <: Any

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ForcingWithoutTime ​

Sindbad.Types.ForcingWithoutTime Type

ForcingWithoutTime

Forcing variable without time dimension

Type Hierarchy

ForcingWithoutTime <: ForcingTime <: InputTypes <: SindbadTypes <: Any

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ForwardDiffGrad ​

Sindbad.Types.ForwardDiffGrad Type

ForwardDiffGrad

Use ForwardDiff.jl for automatic differentiation

Type Hierarchy

ForwardDiffGrad <: MachineLearningGradType <: MachineLearningTypes <: SindbadTypes <: Any

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GSAMethod ​

Sindbad.Types.GSAMethod Type

GSAMethod

Abstract type for global sensitivity analysis methods in SINDBAD

Type Hierarchy

GSAMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

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GSAMorris ​

Sindbad.Types.GSAMorris Type

GSAMorris

Morris method for global sensitivity analysis

Type Hierarchy

GSAMorris <: GSAMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

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GSASobol ​

Sindbad.Types.GSASobol Type

GSASobol

Sobol method for global sensitivity analysis

Type Hierarchy

GSASobol <: GSAMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

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GSASobolDM ​

Sindbad.Types.GSASobolDM Type

GSASobolDM

Sobol method with derivative-based measures for global sensitivity analysis

Type Hierarchy

GSASobolDM <: GSAMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

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GroupView ​

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.

source

InputArray ​

Sindbad.Types.InputArray Type

InputArray

Use standard Julia arrays for input data

Type Hierarchy

InputArray <: InputArrayBackend <: InputTypes <: SindbadTypes <: Any

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InputArrayBackend ​

Sindbad.Types.InputArrayBackend Type

InputArrayBackend

Abstract type for input data array types in SINDBAD

Type Hierarchy

InputArrayBackend <: InputTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

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InputKeyedArray ​

Sindbad.Types.InputKeyedArray Type

InputKeyedArray

Use keyed arrays for input data

Type Hierarchy

InputKeyedArray <: InputArrayBackend <: InputTypes <: SindbadTypes <: Any

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InputNamedDimsArray ​

Sindbad.Types.InputNamedDimsArray Type

InputNamedDimsArray

Use named dimension arrays for input data

Type Hierarchy

InputNamedDimsArray <: InputArrayBackend <: InputTypes <: SindbadTypes <: Any

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InputTypes ​

Sindbad.Types.InputTypes Type

InputTypes

Abstract type for input data and processing related options in SINDBAD

Type Hierarchy

InputTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

  • InterpolationMethod: Abstract type for the methods that put forcing and parameter data on the space and time of the run. The fields of a method are its options in the settings

    • SpaceInterpolationMethod: Abstract type for the methods of interpolation_method.space, which get the spatial axes of the data together

      • SpaceGridInterpolationMethod: Abstract type for Interpolations.jl methods in space that interpolate on the positions of the values along each axis

        • SpaceInterpCubic: cubic B-spline of Interpolations.jl, fitted to the source grids within 16 grids of each block of target grids, so an invalid value makes the target grids that far from it invalid

        • SpaceInterpLanczos: Lanczos resampling of Interpolations.jl with order lobes

        • SpaceInterpLanczosOpencv: Lanczos resampling of order 4 as in OpenCV, Lanczos4OpenCV of Interpolations.jl

        • SpaceInterpLinear: interpolate linearly along each axis, BSpline(Linear()) of Interpolations.jl

        • SpaceInterpNearest: take the value of the nearest grid, BSpline(Constant()) of Interpolations.jl

        • SpaceInterpQuadratic: quadratic B-spline of Interpolations.jl, fitted to the source grids within 12 grids of each block of target grids, so an invalid value makes the target grids that far from it invalid

      • SpaceInterpNone: keep the data on its own grid without interpolation

      • SpaceInterpZhao2004: weight the four surrounding grids of the source by cos(π/2 d/d_max)^power of their great-circle distance d, with d_max the largest distance between the four, as in equations 3 to 5 of Zhao et al. 2005, Remote Sensing of Environment 95

    • TimeInterpolationMethod: Abstract type for the methods of interpolation_method.time, which get the time axis of the data

      • TimeGridInterpolationMethod: Abstract type for Interpolations.jl methods in time that interpolate on the positions of the values

        • TimeInterpCubic: cubic B-spline of Interpolations.jl. It is fitted to the whole series, so one invalid value makes the whole series invalid

        • TimeInterpLanczos: Lanczos resampling of Interpolations.jl with order lobes

        • TimeInterpLanczosOpencv: Lanczos resampling of order 4 as in OpenCV, Lanczos4OpenCV of Interpolations.jl

        • TimeInterpLinear: interpolate linearly in time, BSpline(Linear()) of Interpolations.jl

        • TimeInterpNearest: take the nearest value in time, BSpline(Constant()) of Interpolations.jl

        • TimeInterpNext: take the next value in time, BSpline(Constant(Next)) of Interpolations.jl

        • TimeInterpPrevious: take the previous value in time, BSpline(Constant(Previous)) of Interpolations.jl

        • TimeInterpQuadratic: quadratic B-spline of Interpolations.jl. It is fitted to the whole series, so one invalid value makes the whole series invalid

      • TimeInterpNone: keep the data on its own time axis without interpolation

      • TimeMonotonicInterpolationMethod: Abstract type for monotonic Interpolations.jl methods in time that interpolate on the time coordinates

        • TimeInterpAkimaMonotonic: AkimaMonotonicInterpolation of Interpolations.jl

        • TimeInterpCardinalMonotonic: CardinalMonotonicInterpolation of Interpolations.jl with tension from 0 to 1

        • TimeInterpFiniteDifferenceMonotonic: FiniteDifferenceMonotonicInterpolation of Interpolations.jl

        • TimeInterpFritschButlandMonotonic: FritschButlandMonotonicInterpolation of Interpolations.jl

        • TimeInterpFritschCarlsonMonotonic: FritschCarlsonMonotonicInterpolation of Interpolations.jl

        • TimeInterpLinearMonotonic: LinearMonotonicInterpolation of Interpolations.jl

        • TimeInterpSteffenMonotonic: SteffenMonotonicInterpolation of Interpolations.jl

  • 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

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InputYAXArray ​

Sindbad.Types.InputYAXArray Type

InputYAXArray

Use YAXArray for input data

Type Hierarchy

InputYAXArray <: InputArrayBackend <: InputTypes <: SindbadTypes <: Any

source

InterpolationMethod ​

Sindbad.Types.InterpolationMethod Type

InterpolationMethod

Abstract type for the methods that put forcing and parameter data on the space and time of the run. The fields of a method are its options in the settings

Type Hierarchy

InterpolationMethod <: InputTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • SpaceInterpolationMethod: Abstract type for the methods of interpolation_method.space, which get the spatial axes of the data together

    • SpaceGridInterpolationMethod: Abstract type for Interpolations.jl methods in space that interpolate on the positions of the values along each axis

      • SpaceInterpCubic: cubic B-spline of Interpolations.jl, fitted to the source grids within 16 grids of each block of target grids, so an invalid value makes the target grids that far from it invalid

