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Mads.jl

MADS (Model Analysis & Decision Support)

Mads.jl is MADS main module.

Mads.jl module functions:

# Mads.MFlmMethod.

Matrix Factorization using Levenberg Marquardt

Methods

  • Mads.MFlm(X::Array{T<:Any,2}, nk::Integer; mads, log_W, log_H, retries, maxiter, tol, initW, initH) : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsBSS.jl:121

Arguments

  • X::Array{T<:Any,2} : matrix to factorize
  • nk::Integer : number of features to extract

Keywords

  • initH : initial H (feature) matrix
  • initW : initial W (weight) matrix
  • log_H : log-transform H (feature) matrix[default=false]
  • log_W : log-transform W (weight) matrix [default=false]
  • mads : use MADS Levenberg-Marquardt algorithm [default=true]
  • maxiter : maximum number of iterations [default=10000]
  • retries : number of solution retries [default=1]
  • tol : solution tolerance [default=1.0e-9]

Returns:

  • NMF results

source

# Mads.NMFipoptMethod.

Non-negative Matrix Factorization using JuMP/Ipopt

Methods

  • Mads.NMFipopt(X::Array{T<:Any,2}, nk::Integer; retries, tol, random, maxiter, maxguess, initW, initH, verbosity, quiet) : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsBSS.jl:61

Arguments

  • X::Array{T<:Any,2} : matrix to factorize
  • nk::Integer : number of features to extract

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]
  • retries : number of solution retries [default=1]
  • tol : solution tolerance [default=1.0e-9]
  • verbosity : verbosity output level [default=0]

Returns:

  • NMF results

source

# Mads.NMFmMethod.

Non-negative Matrix Factorization using NMF

Methods

  • Mads.NMFm(X::Array, nk::Integer; retries, maxiter, tol) : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsBSS.jl:22

Arguments

  • X::Array : matrix to factorize
  • nk::Integer : number of features to extract

Keywords

  • maxiter : maximum number of iterations [default=10000]
  • retries : number of solution retries [default=1]
  • tol : solution tolerance [default=1.0e-9]

Returns:

  • NMF results

source

# Mads.addkeyword!Function.

Add a keyword in a class within the Mads dictionary madsdata

Methods

  • Mads.addkeyword!(madsdata::Associative, keyword::String) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:211
  • Mads.addkeyword!(madsdata::Associative, class::String, keyword::String) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:215

Arguments

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

source

# Mads.addsource!Function.

Add an additional contamination source

Methods

  • Mads.addsource!(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsAnasol.jl:19
  • Mads.addsource!(madsdata::Associative, sourceid::Int64) : /Users/monty/.julia/v0.5/Mads/src/MadsAnasol.jl:19

Arguments

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

source

# Mads.addsourceparameters!Method.

Add contaminant source parameters

Methods

  • Mads.addsourceparameters!(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsAnasol.jl:69

Arguments

  • madsdata::Associative : MADS problem dictionary

source

# Mads.allwellsoff!Method.

Turn off all the wells in the MADS problem dictionary

Methods

  • Mads.allwellsoff!(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:552

Arguments

  • madsdata::Associative : MADS problem dictionary

source

# Mads.allwellson!Method.

Turn on all the wells in the MADS problem dictionary

Methods

  • Mads.allwellson!(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:517

Arguments

  • madsdata::Associative : MADS problem dictionary

source

# Mads.amanziFunction.

Execute Amanzi external groundwater flow and transport simulator

Methods

  • Mads.amanzi(filename::String, nproc::Int64, quiet::Bool, observation_filename::String, setup::String; amanzi_exe) : /Users/monty/.julia/v0.5/Mads/src/../src-external/MadsSimulators.jl:15
  • Mads.amanzi(filename::String, nproc::Int64, quiet::Bool, observation_filename::String) : /Users/monty/.julia/v0.5/Mads/src/../src-external/MadsSimulators.jl:15
  • Mads.amanzi(filename::String, nproc::Int64, quiet::Bool) : /Users/monty/.julia/v0.5/Mads/src/../src-external/MadsSimulators.jl:15
  • Mads.amanzi(filename::String, nproc::Int64) : /Users/monty/.julia/v0.5/Mads/src/../src-external/MadsSimulators.jl:15
  • Mads.amanzi(filename::String) : /Users/monty/.julia/v0.5/Mads/src/../src-external/MadsSimulators.jl:15

Arguments

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

Keywords

  • amanzi_exe : full path to the Amanzi executable

source

# Mads.amanzi_output_parserFunction.

Parse Amanzi output provided in an external file (filename)

Methods

  • Mads.amanzi_output_parser(filename::String) : /Users/monty/.julia/v0.5/Mads/src/../src-external/MadsParsers.jl:22
  • Mads.amanzi_output_parser() : /Users/monty/.julia/v0.5/Mads/src/../src-external/MadsParsers.jl:22

Arguments

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

Returns:

  • dictionary with model observations following MADS requirements

Example:

Mads.amanzi_output_parser()
Mads.amanzi_output_parser("observations.out")

source

# Mads.asinetransformMethod.

Arcsine transformation of model parameters

Methods

  • Mads.asinetransform(params::Array{T<:Any,1}, lowerbounds::Array{T<:Any,1}, upperbounds::Array{T<:Any,1}, indexlogtransformed::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsSineTransformations.jl:17

Arguments

  • indexlogtransformed::Array{T<:Any,1} : index vector of log-transformed parameters
  • lowerbounds::Array{T<:Any,1} : lower bounds
  • params::Array{T<:Any,1} : model parameters
  • upperbounds::Array{T<:Any,1} : upper bounds

Returns:

  • Arcsine transformation of model parameters

source

# Mads.bayessamplingFunction.

Bayesian Sampling

Methods

  • Mads.bayessampling(madsdata::Associative; nsteps, burnin, thinning, seed) : /Users/monty/.julia/v0.5/Mads/src/MadsMonteCarlo.jl:78
  • Mads.bayessampling(madsdata::Associative, numsequences::Integer; nsteps, burnin, thinning, seed) : /Users/monty/.julia/v0.5/Mads/src/MadsMonteCarlo.jl:99

Arguments

  • madsdata::Associative : MADS problem dictionary
  • numsequences::Integer : number of sequences executed in parallel

Keywords

  • burnin : number of initial realizations before the MCMC are recorded [default=100]
  • nsteps : number of final realizations in the chain [default=1000]
  • seed : random seed [default=0]
  • thinning : removal of any thinning realization [default=1]

Returns:

  • MCMC chain

Examples:

Mads.bayessampling(madsdata; nsteps=1000, burnin=100, thinning=1, seed=2016)
Mads.bayessampling(madsdata, numsequences; nsteps=1000, burnin=100, thinning=1, seed=2016)

source

# Mads.calibrateMethod.

Calibrate

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::Associative; tolX, tolG, tolOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, usenaive, save_results, localsa) : /Users/monty/.julia/v0.5/Mads/src/MadsCalibrate.jl:166

Arguments

  • madsdata::Associative : 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)

source

# Mads.calibraterandomFunction.

Calibrate with random initial guesses

Methods

  • Mads.calibraterandom(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsCalibrate.jl:39
  • Mads.calibraterandom(madsdata::Associative, numberofsamples::Integer; tolX, tolG, tolOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, usenaive, seed, quiet, all, save_results) : /Users/monty/.julia/v0.5/Mads/src/MadsCalibrate.jl:39

Arguments

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

Keywords

  • all : all model results are returned [default=false]
  • 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)

source

# Mads.calibraterandom_parallelFunction.

Calibrate with random initial guesses in parallel

Methods

  • Mads.calibraterandom_parallel(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsCalibrate.jl:109
  • Mads.calibraterandom_parallel(madsdata::Associative, numberofsamples::Integer; tolX, tolG, tolOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, usenaive, seed, quiet, save_results, localsa) : /Users/monty/.julia/v0.5/Mads/src/MadsCalibrate.jl:109

Arguments

  • madsdata::Associative : 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

source

# Mads.checkmodeloutputdirsMethod.

Check the directories where model outputs should be saved for MADS

Methods

  • Mads.checkmodeloutputdirs(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:452

Arguments

  • madsdata::Associative : MADS problem dictionary

Returns:

  • true or false

source

# Mads.checknodedirFunction.

Check if a directory is readable

Methods

  • Mads.checknodedir(dir::String, waittime::Float64) : /Users/monty/.julia/v0.5/Mads/src/MadsExecute.jl:13
  • Mads.checknodedir(dir::String) : /Users/monty/.julia/v0.5/Mads/src/MadsExecute.jl:13
  • Mads.checknodedir(node::String, dir::String, waittime::Float64) : /Users/monty/.julia/v0.5/Mads/src/MadsExecute.jl:4
  • Mads.checknodedir(node::String, dir::String) : /Users/monty/.julia/v0.5/Mads/src/MadsExecute.jl:4

Arguments

  • dir::String : directory
  • node::String : 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

source

# Mads.checkoutFunction.

Checkout (pull) the latest version of Mads modules

Methods

  • Mads.checkout(modulename::String; git, master, force, pull, required, all) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:100
  • Mads.checkout() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:100

Arguments

  • modulename::String : 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]

source

# Mads.checkparameterrangesMethod.

Check parameter ranges for model parameters

Methods

  • Mads.checkparameterranges(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:675

Arguments

  • madsdata::Associative : MADS problem dictionary

source

# Mads.cleancoverageMethod.

Remove Mads coverage files

Methods

  • Mads.cleancoverage() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsTest.jl:24

source

# Mads.cmadsins_obsMethod.

Call C MADS ins_obs() function from MADS dynamic library

Methods

  • Mads.cmadsins_obs(obsid::Array{T<:Any,1}, instructionfilename::String, inputfilename::String) : /Users/monty/.julia/v0.5/Mads/src/../src-old/MadsCMads.jl:40

Arguments

  • inputfilename::String : input file name
  • instructionfilename::String : instruction file name
  • obsid::Array{T<:Any,1} : observation id

Return:

  • observations

source

# Mads.commitFunction.

Commit the latest version of Mads modules in the repository

Methods

  • Mads.commit(commitmsg::String, modulename::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:230
  • Mads.commit(commitmsg::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:230

Arguments

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

source

# Mads.computemassFunction.

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

Methods

  • Mads.computemass(madsdata::Associative; time) : /Users/monty/.julia/v0.5/Mads/src/MadsAnasol.jl:372
  • Mads.computemass(madsfiles::Union{Regex,String}; time, path) : /Users/monty/.julia/v0.5/Mads/src/MadsAnasol.jl:399

Arguments

  • madsdata::Associative : 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=".")

source

# Mads.computeparametersensititiesMethod.

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

Methods

  • Mads.computeparametersensitities(madsdata::Associative, saresults::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:826

Arguments

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

source

# 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::Array{T<:Any,1}, anasolfunction::Function) : /Users/monty/.julia/v0.5/Mads/src/MadsAnasol.jl:342

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::Array{T<:Any,1} : 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)

source

# Mads.copyrightMethod.

Produce MADS copyright information

Methods

  • Mads.copyright() : /Users/monty/.julia/v0.5/Mads/src/MadsHelp.jl:18

source

# Mads.create_documentationMethod.

Create web documentation files for Mads functions

Methods

  • Mads.create_documentation() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:366

source

# Mads.create_tests_offMethod.

Turn off the generation of MADS tests (default)

Methods

  • Mads.create_tests_off() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:112

source

# Mads.create_tests_onMethod.

Turn on the generation of MADS tests (dangerous)

Methods

  • Mads.create_tests_on() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:103

source

# Mads.createmadsobservationsFunction.

Create Mads dictionary of observations and instruction file

Methods

  • Mads.createmadsobservations(nrow::Int64, ncol::Int64; obstring, pretext, prestring, poststring, filename) : /Users/monty/.julia/v0.5/Mads/src/MadsCreate.jl:87
  • Mads.createmadsobservations(nrow::Int64) : /Users/monty/.julia/v0.5/Mads/src/MadsCreate.jl:87

Arguments

  • ncol::Int64 : number of columns [default 1]
  • nrow::Int64 : number of rows

Keywords

  • filename : file name
  • obstring : observation string
  • poststring : post instruction file string
  • prestring : pre instruction file string
  • pretext : preamble instructions

)

Returns:

  • observation dictionary

source

# Mads.createmadsproblemFunction.

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

Methods

  • Mads.createmadsproblem(infilename::String, outfilename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsCreate.jl:5
  • Mads.createmadsproblem(madsdata::Associative, outfilename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsCreate.jl:30
  • Mads.createmadsproblem(madsdata::Associative, predictions::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsCreate.jl:39
  • Mads.createmadsproblem(madsdata::Associative, predictions::Associative, outfilename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsCreate.jl:35

