YAXArrays.jl

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Read n-d array like-data

DiskArrays.jl

Get your chunks!

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DimensionalData.jl

Select & Index!

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Zarr.jl

Chunkerd, compressed !

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Rasters.jl

Read and manipulate !

Array-oriented data

NetCDF.jl

Scientific binary data.

Raster and vector data

ArchGDAL.jl

GDAL in Julia.

An interface for

GeoInterface.jl

geospatial data in Julia.

A higher level interface

GRIBDatasets.jl

for reading GRIB files.

Array-oriented data

NCDatasets.jl

Scientific binary data.

Group YAXArrays and Datasets ​

The following examples will use the groupby function to calculate temporal and spatial averages.

julia
using YAXArrays, DimensionalData
using NetCDF
using Downloads
using Dates
using Statistics
[ Info: new driver key :netcdf, updating backendlist.

Seasonal Averages from Time Series of Monthly Means ​

The following reproduces the example in xarray by Joe Hamman.

Where the goal is to calculate the seasonal average. And in order to do this properly, is necessary to calculate the weighted average considering that each month has a different number of days.

Download the data ​

julia
url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc"
filename = Downloads.download(url_path, "rasm.nc")
ds_o = Cube(filename)

WARNING

The following rebuild should not be necessary in the future, plus is unpractical to use for large data sets. Out of memory groupby currently is work in progress. Related to https://github.com/rafaqz/DimensionalData.jl/issues/642

julia
axs = dims(ds_o) # get the dimensions
data = ds_o.data[:,:,:] # read the data
_FillValue = ds_o.properties["_FillValue"]
data = replace(data, _FillValue => NaN)
# create new YAXArray
ds = YAXArray(axs, data)

GroupBy: seasons ​

function weighted_seasons(ds) ... end
julia
function weighted_seasons(ds)
    # calculate weights 
    tempo = dims(ds, :Ti)
    month_length = YAXArray((tempo,), daysinmonth.(tempo))
    g_tempo = groupby(month_length, Ti => seasons(; start=December))
    sum_days = sum.(g_tempo, dims=:Ti)
    weights = map(./, g_tempo, sum_days)
    # unweighted seasons
    g_ds = groupby(ds, Ti => seasons(; start=December))
    mean_g = mean.(g_ds, dims=:Ti)
    mean_g = dropdims.(mean_g, dims=:Ti)
    # weighted seasons
    g_dsW = broadcast_dims.(*, weights, g_ds)
    weighted_g = sum.(g_dsW, dims = :Ti);
    weighted_g = dropdims.(weighted_g, dims=:Ti)
    # differences
    diff_g = map(.-, weighted_g, mean_g)
    seasons_g = lookup(mean_g, :Ti)
    return mean_g, weighted_g, diff_g, seasons_g
end

Now, we continue with the groupby operations as usual

julia
julia> g_ds = groupby(ds, Ti => seasons(; start=December))
╭──────────────────────────────────────────────────╮
│ 4-element DimGroupByArray{YAXArray{Float64,2},1} │
├──────────────────────────────────────────────────┴───────────────────── dims ┐
  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{Symbol, Any} with 1 entry:
  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
├────────────────────────────────────────────────────────────────── group dims ┤
  ↓ x, → y, ↗ Ti
└──────────────────────────────────────────────────────────────────────────────┘
 :Dec_Jan_Feb  275×205×9 YAXArray
 :Mar_Apr_May  275×205×9 YAXArray
 :Jun_Jul_Aug  275×205×9 YAXArray
 :Sep_Oct_Nov  275×205×9 YAXArray