      • SpaceInterpLanczos: Lanczos resampling of Interpolations.jl with order lobes

      • SpaceInterpLanczosOpencv: Lanczos resampling of order 4 as in OpenCV, Lanczos4OpenCV of Interpolations.jl

      • SpaceInterpLinear: interpolate linearly along each axis, BSpline(Linear()) of Interpolations.jl

      • SpaceInterpNearest: take the value of the nearest grid, BSpline(Constant()) of Interpolations.jl

      • SpaceInterpQuadratic: quadratic B-spline of Interpolations.jl, fitted to the source grids within 12 grids of each block of target grids, so an invalid value makes the target grids that far from it invalid

    • SpaceInterpNone: keep the data on its own grid without interpolation

    • SpaceInterpZhao2004: weight the four surrounding grids of the source by cos(π/2 d/d_max)^power of their great-circle distance d, with d_max the largest distance between the four, as in equations 3 to 5 of Zhao et al. 2005, Remote Sensing of Environment 95

  • TimeInterpolationMethod: Abstract type for the methods of interpolation_method.time, which get the time axis of the data

    • TimeGridInterpolationMethod: Abstract type for Interpolations.jl methods in time that interpolate on the positions of the values

      • TimeInterpCubic: cubic B-spline of Interpolations.jl. It is fitted to the whole series, so one invalid value makes the whole series invalid

      • TimeInterpLanczos: Lanczos resampling of Interpolations.jl with order lobes

      • TimeInterpLanczosOpencv: Lanczos resampling of order 4 as in OpenCV, Lanczos4OpenCV of Interpolations.jl

      • TimeInterpLinear: interpolate linearly in time, BSpline(Linear()) of Interpolations.jl

      • TimeInterpNearest: take the nearest value in time, BSpline(Constant()) of Interpolations.jl

      • TimeInterpNext: take the next value in time, BSpline(Constant(Next)) of Interpolations.jl

      • TimeInterpPrevious: take the previous value in time, BSpline(Constant(Previous)) of Interpolations.jl

      • TimeInterpQuadratic: quadratic B-spline of Interpolations.jl. It is fitted to the whole series, so one invalid value makes the whole series invalid

    • TimeInterpNone: keep the data on its own time axis without interpolation

    • TimeMonotonicInterpolationMethod: Abstract type for monotonic Interpolations.jl methods in time that interpolate on the time coordinates

      • TimeInterpAkimaMonotonic: AkimaMonotonicInterpolation of Interpolations.jl

      • TimeInterpCardinalMonotonic: CardinalMonotonicInterpolation of Interpolations.jl with tension from 0 to 1

      • TimeInterpFiniteDifferenceMonotonic: FiniteDifferenceMonotonicInterpolation of Interpolations.jl

      • TimeInterpFritschButlandMonotonic: FritschButlandMonotonicInterpolation of Interpolations.jl

      • TimeInterpFritschCarlsonMonotonic: FritschCarlsonMonotonicInterpolation of Interpolations.jl

      • TimeInterpLinearMonotonic: LinearMonotonicInterpolation of Interpolations.jl

      • TimeInterpSteffenMonotonic: SteffenMonotonicInterpolation of Interpolations.jl

source

LandTypes ​

Sindbad.Types.LandTypes Type

LandTypes

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

Type Hierarchy

LandTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

source

LandWrapper ​

Sindbad.Types.LandWrapper Type
julia
LandWrapper{S}

Fields:

  • s::S: The underlying NamedTuple or data structure being wrapped.
source

LoadFoldFromFile ​

Sindbad.Types.LoadFoldFromFile Type

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).

Type Hierarchy

LoadFoldFromFile <: MachineLearningTrainingType <: MachineLearningTypes <: SindbadTypes <: Any

source

LoopTEM ​

Sindbad.Types.LoopTEM Type

LoopTEM

run the TEM over the spinup forcing with the models of the step

Type Hierarchy

LoopTEM <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

LossModelObsMachineLearning ​

Sindbad.Types.LossModelObsMachineLearning Type

LossModelObsMachineLearning

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

Type Hierarchy

LossModelObsMachineLearning <: MachineLearningTrainingType <: MachineLearningTypes <: SindbadTypes <: Any

source

MachineLearningExperimentType ​

Sindbad.Types.MachineLearningExperimentType Type

MachineLearningExperimentType

Abstract type for machine learning related experiments in SINDBAD

Type Hierarchy

MachineLearningExperimentType <: MachineLearningTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • FluxnetParameterLearningWROASTED: Experiment type for learning spatial variability of WROASTED model parameters from FLUXNET data using a machine learning model in SINDBAD.MachineLearning
source

MachineLearningGradType ​

Sindbad.Types.MachineLearningGradType Type

MachineLearningGradType

Abstract type for automatic differentiation or finite differences for gradient calculations

Type Hierarchy

MachineLearningGradType <: MachineLearningTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

source

MachineLearningModelType ​

Sindbad.Types.MachineLearningModelType Type

MachineLearningModelType

Abstract type for machine learning models used in SINDBAD

Type Hierarchy

MachineLearningModelType <: MachineLearningTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

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

MachineLearningOptimizerType ​

Sindbad.Types.MachineLearningOptimizerType Type

MachineLearningOptimizerType

Abstract type for optimizers used for trainingMachine Learningmodels in SINDBAD

Type Hierarchy

MachineLearningOptimizerType <: MachineLearningTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • OptimisersAdam: Use Optimisers.jl Adam optimizer for trainingMachine Learningmodels in SINDBAD

  • OptimisersDescent: Use Optimisers.jl Descent optimizer for trainingMachine Learningmodels in SINDBAD

source

MachineLearningPullbackType ​

Sindbad.Types.MachineLearningPullbackType Type

MachineLearningPullbackType

Abstract type for pullback functions used in machine learning training for calculating the Jacobian-vector product

Type Hierarchy

MachineLearningPullbackType <: MachineLearningTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • ZygotePullback: Use Zygote.jl for calculating the pullback function for the Jacobian-vector product during machine learning training
source

MachineLearningTrainingType ​

Sindbad.Types.MachineLearningTrainingType Type

MachineLearningTrainingType

Abstract type for training a hybrid algorithm in SINDBAD

Type Hierarchy

MachineLearningTrainingType <: MachineLearningTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • CalcFoldFromSplit: Use a split of the data to calculate the folds for cross-validation. The default way 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).

  • LossModelObsMachineLearning: 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

source

MachineLearningTypes ​

Sindbad.Types.MachineLearningTypes Type

MachineLearningTypes

Abstract type for types in machine learning related methods in SINDBAD

Type Hierarchy

MachineLearningTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • ActivationType: Abstract type for activation functions used inMachine Learningmodels

    • 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

    • FluxSoftplus: Use Flux.jl Softplus activation function

    • FluxTanh: Use Flux.jl Tanh activation function

  • MachineLearningExperimentType: Abstract type for machine learning related experiments in SINDBAD

    • FluxnetParameterLearningWROASTED: Experiment type for learning spatial variability of WROASTED model parameters from FLUXNET data using a machine learning model in SINDBAD.MachineLearning

  • MachineLearningGradType: 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

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

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

  • MachineLearningOptimizerType: Abstract type for optimizers used for trainingMachine Learningmodels in SINDBAD

    • OptimisersAdam: Use Optimisers.jl Adam optimizer for trainingMachine Learningmodels in SINDBAD

    • OptimisersDescent: Use Optimisers.jl Descent optimizer for trainingMachine Learningmodels in SINDBAD

  • MachineLearningPullbackType: Abstract type for pullback functions used in machine learning training for calculating the Jacobian-vector product

    • ZygotePullback: Use Zygote.jl for calculating the pullback function for the Jacobian-vector product during machine learning training

  • MachineLearningTrainingType: 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 way 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).

