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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,20 @@ | ||
| using GitHub | ||
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| function getavatars(; n = 90) | ||
| contri = GitHub.contributors("JuliaDataCubes/YAXArrays.jl")[1] | ||
| avatars = [] | ||
| contributions = [] | ||
| urls = [] | ||
| for i in eachindex(contri) | ||
| push!(avatars, contri[i]["contributor"].avatar_url.uri) | ||
| push!(contributions, contri[i]["contributions"]) | ||
| push!(urls, contri[i]["contributor"].html_url.uri) | ||
| end | ||
| p = sortperm(contributions, rev=true) | ||
| return avatars[p], urls[p] | ||
| end | ||
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| avatars, urls = getavatars(; n = 90) | ||
| for (i,a) in enumerate(avatars) | ||
| println("""<a href="$(urls[i])" target="_blank"><img src="$(a)"></a>""") | ||
| end |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,57 +1,204 @@ | ||
| # GroupBy | ||
| In the following example we will use the `groupby` function to calculate temporal and spatial averages. | ||
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| ````@example groupby | ||
| The following examples will use the `groupby` function to calculate temporal and spatial averages. | ||
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| ````@example compareXarray | ||
| using YAXArrays, DimensionalData | ||
| using NetCDF | ||
| using Downloads | ||
| using Dates | ||
| using Statistics | ||
| ```` | ||
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| ````@example compare_with_xarray | ||
| url_path = "https://github.com/pydata/xarray-data/blob/master/" | ||
| ### Seasonal Averages from Time Series of Monthly Means | ||
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| The following reproduces the example in [xarray](https://docs.xarray.dev/en/stable/examples/monthly-means.html) by [Joe Hamman](https://github.com/jhamman/). | ||
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| 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. | ||
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| ### Download the data | ||
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| ````@example compareXarray | ||
| url_path = "https://github.com/pydata/xarray-data/raw/master/rasm.nc" | ||
| filename = Downloads.download(url_path, "rasm.nc") | ||
| ds = Cube(filename) | ||
| ds_o = Cube(filename) | ||
| nothing # hide | ||
| ```` | ||
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| ````@example compare_with_xarray | ||
| g = groupby(ds, Ti => season(; start=December)) | ||
| m_g = mean.(g, dims=:Ti) | ||
| dropdims.(m_g, dims=:Ti) | ||
| ```` | ||
| ::: warning | ||
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| # Create a named array for the month length | ||
| The following rebuild should not be necessary in the next release, plus is unpractical to use for large data sets. | ||
| Related to https://github.com/rafaqz/DimensionalData.jl/issues/642 | ||
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| ````@example compare_with_xarray | ||
| tempo = dims(ds, :Ti) | ||
| month_length = YAXArray((tempo,), daysinmonth.(tempo)) | ||
| ::: | ||
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| ````@example compareXarray | ||
| 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) | ||
| nothing # hide | ||
| ```` | ||
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| ::: info | ||
| ## GroupBy: seasons | ||
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| The same is possible with a pure DimArray, namely | ||
| ::: details function weighted_season(ds) ... end | ||
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| ````julia | ||
| month_length = DimArray(daysinmonth.(tempo), (tempo)) | ||
| function weighted_season(ds) | ||
| # calculate weights | ||
| tempo = dims(ds, :Ti) | ||
| month_length = YAXArray((tempo,), daysinmonth.(tempo)) | ||
| g_tempo = groupby(month_length, Ti => season(; start=December)) | ||
| sum_days = sum.(g_tempo, dims=:Ti) | ||
| weights = map(./, g_tempo, sum_days) | ||
| # unweighted seasons | ||
| g_ds = groupby(ds, Ti => season(; 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 = lookup(mean_g, :Ti) | ||
| return mean_g, weighted_g, diff_g, seasons | ||
| end | ||
| ```` | ||
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| ::: | ||
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| Now, we continue with the `groupby` operations as usual | ||
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| g_tempo = groupby(month_length, Ti => season(; start=December)) | ||
| ````@ansi compareXarray | ||
| g_ds = groupby(ds, Ti => season(; start=December)) | ||
| ```` | ||
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| sum_days = sum.(g_tempo, dims=:Ti) | ||
| And the mean per season is calculated as follows | ||
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| ````@ansi compareXarray | ||
| mean_g = mean.(g_ds, dims=:Ti) | ||
| ```` | ||
|
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| ### dropdims | ||
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| Note that now the time dimension has length one, we can use `dropdims` to remove it | ||
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| ````@ansi compareXarray | ||
| mean_g = dropdims.(mean_g, dims=:Ti) | ||
| ```` | ||
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| ### seasons | ||
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| Due to the `groupby` function we will obtain new grouping names, in this case in the time dimension: | ||
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| ````@example compareXarray | ||
| seasons = lookup(mean_g, :Ti) | ||
| ```` | ||
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| Next, we will weight this grouping by days/month in each group. | ||
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| ## GroupBy: weight | ||
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| Create a `YAXArray` for the month length | ||
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| ```julia | ||
| ````@example compareXarray | ||
| tempo = dims(ds, :Ti) | ||
| month_length = YAXArray((tempo,), daysinmonth.(tempo)) | ||
| ```` | ||
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| Now group it by season | ||
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| ````@ansi compareXarray | ||
| g_tempo = groupby(month_length, Ti => season(; start=December)) | ||
| ```` | ||
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| Get the number of days per season | ||
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| ````@ansi compareXarray | ||
| sum_days = sum.(g_tempo, dims=:Ti) | ||
| ```` | ||
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| ### weights | ||
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| Weight the seasonal groups by `sum_days` | ||
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| ````@ansi compareXarray | ||
| weights = map(./, g_tempo, sum_days) | ||
| ```` | ||
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| Verify that the sum per season is 1 | ||
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| ````@ansi compareXarray | ||
| sum.(weights) | ||
| ```` | ||
| ### weighted seasons | ||
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| g_ds = groupby(ds, Ti => season(; start=December)) | ||
| Now, let's weight the seasons | ||
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| ````@ansi compareXarray | ||
| g_dsW = broadcast_dims.(*, weights, g_ds) | ||
| ```` | ||
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| apply a `sum` over the time dimension and drop it | ||
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| ````@ansi compareXarray | ||
| weighted_g = sum.(g_dsW, dims = :Ti); | ||
| weighted_g = dropdims.(weighted_g, dims=:Ti) | ||
| ```` | ||
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| Calculate the differences | ||
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| ````@ansi compareXarray | ||
| diff_g = map(.-, weighted_g, mean_g) | ||
| ```` | ||
| g_ds .* weights | ||
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| All the previous steps are equivalent to calling the function defined at the top: | ||
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| ````julia | ||
| mean_g, weighted_g, diff_g, seasons = weighted_season(ds) | ||
| ```` | ||
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| Once all calculations are done we can plot the results with `Makie.jl` as follows: | ||
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| ````@example compareXarray | ||
| using CairoMakie | ||
| # define plot arguments/attributes | ||
| colorrange = (-30,30) | ||
| colormap = Reverse(:Spectral) | ||
| highclip = :red | ||
| lowclip = :grey15 | ||
| cb_label = ds_o.properties["long_name"] | ||
| ```` | ||
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| ````@example compareXarray | ||
| 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) | ||
| 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)] | ||
| 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 | ||
| ```` | ||
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| which shows a good agreement with the results first published by [Joe Hamman](https://github.com/jhamman/). |
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