AIBECS.jl

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Plot basic things ​

This guide is organized as follows

In this guide we will focus on how-to plot things using AIBECS' built-in recipes for Plots.jl. These recipes are implemented using RecipesBase.jl, which are explained in Plots.jl's documentation.

Throughout we will use the OCIM2 grid and we will create a dummy tracer as a function of location to showcase each plot, just for the sake of the examples herein.

julia
using Plots
using AIBECS
using JLD2 # required by `OCIM2.load`
grd, _ = OCIM2.load()
dummy = cosd.(latvec(grd))
200160-element Vector{Float64}:
 0.3221204417984906
 0.3546048870425357
 0.38666674294141884
 0.41826780077556525
 0.44937040096716135
 0.4799374779597864
 0.5099326043901359
 0.5393200344991993
 0.5680647467311559
 0.5961324854692254
 ⋮
 0.8854560256532099
 0.8688879687250066
 0.9154080085253663
 0.9009688679024191
 0.8854560256532099
 0.8688879687250066
 0.9154080085253663
 0.9009688679024191
 0.8854560256532099

Horizontal plots ​

Horizontal slice ​

The most common thing you plot after a simulation of marine tracers is a horizontal slice. In this case, you just need to provide the tracer (dummy here), the grid object grd, and the depth at which you want to plot.

julia
plothorizontalslice(dummy, grd, depth = 10)

You can supply units for the depth at which you want to see the horizontal slice.

julia
plothorizontalslice(dummy, grd, depth = 10u"m")

And the units should be understood under the hood.

julia
plothorizontalslice(dummy, grd, depth = 3u"km")

If your tracer is supplied with units, those will show in the colorbar label

julia
plothorizontalslice(dummy * u"mol/m^3", grd, depth = 10u"m")

The advantage of Plots.jl recipes like this one is that you can specify other pieces of the plot as you would with built-in functions. The advantage of Plots.jl recipes like this one is that you can specify other pieces of the plot as you would with built-in functions. For example, you can chose the colormap with the color keyword argument.

julia
dummy .*= cosd.(lonvec(grd))
plt = plothorizontalslice(dummy, grd, depth = 100, color = :balance)

And you can finetune attributes after the plot is created.

julia
plot!(plt, xlabel = "Lon", ylabel = "Lat", colorbar_title = "dummy value", title = "The pacific as a whole")

Vertical plots ​

Exploring the vertical distribution of tracers is important after all.

Zonal slices ​

You must specify the longitude

julia
dummy = cosd.(latvec(grd))
dummy .+= sqrt.(depthvec(grd)) / 30
plotmeridionalslice(dummy, grd, lon = 330)

Zonal averages ​

Global zonal average ​

julia
plotzonalaverage(dummy, grd)

If you want a zonal average over a specific region, you can just mask it out

Basin zonal average ​

This is experimental at this stage and relies on OceanBasins.jl. You can create basin masks using this package with

julia
using OceanBasins
OCEANS = oceanpolygons()
basins = sum(i * isbasin(latvec(grd), lonvec(grd), OCEANS) for (i, isbasin) in enumerate([isindian2, ispacific2, isatlantic2, isantarctic]))
plothorizontalslice(basins, grd, depth = 0, seriestype = :heatmap, color = :lightrainbow)

and you can mask a specific region with the mask keyword argument

julia
mPAC = ispacific(latvec(grd), lonvec(grd), OCEANS)
plotzonalaverage(dummy, grd, mask = mPAC)

Meridional slices ​

Just as you should expect at this stage, you can plot a meridional slice with

julia
plotmeridionalslice(dummy, grd, lon = -30)

Depth profiles ​

Sometimes you want a profile at a given station or location

julia
plotdepthprofile(dummy, grd, lonlat = (-30, 30))

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