Mads.jl

Mads.jl functions:

Mads.MFlm — Method

Matrix Factorization using Levenberg Marquardt

Methods:

Mads.MFlm(X::AbstractMatrix{T}, nk::Integer; method, log_W, log_H, retries, initW, initH, tolX, tolG, tolOF, tolOFcount, minOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, quiet) where T<:Number : ~/work/Mads.jl/Mads.jl/src/MadsBlindSourceSeparation.jl:136
Mads.MFlm(X::AbstractMatrix{T}, range::AbstractUnitRange{Int64}; kw...) where T<:Number : ~/work/Mads.jl/Mads.jl/src/MadsBlindSourceSeparation.jl:103

Arguments:

X :: AbstractMatrix{T} : Matrix to factorize
nk :: Integer : Number of features to extract
range :: AbstractUnitRange{Int64} : Range

Keywords:

method : Method
log_W : Log-transform W (weight) matrix [default=false]
log_H : Log-transform H (feature) matrix[default=false]
retries : Number of solution retries [default=1]
initW : Initial W (weight) matrix
initH : Initial H (feature) matrix
tolX : TolX
tolG : Parameter space update tolerance [default=1e-6]
tolOF : Objective function update tolerance [default=1e-3]
tolOFcount : Number of Jacobian runs with small objective function change [default=5]
minOF : MinOF
maxEval : MaxEval
maxIter : MaxIter
maxJacobians : MaxJacobians
lambda : Lambda
lambda_mu : Lambda mu
np_lambda : Np lambda
show_trace : Show trace
quiet : Quiet

Returns:

NMF results

source

Mads.NMFipopt — Function

Non-negative Matrix Factorization using JuMP/Ipopt

Methods:

Mads.NMFipopt(X::AbstractMatrix, nk::Integer, retries::Integer; random, maxiter, maxguess, initW, initH, verbosity, quiet) : ~/work/Mads.jl/Mads.jl/src/MadsBlindSourceSeparation.jl:60
Mads.NMFipopt(X::AbstractMatrix, nk::Integer; ...) : ~/work/Mads.jl/Mads.jl/src/MadsBlindSourceSeparation.jl:60

Arguments:

X :: AbstractMatrix : Matrix to factorize
nk :: Integer : Number of features to extract
retries :: Integer : Number of solution retries [default=1]

Keywords:

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

Returns:

NMF results

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Mads.NMFm — Function

Non-negative Matrix Factorization using NMF

Methods:

Mads.NMFm(X::AbstractArray, nk::Integer, retries::Integer; tol, maxiter) : ~/work/Mads.jl/Mads.jl/src/MadsBlindSourceSeparation.jl:21
Mads.NMFm(X::AbstractArray, nk::Integer; ...) : ~/work/Mads.jl/Mads.jl/src/MadsBlindSourceSeparation.jl:21

Arguments:

X :: AbstractArray : Matrix to factorize
nk :: Integer : Number of features to extract
retries :: Integer : Number of solution retries [default=1]

Keywords:

tol : Solution tolerance [default=1.0e-9]
maxiter : Maximum number of iterations [default=10000]

Returns:

NMF results

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Mads.addkeyword! — Function

Add a keyword in a class within the Mads dictionary madsdata

Methods:

Mads.addkeyword!(madsdata::AbstractDict, class::AbstractString, keyword::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:357
Mads.addkeyword!(madsdata::AbstractDict, keyword::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:353

Arguments:

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

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Mads.addsource! — Function

Add an additional contamination source

Methods:

Mads.addsource!(madsdata::AbstractDict, sourceid::Integer; dict) : ~/work/Mads.jl/Mads.jl/src/MadsAnasol.jl:17
Mads.addsource!(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsAnasol.jl:17

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
sourceid :: Integer : Source id [default=0]

Keywords:

dict : Dict

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Mads.addsourceparameters! — Method

Add contaminant source parameters

Methods:

Mads.addsourceparameters!(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsAnasol.jl:91

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

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Mads.allwellsoff! — Method

Turn off all the wells in the MADS problem dictionary

Methods:

Mads.allwellsoff!(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:630

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

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Mads.allwellson! — Method

Turn on all the wells in the MADS problem dictionary

Methods:

Mads.allwellson!(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:572

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

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Mads.amanzi — Function

Execute Amanzi external groundwater flow and transport simulator

Methods:

Mads.amanzi(filename::AbstractString, nproc::Integer, quiet::Bool, observation_filename::AbstractString, setup::AbstractString; amanzi_exe) : ~/work/Mads.jl/Mads.jl/src/MadsSimulators.jl:12
Mads.amanzi(filename::AbstractString, nproc::Integer, quiet::Bool, observation_filename::AbstractString; ...) : ~/work/Mads.jl/Mads.jl/src/MadsSimulators.jl:12
Mads.amanzi(filename::AbstractString, nproc::Integer, quiet::Bool; ...) : ~/work/Mads.jl/Mads.jl/src/MadsSimulators.jl:12
Mads.amanzi(filename::AbstractString, nproc::Integer; ...) : ~/work/Mads.jl/Mads.jl/src/MadsSimulators.jl:12
Mads.amanzi(filename::AbstractString; ...) : ~/work/Mads.jl/Mads.jl/src/MadsSimulators.jl:12

Arguments:

filename :: AbstractString : Amanzi input file name
nproc :: Integer : Number of processor to be used by Amanzi [default=Mads.nprocs per task default]
quiet :: Bool : Suppress output [default=Mads.quiet]
observation_filename :: AbstractString : Amanzi observation file name [default="observations.out"]
setup :: AbstractString : Bash script to setup Amanzi environmental variables [default="source-amanzi-setup"]

Keywords:

amanzi_exe : Full path to the Amanzi executable

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Mads.amanzi_output_parser — Function

Parse Amanzi output provided in an external file (filename)

Methods:

Mads.amanzi_output_parser() : ~/work/Mads.jl/Mads.jl/src/MadsParsers.jl:20
Mads.amanzi_output_parser(filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsParsers.jl:20

Arguments:

filename :: AbstractString : 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")

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Mads.asinetransform — Function

Arcsine transformation of model parameters

Methods:

Mads.asinetransform(madsdata::AbstractDict, params::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsSineTransformations.jl:1
Mads.asinetransform(params::AbstractVector, lowerbounds::AbstractVector, upperbounds::AbstractVector, indexlogtransformed::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsSineTransformations.jl:11

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
params :: AbstractVector : Model parameters
lowerbounds :: AbstractVector : Lower bounds
upperbounds :: AbstractVector : Upper bounds
indexlogtransformed :: AbstractVector : Index vector of log-transformed parameters

Returns:

Arcsine transformation of model parameters

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Mads.bigdt — Method

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

Methods:

Mads.bigdt(madsdata::AbstractDict, nummodelruns::Integer; numhorizons, maxHorizon, numlikelihoods) : ~/work/Mads.jl/Mads.jl/src/MadsBayesInfoGap.jl:121

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
nummodelruns :: Integer : Number of model runs

Keywords:

numhorizons : Number of info-gap horizons of uncertainty [default=100]
maxHorizon : Maximum info-gap horizons of uncertainty [default=3]
numlikelihoods : Number of Bayesian likelihoods [default=25]

Returns:

dictionary with BIG-DT results

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Mads.boundparameters! — Function

Bound model parameters based on their ranges

Methods:

Mads.boundparameters!(madsdata::AbstractDict, pardict::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:939
Mads.boundparameters!(madsdata::AbstractDict, parvec::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:927

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
pardict :: AbstractDict : Parameter dictionary
parvec :: AbstractVector : Parameter vector

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Mads.calibrate — Method

Calibrate Mads model using a constrained Levenberg-Marquardt technique

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

Methods:

Mads.calibrate(madsdata::AbstractDict; tolX, tolG, tolOF, tolOFcount, minOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, quiet, usenaive, store_optimization_progress, localsa, parallel_optimization) : ~/work/Mads.jl/Mads.jl/src/MadsCalibrate.jl:206

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

tolX : Parameter space tolerance [default=1e-4]
tolG : Parameter space update tolerance [default=1e-6]
tolOF : Objective function update tolerance [default=1e-3]
tolOFcount : Number of Jacobian runs with small objective function change [default=5]
minOF : Objective function update tolerance [default=1e-3]
maxEval : Maximum number of model evaluations [default=1000]
maxIter : Maximum number of optimization iterations [default=100]
maxJacobians : Maximum number of Jacobian solves [default=100]
lambda : Initial Levenberg-Marquardt lambda [default=100.0]
lambda_mu : Lambda multiplication factor [default=10.0]
np_lambda : Number of parallel lambda solves [default=10]
show_trace : Shows solution trace [default=false]
quiet : Quiet
usenaive : Use naive Levenberg-Marquardt solver [default=false]
store_optimization_progress : Save intermediate results [default=true]
localsa : Perform local sensitivity analysis [default=false]
parallel_optimization : Parallel optimization

Returns:

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

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Mads.calibraterandom — Function

Calibrate with random initial guesses

Methods:

Mads.calibraterandom(madsdata::AbstractDict, numberofsamples::Integer; tolX, tolG, tolOF, tolOFcount, minOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, usenaive, seed, rng, quiet, all_results, store_optimization_progress, save_results, first_init) : ~/work/Mads.jl/Mads.jl/src/MadsCalibrate.jl:44
Mads.calibraterandom(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsCalibrate.jl:44

Arguments:

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

Keywords:

tolX : Parameter space tolerance [default=1e-4]
tolG : Parameter space update tolerance [default=1e-6]
tolOF : Objective function update tolerance [default=1e-3]
tolOFcount : Number of Jacobian runs with small objective function change [default=5]
minOF : MinOF
maxEval : Maximum number of model evaluations [default=1000]
maxIter : Maximum number of optimization iterations [default=100]
maxJacobians : Maximum number of Jacobian solves [default=100]
lambda : Initial Levenberg-Marquardt lambda [default=100.0]
lambda_mu : Lambda multiplication factor [default=10.0]
np_lambda : Number of parallel lambda solves [default=10]
show_trace : Shows solution trace [default=false]
usenaive : Use naive Levenberg-Marquardt solver [default=false]
seed : Random seed [default=0]
rng : Rng
quiet : [default=true]
all_results : All model results are returned [default=false]
store_optimization_progress : Save intermediate results [default=true]
save_results : Save results
first_init : First init

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)

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Mads.calibraterandom_parallel — Function

Calibrate with random initial guesses in parallel

Methods:

Mads.calibraterandom_parallel(madsdata::AbstractDict, numberofsamples::Integer; tolX, tolG, tolOF, tolOFcount, minOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, usenaive, seed, rng, quiet, store_optimization_progress, save_results, first_init, localsa, all_results) : ~/work/Mads.jl/Mads.jl/src/MadsCalibrate.jl:123
Mads.calibraterandom_parallel(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsCalibrate.jl:123

Arguments:

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

Keywords:

tolX : Parameter space tolerance [default=1e-4]
tolG : Parameter space update tolerance [default=1e-6]
tolOF : Objective function update tolerance [default=1e-3]
tolOFcount : Number of Jacobian runs with small objective function change [default=5]
minOF : MinOF
maxEval : Maximum number of model evaluations [default=1000]
maxIter : Maximum number of optimization iterations [default=100]
maxJacobians : Maximum number of Jacobian solves [default=100]
lambda : Initial Levenberg-Marquardt lambda [default=100.0]
lambda_mu : Lambda multiplication factor [default=10.0]
np_lambda : Number of parallel lambda solves [default=10]
show_trace : Shows solution trace [default=false]
usenaive : Use naive Levenberg-Marquardt solver [default=false]
seed : Random seed [default=0]
rng : Rng
quiet : Suppress output [default=true]
store_optimization_progress : Save intermediate results [default=true]
save_results : Save results
first_init : First init
localsa : Perform local sensitivity analysis [default=false]
all_results : All results

Returns:

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

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Mads.captureoff — Method

Make MADS not capture

Methods:

Mads.captureoff() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:192

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Mads.captureon — Method

Make MADS capture

Methods:

Mads.captureon() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:183

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Mads.checkhash — Function

Check hash of a file

Methods:

Mads.checkhash(DATA::Mads.DATA) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:54
Mads.checkhash(DATA::Mads.DATA, throw_error::Bool) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:54
Mads.checkhash(input_file::AbstractString, target_hash::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:36
Mads.checkhash(input_file::AbstractString, target_hash::AbstractString, throw_error::Bool) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:36

Arguments:

DATA :: Mads.DATA : DATA
throw_error :: Bool : Throw error
input_file :: AbstractString : Input file
target_hash :: AbstractString : Target hash

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Mads.checkmodeloutputdirs — Method

Check the directories where model outputs should be saved for MADS

Methods:

Mads.checkmodeloutputdirs(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1057

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Returns:

true or false

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Mads.checknodedir — Function

Check if a directory is readable

Methods:

Mads.checknodedir(dir::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsExecute.jl:10
Mads.checknodedir(dir::AbstractString, waittime::Float64) : ~/work/Mads.jl/Mads.jl/src/MadsExecute.jl:10
Mads.checknodedir(node::AbstractString, dir::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsExecute.jl:1
Mads.checknodedir(node::AbstractString, dir::AbstractString, waittime::Float64) : ~/work/Mads.jl/Mads.jl/src/MadsExecute.jl:1

Arguments:

dir :: AbstractString : Directory
waittime :: Float64 : Wait time in seconds [default=10]
node :: AbstractString : Computational node name (e.g. madsmax, wf03, or 127.0.0.1)

Returns:

true if the directory is readable, false otherwise

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Mads.checkobservationranges — Method

Check parameter ranges for model parameters

Methods:

Mads.checkobservationranges(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:838

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

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Mads.checkout — Function

Checkout (pull) the latest version of Mads modules

Methods:

Mads.checkout(; ...) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:108
Mads.checkout(modulename::AbstractString; git, master, force, pull, required, all) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:108

Arguments:

modulename :: AbstractString : Module name

Keywords:

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

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Mads.checkparameterranges — Method

Check parameter ranges for model parameters

Methods:

Mads.checkparameterranges(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:863

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

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Mads.cmadsins_obs — Method

Call C MADS ins_obs() function from MADS dynamic library

Methods:

Mads.cmadsins_obs(obsid::AbstractVector, instructionfilename::AbstractString, inputfilename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsCMads.jl:38

Arguments:

obsid :: AbstractVector : Observation id
instructionfilename :: AbstractString : Instruction file name
inputfilename :: AbstractString : Input file name

Return:

observations

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Mads.commit — Function

Commit the latest version of Mads modules in the repository

Methods:

Mads.commit(commitmsg::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:234
Mads.commit(commitmsg::AbstractString, modulename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:234

Arguments:

commitmsg :: AbstractString : Commit message
modulename :: AbstractString : Module name

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Mads.computemass — Function

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

Methods:

Mads.computemass(madsdata::AbstractDict; time) : ~/work/Mads.jl/Mads.jl/src/MadsAnasol.jl:529
Mads.computemass(madsfiles::Union{Regex, String}; time, path) : ~/work/Mads.jl/Mads.jl/src/MadsAnasol.jl:556

Arguments:

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

Keywords:

time : Computational time [default=0]
path : Search directory for the mads input files [default="."]

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=".")

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Mads.computeparametersensitities — Method

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

Methods:

Mads.computeparametersensitities(madsdata::AbstractDict, saresults::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:867

Arguments:

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

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Mads.contamination — Method

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

Methods:

Mads.contamination(wellx::Number, welly::Number, wellz::Number, n::Number, lambda::Number, theta::Number, vx::Number, vy::Number, vz::Number, ax::Number, ay::Number, az::Number, H::Number, x::Number, y::Number, z::Number, dx::Number, dy::Number, dz::Number, f::Number, t0::Number, t1::Number, t::AbstractVector, anasolfunction::Function) : ~/work/Mads.jl/Mads.jl/src/MadsAnasol.jl:499

Arguments:

wellx :: Number : Observation point (well) X coordinate
welly :: Number : Observation point (well) Y coordinate
wellz :: Number : Observation point (well) Z coordinate
n :: Number : Porosity
lambda :: Number : First-order reaction rate
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
ax :: Number : Dispersivity in X direction (longitudinal)
ay :: Number : Dispersivity in Y direction (transverse horizontal)
az :: Number : Dispersivity in Y direction (transverse vertical)
H :: Number : Hurst coefficient for Fractional Brownian dispersion
x :: Number : X coordinate of contaminant source location
y :: Number : Y coordinate of contaminant source location
z :: Number : Z coordinate of contaminant source location
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
t0 :: Number : Source starting time
t1 :: Number : Source termination time
t :: AbstractVector : Vector of times to compute concentration at the observation point
anasolfunction :: Function : Anasolfunction

Returns:

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

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Mads.copyaquifer2sourceparameters! — Method

Copy aquifer parameters to become contaminant source parameters

Methods:

Mads.copyaquifer2sourceparameters!(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsAnasol.jl:130

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

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Mads.copyright — Method

Produce MADS copyright information

Methods:

Mads.copyright() : ~/work/Mads.jl/Mads.jl/src/MadsHelp.jl:44

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Mads.create_tests_off — Method

Turn off the generation of MADS tests (default)

Methods:

Mads.create_tests_off() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:237

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Mads.create_tests_on — Method

Turn on the generation of MADS tests (dangerous)

Methods:

Mads.create_tests_on() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:228

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Mads.createobservations — Function

Create Mads dictionary of observations and instruction file

Methods:

Mads.createobservations(nrow::Integer, ncol::Integer; obstring, pretext, prestring, poststring, filename) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:64
Mads.createobservations(nrow::Integer; ...) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:64
Mads.createobservations(obs::AbstractMatrix; key, weight, time) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:118
Mads.createobservations(obs::AbstractVector; key, weight, time, min, max, minorig, maxorig, dist, distribution) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:84

Arguments:

nrow :: Integer : Number of rows
ncol :: Integer : Number of columns [default 1]
obs :: AbstractMatrix : Obs
obs :: AbstractVector : Obs

Keywords:

obstring : Observation string
pretext : Preamble instructions
prestring : Pre instruction file string
poststring : Post instruction file string
filename : File name
key : Key
weight : Weight
time : Time
min : Min
max : Max
minorig : Minorig
maxorig : Maxorig
dist : Dist
distribution : Distribution

)

Returns:

observation dictionary

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Mads.createobservations! — Function

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

Methods:

Mads.createobservations!(madsdata::AbstractDict, observation::AbstractDict; logtransform, weight_type, weight) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:512
Mads.createobservations!(madsdata::AbstractDict, time::AbstractVector, observation::AbstractVector; logtransform, weight_type, weight) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:468
Mads.createobservations!(md::AbstractDict, obs::Union{AbstractMatrix, AbstractVector}; kw...) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:153

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
observation :: AbstractDict : Dictionary of observations
time :: AbstractVector : Vector of observation times
observation :: AbstractVector : Dictionary of observations
md :: AbstractDict : Md
obs :: Union{AbstractMatrix, AbstractVector} : Obs

Keywords:

logtransform : Log transform observations [default=false]
weight_type : Weight type [default=constant]
weight : Weight value [default=1]

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Mads.createproblem — Function

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

Methods:

Mads.createproblem(in::Integer, out::Integer, f::Union{AbstractString, Function}; kw...) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:275
Mads.createproblem(infilename::AbstractString, outfilename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:289
Mads.createproblem(madsdata::AbstractDict, args...; kw...) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:352
Mads.createproblem(madsdata::AbstractDict, outfilename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:314
Mads.createproblem(madsdata::AbstractDict, predictions::AbstractDict, outfilename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:320
Mads.createproblem(param::AbstractVector, obs::Union{AbstractMatrix, AbstractVector}, f::Union{AbstractString, Function, Symbol}; modeltype, problemname, paramkey, paramname, paramplotname, paramtype, parammin, parammax, paramlog, paramminorig, parammaxorig, paramdist, distribution, expressions, obskey, obsweight, obstime, obsmin, obsmax, obsminorig, obsmaxorig, obsdist) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:279
Mads.createproblem(paramfile::AbstractString, obsfile::AbstractString, f::Union{AbstractString, Function}; kw...) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:263

Arguments:

in :: Integer : In
out :: Integer : Out
f :: Union{AbstractString, Function} : F
infilename :: AbstractString : Input Mads file
outfilename :: AbstractString : Output Mads file
madsdata :: AbstractDict : MADS problem dictionary
predictions :: AbstractDict : Dictionary of model predictions
param :: AbstractVector : Param
obs :: Union{AbstractMatrix, AbstractVector} : Obs
f :: Union{AbstractString, Function, Symbol} : F
paramfile :: AbstractString : Paramfile
obsfile :: AbstractString : Obsfile

Keywords:

modeltype : Modeltype
problemname : Problemname
paramkey : Paramkey
paramname : Paramname
paramplotname : Paramplotname
paramtype : Paramtype
parammin : Parammin
parammax : Parammax
paramlog : Paramlog
paramminorig : Paramminorig
parammaxorig : Parammaxorig
paramdist : Paramdist
distribution : Distribution
expressions : Expressions
obskey : Obskey
obsweight : Obsweight
obstime : Obstime
obsmin : Obsmin
obsmax : Obsmax
obsminorig : Obsminorig
obsmaxorig : Obsmaxorig
obsdist : Obsdist

Returns:

new MADS problem dictionary

source

Mads.createtempdir — Method

Create temporary directory

Methods:

Mads.createtempdir(tempdirname::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1744

Arguments:

tempdirname :: AbstractString : Temporary directory name

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Mads.deleteNaN! — Method

Delete rows with NaN in a dataframe df

Methods:

Mads.deleteNaN!(df::DataFrames.DataFrame) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:1091

Arguments:

df :: DataFrames.DataFrame : Dataframe

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Mads.deletekeyword! — Function

Delete a keyword in a class within the Mads dictionary madsdata

Methods:

Mads.deletekeyword!(madsdata::AbstractDict, class::AbstractString, keyword::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:385
Mads.deletekeyword!(madsdata::AbstractDict, keyword::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:379

Arguments:

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

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Mads.deleteoffwells! — Method

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

Methods:

Mads.welloff!(madsdata::AbstractDict, wellname::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:644

Arguments:

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

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Mads.deletetimes! — Method

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

Methods:

Mads.welloff!(madsdata::AbstractDict, wellname::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:644

Arguments:

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

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Mads.dependents — Function

Lists module dependents on a module (Mads by default)

Methods:

Mads.dependents() : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:72
Mads.dependents(modulename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:72
Mads.dependents(modulename::AbstractString, filter::Bool) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:72

Arguments:

modulename :: AbstractString : Module name [default="Mads"]
filter :: Bool : Whether to filter modules [default=false]

Returns:

modules that are dependents of the input module

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Mads.detect_stateplane_epsg — Method

detect_stateplane_epsg(x, y; unit=:auto, bbox=:usa, sample=200)

Detect the most likely EPSG code for input State Plane coordinates (x,y) in the USA.

Arguments

x, y: coordinate vectors in a State Plane CRS (units per unit).
unit: :auto, :m, or :ftUS to limit candidate EPSG codes.
bbox: bounding box tuple (lonmin, lonmax, latmin, latmax) or :usa.
sample: number of finite samples to evaluate for detection.

Returns

Int EPSG code for the best-matching CRS, or throws an error if none found.

source

Mads.diff — Function

Diff the latest version of Mads modules in the repository

Methods:

Mads.diff() : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:183
Mads.diff(modulename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:183

Arguments:

modulename :: AbstractString : Module name

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Mads.display — Function

Display image file

Methods:

Mads.display(filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsDisplay.jl:8
Mads.display(filename::AbstractString, open::Bool) : ~/work/Mads.jl/Mads.jl/src/MadsDisplay.jl:8
Mads.display(o; gwo, gho, gw, gh) : ~/work/Mads.jl/Mads.jl/src/MadsDisplay.jl:133
Mads.display(p::Compose.Context; gwo, gho, gw, gh) : ~/work/Mads.jl/Mads.jl/src/MadsDisplay.jl:100
Mads.display(p::Gadfly.Plot; gwo, gho, gw, gh) : ~/work/Mads.jl/Mads.jl/src/MadsDisplay.jl:67

Arguments:

filename :: AbstractString : Image file name
open :: Bool : Open
o :: Any : O
p :: Compose.Context : Plotting object
p :: Gadfly.Plot : Plotting object

Keywords:

gwo : Gwo
gho : Gho
gw : Gw
gh : Gh

source

Mads.documentation_create — Function

Create web documentation

Methods:

Mads.documentation_create() : ~/work/Mads.jl/Mads.jl/src/MadsPublish.jl:65
Mads.documentation_create(modules_doc) : ~/work/Mads.jl/Mads.jl/src/MadsPublish.jl:65
Mads.documentation_create(modules_doc, modules_load) : ~/work/Mads.jl/Mads.jl/src/MadsPublish.jl:65

Arguments:

modules_doc :: Any : Modules doc
modules_load :: Any : Modules load

source

Mads.documentation_deploy — Method

Create web documentation

Methods:

Mads.documentation_deploy(; deploy_config) : ~/work/Mads.jl/Mads.jl/src/MadsPublish.jl:109

Keywords:

deploy_config : Deploy config

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Mads.documentation_deploy_local — Method

documentation_deploy_local(; build=true, remote="origin", branch="gh-pages", target_subdir="dev",
	worktree_dir=joinpath(Mads.dir, "docs", "_gh-pages"), push=false, commit=true,
	message=nothing)

Deploy the locally built docs (docs/build) into a local git worktree for the gh-pages branch.

This is intended for local development and manual publishing when Documenter.deploydocs does not auto-detect a CI environment.

By default this does not push to the remote. To publish to GitHub Pages, call with push=true.

source

Mads.dumpasciifile — Method

Dump ASCII file

Methods:

Mads.dumpasciifile(filename::AbstractString, data) : ~/work/Mads.jl/Mads.jl/src/MadsASCII.jl:29

Arguments:

filename :: AbstractString : ASCII file name
data :: Any : Data to dump

Dumps:

ASCII file with the name in "filename"

source

Mads.dumpjsonfile — Method

Dump a JSON file

Methods:

Mads.dumpjsonfile(filename::AbstractString, data) : ~/work/Mads.jl/Mads.jl/src/MadsJSON.jl:31

Arguments:

filename :: AbstractString : JSON file name
data :: Any : Data to dump

Dumps:

JSON file with the name in "filename"

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Mads.dumpwelldata — Method

Dump well data from MADS problem dictionary into a ASCII file

Methods:

Mads.dumpwelldata(madsdata::AbstractDict, filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1606

Arguments:

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

Dumps:

filename : a ASCII file

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Mads.dumpyamlfile — Method

Dump YAML file

Methods:

Mads.dumpyamlfile(filename::AbstractString, data) : ~/work/Mads.jl/Mads.jl/src/MadsYAML.jl:29

Arguments:

filename :: AbstractString : Output file name
data :: Any : YAML data

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Mads.dumpyamlmadsfile — Method

Dump YAML Mads file

Methods:

Mads.dumpyamlmadsfile(madsdata::AbstractDict, filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsYAML.jl:41

Arguments:

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

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Mads.efast — Method

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

Methods:

Mads.efast(md::AbstractDict; N, M, gamma, seed, checkpointfrequency, save, load, execute, parallel, robustpmap, restartdir, restart, rng) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:1134

Arguments:

md :: AbstractDict : MADS problem dictionary

Keywords:

N : Number of samples [default=100]
M : Maximum number of harmonics [default=6]
gamma : Multiplication factor (Saltelli 1999 recommends gamma = 2 or 4) [default=4]
seed : Random seed [default=0]
checkpointfrequency : Check point frequency [default=N]
save : Save
load : Load
execute : Execute
parallel : Parallel
robustpmap : Robustpmap
restartdir : Directory where files will be stored containing model results for the efast simulation restarts [default="efastcheckpoints"]
restart : Save restart information [default=false]
rng : Rng

source

Mads.emceesampling — Function

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

Methods:

Mads.emceesampling(madsdata::AbstractDict, p0::AbstractMatrix; filename, load, save, execute, numwalkers, nexecutions, burnin, thinning, seed, weightfactor, rng, distributed_function, type, checkoutputs) : ~/work/Mads.jl/Mads.jl/src/MadsMonteCarlo.jl:131
Mads.emceesampling(madsdata::AbstractDict; filename, load, save, execute, numwalkers, nexecutions, burnin, thinning, sigma, seed, rng, kw...) : ~/work/Mads.jl/Mads.jl/src/MadsMonteCarlo.jl:87

Arguments:

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

Keywords:

filename : Filename
load : Load
save : Save
execute : Execute
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]
nexecutions : Nexecutions
burnin : Number of initial realizations before the MCMC are recorded [default=10]
thinning : Removal of any thinning realization [default=1]
seed : Random seed [default=0]
weightfactor : Weight factor [default=1.0]
rng : Rng
distributed_function : Distributed function
type : Type
checkoutputs : Checkoutputs
sigma : A standard deviation parameter used to initialize the walkers [default=0.01]

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.estimationerror — Function

Estimate kriging error

Methods:

Mads.estimationerror(w::AbstractVector, covmat::AbstractMatrix, covvec::AbstractVector, cov0::Number) : ~/work/Mads.jl/Mads.jl/src/MadsKriging.jl:205
Mads.estimationerror(w::AbstractVector, x0::AbstractVector, X::AbstractMatrix, covfn::Function) : ~/work/Mads.jl/Mads.jl/src/MadsKriging.jl:198

Arguments:

w :: AbstractVector : Kriging weights
covmat :: AbstractMatrix : Covariance matrix
covvec :: AbstractVector : Covariance vector
cov0 :: Number : Zero-separation covariance
x0 :: AbstractVector : Estimated locations
X :: AbstractMatrix : Observation matrix
covfn :: Function : Covfn

Returns:

estimation kriging error

source

Mads.evaluatemadsexpression — Method

Evaluate an expression string based on a parameter dictionary

Methods:

Mads.evaluatemadsexpression(expressionstring::AbstractString, parameters::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsMisc.jl:142

Arguments:

expressionstring :: AbstractString : Expression string
parameters :: AbstractDict : Parameter dictionary applied to evaluate the expression string

Returns:

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

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Mads.evaluatemadsexpressions — Function

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

Methods:

Mads.evaluatemadsexpressions(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsMisc.jl:161
Mads.evaluatemadsexpressions(madsdata::AbstractDict, parameters::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsMisc.jl:161

Arguments:

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

Returns:

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

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Mads.example — Method

List available examples

Methods:

Mads.examples() : ~/work/Mads.jl/Mads.jl/src/MadsExamples.jl:6

source

Mads.examples — Method

List available examples

Methods:

Mads.examples() : ~/work/Mads.jl/Mads.jl/src/MadsExamples.jl:6

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Mads.expcov — Method

Exponential spatial covariance function

Methods:

Mads.expcov(h::Number, maxcov::Number, scale::Number) : ~/work/Mads.jl/Mads.jl/src/MadsKriging.jl:31

Arguments:

h :: Number : Separation distance
maxcov :: Number : Maximum covariance
scale :: Number : Scale

Returns:

covariance

source

Mads.exponentialvariogram — Method

Exponential variogram

Methods:

Mads.exponentialvariogram(h::Number, sill::Number, range::Number, nugget::Number) : ~/work/Mads.jl/Mads.jl/src/MadsKriging.jl:83

Arguments:

h :: Number : Separation distance
sill :: Number : Sill
range :: Number : Range
nugget :: Number : Nugget

Returns:

Exponential variogram

source

Mads.filterkeys — Function

Filter dictionary keys based on a string or regular expression

Methods:

Mads.filterkeys(dict::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1273
Mads.filterkeys(dict::AbstractDict, key::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1273
Mads.filterkeys(dict::AbstractDict, key::Regex) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1272

Arguments:

dict :: AbstractDict : Dictionary
key :: AbstractString : The regular expression or string used to filter dictionary keys
key :: Regex : The regular expression or string used to filter dictionary keys

source

Mads.forward — Function

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

Methods:

Mads.forward(madsdata::AbstractDict, paramarray::AbstractArray; parallel, robustpmap, all, checkpointfrequency, checkpointfilename) : ~/work/Mads.jl/Mads.jl/src/MadsForward.jl:53
Mads.forward(madsdata::AbstractDict, paramdict::AbstractDict; all, checkpointfrequency, checkpointfilename) : ~/work/Mads.jl/Mads.jl/src/MadsForward.jl:12
Mads.forward(madsdata::AbstractDict; all) : ~/work/Mads.jl/Mads.jl/src/MadsForward.jl:8

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
paramarray :: AbstractArray : Array of model parameter values
paramdict :: AbstractDict : Dictionary of model parameter values

Keywords:

parallel : Parallel
robustpmap : Robustpmap
all : All model results are returned [default=false]
checkpointfrequency : Check point frequency for storing restart information [default=0]
checkpointfilename : Check point file name [default="checkpoint forward"]

Returns:

dictionary of model predictions

source

Mads.forwardgrid — Function

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::AbstractDict; kw...) : ~/work/Mads.jl/Mads.jl/src/MadsForward.jl:181
Mads.forwardgrid(madsdatain::AbstractDict, paramvalues::AbstractDict; transient) : ~/work/Mads.jl/Mads.jl/src/MadsForward.jl:186

Arguments:

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

Keywords:

transient : Transient

Returns:

3D array with model predictions along a 3D grid

source

Mads.free — Function

Free Mads modules

Methods:

Mads.free(; ...) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:210
Mads.free(modulename::AbstractString; required, all) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:210

Arguments:

modulename :: AbstractString : Module name

Keywords:

required : Only free Mads.required modules [default=false]
all : Free all the modules [default=false]

source

Mads.functions — Function

List available functions in the MADS modules:

Methods:

Mads.functions(; ...) : ~/work/Mads.jl/Mads.jl/src/MadsHelp.jl:57
Mads.functions(m::Union{Module, Symbol}, re::Regex; shortoutput, quiet) : ~/work/Mads.jl/Mads.jl/src/MadsHelp.jl:66
Mads.functions(m::Union{Module, Symbol}, string::AbstractString; shortoutput, quiet) : ~/work/Mads.jl/Mads.jl/src/MadsHelp.jl:96
Mads.functions(m::Union{Module, Symbol}; ...) : ~/work/Mads.jl/Mads.jl/src/MadsHelp.jl:96
Mads.functions(re::Regex; shortoutput, quiet) : ~/work/Mads.jl/Mads.jl/src/MadsHelp.jl:48
Mads.functions(string::AbstractString; shortoutput, quiet) : ~/work/Mads.jl/Mads.jl/src/MadsHelp.jl:57

Arguments:

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

Keywords:

shortoutput : Shortoutput
quiet : Quiet

Examples:

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

source

Mads.gaussiancov — Method

Gaussian spatial covariance function

Methods:

Mads.gaussiancov(h::Number, maxcov::Number, scale::Number) : ~/work/Mads.jl/Mads.jl/src/MadsKriging.jl:17

Arguments:

h :: Number : Separation distance
maxcov :: Number : Maximum covariance
scale :: Number : Scale

Returns:

covariance

source

Mads.gaussianvariogram — Method

Gaussian variogram

Methods:

Mads.gaussianvariogram(h::Number, sill::Number, range::Number, nugget::Number) : ~/work/Mads.jl/Mads.jl/src/MadsKriging.jl:104

Arguments:

h :: Number : Separation distance
sill :: Number : Sill
range :: Number : Range
nugget :: Number : Nugget

Returns:

Gaussian variogram

source

Mads.get_excel_data — Function

Get data from an EXCEL file

Methods:

Mads.get_excel_data(excel_file::AbstractString, sheet_name::AbstractString; header, rows, cols, keytype, floattype, inttype, convertintegers, mapping, usenans, dataframe) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:91
Mads.get_excel_data(excel_file::AbstractString; ...) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:91

Arguments:

excel_file :: AbstractString : Excel file
sheet_name :: AbstractString : Sheet name

Keywords:

header : Header
rows : Rows
cols : Cols
keytype : Keytype
floattype : Floattype
inttype : Inttype
convertintegers : Convertintegers
mapping : Mapping
usenans : Usenans
dataframe : Dataframe

source

Mads.getcovmat — Method

Get spatial covariance matrix

Methods:

Mads.getcovmat(X::AbstractMatrix, covfunction::Function) : ~/work/Mads.jl/Mads.jl/src/MadsKriging.jl:160

Arguments:

X :: AbstractMatrix : Matrix with coordinates of the data points (x or y)
covfunction :: Function : Covfunction

Returns:

spatial covariance matrix

source

Mads.getcovvec! — Method

Get spatial covariance vector

Methods:

Mads.getcovvec!(covvec::AbstractVector, x0::AbstractVector, X::AbstractMatrix, covfn::Function) : ~/work/Mads.jl/Mads.jl/src/MadsKriging.jl:186

Arguments:

covvec :: AbstractVector : Spatial covariance vector
x0 :: AbstractVector : Vector with coordinates of the estimation points (x or y)
X :: AbstractMatrix : Matrix with coordinates of the data points
covfn :: Function : Spatial covariance function

Returns:

spatial covariance vector

source

Mads.getdefaultplotformat — Method

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

Methods:

Mads.getdefaultplotformat() : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:32

source

Mads.getdictvalues — Function

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

Methods:

Mads.getdictvalues(dict::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1293
Mads.getdictvalues(dict::AbstractDict, key::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1293
Mads.getdictvalues(dict::AbstractDict, key::Regex) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1292

Arguments:

dict :: AbstractDict : Dictionary
key :: AbstractString : The key to find value for
key :: Regex : The key to find value for

source

Mads.getdir — Method

Get directory

Methods:

Mads.getdir(filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:860

Arguments:

filename :: AbstractString : File name

Returns:

directory in file name

Example:

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

source

Mads.getdistribution — Function

Parse parameter distribution from a string

Methods:

Mads.getdistribution(dist::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsMisc.jl:194
Mads.getdistribution(dist::AbstractString, inputtype::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsMisc.jl:194

Arguments:

dist :: AbstractString : Parameter distribution
inputtype :: AbstractString : Input type (parameter or observation)

Returns:

distribution

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Mads.getextension — Method

Get file name extension

Methods:

Mads.getextension(filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1037

Arguments:

filename :: AbstractString : File name

Returns:

file name extension

Example:

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

source

Mads.getfilenames — Method

Get file names by expanding wildcards

Methods:

Mads.getfilenames(cmdstring::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:394

Arguments:

cmdstring :: AbstractString : Cmdstring

source

Mads.getimportantsamples — Method

Get important samples

Methods:

Mads.getimportantsamples(samples::AbstractArray, llhoods::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:354

Arguments:

samples :: AbstractArray : Array of samples
llhoods :: AbstractVector : Vector of log-likelihoods

Returns:

array of important samples

source

Mads.getlogparamkeys — Method

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

source

Mads.getmadsinputfile — Method

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

Methods:

Mads.getmadsinputfile() : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:812

Returns:

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

source

Mads.getmadsproblemdir — Method

Get the directory where the Mads data file is located

Methods:

Mads.getmadsproblemdir(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:883

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Example:

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

where madsproblemdir = "../../"

source

Mads.getmadsrootname — Method

Get the MADS problem root name

Methods:

Mads.getmadsrootname(madsdata::AbstractDict; first, version) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:834

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

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

Example:

madsrootname = Mads.getmadsrootname(madsdata)

Returns:

root of file name

source

Mads.getnextmadsfilename — Method

Get next mads file name

Methods:

Mads.getnextmadsfilename(filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1000

Arguments:

filename :: AbstractString : File name

Returns:

next mads file name

source

Mads.getnonlogparamkeys — Method

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

source

Mads.getnonoptparamkeys — Method

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

source

Mads.getobsdist — Method

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

source

Mads.getobsdist — Method

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

source

Mads.getobskeys — Method

Get keys for all observations in the MADS problem dictionary

Methods:

Mads.getobskeys(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:50

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Returns:

keys for all observations in the MADS problem dictionary

source

Mads.getobslog — Method

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

source

Mads.getobslog — Method

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

source

Mads.getobsmax — Method

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

source

Mads.getobsmax — Method

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

source

Mads.getobsmaxorig — Method

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

source

Mads.getobsmaxorig — Method

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

source

Mads.getobsmin — Method

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

source

Mads.getobsmin — Method

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

source

Mads.getobsminorig — Method

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

source

Mads.getobsminorig — Method

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

source

Mads.getobstarget — Method

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

source

Mads.getobstarget — Method

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

source

Mads.getobstime — Method

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

source

Mads.getobstime — Method

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

source

Mads.getobsweight — Method

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

source

Mads.getobsweight — Method

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

source

Mads.getoptparamkeys — Method

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

source

Mads.getoptparams — Function

Get optimizable parameters

Methods:

Mads.getoptparams(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:369
Mads.getoptparams(madsdata::AbstractDict, parameterarray::AbstractArray) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:369
Mads.getoptparams(madsdata::AbstractDict, parameterarray::AbstractArray, optparameterkey::AbstractArray) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:369

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
parameterarray :: AbstractArray : Parameter array
optparameterkey :: AbstractArray : Optimizable parameter keys

Returns:

parameter array

source

Mads.getparamdict — Method

Get a dictionary with all parameters and their respective initial values

Methods:

Mads.getparamdict(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:64

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Returns:

dictionary with all parameters and their respective initial values

source

Mads.getparamdistributions — Method

Get probabilistic distributions of all parameters in the MADS problem dictionary

Note:

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

Methods:

Mads.getparamdistributions(madsdata::AbstractDict; init_dist) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:818

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

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

Returns:

probabilistic distributions

source

Mads.getparamkeys — Method

Get keys of all parameters in the MADS problem dictionary

Methods:

Mads.getparamkeys(madsdata::AbstractDict; filter) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:45

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

filter : Parameter filter

Returns:

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

source

Mads.getparamrandom — Function

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

Methods:

Mads.getparamrandom(madsdata::AbstractDict, numsamples::Integer, parameterkey::Union{AbstractString, Symbol}; init_dist) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:390
Mads.getparamrandom(madsdata::AbstractDict, numsamples::Integer; ...) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:390
Mads.getparamrandom(madsdata::AbstractDict, parameterkey::Union{AbstractString, Symbol}; numsamples, paramdist, init_dist) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:407
Mads.getparamrandom(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:390

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
numsamples :: Integer : Number of samples, [default=1]
parameterkey :: Union{AbstractString, Symbol} : 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.getparamsinit — Method

Get an array with init values for parameters defined by paramkeys

source

Mads.getparamsinit — Method

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

source

Mads.getparamsinit_max — Function

Get an array with init_max values for parameters defined by paramkeys

Methods:

Mads.getparamsinit_max(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:280
Mads.getparamsinit_max(madsdata::AbstractDict, paramkeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:280

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
paramkeys :: AbstractVector : Parameter keys

Returns:

the parameter values

source

Mads.getparamsinit_min — Function

Get an array with init_min values for parameters

Methods:

Mads.getparamsinit_min(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:234
Mads.getparamsinit_min(madsdata::AbstractDict, paramkeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:234

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
paramkeys :: AbstractVector : Parameter keys

Returns:

the parameter values

source

Mads.getparamslog — Method

Get an array with log values for parameters defined by paramkeys

source

Mads.getparamslog — Method

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

source

Mads.getparamslongname — Method

Get an array with longname values for parameters defined by paramkeys

source

Mads.getparamslongname — Method

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

source

Mads.getparamsmax — Function

Get an array with max values for parameters defined by paramkeys

Methods:

Mads.getparamsmax(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:200
Mads.getparamsmax(madsdata::AbstractDict, paramkeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:200

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
paramkeys :: AbstractVector : Parameter keys

Returns:

returns the parameter values

source

Mads.getparamsmin — Function

Get an array with min values for parameters defined by paramkeys

Methods:

Mads.getparamsmin(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:166
Mads.getparamsmin(madsdata::AbstractDict, paramkeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:166

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
paramkeys :: AbstractVector : Parameter keys

Returns:

the parameter values

source

Mads.getparamsplotname — Method

Get an array with plotname values for parameters defined by paramkeys

source

Mads.getparamsplotname — Method

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

source

Mads.getparamsstep — Method

Get an array with step values for parameters defined by paramkeys

source

Mads.getparamsstep — Method

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

source

Mads.getparamstype — Method

Get an array with type values for parameters defined by paramkeys

source

Mads.getparamstype — Method

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

source

Mads.getproblemdir — Method

Get the directory where currently Mads is running

Methods:

Mads.getproblemdir() : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:906

Example:

problemdir = Mads.getproblemdir()

Returns:

Mads problem directory

source

Mads.getprocs — Method

Get the number of processors

Methods:

Mads.getprocs() : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:24

source

Mads.getrestart — Method

Get MADS restart status

Methods:

Mads.getrestart(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:138

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

source

Mads.getrestartdir — Function

Get the directory where Mads restarts will be stored

Methods:

Mads.getrestartdir(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsFunc.jl:382
Mads.getrestartdir(madsdata::AbstractDict, suffix::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsFunc.jl:382
Mads.getrestartdir(madsdata::AbstractDict, suffix::AbstractString, restartdir::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsFunc.jl:382

Arguments:

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

Returns:

restart directory where reusable model results will be stored

source

Mads.getrootname — Method

Get the filename root

Methods:

Mads.getrootname(filename::AbstractString; first, version) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:938

Arguments:

filename :: AbstractString : File name

Keywords:

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

Returns:

root of file name

Example:

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

source

Mads.getseed — Method

Get and return current random seed.

Methods:

Mads.getseed() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:537

source

Mads.getsindx — Method

Get sin-space dx

Methods:

Mads.getsindx(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:416

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Returns:

sin-space dx value

source

Mads.getsourcekeys — Method

Get keys of all source parameters in the MADS problem dictionary

Methods:

Mads.getsourcekeys(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:85

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Returns:

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

source

Mads.gettarget — Method

Get observation target

Methods:

Mads.gettarget(o::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:228

Arguments:

o :: AbstractDict : Observation data

Returns:

observation target

source

Mads.gettargetkeys — Method

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

Methods:

Mads.gettargetkeys(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:64

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Returns:

keys for all targets in the MADS problem dictionary

source

Mads.gettime — Method

Get observation time

Methods:

Mads.gettime(o::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:151

Arguments:

o :: AbstractDict : Observation data

Returns:

observation time ("NaN" it time is missing)

source

Mads.getweight — Method

Get observation weight

Methods:

Mads.getweight(o::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:189

Arguments:

o :: AbstractDict : Observation data

Returns:

observation weight ("NaN" when weight is missing)

source

Mads.getwelldata — Method

Get spatial and temporal data in the Wells class

Methods:

Mads.getwelldata(madsdata::AbstractDict; time) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:754

Arguments:

madsdata :: AbstractDict : Mads problem dictionary

Keywords:

time : Get observation times [default=false]

Returns:

array with spatial and temporal data in the Wells class

source

Mads.getwellkeys — Method

Get keys for all wells in the MADS problem dictionary

Methods:

Mads.getwellkeys(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:81

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Returns:

keys for all wells in the MADS problem dictionary

source

Mads.getwelltargets — Method

Methods:

Mads.getwelltargets(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:788

Arguments:

madsdata :: AbstractDict : Mads problem dictionary

Returns:

array with targets in the Wells class

source

Mads.graphoff — Method

MADS graph output off

Methods:

Mads.graphoff() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:210

source

Mads.graphon — Method

MADS graph output on

Methods:

Mads.graphon() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:201

source

Mads.haskeyword — Function

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

Methods:

Mads.haskeyword(madsdata::AbstractDict, class::AbstractString, keyword::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:319
Mads.haskeyword(madsdata::AbstractDict, keyword::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:316

Arguments:

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

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.help — Method

Produce MADS help information

Methods:

Mads.help() : ~/work/Mads.jl/Mads.jl/src/MadsHelp.jl:35

source

Mads.importeverywhere — Method

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

Methods:

Mads.importeverywhere(filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsFunc.jl:440

Arguments:

filename :: AbstractString : File name

Returns:

Julia function to execute the model

source

Mads.indexkeys — Function

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

Methods:

Mads.indexkeys(dict::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1283
Mads.indexkeys(dict::AbstractDict, key::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1283
Mads.indexkeys(dict::AbstractDict, key::Regex) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1282

