briochemc

briochemc

using HTTP, CSV, Plots, DataFrames, Dates

# Download the data
df = CSV.read(IOBuffer(String(HTTP.get("https://covid.ourworldindata.org/data/total_cases.csv").body)), normalizenames=true)

# Plotting function
function doit(df, countries, alignment)
    cp = get_color_palette(:auto, plot_color(:white), length(countries))
    plot(legend=false)
    for (i,country) in enumerate(countries)

briochemc

using AIBECS, ModelingToolkit, LinearAlgebra

# dispatch for sparse matrices
Base.:*(A::SparseMatrixCSC{Operation,S}, x::StridedVector{Operation}) where {S} =
    (T = Operation; mul!(similar(x, T, A.m), A, x, true, false))
Base.:*(A::SparseMatrixCSC{Tx,S}, x::StridedVector{Operation}) where {Tx,S} =
    (T = LinearAlgebra.promote_op(LinearAlgebra.matprod, Operation, Tx); mul!(similar(x, T, A.m), A, x, true, false))
Base.:*(A::SparseMatrixCSC{Operation,S}, x::StridedVector{Tx}) where {Tx,S} =
    (T = LinearAlgebra.promote_op(LinearAlgebra.matprod, Operation, Tx); mul!(similar(x, T, A.m), A, x, true, false))

briochemc

using Luxor, Colors

function main(fname)
    Drawing(600, 600, fname)
    origin()
    circle(O, 290, :clip)
    sethue("white")
    paint()

    colors = [Luxor.julia_blue, Luxor.julia_red, Luxor.julia_green, Luxor.julia_purple]

briochemc

using AIBECS, Plots
grd, TOCIM1 = OCIM1.load()
T(p) = TOCIM1
function G(x,p)
    @unpack τ, z₀ = p
    return @. 1 - x / τ * (z < z₀)
end
z = depthvec(grd)
F, ∇ₓF = state_function_and_Jacobian(T, G)
struct IdealAgeParameters{U} <: AbstractParameters{U}

briochemc

using Makie
using AbstractPlotting
using FileIO, Colors

# Load World Ocean Atlas 2D fields for PO₄, Si(OH)₄, and NO₄
using WorldOceanAtlasTools
lat, lon, PO₄_2D   = WorldOceanAtlasTools.WOA13_surface_map("p", 0, "1°")
lat, lon, NO₄_2D   = WorldOceanAtlasTools.WOA13_surface_map("n", 0, "1°")
lat, lon, SiOH₄_2D = WorldOceanAtlasTools.WOA13_surface_map("i", 0, "1°")
# Add the image layer to each plot

briochemc

# Load World Ocean Atlas 2D fields for PO₄, Si(OH)₄, and NO₄
using WorldOceanAtlasTools
product_year = 2018
tracer = "p"
period = 0
resolution = "1°"
ds = WorldOceanAtlasTools.WOA_Dataset(product_year, tracer, period, resolution, verbose=false) ;
field = "an"
field3D, lat, lon, depth = WorldOceanAtlasTools.get_gridded_3D_field(ds, tracer, field) ;
map_2D = field3D[:,:,1] ;

briochemc

using SparseArrays, LinearAlgebra, SuiteSparse
using Unitful

"""
    elunit(x)
    
Returns the unit of the elements of `x`.
(Returns a `Unitful.FreeUnits{(),NoDims,nothing}` object `x`'s unit is heterogeneous.)
"""
elunit(::SparseMatrixCSC{U,Int64}) where {U<:Quantity} = unit(U)

briochemc

ENV["MPLBACKEND"]="qt5agg"
"qt5agg"

using PyPlot, PyCall

clf()

ax = subplot(projection=ccrs.NearsidePerspective(central_latitude=10.0, central_longitude=170.0, satellite_height=100000000.0))

ax.add_feature(cfeature.NaturalEarthFeature("physical", "ocean", "50m", edgecolor="face", facecolor="#4063d8"))

briochemc

length(ARGS) == 1 && (sheet = "Sheet1")

# Use the installed packages
using ExcelFiles, DataFrames, Interpolations

# Replace by your file with just the data!
df = DataFrame(load(abspath(ARGS[1]), sheet))
println("Resampling $(ARGS[1]) - $sheet")