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Mads.jl

MADS (Model Analysis & Decision Support)

Mads.jl is MADS main module.

Mads.jl module functions:

<a id='Mads.MFlm-Union{Tuple{T}, Tuple{AbstractMatrix{T}, Integer}} where T<:Number' href='#Mads.MFlm-Union{Tuple{T}, Tuple{AbstractMatrix{T}, Integer}} where T<:Number'># Mads.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, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, quiet) where T<:Number in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBlindSourceSeparation.jl:133
Mads.MFlm(X::AbstractMatrix{T}, range::AbstractRange{Int64}; kw...) where T<:Number in Mads : /Users/vvv/.julia/dev/Mads/src/MadsBlindSourceSeparation.jl:103

Arguments:

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

Keywords:

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

Returns:

NMF results

source

# Mads.NMFipopt — Function.

Non-negative Matrix Factorization using JuMP/Ipopt

Methods:

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

Arguments:

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

Keywords:

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

Returns:

NMF results

source

# Mads.NMFm — Function.

Non-negative Matrix Factorization using NMF

Methods:

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

Arguments:

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

Keywords:

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

Returns:

NMF results

source

# Mads.addkeyword! — Function.

Add a keyword in a class within the Mads dictionary madsdata

Methods:

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

Arguments:

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

source

# Mads.addsource! — Function.

Add an additional contamination source

Methods:

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

Arguments:

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

Keywords:

dict

source

# Mads.addsourceparameters! — Method.

Add contaminant source parameters

Methods:

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

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:602

Arguments:

madsdata::AbstractDict : MADS problem dictionary

source

# Mads.allwellson! — Method.

Turn on all the wells in the MADS problem dictionary

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

source

# Mads.amanzi — Function.

Execute Amanzi external groundwater flow and transport simulator

Methods:

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

Arguments:

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

Keywords:

amanzi_exe : full path to the Amanzi executable

source

# Mads.amanzi_output_parser — Function.

Parse Amanzi output provided in an external file (filename)

Methods:

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

Arguments:

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

Returns:

dictionary with model observations following MADS requirements

Example:

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

source

# Mads.asinetransform — Function.

Arcsine transformation of model parameters

Methods:

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

Arguments:

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

Returns:

Arcsine transformation of model parameters

source

# Mads.bigdt — Method.

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

Methods:

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

Arguments:

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

Keywords:

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

Returns:

dictionary with BIG-DT results

source

# Mads.boundparameters! — Function.

Bound model parameters based on their ranges

Methods:

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

Arguments:

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

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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, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, usenaive, save_results, localsa) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCalibrate.jl:168

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

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

Returns:

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

source

# Mads.calibraterandom — Function.

Calibrate with random initial guesses

Methods:

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

Arguments:

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

Keywords:

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

Returns:

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

Example:

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

source

# Mads.calibraterandom_parallel — Function.

Calibrate with random initial guesses in parallel

Methods:

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

Arguments:

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

Keywords:

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

Returns:

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

source

# Mads.captureoff — Method.

Make MADS not capture

Methods:

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

source

# Mads.captureon — Method.

Make MADS capture

Methods:

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

source

# Mads.check_notebook — Method.

Check is Jupyter notebook exists

Methods:

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

Arguments:

rootname::AbstractString : notebook root name

Keywords:

dir : notebook directory
ndir

source

# Mads.checkmodeloutputdirs — Method.

Check the directories where model outputs should be saved for MADS

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Returns:

true or false

source

# Mads.checknodedir — Function.

Check if a directory is readable

Methods:

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

Arguments:

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

Returns:

true if the directory is readable, false otherwise

source

# Mads.checkout — Function.

Checkout (pull) the latest version of Mads modules

Methods:

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

Arguments:

modulename::AbstractString : module name

Keywords:

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

source

# Mads.checkparameterranges — Method.

Check parameter ranges for model parameters

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

source

# Mads.cleancoverage — Method.

Remove Mads coverage files

Methods:

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

source

# Mads.cmadsins_obs — Method.

Call C MADS ins_obs() function from MADS dynamic library

Methods:

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

Arguments:

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

Return:

observations

source

# Mads.commit — Function.

Commit the latest version of Mads modules in the repository

Methods:

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

Arguments:

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

source

# Mads.computemass — Function.

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

Methods:

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

Arguments:

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

Keywords:

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

Returns:

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

Example:

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

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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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:839

Arguments:

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

source

# 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{T} where T, anasolfunction::Function) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasol.jl:428

Arguments:

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

Returns:

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

source

# Mads.copyaquifer2sourceparameters! — Method.

Copy aquifer parameters to become contaminant source parameters

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

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# Mads.copyright — Method.

Produce MADS copyright information

Methods:

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

source

# Mads.create_documentation — Method.

Create web documentation files for Mads functions

Methods:

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

source

# Mads.create_tests_off — Method.

Turn off the generation of MADS tests (default)

Methods:

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

source

# Mads.create_tests_on — Method.

Turn on the generation of MADS tests (dangerous)

Methods:

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

source

# Mads.createobservations — Function.

Create Mads dictionary of observations and instruction file

Methods:

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

Arguments:

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

Keywords:

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

)

Returns:

observation dictionary

source

# Mads.createobservations! — Function.

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

Methods:

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

Arguments:

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

Keywords:

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

source

# Mads.createproblem — Function.

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

Methods:

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

Arguments:

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

Keywords:

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

Returns:

new MADS problem dictionary

source

# Mads.createtempdir — Method.

Create temporary directory

Methods:

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

Arguments:

tempdirname::AbstractString : temporary directory name

source

# Mads.deleteNaN! — Method.

Delete rows with NaN in a dataframe df

Methods:

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

Arguments:

df::DataFrames.DataFrame : dataframe

source

# Mads.deletekeyword! — Function.

Delete a keyword in a class within the Mads dictionary madsdata

Methods:

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

Arguments:

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

source

# Mads.deleteoffwells! — Method.

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

Methods:

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

Arguments:

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

source

# Mads.deletetimes! — Method.

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

Methods:

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

Arguments:

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

source

# Mads.dependents — Function.

Lists module dependents on a module (Mads by default)

Methods:

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

Arguments:

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

Returns:

modules that are dependents of the input module

source

# Mads.diff — Function.

Diff the latest version of Mads modules in the repository

Methods:

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

Arguments:

modulename::AbstractString : module name

source

# Mads.display — Function.

Display image file

Methods:

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

Arguments:

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

Keywords:

gh
gho
gw
gwo

source

# Mads.dumpasciifile — Method.

Dump ASCII file

Methods:

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

Arguments:

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

Dumps:

ASCII file with the name in "filename"

source

# Mads.dumpjsonfile — Method.

Dump a JSON file

Methods:

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

Arguments:

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

Dumps:

JSON file with the name in "filename"

source

# Mads.dumpwelldata — Method.

Dump well data from MADS problem dictionary into a ASCII file

Methods:

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

Arguments:

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

Dumps:

filename : a ASCII file

source

# Mads.dumpyamlfile — Method.

Dump YAML file

Methods:

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

Arguments:

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

source

# Mads.dumpyamlmadsfile — Method.

Dump YAML Mads file

Methods:

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

Arguments:

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

source

# 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, restartdir, restart) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:1108

Arguments:

md::AbstractDict : MADS problem dictionary

Keywords:

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

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

Arguments:

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

Keywords:

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

Returns:

MCMC chain
log likelihoods of the final samples in the chain

Examples:

Mads.emceesampling(madsdata; numwalkers=10, nsteps=100, burnin=100, thinning=1, seed=2016, sigma=0.01)
Mads.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{T} where T, covmat::AbstractMatrix{T} where T, covvec::AbstractVector{T} where T, cov0::Number) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:205
Mads.estimationerror(w::AbstractVector{T} where T, x0::AbstractVector{T} where T, X::AbstractMatrix{T} where T, covfn::Function) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:198

Arguments:

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

Returns:

estimation kriging error

source

# Mads.evaluatemadsexpression — Method.

Evaluate an expression string based on a parameter dictionary

Methods:

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

Arguments:

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

Returns:

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

source

# Mads.evaluatemadsexpressions — Method.

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

Methods:

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

Arguments:

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

Returns:

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

source

# Mads.expcov — Method.

Exponential spatial covariance function

Methods:

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

Arguments:

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

Returns:

covariance

source

# Mads.exponentialvariogram — Method.

Exponential variogram

Methods:

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

Arguments:

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

Returns:

Exponential variogram

source

# Mads.filterkeys — Function.

Filter dictionary keys based on a string or regular expression

Methods:

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

Arguments:

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

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

Arguments:

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

Keywords:

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

Returns:

dictionary of model predictions

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

Arguments:

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

Returns:

3D array with model predictions along a 3D grid

source

# Mads.free — Function.

Free Mads modules

Methods:

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

Arguments:

modulename::AbstractString : module name

Keywords:

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

source

# Mads.functions — Function.

List available functions in the MADS modules:

Methods:

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

Arguments:

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

Keywords:

quiet
shortoutput

Examples:

Mads.functions()
Mads.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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:17

Arguments:

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

Returns:

covariance

source

# Mads.gaussianvariogram — Method.

Gaussian variogram

Methods:

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

Arguments:

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

Returns:

Gaussian variogram

source

# Mads.getcovmat — Method.

Get spatial covariance matrix

Methods:

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

Arguments:

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

Returns:

spatial covariance matrix

source

# Mads.getcovvec! — Method.

Get spatial covariance vector

Methods:

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

Arguments:

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

Returns:

spatial covariance vector

source

# Mads.getdefaultplotformat — Method.

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

Methods:

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

Arguments:

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

source

# Mads.getdir — Method.

Get directory

Methods:

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

Arguments:

filename::AbstractString : file name

Returns:

directory in file name

Example:

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

source

# Mads.getdistribution — Method.

Parse parameter distribution from a string

Methods:

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

Arguments:

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

Returns:

distribution

source

# Mads.getextension — Method.

Get file name extension

Methods:

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

Arguments:

filename::AbstractString : file name

Returns:

file name extension

Example:

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

source

# Mads.getfilenames — Method.

Get file names by expanding wildcards

Methods:

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

Arguments:

cmdstring::AbstractString

source

# Mads.getimportantsamples — Method.

Get important samples

Methods:

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

Arguments:

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

Returns:

array of important samples

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

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:493

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:444

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

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

Example:

madsrootname = Mads.getmadsrootname(madsdata)

Returns:

root of file name

source

# Mads.getnextmadsfilename — Method.

Get next mads file name

Methods:

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

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:43

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Returns:

keys for all observations in the MADS problem dictionary

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

Arguments:

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

Returns:

parameter array

source

# Mads.getparamdict — Method.

Get dictionary with all parameters and their respective initial values

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Returns:

dictionary with all parameters and their respective initial values

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParameters.jl:659

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

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

Returns:

probabilistic distributions

source

# Mads.getparamkeys — Method.

Get keys of all parameters in the MADS problem dictionary

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

filter : parameter filter

Returns:

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

source

# Mads.getparamrandom — Function.

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

Methods:

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

Arguments:

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

Keywords:

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

Returns:

generated sample

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

Arguments:

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

Returns:

the parameter values

source

# Mads.getparamsinit_min — Function.

Get an array with init_min values for parameters

Methods:

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

Arguments:

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

Returns:

the parameter values

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

Arguments:

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

Returns:

returns the parameter values

source

# Mads.getparamsmin — Function.

Get an array with min values for parameters defined by paramkeys

Methods:

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

Arguments:

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

Returns:

the parameter values

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

Example:

problemdir = Mads.getproblemdir()

Returns:

Mads problem directory

source

# Mads.getprocs — Method.

Get the number of processors

Methods:

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

source

# Mads.getrestart — Method.

Get MADS restart status

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

source

# Mads.getrestartdir — Function.

Get the directory where Mads restarts will be stored

Methods:

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

Arguments:

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

Returns:

restart directory where reusable model results will be stored

source

# Mads.getrootname — Method.

Get file name root

Methods:

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

Arguments:

filename::AbstractString : file name

Keywords:

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

Returns:

root of file name

Example:

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

source

# Mads.getseed — Method.

Get and return current random seed.

Methods:

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

source

# Mads.getsindx — Method.

Get sin-space dx

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Returns:

sin-space dx value

source

# Mads.getsourcekeys — Method.

Get keys of all source parameters in the MADS problem dictionary

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Returns:

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

source

# Mads.gettarget — Method.

Get observation target

Methods:

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

Arguments:

o::AbstractDict : observation data

Returns:

observation target

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsObservations.jl:57

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Returns:

keys for all targets in the MADS problem dictionary

source

# Mads.gettime — Method.

Get observation time

Methods:

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

Arguments:

o::AbstractDict : observation data

Returns:

observation time ("NaN" it time is missing)

source

# Mads.getweight — Method.

Get observation weight

Methods:

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

Arguments:

o::AbstractDict : observation data

Returns:

observation weight ("NaN" when weight is missing)

source

# Mads.getwelldata — Method.

Get spatial and temporal data in the Wells class

Methods:

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

Arguments:

madsdata::AbstractDict : Mads problem dictionary

Keywords:

time : get observation times [default=false]

Returns:

array with spatial and temporal data in the Wells class

source

# Mads.getwellkeys — Method.

Get keys for all wells in the MADS problem dictionary

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Returns:

keys for all wells in the MADS problem dictionary

source

# Mads.getwelltargets — Method.

Methods:

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

Arguments:

madsdata::AbstractDict : Mads problem dictionary

Returns:

array with targets in the Wells class

source

# Mads.graphoff — Method.

MADS graph output off

Methods:

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

source

# Mads.graphon — Method.

MADS graph output on

Methods:

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

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

Arguments:

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

Returns: true or false

Examples:

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

source

# Mads.help — Method.

Produce MADS help information

Methods:

Mads.help() in Mads : /Users/vvv/.julia/dev/Mads/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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:393

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

Arguments:

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

source

# Mads.infogap_jump — Function.

Information Gap Decision Analysis using JuMP

Methods:

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

Arguments:

madsdata::AbstractDict : Mads problem dictionary

Keywords:

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

source

# Mads.infogap_jump_polynomial — Function.

Information Gap Decision Analysis using JuMP

Methods:

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

Arguments:

madsdata::AbstractDict : Mads problem dictionary

Keywords:

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

Returns:

hmin, hmax

source

# Mads.infogap_mpb_lin — Function.

Information Gap Decision Analysis using MathProgBase

Methods:

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

Arguments:

madsdata::AbstractDict : Mads problem dictionary

Keywords:

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

source

# Mads.infogap_mpb_polynomial — Function.

Information Gap Decision Analysis using MathProgBase

Methods:

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

Arguments:

madsdata::AbstractDict : Mads problem dictionary

Keywords:

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

source

# Mads.ins_obs — Method.

Apply Mads instruction file instructionfilename to read model output file modeloutputfilename

Methods:

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

Arguments:

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

Returns:

obsdict : observation dictionary with the model outputs

source

# Mads.instline2regexs — Method.

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

Methods:

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

Arguments:

instline::AbstractString : instruction line

Returns:

regexs : regular expressions
obsnames : observation names
getparamhere : parameters

source

# Mads.invobsweights! — Function.

Set inversely proportional observation weights in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.invwellweights! — Function.

Set inversely proportional well weights in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.islog — Method.

Is parameter with key parameterkey log-transformed?

Methods:

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

Arguments:

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

Returns:

true if log-transformed, false otherwise

source

# Mads.isobs — Method.

Is a dictionary containing all the observations

Methods:

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

Arguments:

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

Returns:

true if the dictionary contain all the observations, false otherwise

source

# Mads.isopt — Method.

Is parameter with key parameterkey optimizable?

Methods:

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

Arguments:

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

Returns:

true if optimizable, false if not

source

# Mads.isparam — Method.

Check if a dictionary containing all the Mads model parameters

Methods:

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

Arguments:

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

Returns:

true if the dictionary containing all the parameters, false otherwise

source

# Mads.ispkgavailable — Method.

Checks if package is available

Methods:

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

Arguments:

modulename::AbstractString : module name

Returns:

true or false

source

# Mads.ispkgavailable_old — Method.

Checks if package is available

Methods:

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

Arguments:

modulename::AbstractString : module name

Keywords:

quiet

Returns:

true or false

source

# Mads.krige — Method.

Kriging

Methods:

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

Arguments:

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

Returns:

kriging estimates at x0mat

source

# Mads.levenberg_marquardt — Function.

Levenberg-Marquardt optimization

Methods:

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

Arguments:

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

Keywords:

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

source

# Mads.linktempdir — Method.

Link files in a temporary directory

Methods:

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

Arguments:

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

source

# Mads.loadasciifile — Method.

Load ASCII file

Methods:

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

Arguments:

filename::AbstractString : ASCII file name

Returns:

data from the file

source

# Mads.loadbigyamlfile — Method.

Load BIG YAML input file

Methods:

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

Arguments:

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

Keywords:

bigfile
format
quiet

Returns:

MADS problem dictionary

source

# Mads.loadjsonfile — Method.

Load a JSON file

Methods:

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

Arguments:

filename::AbstractString : JSON file name

Returns:

data from the JSON file

source

# Mads.loadmadsfile — Method.

Load MADS input file defining a MADS problem dictionary

Methods:

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

Arguments:

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

Keywords:

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

Returns:

MADS problem dictionary

Example:

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

source

# Mads.loadmadsproblem — Method.

Load a predefined Mads problem

Methods:

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

Arguments:

name::AbstractString : predefined MADS problem name

Returns:

MADS problem dictionary

source

# Mads.loadsaltellirestart! — Method.

Load Saltelli sensitivity analysis results for fast simulation restarts

Methods:

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

Arguments:

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

Returns:

true when successfully loaded, false when it is not

source

# Mads.loadyamlfile — Method.

Load YAML file

Methods:

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

Arguments:

filename::AbstractString : file name

Returns:

data in the yaml input file

source

# Mads.localsa — Method.

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

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

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

Dumps:

filename : output plot file

source

# Mads.long_tests_off — Method.

Turn off execution of long MADS tests (default)

Methods:

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

source

# Mads.long_tests_on — Method.

Turn on execution of long MADS tests

Methods:

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

source

# Mads.madscores — Function.

Check the number of processors on a series of servers

Methods:

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

Arguments:

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

source

# Mads.madscritical — Method.

MADS critical error messages

Methods:

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

Arguments:

message::AbstractString : critical error message

source

# Mads.madsdebug — Function.

MADS debug messages (controlled by quiet and debuglevel)

Methods:

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

Arguments:

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

source

# Mads.madsdir — Method.

Change the current directory to the Mads source dictionary

Methods:

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

source

# Mads.madserror — Method.

MADS error messages

Methods:

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

Arguments:

message::AbstractString : error message

source

# Mads.madsinfo — Function.

MADS information/status messages (controlled by quietandverbositylevel`)

Methods:

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

Arguments:

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

source

# Mads.madsload — Function.

Check the load of a series of servers

Methods:

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

Arguments:

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

source

# Mads.madsmathprogbase — Function.

Define MadsModel type applied for Mads execution using MathProgBase

Methods:

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

Arguments:

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

source

# Mads.madsoutput — Function.

MADS output (controlled by quiet and verbositylevel)

Methods:

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

Arguments:

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

source

# Mads.madsup — Function.

Check the uptime of a series of servers

Methods:

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

Arguments:

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

source

# Mads.madswarn — Method.

MADS warning messages

Methods:

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

Arguments:

message::AbstractString : warning message

source

# Mads.makearrayconditionalloglikelihood — Method.

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

Methods:

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

Arguments:

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

Returns:

a conditional log likelihood function that accepts an array

source

# Mads.makearrayconditionalloglikelihood — Method.

Make array of conditional log-likelihoods

Methods:

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

Arguments:

conditionalloglikelihood
madsdata::AbstractDict : MADS problem dictionary

Returns:

array of conditional log-likelihoods

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

Arguments:

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

Returns:

function accepting an array containing the optimal parameter values

source

# Mads.makearrayloglikelihood — Method.

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

Methods:

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

Arguments:

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

Returns:

a log likelihood function that accepts an array

source

# Mads.makebigdt! — Method.

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

Methods:

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

Arguments:

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

Returns:

BIG-DT problem type

source

# Mads.makebigdt — Method.

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

Methods:

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

Arguments:

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

Returns:

BIG-DT problem type

source

# Mads.makecomputeconcentrations — Method.

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

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

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

Returns:

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

Examples:

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

source

# Mads.makedixonprice — Method.

Make dixon price

Methods:

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

Arguments:

n::Integer : number of observations

Returns:

dixon price

source

# Mads.makedixonprice_gradient — Method.

Methods:

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

Arguments:

n::Integer : number of observations

Returns:

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

Arguments:

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

Returns:

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

source

# Mads.makelmfunctions — Function.

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

Methods:

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

Arguments:

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

Returns:

forward model, gradient, objective functions

source

# Mads.makelocalsafunction — Method.

Make gradient function needed for local sensitivity analysis

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

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

Returns:

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:416

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Return:

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

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, calczeroweightobs, calcpredictions) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:66

Arguments:

madsdata_in::AbstractDict : MADS problem dictionary

Keywords:

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

Example:

Mads.makemadscommandfunction(madsdata)

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

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

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

Only Command uses different approaches to get back the model outputs. The script defined under Julia command parses the model outputs using Julia.

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

Options for writing model inputs:

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

Options for reading model outputs:

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

Returns:

Mads function to execute a forward model simulation

source

# Mads.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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:439

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

weightfactor : Weight factor [default=1]

Return:

the conditional log-likelihood

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsFunc.jl:484

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

weightfactor : Weight factor [default=1]

Returns:

the log-likelihood for a given set of model parameters

source

# Mads.makemadsreusablefunction — Function.

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

Methods:

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

Arguments:

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

Keywords:

usedict : Use dictionary [default=true]

Returns:

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

source

# Mads.makempbfunctions — Method.

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

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Returns:

forward model, gradient, objective functions

source

# Mads.makepowell — Method.

Make Powell test function for LM optimization

Methods:

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

Arguments:

n::Integer : number of observations

Returns:

Powell test function for LM optimization

source

# Mads.makepowell_gradient — Method.

ake parameter gradients of the Powell test function for LM optimization

Methods:

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

Arguments:

n::Integer : number of observations

Returns:

arameter gradients of the Powell test function for LM optimization

source

# Mads.makerosenbrock — Method.

Make Rosenbrock test function for LM optimization

Methods:

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

Arguments:

n::Integer : number of observations

Returns:

Rosenbrock test function for LM optimization

source

# Mads.makerosenbrock_gradient — Method.

Make parameter gradients of the Rosenbrock test function for LM optimization

Methods:

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

Arguments:

n::Integer : number of observations

Returns:

parameter gradients of the Rosenbrock test function for LM optimization

source

# Mads.makerotatedhyperellipsoid — Method.

Methods:

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

Arguments:

n::Integer : number of observations

Returns:

rotated hyperellipsoid

source

# Mads.makerotatedhyperellipsoid_gradient — Method.

Methods:

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

Arguments:

n::Integer : number of observations

Returns:

rotated hyperellipsoid gradient

source

# Mads.makesphere — Method.

Make sphere

Methods:

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

Arguments:

n::Integer : number of observations

Returns:

sphere

source

# Mads.makesphere_gradient — Method.

Make sphere gradient

Methods:

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

Arguments:

n::Integer : number of observations

Returns:

sphere gradient

source

# Mads.makesumsquares — Method.

Methods:

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

Arguments:

n::Integer : number of observations

Returns:

sumsquares

source

# Mads.makesumsquares_gradient — Method.

Methods:

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

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSVR.jl:209
Mads.makesvrmodel(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSVR.jl:209

Arguments:

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

Keywords:

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

[default=0]

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

Returns:

function performing SVR predictions
function 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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsSensitivityAnalysis.jl:1082

Arguments:

df::DataFrames.DataFrame : dataframe

source

# Mads.meshgrid — Function.

Create mesh grid

Methods:

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

Arguments:

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

Returns:

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

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, maxEval, maxIter, maxJacobians, lambda, lambda_mu, np_lambda, show_trace, usenaive, save_results, localsa) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsCalibrate.jl:168

Arguments:

madsdata::AbstractDict

Keywords:

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

Returns:

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

source

# Mads.mkdir — Method.

Create a directory (if does not already exist)

Methods:

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

Arguments:

dirname::AbstractString : directory

source

# Mads.modelinformationcriteria — Function.

Model section information criteria

Methods:

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

Arguments:

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

source

# Mads.modobsweights! — Function.

Modify (multiply) observation weights in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.modwellweights! — Function.

Modify (multiply) well weights in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.montecarlo — Method.

Monte Carlo analysis

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

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

Returns:

parameter dictionary containing the data arrays

Dumps:

YAML output file with the parameter dictionary containing the data arrays

Example:

Mads.montecarlo(madsdata; N=100)

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:246

Arguments:

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

Returns:

the LM parameter space step

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:296
Mads.naive_levenberg_marquardt(f::Function, g::Function, x0::AbstractVector{Float64}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:296

Arguments:

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

Keywords:

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

Returns:

source

# Mads.naive_lm_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}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:267

Arguments:

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

Returns:

source

# Mads.noplot — Method.

Disable MADS plotting

Methods:

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

source

# Mads.notebook — Method.

Execute Jupyter notebook in IJulia or as a script

Methods:

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

Arguments:

rootname::AbstractString : notebook root name

Keywords:

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

source

# Mads.notebooks — Method.

Execute Jupyter notebook in IJulia or as a script

Methods:

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

Keywords:

dir : notebook directory
ndir

source

# Mads.notebookscript — Method.

Execute Jupyter notebook as a script

Methods:

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

Keywords:

dir : notebook directory
ndir
script : execute as a script

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}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1022

Arguments:

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

Returns:

true or false

source

# Mads.of — Function.

Compute objective function

Methods:

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

Arguments:

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

source

# Mads.paramarray2dict — Method.

Convert a parameter array to a parameter dictionary of arrays

Methods:

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

Arguments:

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

Returns:

a parameter dictionary of arrays

source

# Mads.paramdict2array — Method.

Convert a parameter dictionary of arrays to a parameter array

Methods:

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

Arguments:

dict::AbstractDict : parameter dictionary of arrays

Returns:

a parameter array

source

# Mads.parsemadsdata! — Method.

Parse loaded MADS problem dictionary

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

source

# Mads.parsenodenames — Function.

Parse string with node names defined in SLURM

Methods:

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

Arguments:

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

Returns:

vector with names of compute nodes (hosts)

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsLevenbergMarquardt.jl:84

Arguments:

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

Returns:

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

source

# Mads.pkgversion_old — Method.

Get package version

Methods:

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

Arguments:

modulestr::AbstractString

Returns:

package version

source

# Mads.plotgrid — Function.

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

Methods:

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

Arguments:

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

Keywords:

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

Examples:

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

source

# Mads.plotlocalsa — Method.

Plot local sensitivity analysis results

Methods:

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

Arguments:

filenameroot::AbstractString : problem file name root

Keywords:

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

Dumps:

filename : output plot file

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:99

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

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

Dumps:

plot of contaminant sources and wells

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsAnasolPlot.jl:15

Arguments:

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

Keywords:

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

Dumps:

image file with name filename and in specified format

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:209
Mads.plotmatches(madsdata::AbstractDict, result::AbstractDict, rx::Union{Regex, AbstractString}; title, notitle, kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:185
Mads.plotmatches(madsdata::AbstractDict, rx::Union{Regex, AbstractString}; kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:177
Mads.plotmatches(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:177

Arguments:

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

Keywords:

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

Dumps:

plot of the matches between model predictions and observations

Examples:

Mads.plotmatches(madsdata; filename="", format="")
Mads.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, kw...) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsPlot.jl:594

Arguments:

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

Keywords:

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

Dumps:

plot of the sensitivity analysis results for the observations

source

# Mads.plotrobustnesscurves — Method.

Plot BIG-DT robustness curves

Methods:

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

Arguments:

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

Keywords:

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

Dumps:

image file with name filename and in specified format

source

# Mads.plotseries — Function.

Create plots of data series

Methods:

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

Arguments:

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

Keywords:

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

Dumps:

Plots of data series

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

Arguments:

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

Keywords:

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

Dumps:

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

source

# Mads.printSAresults — Method.

Print sensitivity analysis results

Methods:

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

Arguments:

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

source

# Mads.printSAresults2 — Method.

Print sensitivity analysis results (method 2)

Methods:

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

Arguments:

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

source

# Mads.printerrormsg — Method.

Print error message

Methods:

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

Arguments:

errmsg : error message

source

# Mads.printobservations — Function.

Print (emit) observations in the MADS problem dictionary

Methods:

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

Arguments:

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

Keywords:

json

source

# Mads.process_notebook — Method.

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

Methods:

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

Arguments:

rootname::AbstractString : notebook root name

Keywords:

dir : notebook directory
ndir

source

# Mads.pull — Function.

Pull (checkout) the latest version of Mads modules

Methods:

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

Arguments:

modulename::AbstractString : module name

source

# Mads.push — Function.

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

Methods:

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

Arguments:

modulename::AbstractString : module name

source

# Mads.quietoff — Method.

Make MADS not quiet

Methods:

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

source

# Mads.quieton — Method.

Make MADS quiet

Methods:

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

source

# Mads.readasciipredictions — Method.

Read MADS predictions from an ASCII file

Methods:

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

Arguments:

filename::AbstractString : ASCII file name

Returns:

MADS predictions

source

# Mads.readmodeloutput — Method.

Read model outputs saved for MADS

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

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

source

# Mads.readobservations — Function.

Read observations

Methods:

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

Arguments:

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

Returns:

dictionary with Mads observations

source

# Mads.readobservations_cmads — Method.

Read observations using C MADS dynamic library

Methods:

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

Arguments:

madsdata::AbstractDict : Mads problem dictionary

Returns:

observations

source

# Mads.readyamlpredictions — Method.

Read MADS model predictions from a YAML file filename

Methods:

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

Arguments:

filename::AbstractString : file name

Returns:

data in yaml input file

source

# Mads.recursivemkdir — Method.

Create directories recursively (if does not already exist)

Methods:

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

Arguments:

s::AbstractString

Keywords:

filename

source

# Mads.recursivermdir — Method.

Remove directories recursively

Methods:

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

Arguments:

s::AbstractString

Keywords:

filename

source

# 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}) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1043

Arguments:

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

Returns:

obsdict : observations

source

# Mads.removesource! — Function.

Remove a contamination source

Methods:

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

Arguments:

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

source

# Mads.removesourceparameters! — Method.

Remove contaminant source parameters

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

source

# Mads.required — Function.

Lists modules required by a module (Mads by default)

Methods:

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

Arguments:

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

Returns:

filtered modules

source

# Mads.resetmodelruns — Method.

Reset the model runs count to be equal to zero

Methods:

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

source

# Mads.residuals — Function.

Compute residuals

Methods:

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

Arguments:

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

Returns:

source

# Mads.restartoff — Method.

MADS restart off

Methods:

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

source

# Mads.restarton — Method.

MADS restart on

Methods:

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

source

# Mads.reweighsamples — Method.

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

Methods:

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

Arguments:

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

Returns:

vector of log-likelihoods after reweighing

source

# Mads.rmdir — Method.

Remove directory

Methods:

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

Arguments:

dir::AbstractString : directory to be removed

Keywords:

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

source

# Mads.rmfile — Method.

Remove file

Methods:

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

Arguments:

filename::AbstractString : file to be removed

Keywords:

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

source

# Mads.rmfiles — Method.

Remove files

Methods:

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

Arguments:

filename::AbstractString

Keywords:

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

source

# Mads.rmfiles_ext — Method.

Remove files with extension ext

Methods:

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

Arguments:

ext::AbstractString : extension

Keywords:

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

source

# Mads.rmfiles_root — Method.

Remove files with root root

Methods:

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

Arguments:

root::AbstractString : root

Keywords:

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

source

# Mads.rosenbrock — Method.

Rosenbrock test function

Methods:

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

Arguments:

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

Returns:

test result

source

# Mads.rosenbrock2_gradient_lm — Method.

Parameter gradients of the Rosenbrock test function

Methods:

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

Arguments:

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

Returns:

parameter gradients

source

# Mads.rosenbrock2_lm — Method.

Rosenbrock test function (more difficult to solve)

Methods:

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

Arguments:

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

source

# Mads.rosenbrock_gradient! — Method.

Parameter gradients of the Rosenbrock test function

Methods:

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

Arguments:

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

source

# 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{T} where T; dx, center) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsTestFunctions.jl:84

Arguments:

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

Keywords:

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

Returns:

parameter gradients

source

# Mads.rosenbrock_hessian! — Method.

Parameter Hessian of the Rosenbrock test function

Methods:

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

Arguments:

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

source

# Mads.rosenbrock_lm — Method.

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

Methods:

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

Arguments:

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

Returns:

test result

source

# Mads.runcmd — Function.

Run external command and pipe stdout and stderr

Methods:

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

Arguments:

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

Keywords:

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

Returns:

command output
command error message

source

# Mads.runremote — Function.

Run remote command on a series of servers

Methods:

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

Arguments:

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

Returns:

output of running remote command

source

# Mads.saltelli — Method.

Saltelli sensitivity analysis

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

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

source

# Mads.saltellibrute — Method.

Saltelli sensitivity analysis (brute force)

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

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

source

# Mads.saltellibruteparallel — Method.

Parallel version of saltellibrute

source

# Mads.saltelliparallel — Method.

Parallel version of saltelli

source

# Mads.sampling — Method.

Methods:

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

Arguments:

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

Keywords:

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

Returns:

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

source

# Mads.savemadsfile — Function.

Save MADS problem dictionary madsdata in MADS input file filename

Methods:

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

Arguments:

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

Keywords:

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

Example:

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

source

# Mads.savemcmcresults — Method.

Save MCMC chain in a file

Methods:

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

Arguments:

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

Dumps:

the file containing MCMC chain

source

# Mads.savesaltellirestart — Method.

Save Saltelli sensitivity analysis results for fast simulation restarts

Methods:

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

Arguments:

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

source

# Mads.scatterplotsamples — Method.

Create histogram/scatter plots of model parameter samples

Methods:

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

Arguments:

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

Keywords:

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

Dumps:

histogram/scatter plots of model parameter samples

source

# Mads.searchdir — Function.

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

Methods:

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

Arguments:

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

Keywords:

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

Returns:

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

Examples:

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

source

# Mads.set_nprocs_per_task — Function.

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

Methods:

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

Arguments:

local_nprocs_per_task::Integer

source

# Mads.setallparamsoff! — Method.

Set all parameters OFF

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

filter : parameter filter

source

# Mads.setallparamson! — Method.

Set all parameters ON

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

Keywords:

filter : parameter filter

source

# Mads.setdebuglevel — Method.

Set MADS debug level

Methods:

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

Arguments:

level::Int64 : debug level

source

# Mads.setdefaultplotformat — Method.

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

Methods:

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

Arguments:

format::AbstractString : plot format

source

# Mads.setdir — Function.

Set the working directory (for parallel environments)

Methods:

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

Arguments:

dir : directory

Example:

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

source

# Mads.setdpi — Method.

Set image dpi

Methods:

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

Arguments:

dpi::Integer

source

# Mads.setexecutionwaittime — Method.

Set maximum execution wait time for forward model runs in seconds

Methods:

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

Arguments:

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

source

# Mads.setmadsinputfile — Method.

Set a default MADS input file

Methods:

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

Arguments:

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

source

# Mads.setmadsservers — Function.

Generate a list of Mads servers

Methods:

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

Arguments:

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

Returns

array string of mads servers

source

# 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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:713
Mads.setmodelinputs(madsdata::AbstractDict) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:713

Arguments:

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

Keywords:

path : path for the files [default=.]

source

# Mads.setnewmadsfilename — Function.

Set new mads file name

Methods:

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

Arguments:

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

Returns:

new file name

source

# Mads.setobservationtargets! — Method.

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

Methods:

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

Arguments:

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

source

# Mads.setobstime! — Function.

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

Methods:

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

Arguments:

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

Examples:

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

source

# Mads.setobsweights! — Function.

Set observation weights in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.setparamoff! — Method.

Set a specific parameter with a key parameterkey OFF

Methods:

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

Arguments:

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

source

# Mads.setparamon! — Method.

Set a specific parameter with a key parameterkey ON

Methods:

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

Arguments:

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

source

# Mads.setparamsdistnormal! — Method.

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

Methods:

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

Arguments:

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

source

# Mads.setparamsdistuniform! — Method.

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

Methods:

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

Arguments:

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

source

# Mads.setparamsinit! — Function.

Set initial optimized parameter guesses in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.setplotfileformat — Method.

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

Methods:

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

Arguments:

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

Returns:

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

source

# 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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:48
Mads.setprocs(np::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:45
Mads.setprocs(np::Integer, nt::Integer) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsParallel.jl:32

Arguments:

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

Keywords:

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

Returns:

vector with names of compute nodes (hosts)

Example:

Mads.setprocs()
Mads.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")

source

# Mads.setseed — Function.

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

Methods:

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

Arguments:

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

source

# Mads.setsindx! — Method.

Set sin-space dx

Methods:

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

Arguments:

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

Returns:

nothing

source

# Mads.setsindx — Method.

Set sin-space dx

Methods:

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

Arguments:

sindx::Number

Returns:

nothing

source

# Mads.setsourceinit! — Function.

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

Methods:

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

Arguments:

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

source

# Mads.settarget! — Method.

Set observation target

Methods:

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

Arguments:

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

source

# Mads.settime! — Method.

Set observation time

Methods:

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

Arguments:

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

source

# Mads.setverbositylevel — Method.

Set MADS verbosity level

Methods:

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

Arguments:

level::Int64 : debug level

source

# Mads.setweight! — Method.

Set observation weight

Methods:

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

Arguments:

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

source

# Mads.setwellweights! — Function.

Set well weights in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.showallparameters — Function.

Show all parameters in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.showobservations — Function.

Show observations in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.showparameters — Function.

Show parameters in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.sinetransform — Function.

Sine transformation of model parameters

Methods:

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

Arguments:

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

Returns:

Sine transformation of model parameters

source

# Mads.sinetransformfunction — Method.

Sine transformation of a function

Methods:

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

Arguments:

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

Returns:

Sine transformation

source

# Mads.sinetransformgradient — Method.

Sine transformation of a gradient function

Methods:

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

Arguments:

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

Keywords:

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

Returns:

Sine transformation of a gradient function

source

# Mads.spaghettiplot — Function.

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

Methods:

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

Arguments:

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

Keywords:

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

Dumps:

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

Example:

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

source

# Mads.spaghettiplots — Function.

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

Methods:

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

Arguments:

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

Keywords:

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

Dumps:

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

Example:

Mads.spaghettiplots(madsdata, paramdictarray; format="", keyword="", xtitle="X", ytitle="Y", obs_plot_dots=true)
Mads.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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsKriging.jl:45

Arguments:

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

Returns:

covariance

source

# Mads.sphericalvariogram — Method.

Spherical variogram

Methods:

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

Arguments:

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

Returns:

Spherical variogram

source

# Mads.sprintf — Method.

Convert @Printf.sprintf macro into sprintf function

source

# Mads.status — Function.

Status of Mads modules

Methods:

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

Arguments:

madsmodule::AbstractString : mads module

Keywords:

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

Returns:

true or false

source

# Mads.stderrcaptureoff — Method.

Restore stderr

Methods:

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

Returns:

standered error

source

# Mads.stderrcaptureon — Method.

Redirect stderr to a reader

Methods:

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

source

# Mads.stdoutcaptureoff — Method.

Restore stdout

Methods:

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

Returns:

standered output

source

# Mads.stdoutcaptureon — Method.

Redirect stdout to a reader

Methods:

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

source

# Mads.stdouterrcaptureoff — Method.

Restore stdout & stderr

Methods:

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

Returns:

standered output amd standered error

source

# Mads.stdouterrcaptureon — Method.

Redirect stdout & stderr to readers

Methods:

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

source

# Mads.svrdump — Method.

Dump SVR models in files

Methods:

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

Arguments:

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

source

# Mads.svrfree — Method.

Free SVR

Methods:

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

Arguments:

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

source

# Mads.svrload — Method.

Load SVR models from files

Methods:

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

Arguments:

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

Returns:

Array of SVR models for each model prediction

source

# Mads.svrpredict — Function.

Predict SVR

Methods:

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

Arguments:

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

Returns:

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

source

# Mads.svrtrain — Function.

Train SVR

Methods:

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

Arguments:

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

Keywords:

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

[default=0]

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

Returns:

Array of SVR models

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1217

Arguments:

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

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) in Mads : /Users/vvv/.julia/dev/Mads/src/MadsIO.jl:1199

Arguments:

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

source

# Mads.tag — Function.

Tag Mads modules with a default argument :patch

Methods:

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

Arguments:

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

source

# Mads.test — Function.

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

Methods:

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

Arguments:

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

Keywords:

madstest : test Mads [default=true]
plotting

source

# Mads.testj — Function.

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

Methods:

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

Arguments:

coverage::Bool : [default=false]

source

# Mads.transposematrix — Method.

Transpose non-numeric matrix

Methods:

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

Arguments:

a::AbstractMatrix{T} where T : matrix

source

# Mads.transposevector — Method.

Transpose non-numeric vector

Methods:

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

Arguments:

a::AbstractVector{T} where T : vector

source

# Mads.untag — Method.

Untag specific version

Methods:

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

Arguments:

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

source

# Mads.vectoroff — Method.

MADS vector calls off

Methods:

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

source

# Mads.vectoron — Method.

MADS vector calls on

Methods:

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

source

# Mads.veryquietoff — Method.

Make MADS not very quiet

Methods:

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

source

# Mads.veryquieton — Method.

Make MADS very quiet

Methods:

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

source

# Mads.void2nan! — Method.

Convert Nothing's into NaN's in a dictionary

Methods:

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

Arguments:

dict::AbstractDict : dictionary

source

# Mads.weightedstats — Method.

Get weighted mean and variance samples

Methods:

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

Arguments:

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

Returns:

vector of sample means
vector of sample variances

source

# Mads.welloff! — Method.

Turn off a specific well in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.wellon! — Method.

Turn on a specific well in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.wellon! — Method.

Turn on a specific well in the MADS problem dictionary

Methods:

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

Arguments:

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

source

# Mads.wells2observations! — Method.

Convert Wells class to Observations class in the MADS problem dictionary

Methods:

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

Arguments:

madsdata::AbstractDict : MADS problem dictionary

source

# Mads.writeparameters — Function.

Write model parameters

Methods:

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

Arguments: