MITgcmTools.jl

testreport(nam::String,ext="")

Run the testreport script for one model config nam (or "all"), with additional options (optional) speficied in ext

using MITgcmTools
testreport(MITgcm_config(configuration="front_relax"),"-norun")
#testreport(MITgcm_config(configuration="all"),"-norun")

verification_experiments()

Get list of all most-standard configurations of MITgcm and return as an Array of MITgcm_config

using MITgcmTools
exps=verification_experiments()

MITgcm_namelist(groups,params)

Data structure representing a MITgcm namelist file, such as data.pkg, which contains

    groups :: Array{Symbol,1} = Array{Symbol,1}(undef, 0)
    params :: Array{OrderedDict{Symbol,Any},1} = Array{OrderedDict{Symbol,Any},1}(undef, 0)

with model parameters (params) being organized in groups as done in the files.

using MITgcmTools
fil=joinpath(MITgcm_path,"verification","advect_xy","run","data")
nml=read_namelist(fil)
MITgcm_namelist(nml.groups,nml.params)
MITgcm_namelist(groups=nml.groups,params=nml.params)
MITgcm_namelist(groups=nml.groups)

MITgcm_config()

Concrete type of AbstractModelConfig for MITgcm which contains

    model :: String = "MITgcm"
    configuration :: String = ""
    options :: Array{String,1} = Array{String,1}(undef, 0)
    inputs :: Array{String,1} = Array{String,1}(undef, 0)
    outputs :: Array{String,1} = Array{String,1}(undef, 0)
    status :: Array{String,1} = Array{String,1}(undef, 0)
    channel :: Channel{Any} = Channel{Any}(10) 
    folder :: String = tempdir()
    ID :: UUID = UUIDs.uuid4()

and with defaults that can be constructed as follows for example

using MITgcmTools
tmp=MITgcm_config()

exps=verification_experiments()
exps[end]

(part of the climate model interface as specialized for MITgcm)

build(config::MITgcm_config)

Build the model using testreport(config,"-norun") which links all code files, headers, etc in place before compiling the model

(part of the climate model interface as specialized for MITgcm)

compile(config::MITgcm_config)

Compile the model using make in build/ that has already been setup.

(part of the climate model interface as specialized for MITgcm)

setup(config::MITgcm_config)

Create a run/ folder and link everything there as needed to be ready to run model as normally done for most-standard MITgcm configurations (incl. prepare_run and mitgcmuv). Call init_git_log(config) to setup git tracker and put!(config.channel,MITgcm_launch) to be executed via launch(config) later.

(part of the climate model interface as specialized for MITgcm)

MITgcm_launch(config::MITgcm_config)

Go to run/ folder and effectively call mitgcmuv > output.txt

(part of the climate model interface as specialized for MITgcm)

clean(config::MITgcm_config)

Cancel any remaining task (config.channel), clean up the build directory (via testreport), and clean the run directory (via rm).

(part of the climate model interface as specialized for MITgcm)

read_mdsio(datafile)

Read a MITgcm mdsio file (".data" binary + ".meta" text pair), and return as an Array

p="./hs94.cs-32x32x5/run/"
x=read_mdsio(p*"surfDiag.0000000020.002.001.data")
y=read_mdsio(p*"pickup.ckptA.002.001.data")
z=read_mdsio(p*"T.0000000000.002.001.data")

read_mdsio(pth::String,fil::String)

Read a MITgcm's MDSIO files (".data" binary + ".meta" text pair), combine, and return as an Array

p="./hs94.cs-32x32x5/run/"
x=read_mdsio(p,"surfDiag.0000000020")
y=read_mdsio(p,"pickup.ckptA")
z=read_mdsio(p,"T.0000000000")

read_meta(metafile)

Read a MITgcm metadata file, parse it, and return as a NamedTuple

p="./hs94.cs-32x32x5/run/"
meta=read_meta(p*"surfDiag.0000000020.002.001.meta")
pairs(meta)
meta.dimList

read_meta(pth::String,fil::String)

Read a MITgcm metadata files, parse them, and return as an array of NamedTuple

p="./hs94.cs-32x32x5/run/"
meta=read_meta(p,"surfDiag.0000000020")
pairs(meta[end])
[meta[i].dimList for i in 1:length(meta)]

read_namelist(fil)

Read a MITgcm namelist file, parse it, and return as a NamedTuple

using MITgcmTools
testreport("advect_xy")
fil=joinpath(MITgcm_path,"verification","advect_xy","run","data")
namelist=read_namelist(fil)

write_namelist(fil)

Save a MITgcm namelist file. In the example below, one is read from file, modified, and then saved to a new file using write_namelist.

using MITgcmTools
fil=joinpath(MITgcm_path,"verification","advect_xy","run","data")
nml=read_namelist(fil)
write_namelist(fil*"_new",namelist)

or

nml=read(fil,MITgcm_namelist())
write(fil*"_new",nml)

read_available_diagnostics(fldname::String; filename="available_diagnostics.log")

Get the information for a particular variable fldname from the available_diagnostics.log text file generated by MITgcm.

read_bin(fil::String,kt::Union{Int,Missing},kk::Union{Int,Missing},prec::DataType,mygrid::gcmgrid)

Read model output from binary file and convert to MeshArray. Other methods:

read_bin(fil::String,prec::DataType,mygrid::gcmgrid)
read_bin(fil::String,mygrid::gcmgrid)

read_flt(dirIn::String,prec::DataType)

Read displacements from MITgcm/pkg/flt output file into a DataFrame.

read_nctiles(fileName,fldName,mygrid)

Read model output from NCTiles file and convert to MeshArray instance.

mygrid=GridSpec("LatLonCap")
fileName="nctiles_grid/GRID"
Depth=read_nctiles(fileName,"Depth",mygrid)
hFacC=read_nctiles(fileName,"hFacC",mygrid)
hFacC=read_nctiles(fileName,"hFacC",mygrid,I=(:,:,1))

findtiles(ni::Int,nj::Int,mygrid::gcmgrid)
findtiles(ni::Int,nj::Int,grid::String="LatLonCap",GridParentDir="../inputs/GRID_LLC90/")

Return a MeshArray map of tile indices, mytiles["tileNo"], for tile size ni,nj and extract grid variables accordingly.

cube2compact(x::Array)

Reshape from e.g. size (192, 32, 5) in cube format to (32, 192, 5) in compact format.

compact2cube(x::Array)

Reshape from e.g. size (32, 192, 5) in cube format to (192, 32, 5) in compact format.

convert2array(fld::MeshArray)

Convert MeshArray to Array (or vice versa otherwise)

convert2gcmfaces(fld::MeshArray)

Convert mitgcm output to MeshArray (or vice versa otherwise)

SeaWaterDensity(Θ,Σ,Π,Π0)

Compute potential density (ρP), in situ density (ρI), and density referenced to PREF (Π0 in decibars) from potential temperature (Θ in °C), salinity (Σ in psu) and pressure (Π in decibars) according to the UNESCO / Jackett & McDougall 1994 equation of state.

Credits: code based on a Matlab implementation by B. Ferron Reference: https://www.jodc.go.jp/info/iocdoc/UNESCOtech/059832eb.pdf Check value: ρI = 1041.83267kg/m^3 for Θ=3°Celcius, Σ=35psu, Π=3000dbar

(ρP,ρI,ρR) = SeaWaterDensity(3.,35.5,3000.)
isapprox(ρI,1041.83267, rtol=1e-6)

MixedLayerDepth(Θ,Σ,Δ,mthd)

Compute mixed layer depth from potential temperature (Θ in °C), salinity (Σ in psu) and depth (Δ in method) according to various formulas (mthd == "BM", "Suga", "Kara"). Inputs must be dense vectors without any missing value (or NaN, etc).

D=collect(0.0:1.0:500.0); tmp=(1.0.-tanh.(5*(-1 .+ 2/D[end]*D)));
T=2.0 .+ 8.0*tmp; S=34.0 .+ 0.5*tmp;
(ρP,ρI,ρR) = SeaWaterDensity(T,S,D);

mld=MixedLayerDepth(T,S,D,"BM"); isapprox(mld,134.0)

using Plots
plot(ρP,-D,w=2,label="Potential Density",ylabel="Depth")
plot!(vec([ρP[1] ρP[end]]),-fill(mld,2),label="Mixed Layer Depth",w=2,c="black",s=:dash)