    • LossModelObsMachineLearning: 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

  • MachineLearningUpdateType: Abstract type for updating the machine learning model during training

    • OptimisersUpdate: Use Optimisers.jl for updating the machine learning model during training
source

MachineLearningUpdateType ​

Sindbad.Types.MachineLearningUpdateType Type

MachineLearningUpdateType

Abstract type for updating the machine learning model during training

Type Hierarchy

MachineLearningUpdateType <: MachineLearningTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • OptimisersUpdate: Use Optimisers.jl for updating the machine learning model during training
source

MetricMaximum ​

Sindbad.Types.MetricMaximum Type

MetricMaximum

Take maximum value across spatial dimensions

Type Hierarchy

MetricMaximum <: SpatialMetricAggr <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

MetricMinimum ​

Sindbad.Types.MetricMinimum Type

MetricMinimum

Take minimum value across spatial dimensions

Type Hierarchy

MetricMinimum <: SpatialMetricAggr <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

MetricSpatial ​

Sindbad.Types.MetricSpatial Type

MetricSpatial

Apply spatial aggregation to metrics

Type Hierarchy

MetricSpatial <: SpatialMetricAggr <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

MetricSum ​

Sindbad.Types.MetricSum Type

MetricSum

Sum values across spatial dimensions

Type Hierarchy

MetricSum <: SpatialMetricAggr <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

MixedGradient ​

Sindbad.Types.MixedGradient Type

MixedGradient

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

Type Hierarchy

MixedGradient <: MachineLearningTrainingType <: MachineLearningTypes <: SindbadTypes <: Any

source

ModelArrayArray ​

Sindbad.Types.ModelArrayArray Type

ModelArrayArray

Use standard Julia arrays for model variables

Type Hierarchy

ModelArrayArray <: ModelArrayType <: ArrayTypes <: SindbadTypes <: Any

source

ModelArrayStaticArray ​

Sindbad.Types.ModelArrayStaticArray Type

ModelArrayStaticArray

Use StaticArrays for model variables

Type Hierarchy

ModelArrayStaticArray <: ModelArrayType <: ArrayTypes <: SindbadTypes <: Any

source

ModelArrayType ​

Sindbad.Types.ModelArrayType Type

ModelArrayType

Abstract type for internal model array types in SINDBAD

Type Hierarchy

ModelArrayType <: ArrayTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • ModelArrayArray: Use standard Julia arrays for model variables

  • ModelArrayStaticArray: Use StaticArrays for model variables

  • ModelArrayView: Use array views for model variables

source

ModelArrayView ​

Sindbad.Types.ModelArrayView Type

ModelArrayView

Use array views for model variables

Type Hierarchy

ModelArrayView <: ModelArrayType <: ArrayTypes <: SindbadTypes <: Any

source

NlsolveFixedpointTrustregionCEcoTWS ​

Sindbad.Types.NlsolveFixedpointTrustregionCEcoTWS Type

NlsolveFixedpointTrustregionCEcoTWS

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

Type Hierarchy

NlsolveFixedpointTrustregionCEcoTWS <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

NlsolveFixedpointTrustregionTWS ​

Sindbad.Types.NlsolveFixedpointTrustregionTWS Type

NlsolveFixedpointTrustregionTWS

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

Type Hierarchy

NlsolveFixedpointTrustregionTWS <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

ODEAutoTsit5Rodas5 ​

Sindbad.Types.ODEAutoTsit5Rodas5 Type

ODEAutoTsit5Rodas5

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

Type Hierarchy

ODEAutoTsit5Rodas5 <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

ODEDP5 ​

Sindbad.Types.ODEDP5 Type

ODEDP5

use the DP5 method from DifferentialEquations.jl for solving ODEs

Type Hierarchy

ODEDP5 <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

ODETsit5 ​

Sindbad.Types.ODETsit5 Type

ODETsit5

use the Tsit5 method from DifferentialEquations.jl for solving ODEs

Type Hierarchy

ODETsit5 <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

OptimBFGS ​

Sindbad.Types.OptimBFGS Type

OptimBFGS

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

Type Hierarchy

OptimBFGS <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

OptimLBFGS ​

Sindbad.Types.OptimLBFGS Type

OptimLBFGS

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

Type Hierarchy

OptimLBFGS <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

OptimisersAdam ​

Sindbad.Types.OptimisersAdam Type

OptimisersAdam

Use Optimisers.jl Adam optimizer for trainingMachine Learningmodels in SINDBAD

Type Hierarchy

OptimisersAdam <: MachineLearningOptimizerType <: MachineLearningTypes <: SindbadTypes <: Any

source

OptimisersDescent ​

Sindbad.Types.OptimisersDescent Type

OptimisersDescent

Use Optimisers.jl Descent optimizer for trainingMachine Learningmodels in SINDBAD

Type Hierarchy

OptimisersDescent <: MachineLearningOptimizerType <: MachineLearningTypes <: SindbadTypes <: Any

source

OptimisersUpdate ​

Sindbad.Types.OptimisersUpdate Type

OptimisersUpdate

Use Optimisers.jl for updating the machine learning model during training

Type Hierarchy

OptimisersUpdate <: MachineLearningUpdateType <: MachineLearningTypes <: SindbadTypes <: Any

source

OptimizationBBOadaptive ​

Sindbad.Types.OptimizationBBOadaptive Type

OptimizationBBOadaptive

Black Box Optimization with adaptive parameters from Optimization.jl

Type Hierarchy

OptimizationBBOadaptive <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

OptimizationBBOxnes ​

Sindbad.Types.OptimizationBBOxnes Type

OptimizationBBOxnes

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

Type Hierarchy

OptimizationBBOxnes <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

OptimizationBFGS ​

Sindbad.Types.OptimizationBFGS Type

OptimizationBFGS

BFGS optimization with box constraints from Optimization.jl

Type Hierarchy

OptimizationBFGS <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

OptimizationFminboxGradientDescent ​

Sindbad.Types.OptimizationFminboxGradientDescent Type

OptimizationFminboxGradientDescent

Gradient descent optimization with box constraints from Optimization.jl

Type Hierarchy

OptimizationFminboxGradientDescent <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

OptimizationFminboxGradientDescentFD ​

Sindbad.Types.OptimizationFminboxGradientDescentFD Type

OptimizationFminboxGradientDescentFD

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

Type Hierarchy

OptimizationFminboxGradientDescentFD <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

OptimizationGCMAESDef ​

Sindbad.Types.OptimizationGCMAESDef Type

OptimizationGCMAESDef

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

Type Hierarchy

OptimizationGCMAESDef <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

OptimizationGCMAESFD ​

Sindbad.Types.OptimizationGCMAESFD Type

OptimizationGCMAESFD

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

Type Hierarchy

OptimizationGCMAESFD <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

OptimizationMultistartOptimization ​

Sindbad.Types.OptimizationMultistartOptimization Type

OptimizationMultistartOptimization

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

Type Hierarchy

OptimizationMultistartOptimization <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

OptimizationNelderMead ​

Sindbad.Types.OptimizationNelderMead Type

OptimizationNelderMead

Nelder-Mead simplex optimization method from Optimization.jl

Type Hierarchy

OptimizationNelderMead <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

OptimizationQuadDirect ​

Sindbad.Types.OptimizationQuadDirect Type

OptimizationQuadDirect

Quadratic Direct optimization method from Optimization.jl

Type Hierarchy

OptimizationQuadDirect <: ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

OutputArray ​

Sindbad.Types.OutputArray Type

OutputArray

Use standard Julia arrays for output

Type Hierarchy

OutputArray <: OutputArrayType <: ArrayTypes <: SindbadTypes <: Any

source

OutputArrayType ​

Sindbad.Types.OutputArrayType Type

OutputArrayType

Abstract type for output array types in SINDBAD

Type Hierarchy

OutputArrayType <: ArrayTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • OutputArray: Use standard Julia arrays for output

  • OutputMArray: Use MArray for output

  • OutputSizedArray: Use SizedArray for output

  • OutputYAXArray: Use YAXArray for output

source

OutputMArray ​

Sindbad.Types.OutputMArray Type

OutputMArray

Use MArray for output

Type Hierarchy

OutputMArray <: OutputArrayType <: ArrayTypes <: SindbadTypes <: Any

source

OutputSizedArray ​

Sindbad.Types.OutputSizedArray Type

OutputSizedArray

Use SizedArray for output

Type Hierarchy

OutputSizedArray <: OutputArrayType <: ArrayTypes <: SindbadTypes <: Any

source

OutputStrategy ​

Sindbad.Types.OutputStrategy Type

OutputStrategy

Abstract type for model output strategies in SINDBAD

Type Hierarchy

OutputStrategy <: SimulationTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

source

OutputYAXArray ​

Sindbad.Types.OutputYAXArray Type

OutputYAXArray

Use YAXArray for output

Type Hierarchy

OutputYAXArray <: OutputArrayType <: ArrayTypes <: SindbadTypes <: Any

source

ParallelizationPackage ​

Sindbad.Types.ParallelizationPackage Type

ParallelizationPackage

Abstract type for using different parallelization packages in SINDBAD

Type Hierarchy

ParallelizationPackage <: SimulationTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • PmapParallelization: Use Distributed workers with pmap, each with its own threads, for parallelization

  • QbmapParallelization: Use Qbmap for parallelization

  • ThreadsParallelization: Use Julia threads for parallelization

source

ParameterOptimizationMethod ​

Sindbad.Types.ParameterOptimizationMethod Type

ParameterOptimizationMethod

Abstract type for optimization methods in SINDBAD

Type Hierarchy

ParameterOptimizationMethod <: ParameterOptimizationTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

source

ParameterOptimizationTypes ​

Sindbad.Types.ParameterOptimizationTypes Type

ParameterOptimizationTypes

Abstract type for optimization related functions and methods in SINDBAD

Type Hierarchy

ParameterOptimizationTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

  • 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

  • DoAggrObs: Apply aggregation to observations

  • DoNotAggrObs: Do not apply aggregation to observations

  • DoNotSpatialWeight: Do not apply spatial weighting to metrics

  • DoSpatialWeight: Apply spatial weighting to metrics

  • 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

  • ParameterOptimizationMethod: 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

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

    • ConcatData: Concatenate data arrays for aggregation

  • 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

source

ParameterScaling ​

Sindbad.Types.ParameterScaling Type

ParameterScaling

Abstract type for parameter scaling methods in SINDBAD

Type Hierarchy

ParameterScaling <: ParameterOptimizationTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • ScaleBounds: Scale parameters relative to their bounds

  • ScaleDefault: Scale parameters relative to default values

  • ScaleNone: No parameter scaling applied

source

PmapParallelization ​

Sindbad.Types.PmapParallelization Type

PmapParallelization

Use Distributed workers with pmap, each with its own threads, for parallelization

Type Hierarchy

PmapParallelization <: ParallelizationPackage <: SimulationTypes <: SindbadTypes <: Any

source

PolyesterForwardDiffGrad ​

Sindbad.Types.PolyesterForwardDiffGrad Type

PolyesterForwardDiffGrad

Use PolyesterForwardDiff.jl for automatic differentiation

Type Hierarchy

PolyesterForwardDiffGrad <: MachineLearningGradType <: MachineLearningTypes <: SindbadTypes <: Any

source

PreAlloc ​

Sindbad.Types.PreAlloc Type

PreAlloc

Abstract type for preallocated land helpers types in prepTEM of SINDBAD

Type Hierarchy

PreAlloc <: LandTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

source

PreAllocArray ​

Sindbad.Types.PreAllocArray Type

PreAllocArray

use a preallocated array for model output

Type Hierarchy

PreAllocArray <: PreAlloc <: LandTypes <: SindbadTypes <: Any

source

PreAllocArrayAll ​

Sindbad.Types.PreAllocArrayAll Type

PreAllocArrayAll

use a preallocated array to output all land variables

Type Hierarchy

PreAllocArrayAll <: PreAlloc <: LandTypes <: SindbadTypes <: Any

source

PreAllocArrayFD ​

Sindbad.Types.PreAllocArrayFD Type

PreAllocArrayFD

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

Type Hierarchy

PreAllocArrayFD <: PreAlloc <: LandTypes <: SindbadTypes <: Any

source

PreAllocArrayMT ​

Sindbad.Types.PreAllocArrayMT Type

PreAllocArrayMT

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

Type Hierarchy

PreAllocArrayMT <: PreAlloc <: LandTypes <: SindbadTypes <: Any

source

PreAllocStacked ​

Sindbad.Types.PreAllocStacked Type

PreAllocStacked

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

Type Hierarchy

PreAllocStacked <: PreAlloc <: LandTypes <: SindbadTypes <: Any

source

PreAllocTimeseries ​

Sindbad.Types.PreAllocTimeseries Type

PreAllocTimeseries

save land output as a preallocated vector for time series of land

Type Hierarchy

PreAllocTimeseries <: PreAlloc <: LandTypes <: SindbadTypes <: Any

source

PreAllocYAXArray ​

Sindbad.Types.PreAllocYAXArray Type

PreAllocYAXArray

use YAX arrays for model output

Type Hierarchy

PreAllocYAXArray <: PreAlloc <: LandTypes <: SindbadTypes <: Any

source

QbmapParallelization ​

Sindbad.Types.QbmapParallelization Type

QbmapParallelization

Use Qbmap for parallelization

Type Hierarchy

QbmapParallelization <: ParallelizationPackage <: SimulationTypes <: SindbadTypes <: Any

source

RunFlag ​

Sindbad.Types.RunFlag Type

RunFlag

Abstract type for model run configuration flags in SINDBAD

Type Hierarchy

RunFlag <: SimulationTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • DoCalcCost: Enable cost calculation between model output and observations