Arguments

  • infilename::String : input Mads file
  • madsdata::Associative : MADS problem dictionary
  • outfilename::String : output Mads file
  • predictions::Associative : dictionary of model predictions

Returns:

  • new madsdata

source

# Mads.createobservations!Function.

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

Methods

  • Mads.createobservations!(madsdata::Associative, time::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:412
  • Mads.createobservations!(madsdata::Associative, time::Array{T<:Any,1}, observation::Array{T<:Any,1}; logtransform, weight_type, weight) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:412
  • Mads.createobservations!(madsdata::Associative, observation::Associative; logtransform, weight_type, weight) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:456

Arguments

  • madsdata::Associative : MADS problem dictionary
  • observation::Array{T<:Any,1} : dictionary of observations
  • observation::Associative : dictionary of observations
  • time::Array{T<:Any,1} : vector of observation times

Keywords

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

source

# Mads.createtempdirMethod.

Create temporary directory

Methods

  • Mads.createtempdir(tempdirname::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:1047

Arguments

  • tempdirname::String : temporary directory name

source

# Mads.deleteNaN!Method.

Delete rows with NaN in a dataframe df

Methods

  • Mads.deleteNaN!(df::DataFrames.DataFrame) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:1050

Arguments

  • df::DataFrames.DataFrame : dataframe

source

# Mads.deletekeyword!Function.

Delete a keyword in a class within the Mads dictionary madsdata

Methods

  • Mads.deletekeyword!(madsdata::Associative, keyword::String) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:238
  • Mads.deletekeyword!(madsdata::Associative, class::String, keyword::String) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:244

Arguments

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

source

# Mads.dependentsFunction.

Lists module dependents on a module (Mads by default)

Methods

  • Mads.dependents(modulename::String, filter::Bool) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:64
  • Mads.dependents(modulename::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:64
  • Mads.dependents() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:64

Arguments

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

Returns:

  • modules that are dependents of the input module

source

# Mads.diffFunction.

Diff the latest version of Mads modules in the repository

Methods

  • Mads.diff(modulename::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:179
  • Mads.diff() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:179

Arguments

  • modulename::String : module name

source

# Mads.displayFunction.

Display image file

Methods

  • Mads.display(p::Gadfly.Plot) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsDisplay.jl:61
  • Mads.display(filename::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsDisplay.jl:7

Arguments

  • filename::String : image file name
  • p::Gadfly.Plot

source

# Mads.dobigdtMethod.

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

Methods

  • Mads.dobigdt(madsdata::Associative, nummodelruns::Int64; numhorizons, numlikelihoods, maxHorizon) : /Users/monty/.julia/v0.5/Mads/src/MadsBayesInfoGap.jl:123

Arguments

  • madsdata::Associative : 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

source

# Mads.dumpasciifileMethod.

Dump ASCII file

Methods

  • Mads.dumpasciifile(filename::String, data) : /Users/monty/.julia/v0.5/Mads/src/MadsASCII.jl:30

Arguments

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

Dumps:

  • ASCII file with the name in "filename"

source

# Mads.dumpjsonfileMethod.

Dump a JSON file

Methods

  • Mads.dumpjsonfile(filename::String, data) : /Users/monty/.julia/v0.5/Mads/src/MadsJSON.jl:38

Arguments

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

Dumps:

  • JSON file with the name in "filename"

source

# Mads.dumpwelldataMethod.

Dump well data from MADS problem dictionary into a ASCII file

Methods

  • Mads.dumpwelldata(madsdata::Associative, filename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:913

Arguments

  • filename::String : output file name
  • madsdata::Associative : MADS problem dictionary

Dumps:

  • filename : a ASCII file

source

# Mads.dumpyamlfileMethod.

Dump YAML file

Methods

  • Mads.dumpyamlfile(filename::String, data; julia) : /Users/monty/.julia/v0.5/Mads/src/MadsYAML.jl:44

Arguments

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

Keywords

  • julia : if true, use julia YAML library (if available); if false (default), use python YAML library (if available)

source

# Mads.dumpyamlmadsfileMethod.

Dump YAML Mads file

Methods

  • Mads.dumpyamlmadsfile(madsdata::Associative, filename::String; julia) : /Users/monty/.julia/v0.5/Mads/src/MadsYAML.jl:63

Arguments

  • filename::String : output file name
  • madsdata::Associative : MADS problem dictionary

Keywords

  • julia : use julia YAML [default=false]

source

# Mads.efastMethod.

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

Methods

  • Mads.efast(md::Associative; N, M, gamma, seed, restart, checkpointfrequency, restartdir) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:1093

Arguments

  • md::Associative : 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]

source

# Mads.emceesamplingFunction.

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

Methods

  • Mads.emceesampling(madsdata::Associative; numwalkers, nsteps, burnin, thinning, sigma, seed, weightfactor) : /Users/monty/.julia/v0.5/Mads/src/MadsMonteCarlo.jl:10
  • Mads.emceesampling(madsdata::Associative, p0::Array; numwalkers, nsteps, burnin, thinning, seed, weightfactor) : /Users/monty/.julia/v0.5/Mads/src/MadsMonteCarlo.jl:33

Arguments

  • madsdata::Associative : 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.emceesampling(madsdata, p0; numwalkers=10, nsteps=100, burnin=10, thinning=1, seed=2016)

source

# Mads.estimationerrorFunction.

Estimate kriging error

Methods

  • Mads.estimationerror(w::Array{T<:Any,1}, x0::Array{T<:Any,1}, X::Array{T<:Any,2}, cov::Function) : /Users/monty/.julia/v0.5/Mads/src/MadsKriging.jl:194
  • Mads.estimationerror(w::Array{T<:Any,1}, covmat::Array{T<:Any,2}, covvec::Array{T<:Any,1}, cov0::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsKriging.jl:201

Arguments

  • X::Array{T<:Any,2} : observation matrix
  • cov0::Number : zero-separation covariance
  • cov::Function : spatial covariance function
  • covmat::Array{T<:Any,2} : covariance matrix
  • covvec::Array{T<:Any,1} : covariance vector
  • w::Array{T<:Any,1} : kriging weights
  • x0::Array{T<:Any,1} : estimated locations

Returns:

  • estimation kriging error

source

# Mads.evaluatemadsexpressionMethod.

Evaluate an expression string based on a parameter dictionary

Methods

  • Mads.evaluatemadsexpression(expressionstring::String, parameters::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsMisc.jl:151

Arguments

  • expressionstring::String : expression string
  • parameters::Associative : parameter dictionary applied to evaluate the expression string

Returns:

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

source

# Mads.evaluatemadsexpressionsMethod.

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

Methods

  • Mads.evaluatemadsexpressions(madsdata::Associative, parameters::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsMisc.jl:170

Arguments

  • madsdata::Associative : MADS problem dictionary
  • parameters::Associative : 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

source

# Mads.expcovMethod.

Exponential spatial covariance function

Methods

  • Mads.expcov(h::Number, maxcov::Number, scale::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsKriging.jl:29

Arguments

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

Returns:

  • covariance

source

# Mads.exponentialvariogramMethod.

Exponential variogram

Methods

  • Mads.exponentialvariogram(h::Number, sill::Number, range::Number, nugget::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsKriging.jl:82

Arguments

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

Returns:

  • Exponential variogram

source

# Mads.filterkeysFunction.

Filter dictionary keys based on a string or regular expression

Methods

  • Mads.filterkeys(dict::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:619
  • Mads.filterkeys(dict::Associative, key::Regex) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:618
  • Mads.filterkeys(dict::Associative, key::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:619

Arguments

  • dict::Associative : dictionary
  • key::Regex : the regular expression or string used to filter dictionary keys
  • key::String : the regular expression or string used to filter dictionary keys

source

# Mads.forwardFunction.

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

Methods

  • Mads.forward(madsdata::Associative; all) : /Users/monty/.julia/v0.5/Mads/src/MadsForward.jl:7
  • Mads.forward(madsdata::Associative, paramvector::Array{T<:Any,1}; all, checkpointfrequency, checkpointfilename) : /Users/monty/.julia/v0.5/Mads/src/MadsForward.jl:11
  • Mads.forward(madsdata::Associative, paramdict::Associative; all, checkpointfrequency, checkpointfilename) : /Users/monty/.julia/v0.5/Mads/src/MadsForward.jl:28
  • Mads.forward(madsdata::Associative, paramarray::Array; all, checkpointfrequency, checkpointfilename) : /Users/monty/.julia/v0.5/Mads/src/MadsForward.jl:58

Arguments

  • madsdata::Associative : MADS problem dictionary
  • paramarray::Array : array of model parameter values
  • paramdict::Associative : dictionary of model parameter values
  • paramvector::Array{T<:Any,1}

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

source

# 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(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsForward.jl:138
  • Mads.forwardgrid(madsdatain::Associative, paramvalues::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsForward.jl:143

Arguments

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

Returns:

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

source

# Mads.freeFunction.

Free Mads modules

Methods

  • Mads.free(modulename::String; required, all) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:206
  • Mads.free() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:206

Arguments

  • modulename::String : module name

Keywords

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

source

# Mads.functionsFunction.

List available functions in the MADS modules:

Methods

  • Mads.functions(string::String; stdout, quiet) : /Users/monty/.julia/v0.5/Mads/src/MadsHelp.jl:31
  • Mads.functions() : /Users/monty/.julia/v0.5/Mads/src/MadsHelp.jl:31
  • Mads.functions(re::Regex; stdout, quiet) : /Users/monty/.julia/v0.5/Mads/src/MadsHelp.jl:22
  • Mads.functions(m::Union{Module,Symbol}) : /Users/monty/.julia/v0.5/Mads/src/MadsHelp.jl:70
  • Mads.functions(m::Union{Module,Symbol}, re::Regex; stdout, quiet) : /Users/monty/.julia/v0.5/Mads/src/MadsHelp.jl:40
  • Mads.functions(m::Union{Module,Symbol}, string::String; stdout, quiet) : /Users/monty/.julia/v0.5/Mads/src/MadsHelp.jl:70

Arguments

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

Keywords

  • quiet
  • stdout

Examples:

Mads.functions()
Mads.functions(BIGUQ)
Mads.functions("get")
Mads.functions(Mads, "get")

source

# Mads.gaussiancovMethod.

Gaussian spatial covariance function

Methods

  • Mads.gaussiancov(h::Number, maxcov::Number, scale::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsKriging.jl:15

Arguments

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

Returns:

  • covariance

source

# Mads.gaussianvariogramMethod.

Gaussian variogram

Methods

  • Mads.gaussianvariogram(h::Number, sill::Number, range::Number, nugget::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsKriging.jl:103

Arguments

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

Returns:

  • Gaussian variogram

source

# Mads.getcovmatMethod.

Get spatial covariance matrix

Methods

  • Mads.getcovmat(X::Array{T<:Any,2}, cov::Function) : /Users/monty/.julia/v0.5/Mads/src/MadsKriging.jl:156

Arguments

  • X::Array{T<:Any,2} : matrix with coordinates of the data points (x or y)
  • cov::Function : spatial covariance function

Returns:

  • spatial covariance matrix

source

# Mads.getcovvec!Method.

Get spatial covariance vector

Methods

  • Mads.getcovvec!(covvec::Array{T<:Any,1}, x0::Array{T<:Any,1}, X::Array{T<:Any,2}, cov::Function) : /Users/monty/.julia/v0.5/Mads/src/MadsKriging.jl:182

Arguments

  • X::Array{T<:Any,2} : matrix with coordinates of the data points
  • cov::Function : spatial covariance function
  • covvec::Array{T<:Any,1} : spatial covariance vector
  • x0::Array{T<:Any,1} : vector with coordinates of the estimation points (x or y)

Returns:

  • spatial covariance vector

source

# Mads.getdictvaluesFunction.

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

Methods

  • Mads.getdictvalues(dict::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:641
  • Mads.getdictvalues(dict::Associative, key::Regex) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:640
  • Mads.getdictvalues(dict::Associative, key::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:641

Arguments

  • dict::Associative : dictionary
  • key::Regex : the key to find value for
  • key::String : the key to find value for

source

# Mads.getdirMethod.

Get directory

Methods

  • Mads.getdir(filename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:258

Arguments

  • filename::String : file name

Returns:

  • directory in file name

Example:

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

source

# Mads.getdistributionMethod.

Parse parameter distribution from a string

Methods

  • Mads.getdistribution(dist::String, i::String, inputtype::String) : /Users/monty/.julia/v0.5/Mads/src/MadsMisc.jl:197

Arguments

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

Returns:

  • distribution

source

# Mads.getextensionMethod.

Get file name extension

Methods

  • Mads.getextension(filename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:432

Arguments

  • filename::String : file name

Returns:

  • file name extension

Example:

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

source

# Mads.getimportantsamplesMethod.

Get important samples

Methods

  • Mads.getimportantsamples(samples::Array, llhoods::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:335

Arguments

  • llhoods::Array{T<:Any,1} : vector of log-likelihoods
  • samples::Array : array of samples

Returns:

  • array of important samples

source

# Mads.getlogparamkeysMethod.

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

source

# Mads.getmadsdirMethod.

Get the directory where currently Mads is running

Methods

  • Mads.getmadsdir() : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:300

Example:

problemdir = Mads.getmadsdir()

Returns:

  • Mads problem directory

source

# Mads.getmadsinputfileMethod.

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

Methods

  • Mads.getmadsinputfile() : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:215

Returns:

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

source

# Mads.getmadsproblemdirMethod.

Get the directory where the Mads data file is located

Methods

  • Mads.getmadsproblemdir(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:281

Arguments

  • madsdata::Associative : MADS problem dictionary

Example:

madsdata = Mads.loadmadsproblem("../../a.mads")
madsproblemdir = Mads.getmadsproblemdir(madsdata)

where madsproblemdir = "../../"

source

# Mads.getmadsrootnameMethod.

Get the MADS problem root name

Methods

  • Mads.getmadsrootname(madsdata::Associative; first, version) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:237

Arguments

  • madsdata::Associative : 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

source

# Mads.getnextmadsfilenameMethod.

Get next mads file name

Methods

  • Mads.getnextmadsfilename(filename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:395

Arguments

  • filename::String : file name

Returns:

  • next mads file name

source

# Mads.getnonlogparamkeysMethod.

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

source

# Mads.getnonoptparamkeysMethod.

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

source

# Mads.getobsdistMethod.

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

source

# Mads.getobsdistMethod.

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

source

# Mads.getobskeysMethod.

Get keys for all observations in the MADS problem dictionary

Methods

  • Mads.getobskeys(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:44

Arguments

  • madsdata::Associative : MADS problem dictionary

Returns:

  • keys for all observations in the MADS problem dictionary

source

# Mads.getobslogMethod.

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

source

# Mads.getobslogMethod.

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

source

# Mads.getobsmaxMethod.

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

source

# Mads.getobsmaxMethod.

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

source

# Mads.getobsminMethod.

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

source

# Mads.getobsminMethod.

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

source

# Mads.getobstargetMethod.

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

source

# Mads.getobstargetMethod.

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

source

# Mads.getobstimeMethod.

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

source

# Mads.getobstimeMethod.

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

source

# Mads.getobsweightMethod.

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

source

# Mads.getobsweightMethod.

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

source

# Mads.getoptparamkeysMethod.

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

source

# Mads.getoptparamsFunction.

Get optimizable parameters

Methods

  • Mads.getoptparams(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:339
  • Mads.getoptparams(madsdata::Associative, parameterarray::Array) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:342
  • Mads.getoptparams(madsdata::Associative, parameterarray::Array, optparameterkey::Array) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:342

Arguments

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

Returns:

  • parameter array

source

# Mads.getparamdictMethod.

Get dictionary with all parameters and their respective initial values

Methods

  • Mads.getparamdict(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:60

Arguments

  • madsdata::Associative : MADS problem dictionary

Returns:

  • dictionary with all parameters and their respective initial values

source

# 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::Associative; init_dist) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:630

Arguments

  • madsdata::Associative : 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

source

# Mads.getparamkeysMethod.

Get keys of all parameters in the MADS problem dictionary

Methods

  • Mads.getparamkeys(madsdata::Associative; filter) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:44

Arguments

  • madsdata::Associative : MADS problem dictionary

Keywords

  • filter : parameter filter

Returns:

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

source

# Mads.getparamrandomFunction.

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

Methods

  • Mads.getparamrandom(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:371
  • Mads.getparamrandom(madsdata::Associative, numsamples::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:371
  • Mads.getparamrandom(madsdata::Associative, numsamples::Integer, parameterkey::String; init_dist) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:371
  • Mads.getparamrandom(madsdata::Associative, parameterkey::String; numsamples, init_dist, paramdist) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:388

Arguments

  • madsdata::Associative : MADS problem dictionary
  • numsamples::Integer : number of samples, [default=1]
  • parameterkey::String : 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

source

# Mads.getparamsinitMethod.

Get an array with init values for parameters defined by paramkeys

source

# Mads.getparamsinitMethod.

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

source

# Mads.getparamsinit_maxFunction.

Get an array with init_max values for parameters defined by paramkeys

Methods

  • Mads.getparamsinit_max(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:294
  • Mads.getparamsinit_max(madsdata::Associative, paramkeys::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:260

Arguments

  • madsdata::Associative : MADS problem dictionary
  • paramkeys::Array{T<:Any,1} : parameter keys

Returns:

  • the parameter values

source

# Mads.getparamsinit_minFunction.

Get an array with init_min values for parameters

Methods

  • Mads.getparamsinit_min(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:243
  • Mads.getparamsinit_min(madsdata::Associative, paramkeys::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:209

Arguments

  • madsdata::Associative : MADS problem dictionary
  • paramkeys::Array{T<:Any,1} : parameter keys

Returns:

  • the parameter values

source

# Mads.getparamslogMethod.

Get an array with log values for parameters defined by paramkeys

source

# Mads.getparamslogMethod.

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

source

# Mads.getparamslongnameMethod.

Get an array with longname values for parameters defined by paramkeys

source

# Mads.getparamslongnameMethod.

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

source

# Mads.getparamsmaxFunction.

Get an array with max values for parameters defined by paramkeys

Methods

  • Mads.getparamsmax(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:192
  • Mads.getparamsmax(madsdata::Associative, paramkeys::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:170

Arguments

  • madsdata::Associative : MADS problem dictionary
  • paramkeys::Array{T<:Any,1} : parameter keys

Returns:

  • returns the parameter values

source

# Mads.getparamsminFunction.

Get an array with min values for parameters defined by paramkeys

Methods

  • Mads.getparamsmin(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:153
  • Mads.getparamsmin(madsdata::Associative, paramkeys::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:131

Arguments

  • madsdata::Associative : MADS problem dictionary
  • paramkeys::Array{T<:Any,1} : parameter keys

Returns:

  • the parameter values

source

# Mads.getparamsplotnameMethod.

Get an array with plotname values for parameters defined by paramkeys

source

# Mads.getparamsplotnameMethod.

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

source

# Mads.getparamsstepMethod.

Get an array with step values for parameters defined by paramkeys

source

# Mads.getparamsstepMethod.

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

source

# Mads.getparamstypeMethod.

Get an array with type values for parameters defined by paramkeys

source

# Mads.getparamstypeMethod.

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

source

# Mads.getprocsMethod.

Get the number of processors

Methods

  • Mads.getprocs() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:25

source

# Mads.getrestartMethod.

Get MADS restart status

Methods

  • Mads.getrestart(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:56

Arguments

  • madsdata::Associative : MADS problem dictionary

source

# Mads.getrestartdirFunction.

Get the directory where Mads restarts will be stored

Methods

  • Mads.getrestartdir(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsFunc.jl:310
  • Mads.getrestartdir(madsdata::Associative, suffix::String) : /Users/monty/.julia/v0.5/Mads/src/MadsFunc.jl:310

Arguments

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

Returns:

  • restart directory where reusable model results will be stored

source

# Mads.getrootnameMethod.

Get file name root

Methods

  • Mads.getrootname(filename::String; first, version) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:330

Arguments

  • filename::String : 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"

source

# Mads.getseedMethod.

Get and return current random seed.

Methods

  • Mads.getseed() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:362

source

# Mads.getsindxMethod.

Get sin-space dx

Methods

  • Mads.getsindx(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:276

Arguments

  • madsdata::Associative : MADS problem dictionary

Returns:

  • sin-space dx

source

# Mads.getsourcekeysMethod.

Get keys of all source parameters in the MADS problem dictionary

Methods

  • Mads.getsourcekeys(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:78

Arguments

  • madsdata::Associative : MADS problem dictionary

Returns:

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

source

# Mads.gettargetMethod.

Get observation target

Methods

  • Mads.gettarget(o::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:219

Arguments

  • o::Associative : observation data

Returns:

  • observation target

source

# Mads.gettargetkeysMethod.

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

Methods

  • Mads.gettargetkeys(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:58

Arguments

  • madsdata::Associative : MADS problem dictionary

Returns:

  • keys for all targets in the MADS problem dictionary

source

# Mads.gettimeMethod.

Get observation time

Methods

  • Mads.gettime(o::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:141

Arguments

  • o::Associative : observation data

Returns:

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

source

# Mads.getweightMethod.

Get observation weight

Methods

  • Mads.getweight(o::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:180

Arguments

  • o::Associative : observation data

Returns:

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

source

# Mads.getwellkeysMethod.

Get keys for all wells in the MADS problem dictionary

Methods

  • Mads.getwellkeys(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:75

Arguments

  • madsdata::Associative : MADS problem dictionary

Returns:

  • keys for all wells in the MADS problem dictionary

source

# Mads.getwellsdataMethod.

Get spatial and temporal data in the Wells class

Methods

  • Mads.getwellsdata(madsdata::Associative; time) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:625

Arguments

  • madsdata::Associative : Mads problem dictionary

Keywords

  • time : get observation times [default=false]

Returns:

  • array with spatial and temporal data in the Wells class

source

# Mads.graphoffMethod.

MADS graph output off

Methods

  • Mads.graphoff() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:94

source

# Mads.graphonMethod.

MADS graph output on

Methods

  • Mads.graphon() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:85

source

# Mads.haskeywordFunction.

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

Methods

  • Mads.haskeyword(madsdata::Associative, keyword::String) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:173
  • Mads.haskeyword(madsdata::Associative, class::String, keyword::String) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:176

Arguments

  • class::String : dictionary class; if not provided searches for keyword in Problem class
  • keyword::String : dictionary key
  • madsdata::Associative : 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"

source

# Mads.helpMethod.

Produce MADS help information

Methods

  • Mads.help() : /Users/monty/.julia/v0.5/Mads/src/MadsHelp.jl:9

source

# Mads.importeverywhereMethod.

Import Julia function everywhere from a file. The first function in the Julia input file is the one that will be called by Mads to perform the model simulations.

Methods

  • Mads.importeverywhere(filename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsFunc.jl:362

Arguments

  • filename::String : file name

Returns:

  • Julia function to execute the model

source

# Mads.indexkeysFunction.

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

Methods

  • Mads.indexkeys(dict::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:630
  • Mads.indexkeys(dict::Associative, key::Regex) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:629
  • Mads.indexkeys(dict::Associative, key::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:630

Arguments

  • dict::Associative : dictionary
  • key::Regex : the key to find index for
  • key::String : the key to find index for

source

# Mads.infogap_jumpFunction.

Information Gap Decision Analysis using JuMP

Methods

  • Mads.infogap_jump() : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsInfoGap.jl:22
  • Mads.infogap_jump(madsdata::Associative; retries, random, maxiter, verbosity, seed, horizons) : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsInfoGap.jl:22

Arguments

  • madsdata::Associative : 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]

source

# Mads.infogap_jump_polinomialFunction.

Information Gap Decision Analysis using JuMP

Methods

  • Mads.infogap_jump_polinomial() : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsInfoGap.jl:126
  • Mads.infogap_jump_polinomial(madsdata::Associative; retries, random, maxiter, verbosity, quiet, plot, model, seed, horizons) : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsInfoGap.jl:126

Arguments

  • madsdata::Associative : 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

source

# Mads.infogap_mpb_linFunction.

Information Gap Decision Analysis using MathProgBase

Methods

  • Mads.infogap_mpb_lin() : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsInfoGap.jl:423
  • Mads.infogap_mpb_lin(madsdata::Associative; retries, random, maxiter, verbosity, seed, horizons, pinit) : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsInfoGap.jl:423

Arguments

  • madsdata::Associative : 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]

source

# Mads.infogap_mpb_polinomialFunction.

Information Gap Decision Analysis using MathProgBase

Methods

  • Mads.infogap_mpb_polinomial() : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsInfoGap.jl:282
  • Mads.infogap_mpb_polinomial(madsdata::Associative; retries, random, maxiter, verbosity, seed, horizons, pinit) : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsInfoGap.jl:282

Arguments

  • madsdata::Associative : 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]

source

# Mads.ins_obsMethod.

Apply Mads instruction file instructionfilename to read model output file modeloutputfilename

Methods

  • Mads.ins_obs(instructionfilename::String, modeloutputfilename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:824

Arguments

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

Returns:

  • obsdict : observation dictionary with the model outputs

source

# Mads.instline2regexsMethod.

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

Methods

  • Mads.instline2regexs(instline::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:727

Arguments

  • instline::String : instruction line

Returns:

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

source

# Mads.invobsweights!Method.

Set inversely proportional observation weights in the MADS problem dictionary

Methods

  • Mads.invobsweights!(madsdata::Associative, multiplier::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:322

Arguments

  • madsdata::Associative : MADS problem dictionary
  • multiplier::Number : weight multiplier

source

# Mads.invwellweights!Method.

Set inversely proportional well weights in the MADS problem dictionary

Methods

  • Mads.invwellweights!(madsdata::Associative, multiplier::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:373

Arguments

  • madsdata::Associative : MADS problem dictionary
  • multiplier::Number : weight multiplier

source

# Mads.islogMethod.

Is parameter with key parameterkey log-transformed?

Methods

  • Mads.islog(madsdata::Associative, parameterkey::String) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:415

Arguments

  • madsdata::Associative : MADS problem dictionary
  • parameterkey::String : parameter key

Returns:

  • true if log-transformed, false otherwise

source

# Mads.isobsMethod.

Is a dictionary containing all the observations

Methods

  • Mads.isobs(madsdata::Associative, dict::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:18

Arguments

  • dict::Associative : dictionary
  • madsdata::Associative : MADS problem dictionary

Returns:

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

source

# Mads.isoptMethod.

Is parameter with key parameterkey optimizable?

Methods

  • Mads.isopt(madsdata::Associative, parameterkey::String) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:395

Arguments

  • madsdata::Associative : MADS problem dictionary
  • parameterkey::String : parameter key

Returns:

  • true if optimizable, false if not

source

# Mads.isparamMethod.

Check if a dictionary containing all the Mads model parameters

Methods

  • Mads.isparam(madsdata::Associative, dict::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:17

Arguments

  • dict::Associative : dictionary
  • madsdata::Associative : MADS problem dictionary

Returns:

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

source

# Mads.ispkgavailableMethod.

Checks if package is available

Methods

  • Mads.ispkgavailable(modulename::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:16

Arguments

  • modulename::String : module name

Returns:

  • true or false

source

# Mads.krigeMethod.

Kriging

Methods

  • Mads.krige(x0mat::Array{T<:Any,2}, X::Array{T<:Any,2}, Z::Array{T<:Any,1}, cov::Function) : /Users/monty/.julia/v0.5/Mads/src/MadsKriging.jl:124

Arguments

  • X::Array{T<:Any,2} : coordinates of the observation (conditioning) data
  • Z::Array{T<:Any,1} : values for the observation (conditioning) data
  • cov::Function : spatial covariance function
  • x0mat::Array{T<:Any,2} : point coordinates at which to obtain kriging estimates

Returns:

  • kriging estimates at x0mat

source

# Mads.levenberg_marquardtFunction.

Levenberg-Marquardt optimization

Methods

  • Mads.levenberg_marquardt(f::Function, g::Function, x0) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:330
  • 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) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:330

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::Vector, of::Number, lambda::Number)->nothing]
  • callbackjacobian : call back function for each Jacobian [default=(x::Vector, J::Matrix)->nothing]
  • lambda : initial Levenberg-Marquardt lambda [default=eps(Float32)]
  • lambda_mu : lambda multiplication factor μ [default=10]
  • lambda_nu : lambda multiplication factor ν [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]

source

# Mads.linktempdirMethod.

Link files in a temporary directory

Methods

  • Mads.linktempdir(madsproblemdir::String, tempdirname::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:1073

Arguments

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

source

# Mads.loadasciifileMethod.

Load ASCII file

Methods

  • Mads.loadasciifile(filename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsASCII.jl:14

Arguments

  • filename::String : ASCII file name

Returns:

  • data from the file

source

# Mads.loadjsonfileMethod.

Load a JSON file

Methods

  • Mads.loadjsonfile(filename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsJSON.jl:16

Arguments

  • filename::String : JSON file name

Returns:

  • data from the JSON file

source

# Mads.loadmadsfileMethod.

Load MADS input file defining a MADS problem dictionary

Methods

  • Mads.loadmadsfile(filename::String; julia, format) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:22

Arguments

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

Keywords

  • format : acceptable formats are yaml and json [default=yaml]
  • julia : if true, force using julia parsing functions; if false (default), use python parsing functions

Returns:

  • MADS problem dictionary

Example:

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

source

# Mads.loadsaltellirestart!Method.

Load Saltelli sensitivity analysis results for fast simulation restarts

Methods

  • Mads.loadsaltellirestart!(evalmat::Array, matname::String, restartdir::String) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:582

Arguments

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

Returns:

  • true when successfully loaded, false when it is not

source

# Mads.loadyamlfileMethod.

Load YAML file

Methods

  • Mads.loadyamlfile(filename::String; julia) : /Users/monty/.julia/v0.5/Mads/src/MadsYAML.jl:17

Arguments

  • filename::String : file name

Keywords

  • julia : if true, use julia YAML library (if available); if false (default), use python YAML library (if available)

Returns:

  • data in the yaml input file

source

# Mads.localsaMethod.

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

Methods

  • Mads.localsa(madsdata::Associative; sinspace, keyword, filename, format, datafiles, imagefiles, par, obs, J) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:118

Arguments

  • madsdata::Associative : 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

source

# Mads.long_tests_offMethod.

Turn off execution of long MADS tests (default)

Methods

  • Mads.long_tests_off() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:130

source

# Mads.long_tests_onMethod.

Turn on execution of long MADS tests

Methods

  • Mads.long_tests_on() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:121

source

# Mads.madscoresFunction.

Check the number of processors on a series of servers

Methods

  • Mads.madscores(nodenames::Array{String,1}) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:301
  • Mads.madscores() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:301

Arguments

  • nodenames::Array{String,1} : array with names of machines/nodes [default=Mads.madsservers]

source

# Mads.madscriticalMethod.

MADS critical error messages

Methods

  • Mads.madscritical(message::String) : /Users/monty/.julia/v0.5/Mads/src/MadsLog.jl:77

Arguments

  • message::String : critical error message

source

# Mads.madsdebugFunction.

MADS debug messages (controlled by quiet and debuglevel)

Methods

  • Mads.madsdebug(message::String, level::Int64) : /Users/monty/.julia/v0.5/Mads/src/MadsLog.jl:26
  • Mads.madsdebug(message::String) : /Users/monty/.julia/v0.5/Mads/src/MadsLog.jl:26

Arguments

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

source

# Mads.madserrorMethod.

MADS error messages

Methods

  • Mads.madserror(message::String) : /Users/monty/.julia/v0.5/Mads/src/MadsLog.jl:67

Arguments

  • message::String : error message

source

# Mads.madsinfoFunction.

MADS information/status messages (controlled by quietandverbositylevel`)

Methods

  • Mads.madsinfo(message::String, level::Int64) : /Users/monty/.julia/v0.5/Mads/src/MadsLog.jl:41
  • Mads.madsinfo(message::String) : /Users/monty/.julia/v0.5/Mads/src/MadsLog.jl:41

Arguments

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

source

# Mads.madsloadFunction.

Check the load of a series of servers

Methods

  • Mads.madsload(nodenames::Array{String,1}) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:321
  • Mads.madsload() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:321

Arguments

  • nodenames::Array{String,1} : array with names of machines/nodes [default=Mads.madsservers]

source

# Mads.madsmathprogbaseFunction.

Define MadsModel type applied for Mads execution using MathProgBase

Methods

  • Mads.madsmathprogbase() : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsMathProgBase.jl:18
  • Mads.madsmathprogbase(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsMathProgBase.jl:18

Arguments

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

source

# Mads.madsoutputFunction.

MADS output (controlled by quiet and verbositylevel)

Methods

  • Mads.madsoutput(message::String, level::Int64) : /Users/monty/.julia/v0.5/Mads/src/MadsLog.jl:11
  • Mads.madsoutput(message::String) : /Users/monty/.julia/v0.5/Mads/src/MadsLog.jl:11

Arguments

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

source

# Mads.madsupFunction.

Check the uptime of a series of servers

Methods

  • Mads.madsup(nodenames::Array{String,1}) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:311
  • Mads.madsup() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:311

Arguments

  • nodenames::Array{String,1} : array with names of machines/nodes [default=Mads.madsservers]

source

# Mads.madswarnMethod.

MADS warning messages

Methods

  • Mads.madswarn(message::String) : /Users/monty/.julia/v0.5/Mads/src/MadsLog.jl:55

Arguments

  • message::String : warning message

source

# Mads.makearrayconditionalloglikelihoodMethod.

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

Methods

  • Mads.makearrayconditionalloglikelihood(madsdata::Associative, conditionalloglikelihood) : /Users/monty/.julia/v0.5/Mads/src/MadsMisc.jl:101

Arguments

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

Returns:

  • a conditional log likelihood function that accepts an array

source

# Mads.makearrayconditionalloglikelihoodMethod.

Make array of conditional log-likelihoods

Methods

  • Mads.makearrayconditionalloglikelihood(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsBayesInfoGap.jl:160
  • Mads.makearrayconditionalloglikelihood(madsdata::Associative, conditionalloglikelihood) : /Users/monty/.julia/v0.5/Mads/src/MadsMisc.jl:101

Arguments

  • conditionalloglikelihood
  • madsdata::Associative : MADS problem dictionary

Returns:

  • array of conditional log-likelihoods

source

# Mads.makearrayfunctionFunction.

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

Methods

  • Mads.makearrayfunction(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsMisc.jl:29
  • Mads.makearrayfunction(madsdata::Associative, f::Function) : /Users/monty/.julia/v0.5/Mads/src/MadsMisc.jl:29

Arguments

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

Returns:

  • function accepting an array containing the optimal parameter values

source

# Mads.makearrayloglikelihoodMethod.

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

Methods

  • Mads.makearrayloglikelihood(madsdata::Associative, loglikelihood) : /Users/monty/.julia/v0.5/Mads/src/MadsMisc.jl:124

Arguments

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

Returns:

  • a log likelihood function that accepts an array

source

# Mads.makebigdt!Method.

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

Methods

  • Mads.makebigdt!(madsdata::Associative, choice::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsBayesInfoGap.jl:35

Arguments

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

Returns:

  • BIG-DT problem type

source

# Mads.makebigdtMethod.

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

Methods

  • Mads.makebigdt(madsdata::Associative, choice::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsBayesInfoGap.jl:20

Arguments

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

Returns:

  • BIG-DT problem type

source

# Mads.makecomputeconcentrationsMethod.

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

Methods

  • Mads.makecomputeconcentrations(madsdata::Associative; calczeroweightobs, calcpredictions) : /Users/monty/.julia/v0.5/Mads/src/MadsAnasol.jl:142

Arguments

  • madsdata::Associative : 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)
paramkeys = Mads.getparamkeys(madsdata)
paramdict = OrderedDict(zip(paramkeys, map(key->madsdata["Parameters"][key]["init"], paramkeys)))
forward_preds = computeconcentrations(paramdict)

source

# Mads.makedixonpriceMethod.

Make dixon price

Methods

  • Mads.makedixonprice(n::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:260

Arguments

  • n::Integer : number of observations

Returns:

  • dixon price

source

# Mads.makedixonprice_gradientMethod.

Methods

  • Mads.makedixonprice(n::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:260

Arguments

  • n::Integer : number of observations

Returns:

  • dixon price gradient

source

# 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::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsMisc.jl:74
  • Mads.makedoublearrayfunction(madsdata::Associative, f::Function) : /Users/monty/.julia/v0.5/Mads/src/MadsMisc.jl:74

Arguments

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

Returns:

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

source

# Mads.makelmfunctionsMethod.

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

Methods

  • Mads.makelmfunctions(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:111

Arguments

  • madsdata::Associative : MADS problem dictionary

Returns:

  • forward model, gradient, objective functions

source

# Mads.makelocalsafunctionMethod.

Make gradient function needed for local sensitivity analysis

Methods

  • Mads.makelocalsafunction(madsdata::Associative; multiplycenterbyweights) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:26

Arguments

  • madsdata::Associative : MADS problem dictionary

Keywords

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

Returns:

  • gradient function

source

# 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::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsFunc.jl:385

Arguments

  • madsdata::Associative : MADS problem dictionary

Return:

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

source

# Mads.makemadscommandfunctionMethod.

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

Methods

  • Mads.makemadscommandfunction(madsdata_in::Associative; calczeroweightobs, calcpredictions, obskeys) : /Users/monty/.julia/v0.5/Mads/src/MadsFunc.jl:62

Arguments

  • madsdata_in::Associative : 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 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 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 a 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.
  • 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 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. 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

Options for reading model outputs:

  • 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

source

# 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::Associative; weightfactor) : /Users/monty/.julia/v0.5/Mads/src/MadsFunc.jl:408

Arguments

  • madsdata::Associative : MADS problem dictionary

Keywords

  • weightfactor : Weight factor [default=1]

Return:

  • the conditional log-likelihood

source

# Mads.makemadsloglikelihoodMethod.

Make a function to compute the log-likelihood for a given set of model parameters, associated model predictions and existing observations. The function can be provided as an external function in the MADS problem dictionary under LogLikelihood or computed internally.

Methods

  • Mads.makemadsloglikelihood(madsdata::Associative; weightfactor) : /Users/monty/.julia/v0.5/Mads/src/MadsFunc.jl:443

Arguments

  • madsdata::Associative : MADS problem dictionary

Keywords

  • weightfactor : Weight factor [default=1]

Returns:

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

source

# Mads.makemadsreusablefunctionFunction.

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

Methods

  • Mads.makemadsreusablefunction(madsdata::Associative, madscommandfunction::Function) : /Users/monty/.julia/v0.5/Mads/src/MadsFunc.jl:262
  • Mads.makemadsreusablefunction(madsdata::Associative, madscommandfunction::Function, suffix::String; usedict) : /Users/monty/.julia/v0.5/Mads/src/MadsFunc.jl:262
  • Mads.makemadsreusablefunction(paramkeys::Array{T<:Any,1}, obskeys::Array{T<:Any,1}, madsdatarestart::Union{Bool,String}, madscommandfunction::Function, restartdir::String; usedict) : /Users/monty/.julia/v0.5/Mads/src/MadsFunc.jl:265

Arguments

  • madscommandfunction::Function : Mads function to execute a forward model simulation
  • madsdata::Associative : MADS problem dictionary
  • madsdatarestart::Union{Bool,String} : Restart type (memory/disk) or on/off status
  • obskeys::Array{T<:Any,1} : Dictionary of observation keys
  • paramkeys::Array{T<:Any,1} : Dictionary of parameter keys
  • restartdir::String : Restart directory where the reusable model results are stored
  • suffix::String : 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)

source

# Mads.makempbfunctionsMethod.

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

Methods

  • Mads.makempbfunctions(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/../src-new/MadsMathProgBase.jl:92

Arguments

  • madsdata::Associative : MADS problem dictionary

Returns:

  • forward model, gradient, objective functions

source

# Mads.makepowellMethod.

Make Powell test function for LM optimization

Methods

  • Mads.makepowell(n::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:163

Arguments

  • n::Integer : number of observations

Returns:

  • Powell test function for LM optimization

source

# Mads.makepowell_gradientMethod.

ake parameter gradients of the Powell test function for LM optimization

Methods

  • Mads.makepowell_gradient(n::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:187

Arguments

  • n::Integer : number of observations

Returns:

  • arameter gradients of the Powell test function for LM optimization

source

# Mads.makerosenbrockMethod.

Make Rosenbrock test function for LM optimization

Methods

  • Mads.makerosenbrock(n::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:118

Arguments

  • n::Integer : number of observations

Returns:

  • Rosenbrock test function for LM optimization

source

# Mads.makerosenbrock_gradientMethod.

Make parameter gradients of the Rosenbrock test function for LM optimization

Methods

  • Mads.makerosenbrock_gradient(n::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:140

Arguments

  • n::Integer : number of observations

Returns:

  • parameter gradients of the Rosenbrock test function for LM optimization

source

# Mads.makerotatedhyperellipsoidMethod.

Methods

  • Mads.makerotatedhyperellipsoid(n::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:339

Arguments

  • n::Integer : number of observations

Returns:

  • rotated hyperellipsoid

source

# Mads.makerotatedhyperellipsoid_gradientMethod.

Methods

  • Mads.makerotatedhyperellipsoid_gradient(n::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:363

Arguments

  • n::Integer : number of observations

Returns:

  • rotated hyperellipsoid gradient

source

# Mads.makesphereMethod.

Make sphere

Methods

  • Mads.makesphere(n::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:218

Arguments

  • n::Integer : number of observations

Returns:

  • sphere

source

# Mads.makesphere_gradientMethod.

Make sphere gradient

Methods

  • Mads.makesphere_gradient(n::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:239

Arguments

  • n::Integer : number of observations

Returns:

  • sphere gradient

source

# Mads.makesumsquaresMethod.

Methods

  • Mads.makesumsquares(n::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:301

Arguments

  • n::Integer : number of observations

Returns:

  • sumsquares

source

# Mads.makesumsquares_gradientMethod.

Methods

  • Mads.makesumsquares_gradient(n::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:320

Arguments

  • n::Integer : number of observations

Returns:

  • sumsquares gradient

source

# Mads.makesvrmodelFunction.

Make SVR model functions (executor and cleaner)

Methods

  • Mads.makesvrmodel(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsSVR.jl:210
  • Mads.makesvrmodel(madsdata::Associative, numberofsamples::Integer; check, addminmax, loadsvr, savesvr, svm_type, kernel_type, degree, gamma, coef0, C, nu, eps, cache_size, tol, shrinking, probability, verbose, seed) : /Users/monty/.julia/v0.5/Mads/src/MadsSVR.jl:210

Arguments

  • madsdata::Associative : 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]
  • eps : 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 loading existing SVR models
  • function saving SVR models
  • function removing SVR models from the memory

source

# Mads.maxtorealmax!Method.

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

Methods

  • Mads.maxtorealmax!(df::DataFrames.DataFrame) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:1067

Arguments

  • df::DataFrames.DataFrame : dataframe

source

# Mads.meshgridMethod.

Create mesh grid

Methods

  • Mads.meshgrid(x::Array{T<:Any,1}, y::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:332

Arguments

  • x::Array{T<:Any,1} : vector of grid x coordinates
  • y::Array{T<:Any,1} : vector of grid y coordinates

Returns:

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

source

# Mads.mkdirMethod.

Create a directory (if does not already exist)

Methods

  • Mads.mkdir(dirname::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:1100

Arguments

  • dirname::String : directory

source

# Mads.modelinformationcriteriaFunction.

Model section information criteria

Methods

  • Mads.modelinformationcriteria(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsModelSelection.jl:11
  • Mads.modelinformationcriteria(madsdata::Associative, par::Array{Float64,N<:Any}) : /Users/monty/.julia/v0.5/Mads/src/MadsModelSelection.jl:11

Arguments

  • madsdata::Associative : MADS problem dictionary
  • par::Array{Float64,N<:Any} : parameter array

source

# Mads.modobsweights!Method.

Modify (multiply) observation weights in the MADS problem dictionary

Methods

  • Mads.modobsweights!(madsdata::Associative, value::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:308

Arguments

  • madsdata::Associative : MADS problem dictionary
  • value::Number : value for modifing observation weights

source

# Mads.modwellweights!Method.

Modify (multiply) well weights in the MADS problem dictionary

Methods

  • Mads.modwellweights!(madsdata::Associative, value::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:356

Arguments

  • madsdata::Associative : MADS problem dictionary
  • value::Number : value for well weights

source

# Mads.montecarloMethod.

Monte Carlo analysis

Methods

  • Mads.montecarlo(madsdata::Associative; N, filename) : /Users/monty/.julia/v0.5/Mads/src/MadsMonteCarlo.jl:187

Arguments

  • madsdata::Associative : 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)

source

# Mads.naive_get_deltaxMethod.

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

Methods

  • Mads.naive_get_deltax(JpJ::Array{Float64,2}, Jp::Array{Float64,2}, f0::Array{Float64,1}, lambda::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:218

Arguments

  • Jp::Array{Float64,2} : Jacobian matrix times model parameters
  • JpJ::Array{Float64,2} : Jacobian matrix times model parameters times transposed Jacobian matrix
  • f0::Array{Float64,1} : initial model observations
  • lambda::Number : Levenberg-Marquardt lambda

Returns:

  • the LM parameter space step

source

# Mads.naive_levenberg_marquardtFunction.

Naive Levenberg-Marquardt optimization

Methods

  • Mads.naive_levenberg_marquardt(f::Function, g::Function, x0::Array{Float64,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:268
  • Mads.naive_levenberg_marquardt(f::Function, g::Function, x0::Array{Float64,1}, o::Function; maxIter, maxEval, np_lambda, lambda, lambda_mu) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:268

Arguments

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

Keywords

  • lambda : initial Levenberg-Marquardt lambda [default=100]
  • lambda_mu : lambda multiplication factor μ [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:

source

# Mads.naive_lm_iterationMethod.

Naive Levenberg-Marquardt optimization: perform LM iteration

Methods

  • Mads.naive_lm_iteration(f::Function, g::Function, o::Function, x0::Array{Float64,1}, f0::Array{Float64,1}, lambdas::Array{Float64,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:239

Arguments

  • f0::Array{Float64,1} : initial model observations
  • f::Function : forward model function
  • g::Function : gradient function for the forward model
  • lambdas::Array{Float64,1} : Levenberg-Marquardt lambdas
  • o::Function : objective function
  • x0::Array{Float64,1} : initial parameter guess

Returns:

source

# Mads.noplotMethod.

Disable MADS plotting

Methods

  • Mads.noplot() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:234

source

# Mads.obslineismatchMethod.

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

Methods

  • Mads.obslineismatch(obsline::String, regexs::Array{Regex,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:776

Arguments

  • obsline::String : instruction line
  • regexs::Array{Regex,1} : regular expressions

Returns:

  • true or false

source

# Mads.ofFunction.

Compute objective function

Methods

  • Mads.of(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:59
  • Mads.of(madsdata::Associative, resultvec::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:52
  • Mads.of(madsdata::Associative, resultdict::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:56

Arguments

  • madsdata::Associative : MADS problem dictionary
  • resultdict::Associative : result dictionary
  • resultvec::Array{T<:Any,1} : result vector

source

# Mads.paramarray2dictMethod.

Convert a parameter array to a parameter dictionary of arrays

Methods

  • Mads.paramarray2dict(madsdata::Associative, array::Array) : /Users/monty/.julia/v0.5/Mads/src/MadsMonteCarlo.jl:241

Arguments

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

Returns:

  • a parameter dictionary of arrays

source

# Mads.paramdict2arrayMethod.

Convert a parameter dictionary of arrays to a parameter array

Methods

  • Mads.paramdict2array(dict::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsMonteCarlo.jl:260

Arguments

  • dict::Associative : parameter dictionary of arrays

Returns:

  • a parameter array

source

# Mads.parsemadsdata!Method.

Parse loaded MADS problem dictionary

Methods

  • Mads.parsemadsdata!(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:51

Arguments

  • madsdata::Associative : MADS problem dictionary

source

# Mads.parsenodenamesFunction.

Parse string with node names defined in SLURM

Methods

  • Mads.parsenodenames(nodenames::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:203
  • Mads.parsenodenames(nodenames::String, ntasks_per_node::Integer) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:203

Arguments

  • nodenames::String : 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)

source

# Mads.partialofMethod.

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

Methods

  • Mads.partialof(madsdata::Associative, resultdict::Associative, regex::Regex) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:85

Arguments

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

Returns:

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

source

# Mads.pkgversionMethod.

Get package version

Methods

  • Mads.pkgversion(modulestr::String) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:375

Arguments

  • modulestr::String

Returns:

  • package version

source

# 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::Associative; addtitle, title, filename, format) : /Users/monty/.julia/v0.5/Mads/src/MadsPlotPy.jl:57
  • Mads.plotgrid(madsdata::Associative, s::Array{Float64,N<:Any}; addtitle, title, filename, format) : /Users/monty/.julia/v0.5/Mads/src/MadsPlotPy.jl:6
  • Mads.plotgrid(madsdata::Associative, parameters::Associative; addtitle, title, filename, format) : /Users/monty/.julia/v0.5/Mads/src/MadsPlotPy.jl:62

Arguments

  • madsdata::Associative : MADS problem dictionary
  • parameters::Associative : dictionary with model parameters
  • s::Array{Float64,N<:Any} : 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.plotgrid(madsdata; addtitle=true, title="", filename="", format="")
Mads.plotgrid(madsdata, parameters; addtitle=true, title="", filename="", format="")

source

# Mads.plotlocalsaMethod.

Plot local sensitivity analysis results

Methods

  • Mads.plotlocalsa(filenameroot::String; keyword, filename, format) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:1113

Arguments

  • filenameroot::String : 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

source

# Mads.plotmadsproblemMethod.

Plot contaminant sources and wells defined in MADS problem dictionary

Methods

  • Mads.plotmadsproblem(madsdata::Associative; imagefile, format, filename, keyword) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:75

Arguments

  • madsdata::Associative : MADS problem dictionary

Keywords

  • filename : output file name
  • format : output plot format (png, pdf, etc.) [default=Mads.graphbackend]
  • imagefile : dump image file [default=false]
  • keyword : to be added in the filename

Dumps:

  • plot of contaminant sources and wells

source

# Mads.plotmassMethod.

Plot injected/reduced contaminant mass

Methods

  • Mads.plotmass(lambda::Array{Float64,1}, mass_injected::Array{Float64,1}, mass_reduced::Array{Float64,1}, filename::String; format) : /Users/monty/.julia/v0.5/Mads/src/MadsAnasolPlot.jl:19

Arguments

  • filename::String : output filename for the generated plot
  • lambda::Array{Float64,1} : array with all the lambda values
  • mass_injected::Array{Float64,1} : array with associated total injected mass
  • mass_reduced::Array{Float64,1} : array with associated total reduced mass

Keywords

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

Dumps:

  • image file with name filename and in specified format

source

# Mads.plotmatchesFunction.

Plot the matches between model predictions and observations

Methods

  • Mads.plotmatches(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:146
  • Mads.plotmatches(madsdata::Associative, rx::Regex; plotdata, filename, format, title, xtitle, ytitle, ymin, ymax, separate_files, hsize, vsize, linewidth, pointsize, obs_plot_dots, noise, dpi, colors, display) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:146
  • Mads.plotmatches(madsdata::Associative, dict_in::Associative; plotdata, filename, format, title, xtitle, ytitle, ymin, ymax, separate_files, hsize, vsize, linewidth, pointsize, obs_plot_dots, noise, dpi, colors, display) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:178
  • Mads.plotmatches(madsdata::Associative, result::Associative, rx::Regex; plotdata, filename, format, key2time, title, xtitle, ytitle, ymin, ymax, separate_files, hsize, vsize, linewidth, pointsize, obs_plot_dots, noise, dpi, colors, display) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:154

Arguments

  • dict_in::Associative : dictionary with model parameters
  • madsdata::Associative : MADS problem dictionary
  • result::Associative : dictionary with model predictions
  • rx::Regex : regular expression to filter the outputs

Keywords

  • colors : array with plot colors
  • display : display plots [default=false]
  • dpi : graph resolution [default=Mads.dpi]
  • filename : output file name
  • format : output plot format (png, pdf, etc.) [default=Mads.graphbackend]
  • hsize : graph horizontal size [default=6Gadfly.inch]
  • key2time : user provided function to convert observation keys to observation times
  • linewidth : line width [default=2Gadfly.pt]
  • noise : random noise magnitude [default=0; no noise]
  • 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=4Gadfly.pt]
  • separate_files : plot data for multiple wells separately [default=false]
  • title : graph title
  • vsize : graph vertical size [default=4Gadfly.inch]
  • 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.plotmatches(madsdata, dict_in; filename="", format="")
Mads.plotmatches(madsdata, result; filename="", format="")
Mads.plotmatches(madsdata, result, r"NO3"; filename="", format="")

source

# Mads.plotobsSAresultsMethod.

Plot the sensitivity analysis results for the observations

Methods

  • Mads.plotobsSAresults(madsdata::Associative, result::Associative; filter, keyword, filename, format, debug, separate_files, xtitle, ytitle, linewidth, pointsize) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:550

Arguments

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

Keywords

  • debug : [default=false]
  • 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
  • linewidth : line width [default=2Gadfly.pt]
  • pointsize : point size [default=2Gadfly.pt]
  • separate_files : plot data for multiple wells separately [default=false]
  • xtitle : x-axis title [default="Time [years]"]
  • ytitle : y-axis title [default="Concentration [ppb]"]

Dumps:

  • plot of the sensitivity analysis results for the observations

source

# Mads.plotrobustnesscurvesMethod.

Plot BIG-DT robustness curves

Methods

  • Mads.plotrobustnesscurves(madsdata::Associative, bigdtresults::Dict; filename, format, maxprob, maxhoriz) : /Users/monty/.julia/v0.5/Mads/src/MadsBayesInfoGapPlot.jl:20

Arguments

  • bigdtresults::Dict : BIG-DT results
  • madsdata::Associative : 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

source

# Mads.plotseriesFunction.

Create plots of data series

Methods

  • Mads.plotseries(X::Array{T<:Any,2}) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:1050
  • Mads.plotseries(X::Array{T<:Any,2}, filename::String; format, xtitle, ytitle, title, name, combined, hsize, vsize, linewidth, dpi, colors) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:1050

Arguments

  • X::Array{T<:Any,2} : matrix with the series data
  • filename::String : output file name

Keywords

  • colors : colors to use in plots
  • combined : combine plots [default=true]
  • dpi : graph resolution [default=Mads.dpi]
  • format : output plot format (png, pdf, etc.) [default=Mads.graphbackend]
  • hsize : horizontal size [default=6Gadfly.inch]
  • linewidth : width of the lines in plot [default=2Gadfly.pt]
  • name : series name [default=Sources]
  • title : plot title [default=Sources]
  • vsize : vertical size [default=4Gadfly.inch]
  • xtitle : x-axis title [default=X]
  • ytitle : y-axis title [default=Y]

Dumps:

  • Plots of data series

source

# Mads.plotwellSAresultsFunction.

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

Methods

  • Mads.plotwellSAresults(madsdata::Associative, result::Associative; xtitle, ytitle, filename, format) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:415
  • Mads.plotwellSAresults(madsdata::Associative, result::Associative, wellname::String; xtitle, ytitle, filename, format) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:426

Arguments

  • madsdata::Associative : MADS problem dictionary
  • result::Associative : sensitivity analysis results
  • wellname::String : well name

Keywords

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

Dumps:

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

source

# Mads.printSAresultsMethod.

Print sensitivity analysis results

Methods

  • Mads.printSAresults(madsdata::Associative, results::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:903

Arguments

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

source

# Mads.printSAresults2Method.

Print sensitivity analysis results (method 2)

Methods

  • Mads.printSAresults2(madsdata::Associative, results::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:985

Arguments

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

source

# Mads.printerrormsgMethod.

Print error message

Methods

  • Mads.printerrormsg(e) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:313

Arguments

  • e : error message

source

# Mads.pullFunction.

Pull (checkout) the latest version of Mads modules

Methods

  • Mads.pull(modulename::String; kw...) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:84
  • Mads.pull() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:84

Arguments

  • modulename::String : module name

source

# Mads.pushFunction.

Push the latest version of Mads modules in the default remote repository

Methods

  • Mads.push(modulename::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:153
  • Mads.push() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:153

Arguments

  • modulename::String : module name

source

# Mads.quietoffMethod.

Make MADS not quiet

Methods

  • Mads.quietoff() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:75

source

# Mads.quietonMethod.

Make MADS quiet

Methods

  • Mads.quieton() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:65

source

# Mads.readasciipredictionsMethod.

Read MADS predictions from an ASCII file

Methods

  • Mads.readasciipredictions(filename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsASCII.jl:44

Arguments

  • filename::String : ASCII file name

Returns:

  • MADS predictions

source

# Mads.readmodeloutputMethod.

Read model outputs saved for MADS

Methods

  • Mads.readmodeloutput(madsdata::Associative; obskeys) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:567

Arguments

  • madsdata::Associative : MADS problem dictionary

Keywords

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

source

# Mads.readobservationsFunction.

Read observations

Methods

  • Mads.readobservations(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:873
  • Mads.readobservations(madsdata::Associative, obskeys::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:873

Arguments

  • madsdata::Associative : MADS problem dictionary
  • obskeys::Array{T<:Any,1} : observation keys [default=getobskeys(madsdata)]

Returns:

  • dictionary with Mads observations

source

# Mads.readobservations_cmadsMethod.

Read observations using C MADS dynamic library

Methods

  • Mads.readobservations_cmads(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/../src-old/MadsCMads.jl:15

Arguments

  • madsdata::Associative : Mads problem dictionary

Returns:

  • observations

source

# Mads.readyamlpredictionsMethod.

Read MADS model predictions from a YAML file filename

Methods

  • Mads.readyamlpredictions(filename::String; julia) : /Users/monty/.julia/v0.5/Mads/src/MadsYAML.jl:126

Arguments

  • filename::String : file name

Keywords

  • julia : if true, use julia YAML library (if available); if false (default), use python YAML library (if available)

Returns:

  • data in yaml input file

source

# Mads.regexs2obsMethod.

Get observations for a set of regular expressions

Methods

  • Mads.regexs2obs(obsline::String, regexs::Array{Regex,1}, obsnames::Array{String,1}, getparamhere::Array{Bool,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:794

Arguments

  • getparamhere::Array{Bool,1} : parameters
  • obsline::String : observation line
  • obsnames::Array{String,1} : observation names
  • regexs::Array{Regex,1} : regular expressions

Returns:

  • obsdict : observations

source

# Mads.reloadMethod.

Reload Mads modules

Methods

  • Mads.reload() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsTest.jl:38

source

# Mads.removesource!Function.

Remove a contamination source

Methods

  • Mads.removesource!(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsAnasol.jl:44
  • Mads.removesource!(madsdata::Associative, sourceid::Int64) : /Users/monty/.julia/v0.5/Mads/src/MadsAnasol.jl:44

Arguments

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

source

# Mads.removesourceparameters!Method.

Remove contaminant source parameters

Methods

  • Mads.removesourceparameters!(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsAnasol.jl:99

Arguments

  • madsdata::Associative : MADS problem dictionary

source

# Mads.requiredFunction.

Lists modules required by a module (Mads by default)

Methods

  • Mads.required(modulename::String, filtermodule::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:38
  • Mads.required(modulename::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:38
  • Mads.required() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:38

Arguments

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

Returns:

  • filtered modules

source

# Mads.resetmodelrunsMethod.

Reset the model runs count to be equal to zero

Methods

  • Mads.resetmodelruns() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:169

source

# Mads.residualsFunction.

Compute residuals

Methods

  • Mads.residuals(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:33
  • Mads.residuals(madsdata::Associative, resultvec::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:7
  • Mads.residuals(madsdata::Associative, resultdict::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsLevenbergMarquardt.jl:30

Arguments

  • madsdata::Associative : MADS problem dictionary
  • resultdict::Associative : result dictionary
  • resultvec::Array{T<:Any,1} : result vector

Returns:

source

# Mads.restartoffMethod.

MADS restart off

Methods

  • Mads.restartoff() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:45

source

# Mads.restartonMethod.

MADS restart on

Methods

  • Mads.restarton() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:36

source

# Mads.reweighsamplesMethod.

Reweigh samples using importance sampling – returns a vector of log-likelihoods after reweighing

Methods

  • Mads.reweighsamples(madsdata::Associative, predictions::Array, oldllhoods::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:309

Arguments

  • madsdata::Associative : MADS problem dictionary
  • oldllhoods::Array{T<:Any,1} : 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

source

# Mads.rmdirMethod.

Remove directory

Methods

  • Mads.rmdir(dir::String; path) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:972

Arguments

  • dir::String : directory to be removed

Keywords

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

source

# Mads.rmfileMethod.

Remove file

Methods

  • Mads.rmfile(filename::String; path) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:988

Arguments

  • filename::String : file to be removed

Keywords

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

source

# Mads.rmfilesMethod.

Remove files

Methods

  • Mads.rmfile(filename::String; path) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:988

Arguments

  • filename::String

Keywords

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

source

# Mads.rmfiles_extMethod.

Remove files with extension ext

Methods

  • Mads.rmfiles_ext(ext::String; path) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:1017

Arguments

  • ext::String : extension

Keywords

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

source

# Mads.rmfiles_rootMethod.

Remove files with root root

Methods

  • Mads.rmfiles_root(root::String; path) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:1030

Arguments

  • root::String : root

Keywords

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

source

# Mads.rosenbrockMethod.

Rosenbrock test function

Methods

  • Mads.rosenbrock(x::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:43

Arguments

  • x::Array{T<:Any,1} : parameter vector

Returns:

  • test result

source

# Mads.rosenbrock2_gradient_lmMethod.

Parameter gradients of the Rosenbrock test function

Methods

  • Mads.rosenbrock2_gradient_lm(x::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:24

Arguments

  • x::Array{T<:Any,1} : parameter vector

Returns:

  • parameter gradients

source

# Mads.rosenbrock2_lmMethod.

Rosenbrock test function (more difficult to solve)

Methods

  • Mads.rosenbrock2_lm(x::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:10

Arguments

  • x::Array{T<:Any,1} : parameter vector

source

# Mads.rosenbrock_gradient!Method.

Parameter gradients of the Rosenbrock test function

Methods

  • Mads.rosenbrock_gradient!(x::Array{T<:Any,1}, grad::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:68

Arguments

  • grad::Array{T<:Any,1} : gradient vector
  • x::Array{T<:Any,1} : parameter vector

source

# 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::Array{T<:Any,1}; dx, center) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:85

Arguments

  • x::Array{T<:Any,1} : parameter vector

Keywords

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

Returns:

  • parameter gradients

source

# Mads.rosenbrock_hessian!Method.

Parameter Hessian of the Rosenbrock test function

Methods

  • Mads.rosenbrock_hessian!(x::Array{T<:Any,1}, hess::Array{T<:Any,2}) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:101

Arguments

  • hess::Array{T<:Any,2} : Hessian matrix
  • x::Array{T<:Any,1} : parameter vector

source

# Mads.rosenbrock_lmMethod.

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

Methods

  • Mads.rosenbrock_lm(x::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsTestFunctions.jl:57

Arguments

  • x::Array{T<:Any,1} : parameter vector

Returns:

  • test result

source

# Mads.runcmdFunction.

Run external command and pipe stdout and stderr

Methods

  • Mads.runcmd(cmdstring::String; pipe, quiet, waittime) : /Users/monty/.julia/v0.5/Mads/src/MadsExecute.jl:97
  • Mads.runcmd(cmd::Cmd; pipe, quiet, waittime) : /Users/monty/.julia/v0.5/Mads/src/MadsExecute.jl:42

Arguments

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

source

# Mads.runremoteFunction.

Run remote command on a series of servers

Methods

  • Mads.runremote(cmd::String, nodenames::Array{String,1}) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:279
  • Mads.runremote(cmd::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:279

Arguments

  • cmd::String : remote command
  • nodenames::Array{String,1} : names of machines/nodes [default=Mads.madsservers]

Returns:

  • output of running remote command

source

# Mads.saltelliMethod.

Saltelli sensitivity analysis

Methods

  • Mads.saltelli(madsdata::Associative; N, seed, parallel, restartdir, checkpointfrequency) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:622

Arguments

  • madsdata::Associative : 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]

source

# Mads.saltellibruteMethod.

Saltelli sensitivity analysis (brute force)

Methods

  • Mads.saltellibrute(madsdata::Associative; N, seed, restartdir) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:434

Arguments

  • madsdata::Associative : 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]

source

# Mads.saltellibruteparallelMethod.

Parallel version of saltellibrute

source

# Mads.saltelliparallelMethod.

Parallel version of saltelli

source

# Mads.samplingMethod.

Methods

  • Mads.sampling(param::Array{T<:Any,1}, J::Array, numsamples::Number; seed, scale) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:256

Arguments

  • J::Array : Jacobian matrix
  • numsamples::Number : Number of samples
  • param::Array{T<:Any,1} : Parameter vector

Keywords

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

Returns:

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

source

# Mads.savemadsfileFunction.

Save MADS problem dictionary madsdata in MADS input file filename

Methods

  • Mads.savemadsfile(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:146
  • Mads.savemadsfile(madsdata::Associative, filename::String; julia, explicit) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:146
  • Mads.savemadsfile(madsdata::Associative, parameters::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:152
  • Mads.savemadsfile(madsdata::Associative, parameters::Associative, filename::String; julia, explicit) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:152

Arguments

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

Keywords

  • explicit : if true ignores MADS YAML file modifications and rereads the original input file [default=false]
  • julia : if true use Julia JSON module to save [default=false]

Example:

Mads.savemadsfile(madsdata)
Mads.savemadsfile(madsdata, "test.mads")
Mads.savemadsfile(madsdata, parameters, "test.mads")
Mads.savemadsfile(madsdata, parameters, "test.mads", explicit=true)

source

# Mads.savemcmcresultsMethod.

Save MCMC chain in a file

Methods

  • Mads.savemcmcresults(chain::Array, filename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsMonteCarlo.jl:142

Arguments

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

Dumps:

  • the file containing MCMC chain

source

# Mads.savesaltellirestartMethod.

Save Saltelli sensitivity analysis results for fast simulation restarts

Methods

  • Mads.savesaltellirestart(evalmat::Array, matname::String, restartdir::String) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:603

Arguments

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

source

# Mads.scatterplotsamplesMethod.

Create histogram/scatter plots of model parameter samples

Methods

  • Mads.scatterplotsamples(madsdata::Associative, samples::Array{T<:Any,2}, filename::String; format, pointsize) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:378

Arguments

  • filename::String : output file name
  • madsdata::Associative : MADS problem dictionary
  • samples::Array{T<:Any,2} : 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

source

# 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::String; path) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:596
  • Mads.searchdir(key::Regex; path) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:595

Arguments

  • key::Regex : matching pattern for Mads input files (string or regular expression accepted)
  • key::String : 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 = ".")`

source

# Mads.set_nprocs_per_taskFunction.

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

Methods

  • Mads.set_nprocs_per_task() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:27
  • Mads.set_nprocs_per_task(local_nprocs_per_task::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:27

Arguments

  • local_nprocs_per_task::Integer

source

# Mads.setallparamsoff!Method.

Set all parameters OFF

Methods

  • Mads.setallparamsoff!(madsdata::Associative; filter) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:444

Arguments

  • madsdata::Associative : MADS problem dictionary

Keywords

  • filter : parameter filter

source

# Mads.setallparamson!Method.

Set all parameters ON

Methods

  • Mads.setallparamson!(madsdata::Associative; filter) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:430

Arguments

  • madsdata::Associative : MADS problem dictionary

Keywords

  • filter : parameter filter

source

# Mads.setdebuglevelMethod.

Set MADS debug level

Methods

  • Mads.setdebuglevel(level::Int64) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:140

Arguments

  • level::Int64 : debug level

source

# Mads.setdefaultplotformatMethod.

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

Methods

  • Mads.setdefaultplotformat(format::String) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:16

Arguments

  • format::String : plot format

source

# Mads.setdirFunction.

Set the working directory (for parallel environments)

Methods

  • Mads.setdir() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:249
  • Mads.setdir(dir) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:244

Arguments

  • dir : directory

Example:

@everywhere Mads.setdir()
@everywhere Mads.setdir("/home/monty")

source

# Mads.setexecutionwaittimeMethod.

Set maximum execution wait time for forward model runs in seconds

Methods

  • Mads.setexecutionwaittime(waitime::Float64) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:160

Arguments

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

source

# Mads.setmadsinputfileMethod.

Set a default MADS input file

Methods

  • Mads.setmadsinputfile(filename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:202

Arguments

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

source

# Mads.setmodelinputsMethod.

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

Methods

  • Mads.setmodelinputs(madsdata::Associative, parameters::Associative; path) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:499

Arguments

  • madsdata::Associative : MADS problem dictionary
  • parameters::Associative : parameters

Keywords

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

source

# Mads.setnewmadsfilenameFunction.

Set new mads file name

Methods

  • Mads.setnewmadsfilename(filename::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:356
  • Mads.setnewmadsfilename(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:353

Arguments

  • filename::String : file name
  • madsdata::Associative : MADS problem dictionary

Returns:

  • new file name

source

# Mads.setobservationtargets!Method.

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

Methods

  • Mads.setobservationtargets!(madsdata::Associative, predictions::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:496

Arguments

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

source

# Mads.setobstime!Function.

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

Methods

  • Mads.setobstime!(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:248
  • Mads.setobstime!(madsdata::Associative, separator::String) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:248
  • Mads.setobstime!(madsdata::Associative, rx::Regex) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:259

Arguments

  • madsdata::Associative : MADS problem dictionary
  • rx::Regex : regular expression to match
  • separator::String : separator [default=_]

Examples:

Mads.setobstime!(madsdata, "_t")
Mads.setobstime!(madsdata, r"[A-x]*_t([0-9,.]+)")

source

# Mads.setobsweights!Method.

Set observation weights in the MADS problem dictionary

Methods

  • Mads.setobsweights!(madsdata::Associative, value::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:294

Arguments

  • madsdata::Associative : MADS problem dictionary
  • value::Number : value for observation weights

source

# Mads.setoptparamsinit!Function.

Set initial optimized parameter guesses in the MADS problem dictionary

Methods

  • Mads.setparamsinit!(madsdata::Associative, paramdict::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:318

Arguments

  • madsdata::Associative : MADS problem dictionary
  • paramdict::Associative : dictionary with initial model parameter values

source

# Mads.setparamoff!Method.

Set a specific parameter with a key parameterkey OFF

Methods

  • Mads.setparamoff!(madsdata::Associative, parameterkey::String) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:469

Arguments

  • madsdata::Associative : MADS problem dictionary
  • parameterkey::String : parameter key

source

# Mads.setparamon!Method.

Set a specific parameter with a key parameterkey ON

Methods

  • Mads.setparamon!(madsdata::Associative, parameterkey::String) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:458

Arguments

  • madsdata::Associative : MADS problem dictionary
  • parameterkey::String : parameter key

source

# Mads.setparamsdistnormal!Method.

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

Methods

  • Mads.setparamsdistnormal!(madsdata::Associative, mean::Array{T<:Any,1}, stddev::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:481

Arguments

  • madsdata::Associative : MADS problem dictionary
  • mean::Array{T<:Any,1} : array with the mean values
  • stddev::Array{T<:Any,1} : array with the standard deviation values

source

# Mads.setparamsdistuniform!Method.

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

Methods

  • Mads.setparamsdistuniform!(madsdata::Associative, min::Array{T<:Any,1}, max::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:496

Arguments

  • madsdata::Associative : MADS problem dictionary
  • max::Array{T<:Any,1} : array with the maximum values
  • min::Array{T<:Any,1} : array with the minimum values

source

# Mads.setparamsinit!Method.

Set initial parameter guesses in the MADS problem dictionary

Methods

  • Mads.setparamsinit!(madsdata::Associative, paramdict::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:318

Arguments

  • madsdata::Associative : MADS problem dictionary
  • paramdict::Associative : dictionary with initial model parameter values

source

# 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::String, format::String) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:36

Arguments

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

Returns:

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

source

# Mads.setprocsFunction.

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

Methods

  • Mads.setprocs(; ntasks_per_node, mads_servers, test, dir, exename, nprocs_per_task, nodenames, quiet) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:45
  • Mads.setprocs(np::Integer) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:42
  • Mads.setprocs(np::Integer, nt::Integer) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsParallel.jl:29

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]

Returns:

  • vector with names of compute nodes (hosts)

Example:

Mads.setprocs()
Mads.setprocs(4)
Mads.setprocs(4, 8)
Mads.setprocs(ntasks_per_node=4)
Mads.setprocs(ntasks_per_node=32, mads_servers=true)
Mads.setprocs(ntasks_per_node=64, nodenames=Mads.madsservers)
Mads.setprocs(ntasks_per_node=64, nodenames=["madsmax", "madszem"])
Mads.setprocs(ntasks_per_node=64, nodenames="wc[096-157,160,175]")
Mads.setprocs(ntasks_per_node=64, mads_servers=true, exename="/home/monty/bin/julia", dir="/home/monty")

source

# Mads.setseedFunction.

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

Methods

  • Mads.setseed() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:347
  • Mads.setseed(seed::Integer) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:347
  • Mads.setseed(seed::Integer, quiet::Bool) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:347

Arguments

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

source

# Mads.settarget!Method.

Set observation target

Methods

  • Mads.settarget!(o::Associative, target::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:238

Arguments

  • o::Associative : observation data
  • target::Number : observation target

source

# Mads.settime!Method.

Set observation time

Methods

  • Mads.settime!(o::Associative, time::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:160

Arguments

  • o::Associative : observation data
  • time::Number : observation time

source

# Mads.setverbositylevelMethod.

Set MADS verbosity level

Methods

  • Mads.setverbositylevel(level::Int64) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:150

Arguments

  • level::Int64 : debug level

source

# Mads.setweight!Method.

Set observation weight

Methods

  • Mads.setweight!(o::Associative, weight::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:199

Arguments

  • o::Associative : observation data
  • weight::Number : observation weight

source

# Mads.setwellweights!Method.

Set well weights in the MADS problem dictionary

Methods

  • Mads.setwellweights!(madsdata::Associative, value::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:339

Arguments

  • madsdata::Associative : MADS problem dictionary
  • value::Number : value for well weights

source

# Mads.showallparametersMethod.

Show all parameters in the MADS problem dictionary

Methods

  • Mads.showallparameters(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:576

Arguments

  • madsdata::Associative : MADS problem dictionary

source

# Mads.showobservationsMethod.

Show observations in the MADS problem dictionary

Methods

  • Mads.showobservations(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:392

Arguments

  • madsdata::Associative : MADS problem dictionary

source

# Mads.showparametersMethod.

Show parameters in the MADS problem dictionary

Methods

  • Mads.showparameters(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsParameters.jl:540

Arguments

  • madsdata::Associative : MADS problem dictionary

source

# Mads.sinetransformMethod.

Sine transformation of model parameters

Methods

  • Mads.sinetransform(sineparams::Array{T<:Any,1}, lowerbounds::Array{T<:Any,1}, upperbounds::Array{T<:Any,1}, indexlogtransformed::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsSineTransformations.jl:37

Arguments

  • indexlogtransformed::Array{T<:Any,1} : index vector of log-transformed parameters
  • lowerbounds::Array{T<:Any,1} : lower bounds
  • sineparams::Array{T<:Any,1} : model parameters
  • upperbounds::Array{T<:Any,1} : upper bounds

Returns:

  • Sine transformation of model parameters

source

# Mads.sinetransformfunctionMethod.

Sine transformation of a function

Methods

  • Mads.sinetransformfunction(f::Function, lowerbounds::Array{T<:Any,1}, upperbounds::Array{T<:Any,1}, indexlogtransformed::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsSineTransformations.jl:56

Arguments

  • f::Function : function
  • indexlogtransformed::Array{T<:Any,1} : index vector of log-transformed parameters
  • lowerbounds::Array{T<:Any,1} : lower bounds
  • upperbounds::Array{T<:Any,1} : upper bounds

Returns:

  • Sine transformation

source

# Mads.sinetransformgradientMethod.

Sine transformation of a gradient function

Methods

  • Mads.sinetransformgradient(g::Function, lowerbounds::Array{T<:Any,1}, upperbounds::Array{T<:Any,1}, indexlogtransformed::Array{T<:Any,1}; sindx) : /Users/monty/.julia/v0.5/Mads/src/MadsSineTransformations.jl:77

Arguments

  • g::Function : gradient function
  • indexlogtransformed::Array{T<:Any,1} : index vector of log-transformed parameters
  • lowerbounds::Array{T<:Any,1} : vector with parameter lower bounds
  • upperbounds::Array{T<:Any,1} : 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

source

# Mads.spaghettiplotFunction.

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

Methods

  • Mads.spaghettiplot(madsdata::Associative, number_of_samples::Integer; plotdata, filename, keyword, format, xtitle, ytitle, yfit, obs_plot_dots, seed, linewidth, pointsize) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:841
  • Mads.spaghettiplot(madsdata::Associative, dictarray::Associative; plotdata, filename, keyword, format, xtitle, ytitle, yfit, obs_plot_dots, seed, linewidth, pointsize) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:845
  • Mads.spaghettiplot(madsdata::Associative, array::Array; plotdata, filename, keyword, format, xtitle, ytitle, yfit, obs_plot_dots, seed, linewidth, pointsize) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:882

Arguments

  • array::Array : data arrays to be plotted
  • dictarray::Associative : dictionary array containing the data arrays to be plotted
  • madsdata::Associative : MADS problem dictionary
  • 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]
  • 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]
  • seed : random seed [default=0]
  • xtitle : x axis title [default=X]
  • yfit : fit vertical axis range [default=false]
  • 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.spaghettiplot(madsdata, array; filename="", keyword = "", format="", xtitle="X", ytitle="Y", obs_plot_dots=true)
Mads.spaghettiplot(madsdata, number_of_samples; filename="", keyword = "", format="", xtitle="X", ytitle="Y", obs_plot_dots=true)

source

# Mads.spaghettiplotsFunction.

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

Methods

  • Mads.spaghettiplots(madsdata::Associative, number_of_samples::Integer; format, keyword, xtitle, ytitle, obs_plot_dots, seed, linewidth, pointsize) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:704
  • Mads.spaghettiplots(madsdata::Associative, paramdictarray::DataStructures.OrderedDict; format, keyword, xtitle, ytitle, obs_plot_dots, seed, linewidth, pointsize) : /Users/monty/.julia/v0.5/Mads/src/MadsPlot.jl:708

Arguments

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

Keywords

  • format : output plot format (png, pdf, etc.) [default=Mads.graphbackend]
  • 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]
  • 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.spaghettiplots(madsdata, number_of_samples; format="", keyword="", xtitle="X", ytitle="Y", obs_plot_dots=true)

source

# Mads.sphericalcovMethod.

Spherical spatial covariance function

Methods

  • Mads.sphericalcov(h::Number, maxcov::Number, scale::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsKriging.jl:43

Arguments

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

Returns:

  • covariance

source

# Mads.sphericalvariogramMethod.

Spherical variogram

Methods

  • Mads.sphericalvariogram(h::Number, sill::Number, range::Number, nugget::Number) : /Users/monty/.julia/v0.5/Mads/src/MadsKriging.jl:59

Arguments

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

Returns:

  • Spherical variogram

source

# Mads.sprintfMethod.

Convert @sprintf macro into sprintf function

source

# Mads.statusFunction.

Status of Mads modules

Methods

  • Mads.status(madsmodule::String; gitmore, git) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:254
  • Mads.status(; git, gitmore) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:249

Arguments

  • madsmodule::String : mads module

Keywords

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

Returns:

  • true or false

source

# Mads.stderrcaptureoffMethod.

Restore STDERR

Methods

  • Mads.stderrcaptureoff() : /Users/monty/.julia/v0.5/Mads/src/MadsSTDOUT.jl:116

Returns:

  • standered error

source

# Mads.stderrcaptureonMethod.

Redirect STDERR to a reader

Methods

  • Mads.stderrcaptureon() : /Users/monty/.julia/v0.5/Mads/src/MadsSTDOUT.jl:99

source

# Mads.stdoutcaptureoffMethod.

Restore STDOUT

Methods

  • Mads.stdoutcaptureoff() : /Users/monty/.julia/v0.5/Mads/src/MadsSTDOUT.jl:86

Returns:

  • standered output

source

# Mads.stdoutcaptureonMethod.

Redirect STDOUT to a reader

Methods

  • Mads.stdoutcaptureon() : /Users/monty/.julia/v0.5/Mads/src/MadsSTDOUT.jl:69

source

# Mads.stdouterrcaptureoffMethod.

Restore STDOUT & STDERR

Methods

  • Mads.stdouterrcaptureoff() : /Users/monty/.julia/v0.5/Mads/src/MadsSTDOUT.jl:143

Returns:

  • standered output amd standered error

source

# Mads.stdouterrcaptureonMethod.

Redirect STDOUT & STDERR to readers

Methods

  • Mads.stdouterrcaptureon() : /Users/monty/.julia/v0.5/Mads/src/MadsSTDOUT.jl:129

source

# Mads.svrdumpMethod.

Dump SVR models in files

Methods

  • Mads.svrdump(svrmodel::Array{SVR.svmmodel,1}, rootname::String, numberofsamples::Int64) : /Users/monty/.julia/v0.5/Mads/src/MadsSVR.jl:141

Arguments

  • numberofsamples::Int64 : number of samples
  • rootname::String : root name
  • svrmodel::Array{SVR.svmmodel,1} : array of SVR models

source

# Mads.svrfreeMethod.

Free SVR

Methods

  • Mads.svrfree(svrmodel::Array{SVR.svmmodel,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsSVR.jl:123

Arguments

  • svrmodel::Array{SVR.svmmodel,1} : array of SVR models

source

# Mads.svrloadMethod.

Load SVR models from files

Methods

  • Mads.svrload(npred::Int64, rootname::String, numberofsamples::Int64) : /Users/monty/.julia/v0.5/Mads/src/MadsSVR.jl:164

Arguments

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

Returns:

  • Array of SVR models for each model prediction

source

# Mads.svrpredictFunction.

Predict SVR

Methods

  • Mads.svrpredict(svrmodel::Array{SVR.svmmodel,1}, paramarray::Array{Float64,2}) : /Users/monty/.julia/v0.5/Mads/src/MadsSVR.jl:96

Arguments

  • paramarray::Array{Float64,2} : parameter array
  • svrmodel::Array{SVR.svmmodel,1} : array of SVR models

Returns:

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

source

# Mads.svrtrainFunction.

Train SVR

Methods

  • Mads.svrtrain(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsSVR.jl:39
  • Mads.svrtrain(madsdata::Associative, paramarray::Array{Float64,2}; check, savesvr, addminmax, svm_type, kernel_type, degree, gamma, coef0, C, nu, cache_size, eps, shrinking, probability, verbose, tol) : /Users/monty/.julia/v0.5/Mads/src/MadsSVR.jl:6
  • Mads.svrtrain(madsdata::Associative, numberofsamples::Integer; addminmax, kw...) : /Users/monty/.julia/v0.5/Mads/src/MadsSVR.jl:39

Arguments

  • madsdata::Associative : MADS problem dictionary
  • numberofsamples::Integer : number of random samples in the training set [default=100]
  • paramarray::Array{Float64,2}

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]
  • eps : 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

source

# Mads.symlinkdirMethod.

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

Methods

  • Mads.symlinkdir(filename::String, dirtarget::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:958

Arguments

  • dirtarget::String : target directory
  • filename::String : file name

source

# Mads.symlinkdirfilesMethod.

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

Methods

  • Mads.symlinkdirfiles(dirsource::String, dirtarget::String) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:940

Arguments

  • dirsource::String : source directory
  • dirtarget::String : target directory

source

# Mads.tagFunction.

Tag Mads modules with a default argument :patch

Methods

  • Mads.tag(madsmodule::String, versionsym::Symbol) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:312
  • Mads.tag(madsmodule::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:312
  • Mads.tag(versionsym::Symbol) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:307
  • Mads.tag() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:307

Arguments

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

source

# 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::String; madstest) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsTest.jl:52
  • Mads.test() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsTest.jl:52

Arguments

  • testname::String : name of the test to execute (module or example)

Keywords

  • madstest : test Mads [default=true]

source

# Mads.testjFunction.

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

Methods

  • Mads.testj(coverage::Bool) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsTest.jl:11
  • Mads.testj() : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsTest.jl:11

Arguments

  • coverage::Bool : [default=false]

source

# Mads.transposematrixMethod.

Transpose non-numeric matrix

Methods

  • Mads.transposematrix(a::Array{T<:Any,2}) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:303

Arguments

  • a::Array{T<:Any,2} : matrix

source

# Mads.transposevectorMethod.

Transpose non-numeric vector

Methods

  • Mads.transposevector(a::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:293

Arguments

  • a::Array{T<:Any,1} : vector

source

# Mads.untagMethod.

Untag specific version

Methods

  • Mads.untag(madsmodule::String, version::String) : /Users/monty/.julia/v0.5/Mads/src/../src-interactive/MadsPublish.jl:347

Arguments

  • madsmodule::String : mads module name
  • version::String : version

source

# Mads.vectoroffMethod.

MADS vector calls off

Methods

  • Mads.vectoroff() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:18

source

# Mads.vectoronMethod.

MADS vector calls on

Methods

  • Mads.vectoron() : /Users/monty/.julia/v0.5/Mads/src/MadsHelpers.jl:9

source

# Mads.void2nan!Method.

Convert Void's into NaN's in a dictionary

Methods

  • Mads.void2nan!(dict::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:1025

Arguments

  • dict::Associative : dictionary

source

# Mads.weightedstatsMethod.

Get weighted mean and variance samples

Methods

  • Mads.weightedstats(samples::Array, llhoods::Array{T<:Any,1}) : /Users/monty/.julia/v0.5/Mads/src/MadsSenstivityAnalysis.jl:366

Arguments

  • llhoods::Array{T<:Any,1} : vector of log-likelihoods
  • samples::Array : array of samples

Returns:

  • vector of sample means
  • vector of sample variances

source

# Mads.welloff!Method.

Turn off a specific well in the MADS problem dictionary

Methods

  • Mads.welloff!(madsdata::Associative, wellname::String) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:566

Arguments

  • madsdata::Associative : MADS problem dictionary
  • wellname::String : name of the well to be turned off

source

# Mads.wellon!Method.

Turn on a specific well in the MADS problem dictionary

Methods

  • Mads.wellon!(madsdata::Associative, wellname::String) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:531

Arguments

  • madsdata::Associative : MADS problem dictionary
  • wellname::String : name of the well to be turned on

source

# Mads.wells2observations!Method.

Convert Wells class to Observations class in the MADS problem dictionary

Methods

  • Mads.wells2observations!(madsdata::Associative) : /Users/monty/.julia/v0.5/Mads/src/MadsObservations.jl:587

Arguments

  • madsdata::Associative : MADS problem dictionary

source

# Mads.writeparametersMethod.

Write model parameters

Methods

  • Mads.writeparameters(madsdata::Associative, parameters::Associative; respect_space) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:707

Arguments

  • madsdata::Associative : MADS problem dictionary
  • parameters::Associative : parameters

Keywords

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

source

# Mads.writeparametersviatemplateMethod.

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

Methods

  • Mads.writeparametersviatemplate(parameters, templatefilename, outputfilename; respect_space) : /Users/monty/.julia/v0.5/Mads/src/MadsIO.jl:661

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]

source

# Mads.@stderrcaptureMacro.

Capture STDERR of a block

source

# Mads.@stdoutcaptureMacro.

Capture STDOUT of a block

source

# Mads.@stdouterrcaptureMacro.

Capture STDERR & STDERR of a block

source

# Mads.@tryimportMacro.

Try to import a module

source

# Mads.MadsModelType.

MadsModel type applied for MathProgBase analyses

source