And the mean per season is calculated as follows

julia
julia> mean_g = mean.(g_ds, dims=:Ti)
╭──────────────────────────────────────────────────────────────────────────────╮
│ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, DimensionalData.Dimensions.Ti{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
├──────────────────────────────────────────────────────────────────────── dims ┤
  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{Symbol, Any} with 1 entry:
  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
└──────────────────────────────────────────────────────────────────────────────┘
 :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843;;;]
 :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762;;;]
 :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537;;;]
 :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404;;;]

dropdims ​

Note that now the time dimension has length one, we can use dropdims to remove it

julia
julia> mean_g = dropdims.(mean_g, dims=:Ti)
╭──────────────────────────────────────────────────────────────────────────────╮
│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
├──────────────────────────────────────────────────────────────────────── dims ┤
  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{Symbol, Any} with 1 entry:
  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
└──────────────────────────────────────────────────────────────────────────────┘
 :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1372 11.3835; NaN NaN … 11.3252 11.5843]
 :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1363 21.018; NaN NaN … 21.4325 21.1762]
 :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2818 27.9432; NaN NaN … 28.619 28.0537]
 :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.7119 21.7158; NaN NaN … 21.9682 21.9404]

seasons ​

Due to the groupby function we will obtain new grouping names, in this case in the time dimension:

julia
seasons_g = lookup(mean_g, :Ti)
Categorical{Symbol} Unordered
wrapping: 4-element Vector{Symbol}:
 :Dec_Jan_Feb
 :Mar_Apr_May
 :Jun_Jul_Aug
 :Sep_Oct_Nov

Next, we will weight this grouping by days/month in each group.

GroupBy: weight ​

Create a YAXArray for the month length

julia
tempo = dims(ds, :Ti)
month_length = YAXArray((tempo,), daysinmonth.(tempo))
╭──────────────────────────────╮
│ 36-element YAXArray{Int64,1} │
├──────────────────────────────┴───────────────────────────────────────── dims ┐
  ↓ Ti Sampled{CFTime.DateTimeNoLeap} [CFTime.DateTimeNoLeap(1980-09-16T12:00:00), …, CFTime.DateTimeNoLeap(1983-08-17T00:00:00)] ForwardOrdered Irregular Points
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{String, Any}()
├──────────────────────────────────────────────────────────── loaded in memory ┤
  data size: 288.0 bytes
└──────────────────────────────────────────────────────────────────────────────┘

Now group it by season

julia
julia> g_tempo = groupby(month_length, Ti => seasons(; start=December))
╭────────────────────────────────────────────────╮
│ 4-element DimGroupByArray{YAXArray{Int64,0},1} │
├────────────────────────────────────────────────┴─────────────────────── dims ┐
  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{Symbol, Any} with 1 entry:
  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
├────────────────────────────────────────────────────────────────── group dims ┤
  ↓ Ti
└──────────────────────────────────────────────────────────────────────────────┘
 :Dec_Jan_Feb  9-element YAXArray
 :Mar_Apr_May  9-element YAXArray
 :Jun_Jul_Aug  9-element YAXArray
 :Sep_Oct_Nov  9-element YAXArray

Get the number of days per season

julia
julia> sum_days = sum.(g_tempo, dims=:Ti)
╭──────────────────────────────────────────────────────────────────────────────╮
│ 4-element DimArray{YAXArray{Int64, 1, Vector{Int64}, Tuple{DimensionalData.Dimensions.Ti{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} │
├──────────────────────────────────────────────────────────────────────── dims ┤
  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{Symbol, Any} with 1 entry:
  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
└──────────────────────────────────────────────────────────────────────────────┘
 :Dec_Jan_Feb  [270]
 :Mar_Apr_May  [276]
 :Jun_Jul_Aug  [276]
 :Sep_Oct_Nov  [273]

weights ​

Weight the seasonal groups by sum_days

julia
julia> weights = map(./, g_tempo, sum_days)
╭──────────────────────────────────────────────────────────────────────────────╮
│ 4-element DimArray{YAXArray{Float64, 1, Vector{Float64}, Tuple{DimensionalData.Dimensions.Ti{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, Dict{String, Any}},1} groupby │
├──────────────────────────────────────────────────────────────────────── dims ┤
  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{Symbol, Any} with 1 entry:
  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
└──────────────────────────────────────────────────────────────────────────────┘
 :Dec_Jan_Feb  …  [0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704, 0.114815, 0.114815, 0.103704]
 :Mar_Apr_May     [0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319]
 :Jun_Jul_Aug     [0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319, 0.108696, 0.112319, 0.112319]
 :Sep_Oct_Nov     [0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989, 0.10989, 0.113553, 0.10989]

Verify that the sum per season is 1

julia
julia> sum.(weights)
╭───────────────────────────────╮
│ 4-element DimArray{Float64,1} │
├───────────────────────────────┴──────────────────────────────────────── dims ┐
  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{Symbol, Any} with 1 entry:
  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
└──────────────────────────────────────────────────────────────────────────────┘
 :Dec_Jan_Feb  1.0
 :Mar_Apr_May  1.0
 :Jun_Jul_Aug  1.0
 :Sep_Oct_Nov  1.0

weighted seasons ​

Now, let's weight the seasons

julia
julia> g_dsW = broadcast_dims.(*, weights, g_ds)
╭──────────────────────────────────────────────────────────────────────────────╮
│ 4-element DimArray{YAXArray{Float64, 3, Array{Float64, 3}, Tuple{DimensionalData.Dimensions.Ti{DimensionalData.Dimensions.Lookups.Sampled{CFTime.DateTimeNoLeap, Vector{CFTime.DateTimeNoLeap}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Irregular{Tuple{Nothing, Nothing}}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
├──────────────────────────────────────────────────────────────────────── dims ┤
  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{Symbol, Any} with 1 entry:
  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
└──────────────────────────────────────────────────────────────────────────────┘
 :Dec_Jan_Feb  …  [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 … 1.32149 1.33565; NaN NaN … 1.29564 1.32555; … ; NaN NaN … 1.3188 1.3169; NaN NaN … 1.17863 1.18434;;; NaN NaN … 1.29816 1.34218; NaN NaN … 1.30113 1.35483; … ; NaN NaN … 1.30142 1.31753; NaN NaN … 1.16258 1.17647;;; NaN NaN … 1.34549 1.37878; NaN NaN … 1.36836 1.41634; … ; NaN NaN … 1.34832 1.38364; NaN NaN … 1.17852 1.16713]
 :Mar_Apr_May     [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 … 1.87705 1.90365; NaN NaN … 2.30018 2.35432; … ; NaN NaN … 2.41049 2.43254; NaN NaN … 2.65105 2.69085;;; NaN NaN … 1.86457 1.90712; NaN NaN … 2.2894 2.34818; … ; NaN NaN … 2.3866 2.41241; NaN NaN … 2.61197 2.64976;;; NaN NaN … 1.89237 1.8984; NaN NaN … 2.29473 2.312; … ; NaN NaN … 2.36142 2.36126; NaN NaN … 2.56632 2.59085]
 :Jun_Jul_Aug     [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 … 3.21209 3.25153; NaN NaN … 3.23 3.28008; … ; NaN NaN … 3.12575 3.15532; NaN NaN … 3.2434 3.26274;;; NaN NaN … 3.17434 3.21699; NaN NaN … 3.18892 3.24375; … ; NaN NaN … 3.06755 3.1083; NaN NaN … 3.19241 3.22211;;; NaN NaN … 3.1437 3.15644; NaN NaN … 3.16631 3.18583; … ; NaN NaN … 3.03361 3.05846; NaN NaN … 3.16581 3.16824]
 :Sep_Oct_Nov     [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 … 2.97047 3.00388; NaN NaN … 2.77587 2.80759; … ; NaN NaN … 2.60175 2.60918; NaN NaN … 1.4947 1.52419;;; NaN NaN … 2.94534 2.97649; NaN NaN … 2.75891 2.79502; … ; NaN NaN … 2.57695 2.59212; NaN NaN … 1.46506 1.49909;;; NaN NaN … 2.9192 2.93743; NaN NaN … 2.7593 2.77687; … ; NaN NaN … 2.57873 2.63006; NaN NaN … 1.48367 1.50089]

apply a sum over the time dimension and drop it

julia
julia> weighted_g = sum.(g_dsW, dims = :Ti);

julia> weighted_g = dropdims.(weighted_g, dims=:Ti)
╭──────────────────────────────────────────────────────────────────────────────╮
│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
├──────────────────────────────────────────────────────────────────────── dims ┤
  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{Symbol, Any} with 1 entry:
  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
└──────────────────────────────────────────────────────────────────────────────┘
 :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 11.1181 11.372; NaN NaN … 11.3069 11.5743]
 :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.1242 21.0057; NaN NaN … 21.4198 21.1644]
 :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 28.2747 27.9362; NaN NaN … 28.6122 28.0465]
 :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 21.73 21.7341; NaN NaN … 21.986 21.959]

Calculate the differences

julia
julia> diff_g = map(.-, weighted_g, mean_g)
╭──────────────────────────────────────────────────────────────────────────────╮
│ 4-element DimArray{YAXArray{Float64, 2, Matrix{Float64}, Tuple{Dim{:x, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}, Dim{:y, DimensionalData.Dimensions.Lookups.Sampled{Int64, UnitRange{Int64}, DimensionalData.Dimensions.Lookups.ForwardOrdered, DimensionalData.Dimensions.Lookups.Regular{Int64}, DimensionalData.Dimensions.Lookups.Points, DimensionalData.Dimensions.Lookups.NoMetadata}}}, DimensionalData.Dimensions.Lookups.NoMetadata},1} │
├──────────────────────────────────────────────────────────────────────── dims ┤
  ↓ Ti Categorical{Symbol} [:Dec_Jan_Feb, :Mar_Apr_May, :Jun_Jul_Aug, :Sep_Oct_Nov] Unordered
├──────────────────────────────────────────────────────────────────── metadata ┤
  Dict{Symbol, Any} with 1 entry:
  :groupby => :Ti=>CyclicBins(month; cycle=12, step=3, start=12)…
└──────────────────────────────────────────────────────────────────────────────┘
 :Dec_Jan_Feb  …  [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.019016 -0.0115514; NaN NaN … -0.0183003 -0.00990356]
 :Mar_Apr_May     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.0121037 -0.0123091; NaN NaN … -0.0127077 -0.0117519]
 :Jun_Jul_Aug     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … -0.00709111 -0.00693713; NaN NaN … -0.00684233 -0.00722034]
 :Sep_Oct_Nov     [NaN NaN … NaN NaN; NaN NaN … NaN NaN; … ; NaN NaN … 0.0180572 0.0182373; NaN NaN … 0.0178074 0.018571]

All the previous steps are equivalent to calling the function defined at the top:

julia
mean_g, weighted_g, diff_g, seasons_g = weighted_seasons(ds)

Once all calculations are done we can plot the results with Makie.jl as follows:

julia
using CairoMakie
# define plot arguments/attributes
colorrange = (-30,30)
colormap = Reverse(:Spectral)
highclip = :red
lowclip = :grey15
cb_label =  ds_o.properties["long_name"]
"Surface air temperature"
julia
with_theme(theme_ggplot2()) do
    hm_o, hm_d, hm_w = nothing, nothing, nothing
    # the figure
    fig = Figure(; size = (850,500))
    axs = [Axis(fig[i,j], aspect=DataAspect()) for i in 1:3, j in 1:4]
    for (j, s) in enumerate(seasons_g)
        hm_o = heatmap!(axs[1,j], mean_g[Ti=At(s)]; colorrange, lowclip, highclip, colormap)
        hm_w = heatmap!(axs[2,j], weighted_g[Ti=At(s)]; colorrange, lowclip, highclip, colormap)
        hm_d = heatmap!(axs[3,j], diff_g[Ti=At(s)]; colorrange=(-0.1,0.1), lowclip, highclip,
            colormap=:diverging_bwr_20_95_c54_n256)
    end
    Colorbar(fig[1:2,5], hm_o, label=cb_label)
    Colorbar(fig[3,5], hm_d, label="Tair")
    hidedecorations!.(axs, grid=false, ticks=false, label=false)
    # some labels
    [axs[1,j].title = string.(s) for (j,s) in enumerate(seasons_g)]
    Label(fig[0,1:5], "Seasonal Surface Air Temperature", fontsize=18, font=:bold)
    axs[1,1].ylabel = "Unweighted"
    axs[2,1].ylabel = "Weighted"
    axs[3,1].ylabel = "Difference"
    colgap!(fig.layout, 5)
    rowgap!(fig.layout, 5)
    fig
end

which shows a good agreement with the results first published by Joe Hamman.