  • DoFilterNanPixels: Enable filtering of NaN values in spatial data

  • DoNotCalcCost: Disable cost calculation between model output and observations

  • DoNotFilterNanPixels: Disable filtering of NaN values in spatial data

  • DoNotRunForward: Disable forward model run

  • DoNotRunLazy: Disable lazy run mode for running experiements based on Diskarrays and YAXArrays

  • DoNotRunOptimization: Disable model parameter optimization

  • DoNotSaveInfo: Disable saving of model information

  • DoNotSpinupTEM: Disable terrestrial ecosystem model spinup

  • DoNotStoreSpinup: Disable storing of spinup results

  • DoRunForward: Enable forward model run

  • DoRunLazy: Enable lazy run mode for running experiements based on Diskarrays and YAXArrays

  • DoRunOptimization: Enable model parameter optimization

  • DoSaveInfo: Enable saving of model information

  • DoSpinupTEM: Enable terrestrial ecosystem model spinup

  • DoStoreSpinup: Enable storing of spinup results

source

SSPDynamicSSTsit5 ​

Sindbad.Types.SSPDynamicSSTsit5 Type

SSPDynamicSSTsit5

use the SteadyState solver with DynamicSS and Tsit5 methods

Type Hierarchy

SSPDynamicSSTsit5 <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

SSPSSRootfind ​

Sindbad.Types.SSPSSRootfind Type

SSPSSRootfind

use the SteadyState solver with SSRootfind method

Type Hierarchy

SSPSSRootfind <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

ScaleBounds ​

Sindbad.Types.ScaleBounds Type

ScaleBounds

Scale parameters relative to their bounds

Type Hierarchy

ScaleBounds <: ParameterScaling <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

ScaleDefault ​

Sindbad.Types.ScaleDefault Type

ScaleDefault

Scale parameters relative to default values

Type Hierarchy

ScaleDefault <: ParameterScaling <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

ScaleNone ​

Sindbad.Types.ScaleNone Type

ScaleNone

No parameter scaling applied

Type Hierarchy

ScaleNone <: ParameterScaling <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

SequenceDefault ​

Sindbad.Types.SequenceDefault Type

SequenceDefault

spin the selected spinup models up on the mean seasonal cycle, bracketed by a run over all years, and use this when the experiment settings give no sequence

Type Hierarchy

SequenceDefault <: SpinupSequence <: SpinupTypes <: SindbadTypes <: Any

source

SequenceList ​

Sindbad.Types.SequenceList Type

SequenceList

use the explicit list of spinup sequences given in the experiment settings

Type Hierarchy

SequenceList <: SpinupSequence <: SpinupTypes <: SindbadTypes <: Any

source

SequenceWithAge ​

Sindbad.Types.SequenceWithAge Type

SequenceWithAge

build the spinup sequence from the disturbance year of the location so that the years since disturbance are replayed

Type Hierarchy

SequenceWithAge <: SpinupSequence <: SpinupTypes <: SindbadTypes <: Any

source

SequenceWithSpinupInput ​

Sindbad.Types.SequenceWithSpinupInput Type

SequenceWithSpinupInput

build the spinup sequence with spinup input from previously saved cEco and TWS pools through forcing json

Type Hierarchy

SequenceWithSpinupInput <: SpinupSequence <: SpinupTypes <: SindbadTypes <: Any

source

SimulationTypes ​

Sindbad.Types.SimulationTypes Type

SimulationTypes

Abstract type for model simulation run flags and experimental setup and simulations in SINDBAD

Type Hierarchy

SimulationTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

    • PmapParallelization: Use Distributed workers with pmap, each with its own threads, for parallelization

    • 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

    • DoFilterNanPixels: Enable filtering of NaN values in spatial data

    • DoNotCalcCost: Disable cost calculation between model output and observations

    • DoNotFilterNanPixels: Disable filtering of NaN values in spatial data

    • DoNotRunForward: Disable forward model run

    • DoNotRunLazy: Disable lazy run mode for running experiements based on Diskarrays and YAXArrays

    • DoNotRunOptimization: Disable model parameter optimization

    • DoNotSaveInfo: Disable saving of model information

    • DoNotSpinupTEM: Disable terrestrial ecosystem model spinup

    • DoNotStoreSpinup: Disable storing of spinup results

    • DoRunForward: Enable forward model run

    • DoRunLazy: Enable lazy run mode for running experiements based on Diskarrays and YAXArrays

    • DoRunOptimization: Enable model parameter optimization

    • DoSaveInfo: Enable saving of model information

    • DoSpinupTEM: Enable terrestrial ecosystem model spinup

    • DoStoreSpinup: Enable storing of spinup results

source

SpaceGridInterpolationMethod ​

Sindbad.Types.SpaceGridInterpolationMethod Type

SpaceGridInterpolationMethod

Abstract type for Interpolations.jl methods in space that interpolate on the positions of the values along each axis

Type Hierarchy

SpaceGridInterpolationMethod <: SpaceInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • SpaceInterpCubic: cubic B-spline of Interpolations.jl, fitted to the source grids within 16 grids of each block of target grids, so an invalid value makes the target grids that far from it invalid

  • SpaceInterpLanczos: Lanczos resampling of Interpolations.jl with order lobes

  • SpaceInterpLanczosOpencv: Lanczos resampling of order 4 as in OpenCV, Lanczos4OpenCV of Interpolations.jl

  • SpaceInterpLinear: interpolate linearly along each axis, BSpline(Linear()) of Interpolations.jl

  • SpaceInterpNearest: take the value of the nearest grid, BSpline(Constant()) of Interpolations.jl

  • SpaceInterpQuadratic: quadratic B-spline of Interpolations.jl, fitted to the source grids within 12 grids of each block of target grids, so an invalid value makes the target grids that far from it invalid

source

SpaceID ​

Sindbad.Types.SpaceID Type

SpaceID

Use site ID (all caps) for spatial subsetting

Type Hierarchy

SpaceID <: SpatialSubsetter <: InputTypes <: SindbadTypes <: Any

source

SpaceId ​

Sindbad.Types.SpaceId Type

SpaceId

Use site ID (capitalized) for spatial subsetting

Type Hierarchy

SpaceId <: SpatialSubsetter <: InputTypes <: SindbadTypes <: Any

source

SpaceInterpNearest ​

Sindbad.Types.SpaceInterpNearest Type

SpaceInterpNearest

take the value of the nearest grid, BSpline(Constant()) of Interpolations.jl

Type Hierarchy

SpaceInterpNearest <: SpaceGridInterpolationMethod <: SpaceInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

SpaceInterpCubic ​

Sindbad.Types.SpaceInterpCubic Type

SpaceInterpCubic

cubic B-spline of Interpolations.jl, fitted to the source grids within 16 grids of each block of target grids, so an invalid value makes the target grids that far from it invalid

Type Hierarchy

SpaceInterpCubic <: SpaceGridInterpolationMethod <: SpaceInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

SpaceInterpLanczos ​

Sindbad.Types.SpaceInterpLanczos Type

SpaceInterpLanczos

Lanczos resampling of Interpolations.jl with order lobes

Type Hierarchy

SpaceInterpLanczos <: SpaceGridInterpolationMethod <: SpaceInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

SpaceInterpLanczosOpencv ​

Sindbad.Types.SpaceInterpLanczosOpencv Type

SpaceInterpLanczosOpencv

Lanczos resampling of order 4 as in OpenCV, Lanczos4OpenCV of Interpolations.jl

Type Hierarchy

SpaceInterpLanczosOpencv <: SpaceGridInterpolationMethod <: SpaceInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

SpaceInterpLinear ​

Sindbad.Types.SpaceInterpLinear Type

SpaceInterpLinear

interpolate linearly along each axis, BSpline(Linear()) of Interpolations.jl

Type Hierarchy

SpaceInterpLinear <: SpaceGridInterpolationMethod <: SpaceInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

SpaceInterpNearest ​

Missing docstring.

Missing docstring for SpaceInterpNearest. Check Documenter's build log for details.

SpaceInterpNone ​

Sindbad.Types.SpaceInterpNone Type

SpaceInterpNone

keep the data on its own grid without interpolation

Type Hierarchy

SpaceInterpNone <: SpaceInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

SpaceInterpQuadratic ​

Sindbad.Types.SpaceInterpQuadratic Type

SpaceInterpQuadratic

quadratic B-spline of Interpolations.jl, fitted to the source grids within 12 grids of each block of target grids, so an invalid value makes the target grids that far from it invalid

Type Hierarchy

SpaceInterpQuadratic <: SpaceGridInterpolationMethod <: SpaceInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

SpaceInterpZhao2004 ​

Sindbad.Types.SpaceInterpZhao2004 Type

SpaceInterpZhao2004

weight the four surrounding grids of the source by cos(π/2 d/d_max)^power of their great-circle distance d, with d_max the largest distance between the four, as in equations 3 to 5 of Zhao et al. 2005, Remote Sensing of Environment 95

Type Hierarchy

SpaceInterpZhao2004 <: SpaceInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

SpaceInterpolationMethod ​

Sindbad.Types.SpaceInterpolationMethod Type

SpaceInterpolationMethod

Abstract type for the methods of interpolation_method.space, which get the spatial axes of the data together

Type Hierarchy

SpaceInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • SpaceGridInterpolationMethod: Abstract type for Interpolations.jl methods in space that interpolate on the positions of the values along each axis

    • SpaceInterpCubic: cubic B-spline of Interpolations.jl, fitted to the source grids within 16 grids of each block of target grids, so an invalid value makes the target grids that far from it invalid

    • SpaceInterpLanczos: Lanczos resampling of Interpolations.jl with order lobes

    • SpaceInterpLanczosOpencv: Lanczos resampling of order 4 as in OpenCV, Lanczos4OpenCV of Interpolations.jl

    • SpaceInterpLinear: interpolate linearly along each axis, BSpline(Linear()) of Interpolations.jl

    • SpaceInterpNearest: take the value of the nearest grid, BSpline(Constant()) of Interpolations.jl

    • SpaceInterpQuadratic: quadratic B-spline of Interpolations.jl, fitted to the source grids within 12 grids of each block of target grids, so an invalid value makes the target grids that far from it invalid

  • SpaceInterpNone: keep the data on its own grid without interpolation

  • SpaceInterpZhao2004: weight the four surrounding grids of the source by cos(π/2 d/d_max)^power of their great-circle distance d, with d_max the largest distance between the four, as in equations 3 to 5 of Zhao et al. 2005, Remote Sensing of Environment 95

source

SpaceTime ​

Sindbad.Types.SpaceTime Type

SpaceTime

Aggregate data first over space, then over time

Type Hierarchy

SpaceTime <: DataAggrOrder <: ParameterOptimizationTypes <: SindbadTypes <: Any

source

Spaceid ​

Sindbad.Types.Spaceid Type

Spaceid

Use site ID for spatial subsetting

Type Hierarchy

Spaceid <: SpatialSubsetter <: InputTypes <: SindbadTypes <: Any

source

Spacelat ​

Sindbad.Types.Spacelat Type

Spacelat

Use latitude for spatial subsetting

Type Hierarchy

Spacelat <: SpatialSubsetter <: InputTypes <: SindbadTypes <: Any

source

Spacelatitude ​

Sindbad.Types.Spacelatitude Type

Spacelatitude

Use full latitude for spatial subsetting

Type Hierarchy

Spacelatitude <: SpatialSubsetter <: InputTypes <: SindbadTypes <: Any

source

Spacelon ​

Sindbad.Types.Spacelon Type

Spacelon

Use longitude for spatial subsetting

Type Hierarchy

Spacelon <: SpatialSubsetter <: InputTypes <: SindbadTypes <: Any

source

Spacelongitude ​

Sindbad.Types.Spacelongitude Type

Spacelongitude

Use full longitude for spatial subsetting

Type Hierarchy

Spacelongitude <: SpatialSubsetter <: InputTypes <: SindbadTypes <: Any

source

Spacesite ​

Sindbad.Types.Spacesite Type

Spacesite

Use site location for spatial subsetting

Type Hierarchy

Spacesite <: SpatialSubsetter <: InputTypes <: SindbadTypes <: Any

source

SpatialDataAggr ​

Sindbad.Types.SpatialDataAggr Type

SpatialDataAggr

Abstract type for spatial data aggregation methods in SINDBAD

Type Hierarchy

SpatialDataAggr <: ParameterOptimizationTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • ConcatData: Concatenate data arrays for aggregation
source

SpatialMetricAggr ​

Sindbad.Types.SpatialMetricAggr Type

SpatialMetricAggr

Abstract type for spatial metric aggregation methods in SINDBAD

Type Hierarchy

SpatialMetricAggr <: ParameterOptimizationTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

source

SpatialSubsetter ​

Sindbad.Types.SpatialSubsetter Type

SpatialSubsetter

Abstract type for spatial subsetting methods in SINDBAD

Type Hierarchy

SpatialSubsetter <: InputTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • 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

source

SpinupInput ​

Sindbad.Types.SpinupInput Type

SpinupInput

replace the pools with the forcing variables of the same name, e.g., the main pools of a restart file, and fill their component pools

Type Hierarchy

SpinupInput <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

SpinupMode ​

Sindbad.Types.SpinupMode Type

SpinupMode

Abstract type for model spinup modes in SINDBAD

Type Hierarchy

SpinupMode <: SpinupTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • ClearanceA0: cut the vegetation carbon pools down to c_remain, as EtaScaleA0H does without the scaling by ηH

  • 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

  • EtaScaleH: scale only the heterotrophic carbon pools using diagnostic scalars for ηH

  • LoopTEM: run the TEM over the spinup forcing with the models of the step

  • 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

  • SpinupInput: replace the pools with the forcing variables of the same name, e.g., the main pools of a restart file, and fill their component pools

  • 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

source

SpinupSequence ​

Sindbad.Types.SpinupSequence Type

SpinupSequence

Abstract type for methods that build the spinup sequence of a location in SINDBAD

Type Hierarchy

SpinupSequence <: SpinupTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • SequenceDefault: spin the selected spinup models up on the mean seasonal cycle, bracketed by a run over all years, and use this when the experiment settings give no sequence

  • SequenceList: use the explicit list of spinup sequences given in the experiment settings

  • SequenceWithAge: build the spinup sequence from the disturbance year of the location so that the years since disturbance are replayed

  • SequenceWithSpinupInput: build the spinup sequence with spinup input from previously saved cEco and TWS pools through forcing json

source

SpinupStep ​

Sindbad.Types.SpinupStep Type

SpinupStep

Basic Spinup sequence without time aggregation

Type Hierarchy

SpinupStep <: SpinupTypes <: SindbadTypes <: Any

source

SpinupStepWithAggregator ​

Sindbad.Types.SpinupStepWithAggregator Type

SpinupStepWithAggregator

Spinup sequence with time aggregation for corresponding forcingtime series

Type Hierarchy

SpinupStepWithAggregator <: SpinupTypes <: SindbadTypes <: Any

source

SpinupTypes ​

Sindbad.Types.SpinupTypes Type

SpinupTypes

Abstract type for model spinup related functions and methods in SINDBAD

Type Hierarchy

SpinupTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • SpinupMode: Abstract type for model spinup modes in SINDBAD

    • ClearanceA0: cut the vegetation carbon pools down to c_remain, as EtaScaleA0H does without the scaling by ηH

    • 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

    • EtaScaleH: scale only the heterotrophic carbon pools using diagnostic scalars for ηH

    • LoopTEM: run the TEM over the spinup forcing with the models of the step

    • 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

    • SpinupInput: replace the pools with the forcing variables of the same name, e.g., the main pools of a restart file, and fill their component pools

    • 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: Abstract type for methods that build the spinup sequence of a location in SINDBAD

    • SequenceDefault: spin the selected spinup models up on the mean seasonal cycle, bracketed by a run over all years, and use this when the experiment settings give no sequence

    • SequenceList: use the explicit list of spinup sequences given in the experiment settings

    • SequenceWithAge: build the spinup sequence from the disturbance year of the location so that the years since disturbance are replayed

    • SequenceWithSpinupInput: build the spinup sequence with spinup input from previously saved cEco and TWS pools through forcing json

  • SpinupStep: Basic Spinup sequence without time aggregation

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

source

Spinup_TWS ​

Sindbad.Types.Spinup_TWS Type

Spinup_TWS

Spinup spinup_mode for Total Water Storage (TWS)

Type Hierarchy

Spinup_TWS <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

Spinup_cEco ​

Sindbad.Types.Spinup_cEco Type

Spinup_cEco

Spinup spinup_mode for cEco

Type Hierarchy

Spinup_cEco <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

Spinup_cEco_TWS ​

Sindbad.Types.Spinup_cEco_TWS Type

Spinup_cEco_TWS

Spinup spinup_mode for cEco and TWS

Type Hierarchy

Spinup_cEco_TWS <: SpinupMode <: SpinupTypes <: SindbadTypes <: Any

source

ThreadsParallelization ​

Sindbad.Types.ThreadsParallelization Type

ThreadsParallelization

Use Julia threads for parallelization

Type Hierarchy

ThreadsParallelization <: ParallelizationPackage <: SimulationTypes <: SindbadTypes <: Any

source

TimeGridInterpolationMethod ​

Sindbad.Types.TimeGridInterpolationMethod Type

TimeGridInterpolationMethod

Abstract type for Interpolations.jl methods in time that interpolate on the positions of the values

Type Hierarchy

TimeGridInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • TimeInterpCubic: cubic B-spline of Interpolations.jl. It is fitted to the whole series, so one invalid value makes the whole series invalid

  • TimeInterpLanczos: Lanczos resampling of Interpolations.jl with order lobes

  • TimeInterpLanczosOpencv: Lanczos resampling of order 4 as in OpenCV, Lanczos4OpenCV of Interpolations.jl

  • TimeInterpLinear: interpolate linearly in time, BSpline(Linear()) of Interpolations.jl

  • TimeInterpNearest: take the nearest value in time, BSpline(Constant()) of Interpolations.jl

  • TimeInterpNext: take the next value in time, BSpline(Constant(Next)) of Interpolations.jl

  • TimeInterpPrevious: take the previous value in time, BSpline(Constant(Previous)) of Interpolations.jl

  • TimeInterpQuadratic: quadratic B-spline of Interpolations.jl. It is fitted to the whole series, so one invalid value makes the whole series invalid

source

TimeInterpAkimaMonotonic ​

Sindbad.Types.TimeInterpAkimaMonotonic Type

TimeInterpAkimaMonotonic

AkimaMonotonicInterpolation of Interpolations.jl

Type Hierarchy

TimeInterpAkimaMonotonic <: TimeMonotonicInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

TimeInterpCardinalMonotonic ​

Sindbad.Types.TimeInterpCardinalMonotonic Type

TimeInterpCardinalMonotonic

CardinalMonotonicInterpolation of Interpolations.jl with tension from 0 to 1

Type Hierarchy

TimeInterpCardinalMonotonic <: TimeMonotonicInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

TimeInterpNearest ​

Sindbad.Types.TimeInterpNearest Type

TimeInterpNearest

take the nearest value in time, BSpline(Constant()) of Interpolations.jl

Type Hierarchy

TimeInterpNearest <: TimeGridInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

TimeInterpCubic ​

Sindbad.Types.TimeInterpCubic Type

TimeInterpCubic

cubic B-spline of Interpolations.jl. It is fitted to the whole series, so one invalid value makes the whole series invalid

Type Hierarchy

TimeInterpCubic <: TimeGridInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

TimeInterpFiniteDifferenceMonotonic ​

Sindbad.Types.TimeInterpFiniteDifferenceMonotonic Type

TimeInterpFiniteDifferenceMonotonic

FiniteDifferenceMonotonicInterpolation of Interpolations.jl

Type Hierarchy

TimeInterpFiniteDifferenceMonotonic <: TimeMonotonicInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

source

TimeInterpFritschButlandMonotonic ​

Sindbad.Types.TimeInterpFritschButlandMonotonic Type

TimeInterpFritschButlandMonotonic

FritschButlandMonotonicInterpolation of Interpolations.jl

Type Hierarchy

TimeInterpFritschButlandMonotonic <: TimeMonotonicInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

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TimeInterpFritschCarlsonMonotonic ​

Sindbad.Types.TimeInterpFritschCarlsonMonotonic Type

TimeInterpFritschCarlsonMonotonic

FritschCarlsonMonotonicInterpolation of Interpolations.jl

Type Hierarchy

TimeInterpFritschCarlsonMonotonic <: TimeMonotonicInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

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TimeInterpLanczos ​

Sindbad.Types.TimeInterpLanczos Type

TimeInterpLanczos

Lanczos resampling of Interpolations.jl with order lobes

Type Hierarchy

TimeInterpLanczos <: TimeGridInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

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TimeInterpLanczosOpencv ​

Sindbad.Types.TimeInterpLanczosOpencv Type

TimeInterpLanczosOpencv

Lanczos resampling of order 4 as in OpenCV, Lanczos4OpenCV of Interpolations.jl

Type Hierarchy

TimeInterpLanczosOpencv <: TimeGridInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

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TimeInterpLinear ​

Sindbad.Types.TimeInterpLinear Type

TimeInterpLinear

interpolate linearly in time, BSpline(Linear()) of Interpolations.jl

Type Hierarchy

TimeInterpLinear <: TimeGridInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

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TimeInterpLinearMonotonic ​

Sindbad.Types.TimeInterpLinearMonotonic Type

TimeInterpLinearMonotonic

LinearMonotonicInterpolation of Interpolations.jl

Type Hierarchy

TimeInterpLinearMonotonic <: TimeMonotonicInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

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TimeInterpNearest ​

Missing docstring.

Missing docstring for TimeInterpNearest. Check Documenter's build log for details.

TimeInterpNext ​

Sindbad.Types.TimeInterpNext Type

TimeInterpNext

take the next value in time, BSpline(Constant(Next)) of Interpolations.jl

Type Hierarchy

TimeInterpNext <: TimeGridInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

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TimeInterpNone ​

Sindbad.Types.TimeInterpNone Type

TimeInterpNone

keep the data on its own time axis without interpolation

Type Hierarchy

TimeInterpNone <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

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TimeInterpPrevious ​

Sindbad.Types.TimeInterpPrevious Type

TimeInterpPrevious

take the previous value in time, BSpline(Constant(Previous)) of Interpolations.jl

Type Hierarchy

TimeInterpPrevious <: TimeGridInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

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TimeInterpQuadratic ​

Sindbad.Types.TimeInterpQuadratic Type

TimeInterpQuadratic

quadratic B-spline of Interpolations.jl. It is fitted to the whole series, so one invalid value makes the whole series invalid

Type Hierarchy

TimeInterpQuadratic <: TimeGridInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

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TimeInterpSteffenMonotonic ​

Sindbad.Types.TimeInterpSteffenMonotonic Type

TimeInterpSteffenMonotonic

SteffenMonotonicInterpolation of Interpolations.jl

Type Hierarchy

TimeInterpSteffenMonotonic <: TimeMonotonicInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

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TimeInterpolationMethod ​

Sindbad.Types.TimeInterpolationMethod Type

TimeInterpolationMethod

Abstract type for the methods of interpolation_method.time, which get the time axis of the data

Type Hierarchy

TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • TimeGridInterpolationMethod: Abstract type for Interpolations.jl methods in time that interpolate on the positions of the values

    • TimeInterpCubic: cubic B-spline of Interpolations.jl. It is fitted to the whole series, so one invalid value makes the whole series invalid

    • TimeInterpLanczos: Lanczos resampling of Interpolations.jl with order lobes

    • TimeInterpLanczosOpencv: Lanczos resampling of order 4 as in OpenCV, Lanczos4OpenCV of Interpolations.jl

    • TimeInterpLinear: interpolate linearly in time, BSpline(Linear()) of Interpolations.jl

    • TimeInterpNearest: take the nearest value in time, BSpline(Constant()) of Interpolations.jl

    • TimeInterpNext: take the next value in time, BSpline(Constant(Next)) of Interpolations.jl

    • TimeInterpPrevious: take the previous value in time, BSpline(Constant(Previous)) of Interpolations.jl

    • TimeInterpQuadratic: quadratic B-spline of Interpolations.jl. It is fitted to the whole series, so one invalid value makes the whole series invalid

  • TimeInterpNone: keep the data on its own time axis without interpolation

  • TimeMonotonicInterpolationMethod: Abstract type for monotonic Interpolations.jl methods in time that interpolate on the time coordinates

    • TimeInterpAkimaMonotonic: AkimaMonotonicInterpolation of Interpolations.jl

    • TimeInterpCardinalMonotonic: CardinalMonotonicInterpolation of Interpolations.jl with tension from 0 to 1

    • TimeInterpFiniteDifferenceMonotonic: FiniteDifferenceMonotonicInterpolation of Interpolations.jl

    • TimeInterpFritschButlandMonotonic: FritschButlandMonotonicInterpolation of Interpolations.jl

    • TimeInterpFritschCarlsonMonotonic: FritschCarlsonMonotonicInterpolation of Interpolations.jl

    • TimeInterpLinearMonotonic: LinearMonotonicInterpolation of Interpolations.jl

    • TimeInterpSteffenMonotonic: SteffenMonotonicInterpolation of Interpolations.jl

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TimeMonotonicInterpolationMethod ​

Sindbad.Types.TimeMonotonicInterpolationMethod Type

TimeMonotonicInterpolationMethod

Abstract type for monotonic Interpolations.jl methods in time that interpolate on the time coordinates

Type Hierarchy

TimeMonotonicInterpolationMethod <: TimeInterpolationMethod <: InterpolationMethod <: InputTypes <: SindbadTypes <: Any

Extended help

Available methods/subtypes:

  • TimeInterpAkimaMonotonic: AkimaMonotonicInterpolation of Interpolations.jl

  • TimeInterpCardinalMonotonic: CardinalMonotonicInterpolation of Interpolations.jl with tension from 0 to 1

  • TimeInterpFiniteDifferenceMonotonic: FiniteDifferenceMonotonicInterpolation of Interpolations.jl

  • TimeInterpFritschButlandMonotonic: FritschButlandMonotonicInterpolation of Interpolations.jl

  • TimeInterpFritschCarlsonMonotonic: FritschCarlsonMonotonicInterpolation of Interpolations.jl

  • TimeInterpLinearMonotonic: LinearMonotonicInterpolation of Interpolations.jl

  • TimeInterpSteffenMonotonic: SteffenMonotonicInterpolation of Interpolations.jl

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TimeSpace ​

Sindbad.Types.TimeSpace Type

TimeSpace

Aggregate data first over time, then over space

Type Hierarchy

TimeSpace <: DataAggrOrder <: ParameterOptimizationTypes <: SindbadTypes <: Any

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VisualizationPlots ​

Sindbad.Types.VisualizationPlots Type

VisualizationPlots

Dispatch type for visualizations using Plots.jl

Type Hierarchy

VisualizationPlots <: SindbadTypes <: Any

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VisualizationTypes ​

Sindbad.Types.VisualizationTypes Type

VisualizationTypes

Default dispatch type for visualization; used when no plotting backend is selected/available, dispatching to a fallback that logs an info message instead of plotting

Type Hierarchy

VisualizationTypes <: SindbadTypes <: Any

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ZygoteGrad ​

Sindbad.Types.ZygoteGrad Type

ZygoteGrad

Use Zygote.jl for automatic differentiation

Type Hierarchy

ZygoteGrad <: MachineLearningGradType <: MachineLearningTypes <: SindbadTypes <: Any

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ZygotePullback ​

Sindbad.Types.ZygotePullback Type

ZygotePullback

Use Zygote.jl for calculating the pullback function for the Jacobian-vector product during machine learning training

Type Hierarchy

ZygotePullback <: MachineLearningPullbackType <: MachineLearningTypes <: SindbadTypes <: Any

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