RegionGrids.jl

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How do you use RegionGrids.jl? ​

The Basic Outline ​

In practice, we would use GeoRegions and RegionGrids as follows:

using GeoRegions
using RegionGrids

# 1. Get gridded data, and longitude/latitude

data = ...
dlon = ... # longitudes for gridded data points
dlat = ... # latitudes for gridded data points

# 2. Define a Geographic Region using a shape defined by vectors for longitude and latitude respectively

geo = GeoRegion(lon,lat)

# 3. Create a RegionGrid for data extraction using the GeoRegion, and longitude and latitude vectors

ggrd = RegionGrid(geo,dlon,dlat)

# 4. Extract the data within the GeoRegion of interest

longitude and latitude vectors respectively
ndata  = extract(data,ggrd)
newlon = ggrd.lon
newlat = ggrd.lat

An Example ​

Setup ​

julia
using GeoRegions
using RegionGrids
using DelimitedFiles
using CairoMakie

download("https://raw.githubusercontent.com/natgeo-wong/GeoPlottingData/main/coastline_resl.txt","coast.cst")
coast = readdlm("coast.cst",comments=true)
clon  = coast[:,1]
clat  = coast[:,2]
nothing

Defining some data ​

julia
lon = collect(0:5:360);  nlon = length(lon)
lat = collect(-90:5:90); nlat = length(lat)
data = rand(nlon,nlat)
73×37 Matrix{Float64}:
 0.520607  0.656478   0.8623     0.364012   …  0.407073  0.10893    0.499349
 0.685958  0.170849   0.584417   0.431761      0.467307  0.315705   0.294867
 0.651259  0.536291   0.507484   0.148737      0.608901  0.515085   0.174403
 0.162005  0.770139   0.790914   0.729053      0.704964  0.917222   0.405027
 0.79426   0.767143   0.630531   0.679615      0.216706  0.64735    0.326352
 0.121802  0.586907   0.86278    0.190179   …  0.257755  0.888234   0.0607443
 0.556957  0.481187   0.293685   0.405705      0.919959  0.384567   0.783191
 0.81301   0.31472    0.407746   0.530773      0.90862   0.974702   0.490176
 0.810525  0.3017     0.450825   0.377438      0.3316    0.647843   0.233956
 0.448305  0.0385708  0.336562   0.0236978     0.996079  0.0341095  0.0573869
 ⋮                                          ⋱            ⋮          
 0.767291  0.794692   0.391959   0.871937      0.317132  0.75175    0.492259
 0.145043  0.247796   0.335396   0.818748   …  0.659016  0.903467   0.0589598
 0.217171  0.689141   0.60046    0.559253      0.423461  0.198745   0.0189442
 0.824684  0.371659   0.0807141  0.706745      0.877306  0.634762   0.0403287
 0.168069  0.892426   0.61952    0.0926675     0.968932  0.584513   0.711143
 0.256901  0.375477   0.151346   0.15687       0.399286  0.0752857  0.503156
 0.588399  0.934283   0.34465    0.94413    …  0.158606  0.750039   0.56092
 0.394082  0.687597   0.867584   0.889833      0.34219   0.427055   0.909798
 0.402845  0.449754   0.985092   0.244497      0.58814   0.115636   0.354293

Defining a Region of Interest ​

Next, we proceed to define a GeoRegion and extract its coordinates:

julia
geo = GeoRegion([10,230,-50,10],[50,10,-40,50])
slon,slat = coordinates(geo) # extract the coordinates
([10.0, 230.0, -50.0, 10.0], [50.0, 10.0, -40.0, 50.0])

Let us create a RegionGrid for Data Extraction ​

Following which, we can define a RegionGrid:

julia
ggrd = RegionGrid(geo,lon,lat)
The RLinearMask Grid type has the following properties:
    Longitude Indices     (ilon) : [63, 64, 65, 66, 67, 68, 69, 70, 71, 72  …  38, 39, 40, 41, 42, 43, 44, 45, 46, 47]
    Latitude Indices      (ilat) : [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]
    Longitude Points       (lon) : [-50, -45, -40, -35, -30, -25, -20, -15, -10, -5  …  185, 190, 195, 200, 205, 210, 215, 220, 225, 230]
    Latitude Points        (lat) : [-40, -35, -30, -25, -20, -15, -10, -5, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50]
    Rotated X Coordinates    (X)
    Rotated Y Coordinates    (Y)
    Rotation (°)             (θ) : 0.0
    RegionGrid Mask       (mask)
    RegionGrid Weights (weights)
    RegionGrid Size              : 58 lon points x 19 lat points
    RegionGrid Validity		  : 465 / 1102

And then use this RegionGrid to extract data for the GeoRegion of interest:

julia
ndata = extract(data,ggrd)
58×19 Matrix{Float64}:
   0.241761  NaN           …  NaN         NaN  NaN  NaN  NaN  NaN
 NaN           0.687105       NaN         NaN  NaN  NaN  NaN  NaN
 NaN           0.509172       NaN         NaN  NaN  NaN  NaN  NaN
 NaN           0.00676107     NaN         NaN  NaN  NaN  NaN  NaN
 NaN           0.28973        NaN         NaN  NaN  NaN  NaN  NaN
 NaN           0.450687    …  NaN         NaN  NaN  NaN  NaN  NaN
 NaN         NaN              NaN         NaN  NaN  NaN  NaN  NaN
 NaN         NaN              NaN         NaN  NaN  NaN  NaN  NaN
 NaN         NaN              NaN         NaN  NaN  NaN  NaN  NaN
 NaN         NaN                0.815507  NaN  NaN  NaN  NaN  NaN
   ⋮                       ⋱                     ⋮            
 NaN         NaN              NaN         NaN  NaN  NaN  NaN  NaN
 NaN         NaN           …  NaN         NaN  NaN  NaN  NaN  NaN
 NaN         NaN              NaN         NaN  NaN  NaN  NaN  NaN
 NaN         NaN              NaN         NaN  NaN  NaN  NaN  NaN
 NaN         NaN              NaN         NaN  NaN  NaN  NaN  NaN
 NaN         NaN              NaN         NaN  NaN  NaN  NaN  NaN
 NaN         NaN           …  NaN         NaN  NaN  NaN  NaN  NaN
 NaN         NaN              NaN         NaN  NaN  NaN  NaN  NaN
 NaN         NaN              NaN         NaN  NaN  NaN  NaN  NaN

Data Visualization ​

julia
fig = Figure()

ax1 = Axis(
    fig[1,1],width=400,height=200,
    limits=(0,360,-90,90)
)
heatmap!(ax1,lon,lat,data,colorrange=(-1,1))
lines!(ax1,slon,slat,color=:black,linewidth=5)
lines!(ax1,slon.+360,slat,color=:black,linewidth=5)

ax2 = Axis(
    fig[2,1],width=400,height=200,
    limits=(0,360,-90,90)
)
heatmap!(ax2,ggrd.lon,ggrd.lat,ndata,colorrange=(-1,1))
heatmap!(ax2,ggrd.lon.+360,ggrd.lat,ndata,colorrange=(-1,1))
lines!(ax2,slon,slat,color=:black,linewidth=5)
lines!(ax2,slon.+360,slat,color=:black,linewidth=5)

resize_to_layout!(fig)
fig