MADS (Model Analysis & Decision Support)

<a id='Mads.MFlm-Union{Tuple{T}, Tuple{AbstractMatrix{T}, Integer}} where T<:Number' href='#Mads.MFlm-Union{Tuple{T}, Tuple{AbstractMatrix{T}, Integer}} where T<:Number'># Mads.MFlmMethod.

Matrix Factorization using Levenberg Marquardt

Methods:

• Mads.MFlm(X::AbstractMatrix{T}, nk::Integer; method, log_W, log_H, retries, initW, initH, tolX, tolG, tolOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, quiet) where T<:Number in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBlindSourceSeparation.jl:133
• Mads.MFlm(X::AbstractMatrix{T}, range::AbstractRange{Int64}; kw...) where T<:Number in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBlindSourceSeparation.jl:103

Arguments:

• X::AbstractMatrix{T} : matrix to factorize
• nk::Integer : number of features to extract
• range::AbstractRange{Int64}

Keywords:

• initH : initial H (feature) matrix
• initW : initial W (weight) matrix
• lambda
• lambda_mu
• log_H : log-transform H (feature) matrix[default=false]
• log_W : log-transform W (weight) matrix [default=false]
• maxEval
• maxIter
• maxJacobians
• method
• np_lambda
• quiet
• retries : number of solution retries [default=1]
• show_trace
• tolG
• tolOF
• tolX

Returns:

• NMF results

# Mads.NMFipoptFunction.

Non-negative Matrix Factorization using JuMP/Ipopt

Methods:

• Mads.NMFipopt(X::AbstractMatrix{T} where T, nk::Integer, retries::Integer; random, maxiter, maxguess, initW, initH, verbosity, quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBlindSourceSeparation.jl:60
• Mads.NMFipopt(X::AbstractMatrix{T} where T, nk::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBlindSourceSeparation.jl:60

Arguments:

• X::AbstractMatrix{T} where T : matrix to factorize
• nk::Integer : number of features to extract
• retries::Integer : number of solution retries [default=1]

Keywords:

• initH : initial H (feature) matrix
• initW : initial W (weight) matrix
• maxguess : guess about the maximum for the H (feature) matrix [default=1]
• maxiter : maximum number of iterations [default=100000]
• quiet : quiet [default=false]
• random : random initial guesses [default=false]
• verbosity : verbosity output level [default=0]

Returns:

• NMF results

# Mads.NMFmFunction.

Non-negative Matrix Factorization using NMF

Methods:

• Mads.NMFm(X::Array, nk::Integer, retries::Integer; tol, maxiter) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBlindSourceSeparation.jl:21
• Mads.NMFm(X::Array, nk::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBlindSourceSeparation.jl:21

Arguments:

• X::Array : matrix to factorize
• nk::Integer : number of features to extract
• retries::Integer : number of solution retries [default=1]

Keywords:

• maxiter : maximum number of iterations [default=10000]
• tol : solution tolerance [default=1.0e-9]

Returns:

• NMF results

# Mads.addkeyword!Function.

Add a keyword in a class within the Mads dictionary madsdata

Methods:

• Mads.addkeyword!(madsdata::AbstractDict, class::AbstractString, keyword::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:288
• Mads.addkeyword!(madsdata::AbstractDict, keyword::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:284

Arguments:

• class::AbstractString : dictionary class; if not provided searches for keyword in Problem class
• keyword::AbstractString : dictionary key
• madsdata::AbstractDict : MADS problem dictionary

# Mads.addsource!Function.

Methods:

• Mads.addsource!(madsdata::AbstractDict, sourceid::Int64; dict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasol.jl:18
• Mads.addsource!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasol.jl:18

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• sourceid::Int64 : source id [default=0]

Keywords:

• dict

# Mads.addsourceparameters!Method.

Methods:

• Mads.addsourceparameters!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasol.jl:75

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

# Mads.allwellsoff!Method.

Turn off all the wells in the MADS problem dictionary

Methods:

• Mads.allwellsoff!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:602

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

# Mads.allwellson!Method.

Turn on all the wells in the MADS problem dictionary

Methods:

• Mads.allwellson!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:544

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

# Mads.amanziFunction.

Execute Amanzi external groundwater flow and transport simulator

Methods:

• Mads.amanzi(filename::AbstractString, nproc::Int64, quiet::Bool, observation_filename::AbstractString, setup::AbstractString; amanzi_exe) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSimulators.jl:14
• Mads.amanzi(filename::AbstractString, nproc::Int64, quiet::Bool, observation_filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSimulators.jl:14
• Mads.amanzi(filename::AbstractString, nproc::Int64, quiet::Bool) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSimulators.jl:14
• Mads.amanzi(filename::AbstractString, nproc::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSimulators.jl:14
• Mads.amanzi(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSimulators.jl:14

Arguments:

• filename::AbstractString : amanzi input file name
• nproc::Int64 : number of processor to be used by Amanzi [default=Mads.nprocs_per_task_default]
• observation_filename::AbstractString : Amanzi observation file name [default="observations.out"]
• quiet::Bool : suppress output [default=Mads.quiet]
• setup::AbstractString : bash script to setup Amanzi environmental variables [default="source-amanzi-setup"]

Keywords:

• amanzi_exe : full path to the Amanzi executable

# Mads.amanzi_output_parserFunction.

Parse Amanzi output provided in an external file (filename)

Methods:

• Mads.amanzi_output_parser(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParsers.jl:21
• Mads.amanzi_output_parser() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParsers.jl:21

Arguments:

• filename::AbstractString : external file name [default="observations.out"]

Returns:

• dictionary with model observations following MADS requirements

Example:

Mads.amanzi_output_parser()


# Mads.asinetransformFunction.

Arcsine transformation of model parameters

Methods:

• Mads.asinetransform(params::AbstractVector{T} where T, lowerbounds::AbstractVector{T} where T, upperbounds::AbstractVector{T} where T, indexlogtransformed::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSineTransformations.jl:13
• Mads.asinetransform(madsdata::AbstractDict, params::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSineTransformations.jl:3

Arguments:

• indexlogtransformed::AbstractVector{T} where T : index vector of log-transformed parameters
• lowerbounds::AbstractVector{T} where T : lower bounds
• madsdata::AbstractDict : MADS problem dictionary
• params::AbstractVector{T} where T : model parameters
• upperbounds::AbstractVector{T} where T : upper bounds

Returns:

• Arcsine transformation of model parameters

# Mads.bigdtMethod.

Perform Bayesian Information Gap Decision Theory (BIG-DT) analysis

Methods:

• Mads.bigdt(madsdata::AbstractDict, nummodelruns::Int64; numhorizons, maxHorizon, numlikelihoods) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBayesInfoGap.jl:122

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• nummodelruns::Int64 : number of model runs

Keywords:

• maxHorizon : maximum info-gap horizons of uncertainty [default=3]
• numhorizons : number of info-gap horizons of uncertainty [default=100]
• numlikelihoods : number of Bayesian likelihoods [default=25]

Returns:

• dictionary with BIG-DT results

# Mads.boundparameters!Function.

Bound model parameters based on their ranges

Methods:

• Mads.boundparameters!(madsdata::AbstractDict, pardict::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:778
• Mads.boundparameters!(madsdata::AbstractDict, parvec::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:766

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• pardict::AbstractDict : Parameter dictionary
• parvec::AbstractVector{T} where T : Parameter vector

# Mads.calibrateMethod.

Calibrate Mads model using a constrained Levenberg-Marquardt technique

Mads.calibrate(madsdata; tolX=1e-3, tolG=1e-6, maxEval=1000, maxIter=100, maxJacobians=100, lambda=100.0, lambda_mu=10.0, np_lambda=10, show_trace=false, usenaive=false)

Methods:

• Mads.calibrate(madsdata::AbstractDict; tolX, tolG, tolOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, usenaive, save_results, localsa) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCalibrate.jl:168

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• lambda : initial Levenberg-Marquardt lambda [default=100.0]
• lambda_mu : lambda multiplication factor [default=10.0]
• localsa : perform local sensitivity analysis [default=false]
• maxEval : maximum number of model evaluations [default=1000]
• maxIter : maximum number of optimization iterations [default=100]
• maxJacobians : maximum number of Jacobian solves [default=100]
• np_lambda : number of parallel lambda solves [default=10]
• save_results : save intermediate results [default=true]
• show_trace : shows solution trace [default=false]
• tolG : parameter space update tolerance [default=1e-6]
• tolOF : objective function tolerance [default=1e-3]
• tolX : parameter space tolerance [default=1e-4]
• usenaive : use naive Levenberg-Marquardt solver [default=false]

Returns:

• model parameter dictionary with the optimal values at the minimum
• optimization algorithm results (e.g. results.minimizer)

# Mads.calibraterandomFunction.

Calibrate with random initial guesses

Methods:

• Mads.calibraterandom(madsdata::AbstractDict, numberofsamples::Integer; tolX, tolG, tolOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, usenaive, seed, quiet, all, save_results, first_init) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCalibrate.jl:41
• Mads.calibraterandom(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCalibrate.jl:41

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• numberofsamples::Integer : number of random initial samples [default=1]

Keywords:

• all : all model results are returned [default=false]
• first_init
• lambda : initial Levenberg-Marquardt lambda [default=100.0]
• lambda_mu : lambda multiplication factor [default=10.0]
• maxEval : maximum number of model evaluations [default=1000]
• maxIter : maximum number of optimization iterations [default=100]
• maxJacobians : maximum number of Jacobian solves [default=100]
• np_lambda : number of parallel lambda solves [default=10]
• quiet : [default=true]
• save_results : save intermediate results [default=true]
• seed : random seed [default=0]
• show_trace : shows solution trace [default=false]
• tolG : parameter space update tolerance [default=1e-6]
• tolOF : objective function tolerance [default=1e-3]
• tolX : parameter space tolerance [default=1e-4]
• usenaive : use naive Levenberg-Marquardt solver [default=false]

Returns:

• model parameter dictionary with the optimal values at the minimum
• optimization algorithm results (e.g. bestresult[2].minimizer)

Example:

Mads.calibraterandom(madsdata; tolX=1e-3, tolG=1e-6, maxEval=1000, maxIter=100, maxJacobians=100, lambda=100.0, lambda_mu=10.0, np_lambda=10, show_trace=false, usenaive=false)
Mads.calibraterandom(madsdata, numberofsamples; tolX=1e-3, tolG=1e-6, maxEval=1000, maxIter=100, maxJacobians=100, lambda=100.0, lambda_mu=10.0, np_lambda=10, show_trace=false, usenaive=false)


# Mads.calibraterandom_parallelFunction.

Calibrate with random initial guesses in parallel

Methods:

• Mads.calibraterandom_parallel(madsdata::AbstractDict, numberofsamples::Integer; tolX, tolG, tolOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, usenaive, seed, quiet, save_results, localsa) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCalibrate.jl:112
• Mads.calibraterandom_parallel(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCalibrate.jl:112

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• numberofsamples::Integer : number of random initial samples [default=1]

Keywords:

• lambda : initial Levenberg-Marquardt lambda [default=100.0]
• lambda_mu : lambda multiplication factor [default=10.0]
• localsa : perform local sensitivity analysis [default=false]
• maxEval : maximum number of model evaluations [default=1000]
• maxIter : maximum number of optimization iterations [default=100]
• maxJacobians : maximum number of Jacobian solves [default=100]
• np_lambda : number of parallel lambda solves [default=10]
• quiet : suppress output [default=true]
• save_results : save intermediate results [default=true]
• seed : random seed [default=0]
• show_trace : shows solution trace [default=false]
• tolG : parameter space update tolerance [default=1e-6]
• tolOF : objective function tolerance [default=1e-3]
• tolX : parameter space tolerance [default=1e-4]
• usenaive : use naive Levenberg-Marquardt solver [default=false]

Returns:

• vector with all objective function values
• boolean vector (converged/not converged)
• array with estimate model parameters

# Mads.captureoffMethod.

Methods:

• Mads.captureoff() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:140

# Mads.captureonMethod.

Methods:

• Mads.captureon() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:131

# Mads.check_notebookMethod.

Check is Jupyter notebook exists

Methods:

• Mads.check_notebook(rootname::AbstractString; dir, ndir) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsNotebooks.jl:97

Arguments:

• rootname::AbstractString : notebook root name

Keywords:

• dir : notebook directory
• ndir

# Mads.checkmodeloutputdirsMethod.

Check the directories where model outputs should be saved for MADS

Methods:

• Mads.checkmodeloutputdirs(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:666

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Returns:

• true or false

# Mads.checknodedirFunction.

Check if a directory is readable

Methods:

• Mads.checknodedir(dir::AbstractString, waittime::Float64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsExecute.jl:12
• Mads.checknodedir(dir::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsExecute.jl:12
• Mads.checknodedir(node::AbstractString, dir::AbstractString, waittime::Float64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsExecute.jl:3
• Mads.checknodedir(node::AbstractString, dir::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsExecute.jl:3

Arguments:

• dir::AbstractString : directory
• node::AbstractString : computational node name (e.g. madsmax.lanl.gov, wf03, or 127.0.0.1)
• waittime::Float64 : wait time in seconds [default=10]

Returns:

• true if the directory is readable, false otherwise

# Mads.checkoutFunction.

Methods:

• Mads.checkout(modulename::AbstractString; git, master, force, pull, required, all) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:78
• Mads.checkout() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:78

Arguments:

• modulename::AbstractString : module name

Keywords:

• all : whether to checkout all the modules [default=false]
• force : whether to overwrite local changes when checkout [default=false]
• git : whether to use "git checkout" [default=true]
• master : whether on master branch [default=false]
• pull : whether to run "git pull" [default=true]
• required : whether only checkout Mads.required modules [default=false]

# Mads.checkparameterrangesMethod.

Check parameter ranges for model parameters

Methods:

• Mads.checkparameterranges(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:704

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

# Mads.cleancoverageMethod.

Methods:

• Mads.cleancoverage() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTest.jl:22

# Mads.cmadsins_obsMethod.

Methods:

• Mads.cmadsins_obs(obsid::AbstractVector{T} where T, instructionfilename::AbstractString, inputfilename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCMads.jl:39

Arguments:

• inputfilename::AbstractString : input file name
• instructionfilename::AbstractString : instruction file name
• obsid::AbstractVector{T} where T : observation id

Return:

• observations

# Mads.commitFunction.

Methods:

• Mads.commit(commitmsg::AbstractString, modulename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:226
• Mads.commit(commitmsg::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:226

Arguments:

• commitmsg::AbstractString : commit message
• modulename::AbstractString : module name

# Mads.computemassFunction.

Compute injected/reduced contaminant mass (for a given set of mads input files when "path" is provided)

Methods:

• Mads.computemass(madsfiles::Union{Regex, String}; time, path) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasol.jl:485
• Mads.computemass(madsdata::AbstractDict; time) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasol.jl:458

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• madsfiles::Union{Regex, String} : matching pattern for Mads input files (string or regular expression accepted)

Keywords:

• path : search directory for the mads input files [default="."]
• time : computational time [default=0]

Returns:

• array with all the lambda values
• array with associated total injected mass
• array with associated total reduced mass

Example:

Mads.computemass(madsfiles; time=0, path=".")


# Mads.computeparametersensititiesMethod.

Compute sensitivities for each model parameter; averaging the sensitivity indices over the entire observation range

Methods:

• Mads.computeparametersensitities(madsdata::AbstractDict, saresults::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:839

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• saresults::AbstractDict : dictionary with sensitivity analysis results

# Mads.contaminationMethod.

Compute concentration for a point in space and time (x,y,z,t)

Methods:

• Mads.contamination(wellx::Number, welly::Number, wellz::Number, n::Number, lambda::Number, theta::Number, vx::Number, vy::Number, vz::Number, ax::Number, ay::Number, az::Number, H::Number, x::Number, y::Number, z::Number, dx::Number, dy::Number, dz::Number, f::Number, t0::Number, t1::Number, t::AbstractVector{T} where T, anasolfunction::Function) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasol.jl:428

Arguments:

• H::Number : Hurst coefficient for Fractional Brownian dispersion
• anasolfunction::Function
• ax::Number : dispersivity in X direction (longitudinal)
• ay::Number : dispersivity in Y direction (transverse horizontal)
• az::Number : dispersivity in Y direction (transverse vertical)
• dx::Number : source size (extent) in X direction
• dy::Number : source size (extent) in Y direction
• dz::Number : source size (extent) in Z direction
• f::Number : source mass flux
• lambda::Number : first-order reaction rate
• n::Number : porosity
• t0::Number : source starting time
• t1::Number : source termination time
• t::AbstractVector{T} where T : vector of times to compute concentration at the observation point
• theta::Number : groundwater flow direction
• vx::Number : advective transport velocity in X direction
• vy::Number : advective transport velocity in Y direction
• vz::Number : advective transport velocity in Z direction
• wellx::Number : observation point (well) X coordinate
• welly::Number : observation point (well) Y coordinate
• wellz::Number : observation point (well) Z coordinate
• x::Number : X coordinate of contaminant source location
• y::Number : Y coordinate of contaminant source location
• z::Number : Z coordinate of contaminant source location

Returns:

• a vector of predicted concentration at (wellx, welly, wellz, t)

# Mads.copyaquifer2sourceparameters!Method.

Copy aquifer parameters to become contaminant source parameters

Methods:

• Mads.copyaquifer2sourceparameters!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasol.jl:114

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

# Mads.copyrightMethod.

Methods:

• Mads.copyright() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelp.jl:44

# Mads.create_documentationMethod.

Create web documentation files for Mads functions

Methods:

• Mads.create_documentation() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:386

# Mads.create_tests_offMethod.

Turn off the generation of MADS tests (default)

Methods:

• Mads.create_tests_off() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:185

# Mads.create_tests_onMethod.

Turn on the generation of MADS tests (dangerous)

Methods:

• Mads.create_tests_on() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:176

# Mads.createobservationsFunction.

Create Mads dictionary of observations and instruction file

Methods:

• Mads.createobservations(obs::AbstractMatrix{T} where T; key, weight, time) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCreate.jl:56
• Mads.createobservations(obs::AbstractVector{T} where T; key, weight, time, min, max, dist) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCreate.jl:43
• Mads.createobservations(nrow::Int64, ncol::Int64; obstring, pretext, prestring, poststring, filename) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCreate.jl:25
• Mads.createobservations(nrow::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCreate.jl:25

Arguments:

• ncol::Int64 : number of columns [default 1]
• nrow::Int64 : number of rows
• obs::AbstractMatrix{T} where T
• obs::AbstractVector{T} where T

Keywords:

• dist
• filename : file name
• key
• max
• min
• obstring : observation string
• poststring : post instruction file string
• prestring : pre instruction file string
• pretext : preamble instructions
• time
• weight

)

Returns:

• observation dictionary

# Mads.createobservations!Function.

Create observations in the MADS problem dictionary based on time and observation vectors

Methods:

• Mads.createobservations!(madsdata::AbstractDict, observation::AbstractDict; logtransform, weight_type, weight) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:483
• Mads.createobservations!(madsdata::AbstractDict, time::AbstractVector{T} where T, observation::AbstractVector{T} where T; logtransform, weight_type, weight) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:439
• Mads.createobservations!(md::AbstractDict, obs::AbstractVecOrMat{T} where T; kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCreate.jl:91

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• md::AbstractDict
• obs::AbstractVecOrMat{T} where T
• observation::AbstractDict : dictionary of observations
• observation::AbstractVector{T} where T : dictionary of observations
• time::AbstractVector{T} where T : vector of observation times

Keywords:

• logtransform : log transform observations [default=false]
• weight : weight value [default=1]
• weight_type : weight type [default=constant]

# Mads.createproblemFunction.

Create a new Mads problem where the observation targets are computed based on the model predictions

Methods:

• Mads.createproblem(madsdata::AbstractDict, predictions::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCreate.jl:197
• Mads.createproblem(madsdata::AbstractDict, predictions::AbstractDict, outfilename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCreate.jl:193
• Mads.createproblem(madsdata::AbstractDict, outfilename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCreate.jl:188
• Mads.createproblem(infilename::AbstractString, outfilename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCreate.jl:163
• Mads.createproblem(param::AbstractVector{T} where T, obs::AbstractVecOrMat{T} where T, f::Union{AbstractString, Function}; problemname, paramkey, paramname, paramplotname, paramtype, parammin, parammax, paramdist, paramlog, obskey, obsweight, obstime, obsmin, obsmax, obsdist) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCreate.jl:153
• Mads.createproblem(in::Integer, out::Integer, f::Union{AbstractString, Function}; kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCreate.jl:150

Arguments:

• f::Union{AbstractString, Function}
• in::Integer
• infilename::AbstractString : input Mads file
• madsdata::AbstractDict : MADS problem dictionary
• obs::AbstractVecOrMat{T} where T
• out::Integer
• outfilename::AbstractString : output Mads file
• param::AbstractVector{T} where T
• predictions::AbstractDict : dictionary of model predictions

Keywords:

• obsdist
• obskey
• obsmax
• obsmin
• obstime
• obsweight
• paramdist
• paramkey
• paramlog
• parammax
• parammin
• paramname
• paramplotname
• paramtype
• problemname

Returns:

# Mads.createtempdirMethod.

Create temporary directory

Methods:

• Mads.createtempdir(tempdirname::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1306

Arguments:

• tempdirname::AbstractString : temporary directory name

# Mads.deleteNaN!Method.

Delete rows with NaN in a dataframe df

Methods:

• Mads.deleteNaN!(df::DataFrames.DataFrame) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:1065

Arguments:

• df::DataFrames.DataFrame : dataframe

# Mads.deletekeyword!Function.

Delete a keyword in a class within the Mads dictionary madsdata

Methods:

• Mads.deletekeyword!(madsdata::AbstractDict, class::AbstractString, keyword::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:317
• Mads.deletekeyword!(madsdata::AbstractDict, keyword::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:311

Arguments:

• class::AbstractString : dictionary class; if not provided searches for keyword in Problem class
• keyword::AbstractString : dictionary key
• madsdata::AbstractDict : MADS problem dictionary

# Mads.deleteoffwells!Method.

Delete all wells marked as being off in the MADS problem dictionary

Methods:

• Mads.welloff!(madsdata::AbstractDict, wellname::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:616

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• wellname::AbstractString : name of the well to be turned off

# Mads.deletetimes!Method.

Delete all times in the MADS problem dictionary in a given list.

Methods:

• Mads.welloff!(madsdata::AbstractDict, wellname::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:616

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• wellname::AbstractString : name of the well to be turned off

# Mads.dependentsFunction.

Lists module dependents on a module (Mads by default)

Methods:

• Mads.dependents(modulename::AbstractString, filter::Bool) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:42
• Mads.dependents(modulename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:42
• Mads.dependents() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:42

Arguments:

• filter::Bool : whether to filter modules [default=false]
• modulename::AbstractString : module name [default="Mads"]

Returns:

• modules that are dependents of the input module

# Mads.diffFunction.

Methods:

• Mads.diff(modulename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:169
• Mads.diff() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:169

Arguments:

• modulename::AbstractString : module name

# Mads.displayFunction.

Display image file

Methods:

• Mads.display(o; gwo, gho, gw, gh) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsDisplay.jl:134
• Mads.display(p::Compose.Context; gwo, gho, gw, gh) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsDisplay.jl:101
• Mads.display(p::Gadfly.Plot; gwo, gho, gw, gh) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsDisplay.jl:68
• Mads.display(filename::AbstractString, open::Bool) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsDisplay.jl:7
• Mads.display(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsDisplay.jl:7

Arguments:

• filename::AbstractString : image file name
• o
• open::Bool
• p::Compose.Context : plotting object
• p::Gadfly.Plot : plotting object

Keywords:

• gh
• gho
• gw
• gwo

# Mads.dumpasciifileMethod.

Dump ASCII file

Methods:

• Mads.dumpasciifile(filename::AbstractString, data) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsASCII.jl:30

Arguments:

• data : data to dump
• filename::AbstractString : ASCII file name

Dumps:

• ASCII file with the name in "filename"

# Mads.dumpjsonfileMethod.

Dump a JSON file

Methods:

• Mads.dumpjsonfile(filename::AbstractString, data) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsJSON.jl:38

Arguments:

• data : data to dump
• filename::AbstractString : JSON file name

Dumps:

• JSON file with the name in "filename"

# Mads.dumpwelldataMethod.

Dump well data from MADS problem dictionary into a ASCII file

Methods:

• Mads.dumpwelldata(madsdata::AbstractDict, filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1172

Arguments:

• filename::AbstractString : output file name
• madsdata::AbstractDict : MADS problem dictionary

Dumps:

• filename : a ASCII file

# Mads.dumpyamlfileMethod.

Dump YAML file

Methods:

• Mads.dumpyamlfile(filename::AbstractString, data) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsYAML.jl:33

Arguments:

• data : YAML data
• filename::AbstractString : output file name

# Mads.dumpyamlmadsfileMethod.

Methods:

• Mads.dumpyamlmadsfile(madsdata::AbstractDict, filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsYAML.jl:45

Arguments:

• filename::AbstractString : output file name
• madsdata::AbstractDict : MADS problem dictionary

# Mads.efastMethod.

Sensitivity analysis using Saltelli's extended Fourier Amplitude Sensitivity Testing (eFAST) method

Methods:

• Mads.efast(md::AbstractDict; N, M, gamma, seed, checkpointfrequency, restartdir, restart) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:1108

Arguments:

• md::AbstractDict : MADS problem dictionary

Keywords:

• M : maximum number of harmonics [default=6]
• N : number of samples [default=100]
• checkpointfrequency : check point frequency [default=N]
• gamma : multiplication factor (Saltelli 1999 recommends gamma = 2 or 4) [default=4]
• restart : save restart information [default=false]
• restartdir : directory where files will be stored containing model results for the efast simulation restarts [default="efastcheckpoints"]
• seed : random seed [default=0]

# Mads.emceesamplingFunction.

Bayesian sampling with Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler (aka Emcee)

Methods:

• Mads.emceesampling(madsdata::AbstractDict, p0::Array; numwalkers, nsteps, burnin, thinning, seed, weightfactor) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMonteCarlo.jl:31
• Mads.emceesampling(madsdata::AbstractDict; numwalkers, nsteps, burnin, thinning, sigma, seed, weightfactor) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMonteCarlo.jl:8

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• p0::Array : initial parameters (matrix of size (number of parameters, number of walkers) or (length(Mads.getoptparamkeys(madsdata)), numwalkers))

Keywords:

• burnin : number of initial realizations before the MCMC are recorded [default=10]
• nsteps : number of final realizations in the chain [default=100]
• numwalkers : number of walkers (if in parallel this can be the number of available processors; in general, the higher the number of walkers, the better the results and computational time [default=10]
• seed : random seed [default=0]
• sigma : a standard deviation parameter used to initialize the walkers [default=0.01]
• thinning : removal of any thinning realization [default=1]
• weightfactor : weight factor [default=1.0]

Returns:

• MCMC chain
• log likelihoods of the final samples in the chain

Examples:

Mads.emceesampling(madsdata; numwalkers=10, nsteps=100, burnin=100, thinning=1, seed=2016, sigma=0.01)


# Mads.estimationerrorFunction.

Estimate kriging error

Methods:

• Mads.estimationerror(w::AbstractVector{T} where T, covmat::AbstractMatrix{T} where T, covvec::AbstractVector{T} where T, cov0::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:205
• Mads.estimationerror(w::AbstractVector{T} where T, x0::AbstractVector{T} where T, X::AbstractMatrix{T} where T, covfn::Function) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:198

Arguments:

• X::AbstractMatrix{T} where T : observation matrix
• cov0::Number : zero-separation covariance
• covfn::Function
• covmat::AbstractMatrix{T} where T : covariance matrix
• covvec::AbstractVector{T} where T : covariance vector
• w::AbstractVector{T} where T : kriging weights
• x0::AbstractVector{T} where T : estimated locations

Returns:

• estimation kriging error

# Mads.evaluatemadsexpressionMethod.

Evaluate an expression string based on a parameter dictionary

Methods:

• Mads.evaluatemadsexpression(expressionstring::AbstractString, parameters::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMisc.jl:154

Arguments:

• expressionstring::AbstractString : expression string
• parameters::AbstractDict : parameter dictionary applied to evaluate the expression string

Returns:

• dictionary containing the expression names as keys, and the values of the expression as values

# Mads.evaluatemadsexpressionsMethod.

Evaluate all the expressions in the Mads problem dictiorany based on a parameter dictionary

Methods:

• Mads.evaluatemadsexpressions(madsdata::AbstractDict, parameters::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMisc.jl:173

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• parameters::AbstractDict : parameter dictionary applied to evaluate the expression strings

Returns:

• dictionary containing the parameter and expression names as keys, and the values of the expression as values

# Mads.expcovMethod.

Exponential spatial covariance function

Methods:

• Mads.expcov(h::Number, maxcov::Number, scale::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:31

Arguments:

• h::Number : separation distance
• maxcov::Number : maximum covariance
• scale::Number : scale

Returns:

• covariance

# Mads.exponentialvariogramMethod.

Exponential variogram

Methods:

• Mads.exponentialvariogram(h::Number, sill::Number, range::Number, nugget::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:83

Arguments:

• h::Number : separation distance
• nugget::Number : nugget
• range::Number : range
• sill::Number : sill

Returns:

• Exponential variogram

# Mads.filterkeysFunction.

Filter dictionary keys based on a string or regular expression

Methods:

• Mads.filterkeys(dict::AbstractDict, key::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:854
• Mads.filterkeys(dict::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:854
• Mads.filterkeys(dict::AbstractDict, key::Regex) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:853

Arguments:

• dict::AbstractDict : dictionary
• key::AbstractString : the regular expression or string used to filter dictionary keys
• key::Regex : the regular expression or string used to filter dictionary keys

# Mads.forwardFunction.

Perform a forward run using the initial or provided values for the model parameters

Methods:

• Mads.forward(madsdata::AbstractDict, paramarray::AbstractArray; all, checkpointfrequency, checkpointfilename) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsForward.jl:45
• Mads.forward(madsdata::AbstractDict, paramdict::AbstractDict; all, checkpointfrequency, checkpointfilename) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsForward.jl:11
• Mads.forward(madsdata::AbstractDict; all) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsForward.jl:7

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• paramarray::AbstractArray : array of model parameter values
• paramdict::AbstractDict : dictionary of model parameter values

Keywords:

• all : all model results are returned [default=false]
• checkpointfilename : check point file name [default="checkpoint_forward"]
• checkpointfrequency : check point frequency for storing restart information [default=0]

Returns:

• dictionary of model predictions

# Mads.forwardgridFunction.

Perform a forward run over a 3D grid defined in madsdata using the initial or provided values for the model parameters

Methods:

• Mads.forwardgrid(madsdatain::AbstractDict, paramvalues::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsForward.jl:138
• Mads.forwardgrid(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsForward.jl:133

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• madsdatain::AbstractDict : MADS problem dictionary
• paramvalues::AbstractDict : dictionary of model parameter values

Returns:

• 3D array with model predictions along a 3D grid

# Mads.freeFunction.

Methods:

• Mads.free(modulename::AbstractString; required, all) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:202
• Mads.free() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:202

Arguments:

• modulename::AbstractString : module name

Keywords:

• all : free all the modules [default=false]
• required : only free Mads.required modules [default=false]

# Mads.functionsFunction.

List available functions in the MADS modules:

Methods:

• Mads.functions(m::Union{Module, Symbol}, string::AbstractString; shortoutput, quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelp.jl:96
• Mads.functions(m::Union{Module, Symbol}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelp.jl:96
• Mads.functions(m::Union{Module, Symbol}, re::Regex; shortoutput, quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelp.jl:66
• Mads.functions(string::AbstractString; shortoutput, quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelp.jl:57
• Mads.functions() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelp.jl:57
• Mads.functions(re::Regex; shortoutput, quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelp.jl:48

Arguments:

• m::Union{Module, Symbol} : MADS module
• re::Regex
• string::AbstractString : string to display functions with matching names

Keywords:

• quiet
• shortoutput

Examples:

Mads.functions()


# Mads.gaussiancovMethod.

Gaussian spatial covariance function

Methods:

• Mads.gaussiancov(h::Number, maxcov::Number, scale::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:17

Arguments:

• h::Number : separation distance
• maxcov::Number : maximum covariance
• scale::Number : scale

Returns:

• covariance

# Mads.gaussianvariogramMethod.

Gaussian variogram

Methods:

• Mads.gaussianvariogram(h::Number, sill::Number, range::Number, nugget::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:104

Arguments:

• h::Number : separation distance
• nugget::Number : nugget
• range::Number : range
• sill::Number : sill

Returns:

• Gaussian variogram

# Mads.getcovmatMethod.

Get spatial covariance matrix

Methods:

• Mads.getcovmat(X::AbstractMatrix{T} where T, covfunction::Function) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:160

Arguments:

• X::AbstractMatrix{T} where T : matrix with coordinates of the data points (x or y)
• covfunction::Function

Returns:

• spatial covariance matrix

# Mads.getcovvec!Method.

Get spatial covariance vector

Methods:

• Mads.getcovvec!(covvec::AbstractVector{T} where T, x0::AbstractVector{T} where T, X::AbstractMatrix{T} where T, covfn::Function) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:186

Arguments:

• X::AbstractMatrix{T} where T : matrix with coordinates of the data points
• covfn::Function : spatial covariance function
• covvec::AbstractVector{T} where T : spatial covariance vector
• x0::AbstractVector{T} where T : vector with coordinates of the estimation points (x or y)

Returns:

• spatial covariance vector

# Mads.getdefaultplotformatMethod.

Set the default plot format (SVG is the default format)

Methods:

• Mads.getdefaultplotformat() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:32

# Mads.getdictvaluesFunction.

Get dictionary values for keys based on a string or regular expression

Methods:

• Mads.getdictvalues(dict::AbstractDict, key::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:876
• Mads.getdictvalues(dict::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:876
• Mads.getdictvalues(dict::AbstractDict, key::Regex) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:875

Arguments:

• dict::AbstractDict : dictionary
• key::AbstractString : the key to find value for
• key::Regex : the key to find value for

# Mads.getdirMethod.

Get directory

Methods:

• Mads.getdir(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:470

Arguments:

• filename::AbstractString : file name

Returns:

• directory in file name

Example:

d = Mads.getdir("a.mads") # d = "."


# Mads.getdistributionMethod.

Parse parameter distribution from a string

Methods:

• Mads.getdistribution(dist::AbstractString, i::AbstractString, inputtype::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMisc.jl:202

Arguments:

• dist::AbstractString : parameter distribution
• i::AbstractString
• inputtype::AbstractString : input type (parameter or observation)

Returns:

• distribution

# Mads.getextensionMethod.

Get file name extension

Methods:

• Mads.getextension(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:646

Arguments:

• filename::AbstractString : file name

Returns:

• file name extension

Example:

ext = Mads.getextension("a.mads") # ext = "mads"


# Mads.getfilenamesMethod.

Get file names by expanding wildcards

Methods:

• Mads.getfilenames(cmdstring::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:10

Arguments:

• cmdstring::AbstractString

# Mads.getimportantsamplesMethod.

Get important samples

Methods:

• Mads.getimportantsamples(samples::Array, llhoods::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:348

Arguments:

• llhoods::AbstractVector{T} where T : vector of log-likelihoods
• samples::Array : array of samples

Returns:

• array of important samples

# Mads.getlogparamkeysMethod.

Get the keys in the MADS problem dictionary for parameters that are log-transformed (log)

# Mads.getmadsinputfileMethod.

Get the default MADS input file set as a MADS global variable using setmadsinputfile(filename)

Methods:

• Mads.getmadsinputfile() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:422

Returns:

• input file name (e.g. input_file_name.mads)

# Mads.getmadsproblemdirMethod.

Get the directory where the Mads data file is located

Methods:

• Mads.getmadsproblemdir(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:493

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Example:

madsdata = Mads.loadmadsfile("../../a.mads")


where madsproblemdir = "../../"

# Mads.getmadsrootnameMethod.

Get the MADS problem root name

Methods:

• Mads.getmadsrootname(madsdata::AbstractDict; first, version) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:444

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• first : use the first . in filename as the seperator between root name and extention [default=true]
• version : delete version information from filename for the returned rootname [default=false]

Example:

madsrootname = Mads.getmadsrootname(madsdata)


Returns:

• root of file name

# Mads.getnextmadsfilenameMethod.

Methods:

• Mads.getnextmadsfilename(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:609

Arguments:

• filename::AbstractString : file name

Returns:

# Mads.getnonlogparamkeysMethod.

Get the keys in the MADS problem dictionary for parameters that are NOT log-transformed (log)

# Mads.getnonoptparamkeysMethod.

Get the keys in the MADS problem dictionary for parameters that are NOT optimized (opt)

# Mads.getobsdistMethod.

Get an array with dist values for observations in the MADS problem dictionary defined by obskeys

# Mads.getobsdistMethod.

Get an array with dist values for all observations in the MADS problem dictionary

# Mads.getobskeysMethod.

Get keys for all observations in the MADS problem dictionary

Methods:

• Mads.getobskeys(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:43

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Returns:

• keys for all observations in the MADS problem dictionary

# Mads.getobslogMethod.

Get an array with log values for observations in the MADS problem dictionary defined by obskeys

# Mads.getobslogMethod.

Get an array with log values for all observations in the MADS problem dictionary

# Mads.getobsmaxMethod.

Get an array with max values for observations in the MADS problem dictionary defined by obskeys

# Mads.getobsmaxMethod.

Get an array with max values for all observations in the MADS problem dictionary

# Mads.getobsminMethod.

Get an array with min values for observations in the MADS problem dictionary defined by obskeys

# Mads.getobsminMethod.

Get an array with min values for all observations in the MADS problem dictionary

# Mads.getobstargetMethod.

Get an array with target values for observations in the MADS problem dictionary defined by obskeys

# Mads.getobstargetMethod.

Get an array with target values for all observations in the MADS problem dictionary

# Mads.getobstimeMethod.

Get an array with time values for observations in the MADS problem dictionary defined by obskeys

# Mads.getobstimeMethod.

Get an array with time values for all observations in the MADS problem dictionary

# Mads.getobsweightMethod.

Get an array with weight values for observations in the MADS problem dictionary defined by obskeys

# Mads.getobsweightMethod.

Get an array with weight values for all observations in the MADS problem dictionary

# Mads.getoptparamkeysMethod.

Get the keys in the MADS problem dictionary for parameters that are optimized (opt)

# Mads.getoptparamsFunction.

Get optimizable parameters

Methods:

• Mads.getoptparams(madsdata::AbstractDict, parameterarray::Array, optparameterkey::Array) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:362
• Mads.getoptparams(madsdata::AbstractDict, parameterarray::Array) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:362
• Mads.getoptparams(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:362

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• optparameterkey::Array : optimizable parameter keys
• parameterarray::Array : parameter array

Returns:

• parameter array

# Mads.getparamdictMethod.

Get dictionary with all parameters and their respective initial values

Methods:

• Mads.getparamdict(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:59

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Returns:

• dictionary with all parameters and their respective initial values

# Mads.getparamdistributionsMethod.

Get probabilistic distributions of all parameters in the MADS problem dictionary

Note:

Probabilistic distribution of parameters can be defined only if dist or min/max model parameter fields are specified in the MADS problem dictionary madsdata.

Methods:

• Mads.getparamdistributions(madsdata::AbstractDict; init_dist) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:659

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• init_dist : if true use the distribution defined for initialization in the MADS problem dictionary (defined using init_dist parameter field); else use the regular distribution defined in the MADS problem dictionary (defined using dist parameter field [default=false]

Returns:

• probabilistic distributions

# Mads.getparamkeysMethod.

Get keys of all parameters in the MADS problem dictionary

Methods:

• Mads.getparamkeys(madsdata::AbstractDict; filter) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:43

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• filter : parameter filter

Returns:

• array with the keys of all parameters in the MADS problem dictionary

# Mads.getparamrandomFunction.

Get independent sampling of model parameters defined in the MADS problem dictionary

Methods:

• Mads.getparamrandom(madsdata::AbstractDict, parameterkey::AbstractString; numsamples, paramdist, init_dist) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:401
• Mads.getparamrandom(madsdata::AbstractDict, numsamples::Integer, parameterkey::AbstractString; init_dist) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:384
• Mads.getparamrandom(madsdata::AbstractDict, numsamples::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:384
• Mads.getparamrandom(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:384

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• numsamples::Integer : number of samples, [default=1]
• parameterkey::AbstractString : model parameter key

Keywords:

• init_dist : if true use the distribution set for initialization in the MADS problem dictionary (defined using init_dist parameter field); if false (default) use the regular distribution set in the MADS problem dictionary (defined using dist parameter field)
• numsamples : number of samples
• paramdist : dictionary of parameter distributions

Returns:

• generated sample

# Mads.getparamsinitMethod.

Get an array with init values for parameters defined by paramkeys

# Mads.getparamsinitMethod.

Get an array with init values for all the MADS model parameters

# Mads.getparamsinit_maxFunction.

Get an array with init_max values for parameters defined by paramkeys

Methods:

• Mads.getparamsinit_max(madsdata::AbstractDict, paramkeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:273
• Mads.getparamsinit_max(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:273

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• paramkeys::AbstractVector{T} where T : parameter keys

Returns:

• the parameter values

# Mads.getparamsinit_minFunction.

Get an array with init_min values for parameters

Methods:

• Mads.getparamsinit_min(madsdata::AbstractDict, paramkeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:227
• Mads.getparamsinit_min(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:227

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• paramkeys::AbstractVector{T} where T : parameter keys

Returns:

• the parameter values

# Mads.getparamslogMethod.

Get an array with log values for parameters defined by paramkeys

# Mads.getparamslogMethod.

Get an array with log values for all the MADS model parameters

# Mads.getparamslongnameMethod.

Get an array with longname values for parameters defined by paramkeys

# Mads.getparamslongnameMethod.

Get an array with longname values for all the MADS model parameters

# Mads.getparamsmaxFunction.

Get an array with max values for parameters defined by paramkeys

Methods:

• Mads.getparamsmax(madsdata::AbstractDict, paramkeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:193
• Mads.getparamsmax(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:193

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• paramkeys::AbstractVector{T} where T : parameter keys

Returns:

• returns the parameter values

# Mads.getparamsminFunction.

Get an array with min values for parameters defined by paramkeys

Methods:

• Mads.getparamsmin(madsdata::AbstractDict, paramkeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:159
• Mads.getparamsmin(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:159

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• paramkeys::AbstractVector{T} where T : parameter keys

Returns:

• the parameter values

# Mads.getparamsplotnameMethod.

Get an array with plotname values for parameters defined by paramkeys

# Mads.getparamsplotnameMethod.

Get an array with plotname values for all the MADS model parameters

# Mads.getparamsstepMethod.

Get an array with step values for parameters defined by paramkeys

# Mads.getparamsstepMethod.

Get an array with step values for all the MADS model parameters

# Mads.getparamstypeMethod.

Get an array with type values for parameters defined by paramkeys

# Mads.getparamstypeMethod.

Get an array with type values for all the MADS model parameters

# Mads.getproblemdirMethod.

Get the directory where currently Mads is running

Methods:

• Mads.getproblemdir() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:516

Example:

problemdir = Mads.getproblemdir()


Returns:

# Mads.getprocsMethod.

Get the number of processors

Methods:

• Mads.getprocs() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:28

# Mads.getrestartMethod.

Methods:

• Mads.getrestart(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:86

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

# Mads.getrestartdirFunction.

Get the directory where Mads restarts will be stored

Methods:

• Mads.getrestartdir(madsdata::AbstractDict, suffix::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:341
• Mads.getrestartdir(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:341

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• suffix::AbstractString : Suffix to be added to the name of restart directory

Returns:

• restart directory where reusable model results will be stored

# Mads.getrootnameMethod.

Get file name root

Methods:

• Mads.getrootname(filename::AbstractString; first, version) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:546

Arguments:

• filename::AbstractString : file name

Keywords:

• first : use the first . in filename as the seperator between root name and extention [default=true]
• version : delete version information from filename for the returned rootname [default=false]

Returns:

• root of file name

Example:

r = Mads.getrootname("a.rnd.dat") # r = "a"
r = Mads.getrootname("a.rnd.dat", first=false) # r = "a.rnd"


# Mads.getseedMethod.

Get and return current random seed.

Methods:

• Mads.getseed() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:475
• Mads.getseed() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:475
• Mads.getseed() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:475
• Mads.getseed() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:475
• Mads.getseed() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:475

# Mads.getsindxMethod.

Get sin-space dx

Methods:

• Mads.getsindx(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:349

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Returns:

• sin-space dx value

# Mads.getsourcekeysMethod.

Get keys of all source parameters in the MADS problem dictionary

Methods:

• Mads.getsourcekeys(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:77

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Returns:

• array with keys of all source parameters in the MADS problem dictionary

# Mads.gettargetMethod.

Get observation target

Methods:

• Mads.gettarget(o::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:222

Arguments:

• o::AbstractDict : observation data

Returns:

• observation target

# Mads.gettargetkeysMethod.

Get keys for all targets (observations with weights greater than zero) in the MADS problem dictionary

Methods:

• Mads.gettargetkeys(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:57

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Returns:

• keys for all targets in the MADS problem dictionary

# Mads.gettimeMethod.

Get observation time

Methods:

• Mads.gettime(o::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:144

Arguments:

• o::AbstractDict : observation data

Returns:

• observation time ("NaN" it time is missing)

# Mads.getweightMethod.

Get observation weight

Methods:

• Mads.getweight(o::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:183

Arguments:

• o::AbstractDict : observation data

Returns:

• observation weight ("NaN" when weight is missing)

# Mads.getwelldataMethod.

Get spatial and temporal data in the Wells class

Methods:

• Mads.getwelldata(madsdata::AbstractDict; time) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:711

Arguments:

• madsdata::AbstractDict : Mads problem dictionary

Keywords:

• time : get observation times [default=false]

Returns:

• array with spatial and temporal data in the Wells class

# Mads.getwellkeysMethod.

Get keys for all wells in the MADS problem dictionary

Methods:

• Mads.getwellkeys(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:74

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Returns:

• keys for all wells in the MADS problem dictionary

# Mads.getwelltargetsMethod.

Methods:

• Mads.getwelltargets(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:745

Arguments:

• madsdata::AbstractDict : Mads problem dictionary

Returns:

• array with targets in the Wells class

# Mads.graphoffMethod.

Methods:

• Mads.graphoff() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:158

# Mads.graphonMethod.

Methods:

• Mads.graphon() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:149

# Mads.haskeywordFunction.

Check for a keyword in a class within the Mads dictionary madsdata

Methods:

• Mads.haskeyword(madsdata::AbstractDict, class::AbstractString, keyword::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:249
• Mads.haskeyword(madsdata::AbstractDict, keyword::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:246

Arguments:

• class::AbstractString : dictionary class; if not provided searches for keyword in Problem class
• keyword::AbstractString : dictionary key
• madsdata::AbstractDict : MADS problem dictionary

Returns: true or false

Examples:

- Mads.haskeyword(madsdata, "disp") ... searches in Problem class by default
- Mads.haskeyword(madsdata, "Wells", "R-28") ... searches in Wells class for a keyword "R-28"


# Mads.helpMethod.

Methods:

• Mads.help() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelp.jl:35

# Mads.importeverywhereMethod.

Import Julia function everywhere from a file. The first function in the Julia input file is the one that will be targeted by Mads for execution.

Methods:

• Mads.importeverywhere(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:393

Arguments:

• filename::AbstractString : file name

Returns:

• Julia function to execute the model

# Mads.indexkeysFunction.

Find indexes for dictionary keys based on a string or regular expression

Methods:

• Mads.indexkeys(dict::AbstractDict, key::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:865
• Mads.indexkeys(dict::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:865
• Mads.indexkeys(dict::AbstractDict, key::Regex) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:864

Arguments:

• dict::AbstractDict : dictionary
• key::AbstractString : the key to find index for
• key::Regex : the key to find index for

# Mads.infogap_jumpFunction.

Information Gap Decision Analysis using JuMP

Methods:

• Mads.infogap_jump(madsdata::AbstractDict; horizons, retries, random, maxiter, verbosity, seed) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsInfoGap.jl:23
• Mads.infogap_jump() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsInfoGap.jl:23

Arguments:

• madsdata::AbstractDict : Mads problem dictionary

Keywords:

• horizons : info-gap horizons of uncertainty [default=[0.05, 0.1, 0.2, 0.5]]
• maxiter : maximum number of iterations [default=3000]
• random : random initial guesses [default=false]
• retries : number of solution retries [default=1]
• seed : random seed [default=0]
• verbosity : verbosity output level [default=0]

# Mads.infogap_jump_polynomialFunction.

Information Gap Decision Analysis using JuMP

Methods:

• Mads.infogap_jump_polynomial(madsdata::AbstractDict; horizons, retries, random, maxiter, verbosity, quiet, plot, model, seed) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsInfoGap.jl:128
• Mads.infogap_jump_polynomial() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsInfoGap.jl:128

Arguments:

• madsdata::AbstractDict : Mads problem dictionary

Keywords:

• horizons : info-gap horizons of uncertainty [default=[0.05, 0.1, 0.2, 0.5]]
• maxiter : maximum number of iterations [default=3000]
• model : model id [default=1]
• plot : activate plotting [default=false]
• quiet : quiet [default=false]
• random : random initial guesses [default=false]
• retries : number of solution retries [default=1]
• seed : random seed [default=0]
• verbosity : verbosity output level [default=0]

Returns:

• hmin, hmax

# Mads.infogap_mpb_linFunction.

Information Gap Decision Analysis using MathProgBase

Methods:

• Mads.infogap_mpb_lin(madsdata::AbstractDict; horizons, retries, random, maxiter, verbosity, seed, pinit) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsInfoGap.jl:442
• Mads.infogap_mpb_lin() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsInfoGap.jl:442

Arguments:

• madsdata::AbstractDict : Mads problem dictionary

Keywords:

• horizons : info-gap horizons of uncertainty [default=[0.05, 0.1, 0.2, 0.5]]
• maxiter : maximum number of iterations [default=3000]
• pinit : vector with initial parameters
• random : random initial guesses [default=false]
• retries : number of solution retries [default=1]
• seed : random seed [default=0]
• verbosity : verbosity output level [default=0]

# Mads.infogap_mpb_polynomialFunction.

Information Gap Decision Analysis using MathProgBase

Methods:

• Mads.infogap_mpb_polynomial(madsdata::AbstractDict; horizons, retries, random, maxiter, verbosity, seed, pinit) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsInfoGap.jl:301
• Mads.infogap_mpb_polynomial() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsInfoGap.jl:301

Arguments:

• madsdata::AbstractDict : Mads problem dictionary

Keywords:

• horizons : info-gap horizons of uncertainty [default=[0.05, 0.1, 0.2, 0.5]]
• maxiter : maximum number of iterations [default=3000]
• pinit : vector with initial parameters
• random : random initial guesses [default=false]
• retries : number of solution retries [default=1]
• seed : random seed [default=0]
• verbosity : verbosity output level [default=0]

# Mads.ins_obsMethod.

Apply Mads instruction file instructionfilename to read model output file modeloutputfilename

Methods:

• Mads.ins_obs(instructionfilename::AbstractString, modeloutputfilename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1073

Arguments:

• instructionfilename::AbstractString : instruction file name
• modeloutputfilename::AbstractString : model output file name

Returns:

• obsdict : observation dictionary with the model outputs

# Mads.instline2regexsMethod.

Convert an instruction line in the Mads instruction file into regular expressions

Methods:

• Mads.instline2regexs(instline::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:973

Arguments:

• instline::AbstractString : instruction line

Returns:

• regexs : regular expressions
• obsnames : observation names
• getparamhere : parameters

# Mads.invobsweights!Function.

Set inversely proportional observation weights in the MADS problem dictionary

Methods:

• Mads.invobsweights!(madsdata::AbstractDict, multiplier::Number, obskeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:328
• Mads.invobsweights!(madsdata::AbstractDict, multiplier::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:328
• Mads.invobsweights!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:328

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• multiplier::Number : weight multiplier
• obskeys::AbstractVector{T} where T

# Mads.invwellweights!Function.

Set inversely proportional well weights in the MADS problem dictionary

Methods:

• Mads.invwellweights!(madsdata::AbstractDict, multiplier::Number, wellkeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:380
• Mads.invwellweights!(madsdata::AbstractDict, multiplier::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:380

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• multiplier::Number : weight multiplier
• wellkeys::AbstractVector{T} where T

# Mads.islogMethod.

Is parameter with key parameterkey log-transformed?

Methods:

• Mads.islog(madsdata::AbstractDict, parameterkey::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:435

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• parameterkey::AbstractString : parameter key

Returns:

• true if log-transformed, false otherwise

# Mads.isobsMethod.

Is a dictionary containing all the observations

Methods:

• Mads.isobs(madsdata::AbstractDict, dict::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:17

Arguments:

• dict::AbstractDict : dictionary
• madsdata::AbstractDict : MADS problem dictionary

Returns:

• true if the dictionary contain all the observations, false otherwise

# Mads.isoptMethod.

Is parameter with key parameterkey optimizable?

Methods:

• Mads.isopt(madsdata::AbstractDict, parameterkey::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:415

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• parameterkey::AbstractString : parameter key

Returns:

• true if optimizable, false if not

# Mads.isparamMethod.

Check if a dictionary containing all the Mads model parameters

Methods:

• Mads.isparam(madsdata::AbstractDict, dict::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:16

Arguments:

• dict::AbstractDict : dictionary
• madsdata::AbstractDict : MADS problem dictionary

Returns:

• true if the dictionary containing all the parameters, false otherwise

# Mads.ispkgavailableMethod.

Checks if package is available

Methods:

• Mads.ispkgavailable(modulename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:533

Arguments:

• modulename::AbstractString : module name

Returns:

• true or false

# Mads.ispkgavailable_oldMethod.

Checks if package is available

Methods:

• Mads.ispkgavailable_old(modulename::AbstractString; quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:511

Arguments:

• modulename::AbstractString : module name

Keywords:

• quiet

Returns:

• true or false

# Mads.krigeMethod.

Kriging

Methods:

• Mads.krige(x0mat::AbstractMatrix{T} where T, X::AbstractMatrix{T} where T, Z::AbstractVector{T} where T, covfn::Function) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:125

Arguments:

• X::AbstractMatrix{T} where T : coordinates of the observation (conditioning) data
• Z::AbstractVector{T} where T : values for the observation (conditioning) data
• covfn::Function : spatial covariance function
• x0mat::AbstractMatrix{T} where T : point coordinates at which to obtain kriging estimates

Returns:

• kriging estimates at x0mat

# Mads.levenberg_marquardtFunction.

Levenberg-Marquardt optimization

Methods:

• Mads.levenberg_marquardt(f::Function, g::Function, x0, o::Function; root, tolX, tolG, tolOF, maxEval, maxIter, maxJacobians, lambda, lambda_scale, lambda_mu, lambda_nu, np_lambda, show_trace, alwaysDoJacobian, callbackiteration, callbackjacobian) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:337
• Mads.levenberg_marquardt(f::Function, g::Function, x0) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:337

Arguments:

• f::Function : forward model function
• g::Function : gradient function for the forward model
• o::Function : objective function [default=x->(x'*x)[1]]
• x0 : initial parameter guess

Keywords:

• alwaysDoJacobian : computer Jacobian each iteration [default=false]
• callbackiteration : call back function for each iteration [default=(best_x::AbstractVector, of::Number, lambda::Number)->nothing]
• callbackjacobian : call back function for each Jacobian [default=(x::AbstractVector, J::AbstractMatrix)->nothing]
• lambda : initial Levenberg-Marquardt lambda [default=eps(Float32)]
• lambda_mu : lambda multiplication factor Î¼ [default=10]
• lambda_nu : lambda multiplication factor Î½ [default=2]
• lambda_scale : lambda scaling factor [default=1e-3,]
• maxEval : maximum number of model evaluations [default=1001]
• maxIter : maximum number of optimization iterations [default=100]
• maxJacobians : maximum number of Jacobian solves [default=100]
• np_lambda : number of parallel lambda solves [default=10]
• root : Mads problem root name
• show_trace : shows solution trace [default=false]
• tolG : parameter space update tolerance [default=1e-6]
• tolOF : objective function update tolerance [default=1e-3]
• tolX : parameter space tolerance [default=1e-4]

# Mads.linktempdirMethod.

Link files in a temporary directory

Methods:

• Mads.linktempdir(madsproblemdir::AbstractString, tempdirname::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1332

Arguments:

• madsproblemdir::AbstractString : Mads problem directory
• tempdirname::AbstractString : temporary directory name

# Mads.loadasciifileMethod.

Methods:

• Mads.loadasciifile(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsASCII.jl:15

Arguments:

• filename::AbstractString : ASCII file name

Returns:

• data from the file

# Mads.loadbigyamlfileMethod.

Methods:

• Mads.loadmadsfile(filename::AbstractString; bigfile, format, quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:48

Arguments:

• filename::AbstractString : input file name (e.g. input_file_name.mads)

Keywords:

• bigfile
• format
• quiet

Returns:

# Mads.loadjsonfileMethod.

Methods:

• Mads.loadjsonfile(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsJSON.jl:16

Arguments:

• filename::AbstractString : JSON file name

Returns:

• data from the JSON file

# Mads.loadmadsfileMethod.

Methods:

• Mads.loadmadsfile(filename::AbstractString; bigfile, format, quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:48

Arguments:

• filename::AbstractString : input file name (e.g. input_file_name.mads)

Keywords:

• bigfile
• format : acceptable formats are yaml and json [default=yaml]
• quiet

Returns:

Example:

md = Mads.loadmadsfile("input_file_name.mads")


# Mads.loadmadsproblemMethod.

Methods:

• Mads.loadmadsproblem(name::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCreate.jl:14

Arguments:

• name::AbstractString : predefined MADS problem name

Returns:

# Mads.loadsaltellirestart!Method.

Load Saltelli sensitivity analysis results for fast simulation restarts

Methods:

• Mads.loadsaltellirestart!(evalmat::Array, matname::AbstractString, restartdir::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:595

Arguments:

• evalmat::Array : loaded array
• matname::AbstractString : matrix (array) name (defines the name of the loaded file)
• restartdir::AbstractString : directory where files will be stored containing model results for fast simulation restarts

Returns:

• true when successfully loaded, false when it is not

# Mads.loadyamlfileMethod.

Methods:

• Mads.loadyamlfile(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsYAML.jl:17

Arguments:

• filename::AbstractString : file name

Returns:

• data in the yaml input file

# Mads.localsaMethod.

Local sensitivity analysis based on eigen analysis of the parameter covariance matrix

Methods:

• Mads.localsa(madsdata::AbstractDict; sinspace, keyword, filename, format, datafiles, imagefiles, par, obs, J) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:124

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• J : Jacobian matrix
• datafiles : flag to write data files [default=true]
• filename : output file name
• format : output plot format (png, pdf, etc.)
• imagefiles : flag to create image files [default=Mads.graphoutput]
• keyword : keyword to be added in the filename root
• obs : observations for the parameter set
• par : parameter set
• sinspace : apply sin transformation [default=true]

Dumps:

• filename : output plot file

# Mads.long_tests_offMethod.

Turn off execution of long MADS tests (default)

Methods:

• Mads.long_tests_off() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:203

# Mads.long_tests_onMethod.

Turn on execution of long MADS tests

Methods:

• Mads.long_tests_on() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:194

# Mads.madscoresFunction.

Check the number of processors on a series of servers

Methods:

• Mads.madscores(nodenames::Vector{String}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:307
• Mads.madscores() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:307

Arguments:

• nodenames::Vector{String} : array with names of machines/nodes [default=madsservers]

# Mads.madscriticalMethod.

Methods:

• Mads.madscritical(message::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLog.jl:72

Arguments:

• message::AbstractString : critical error message

# Mads.madsdebugFunction.

MADS debug messages (controlled by quiet and debuglevel)

Methods:

• Mads.madsdebug(message::AbstractString, level::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLog.jl:25
• Mads.madsdebug(message::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLog.jl:25

Arguments:

• level::Int64 : output verbosity level [default=0]
• message::AbstractString : debug message

# Mads.madsdirMethod.

Change the current directory to the Mads source dictionary

Methods:

• Mads.madsdir() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:19

# Mads.madserrorMethod.

Methods:

• Mads.madserror(message::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLog.jl:62

Arguments:

• message::AbstractString : error message

# Mads.madsinfoFunction.

MADS information/status messages (controlled by quietandverbositylevel)

Methods:

• Mads.madsinfo(message::AbstractString, level::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLog.jl:40
• Mads.madsinfo(message::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLog.jl:40

Arguments:

• level::Int64 : output verbosity level [default=0]
• message::AbstractString : information/status message

# Mads.madsloadFunction.

Check the load of a series of servers

Methods:

• Mads.madsload(nodenames::Vector{String}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:327
• Mads.madsload() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:327

Arguments:

• nodenames::Vector{String} : array with names of machines/nodes [default=madsservers]

# Mads.madsmathprogbaseFunction.

Define MadsModel type applied for Mads execution using MathProgBase

Methods:

• Mads.madsmathprogbase(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMathProgBase.jl:16
• Mads.madsmathprogbase() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMathProgBase.jl:16

Arguments:

• madsdata::AbstractDict : MADS problem dictionary [default=Dict()]

# Mads.madsoutputFunction.

MADS output (controlled by quiet and verbositylevel)

Methods:

• Mads.madsoutput(message::AbstractString, level::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLog.jl:10
• Mads.madsoutput(message::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLog.jl:10

Arguments:

• level::Int64 : output verbosity level [default=0]
• message::AbstractString : output message

# Mads.madsupFunction.

Check the uptime of a series of servers

Methods:

• Mads.madsup(nodenames::Vector{String}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:317
• Mads.madsup() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:317

Arguments:

• nodenames::Vector{String} : array with names of machines/nodes [default=madsservers]

# Mads.madswarnMethod.

Methods:

• Mads.madswarn(message::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLog.jl:52

Arguments:

• message::AbstractString : warning message

# Mads.makearrayconditionalloglikelihoodMethod.

Make a conditional log likelihood function that accepts an array containing the optimal parameter values

Methods:

• Mads.makearrayconditionalloglikelihood(madsdata::AbstractDict, conditionalloglikelihood) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMisc.jl:104

Arguments:

• conditionalloglikelihood : conditional log likelihood
• madsdata::AbstractDict : MADS problem dictionary

Returns:

• a conditional log likelihood function that accepts an array

# Mads.makearrayconditionalloglikelihoodMethod.

Make array of conditional log-likelihoods

Methods:

• Mads.makearrayconditionalloglikelihood(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBayesInfoGap.jl:159
• Mads.makearrayconditionalloglikelihood(madsdata::AbstractDict, conditionalloglikelihood) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMisc.jl:104

Arguments:

• conditionalloglikelihood
• madsdata::AbstractDict : MADS problem dictionary

Returns:

• array of conditional log-likelihoods

# Mads.makearrayfunctionFunction.

Make a version of the function f that accepts an array containing the optimal parameter values

Methods:

• Mads.makearrayfunction(madsdata::AbstractDict, f::Function) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMisc.jl:31
• Mads.makearrayfunction(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMisc.jl:31

Arguments:

• f::Function : function [default=makemadscommandfunction(madsdata)]
• madsdata::AbstractDict : MADS problem dictionary

Returns:

• function accepting an array containing the optimal parameter values

# Mads.makearrayloglikelihoodMethod.

Make a log likelihood function that accepts an array containing the optimal parameter values

Methods:

• Mads.makearrayloglikelihood(madsdata::AbstractDict, loglikelihood) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMisc.jl:127

Arguments:

• loglikelihood : log likelihood
• madsdata::AbstractDict : MADS problem dictionary

Returns:

• a log likelihood function that accepts an array

# Mads.makebigdt!Method.

Setup Bayesian Information Gap Decision Theory (BIG-DT) problem

Methods:

• Mads.makebigdt!(madsdata::AbstractDict, choice::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBayesInfoGap.jl:34

Arguments:

• choice::AbstractDict : dictionary of BIG-DT choices (scenarios)
• madsdata::AbstractDict : MADS problem dictionary

Returns:

• BIG-DT problem type

# Mads.makebigdtMethod.

Setup Bayesian Information Gap Decision Theory (BIG-DT) problem

Methods:

• Mads.makebigdt(madsdata::AbstractDict, choice::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBayesInfoGap.jl:19

Arguments:

• choice::AbstractDict : dictionary of BIG-DT choices (scenarios)
• madsdata::AbstractDict : MADS problem dictionary

Returns:

• BIG-DT problem type

# Mads.makecomputeconcentrationsMethod.

Create a function to compute concentrations for all the observation points using Anasol

Methods:

• Mads.makecomputeconcentrations(madsdata::AbstractDict; calczeroweightobs, calcpredictions) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasol.jl:178

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• calcpredictions : calculate zero weight predictions [default=true]
• calczeroweightobs : calculate zero weight observations[default=false]

Returns:

• function to compute concentrations; the new function returns a dictionary of observations and model predicted concentrations

Examples:

computeconcentrations = Mads.makecomputeconcentrations(madsdata)
forward_preds = computeconcentrations(paramdict)


# Mads.makedixonpriceMethod.

Make dixon price

Methods:

• Mads.makedixonprice(n::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:259

Arguments:

• n::Integer : number of observations

Returns:

• dixon price

# Mads.makedixonprice_gradientMethod.

Methods:

• Mads.makedixonprice(n::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:259

Arguments:

• n::Integer : number of observations

Returns:

# Mads.makedoublearrayfunctionFunction.

Make a version of the function f that accepts an array containing the optimal parameter values, and returns an array of observations

Methods:

• Mads.makedoublearrayfunction(madsdata::AbstractDict, f::Function) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMisc.jl:77
• Mads.makedoublearrayfunction(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMisc.jl:77

Arguments:

• f::Function : function [default=makemadscommandfunction(madsdata)]
• madsdata::AbstractDict : MADS problem dictionary

Returns:

• function accepting an array containing the optimal parameter values, and returning an array of observations

# Mads.makelmfunctionsFunction.

Make forward model, gradient, objective functions needed for Levenberg-Marquardt optimization

Methods:

• Mads.makelmfunctions(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:121
• Mads.makelmfunctions(f::Function) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:100

Arguments:

• f::Function : Function
• madsdata::AbstractDict : MADS problem dictionary

Returns:

• forward model, gradient, objective functions

# Mads.makelocalsafunctionMethod.

Make gradient function needed for local sensitivity analysis

Methods:

• Mads.makelocalsafunction(madsdata::AbstractDict; multiplycenterbyweights) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:25

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• multiplycenterbyweights : multiply center by observation weights [default=true]

Returns:

# Mads.makelogpriorMethod.

Make a function to compute the prior log-likelihood of the model parameters listed in the MADS problem dictionary madsdata

Methods:

• Mads.makelogprior(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:416

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Return:

• the prior log-likelihood of the model parameters listed in the MADS problem dictionary madsdata

# Mads.makemadscommandfunctionMethod.

Make MADS function to execute the model defined in the input MADS problem dictionary

Methods:

• Mads.makemadscommandfunction(madsdata_in::AbstractDict; obskeys, calczeroweightobs, calcpredictions) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:68

Arguments:

• madsdata_in::AbstractDict : MADS problem dictionary

Keywords:

• calcpredictions : Calculate predictions [default=true]
• calczeroweightobs : Calculate zero weight observations [default=false]
• obskeys

Example:

Mads.makemadscommandfunction(madsdata)


MADS can be coupled with any internal or external model. The model coupling is defined in the MADS problem dictionary. The expectations is that for a given set of model inputs, the model will produce a model output that will be provided to MADS. The fields in the MADS problem dictionary that can be used to define the model coupling are:

• Model : execute a Julia function defined in an external input Julia file. The function that should accept a parameter dictionary with all the model parameters as an input argument and should return an observation dictionary with all the model predicted observations. MADS will execute the first function defined in the file.
• MADS model : create a Julia function based on an external input Julia file. The input file should contain a function that accepts as an argument the MADS problem dictionary. MADS will execute the first function defined in the file. This function should create a Julia function that will accept a parameter dictionary with all the model parameters as an input argument and will return an observation dictionary with all the model predicted observations.
• Julia model : execute an internal Julia function that accepts a parameter dictionary with all the model parameters as an input argument and will return an observation dictionary with all the model predicted observations.
• Julia function : execute an internal Julia function that accepts a parameter vector with all the model parameters as an input argument and will return an observation vector with all the model predicted observations.
• Command : execute an external UNIX command or script that will execute an external model.
• Julia command : execute a Julia script that will execute an external model. The Julia script is defined in an external Julia file. The input file should contain a function that accepts a parameter dictionary with all the model parameters as an input argument; MADS will execute the first function defined in the file. The Julia script should be capable to (1) execute the model (making a system call of an external model), (2) parse the model outputs, (3) return an observation dictionary with model predictions.

Both Command and Julia command can use different approaches to pass model parameters to the external model.

Only Command uses different approaches to get back the model outputs.

The script defined under Julia command parses the model outputs using Julia.

The available options for writing model inputs and reading model outputs are as follows.

Options for writing model inputs:

• Templates : template files for writing model input files as defined at http://mads.lanl.gov
• ASCIIParameters : model parameters written in a ASCII file
• JLDParameters : model parameters written in a JLD file
• YAMLParameters : model parameters written in a YAML file
• JSONParameters : model parameters written in a JSON file

• Instructions : instruction files for reading model output files as defined at http://mads.lanl.gov
• ASCIIPredictions : model predictions read from a ASCII file
• JLDPredictions : model predictions read from a JLD file
• YAMLPredictions : model predictions read from a YAML file
• JSONPredictions : model predictions read from a JSON file

Returns:

• Mads function to execute a forward model simulation

# Mads.makemadsconditionalloglikelihoodMethod.

Make a function to compute the conditional log-likelihood of the model parameters conditioned on the model predictions/observations. Model parameters and observations are defined in the MADS problem dictionary madsdata.

Methods:

• Mads.makemadsconditionalloglikelihood(madsdata::AbstractDict; weightfactor) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:439

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• weightfactor : Weight factor [default=1]

Return:

• the conditional log-likelihood

# Mads.makemadsloglikelihoodMethod.

Make a function to compute the log-likelihood for a given set of model parameters, associated model predictions and existing observations. By default, the Log-likelihood function computed internally. The Log-likelihood can be constructed from an external Julia function defined the MADS problem dictionary under LogLikelihood or ConditionalLogLikelihood.

In the case of a LogLikelihood external Julia function, the first function in the file provided should be a function that takes as arguments:

• dictionary of model parameters
• dictionary of model predictions
• dictionary of respective observations

In the case of a ConditionalLogLikelihood external Julia function, the first function in the file provided should be a function that takes as arguments:

• dictionary of model predictions
• dictionary of respective observations

Methods:

• Mads.makemadsloglikelihood(madsdata::AbstractDict; weightfactor) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:484

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• weightfactor : Weight factor [default=1]

Returns:

• the log-likelihood for a given set of model parameters

# Mads.makemadsreusablefunctionFunction.

Make Reusable Mads function to execute a forward model simulation (automatically restarts if restart data exists)

Methods:

• Mads.makemadsreusablefunction(paramkeys::AbstractVector{T} where T, obskeys::AbstractVector{T} where T, madsdatarestart::Union{Bool, String}, madscommandfunction::Function, restartdir::AbstractString; usedict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:296
• Mads.makemadsreusablefunction(madsdata::AbstractDict, madscommandfunction::Function, suffix::AbstractString; usedict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:293
• Mads.makemadsreusablefunction(madsdata::AbstractDict, madscommandfunction::Function) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:293

Arguments:

• madscommandfunction::Function : Mads function to execute a forward model simulation
• madsdata::AbstractDict : MADS problem dictionary
• madsdatarestart::Union{Bool, String} : Restart type (memory/disk) or on/off status
• obskeys::AbstractVector{T} where T : Dictionary of observation keys
• paramkeys::AbstractVector{T} where T : Dictionary of parameter keys
• restartdir::AbstractString : Restart directory where the reusable model results are stored
• suffix::AbstractString : Suffix to be added to the name of restart directory

Keywords:

• usedict : Use dictionary [default=true]

Returns:

• Reusable Mads function to execute a forward model simulation (automatically restarts if restart data exists)

# Mads.makempbfunctionsMethod.

Make forward model, gradient, objective functions needed for MathProgBase optimization

Methods:

• Mads.makempbfunctions(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMathProgBase.jl:90

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Returns:

• forward model, gradient, objective functions

# Mads.makepowellMethod.

Make Powell test function for LM optimization

Methods:

• Mads.makepowell(n::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:162

Arguments:

• n::Integer : number of observations

Returns:

• Powell test function for LM optimization

# Mads.makepowell_gradientMethod.

ake parameter gradients of the Powell test function for LM optimization

Methods:

• Mads.makepowell_gradient(n::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:186

Arguments:

• n::Integer : number of observations

Returns:

• arameter gradients of the Powell test function for LM optimization

# Mads.makerosenbrockMethod.

Make Rosenbrock test function for LM optimization

Methods:

• Mads.makerosenbrock(n::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:117

Arguments:

• n::Integer : number of observations

Returns:

• Rosenbrock test function for LM optimization

# Mads.makerosenbrock_gradientMethod.

Make parameter gradients of the Rosenbrock test function for LM optimization

Methods:

• Mads.makerosenbrock_gradient(n::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:139

Arguments:

• n::Integer : number of observations

Returns:

• parameter gradients of the Rosenbrock test function for LM optimization

# Mads.makerotatedhyperellipsoidMethod.

Methods:

• Mads.makerotatedhyperellipsoid(n::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:338

Arguments:

• n::Integer : number of observations

Returns:

• rotated hyperellipsoid

# Mads.makerotatedhyperellipsoid_gradientMethod.

Methods:

• Mads.makerotatedhyperellipsoid_gradient(n::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:362

Arguments:

• n::Integer : number of observations

Returns:

# Mads.makesphereMethod.

Make sphere

Methods:

• Mads.makesphere(n::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:217

Arguments:

• n::Integer : number of observations

Returns:

• sphere

# Mads.makesphere_gradientMethod.

Methods:

• Mads.makesphere_gradient(n::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:238

Arguments:

• n::Integer : number of observations

Returns:

# Mads.makesumsquaresMethod.

Methods:

• Mads.makesumsquares(n::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:300

Arguments:

• n::Integer : number of observations

Returns:

• sumsquares

# Mads.makesumsquares_gradientMethod.

Methods:

• Mads.makesumsquares_gradient(n::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:319

Arguments:

• n::Integer : number of observations

Returns:

# Mads.makesvrmodelFunction.

Make SVR model functions (executor and cleaner)

Methods:

• Mads.makesvrmodel(madsdata::AbstractDict, numberofsamples::Integer; check, addminmax, loadsvr, savesvr, svm_type, kernel_type, degree, gamma, coef0, C, nu, epsilon, cache_size, tol, shrinking, probability, verbose, seed) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSVR.jl:209
• Mads.makesvrmodel(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSVR.jl:209

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• numberofsamples::Integer : number of samples [default=100]

Keywords:

• C : cost; penalty parameter of the error term [default=1000.0]
• addminmax : add parameter minimum / maximum range values in the training set [default=true]
• cache_size : size of the kernel cache [default=100.0]
• check : check SVR performance [default=false]
• coef0 : independent term in kernel function; important only in POLY and SIGMOND kernel types

[default=0]

• degree : degree of the polynomial kernel [default=3]
• epsilon : epsilon in the EPSILON_SVR model; defines an epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value [default=0.001]
• gamma : coefficient for RBF, POLY and SIGMOND kernel types [default=1/numberofsamples]
• kernel_type : kernel type[default=SVR.RBF]
• loadsvr : load SVR models [default=false]
• nu : upper bound on the fraction of training errors / lower bound of the fraction of support vectors; acceptable range (0, 1]; applied if NU_SVR model [default=0.5]
• probability : train to estimate probabilities [default=false]
• savesvr : save SVR models [default=false]
• seed : random seed [default=0]
• shrinking : apply shrinking heuristic [default=true]
• svm_type : SVM type [default=SVR.EPSILON_SVR]
• tol : tolerance of termination criterion [default=0.001]
• verbose : verbose output [default=false]

Returns:

• function performing SVR predictions
• function saving SVR models
• function removing SVR models from the memory

# Mads.maxtofloatmax!Method.

Scale down values larger than max(Float32) in a dataframe df so that Gadfly can plot the data

Methods:

• Mads.maxtofloatmax!(df::DataFrames.DataFrame) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:1082

Arguments:

• df::DataFrames.DataFrame : dataframe

# Mads.meshgridFunction.

Create mesh grid

Methods:

• Mads.meshgrid(nx::Number, ny::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:433
• Mads.meshgrid(x::AbstractVector{T} where T, y::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:426

Arguments:

• nx::Number
• ny::Number
• x::AbstractVector{T} where T : vector of grid x coordinates
• y::AbstractVector{T} where T : vector of grid y coordinates

Returns:

• 2D grid coordinates based on the coordinates contained in vectors x and y

# Mads.minimizeMethod.

Minimize Julia function using a constrained Levenberg-Marquardt technique

Mads.calibrate(madsdata; tolX=1e-3, tolG=1e-6, maxEval=1000, maxIter=100, maxJacobians=100, lambda=100.0, lambda_mu=10.0, np_lambda=10, show_trace=false, usenaive=false)

Methods:

• Mads.calibrate(madsdata::AbstractDict; tolX, tolG, tolOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, usenaive, save_results, localsa) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCalibrate.jl:168

Arguments:

• madsdata::AbstractDict

Keywords:

• lambda : initial Levenberg-Marquardt lambda [default=100.0]
• lambda_mu : lambda multiplication factor [default=10.0]
• localsa
• maxEval : maximum number of model evaluations [default=1000]
• maxIter : maximum number of optimization iterations [default=100]
• maxJacobians : maximum number of Jacobian solves [default=100]
• np_lambda : number of parallel lambda solves [default=10]
• save_results
• show_trace : shows solution trace [default=false]
• tolG : parameter space update tolerance [default=1e-6]
• tolOF : objective function tolerance [default=1e-3]
• tolX : parameter space tolerance [default=1e-4]
• usenaive

Returns:

• vector with the optimal parameter values at the minimum
• optimization algorithm results (e.g. results.minimizer)

# Mads.mkdirMethod.

Create a directory (if does not already exist)

Methods:

• Mads.mkdir(dirname::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1359

Arguments:

• dirname::AbstractString : directory

# Mads.modelinformationcriteriaFunction.

Model section information criteria

Methods:

• Mads.modelinformationcriteria(madsdata::AbstractDict, par::Array{Float64, N} where N) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsModelSelection.jl:11
• Mads.modelinformationcriteria(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsModelSelection.jl:11

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• par::Array{Float64, N} where N : parameter array

# Mads.modobsweights!Function.

Modify (multiply) observation weights in the MADS problem dictionary

Methods:

• Mads.modobsweights!(madsdata::AbstractDict, value::Number, obskeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:315
• Mads.modobsweights!(madsdata::AbstractDict, value::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:315

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• obskeys::AbstractVector{T} where T
• value::Number : value for modifing observation weights

# Mads.modwellweights!Function.

Modify (multiply) well weights in the MADS problem dictionary

Methods:

• Mads.modwellweights!(madsdata::AbstractDict, value::Number, wellkeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:362
• Mads.modwellweights!(madsdata::AbstractDict, value::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:362

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• value::Number : value for well weights
• wellkeys::AbstractVector{T} where T

# Mads.montecarloMethod.

Monte Carlo analysis

Methods:

• Mads.montecarlo(madsdata::AbstractDict; N, filename) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMonteCarlo.jl:188

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• N : number of samples [default=100]
• filename : file name to save Monte-Carlo results

Returns:

• parameter dictionary containing the data arrays

Dumps:

• YAML output file with the parameter dictionary containing the data arrays

Example:

Mads.montecarlo(madsdata; N=100)


# Mads.naive_get_deltaxMethod.

Naive Levenberg-Marquardt optimization: get the LM parameter space step

Methods:

• Mads.naive_get_deltax(JpJ::AbstractMatrix{Float64}, Jp::AbstractMatrix{Float64}, f0::AbstractVector{Float64}, lambda::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:246

Arguments:

• Jp::AbstractMatrix{Float64} : Jacobian matrix times model parameters
• JpJ::AbstractMatrix{Float64} : Jacobian matrix times model parameters times transposed Jacobian matrix
• f0::AbstractVector{Float64} : initial model observations
• lambda::Number : Levenberg-Marquardt lambda

Returns:

• the LM parameter space step

# Mads.naive_levenberg_marquardtFunction.

Naive Levenberg-Marquardt optimization

Methods:

• Mads.naive_levenberg_marquardt(f::Function, g::Function, x0::AbstractVector{Float64}, o::Function; maxIter, maxEval, lambda, lambda_mu, np_lambda) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:296
• Mads.naive_levenberg_marquardt(f::Function, g::Function, x0::AbstractVector{Float64}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:296

Arguments:

• f::Function : forward model function
• g::Function : gradient function for the forward model
• o::Function : objective function [default=x->(x'*x)[1]]
• x0::AbstractVector{Float64} : initial parameter guess

Keywords:

• lambda : initial Levenberg-Marquardt lambda [default=100]
• lambda_mu : lambda multiplication factor Î¼ [default=10]
• maxEval : maximum number of model evaluations [default=101]
• maxIter : maximum number of optimization iterations [default=10]
• np_lambda : number of parallel lambda solves [default=10]

Returns:

# Mads.naive_lm_iterationMethod.

Naive Levenberg-Marquardt optimization: perform LM iteration

Methods:

• Mads.naive_lm_iteration(f::Function, g::Function, o::Function, x0::AbstractVector{Float64}, f0::AbstractVector{Float64}, lambdas::AbstractVector{Float64}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:267

Arguments:

• f0::AbstractVector{Float64} : initial model observations
• f::Function : forward model function
• g::Function : gradient function for the forward model
• lambdas::AbstractVector{Float64} : Levenberg-Marquardt lambdas
• o::Function : objective function
• x0::AbstractVector{Float64} : initial parameter guess

Returns:

# Mads.noplotMethod.

Methods:

• Mads.noplot() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:240

# Mads.notebookMethod.

Execute Jupyter notebook in IJulia or as a script

Methods:

• Mads.notebook(rootname::AbstractString; script, dir, ndir, check) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsNotebooks.jl:21

Arguments:

• rootname::AbstractString : notebook root name

Keywords:

• check : check of notebook exists
• dir : notebook directory
• ndir
• script : execute as a script

# Mads.notebooksMethod.

Execute Jupyter notebook in IJulia or as a script

Methods:

• Mads.notebooks(; dir, ndir) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsNotebooks.jl:53

Keywords:

• dir : notebook directory
• ndir

# Mads.notebookscriptMethod.

Execute Jupyter notebook as a script

Methods:

• Mads.notebookscript(a...; script, dir, ndir, k...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsNotebooks.jl:10

Keywords:

• dir : notebook directory
• ndir
• script : execute as a script

# Mads.obslineoccursinMethod.

Match an instruction line in the Mads instruction file with model input file

Methods:

• Mads.obslineoccursin(obsline::AbstractString, regexs::Vector{Regex}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1022

Arguments:

• obsline::AbstractString : instruction line
• regexs::Vector{Regex} : regular expressions

Returns:

• true or false

# Mads.ofFunction.

Compute objective function

Methods:

• Mads.of(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:58
• Mads.of(madsdata::AbstractDict, resultdict::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:55
• Mads.of(madsdata::AbstractDict, resultvec::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:51

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• resultdict::AbstractDict : result dictionary
• resultvec::AbstractVector{T} where T : result vector

# Mads.paramarray2dictMethod.

Convert a parameter array to a parameter dictionary of arrays

Methods:

• Mads.paramarray2dict(madsdata::AbstractDict, array::Array) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMonteCarlo.jl:242

Arguments:

• array::Array : parameter array
• madsdata::AbstractDict : MADS problem dictionary

Returns:

• a parameter dictionary of arrays

# Mads.paramdict2arrayMethod.

Convert a parameter dictionary of arrays to a parameter array

Methods:

• Mads.paramdict2array(dict::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMonteCarlo.jl:261

Arguments:

• dict::AbstractDict : parameter dictionary of arrays

Returns:

• a parameter array

# Mads.parsemadsdata!Method.

Methods:

• Mads.parsemadsdata!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:193
• Mads.parsemadsdata!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:193
• Mads.parsemadsdata!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:193
• Mads.parsemadsdata!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:193

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

# Mads.parsenodenamesFunction.

Parse string with node names defined in SLURM

Methods:

• Mads.parsenodenames(nodenames::AbstractString, ntasks_per_node::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:209
• Mads.parsenodenames(nodenames::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:209

Arguments:

• nodenames::AbstractString : string with node names defined in SLURM
• ntasks_per_node::Integer : number of parallel tasks per node [default=1]

Returns:

• vector with names of compute nodes (hosts)

# Mads.partialofMethod.

Compute the sum of squared residuals for observations that match a regular expression

Methods:

• Mads.partialof(madsdata::AbstractDict, resultdict::AbstractDict, regex::Regex) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:84

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• regex::Regex : regular expression
• resultdict::AbstractDict : result dictionary

Returns:

• the sum of squared residuals for observations that match the regular expression

# Mads.pkgversion_oldMethod.

Get package version

Methods:

• Mads.pkgversion_old(modulestr::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:487

Arguments:

• modulestr::AbstractString

Returns:

• package version

# Mads.plotgridFunction.

Plot a 3D grid solution based on model predictions in array s, initial parameters, or user provided parameter values

Methods:

• Mads.plotgrid(madsdata::AbstractDict, parameters::AbstractDict; addtitle, title, filename, format) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlotPy.jl:60
• Mads.plotgrid(madsdata::AbstractDict; addtitle, title, filename, format) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlotPy.jl:55
• Mads.plotgrid(madsdata::AbstractDict, s::Array{Float64, N} where N; addtitle, title, filename, format) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlotPy.jl:4

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• parameters::AbstractDict : dictionary with model parameters
• s::Array{Float64, N} where N : model predictions array

Keywords:

• addtitle : add plot title [default=true]
• filename : output file name
• format : output plot format (png, pdf, etc.)
• title : plot title

Examples:

Mads.plotgrid(madsdata, s; addtitle=true, title="", filename="", format="")


# Mads.plotlocalsaMethod.

Plot local sensitivity analysis results

Methods:

• Mads.plotlocalsa(filenameroot::AbstractString; keyword, filename, format) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:1251

Arguments:

• filenameroot::AbstractString : problem file name root

Keywords:

• filename : output file name
• format : output plot format (png, pdf, etc.)
• keyword : keyword to be added in the filename root

Dumps:

• filename : output plot file

# Mads.plotmadsproblemMethod.

Plot contaminant sources and wells defined in MADS problem dictionary

Methods:

• Mads.plotmadsproblem(madsdata::AbstractDict; format, filename, keyword, hsize, vsize, quiet, gm) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:99

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• filename : output file name
• format : output plot format (png, pdf, etc.) [default=Mads.graphbackend]
• gm
• hsize
• keyword : to be added in the filename
• quiet
• vsize

Dumps:

• plot of contaminant sources and wells

# Mads.plotmassMethod.

Plot injected/reduced contaminant mass

Methods:

• Mads.plotmass(lambda::AbstractVector{Float64}, mass_injected::AbstractVector{Float64}, mass_reduced::AbstractVector{Float64}, filename::AbstractString; format) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasolPlot.jl:15

Arguments:

• filename::AbstractString : output filename for the generated plot
• lambda::AbstractVector{Float64} : array with all the lambda values
• mass_injected::AbstractVector{Float64} : array with associated total injected mass
• mass_reduced::AbstractVector{Float64} : array with associated total reduced mass

Keywords:

• format : output plot format (png, pdf, etc.)

Dumps:

• image file with name filename and in specified format

# Mads.plotmatchesFunction.

Plot the matches between model predictions and observations

Methods:

• Mads.plotmatches(madsdata::AbstractDict, dict_in::AbstractDict; plotdata, filename, format, title, xtitle, ytitle, ymin, ymax, xmin, xmax, separate_files, hsize, vsize, linewidth, pointsize, obs_plot_dots, noise, dpi, colors, display, notitle) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:209
• Mads.plotmatches(madsdata::AbstractDict, result::AbstractDict, rx::Union{Regex, AbstractString}; title, notitle, kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:185
• Mads.plotmatches(madsdata::AbstractDict, rx::Union{Regex, AbstractString}; kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:177
• Mads.plotmatches(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:177

Arguments:

• dict_in::AbstractDict : dictionary with model parameters
• madsdata::AbstractDict : MADS problem dictionary
• result::AbstractDict : dictionary with model predictions
• rx::Union{Regex, AbstractString} : regular expression to filter the outputs

Keywords:

• colors : array with plot colors
• display : display plots [default=false]
• dpi : graph resolution [default=Mads.imagedpi]
• filename : output file name
• format : output plot format (png, pdf, etc.) [default=Mads.graphbackend]
• hsize : graph horizontal size [default=8Gadfly.inch]
• linewidth : line width [default=2Gadfly.pt]
• noise : random noise magnitude [default=0; no noise]
• notitle
• obs_plot_dots : plot data as dots or line [default=true]
• plotdata : plot data (if false model predictions are ploted only) [default=true]
• pointsize : data dot size [default=2Gadfly.pt]
• separate_files : plot data for multiple wells separately [default=false]
• title : graph title
• vsize : graph vertical size [default=4Gadfly.inch]
• xmax
• xmin
• xtitle : x-axis title [default="Time"]
• ymax
• ymin
• ytitle : y-axis title [default="y"]

Dumps:

• plot of the matches between model predictions and observations

Examples:

Mads.plotmatches(madsdata; filename="", format="")


# Mads.plotobsSAresultsMethod.

Plot the sensitivity analysis results for the observations

Methods:

• Mads.plotobsSAresults(madsdata::AbstractDict, result::AbstractDict; filter, keyword, filename, format, separate_files, xtitle, ytitle, plotlabels, quiet, kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:594

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• result::AbstractDict : sensitivity analysis results

Keywords:

• filename : output file name
• filter : string or regex to plot only observations containing filter
• format : output plot format (png, pdf, etc.) [default=Mads.graphbackend]
• keyword : to be added in the auto-generated filename
• plotlabels
• quiet
• separate_files : plot data for multiple wells separately [default=false]
• xtitle : x-axis title
• ytitle : y-axis title

Dumps:

• plot of the sensitivity analysis results for the observations

# Mads.plotrobustnesscurvesMethod.

Plot BIG-DT robustness curves

Methods:

• Mads.plotrobustnesscurves(madsdata::AbstractDict, bigdtresults::Dict; filename, format, maxprob, maxhoriz) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBayesInfoGapPlot.jl:19

Arguments:

• bigdtresults::Dict : BIG-DT results
• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• filename : output file name used to dump plots
• format : output plot format (png, pdf, etc.)
• maxhoriz : maximum horizon [default=Inf]
• maxprob : maximum probability [default=1.0]

Dumps:

• image file with name filename and in specified format

# Mads.plotseriesFunction.

Create plots of data series

Methods:

• Mads.plotseries(X::AbstractArray, filename::AbstractString; nT, nS, format, xtitle, ytitle, title, logx, logy, keytitle, name, names, combined, hsize, vsize, linewidth, linestyle, pointsize, key_position, major_label_font_size, minor_label_font_size, dpi, colors, opacity, xmin, xmax, ymin, ymax, xaxis, plotline, plotdots, firstred, lastred, nextgray, code, returnplot, colorkey, background_color, gm, gl, quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:1062
• Mads.plotseries(X::AbstractArray) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:1062

Arguments:

• X::AbstractArray : matrix with the series data
• filename::AbstractString : output file name

Keywords:

• background_color
• code
• colorkey
• colors : colors to use in plots
• combined : combine plots [default=true]
• dpi : graph resolution [default=Mads.imagedpi]
• firstred
• format : output plot format (png, pdf, etc.) [default=Mads.graphbackend]
• gl
• gm
• hsize : horizontal size [default=8Gadfly.inch]
• key_position
• keytitle
• lastred
• linestyle
• linewidth : width of the lines in plot [default=2Gadfly.pt]
• logx
• logy
• major_label_font_size
• minor_label_font_size
• nS
• nT
• name : series name [default=Sources]
• names
• nextgray
• opacity
• plotdots
• plotline
• pointsize
• quiet
• returnplot
• title : plot title [default=Sources]
• vsize : vertical size [default=4Gadfly.inch]
• xaxis
• xmax
• xmin
• xtitle : x-axis title [default=X]
• ymax
• ymin
• ytitle : y-axis title [default=Y]

Dumps:

• Plots of data series

# Mads.plotwellSAresultsFunction.

Plot the sensitivity analysis results for all the wells in the MADS problem dictionary (wells class expected)

Methods:

• Mads.plotwellSAresults(madsdata::AbstractDict, result::AbstractDict, wellname::AbstractString; xtitle, ytitle, filename, format, quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:472
• Mads.plotwellSAresults(madsdata::AbstractDict, result::AbstractDict; xtitle, ytitle, filename, format, quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:461

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• result::AbstractDict : sensitivity analysis results
• wellname::AbstractString : well name

Keywords:

• filename : output file name
• format : output plot format (png, pdf, etc.) [default=Mads.graphbackend]
• quiet
• xtitle : x-axis title
• ytitle : y-axis title

Dumps:

• Plot of the sensitivity analysis results for all the wells in the MADS problem dictionary

# Mads.printSAresultsMethod.

Print sensitivity analysis results

Methods:

• Mads.printSAresults(madsdata::AbstractDict, results::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:918

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• results::AbstractDict : dictionary with sensitivity analysis results

# Mads.printSAresults2Method.

Print sensitivity analysis results (method 2)

Methods:

• Mads.printSAresults2(madsdata::AbstractDict, results::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:1000

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• results::AbstractDict : dictionary with sensitivity analysis results

# Mads.printerrormsgMethod.

Print error message

Methods:

• Mads.printerrormsg(errmsg) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:417

Arguments:

• errmsg : error message

# Mads.printobservationsFunction.

Print (emit) observations in the MADS problem dictionary

Methods:

• Mads.printobservations(madsdata::AbstractDict, filename::AbstractString; json) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:427
• Mads.printobservations(madsdata::AbstractDict, io::IO, obskeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:419
• Mads.printobservations(madsdata::AbstractDict, io::IO) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:419
• Mads.printobservations(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:419

Arguments:

• filename::AbstractString : output file name
• io::IO : output stream
• madsdata::AbstractDict : MADS problem dictionary
• obskeys::AbstractVector{T} where T

Keywords:

• json

# Mads.process_notebookMethod.

Process Jupyter notebook to generate html, markdown, latex, and script versions

Methods:

• Mads.process_notebook(rootname::AbstractString; dir, ndir) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsNotebooks.jl:69

Arguments:

• rootname::AbstractString : notebook root name

Keywords:

• dir : notebook directory
• ndir

# Mads.pullFunction.

Methods:

• Mads.pull(modulename::AbstractString; kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:62
• Mads.pull() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:62

Arguments:

• modulename::AbstractString : module name

# Mads.pushFunction.

Methods:

• Mads.push(modulename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:137
• Mads.push() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:137

Arguments:

• modulename::AbstractString : module name

# Mads.quietoffMethod.

Methods:

• Mads.quietoff() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:104

# Mads.quietonMethod.

Methods:

• Mads.quieton() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:95

# Mads.readasciipredictionsMethod.

Methods:

• Mads.readasciipredictions(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsASCII.jl:44

Arguments:

• filename::AbstractString : ASCII file name

Returns:

# Mads.readmodeloutputMethod.

Methods:

• Mads.readmodeloutput(madsdata::AbstractDict; obskeys) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:790

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• obskeys : observation keys [default=getobskeys(madsdata)]

# Mads.readobservationsFunction.

Methods:

• Mads.readobservations(madsdata::AbstractDict, obskeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1134
• Mads.readobservations(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1134

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• obskeys::AbstractVector{T} where T : observation keys [default=getobskeys(madsdata)]

Returns:

# Mads.readobservations_cmadsMethod.

Methods:

• Mads.readobservations_cmads(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCMads.jl:14

Arguments:

• madsdata::AbstractDict : Mads problem dictionary

Returns:

• observations

# Mads.readyamlpredictionsMethod.

Read MADS model predictions from a YAML file filename

Methods:

• Mads.readyamlpredictions(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsYAML.jl:108

Arguments:

• filename::AbstractString : file name

Returns:

• data in yaml input file

# Mads.recursivemkdirMethod.

Create directories recursively (if does not already exist)

Methods:

• Mads.recursivemkdir(s::AbstractString; filename) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1371

Arguments:

• s::AbstractString

Keywords:

• filename

# Mads.recursivermdirMethod.

Remove directories recursively

Methods:

• Mads.recursivermdir(s::AbstractString; filename) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1416

Arguments:

• s::AbstractString

Keywords:

• filename

# Mads.regexs2obsMethod.

Get observations for a set of regular expressions

Methods:

• Mads.regexs2obs(obsline::AbstractString, regexs::Vector{Regex}, obsnames::Vector{String}, getparamhere::Vector{Bool}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1043

Arguments:

• getparamhere::Vector{Bool} : parameters
• obsline::AbstractString : observation line
• obsnames::Vector{String} : observation names
• regexs::Vector{Regex} : regular expressions

Returns:

• obsdict : observations

# Mads.removesource!Function.

Remove a contamination source

Methods:

• Mads.removesource!(madsdata::AbstractDict, sourceid::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasol.jl:50
• Mads.removesource!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasol.jl:50

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• sourceid::Int64 : source id [default=0]

# Mads.removesourceparameters!Method.

Remove contaminant source parameters

Methods:

• Mads.removesourceparameters!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasol.jl:135

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

# Mads.requiredFunction.

Lists modules required by a module (Mads by default)

Methods:

• Mads.required(modulename::AbstractString, filtermodule::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:16
• Mads.required(modulename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:16
• Mads.required() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:16

Arguments:

• filtermodule::AbstractString : filter module name
• modulename::AbstractString : module name [default="Mads"]

Returns:

• filtered modules

# Mads.resetmodelrunsMethod.

Reset the model runs count to be equal to zero

Methods:

• Mads.resetmodelruns() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:242

# Mads.residualsFunction.

Compute residuals

Methods:

• Mads.residuals(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:32
• Mads.residuals(madsdata::AbstractDict, resultdict::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:29
• Mads.residuals(madsdata::AbstractDict, resultvec::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:6

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• resultdict::AbstractDict : result dictionary
• resultvec::AbstractVector{T} where T : result vector

Returns:

# Mads.restartoffMethod.

Methods:

• Mads.restartoff() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:76

# Mads.restartonMethod.

Methods:

• Mads.restarton() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:67

# Mads.reweighsamplesMethod.

Reweigh samples using importance sampling â€“ returns a vector of log-likelihoods after reweighing

Methods:

• Mads.reweighsamples(madsdata::AbstractDict, predictions::Array, oldllhoods::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:322

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• oldllhoods::AbstractVector{T} where T : the log likelihoods of the parameters in the old distribution
• predictions::Array : the model predictions for each of the samples

Returns:

• vector of log-likelihoods after reweighing

# Mads.rmdirMethod.

Remove directory

Methods:

• Mads.rmdir(dir::AbstractString; path) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1231

Arguments:

• dir::AbstractString : directory to be removed

Keywords:

• path : path of the directory [default=current path]

# Mads.rmfileMethod.

Remove file

Methods:

• Mads.rmfile(filename::AbstractString; path) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1247

Arguments:

• filename::AbstractString : file to be removed

Keywords:

• path : path of the file [default=current path]

# Mads.rmfilesMethod.

Remove files

Methods:

• Mads.rmfile(filename::AbstractString; path) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1247

Arguments:

• filename::AbstractString

Keywords:

• path : path of the file [default=current path]

# Mads.rmfiles_extMethod.

Remove files with extension ext

Methods:

• Mads.rmfiles_ext(ext::AbstractString; path) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1276

Arguments:

• ext::AbstractString : extension

Keywords:

• path : path of the files to be removed [default=.]

# Mads.rmfiles_rootMethod.

Remove files with root root

Methods:

• Mads.rmfiles_root(root::AbstractString; path) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1289

Arguments:

• root::AbstractString : root

Keywords:

• path : path of the files to be removed [default=.]

# Mads.rosenbrockMethod.

Rosenbrock test function

Methods:

• Mads.rosenbrock(x::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:42

Arguments:

• x::AbstractVector{T} where T : parameter vector

Returns:

• test result

# Mads.rosenbrock2_gradient_lmMethod.

Parameter gradients of the Rosenbrock test function

Methods:

• Mads.rosenbrock2_gradient_lm(x::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:23

Arguments:

• x::AbstractVector{T} where T : parameter vector

Returns:

# Mads.rosenbrock2_lmMethod.

Rosenbrock test function (more difficult to solve)

Methods:

• Mads.rosenbrock2_lm(x::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:9

Arguments:

• x::AbstractVector{T} where T : parameter vector

# Mads.rosenbrock_gradient!Method.

Parameter gradients of the Rosenbrock test function

Methods:

• Mads.rosenbrock_gradient!(x::AbstractVector{T} where T, grad::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:67

Arguments:

• grad::AbstractVector{T} where T : gradient vector
• x::AbstractVector{T} where T : parameter vector

# Mads.rosenbrock_gradient_lmMethod.

Parameter gradients of the Rosenbrock test function for LM optimization (returns the gradients for the 2 components separately)

Methods:

• Mads.rosenbrock_gradient_lm(x::AbstractVector{T} where T; dx, center) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:84

Arguments:

• x::AbstractVector{T} where T : parameter vector

Keywords:

• center : array with parameter observations at the center applied to compute numerical derivatives [default=Array{Float64}(undef, 0)]
• dx : apply parameter step to compute numerical derivatives [default=false]

Returns:

# Mads.rosenbrock_hessian!Method.

Parameter Hessian of the Rosenbrock test function

Methods:

• Mads.rosenbrock_hessian!(x::AbstractVector{T} where T, hess::AbstractMatrix{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:100

Arguments:

• hess::AbstractMatrix{T} where T : Hessian matrix
• x::AbstractVector{T} where T : parameter vector

# Mads.rosenbrock_lmMethod.

Rosenbrock test function for LM optimization (returns the 2 components separately)

Methods:

• Mads.rosenbrock_lm(x::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:56

Arguments:

• x::AbstractVector{T} where T : parameter vector

Returns:

• test result

# Mads.runcmdFunction.

Run external command and pipe stdout and stderr

Methods:

• Mads.runcmd(cmdstring::AbstractString; quiet, pipe, waittime) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsExecute.jl:100
• Mads.runcmd(cmd::Cmd; quiet, pipe, waittime) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsExecute.jl:41

Arguments:

• cmd::Cmd : command (as a julia command; e.g. ls)
• cmdstring::AbstractString : command (as a string; e.g. "ls")

Keywords:

• pipe : [default=false]
• quiet : [default=Mads.quiet]
• waittime : wait time is second [default=Mads.executionwaittime]

Returns:

• command output
• command error message

# Mads.runremoteFunction.

Run remote command on a series of servers

Methods:

• Mads.runremote(cmd::AbstractString, nodenames::Vector{String}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:285
• Mads.runremote(cmd::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:285

Arguments:

• cmd::AbstractString : remote command
• nodenames::Vector{String} : names of machines/nodes [default=madsservers]

Returns:

• output of running remote command

# Mads.saltelliMethod.

Saltelli sensitivity analysis

Methods:

• Mads.saltelli(madsdata::AbstractDict; N, seed, restartdir, parallel, checkpointfrequency) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:635

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• N : number of samples [default=100]
• checkpointfrequency : check point frequency [default=N]
• parallel : set to true if the model runs should be performed in parallel [default=false]
• restartdir : directory where files will be stored containing model results for fast simulation restarts
• seed : random seed [default=0]

# Mads.saltellibruteMethod.

Saltelli sensitivity analysis (brute force)

Methods:

• Mads.saltellibrute(madsdata::AbstractDict; N, seed, restartdir) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:447

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• N : number of samples [default=1000]
• restartdir : directory where files will be stored containing model results for fast simulation restarts
• seed : random seed [default=0]

# Mads.saltellibruteparallelMethod.

Parallel version of saltellibrute

# Mads.saltelliparallelMethod.

Parallel version of saltelli

# Mads.samplingMethod.

Methods:

• Mads.sampling(param::AbstractVector{T} where T, J::Array, numsamples::Number; seed, scale) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:271

Arguments:

• J::Array : Jacobian matrix
• numsamples::Number : Number of samples
• param::AbstractVector{T} where T : Parameter vector

Keywords:

• scale : data scaling [default=1]
• seed : random esee [default=0]

Returns:

• generated samples (vector or array)
• vector of log-likelihoods

# Mads.savemadsfileFunction.

Save MADS problem dictionary madsdata in MADS input file filename

Methods:

• Mads.savemadsfile(madsdata::AbstractDict, parameters::AbstractDict, filename::AbstractString; explicit, observations_separate) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:349
• Mads.savemadsfile(madsdata::AbstractDict, parameters::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:349
• Mads.savemadsfile(madsdata::AbstractDict, filename::AbstractString; observations_separate, filenameobs) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:332
• Mads.savemadsfile(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:332

Arguments:

• filename::AbstractString : input file name (e.g. input_file_name.mads)
• madsdata::AbstractDict : MADS problem dictionary
• parameters::AbstractDict : Dictionary with parameters (optional)

Keywords:

• explicit : if true ignores MADS YAML file modifications and rereads the original input file [default=false]
• filenameobs
• observations_separate

Example:

Mads.savemadsfile(madsdata)


# Mads.savemcmcresultsMethod.

Save MCMC chain in a file

Methods:

• Mads.savemcmcresults(chain::Array, filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsMonteCarlo.jl:143

Arguments:

• chain::Array : MCMC chain
• filename::AbstractString : file name

Dumps:

• the file containing MCMC chain

# Mads.savesaltellirestartMethod.

Save Saltelli sensitivity analysis results for fast simulation restarts

Methods:

• Mads.savesaltellirestart(evalmat::Array, matname::AbstractString, restartdir::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:616

Arguments:

• evalmat::Array : saved array
• matname::AbstractString : matrix (array) name (defines the name of the loaded file)
• restartdir::AbstractString : directory where files will be stored containing model results for fast simulation restarts

# Mads.scatterplotsamplesMethod.

Create histogram/scatter plots of model parameter samples

Methods:

• Mads.scatterplotsamples(madsdata::AbstractDict, samples::AbstractMatrix{T} where T, filename::AbstractString; format, pointsize) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:428

Arguments:

• filename::AbstractString : output file name
• madsdata::AbstractDict : MADS problem dictionary
• samples::AbstractMatrix{T} where T : matrix with model parameters

Keywords:

• format : output plot format (png, pdf, etc.) [default=Mads.graphbackend]
• pointsize : point size [default=0.9Gadfly.mm]

Dumps:

• histogram/scatter plots of model parameter samples

# Mads.searchdirFunction.

Get files in the current directory or in a directory defined by path matching pattern key which can be a string or regular expression

Methods:

• Mads.searchdir(key::AbstractString; path) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:831
• Mads.searchdir(key::Regex; path) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:830

Arguments:

• key::AbstractString : matching pattern for Mads input files (string or regular expression accepted)
• key::Regex : matching pattern for Mads input files (string or regular expression accepted)

Keywords:

• path : search directory for the mads input files [default=.]

Returns:

• filename : an array with file names matching the pattern in the specified directory

Examples:

- Mads.searchdir("a")
- Mads.searchdir(r"[A-B]"; path = ".")
- Mads.searchdir(r".*.cov"; path = ".")


# Mads.set_nprocs_per_taskFunction.

Set number of processors needed for each parallel task at each node

Methods:

• Mads.set_nprocs_per_task(local_nprocs_per_task::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:58
• Mads.set_nprocs_per_task() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:58

Arguments:

• local_nprocs_per_task::Integer

# Mads.setallparamsoff!Method.

Set all parameters OFF

Methods:

• Mads.setallparamsoff!(madsdata::AbstractDict; filter) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:464

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• filter : parameter filter

# Mads.setallparamson!Method.

Set all parameters ON

Methods:

• Mads.setallparamson!(madsdata::AbstractDict; filter) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:450

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

Keywords:

• filter : parameter filter

# Mads.setdebuglevelMethod.

Methods:

• Mads.setdebuglevel(level::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:213

Arguments:

• level::Int64 : debug level

# Mads.setdefaultplotformatMethod.

Set the default plot format (SVG is the default format)

Methods:

• Mads.setdefaultplotformat(format::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:19

Arguments:

• format::AbstractString : plot format

# Mads.setdirFunction.

Set the working directory (for parallel environments)

Methods:

• Mads.setdir() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:255
• Mads.setdir(dir) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:250

Arguments:

• dir : directory

Example:

@Distributed.everywhere Mads.setdir()


# Mads.setdpiMethod.

Set image dpi

Methods:

• Mads.setdpi(dpi::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:167

Arguments:

• dpi::Integer

# Mads.setexecutionwaittimeMethod.

Set maximum execution wait time for forward model runs in seconds

Methods:

• Mads.setexecutionwaittime(waitime::Float64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:233

Arguments:

• waitime::Float64 : maximum execution wait time for forward model runs in seconds

# Mads.setmadsinputfileMethod.

Set a default MADS input file

Methods:

• Mads.setmadsinputfile(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:409

Arguments:

• filename::AbstractString : input file name (e.g. input_file_name.mads)

# Mads.setmadsserversFunction.

Generate a list of Mads servers

Methods:

• Mads.setmadsservers(first::Int64, last::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:340
• Mads.setmadsservers(first::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:340
• Mads.setmadsservers() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:340

Arguments:

• first::Int64 : first [default=0]
• last::Int64 : last [default=18]

Returns

• array string of mads servers

# Mads.setmodelinputsFunction.

Set model input files; delete files where model output should be saved for MADS

Methods:

• Mads.setmodelinputs(madsdata::AbstractDict, parameters::AbstractDict; path) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:713
• Mads.setmodelinputs(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:713

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• parameters::AbstractDict : parameters

Keywords:

• path : path for the files [default=.]

# Mads.setnewmadsfilenameFunction.

Methods:

• Mads.setnewmadsfilename(filename::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:572
• Mads.setnewmadsfilename(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:569

Arguments:

• filename::AbstractString : file name
• madsdata::AbstractDict : MADS problem dictionary

Returns:

• new file name

# Mads.setobservationtargets!Method.

Set observations (calibration targets) in the MADS problem dictionary based on a predictions dictionary

Methods:

• Mads.setobservationtargets!(madsdata::AbstractDict, predictions::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:523

Arguments:

• madsdata::AbstractDict : Mads problem dictionary
• predictions::AbstractDict : dictionary with model predictions

# Mads.setobstime!Function.

Set observation time based on the observation name in the MADS problem dictionary

Methods:

• Mads.setobstime!(madsdata::AbstractDict, rx::Regex, obskeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:261
• Mads.setobstime!(madsdata::AbstractDict, rx::Regex) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:261
• Mads.setobstime!(madsdata::AbstractDict, separator::AbstractString, obskeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:251
• Mads.setobstime!(madsdata::AbstractDict, separator::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:251
• Mads.setobstime!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:251

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• obskeys::AbstractVector{T} where T
• rx::Regex : regular expression to match
• separator::AbstractString : separator [default=_]

Examples:

Mads.setobstime!(madsdata, "_t")


# Mads.setobsweights!Function.

Set observation weights in the MADS problem dictionary

Methods:

• Mads.setobsweights!(madsdata::AbstractDict, v::AbstractVector{T} where T, obskeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:293
• Mads.setobsweights!(madsdata::AbstractDict, v::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:293
• Mads.setobsweights!(madsdata::AbstractDict, value::Number, obskeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:288
• Mads.setobsweights!(madsdata::AbstractDict, value::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:288

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• obskeys::AbstractVector{T} where T
• v::AbstractVector{T} where T : vector of observation weights
• value::Number : value for observation weights

# Mads.setparamoff!Method.

Set a specific parameter with a key parameterkey OFF

Methods:

• Mads.setparamoff!(madsdata::AbstractDict, parameterkey::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:489

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• parameterkey::AbstractString : parameter key

# Mads.setparamon!Method.

Set a specific parameter with a key parameterkey ON

Methods:

• Mads.setparamon!(madsdata::AbstractDict, parameterkey::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:478

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• parameterkey::AbstractString : parameter key

# Mads.setparamsdistnormal!Method.

Set normal parameter distributions for all the model parameters in the MADS problem dictionary

Methods:

• Mads.setparamsdistnormal!(madsdata::AbstractDict, mean::AbstractVector{T} where T, stddev::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:501

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• mean::AbstractVector{T} where T : array with the mean values
• stddev::AbstractVector{T} where T : array with the standard deviation values

# Mads.setparamsdistuniform!Method.

Set uniform parameter distributions for all the model parameters in the MADS problem dictionary

Methods:

• Mads.setparamsdistuniform!(madsdata::AbstractDict, min::AbstractVector{T} where T, max::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:516

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• max::AbstractVector{T} where T : array with the maximum values
• min::AbstractVector{T} where T : array with the minimum values

# Mads.setparamsinit!Function.

Set initial optimized parameter guesses in the MADS problem dictionary

Methods:

• Mads.setparamsinit!(madsdata::AbstractDict, paramdict::AbstractDict, idx::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:317
• Mads.setparamsinit!(madsdata::AbstractDict, paramdict::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:317

Arguments:

• idx::Int64 : index of the dictionary of arrays with initial model parameter values
• madsdata::AbstractDict : MADS problem dictionary
• paramdict::AbstractDict : dictionary with initial model parameter values

# Mads.setplotfileformatMethod.

Set image file format based on the filename extension, or sets the filename extension based on the requested format. The default format is SVG. PNG, PDF, ESP, and PS are also supported.

Methods:

• Mads.setplotfileformat(filename::AbstractString, format::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:48

Arguments:

• filename::AbstractString : output file name
• format::AbstractString : output plot format (png, pdf, etc.) [default=Mads.graphbackend]

Returns:

• output file name
• output plot format (png, pdf, etc.)

# Mads.setprocsFunction.

Set the available processors based on environmental variables (supports SLURM only at the moment)

Methods:

• Mads.setprocs(; ntasks_per_node, nprocs_per_task, nodenames, mads_servers, test, quiet, veryquiet, dir, exename) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:48
• Mads.setprocs(np::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:45
• Mads.setprocs(np::Integer, nt::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:32

Arguments:

• np::Integer : number of processors [default=1]
• nt::Integer : number of threads[default=1]

Keywords:

• dir : common directory shared by all the jobs
• exename : location of the julia executable (the same version of julia is needed on all the workers)
• mads_servers : if true use MADS servers (LANL only) [default=false]
• nodenames : array with names of machines/nodes to be invoked
• nprocs_per_task : number of processors needed for each parallel task at each node [default=Mads.nprocs_per_task]
• ntasks_per_node : number of parallel tasks per node [default=0]
• quiet : suppress output [default=Mads.quiet]
• test : test the servers and connect to each one ones at a time [default=false]
• veryquiet

Returns:

• vector with names of compute nodes (hosts)

Example:

Mads.setprocs()


# Mads.setseedFunction.

Set / get current random seed. seed < 0 gets seed, anything else sets it.

Methods:

• Mads.setseed(seed::Integer, quiet::Bool) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:459
• Mads.setseed(seed::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:459
• Mads.setseed() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:459
• Mads.setseed(seed::Integer, quiet::Bool) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:459
• Mads.setseed(seed::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:459
• Mads.setseed() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:459
• Mads.setseed(seed::Integer, quiet::Bool) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:459
• Mads.setseed(seed::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:459
• Mads.setseed() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:459

Arguments:

• quiet::Bool : [default=true]
• seed::Integer : random seed

# Mads.setsindx!Method.

Set sin-space dx

Methods:

• Mads.setsindx!(madsdata::AbstractDict, sindx::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:370

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• sindx::Number : sin-space dx value

Returns:

• nothing

# Mads.setsindxMethod.

Set sin-space dx

Methods:

• Mads.setsindx(sindx::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:387

Arguments:

• sindx::Number

Returns:

• nothing

# Mads.setsourceinit!Function.

Set initial optimized parameter guesses in the MADS problem dictionary for the Source class

Methods:

• Mads.setparamsinit!(madsdata::AbstractDict, paramdict::AbstractDict, idx::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:317
• Mads.setparamsinit!(madsdata::AbstractDict, paramdict::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:317

Arguments:

• idx::Int64 : index of the dictionary of arrays with initial model parameter values
• madsdata::AbstractDict : MADS problem dictionary
• paramdict::AbstractDict : dictionary with initial model parameter values

# Mads.settarget!Method.

Set observation target

Methods:

• Mads.settarget!(o::AbstractDict, target::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:241

Arguments:

• o::AbstractDict : observation data
• target::Number : observation target

# Mads.settime!Method.

Set observation time

Methods:

• Mads.settime!(o::AbstractDict, time::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:163

Arguments:

• o::AbstractDict : observation data
• time::Number : observation time

# Mads.setverbositylevelMethod.

Methods:

• Mads.setverbositylevel(level::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:223

Arguments:

• level::Int64 : debug level

# Mads.setweight!Method.

Set observation weight

Methods:

• Mads.setweight!(o::AbstractDict, weight::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:202

Arguments:

• o::AbstractDict : observation data
• weight::Number : observation weight

# Mads.setwellweights!Function.

Set well weights in the MADS problem dictionary

Methods:

• Mads.setwellweights!(madsdata::AbstractDict, value::Number, wellkeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:344
• Mads.setwellweights!(madsdata::AbstractDict, value::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:344

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• value::Number : value for well weights
• wellkeys::AbstractVector{T} where T

# Mads.showallparametersFunction.

Show all parameters in the MADS problem dictionary

Methods:

• Mads.showallparameters(madsdata::AbstractDict, result::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:581
• Mads.showallparameters(madsdata::AbstractDict, parkeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:577
• Mads.showallparameters(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:577

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• parkeys::AbstractVector{T} where T
• result::AbstractDict

# Mads.showobservationsFunction.

Show observations in the MADS problem dictionary

Methods:

• Mads.showobservations(madsdata::AbstractDict, obskeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:400
• Mads.showobservations(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:400

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• obskeys::AbstractVector{T} where T

# Mads.showparametersFunction.

Show parameters in the MADS problem dictionary

Methods:

• Mads.showparameters(madsdata::AbstractDict, parkeys::AbstractVector{T} where T, all::Bool) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:563
• Mads.showparameters(madsdata::AbstractDict, parkeys::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:563
• Mads.showparameters(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:563
• Mads.showparameters(madsdata::AbstractDict, result::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:558

Arguments:

• all::Bool
• madsdata::AbstractDict : MADS problem dictionary
• parkeys::AbstractVector{T} where T
• result::AbstractDict

# Mads.sinetransformFunction.

Sine transformation of model parameters

Methods:

• Mads.sinetransform(sineparams::AbstractVector{T} where T, lowerbounds::AbstractVector{T} where T, upperbounds::AbstractVector{T} where T, indexlogtransformed::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSineTransformations.jl:45
• Mads.sinetransform(madsdata::AbstractDict, params::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSineTransformations.jl:35

Arguments:

• indexlogtransformed::AbstractVector{T} where T : index vector of log-transformed parameters
• lowerbounds::AbstractVector{T} where T : lower bounds
• madsdata::AbstractDict : MADS problem dictionary
• params::AbstractVector{T} where T
• sineparams::AbstractVector{T} where T : model parameters
• upperbounds::AbstractVector{T} where T : upper bounds

Returns:

• Sine transformation of model parameters

# Mads.sinetransformfunctionMethod.

Sine transformation of a function

Methods:

• Mads.sinetransformfunction(f::Function, lowerbounds::AbstractVector{T} where T, upperbounds::AbstractVector{T} where T, indexlogtransformed::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSineTransformations.jl:79

Arguments:

• f::Function : function
• indexlogtransformed::AbstractVector{T} where T : index vector of log-transformed parameters
• lowerbounds::AbstractVector{T} where T : lower bounds
• upperbounds::AbstractVector{T} where T : upper bounds

Returns:

• Sine transformation

# Mads.sinetransformgradientMethod.

Sine transformation of a gradient function

Methods:

• Mads.sinetransformgradient(g::Function, lowerbounds::AbstractVector{T} where T, upperbounds::AbstractVector{T} where T, indexlogtransformed::AbstractVector{T} where T; sindx) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSineTransformations.jl:100

Arguments:

• g::Function : gradient function
• indexlogtransformed::AbstractVector{T} where T : index vector of log-transformed parameters
• lowerbounds::AbstractVector{T} where T : vector with parameter lower bounds
• upperbounds::AbstractVector{T} where T : vector with parameter upper bounds

Keywords:

• sindx : sin-space parameter step applied to compute numerical derivatives [default=0.1]

Returns:

• Sine transformation of a gradient function

# Mads.spaghettiplotFunction.

Generate a combined spaghetti plot for the selected (type != null) model parameter

Methods:

• Mads.spaghettiplot(madsdata::AbstractDict, matrix::AbstractMatrix{T} where T; plotdata, filename, keyword, format, title, xtitle, ytitle, yfit, obs_plot_dots, linewidth, pointsize, grayscale, xmin, xmax, ymin, ymax, quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:876
• Mads.spaghettiplot(madsdata::AbstractDict, dictarray::AbstractDict; seed, kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:839
• Mads.spaghettiplot(madsdata::AbstractDict, number_of_samples::Integer; kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:835

Arguments:

• dictarray::AbstractDict : dictionary array containing the data arrays to be plotted
• madsdata::AbstractDict : MADS problem dictionary
• matrix::AbstractMatrix{T} where T
• number_of_samples::Integer : number of samples

Keywords:

• filename : output file name used to output the produced plots
• format : output plot format (png, pdf, etc.) [default=Mads.graphbackend]
• grayscale
• keyword : keyword to be added in the file name used to output the produced plots (if filename is not defined)
• linewidth : width of the lines in plot [default=2Gadfly.pt]
• obs_plot_dots : plot observation as dots (true [default] or false)
• plotdata : plot data (if false model predictions are plotted only) [default=true]
• pointsize : size of the markers in plot [default=4Gadfly.pt]
• quiet
• seed : random seed [default=0]
• title
• xmax
• xmin
• xtitle : x axis title [default=X]
• yfit : fit vertical axis range [default=false]
• ymax
• ymin
• ytitle : y axis title [default=Y]

Dumps:

• Image file with a spaghetti plot (<mads_rootname>-<keyword>-<number_of_samples>-spaghetti.<default_image_extension>)

Example:

Mads.spaghettiplot(madsdata, dictarray; filename="", keyword = "", format="", xtitle="X", ytitle="Y", obs_plot_dots=true)


# Mads.spaghettiplotsFunction.

Generate separate spaghetti plots for each selected (type != null) model parameter

Methods:

• Mads.spaghettiplots(madsdata::AbstractDict, paramdictarray::OrderedCollections.OrderedDict; format, keyword, xtitle, ytitle, obs_plot_dots, seed, linewidth, pointsize, grayscale, quiet) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:683
• Mads.spaghettiplots(madsdata::AbstractDict, number_of_samples::Integer; seed, kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:678

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• number_of_samples::Integer : number of samples
• paramdictarray::OrderedCollections.OrderedDict : parameter dictionary containing the data arrays to be plotted

Keywords:

• format : output plot format (png, pdf, etc.) [default=Mads.graphbackend]
• grayscale
• keyword : keyword to be added in the file name used to output the produced plots
• linewidth : width of the lines on the plot [default=2Gadfly.pt]
• obs_plot_dots : plot observation as dots (true (default) or false)
• pointsize : size of the markers on the plot [default=4Gadfly.pt]
• quiet
• seed : random seed [default=0]
• xtitle : x axis title [default=X]
• ytitle : y axis title [default=Y]

Dumps:

• A series of image files with spaghetti plots for each selected (type != null) model parameter (<mads_rootname>-<keyword>-<param_key>-<number_of_samples>-spaghetti.<default_image_extension>)

Example:

Mads.spaghettiplots(madsdata, paramdictarray; format="", keyword="", xtitle="X", ytitle="Y", obs_plot_dots=true)


# Mads.sphericalcovMethod.

Spherical spatial covariance function

Methods:

• Mads.sphericalcov(h::Number, maxcov::Number, scale::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:45

Arguments:

• h::Number : separation distance
• maxcov::Number : max covariance
• scale::Number : scale

Returns:

• covariance

# Mads.sphericalvariogramMethod.

Spherical variogram

Methods:

• Mads.sphericalvariogram(h::Number, sill::Number, range::Number, nugget::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:60

Arguments:

• h::Number : separation distance
• nugget::Number : nugget
• range::Number : range
• sill::Number : sill

Returns:

• Spherical variogram

# Mads.sprintfMethod.

Convert @Printf.sprintf macro into sprintf function

# Mads.statusFunction.

Methods:

• Mads.status(madsmodule::AbstractString; git, gitmore) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:256
• Mads.status(; git, gitmore) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:251

Arguments:

• madsmodule::AbstractString : mads module

Keywords:

• git : use git [default=true or Mads.madsgit]
• gitmore : use even more git [default=false]

Returns:

• true or false

# Mads.stderrcaptureoffMethod.

Restore stderr

Methods:

• Mads.stderrcaptureoff() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCapture.jl:139

Returns:

• standered error

# Mads.stderrcaptureonMethod.

Methods:

• Mads.stderrcaptureon() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCapture.jl:120

# Mads.stdoutcaptureoffMethod.

Restore stdout

Methods:

• Mads.stdoutcaptureoff() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCapture.jl:105

Returns:

• standered output

# Mads.stdoutcaptureonMethod.

Methods:

• Mads.stdoutcaptureon() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCapture.jl:86

# Mads.stdouterrcaptureoffMethod.

Restore stdout & stderr

Methods:

• Mads.stdouterrcaptureoff() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCapture.jl:170

Returns:

• standered output amd standered error

# Mads.stdouterrcaptureonMethod.

Redirect stdout & stderr to readers

Methods:

• Mads.stdouterrcaptureon() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCapture.jl:154

# Mads.svrdumpMethod.

Dump SVR models in files

Methods:

• Mads.svrdump(svrmodel::Vector{SVR.svmmodel}, rootname::AbstractString, numberofsamples::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSVR.jl:140

Arguments:

• numberofsamples::Int64 : number of samples
• rootname::AbstractString : root name
• svrmodel::Vector{SVR.svmmodel} : array of SVR models

# Mads.svrfreeMethod.

Free SVR

Methods:

• Mads.svrfree(svrmodel::Vector{SVR.svmmodel}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSVR.jl:122

Arguments:

• svrmodel::Vector{SVR.svmmodel} : array of SVR models

# Mads.svrloadMethod.

Methods:

• Mads.svrload(npred::Int64, rootname::AbstractString, numberofsamples::Int64) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSVR.jl:163

Arguments:

• npred::Int64 : number of model predictions
• numberofsamples::Int64 : number of samples
• rootname::AbstractString : root name

Returns:

• Array of SVR models for each model prediction

# Mads.svrpredictFunction.

Predict SVR

Methods:

• Mads.svrpredict(svrmodel::Vector{SVR.svmmodel}, paramarray::Matrix{Float64}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSVR.jl:95

Arguments:

• paramarray::Matrix{Float64} : parameter array
• svrmodel::Vector{SVR.svmmodel} : array of SVR models

Returns:

• SVR predicted observations (dependent variables) for a given set of parameters (independent variables)

# Mads.svrtrainFunction.

Train SVR

Methods:

• Mads.svrtrain(madsdata::AbstractDict, numberofsamples::Integer; addminmax, kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSVR.jl:38
• Mads.svrtrain(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSVR.jl:38
• Mads.svrtrain(madsdata::AbstractDict, paramarray::Matrix{Float64}; check, savesvr, addminmax, svm_type, kernel_type, degree, gamma, coef0, C, nu, cache_size, epsilon, shrinking, probability, verbose, tol) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSVR.jl:5

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• numberofsamples::Integer : number of random samples in the training set [default=100]
• paramarray::Matrix{Float64}

Keywords:

• C : cost; penalty parameter of the error term [default=1000.0]
• addminmax : add parameter minimum / maximum range values in the training set [default=true]
• cache_size : size of the kernel cache [default=100.0]
• check : check SVR performance [default=false]
• coef0 : independent term in kernel function; important only in POLY and SIGMOND kernel types

[default=0]

• degree : degree of the polynomial kernel [default=3]
• epsilon : epsilon in the EPSILON_SVR model; defines an epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value [default=0.001]
• gamma : coefficient for RBF, POLY and SIGMOND kernel types [default=1/numberofsamples]
• kernel_type : kernel type[default=SVR.RBF]
• nu : upper bound on the fraction of training errors / lower bound of the fraction of support vectors; acceptable range (0, 1]; applied if NU_SVR model [default=0.5]
• probability : train to estimate probabilities [default=false]
• savesvr : save SVR models [default=false]
• shrinking : apply shrinking heuristic [default=true]
• svm_type : SVM type [default=SVR.EPSILON_SVR]
• tol : tolerance of termination criterion [default=0.001]
• verbose : verbose output [default=false]

Returns:

• Array of SVR models

# Mads.symlinkdirMethod.

Create a symbolic link of a file filename in a directory dirtarget

Methods:

• Mads.symlinkdir(filename::AbstractString, dirtarget::AbstractString, dirsource::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1217

Arguments:

• dirsource::AbstractString
• dirtarget::AbstractString : target directory
• filename::AbstractString : file name

# Mads.symlinkdirfilesMethod.

Create a symbolic link of all the files in a directory dirsource in a directory dirtarget

Methods:

• Mads.symlinkdirfiles(dirsource::AbstractString, dirtarget::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1199

Arguments:

• dirsource::AbstractString : source directory
• dirtarget::AbstractString : target directory

# Mads.tagFunction.

Tag Mads modules with a default argument :patch

Methods:

• Mads.tag(madsmodule::AbstractString, versionsym::Symbol) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:326
• Mads.tag(madsmodule::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:326
• Mads.tag(versionsym::Symbol) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:321
• Mads.tag() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:321

Arguments:

• madsmodule::AbstractString : mads module name
• versionsym::Symbol : version symbol [default=:patch]

# Mads.testFunction.

Perform Mads tests (the tests will be in parallel if processors are defined; tests use the current Mads version in the workspace; reload("Mads.jl") if needed)

Methods:

• Mads.test(testname::AbstractString; madstest, plotting) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTest.jl:38
• Mads.test() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTest.jl:38

Arguments:

• testname::AbstractString : name of the test to execute; module or example

Keywords:

• madstest : test Mads [default=true]
• plotting

# Mads.testjFunction.

Execute Mads tests using Julia Pkg.test (the default Pkg.test in Julia is executed in serial)

Methods:

• Mads.testj(coverage::Bool) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTest.jl:9
• Mads.testj() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTest.jl:9

Arguments:

• coverage::Bool : [default=false]

# Mads.transposematrixMethod.

Transpose non-numeric matrix

Methods:

• Mads.transposematrix(a::AbstractMatrix{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:407

Arguments:

• a::AbstractMatrix{T} where T : matrix

# Mads.transposevectorMethod.

Transpose non-numeric vector

Methods:

• Mads.transposevector(a::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:397

Arguments:

• a::AbstractVector{T} where T : vector

# Mads.untagMethod.

Untag specific version

Methods:

• Mads.untag(madsmodule::AbstractString, version::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPublish.jl:361

Arguments:

• madsmodule::AbstractString : mads module name
• version::AbstractString : version

# Mads.vectoroffMethod.

Methods:

• Mads.vectoroff() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:49

# Mads.vectoronMethod.

Methods:

• Mads.vectoron() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:40

# Mads.veryquietoffMethod.

Methods:

• Mads.veryquietoff() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:122

# Mads.veryquietonMethod.

Methods:

• Mads.veryquieton() in Mads : /Users/vvv/.julia/dev/Mads/src/MadsHelpers.jl:113

# Mads.void2nan!Method.

Convert Nothing's into NaN's in a dictionary

Methods:

• Mads.void2nan!(dict::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:1040

Arguments:

• dict::AbstractDict : dictionary

# Mads.weightedstatsMethod.

Get weighted mean and variance samples

Methods:

• Mads.weightedstats(samples::Array, llhoods::AbstractVector{T} where T) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:379

Arguments:

• llhoods::AbstractVector{T} where T : vector of log-likelihoods
• samples::Array : array of samples

Returns:

• vector of sample means
• vector of sample variances

# Mads.welloff!Method.

Turn off a specific well in the MADS problem dictionary

Methods:

• Mads.welloff!(madsdata::AbstractDict, wellname::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:616

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• wellname::AbstractString : name of the well to be turned off

# Mads.wellon!Method.

Turn on a specific well in the MADS problem dictionary

Methods:

• Mads.wellon!(madsdata::AbstractDict, wellname::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:558

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• wellname::AbstractString : name of the well to be turned on

# Mads.wellon!Method.

Turn on a specific well in the MADS problem dictionary

Methods:

• Mads.wellon!(madsdata::AbstractDict, rx::Regex) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:580
• Mads.wellon!(madsdata::AbstractDict, wellname::AbstractString) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:558

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• rx::Regex
• wellname::AbstractString : name of the well to be turned on

# Mads.wells2observations!Method.

Convert Wells class to Observations class in the MADS problem dictionary

Methods:

• Mads.wells2observations!(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:671

Arguments:

• madsdata::AbstractDict : MADS problem dictionary

# Mads.writeparametersFunction.

Write model parameters

Methods:

• Mads.writeparameters(madsdata::AbstractDict, parameters::AbstractDict; respect_space) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:941
• Mads.writeparameters(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:941

Arguments:

• madsdata::AbstractDict : MADS problem dictionary
• parameters::AbstractDict : parameters

Keywords:

• respect_space : respect provided space in the template file to fit model parameters [default=false]

# Mads.writeparametersviatemplateMethod.

Write parameters via MADS template (templatefilename) to an output file (outputfilename)

Methods:

• Mads.writeparametersviatemplate(parameters, templatefilename, outputfilename; respect_space) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:895

Arguments:

• outputfilename : output file name
• parameters : parameters
• templatefilename : tmplate file name

Keywords:

• respect_space : respect provided space in the template file to fit model parameters [default=false]

# Mads.@stderrcaptureMacro.

Capture stderr of a block

# Mads.@stdoutcaptureMacro.

Capture stdout of a block

# Mads.@stdouterrcaptureMacro.

Capture stderr & stderr of a block

# Mads.@tryimportMacro.

Try to import a module in Mads

# Mads.@tryimportmainMacro.

Try to import a module in Main

# Mads.MadsModel`Type.

MadsModel type applied for MathProgBase analyses