Arguments:

dict :: AbstractDict : Dictionary
key :: AbstractString : The key to find index for
key :: Regex : The key to find index for

source

Mads.infogap_jump — Function

Information Gap Decision Analysis using JuMP

Methods:

Mads.infogap_jump(; ...) : ~/work/Mads.jl/Mads.jl/src/MadsInfoGap.jl:23
Mads.infogap_jump(madsdata::AbstractDict; horizons, retries, random, maxiter, verbosity, seed) : ~/work/Mads.jl/Mads.jl/src/MadsInfoGap.jl:23

Arguments:

madsdata :: AbstractDict : Mads problem dictionary

Keywords:

horizons : Info-gap horizons of uncertainty [default=[0.05, 0.1, 0.2, 0.5]]
retries : Number of solution retries [default=1]
random : Random initial guesses [default=false]
maxiter : Maximum number of iterations [default=3000]
verbosity : Verbosity output level [default=0]
seed : Random seed [default=0]

source

Mads.infogap_jump_polynomial — Function

Information Gap Decision Analysis using JuMP

Methods:

Mads.infogap_jump_polynomial(; ...) : ~/work/Mads.jl/Mads.jl/src/MadsInfoGap.jl:128
Mads.infogap_jump_polynomial(madsdata::AbstractDict; horizons, retries, random, maxiter, verbosity, quiet, plot, model, seed) : ~/work/Mads.jl/Mads.jl/src/MadsInfoGap.jl:128

Arguments:

madsdata :: AbstractDict : Mads problem dictionary

Keywords:

horizons : Info-gap horizons of uncertainty [default=[0.05, 0.1, 0.2, 0.5]]
retries : Number of solution retries [default=1]
random : Random initial guesses [default=false]
maxiter : Maximum number of iterations [default=3000]
verbosity : Verbosity output level [default=0]
quiet : Quiet [default=false]
plot : Activate plotting [default=false]
model : Model id [default=1]
seed : Random seed [default=0]

Returns:

hmin, hmax

source

Mads.infogap_moi_lin — Function

Information Gap Decision Analysis using MathOptInterface

Methods:

Mads.infogap_moi_lin(; ...) : ~/work/Mads.jl/Mads.jl/src/MadsInfoGap.jl:442
Mads.infogap_moi_lin(madsdata::AbstractDict; horizons, retries, random, maxiter, verbosity, seed, pinit) : ~/work/Mads.jl/Mads.jl/src/MadsInfoGap.jl:442

Arguments:

madsdata :: AbstractDict : Mads problem dictionary

Keywords:

horizons : Info-gap horizons of uncertainty [default=[0.05, 0.1, 0.2, 0.5]]
retries : Number of solution retries [default=1]
random : Random initial guesses [default=false]
maxiter : Maximum number of iterations [default=3000]
verbosity : Verbosity output level [default=0]
seed : Random seed [default=0]
pinit : Vector with initial parameters

source

Mads.infogap_moi_polynomial — Function

Information Gap Decision Analysis using MathOptInterface

Methods:

Mads.infogap_moi_polynomial(; ...) : ~/work/Mads.jl/Mads.jl/src/MadsInfoGap.jl:301
Mads.infogap_moi_polynomial(madsdata::AbstractDict; horizons, retries, random, maxiter, verbosity, seed, rng, pinit) : ~/work/Mads.jl/Mads.jl/src/MadsInfoGap.jl:301

Arguments:

madsdata :: AbstractDict : Mads problem dictionary

Keywords:

horizons : Info-gap horizons of uncertainty [default=[0.05, 0.1, 0.2, 0.5]]
retries : Number of solution retries [default=1]
random : Random initial guesses [default=false]
maxiter : Maximum number of iterations [default=3000]
verbosity : Verbosity output level [default=0]
seed : Random seed [default=0]
rng : Rng
pinit : Vector with initial parameters

source

Mads.ins_obs — Method

Apply Mads instruction file instructionfilename to read model output file modeloutputfilename

Methods:

Mads.ins_obs(instructionfilename::AbstractString, modeloutputfilename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1499

Arguments:

instructionfilename :: AbstractString : Instruction file name
modeloutputfilename :: AbstractString : Model output file name

Returns:

obsdict : observation dictionary with the model outputs

source

Mads.instline2regexs — Method

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

Methods:

Mads.instline2regexs(instline::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1399

Arguments:

instline :: AbstractString : Instruction line

Returns:

regexs : regular expressions
obsnames : observation names
getparamhere : parameters

source

Mads.invobsweights! — Function

Set inversely proportional observation weights in the MADS problem dictionary

Methods:

Mads.invobsweights!(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:332
Mads.invobsweights!(madsdata::AbstractDict, multiplier::Number) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:332
Mads.invobsweights!(madsdata::AbstractDict, multiplier::Number, obskeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:332

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
multiplier :: Number : Weight multiplier
obskeys :: AbstractVector : Obskeys

source

Mads.invwellweights! — Function

Set inversely proportional well weights in the MADS problem dictionary

Methods:

Mads.invwellweights!(madsdata::AbstractDict, multiplier::Number) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:384
Mads.invwellweights!(madsdata::AbstractDict, multiplier::Number, wellkeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:384

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
multiplier :: Number : Weight multiplier
wellkeys :: AbstractVector : Wellkeys

source

Mads.islog — Method

Is parameter with key parameterkey log-transformed?

Methods:

Mads.islog(madsdata::AbstractDict, parameterkey::Union{AbstractString, Symbol}) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:441

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
parameterkey :: Union{AbstractString, Symbol} : Parameter key

Returns:

true if log-transformed, false otherwise

source

Mads.isnull — Function

Check if input value is nulltype

Methods:

Mads.isnull(x) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:709
Mads.isnull(x::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:721
Mads.isnull(x::Missing) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:715
Mads.isnull(x::Nothing) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:712
Mads.isnull(x::Real) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:718

Arguments:

x :: Any : Input value
x :: AbstractString : Input value
x :: Missing : Input value
x :: Nothing : Input value
x :: Real : Input value

Returns:

true if the input value does not contain information; false otherwise

source

Mads.isobs — Method

Is a dictionary containing all the observations

Methods:

Mads.isobs(madsdata::AbstractDict, dict::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:24

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
dict :: AbstractDict : Dictionary

Returns:

true if the dictionary contain all the observations, false otherwise

source

Mads.isopt — Method

Is a parameter with key parameterkey optimizable?

Methods:

Mads.isopt(madsdata::AbstractDict, parameterkey::Union{AbstractString, Symbol}) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:421

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
parameterkey :: Union{AbstractString, Symbol} : Parameter key

Returns:

true if optimizable, false if not

source

Mads.isparam — Method

Check if a dictionary containing all the Mads model parameters

Methods:

Mads.isparam(madsdata::AbstractDict, dict::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:15

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
dict :: AbstractDict : Dictionary

Returns:

true if the dictionary contains all the parameters, false otherwise

source

Mads.ispkgavailable — Method

Checks if package is available

Methods:

Mads.ispkgavailable(modulename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:617

Arguments:

modulename :: AbstractString : Module name

Returns:

true or false

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Mads.ispkgavailable_old — Method

Checks if package is available

Methods:

Mads.ispkgavailable_old(modulename::AbstractString; quiet) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:595

Arguments:

modulename :: AbstractString : Module name

Keywords:

quiet : Quiet

Returns:

true or false

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Mads.krige — Method

Kriging

Methods:

Mads.krige(x0mat::AbstractMatrix, X::AbstractMatrix, Z::AbstractVector, covfn::Function) : ~/work/Mads.jl/Mads.jl/src/MadsKriging.jl:125

Arguments:

x0mat :: AbstractMatrix : Point coordinates at which to obtain kriging estimates
X :: AbstractMatrix : Coordinates of the observation (conditioning) data
Z :: AbstractVector : Values for the observation (conditioning) data
covfn :: Function : Spatial covariance function

Returns:

kriging estimates at x0mat

source

Mads.levenberg_marquardt — Function

Levenberg-Marquardt optimization

Methods:

Mads.levenberg_marquardt(f::Function, g::Function, x0, o::Function; root, tolX, tolG, tolOF, tolOFcount, minOF, maxEval, maxIter, maxJacobians, lambda, lambda_scale, lambda_mu, lambda_nu, np_lambda, show_trace, quiet, callbackinitial, callbackiteration, callbackjacobian, callbackfinal, parallel_execution, center_provided) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:367
Mads.levenberg_marquardt(f::Function, g::Function, x0; ...) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:367

Arguments:

f :: Function : Forward model function
g :: Function : Gradient function for the forward model
x0 :: Any : Initial parameter guess
o :: Function : Objective function [default=x->(x'*x)[1]]

Keywords:

root : Mads problem root name
tolX : Parameter space tolerance [default=1e-4]
tolG : Parameter space update tolerance [default=1e-6]
tolOF : Objective function update tolerance [default=1e-3]
tolOFcount : Number of Jacobian runs with small objective function change [default=5]
minOF : Objective function update tolerance [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]
lambda : Initial Levenberg-Marquardt lambda [default=eps(Float32)]
lambda_scale : Lambda scaling factor [default=1e-3,]
lambda_mu : Lambda multiplication factor μ [default=10]
lambda_nu : Lambda multiplication factor ν [default=2]
np_lambda : Number of parallel lambda solves [default=10]
show_trace : Shows solution trace [default=false]
quiet : Quiet
callbackinitial : Callbackinitial
callbackiteration : Call back function for each iteration [default=(best x::AbstractVector, of::Number, lambda::Number)->nothing]
callbackjacobian : Call back function for each Jacobian [default=(x::AbstractVector, J::AbstractMatrix)->nothing]
callbackfinal : Final call back function [default=(best x::AbstractVector, of::Number, lambda::Number)->nothing]
parallel_execution : Parallel execution
center_provided : Center provided

source

Mads.linktempdir — Method

Link files in a temporary directory

Methods:

Mads.linktempdir(madsproblemdir::AbstractString, tempdirname::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1770

Arguments:

madsproblemdir :: AbstractString : Mads problem directory
tempdirname :: AbstractString : Temporary directory name

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Mads.loadasciifile — Method

Load ASCII file

Methods:

Mads.loadasciifile(filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsASCII.jl:14

Arguments:

filename :: AbstractString : ASCII file name

Returns:

data from the file

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Mads.loadbigyamlfile — Method

Load BIG YAML input file

Methods:

Mads.loadmadsfile(filename::AbstractString; bigfile, format, quiet, dicttype) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:432

Arguments:

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

Keywords:

bigfile : Bigfile
format : Format
quiet : Quiet
dicttype : Dicttype

Returns:

MADS problem dictionary

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Mads.loadjsonfile — Method

Load a JSON file

Methods:

Mads.loadjsonfile(filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsJSON.jl:15

Arguments:

filename :: AbstractString : JSON file name

Returns:

data from the JSON file

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Mads.loadmadsfile — Method

Load MADS input file defining a MADS problem dictionary

Methods:

Mads.loadmadsfile(filename::AbstractString; bigfile, format, quiet, dicttype) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:432

Arguments:

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

Keywords:

bigfile : Bigfile
format : Acceptable formats are yaml and json [default=yaml]
quiet : Quiet
dicttype : Dicttype

Returns:

MADS problem dictionary

Example:

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

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Mads.loadmadsproblem — Method

Load a predefined Mads problem

Methods:

Mads.loadmadsproblem(name::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsCreate.jl:14

Arguments:

name :: AbstractString : Predefined MADS problem name

Returns:

MADS problem dictionary

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Mads.loadsaltellirestart! — Method

Load Saltelli sensitivity analysis results for fast simulation restarts

Methods:

Mads.loadsaltellirestart!(evalmat::AbstractArray, matname::AbstractString, restartdir::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:600

Arguments:

evalmat :: AbstractArray : Loaded array
matname :: AbstractString : Matrix (array) name (defines the name of the loaded file)
restartdir :: AbstractString : Directory where files will be stored containing model results for fast simulation restarts

Returns:

true when successfully loaded, false when it is not

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Mads.loadyamlfile — Method

Load YAML file

Methods:

Mads.loadyamlfile(filename::AbstractString; dicttype) : ~/work/Mads.jl/Mads.jl/src/MadsYAML.jl:16

Arguments:

filename :: AbstractString : File name

Keywords:

dicttype : Dicttype

Returns:

data in the yaml input file

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Mads.localsa — Method

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

Methods:

Mads.localsa(madsdata::AbstractDict; sinspace, keyword, filename, format, xtitle, ytitle, datafiles, restart, imagefiles, par, obs, J) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:123

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

sinspace : Apply sin transformation [default=true]
keyword : Keyword to be added in the filename root
filename : Output file name
format : Output plot format (png, pdf, etc.)
xtitle : Xtitle
ytitle : Ytitle
datafiles : Flag to write data files [default=true]
restart : Restart
imagefiles : Flag to create image files [default=Mads.graphoutput]
par : Parameter set
obs : Observations for the parameter set
J : Jacobian matrix

Dumps:

filename : output plot file

source

Mads.long_tests_off — Method

Turn off execution of long MADS tests (default)

Methods:

Mads.long_tests_off() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:255

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Mads.long_tests_on — Method

Turn on execution of long MADS tests

Methods:

Mads.long_tests_on() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:246

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Mads.madsMathOptInterface — Function

Define MadsModel type applied for Mads execution using MathOptInterface

Methods:

Mads.madsMathOptInterface() : ~/work/Mads.jl/Mads.jl/src/MadsMathOptInterface.jl:16
Mads.madsMathOptInterface(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsMathOptInterface.jl:16

Arguments:

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

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Mads.madscores — Function

Check the number of processors on a series of servers

Methods:

Mads.madscores() : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:302
Mads.madscores(nodenames::Vector{String}) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:302

Arguments:

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

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Mads.madscritical — Method

MADS critical error messages

Methods:

Mads.madscritical(message::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsLog.jl:70

Arguments:

message :: AbstractString : Critical error message

source

Mads.madsdebug — Function

MADS debug messages (controlled by quiet and debuglevel)

Methods:

Mads.madsdebug(message::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsLog.jl:23
Mads.madsdebug(message::AbstractString, level::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsLog.jl:23

Arguments:

message :: AbstractString : Debug message
level :: Integer : Output verbosity level [default=0]

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Mads.madsdir — Method

Change the current directory to the Mads source dictionary

Methods:

Mads.madsdir() : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:403

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Mads.madserror — Method

MADS error messages

Methods:

Mads.madserror(message::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsLog.jl:60

Arguments:

message :: AbstractString : Error message

source

Mads.madsinfo — Function

MADS information/status messages (controlled by quietandverbositylevel`)

Methods:

Mads.madsinfo(message::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsLog.jl:38
Mads.madsinfo(message::AbstractString, level::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsLog.jl:38

Arguments:

message :: AbstractString : Information/status message
level :: Integer : Output verbosity level [default=0]

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Mads.madsload — Function

Check the load of a series of servers

Methods:

Mads.madsload() : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:322
Mads.madsload(nodenames::Vector{String}) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:322

Arguments:

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

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Mads.madsoutput — Function

MADS output (controlled by quiet and verbositylevel)

Methods:

Mads.madsoutput(message::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsLog.jl:8
Mads.madsoutput(message::AbstractString, level::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsLog.jl:8

Arguments:

message :: AbstractString : Output message
level :: Integer : Output verbosity level [default=0]

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Mads.madsup — Function

Check the uptime of a series of servers

Methods:

Mads.madsup() : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:312
Mads.madsup(nodenames::Vector{String}) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:312

Arguments:

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

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Mads.madswarn — Method

MADS warning messages

Methods:

Mads.madswarn(message::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsLog.jl:50

Arguments:

message :: AbstractString : Warning message

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Mads.makearrayconditionalloglikelihood — Method

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

Methods:

Mads.makearrayconditionalloglikelihood(madsdata::AbstractDict, conditionalloglikelihood) : ~/work/Mads.jl/Mads.jl/src/MadsMisc.jl:89

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
conditionalloglikelihood :: Any : Conditional log likelihood

Returns:

a conditional log likelihood function that accepts an array

source

Mads.makearrayconditionalloglikelihood — Method

Make array of conditional log-likelihoods

Methods:

Mads.makearrayconditionalloglikelihood(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsBayesInfoGap.jl:158
Mads.makearrayconditionalloglikelihood(madsdata::AbstractDict, conditionalloglikelihood) : ~/work/Mads.jl/Mads.jl/src/MadsMisc.jl:89

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
conditionalloglikelihood :: Any : Conditionalloglikelihood

Returns:

array of conditional log-likelihoods

source

Mads.makearrayfunction — Function

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

Methods:

Mads.makearrayfunction(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsMisc.jl:17
Mads.makearrayfunction(madsdata::AbstractDict, f::Function) : ~/work/Mads.jl/Mads.jl/src/MadsMisc.jl:17

Arguments:

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

Returns:

function accepting an array containing the optimal parameter values

source

Mads.makearrayloglikelihood — Method

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

Methods:

Mads.makearrayloglikelihood(madsdata::AbstractDict, loglikelihood) : ~/work/Mads.jl/Mads.jl/src/MadsMisc.jl:113

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
loglikelihood :: Any : Log likelihood

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::AbstractDict, choice::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsBayesInfoGap.jl:33

Arguments:

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

Returns:

BIG-DT problem type

source

Mads.makebigdt — Method

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

Methods:

Mads.makebigdt(madsdata::AbstractDict, choice::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsBayesInfoGap.jl:18

Arguments:

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

Returns:

BIG-DT problem type

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Mads.makecomputeconcentrations — Method

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

Methods:

Mads.makecomputeconcentrations(madsdata::AbstractDict; calc_zero_weight_obs, calc_predictions, source_label) : ~/work/Mads.jl/Mads.jl/src/MadsAnasol.jl:204

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

calc_zero_weight_obs : Calculate zero weight observations[default=false]
calc_predictions : Calculate zero weight predictions [default=true]
source_label : Source label

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)

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Mads.makedixonprice — Method

Make dixon price

Methods:

Mads.makedixonprice(n::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:257

Arguments:

n :: Integer : Number of observations

Returns:

dixon price

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Mads.makedixonprice_gradient — Method

Methods:

Mads.makedixonprice(n::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:257

Arguments:

n :: Integer : Number of observations

Returns:

dixon price gradient

source

Mads.makedoublearrayfunction — Function

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

Methods:

Mads.makedoublearrayfunction(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsMisc.jl:63
Mads.makedoublearrayfunction(madsdata::AbstractDict, f::Function) : ~/work/Mads.jl/Mads.jl/src/MadsMisc.jl:63

Arguments:

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

Returns:

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

source

Mads.makelmfunctions — Function

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

Methods:

Mads.makelmfunctions(f::Function) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:118
Mads.makelmfunctions(madsdata::AbstractDict; parallel_gradients) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:139

Arguments:

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

Keywords:

parallel_gradients : Parallel gradients

Returns:

forward model, gradient, objective functions

source

Mads.makelocalsafunction — Method

Make gradient function needed for local sensitivity analysis

Methods:

Mads.makelocalsafunction(madsdata::AbstractDict; restart, multiplycenterbyweights) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:24

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

restart : Restart
multiplycenterbyweights : Multiply center by observation weights [default=true]

Returns:

gradient function

source

Mads.makelogprior — Method

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

Methods:

Mads.makelogprior(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsFunc.jl:468

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Return:

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

source

Mads.makemadscommandfunction — Method

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

Methods:

Mads.makemadscommandfunction(madsdata_in::AbstractDict; obskeys, calc_zero_weight_obs, calc_predictions, quiet) : ~/work/Mads.jl/Mads.jl/src/MadsFunc.jl:68

Arguments:

madsdata_in :: AbstractDict : Madsdata in

Keywords:

obskeys : Obskeys
calc_zero_weight_obs : Calculate zero weight observations [default=false]
calc_predictions : Calculate predictions [default=true]
quiet : Quiet

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 expectation is that for a given set of model inputs, the model will produce a model output that will be provided to MADS. The fields in the MADS problem dictionary that can be used to define the model coupling are:

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

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

Methods:

Mads.makemadsconditionalloglikelihood(madsdata::AbstractDict; weightfactor) : ~/work/Mads.jl/Mads.jl/src/MadsFunc.jl:491

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

weightfactor : Weight factor [default=1]

Return:

the conditional log-likelihood

source

Mads.makemadsloglikelihood — Method

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

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

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

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

dictionary of model predictions
dictionary of respective observations

Methods:

Mads.makemadsloglikelihood(madsdata::AbstractDict; weightfactor) : ~/work/Mads.jl/Mads.jl/src/MadsFunc.jl:536

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

weightfactor : Weight factor [default=1]

Returns:

the log-likelihood for a given set of model parameters

source

Mads.makemadsreusablefunction — Function

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

Methods:

Mads.makemadsreusablefunction(madsdata::AbstractDict, madscommandfunction::Function, suffix::AbstractString; usedict) : ~/work/Mads.jl/Mads.jl/src/MadsFunc.jl:335
Mads.makemadsreusablefunction(madsdata::AbstractDict, madscommandfunction::Function; ...) : ~/work/Mads.jl/Mads.jl/src/MadsFunc.jl:335
Mads.makemadsreusablefunction(paramkeys::AbstractVector, obskeys::AbstractVector, madsdatarestart::Union{Bool, String}, madscommandfunction::Function, restartdir::AbstractString; usedict) : ~/work/Mads.jl/Mads.jl/src/MadsFunc.jl:338

Arguments:

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

Keywords:

usedict : Use dictionary [default=true]

Returns:

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

source

Mads.makemoifunctions — Method

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

Methods:

Mads.makemoifunctions(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsMathOptInterface.jl:90

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Returns:

forward model, gradient, objective functions

source

Mads.makepowell — Method

Make Powell test function for LM optimization

Methods:

Mads.makepowell(n::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:160

Arguments:

n :: Integer : Number of observations

Returns:

Powell test function for LM optimization

source

Mads.makepowell_gradient — Method

ake parameter gradients of the Powell test function for LM optimization

Methods:

Mads.makepowell_gradient(n::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:184

Arguments:

n :: Integer : Number of observations

Returns:

arameter gradients of the Powell test function for LM optimization

source

Mads.makerosenbrock — Method

Make Rosenbrock test function for LM optimization

Methods:

Mads.makerosenbrock(n::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:115

Arguments:

n :: Integer : Number of observations

Returns:

Rosenbrock test function for LM optimization

source

Mads.makerosenbrock_gradient — Method

Make parameter gradients of the Rosenbrock test function for LM optimization

Methods:

Mads.makerosenbrock_gradient(n::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:137

Arguments:

n :: Integer : Number of observations

Returns:

parameter gradients of the Rosenbrock test function for LM optimization

source

Mads.makerotatedhyperellipsoid — Method

Methods:

Mads.makerotatedhyperellipsoid(n::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:336

Arguments:

n :: Integer : Number of observations

Returns:

rotated hyperellipsoid

source

Mads.makerotatedhyperellipsoid_gradient — Method

Methods:

Mads.makerotatedhyperellipsoid_gradient(n::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:360

Arguments:

n :: Integer : Number of observations

Returns:

rotated hyperellipsoid gradient

source

Mads.makesphere — Method

Make sphere

Methods:

Mads.makesphere(n::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:215

Arguments:

n :: Integer : Number of observations

Returns:

sphere

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Mads.makesphere_gradient — Method

Make sphere gradient

Methods:

Mads.makesphere_gradient(n::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:236

Arguments:

n :: Integer : Number of observations

Returns:

sphere gradient

source

Mads.makesumsquares — Method

Methods:

Mads.makesumsquares(n::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:298

Arguments:

n :: Integer : Number of observations

Returns:

sumsquares

source

Mads.makesumsquares_gradient — Method

Methods:

Mads.makesumsquares_gradient(n::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:317

Arguments:

n :: Integer : Number of observations

Returns:

sumsquares gradient

source

Mads.makesvrmodel — Function

Make SVR model functions (executor and cleaner)

Methods:

Mads.makesvrmodel(madsdata::AbstractDict, numberofsamples::Integer; check, addminmax, loadsvr, savesvr, svm_type, kernel_type, degree, gamma, coef0, C, nu, epsilon, cache_size, tol, shrinking, probability, verbose, seed) : ~/work/Mads.jl/Mads.jl/src/MadsSVR.jl:203
Mads.makesvrmodel(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsSVR.jl:203

Arguments:

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

Keywords:

check : Check SVR performance [default=false]
addminmax : Add parameter minimum / maximum range values in the training set [default=true]
loadsvr : Load SVR models [default=false]
savesvr : Save SVR models [default=false]
svm_type : SVM type [default=SVR.EPSILON SVR]
kernel_type : Kernel type[default=SVR.RBF]
degree : Degree of the polynomial kernel [default=3]
gamma : Coefficient for RBF, POLY and SIGMOND kernel types [default=1/numberofsamples]
coef0 : Independent term in kernel function; important only in POLY and SIGMOND kernel types [default=0]
C : Cost; penalty parameter of the error term [default=1000.0]
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]
epsilon : Epsilon in the EPSILON SVR model; defines an epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value [default=0.001]
cache_size : Size of the kernel cache [default=100.0]
tol : Tolerance of termination criterion [default=0.001]
shrinking : Apply shrinking heuristic [default=true]
probability : Train to estimate probabilities [default=false]
verbose : Verbose output [default=false]
seed : Random seed [default=0]

Returns:

function performing SVR predictions
function loading existing SVR models
function saving SVR models
function removing SVR models from the memory

source

Mads.maxtofloatmax! — Method

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

Methods:

Mads.maxtofloatmax!(df::DataFrames.DataFrame) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:1108

Arguments:

df :: DataFrames.DataFrame : Dataframe

source

Mads.meshgrid — Function

Create mesh grid

Methods:

Mads.meshgrid(nx::Number, ny::Number) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:495
Mads.meshgrid(x::AbstractVector, y::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:488

Arguments:

nx :: Number : Nx
ny :: Number : Ny
x :: AbstractVector : Vector of grid x coordinates
y :: AbstractVector : Vector of grid y coordinates

Returns:

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

source

Mads.minimize — Method

Minimize Julia function using a constrained Levenberg-Marquardt technique

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

Methods:

Mads.calibrate(madsdata::AbstractDict; tolX, tolG, tolOF, tolOFcount, minOF, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, quiet, usenaive, store_optimization_progress, localsa, parallel_optimization) : ~/work/Mads.jl/Mads.jl/src/MadsCalibrate.jl:206

Arguments:

madsdata :: AbstractDict : Madsdata

Keywords:

tolX : Parameter space tolerance [default=1e-4]
tolG : Parameter space update tolerance [default=1e-6]
tolOF : Objective function update tolerance [default=1e-3]
tolOFcount : Number of Jacobian runs with small objective function change [default=5]
minOF : Objective function update tolerance [default=1e-3]
maxEval : Maximum number of model evaluations [default=1000]
maxIter : Maximum number of optimization iterations [default=100]
maxJacobians : Maximum number of Jacobian solves [default=100]
lambda : Initial Levenberg-Marquardt lambda [default=100.0]
lambda_mu : Lambda multiplication factor [default=10.0]
np_lambda : Number of parallel lambda solves [default=10]
show_trace : Shows solution trace [default=false]
quiet : Quiet
usenaive : Usenaive
store_optimization_progress : Store optimization progress
localsa : Localsa
parallel_optimization : Parallel optimization

Returns:

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

source

Mads.mkdir — Method

Create a directory (if does not already exist)

Methods:

Mads.mkdir(dirname::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1797

Arguments:

dirname :: AbstractString : Directory

source

Mads.modelinformationcriteria — Function

Model section information criteria

Methods:

Mads.modelinformationcriteria(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsModelSelection.jl:11
Mads.modelinformationcriteria(madsdata::AbstractDict, par::AbstractVector{Float64}) : ~/work/Mads.jl/Mads.jl/src/MadsModelSelection.jl:11

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
par :: AbstractVector{Float64} : Parameter array

source

Mads.modobsweights! — Function

Modify (multiply) observation weights in the MADS problem dictionary

Methods:

Mads.modobsweights!(madsdata::AbstractDict, value::Number) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:319
Mads.modobsweights!(madsdata::AbstractDict, value::Number, obskeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:319

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
value :: Number : Value for modifing observation weights
obskeys :: AbstractVector : Obskeys

source

Mads.modwellweights! — Function

Modify (multiply) well weights in the MADS problem dictionary

Methods:

Mads.modwellweights!(madsdata::AbstractDict, value::Number) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:366
Mads.modwellweights!(madsdata::AbstractDict, value::Number, wellkeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:366

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
value :: Number : Value for well weights
wellkeys :: AbstractVector : Wellkeys

source

Mads.montecarlo — Method

Monte Carlo analysis

Methods:

Mads.montecarlo(madsdata::AbstractDict; compute, N, filename) : ~/work/Mads.jl/Mads.jl/src/MadsMonteCarlo.jl:285

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

compute : Compute
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_deltax — Method

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

Methods:

Mads.naive_get_deltax(JpJ::AbstractMatrix{Float64}, Jp::AbstractMatrix{Float64}, f0::AbstractVector{Float64}, lambda::Number) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:274

Arguments:

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

Returns:

the LM parameter space step

source

Mads.naive_levenberg_marquardt — Function

Naive Levenberg-Marquardt optimization

Methods:

Mads.naive_levenberg_marquardt(f::Function, g::Function, x0::AbstractVector{Float64}, o::Function; maxIter, maxEval, lambda, lambda_mu, np_lambda) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:324
Mads.naive_levenberg_marquardt(f::Function, g::Function, x0::AbstractVector{Float64}; ...) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:324

Arguments:

f :: Function : Forward model function
g :: Function : Gradient function for the forward model
x0 :: AbstractVector{Float64} : Initial parameter guess
o :: Function : Objective function [default=x->(x'*x)[1]]

Keywords:

maxIter : Maximum number of optimization iterations [default=10]
maxEval : Maximum number of model evaluations [default=101]
lambda : Initial Levenberg-Marquardt lambda [default=100]
lambda_mu : Lambda multiplication factor μ [default=10]
np_lambda : Number of parallel lambda solves [default=10]

Returns:

source

Mads.naive_lm_iteration — Method

Naive Levenberg-Marquardt optimization: perform LM iteration

Methods:

Mads.naive_lm_iteration(f::Function, g::Function, o::Function, x0::AbstractVector{Float64}, f0::AbstractVector{Float64}, lambdas::AbstractVector{Float64}) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:295

Arguments:

f :: Function : Forward model function
g :: Function : Gradient function for the forward model
o :: Function : Objective function
x0 :: AbstractVector{Float64} : Initial parameter guess
f0 :: AbstractVector{Float64} : Initial model observations
lambdas :: AbstractVector{Float64} : Levenberg-Marquardt lambdas

Returns:

source

Mads.noplot — Method

Disable MADS plotting

Methods:

Mads.noplot() : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:236

source

Mads.notebook — Method

Execute Jupyter notebook in IJulia or as a script

Methods:

Mads.notebook(rootname::AbstractString; script, notebook_directory, check) : ~/work/Mads.jl/Mads.jl/src/MadsNotebooks.jl:20

Arguments:

rootname :: AbstractString : Notebook root name

Keywords:

script : Execute as a script
notebook_directory : Notebook directory
check : Check of notebook exists

source

Mads.notebook_check — Method

Check is Jupyter notebook exists

Methods:

Mads.notebook_check(rootname::AbstractString; notebook_directory) : ~/work/Mads.jl/Mads.jl/src/MadsNotebooks.jl:104

Arguments:

rootname :: AbstractString : Notebook root name

Keywords:

notebook_directory : Notebook directory

source

Mads.notebook_export — Method

Export Jupyter notebook in html, markdown, latex, and script versions

Methods:

Mads.notebook_export(rootname::AbstractString; notebook_directory) : ~/work/Mads.jl/Mads.jl/src/MadsNotebooks.jl:67

Arguments:

rootname :: AbstractString : Notebook root name

Keywords:

notebook_directory : Notebook directory

source

Mads.notebooks — Method

Open Jupyter in the Mads notebook directory

Methods:

Mads.notebooks(; notebook_directory) : ~/work/Mads.jl/Mads.jl/src/MadsNotebooks.jl:52

Keywords:

notebook_directory : Notebook directory

source

Mads.notebookscript — Method

Execute Jupyter notebook as a script

Methods:

Mads.notebookscript(a...; script, notebook_directory, k...) : ~/work/Mads.jl/Mads.jl/src/MadsNotebooks.jl:9

Keywords:

script : Execute as a script
notebook_directory : Notebook directory

source

Mads.obslineoccursin — Method

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

Methods:

Mads.obslineoccursin(obsline::AbstractString, regexs::Vector{Regex}) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1448

Arguments:

obsline :: AbstractString : Instruction line
regexs :: Vector{Regex} : Regular expressions

Returns:

true or false

source

Mads.of — Function

Compute objective function

Methods:

Mads.of(madsdata::AbstractDict, M::AbstractMatrix; filter) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:64
Mads.of(madsdata::AbstractDict, d::AbstractDict; filter) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:53
Mads.of(madsdata::AbstractDict, resultvec::AbstractVector; filter) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:49
Mads.of(madsdata::AbstractDict; filter) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:77

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
M :: AbstractMatrix : M
d :: AbstractDict : D
resultvec :: AbstractVector : Result vector

Keywords:

filter : Filter

source

Mads.parallel_optimization_off — Method

Turn off parallel optimization of jacobians and lambdas

Methods:

Mads.parallel_optimization_off() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:273

source

Mads.parallel_optimization_on — Method

Turn on parallel optimization of jacobians and lambdas

Methods:

Mads.parallel_optimization_on() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:264

source

Mads.paramarray2dict — Method

Convert a parameter array to a parameter dictionary of arrays

Methods:

Mads.paramarray2dict(madsdata::AbstractDict, array::AbstractArray) : ~/work/Mads.jl/Mads.jl/src/MadsMonteCarlo.jl:355

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
array :: AbstractArray : Parameter array

Returns:

a parameter dictionary of arrays

source

Mads.paramdict2array — Method

Convert a parameter dictionary of arrays to a parameter array

Methods:

Mads.paramdict2array(dict::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsMonteCarlo.jl:374

Arguments:

dict :: AbstractDict : Parameter dictionary of arrays

Returns:

a parameter array

source

Mads.parsemadsdata! — Method

Parse loaded MADS problem dictionary

Methods:

Mads.parsemadsdata!(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:579

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

source

Mads.parsenodenames — Function

Parse string with node names defined in SLURM

Methods:

Mads.parsenodenames(nodenames::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:205
Mads.parsenodenames(nodenames::AbstractString, ntasks_per_node::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:205

Arguments:

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

Returns:

vector with names of compute nodes (hosts)

source

Mads.partialof — Method

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

Methods:

Mads.partialof(madsdata::AbstractDict, resultdict::AbstractDict, regex::Regex) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:102

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
resultdict :: AbstractDict : Result dictionary
regex :: Regex : Regular expression

Returns:

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

source

Mads.pkgversion_old — Method

Get package version

Methods:

Mads.pkgversion_old(modulestr::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:571

Arguments:

modulestr :: AbstractString : Modulestr

Returns:

package version

source

Mads.plotlocalsa — Method

Plot local sensitivity analysis results

Methods:

Mads.plotlocalsa(filenameroot::AbstractString; keyword, filename, format) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:1370

Arguments:

filenameroot :: AbstractString : Problem file name root

Keywords:

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

Dumps:

filename : output plot file

source

Mads.plotmadsproblem — Method

Plot contaminant sources and wells defined in MADS problem dictionary

Methods:

Mads.plotmadsproblem(madsdata::AbstractDict; format, filename, keyword, hsize, vsize, quiet, gm) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:118

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

format : Output plot format (png, pdf, etc.) [default=Mads.graphbackend]
filename : Output file name
keyword : To be added in the filename
hsize : Hsize
vsize : Vsize
quiet : Quiet
gm : Gm

Dumps:

plot of contaminant sources and wells

source

Mads.plotmass — Method

Plot injected/reduced contaminant mass

Methods:

Mads.plotmass(lambda::AbstractVector{Float64}, mass_injected::AbstractVector{Float64}, mass_reduced::AbstractVector{Float64}, filename::AbstractString; format, hsize, vsize) : ~/work/Mads.jl/Mads.jl/src/MadsAnasolPlot.jl:17

Arguments:

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

Keywords:

format : Output plot format (png, pdf, etc.)
hsize : Hsize
vsize : Vsize

Dumps:

image file with name filename and in specified format

source

Mads.plotmatches — Function

Plot the matches between model predictions and observations

Methods:

Mads.plotmatches(madsdata::AbstractDict, dict_in::AbstractDict; plotdata, filename, format, title, xtitle, ytitle, ymin, ymax, xmin, xmax, separate_files, hsize, vsize, linewidth, pointsize, obs_plot_dots, noise, dpi, colors, display, notitle, truthtitle, predictiontitle, gmk) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:234
Mads.plotmatches(madsdata::AbstractDict, params::AbstractVector, arg...; kw...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:203
Mads.plotmatches(madsdata::AbstractDict, result::AbstractDict, rx::Union{Regex, AbstractString}; title, notitle, kw...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:208
Mads.plotmatches(madsdata::AbstractDict, rx::Union{Regex, AbstractString}; kw...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:195
Mads.plotmatches(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:195

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
dict_in :: AbstractDict : Dictionary with model parameters
params :: AbstractVector : Params
result :: AbstractDict : Dictionary with model predictions
rx :: Union{Regex, AbstractString} : Regular expression to filter the outputs

Keywords:

plotdata : Plot data (if false model predictions are ploted only) [default=true]
filename : Output file name
format : Output plot format (png, pdf, etc.) [default=Mads.graphbackend]
title : Graph title
xtitle : X-axis title [default="Time"]
ytitle : Y-axis title [default="y"]
ymin : Ymin
ymax : Ymax
xmin : Xmin
xmax : Xmax
separate_files : Plot data for multiple wells separately [default=false]
hsize : Graph horizontal size [default=8Gadfly.inch]
vsize : Graph vertical size [default=4Gadfly.inch]
linewidth : Line width [default=2Gadfly.pt]
pointsize : Data dot size [default=2Gadfly.pt]
obs_plot_dots : Plot data as dots or line [default=true]
noise : Random noise magnitude [default=0; no noise]
dpi : Graph resolution [default=Mads.imagedpi]
colors : Array with plot colors
display : Display plots [default=false]
notitle : Notitle
truthtitle : Truthtitle
predictiontitle : Predictiontitle
gmk : Gmk

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.plotobsSAresults — Method

Plot the sensitivity analysis results for the observations

Methods:

Mads.plotobsSAresults(madsdata::AbstractDict, result::AbstractDict; filter, keyword, filename, format, separate_files, xtitle, ytitle, plotlabels, quiet, order, select, kw...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:631

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
result :: AbstractDict : Sensitivity analysis results

Keywords:

filter : String or regex to plot only observations containing filter
keyword : To be added in the auto-generated filename
filename : Output file name
format : Output plot format (png, pdf, etc.) [default=Mads.graphbackend]
separate_files : Plot data for multiple wells separately [default=false]
xtitle : X-axis title
ytitle : Y-axis title
plotlabels : Plotlabels
quiet : Quiet
order : Order
select : Select

Dumps:

plot of the sensitivity analysis results for the observations

source

Mads.plotrobustnesscurves — Method

Plot BIG-DT robustness curves

Methods:

Mads.plotrobustnesscurves(madsdata::AbstractDict, bigdtresults::AbstractDict; filename, format, maxprob, maxhoriz) : ~/work/Mads.jl/Mads.jl/src/MadsBayesInfoGapPlot.jl:18

Arguments:

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

Keywords:

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

Dumps:

image file with name filename and in specified format

source

Mads.plotseries — Function

Create plots of data series

Methods:

Mads.plotseries(X::Union{AbstractMatrix, AbstractVector}, filename::AbstractString; nS, separate_files, normalize, name, names, kw...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:1111
Mads.plotseries(X::Union{AbstractMatrix, AbstractVector}; ...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:1111
Mads.plotseries(df::DataFrames.DataFrame, filename::AbstractString; names, kw...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:1108
Mads.plotseries(df::DataFrames.DataFrame; ...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:1108
Mads.plotseries(dict::AbstractDict, filename::AbstractString; separate_files, normalize, names, kw...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:1087
Mads.plotseries(dict::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:1087

Arguments:

X :: Union{AbstractMatrix, AbstractVector} : Matrix with the series data
filename :: AbstractString : Output file name
df :: DataFrames.DataFrame : Df
dict :: AbstractDict : Dict

Keywords:

nS : NS
separate_files : Separate files
normalize : Normalize
name : Series name [default=Sources]
names : Names

Dumps:

Plots of data series

source

Mads.plotseriesengine — Function

Engine to create plots of data series

Methods:

Mads.plotseriesengine(X::Union{AbstractMatrix, AbstractVector}, filename::AbstractString; nT, nS, format, xtitle, ytitle, title, logx, logy, keytitle, name, names, combined, hsize, vsize, linewidth, linestyle, pointsize, key_position, font_size, key_title_font_size, key_label_font_size, major_label_font_size, minor_label_font_size, dpi, colors, alpha, alphas, xmin, xmax, ymin, ymax, xaxis, plotline, plotdots, nextgray, lastcolored, code, returnplot, colorkey, background_color, gm, gl, quiet, truth, gall) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:1174
Mads.plotseriesengine(X::Union{AbstractMatrix, AbstractVector}; ...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:1174

Arguments:

X :: Union{AbstractMatrix, AbstractVector} : Matrix with the series data
filename :: AbstractString : Output file name

Keywords:

nT : NT
nS : NS
format : Output plot format (png, pdf, etc.) [default=Mads.graphbackend]
xtitle : X-axis title [default=X]
ytitle : Y-axis title [default=Y]
title : Plot title [default=Sources]
logx : Logx
logy : Logy
keytitle : Keytitle
name : Series name [default=Sources]
names : Names
combined : Combine plots [default=true]
hsize : Horizontal size [default=8Gadfly.inch]
vsize : Vertical size [default=4Gadfly.inch]
linewidth : Width of the lines in plot [default=2Gadfly.pt]
linestyle : Linestyle
pointsize : Pointsize
key_position : Key position
font_size : Font size
key_title_font_size : Key title font size
key_label_font_size : Key label font size
major_label_font_size : Major label font size
minor_label_font_size : Minor label font size
dpi : Graph resolution [default=Mads.imagedpi]
colors : Colors to use in plots
alpha : Alpha
alphas : Alphas
xmin : Xmin
xmax : Xmax
ymin : Ymin
ymax : Ymax
xaxis : Xaxis
plotline : Plotline
plotdots : Plotdots
nextgray : Nextgray
lastcolored : Lastcolored
code : Code
returnplot : Returnplot
colorkey : Colorkey
background_color : Background color
gm : Gm
gl : Gl
quiet : Quiet
truth : Truth
gall : Gall

Dumps:

Plots of data series

source

Mads.plotwellSAresults — Function

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

Methods:

Mads.plotwellSAresults(madsdata::AbstractDict, result::AbstractDict, wellname::AbstractString; xtitle, ytitle, filename, format, quiet) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:511
Mads.plotwellSAresults(madsdata::AbstractDict, result::AbstractDict; xtitle, ytitle, filename, format, quiet) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:500

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
result :: AbstractDict : Sensitivity analysis results
wellname :: AbstractString : Well name

Keywords:

xtitle : X-axis title
ytitle : Y-axis title
filename : Output file name
format : Output plot format (png, pdf, etc.) [default=Mads.graphbackend]
quiet : Quiet

Dumps:

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

source

Mads.printSAresults — Method

Print sensitivity analysis results

Methods:

Mads.printSAresults(madsdata::AbstractDict, results::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:946

Arguments:

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

source

Mads.printSAresults2 — Method

Print sensitivity analysis results (method 2)

Methods:

Mads.printSAresults2(madsdata::AbstractDict, results::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:1027

Arguments:

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

source

Mads.printerrormsg — Method

Print error message

Methods:

Mads.printerrormsg(errmsg) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:479

Arguments:

errmsg :: Any : Error message

source

Mads.printobservations — Function

Print (emit) observations in the MADS problem dictionary

Methods:

Mads.printobservations(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:448
Mads.printobservations(madsdata::AbstractDict, filename::AbstractString; json) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:456
Mads.printobservations(madsdata::AbstractDict, io::IO) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:448
Mads.printobservations(madsdata::AbstractDict, io::IO, obskeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:448

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
filename :: AbstractString : Output file name
io :: IO : Output stream
obskeys :: AbstractVector : Obskeys

Keywords:

json : Json

source

Mads.pull — Function

Pull (checkout) the latest version of Mads modules

Methods:

Mads.pull(; ...) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:92
Mads.pull(modulename::AbstractString; kw...) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:92

Arguments:

modulename :: AbstractString : Module name

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Mads.push — Function

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

Methods:

Mads.push() : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:157
Mads.push(modulename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:157

Arguments:

modulename :: AbstractString : Module name

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Mads.quietoff — Method

Make MADS not quiet

Methods:

Mads.quietoff() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:156

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Mads.quieton — Method

Make MADS quiet

Methods:

Mads.quieton() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:147

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Mads.readasciipredictions — Method

Read MADS predictions from an ASCII file

Methods:

Mads.readasciipredictions(filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsASCII.jl:43

Arguments:

filename :: AbstractString : ASCII file name

Returns:

MADS predictions

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Mads.readmodeloutput — Method

Read model outputs saved for MADS

Methods:

Mads.readmodeloutput(madsdata::AbstractDict; obskeys) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1206

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

obskeys : Observation keys [default=getobskeys(madsdata)]

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Mads.readobservations — Function

Read observations

Methods:

Mads.readobservations(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1568
Mads.readobservations(madsdata::AbstractDict, obskeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1568

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
obskeys :: AbstractVector : Observation keys [default=getobskeys(madsdata)]

Returns:

dictionary with Mads observations

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Mads.readobservations_cmads — Method

Read observations using C MADS dynamic library

Methods:

Mads.readobservations_cmads(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsCMads.jl:13

Arguments:

madsdata :: AbstractDict : Mads problem dictionary

Returns:

observations

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Mads.readyamlpredictions — Method

Read MADS model predictions from a YAML file filename

Methods:

Mads.readyamlpredictions(filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsYAML.jl:106

Arguments:

filename :: AbstractString : File name

Returns:

data in yaml input file

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Mads.recursivemkdir — Method

Create directories recursively (if does not already exist)

Methods:

Mads.recursivemkdir(s::AbstractString; filename) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1809

Arguments:

s :: AbstractString : S

Keywords:

filename : Filename

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Mads.recursivermdir — Method

Remove directories recursively

Methods:

Mads.recursivermdir(s::AbstractString; filename) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1854

Arguments:

s :: AbstractString : S

Keywords:

filename : Filename

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Mads.regexs2obs — Method

Get observations for a set of regular expressions

Methods:

Mads.regexs2obs(obsline::AbstractString, regexs::Vector{Regex}, obsnames::Vector{String}, getparamhere::Vector{Bool}) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1469

Arguments:

obsline :: AbstractString : Observation line
regexs :: Vector{Regex} : Regular expressions
obsnames :: Vector{String} : Observation names
getparamhere :: Vector{Bool} : Parameters

Returns:

obsdict : observations

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Mads.removesource! — Function

Remove a contamination source

Methods:

Mads.removesource!(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsAnasol.jl:49
Mads.removesource!(madsdata::AbstractDict, sourceindex::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsAnasol.jl:49

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
sourceindex :: Integer : Sourceindex

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Mads.removesourceparameters! — Method

Remove contaminant source parameters

Methods:

Mads.removesourceparameters!(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsAnasol.jl:151

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

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Mads.required — Function

Lists modules required by a module (Mads by default)

Methods:

Mads.required() : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:46
Mads.required(modulename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:46
Mads.required(modulename::AbstractString, filtermodule::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:46

Arguments:

modulename :: AbstractString : Module name [default="Mads"]
filtermodule :: AbstractString : Filter module name

Returns:

filtered modules

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Mads.resetmodelruns — Method

Reset the model runs count to be equal to zero

Methods:

Mads.resetmodelruns() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:312

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Mads.residuals — Function

Compute residuals

Methods:

Mads.residuals(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:32
Mads.residuals(madsdata::AbstractDict, resultdict::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:29
Mads.residuals(madsdata::AbstractDict, resultvec::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsLevenbergMarquardt.jl:6

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
resultdict :: AbstractDict : Result dictionary
resultvec :: AbstractVector : Result vector

Returns:

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Mads.restartoff — Method

MADS restart off

Methods:

Mads.restartoff() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:128

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Mads.restarton — Method

MADS restart on

Methods:

Mads.restarton() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:119

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Mads.reweighsamples — Method

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

Methods:

Mads.reweighsamples(madsdata::AbstractDict, predictions::AbstractArray, oldllhoods::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:328

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
predictions :: AbstractArray : The model predictions for each of the samples
oldllhoods :: AbstractVector : The log likelihoods of the parameters in the old distribution

Returns:

vector of log-likelihoods after reweighing

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Mads.rmdir — Method

Remove directory

Methods:

Mads.rmdir(dir::AbstractString; path) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1669

Arguments:

dir :: AbstractString : Directory to be removed

Keywords:

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

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Mads.rmfile — Method

Remove file

Methods:

Mads.rmfile(filename::AbstractString; path) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1685

Arguments:

filename :: AbstractString : File to be removed

Keywords:

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

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Mads.rmfiles — Method

Remove files

Methods:

Mads.rmfile(filename::AbstractString; path) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1685

Arguments:

filename :: AbstractString : Filename

Keywords:

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

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Mads.rmfiles_ext — Method

Remove files with extension ext

Methods:

Mads.rmfiles_ext(ext::AbstractString; path) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1714

Arguments:

ext :: AbstractString : Extension

Keywords:

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

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Mads.rmfiles_root — Method

Remove files with root root

Methods:

Mads.rmfiles_root(root::AbstractString; path) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1727

Arguments:

root :: AbstractString : Root

Keywords:

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

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Mads.rosenbrock — Method

Rosenbrock test function

Methods:

Mads.rosenbrock(x::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:40

Arguments:

x :: AbstractVector : Parameter vector

Returns:

test result

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Mads.rosenbrock2_gradient_lm — Method

Parameter gradients of the Rosenbrock test function

Methods:

Mads.rosenbrock2_gradient_lm(x::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:21

Arguments:

x :: AbstractVector : Parameter vector

Returns:

parameter gradients

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Mads.rosenbrock2_lm — Method

Rosenbrock test function (more difficult to solve)

Methods:

Mads.rosenbrock2_lm(x::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:7

Arguments:

x :: AbstractVector : Parameter vector

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Mads.rosenbrock_gradient! — Method

Parameter gradients of the Rosenbrock test function

Methods:

Mads.rosenbrock_gradient!(x::AbstractVector, grad::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:65

Arguments:

x :: AbstractVector : Parameter vector
grad :: AbstractVector : Gradient vector

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Mads.rosenbrock_gradient_lm — Method

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

Methods:

Mads.rosenbrock_gradient_lm(x::AbstractVector; dx, center) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:82

Arguments:

x :: AbstractVector : Parameter vector

Keywords:

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

Returns:

parameter gradients

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Mads.rosenbrock_hessian! — Method

Parameter Hessian of the Rosenbrock test function

Methods:

Mads.rosenbrock_hessian!(x::AbstractVector, hess::AbstractMatrix) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:98

Arguments:

x :: AbstractVector : Parameter vector
hess :: AbstractMatrix : Hessian matrix

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Mads.rosenbrock_lm — Method

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

Methods:

Mads.rosenbrock_lm(x::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsTestFunctions.jl:54

Arguments:

x :: AbstractVector : Parameter vector

Returns:

test result

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Mads.runcmd — Function

Run external command and pipe stdout and stderr

Methods:

Mads.runcmd(cmd::Cmd; quiet, pipe, waittime) : ~/work/Mads.jl/Mads.jl/src/MadsExecute.jl:38
Mads.runcmd(cmdstring::AbstractString; quiet, pipe, waittime) : ~/work/Mads.jl/Mads.jl/src/MadsExecute.jl:103

Arguments:

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

Keywords:

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

Returns:

command output
command error message

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Mads.runremote — Function

Run remote command on a series of servers

Methods:

Mads.runremote(cmd::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:280
Mads.runremote(cmd::AbstractString, nodenames::Vector{String}) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:280

Arguments:

cmd :: AbstractString : Remote command
nodenames :: Vector{String} : Names of machines/nodes [default=madsservers]

Returns:

output of running remote command

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Mads.saltelli — Method

Saltelli sensitivity analysis

Methods:

Mads.saltelli(madsdata::AbstractDict; N, seed, rng, restart, restartdir, parallel, checkpointfrequency, save, load) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:642

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

N : Number of samples [default=100]
seed : Random seed [default=0]
rng : Rng
restart : Restart
restartdir : Directory where files will be stored containing model results for fast simulation restarts
parallel : Set to true if the model runs should be performed in parallel [default=false]
checkpointfrequency : Check point frequency [default=N]
save : Save
load : Load

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Mads.saltellibrute — Method

Saltelli sensitivity analysis (brute force)

Methods:

Mads.saltellibrute(madsdata::AbstractDict; N, seed, rng, restartdir) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:452

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

N : Number of samples [default=1000]
seed : Random seed [default=0]
rng : Rng
restartdir : Directory where files will be stored containing model results for fast simulation restarts

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Mads.saltellibruteparallel — Method

Parallel version of saltellibrute

source

Mads.saltelliparallel — Method

Parallel version of saltelli

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Mads.sampling — Method

Methods:

Mads.sampling(param::AbstractVector, J::AbstractArray, numsamples::Number; seed, rng, scale) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:277

Arguments:

param :: AbstractVector : Parameter vector
J :: AbstractArray : Jacobian matrix
numsamples :: Number : Number of samples

Keywords:

seed : Random esee [default=0]
rng : Rng
scale : Data scaling [default=1]

Returns:

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

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Mads.savemadsfile — Function

Save MADS problem dictionary madsdata in MADS input file filename

Methods:

Mads.savemadsfile(madsdata::AbstractDict, filename::AbstractString; observations_separate, filenameobs) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:723
Mads.savemadsfile(madsdata::AbstractDict, parameters::AbstractDict, filename::AbstractString; explicit, observations_separate) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:740
Mads.savemadsfile(madsdata::AbstractDict, parameters::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:740
Mads.savemadsfile(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:723

Arguments:

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

Keywords:

observations_separate : Observations separate
filenameobs : Filenameobs
explicit : If true ignores MADS YAML file modifications and rereads the original input file [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)

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Mads.savemcmcresults — Method

Save MCMC chain in a file

Methods:

Mads.savemcmcresults(chain::AbstractArray, filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsMonteCarlo.jl:247

Arguments:

chain :: AbstractArray : MCMC chain
filename :: AbstractString : File name

Dumps:

the file containing MCMC chain

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Mads.savesaltellirestart — Method

Save Saltelli sensitivity analysis results for fast simulation restarts

Methods:

Mads.savesaltellirestart(evalmat::AbstractArray, matname::AbstractString, restartdir::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:623

Arguments:

evalmat :: AbstractArray : Saved array
matname :: AbstractString : Matrix (array) name (defines the name of the loaded file)
restartdir :: AbstractString : Directory where files will be stored containing model results for fast simulation restarts

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Mads.scatterplotsamples — Function

Create histogram/scatter plots of model parameter samples

Methods:

Mads.scatterplotsamples(madsdata::AbstractDict, samples::AbstractMatrix, filename::AbstractString; np, format, pointsize, major_label_font_size, minor_label_font_size, highlight_width, dpi, separate_files, plottypes) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:444
Mads.scatterplotsamples(madsdata::AbstractDict, samples::AbstractMatrix; ...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:444

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
samples :: AbstractMatrix : Matrix with model parameters
filename :: AbstractString : Output file name

Keywords:

np : Np
format : Output plot format (png, pdf, etc.) [default=Mads.graphbackend]
pointsize : Point size [default=0.9Gadfly.mm]
major_label_font_size : Major label font size
minor_label_font_size : Minor label font size
highlight_width : Highlight width
dpi : Dpi
separate_files : Separate files
plottypes : Plottypes

Dumps:

histogram/scatter plots of model parameter samples

source

Mads.searchdir — Method

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

Methods:

Mads.searchdir(key::Union{Regex, AbstractString}; path) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1270

Arguments:

key :: Union{Regex, AbstractString} : Matching pattern (string or regular expression accepted)

Keywords:

path : Search directory [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 = ".")`

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Mads.set_nprocs_per_task — Function

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

Methods:

Mads.set_nprocs_per_task() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:110
Mads.set_nprocs_per_task(local_nprocs_per_task::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:110

Arguments:

local_nprocs_per_task :: Integer : Local nprocs per task

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Mads.setallparamsoff! — Method

Set all parameters OFF

Methods:

Mads.setallparamsoff!(madsdata::AbstractDict; filter) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:470

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

filter : Parameter filter

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Mads.setallparamson! — Method

Set all parameters ON

Methods:

Mads.setallparamson!(madsdata::AbstractDict; filter) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:456

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

filter : Parameter filter

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Mads.setdebuglevel — Method

Set MADS debug level

Methods:

Mads.setdebuglevel(level::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:283

Arguments:

level :: Integer : Debug level

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Mads.setdefaultplotformat — Method

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

Methods:

Mads.setdefaultplotformat(format::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:19

Arguments:

format :: AbstractString : Plot format

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Mads.setdir — Function

Set the working directory (for parallel environments)

Methods:

Mads.setdir() : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:251
Mads.setdir(dir) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:246

Arguments:

dir :: Any : Directory

Example:

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

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Mads.setdpi — Method

Set image dpi

Methods:

Mads.setdpi(dpi::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:219

Arguments:

dpi :: Integer : Dpi

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Mads.setexecutionwaittime — Method

Set maximum execution wait time for forward model runs in seconds

Methods:

Mads.setexecutionwaittime(waitime::Float64) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:303

Arguments:

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

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Mads.setmadsinputfile — Method

Set a default MADS input file

Methods:

Mads.setmadsinputfile(filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:799

Arguments:

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

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Mads.setmadsservers — Function

Generate a list of Mads servers

Methods:

Mads.setmadsservers() : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:335
Mads.setmadsservers(first::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:335
Mads.setmadsservers(first::Integer, last::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:335

Arguments:

first :: Integer : First [default=0]
last :: Integer : Last [default=18]

Returns

array string of mads servers

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Mads.setmodelinputs — Function

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

Methods:

Mads.setmodelinputs(madsdata::AbstractDict, parameters::AbstractDict; path) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1116
Mads.setmodelinputs(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1116

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
parameters :: AbstractDict : Parameters

Keywords:

path : Path for the files [default=.]

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Mads.setnewmadsfilename — Function

Set new mads file name

Methods:

Mads.setnewmadsfilename(filename::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:964
Mads.setnewmadsfilename(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:961

Arguments:

filename :: AbstractString : File name
madsdata :: AbstractDict : MADS problem dictionary

Returns:

new file name

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Mads.setobservationtargets! — Method

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

Methods:

Mads.setobservationtargets!(madsdata::AbstractDict, predictions::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:551

Arguments:

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

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Mads.setobstime! — Function

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

Methods:

Mads.setobstime!(madsdata::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:257
Mads.setobstime!(madsdata::AbstractDict, rx::Regex) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:267
Mads.setobstime!(madsdata::AbstractDict, rx::Regex, obskeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:267
Mads.setobstime!(madsdata::AbstractDict, separator::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:257
Mads.setobstime!(madsdata::AbstractDict, separator::AbstractString, obskeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:257

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
rx :: Regex : Regular expression to match
obskeys :: AbstractVector : Obskeys
separator :: AbstractString : Separator [default=]

Examples:

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

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Mads.setobsweights! — Function

Set observation weights in the MADS problem dictionary

Methods:

Mads.setobsweights!(madsdata::AbstractDict, v::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:298
Mads.setobsweights!(madsdata::AbstractDict, v::AbstractVector, obskeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:298
Mads.setobsweights!(madsdata::AbstractDict, value::Number) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:293
Mads.setobsweights!(madsdata::AbstractDict, value::Number, obskeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:293

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
v :: AbstractVector : Vector of observation weights
obskeys :: AbstractVector : Obskeys
value :: Number : Value for observation weights

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Mads.setparamoff! — Method

Set a specific parameter with a key parameterkey OFF

Methods:

Mads.setparamoff!(madsdata::AbstractDict, parameterkey::Union{AbstractString, Symbol}) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:495

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
parameterkey :: Union{AbstractString, Symbol} : Parameter key

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Mads.setparamon! — Method

Set a specific parameter with a key parameterkey ON

Methods:

Mads.setparamon!(madsdata::AbstractDict, parameterkey::Union{AbstractString, Symbol}) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:484

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
parameterkey :: Union{AbstractString, Symbol} : Parameter key

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Mads.setparamsdistnormal! — Method

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

Methods:

Mads.setparamsdistnormal!(madsdata::AbstractDict, mean::AbstractVector, stddev::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:507

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
mean :: AbstractVector : Array with the mean values
stddev :: AbstractVector : Array with the standard deviation values

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Mads.setparamsdistuniform! — Method

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

Methods:

Mads.setparamsdistuniform!(madsdata::AbstractDict, min::AbstractVector, max::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:522

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
min :: AbstractVector : Array with the minimum values
max :: AbstractVector : Array with the maximum values

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Mads.setparamsinit! — Function

Set initial optimized parameter guesses in the MADS problem dictionary

Methods:

Mads.setparamsinit!(madsdata::AbstractDict, paramdict::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:324
Mads.setparamsinit!(madsdata::AbstractDict, paramdict::AbstractDict, idx::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:324

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
paramdict :: AbstractDict : Dictionary with initial model parameter values
idx :: Integer : Index of the dictionary of arrays with initial model parameter values

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Mads.setplotfileformat — Method

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

Methods:

Mads.setplotfileformat(filename::AbstractString, format::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:48

Arguments:

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

Returns:

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

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Mads.setprocs — Function

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

Methods:

Mads.setprocs(; ntasks_per_node, nprocs_per_task, nodenames, mads_servers, test, quiet, veryquiet, dir, exename) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:44
Mads.setprocs(np::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:41
Mads.setprocs(np::Integer, nt::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsParallel.jl:28

Arguments:

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

Keywords:

ntasks_per_node : Number of parallel tasks per node [default=0]
nprocs_per_task : Number of processors needed for each parallel task at each node [default=Mads.nprocs per task]
nodenames : Array with names of machines/nodes to be invoked
mads_servers : If true use MADS servers [default=false]
test : Test the servers and connect to each one ones at a time [default=false]
quiet : Suppress output [default=Mads.quiet]
veryquiet : Veryquiet
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)

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=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")

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Mads.setseed — Function

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

Methods:

Mads.setseed(; ...) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:521
Mads.setseed(seed::Integer, quiet::Bool; rng) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:521
Mads.setseed(seed::Integer; ...) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:521

Arguments:

seed :: Integer : Random seed
quiet :: Bool : [default=true]

Keywords:

rng : Rng

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Mads.setsourceinit! — Function

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

Methods:

Mads.setparamsinit!(madsdata::AbstractDict, paramdict::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:324
Mads.setparamsinit!(madsdata::AbstractDict, paramdict::AbstractDict, idx::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:324

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
paramdict :: AbstractDict : Dictionary with initial model parameter values
idx :: Integer : Index of the dictionary of arrays with initial model parameter values

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Mads.settarget! — Method

Set observation target

Methods:

Mads.settarget!(o::AbstractDict, target::Number) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:247

Arguments:

o :: AbstractDict : Observation data
target :: Number : Observation target

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Mads.settime! — Method

Set observation time

Methods:

Mads.settime!(o::AbstractDict, time::Number) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:169

Arguments:

o :: AbstractDict : Observation data
time :: Number : Observation time

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Mads.setverbositylevel — Method

Set MADS verbosity level

Methods:

Mads.setverbositylevel(level::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:293

Arguments:

level :: Integer : Debug level

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Mads.setweight! — Method

Set observation weight

Methods:

Mads.setweight!(o::AbstractDict, weight::Number) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:208

Arguments:

o :: AbstractDict : Observation data
weight :: Number : Observation weight

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Mads.setwellweights! — Function

Set well weights in the MADS problem dictionary

Methods:

Mads.setwellweights!(madsdata::AbstractDict, value::Number) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:348
Mads.setwellweights!(madsdata::AbstractDict, value::Number, wellkeys::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:348

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
value :: Number : Value for well weights
wellkeys :: AbstractVector : Wellkeys

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Mads.showallparameters — Function

Show all parameters in the MADS problem dictionary

Methods:

Mads.showallparameters(madsdata::AbstractDict, parkeys::AbstractVector; rescale) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:587
Mads.showallparameters(madsdata::AbstractDict, result::AbstractDict; kw...) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:591
Mads.showallparameters(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:587

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
parkeys :: AbstractVector : Parkeys
result :: AbstractDict : Result

Keywords:

rescale : Rescale

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Mads.showobservations — Function

Show observations in the MADS problem dictionary

Methods:

Mads.showobservations(madsdata::AbstractDict, obskeys::AbstractVector; rescale) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:404
Mads.showobservations(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:404

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
obskeys :: AbstractVector : Obskeys

Keywords:

rescale : Rescale

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Mads.showparameters — Function

Show parameters in the MADS problem dictionary

Methods:

Mads.showparameters(madsdata::AbstractDict, parkeys::AbstractVector; all, rescale) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:569
Mads.showparameters(madsdata::AbstractDict, result::AbstractDict; kw...) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:564
Mads.showparameters(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsParameters.jl:569

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
parkeys :: AbstractVector : Parkeys
result :: AbstractDict : Result

Keywords:

all : All
rescale : Rescale

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Mads.sinetransform — Function

Sine transformation of model parameters

Methods:

Mads.sinetransform(madsdata::AbstractDict, params::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsSineTransformations.jl:32
Mads.sinetransform(sineparams::AbstractVector, lowerbounds::AbstractVector, upperbounds::AbstractVector, indexlogtransformed::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsSineTransformations.jl:42

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
params :: AbstractVector : Params
sineparams :: AbstractVector : Model parameters
lowerbounds :: AbstractVector : Lower bounds
upperbounds :: AbstractVector : Upper bounds
indexlogtransformed :: AbstractVector : Index vector of log-transformed parameters

Returns:

Sine transformation of model parameters

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Mads.sinetransformfunction — Method

Sine transformation of a function

Methods:

Mads.sinetransformfunction(f::Function, lowerbounds::AbstractVector, upperbounds::AbstractVector, indexlogtransformed::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsSineTransformations.jl:75

Arguments:

f :: Function : Function
lowerbounds :: AbstractVector : Lower bounds
upperbounds :: AbstractVector : Upper bounds
indexlogtransformed :: AbstractVector : Index vector of log-transformed parameters

Returns:

Sine transformation

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Mads.sinetransformgradient — Method

Sine transformation of a gradient function

Methods:

Mads.sinetransformgradient(g::Function, lowerbounds::AbstractVector, upperbounds::AbstractVector, indexlogtransformed::AbstractVector; sindx) : ~/work/Mads.jl/Mads.jl/src/MadsSineTransformations.jl:96

Arguments:

g :: Function : Gradient function
lowerbounds :: AbstractVector : Vector with parameter lower bounds
upperbounds :: AbstractVector : Vector with parameter upper bounds
indexlogtransformed :: AbstractVector : Index vector of log-transformed parameters

Keywords:

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

Returns:

Sine transformation of a gradient function

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Mads.spaghettiplot — Function

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

Methods:

Mads.spaghettiplot(madsdata::AbstractDict, dictarray::AbstractDict; seed, rng, kw...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:877
Mads.spaghettiplot(madsdata::AbstractDict, matrix::AbstractMatrix; plotdata, filename, keyword, format, title, xtitle, ytitle, yfit, obs_plot_dots, linewidth, pointsize, grayscale, alpha, alphas, xmin, xmax, ymin, ymax, quiet, colors, plot_nontargets, gm, separate_files, hsize, vsize, dpi) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:914
Mads.spaghettiplot(madsdata::AbstractDict, number_of_samples::Integer; kw...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:873

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
dictarray :: AbstractDict : Dictionary array containing the data arrays to be plotted
matrix :: AbstractMatrix : Matrix
number_of_samples :: Integer : Number of samples

Keywords:

seed : Random seed [default=0]
rng : Rng
plotdata : Plot data (if false model predictions are plotted only) [default=true]
filename : Output file name used to output the produced plots
keyword : Keyword to be added in the file name used to output the produced plots (if filename is not defined)
format : Output plot format (png, pdf, etc.) [default=Mads.graphbackend]
title : Title
xtitle : x axis title [default=X]
ytitle : y axis title [default=Y]
yfit : Fit vertical axis range [default=false]
obs_plot_dots : Plot observation as dots (true [default] or false)
linewidth : Width of the lines in plot [default=2Gadfly.pt]
pointsize : Size of the markers in plot [default=4Gadfly.pt]
grayscale : Grayscale
alpha : Alpha
alphas : Alphas
xmin : Xmin
xmax : Xmax
ymin : Ymin
ymax : Ymax
quiet : Quiet
colors : Colors
plot_nontargets : Plot nontargets
gm : Gm
separate_files : Separate files
hsize : Hsize
vsize : Vsize
dpi : Dpi

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)

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Mads.spaghettiplots — Function

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

Methods:

Mads.spaghettiplots(madsdata::AbstractDict, number_of_samples::Integer; seed, rng, kw...) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:723
Mads.spaghettiplots(madsdata::AbstractDict, paramdictarray::OrderedCollections.OrderedDict; format, keyword, xtitle, ytitle, obs_plot_dots, seed, rng, linewidth, pointsize, grayscale, quiet) : ~/work/Mads.jl/Mads.jl/src/MadsPlot.jl:728

Arguments:

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

Keywords:

seed : Random seed [default=0]
rng : Rng
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
xtitle : x axis title [default=X]
ytitle : y axis title [default=Y]
obs_plot_dots : Plot observation as dots (true (default) or false)
linewidth : Width of the lines on the plot [default=2Gadfly.pt]
pointsize : Size of the markers on the plot [default=4Gadfly.pt]
grayscale : Grayscale
quiet : Quiet

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.sphericalcov — Method

Spherical spatial covariance function

Methods:

Mads.sphericalcov(h::Number, maxcov::Number, scale::Number) : ~/work/Mads.jl/Mads.jl/src/MadsKriging.jl:45

Arguments:

h :: Number : Separation distance
maxcov :: Number : Max covariance
scale :: Number : Scale

Returns:

covariance

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Mads.sphericalvariogram — Method

Spherical variogram

Methods:

Mads.sphericalvariogram(h::Number, sill::Number, range::Number, nugget::Number) : ~/work/Mads.jl/Mads.jl/src/MadsKriging.jl:60

Arguments:

h :: Number : Separation distance
sill :: Number : Sill
range :: Number : Range
nugget :: Number : Nugget

Returns:

Spherical variogram

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Mads.sprintf — Method

Convert @Printf.sprintf macro into sprintf function

source

Mads.status — Function

Status of Mads modules

Methods:

Mads.status(; git, gitmore) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:251
Mads.status(madsmodule::AbstractString; git, gitmore) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:256

Arguments:

madsmodule :: AbstractString : Mads module

Keywords:

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

Returns:

true or false

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Mads.stderrcaptureoff — Method

Restore stderr

Methods:

Mads.stderrcaptureoff() : ~/work/Mads.jl/Mads.jl/src/MadsCapture.jl:137

Returns:

standered error

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Mads.stderrcaptureon — Method

Redirect stderr to a reader

Methods:

Mads.stderrcaptureon() : ~/work/Mads.jl/Mads.jl/src/MadsCapture.jl:118

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Mads.stdoutcaptureoff — Method

Restore stdout

Methods:

Mads.stdoutcaptureoff() : ~/work/Mads.jl/Mads.jl/src/MadsCapture.jl:103

Returns:

standered output

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Mads.stdoutcaptureon — Method

Redirect stdout to a reader

Methods:

Mads.stdoutcaptureon() : ~/work/Mads.jl/Mads.jl/src/MadsCapture.jl:84

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Mads.stdouterrcaptureoff — Method

Restore stdout & stderr

Methods:

Mads.stdouterrcaptureoff() : ~/work/Mads.jl/Mads.jl/src/MadsCapture.jl:168

Returns:

standered output amd standered error

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Mads.stdouterrcaptureon — Method

Redirect stdout & stderr to readers

Methods:

Mads.stdouterrcaptureon() : ~/work/Mads.jl/Mads.jl/src/MadsCapture.jl:152

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Mads.svrdump — Method

Dump SVR models in files

Methods:

Mads.svrdump(svrmodel::AbstractVector{SVR.svmmodel}, rootname::AbstractString, numberofsamples::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsSVR.jl:135

Arguments:

svrmodel :: AbstractVector{SVR.svmmodel} : Array of SVR models
rootname :: AbstractString : Root name
numberofsamples :: Integer : Number of samples

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Mads.svrfree — Method

Free SVR

Methods:

Mads.svrfree(svrmodel::AbstractVector{SVR.svmmodel}) : ~/work/Mads.jl/Mads.jl/src/MadsSVR.jl:117

Arguments:

svrmodel :: AbstractVector{SVR.svmmodel} : Array of SVR models

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Mads.svrload — Method

Load SVR models from files

Methods:

Mads.svrload(npred::Integer, rootname::AbstractString, numberofsamples::Integer) : ~/work/Mads.jl/Mads.jl/src/MadsSVR.jl:158

Arguments:

npred :: Integer : Number of model predictions
rootname :: AbstractString : Root name
numberofsamples :: Integer : Number of samples

Returns:

Array of SVR models for each model prediction

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Mads.svrprediction — Function

Predict SVR

Methods:

Mads.svrprediction(svrmodel::AbstractVector{SVR.svmmodel}, paramarray::AbstractMatrix{Float64}) : ~/work/Mads.jl/Mads.jl/src/MadsSVR.jl:91

Arguments:

svrmodel :: AbstractVector{SVR.svmmodel} : Array of SVR models
paramarray :: AbstractMatrix{Float64} : Parameter array

Returns:

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

source

Mads.svrtraining — Function

Train SVR

Methods:

Mads.svrtraining(madsdata::AbstractDict, numberofsamples::Integer; addminmax, kw...) : ~/work/Mads.jl/Mads.jl/src/MadsSVR.jl:37
Mads.svrtraining(madsdata::AbstractDict, paramarray::AbstractMatrix{Float64}; check, savesvr, addminmax, svm_type, kernel_type, degree, gamma, coef0, C, nu, cache_size, epsilon, shrinking, probability, verbose, tol) : ~/work/Mads.jl/Mads.jl/src/MadsSVR.jl:4
Mads.svrtraining(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsSVR.jl:37

Arguments:

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

Keywords:

addminmax : Add parameter minimum / maximum range values in the training set [default=true]
check : Check SVR performance [default=false]
savesvr : Save SVR models [default=false]
svm_type : SVM type [default=SVR.EPSILON SVR]
kernel_type : Kernel type[default=SVR.RBF]
degree : Degree of the polynomial kernel [default=3]
gamma : Coefficient for RBF, POLY and SIGMOND kernel types [default=1/numberofsamples]
coef0 : Independent term in kernel function; important only in POLY and SIGMOND kernel types [default=0]
C : Cost; penalty parameter of the error term [default=1000.0]
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]
cache_size : Size of the kernel cache [default=100.0]
epsilon : Epsilon in the EPSILON SVR model; defines an epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual value [default=0.001]
shrinking : Apply shrinking heuristic [default=true]
probability : Train to estimate probabilities [default=false]
verbose : Verbose output [default=false]
tol : Tolerance of termination criterion [default=0.001]

Returns:

Array of SVR models

source

Mads.symlinkdir — Method

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

Methods:

Mads.symlinkdir(filename::AbstractString, dirtarget::AbstractString, dirsource::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1651

Arguments:

filename :: AbstractString : File name
dirtarget :: AbstractString : Target directory
dirsource :: AbstractString : Dirsource

source

Mads.symlinkdirfiles — Method

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

Methods:

Mads.symlinkdirfiles(dirsource::AbstractString, dirtarget::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1633

Arguments:

dirsource :: AbstractString : Source directory
dirtarget :: AbstractString : Target directory

source

Mads.tag — Function

Tag Mads modules with a default argument :patch

Methods:

Mads.tag() : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:336
Mads.tag(madsmodule::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:341
Mads.tag(madsmodule::AbstractString, versionsym::Symbol) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:341
Mads.tag(versionsym::Symbol) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:336

Arguments:

madsmodule :: AbstractString : Mads module name
versionsym :: Symbol : Version symbol [default=:patch]

source

Mads.transform_stateplane_to_lonlat — Method

transform_stateplane_to_lonlat(x, y; epsg=nothing, unit=:auto, bbox=:usa, sample=200)

Transform State Plane coordinates (x,y) to longitude/latitude (WGS84) for any US state system.

Keywords

epsg: explicit EPSG Int code (if known). If nothing, auto-detect via detect_stateplane_epsg.
unit: :auto, :m, or :ftUS to guide auto-detection.
bbox: bounding box to guide detection; :usa by default or pass a tuple.
sample: number of points used during detection when epsg is not provided.

Returns

Tuple (lon::Vector{Float64}, lat::Vector{Float64}).

source

Mads.transposematrix — Method

Transpose non-numeric matrix

Methods:

Mads.transposematrix(a::AbstractMatrix) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:469

Arguments:

a :: AbstractMatrix : Matrix

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Mads.transposevector — Method

Transpose non-numeric vector

Methods:

Mads.transposevector(a::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:459

Arguments:

a :: AbstractVector : Vector

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Mads.untag — Method

Untag specific version

Methods:

Mads.untag(madsmodule::AbstractString, version::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsModules.jl:375

Arguments:

madsmodule :: AbstractString : Mads module name
version :: AbstractString : Version

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Mads.vectoroff — Method

MADS vector calls off

Methods:

Mads.vectoroff() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:101

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Mads.vectoron — Method

MADS vector calls on

Methods:

Mads.vectoron() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:92

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Mads.veryquietoff — Method

Make MADS not very quiet

Methods:

Mads.veryquietoff() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:174

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Mads.veryquieton — Method

Make MADS very quiet

Methods:

Mads.veryquieton() : ~/work/Mads.jl/Mads.jl/src/MadsHelpers.jl:165

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Mads.void2nan! — Method

Convert Nothing's into NaN's in a dictionary

Methods:

Mads.void2nan!(dict::AbstractDict) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:1066

Arguments:

dict :: AbstractDict : Dictionary

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Mads.weightedstats — Method

Get weighted mean and variance samples

Methods:

Mads.weightedstats(samples::AbstractArray, llhoods::AbstractVector) : ~/work/Mads.jl/Mads.jl/src/MadsSensitivityAnalysis.jl:385

Arguments:

samples :: AbstractArray : Array of samples
llhoods :: AbstractVector : Vector of log-likelihoods

Returns:

vector of sample means
vector of sample variances

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Mads.welloff! — Method

Turn off a specific well in the MADS problem dictionary

Methods:

Mads.welloff!(madsdata::AbstractDict, wellname::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:644

Arguments:

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

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Mads.wellon! — Method

Turn on a specific well in the MADS problem dictionary

Methods:

Mads.wellon!(madsdata::AbstractDict, wellname::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:586

Arguments:

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

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Mads.wellon! — Method

Turn on a specific well in the MADS problem dictionary

Methods:

Mads.wellon!(madsdata::AbstractDict, rx::Regex) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:608
Mads.wellon!(madsdata::AbstractDict, wellname::AbstractString) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:586

Arguments:

madsdata :: AbstractDict : MADS problem dictionary
rx :: Regex : Rx
wellname :: AbstractString : 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::AbstractDict; separate_sources) : ~/work/Mads.jl/Mads.jl/src/MadsObservations.jl:699

Arguments:

madsdata :: AbstractDict : MADS problem dictionary

Keywords:

separate_sources : Separate sources

source

Mads.writeparameters — Function

Write model parameters

Methods:

Mads.writeparameters(madsdata::AbstractDict, parameters::AbstractDict; respect_space) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1367
Mads.writeparameters(madsdata::AbstractDict; ...) : ~/work/Mads.jl/Mads.jl/src/MadsIO.jl:1367

Arguments: