From 5b48b6d0ba74d7f041a1ae9ad8e402b48fc5daff Mon Sep 17 00:00:00 2001 From: Kevin Phan <98072684+ph-kev@users.noreply.github.com> Date: Thu, 12 Sep 2024 17:52:23 -0700 Subject: [PATCH] Add Visualize.plot_bias! This functionality is already in ClimaCoupler and should belong in ClimaAnalysis. This commit ports over plot_bias! to ClimaAnalysis. --- NEWS.md | 21 ++++++ docs/src/api.md | 1 + docs/src/assets/bias_plot.png | Bin 0 -> 185606 bytes docs/src/visualize.md | 26 +++++++ ext/ClimaAnalysisGeoMakieExt.jl | 121 ++++++++++++++++++++++++++++++++ src/Visualize.jl | 2 + test/test_GeoMakieExt.jl | 36 ++++++++++ 7 files changed, 207 insertions(+) create mode 100644 docs/src/assets/bias_plot.png diff --git a/NEWS.md b/NEWS.md index 1a50ef7..05a5824 100644 --- a/NEWS.md +++ b/NEWS.md @@ -253,6 +253,27 @@ julia> units(se_var) "K^2" ``` +### Plotting bias + +To plot the bias and display information such as the root mean squared error (RMSE) and the +global bias in the plot, you use the function `plot_bias!(fig, sim, obs)`. In the example +below, we plot the bias between our simulation and some observations stored in +"ta\_1d\_average.nc". + +```julia +import ClimaAnalysis +import ClimaAnalysis.Visualize: plot_bias! +import GeoMakie +import CairoMakie + +obs_var = ClimaAnalysis.OutputVar("ta_1d_average.nc") +sim_var = ClimaAnalysis.get(ClimaAnalysis.simdir("simulation_output"), "ta") + +fig = CairoMakie.Figure() +plot_bias!(fig, sim_var, obs_var) +CairoMakie.save("myfigure.pdf", fig) +``` + ## Bug fixes - Increased the default value for `warp_string` to 72. diff --git a/docs/src/api.md b/docs/src/api.md index b39d372..684a2f3 100644 --- a/docs/src/api.md +++ b/docs/src/api.md @@ -106,4 +106,5 @@ Visualize.oceanmask Visualize.landmask Visualize.contour2D_on_globe! Visualize.heatmap2D_on_globe! +Visualize.plot_bias! ``` diff --git a/docs/src/assets/bias_plot.png b/docs/src/assets/bias_plot.png new file mode 100644 index 0000000000000000000000000000000000000000..d09ec706bc88288b178de9d512eebe32e0b703f1 GIT binary patch literal 185606 zcmeFZ^;eZ$7dDE8fRu`K2qIF_A+1}wyF)2SQMy4%Nu|3xm2MP48lk*0(Xw*XWyBF+ZF3TIQ zc@pI?exEf_UBt->arSKVaV{qQ5ngaS2YSh=i1poKz|9uYs zw+{aQx({BFKY8=}>$__Tbp##vt|CyS&R6BKyX1(RsI(k=y}Q(_P{;fxg|>SqlI0qF z)BBZ_wzhUMze9;$J)Li!YQFNDH=hFn+9O#AlAht9T84L0yolEY{BRIfe%{9tgXX3e z-`+pcqj~ay^uNo$IX$p8FqjR%qpG@B5`pg(6!QMYEq{N1S2s72l?>|kJnHLknUB}* z(#bj>ZM}4P>8wvIed{^jOXt161FawKrrOv3#Fr_fI zB|8Mf{I%}1hf~>%7L7-;+_&}r?`?5EnYgU~`eIsMo5RL9kcOX;_OalW4|BJtr{^$_ zkR8T<*IQUl^}@x#*uqzqo6ATx(5Z38T7kE;94qlqHu14D<3RQA`)-|`o!l0~fk8p8 zN;Rr?#DlZP{8oHBn02bGBqb%ky}ywvi8*)uzgsl6IanW8Qc`O3TQItZgGJ4VX@p5V zUFTrgm&A9h$FXpg+ZQg0R(6|&M62AicVS@xx24jUuu}Ta*QK;$YikPw7qr{6I&a;$&yBn=X=}F#8^p zIR%9>o8Wh=T`Tf 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The output might look something like: ![oceanmask](./assets/oceanmask.png) + +### Plotting bias + +After [computing the bias](@ref bias) between observational and simulation data, you may +want to plot the bias and display information such as the root mean squared error (RMSE) and +the global bias in the plot. To do this, you use the function [`plot_bias!(fig, sim, +obs)`](@ref Visualize.plot_bias!). In the example below, we plot the bias between our +simulation and some observations stored in "ta\_1d\_average.nc". + +```julia +import ClimaAnalysis +import ClimaAnalysis.Visualize: plot_bias! +import GeoMakie +import CairoMakie + +obs_var = ClimaAnalysis.OutputVar("ta_1d_average.nc") +sim_var = ClimaAnalysis.get(ClimaAnalysis.simdir("simulation_output"), "ta") + +fig = CairoMakie.Figure() +plot_bias!(fig, sim_var, obs_var) +CairoMakie.save("myfigure.pdf", fig) +``` + +The output produces something like: + +![biasplot](./assets/bias_plot.png) \ No newline at end of file diff --git a/ext/ClimaAnalysisGeoMakieExt.jl b/ext/ClimaAnalysisGeoMakieExt.jl index 4b74514..7c108dc 100644 --- a/ext/ClimaAnalysisGeoMakieExt.jl +++ b/ext/ClimaAnalysisGeoMakieExt.jl @@ -254,4 +254,125 @@ function Visualize.contour2D_on_globe!( ) end +""" + plot_bias!(fig::Makie.Figure, + sim::ClimaAnalysis.OutputVar, + obs::ClimaAnalysis.OutputVar; + cmap_extrema = extrema(ClimaAnalysis.bias(sim, obs).data), + p_loc = (1, 1), + plot_coastline = true, + plot_colorbar = true, + mask = nothing, + more_kwargs) + plot_bias!(grid_layout::Makie.GridLayout, + sim::ClimaAnalysis.OutputVar, + obs::ClimaAnalysis.OutputVar; + cmap_extrema = extrema(ClimaAnalysis.bias(sim, obs).data), + p_loc = (1, 1), + plot_coastline = true, + plot_colorbar = true, + mask = nothing, + more_kwargs) + + +Plot the bias (`sim.data - var.data`) on a projected geoid. The gloal bias and root mean +squared error (RMSE) are computed and can be found in the title of the plot. This function +plots the returned `OutputVar` of `ClimaAnalysis.bias(sim, obs)`. See also +[`ClimaAnalysis.bias`](@ref). + +The plot comes with labels, units, and a colorbar. This function uses a constrained colormap +based on the values of `cmap_extrema`. + +The dimensions have to be longitude and latitude. + +`mask` has to be an object that can be plotted by `Makie.poly`. `ClimaAnalysis` comes with +predefined masks, check out [`Visualize.oceanmask`](@ref) and [`Visualize.landmask`](@ref). + +!!! note + Masking does not affect the colorbar. If you have values defined beneath the map, they + can still affect the colorbar. + +Additional arguments to the plotting and axis functions +======================================================= + +`more_kwargs` can be a dictionary that maps symbols to additional options for: +- the axis (`:axis`) +- the plotting function (`:plot`) +- the colorbar (`:cb`) +- the coastline (`:coast`) +- the mask (`:mask`) + +The coastline is plotted from `GeoMakie.coastline` using the `lines!` plotting function. + +The values are splatted in the relevant functions. Populate them with a +Dictionary of `Symbol`s => values to pass additional options. +""" +function Visualize.plot_bias!( + place::MakiePlace, + sim::ClimaAnalysis.OutputVar, + obs::ClimaAnalysis.OutputVar; + cmap_extrema = extrema(ClimaAnalysis.bias(sim, obs).data), + p_loc = (1, 1), + plot_coastline = true, + plot_colorbar = true, + mask = nothing, + more_kwargs = Dict( + :plot => Dict(), + :cb => Dict(), + :axis => Dict(), + :coast => Dict(:color => :black), + :mask => Dict(), + ), +) + bias_var = ClimaAnalysis.bias(sim, obs) + global_bias = round(bias_var.attributes["global_bias"], sigdigits = 3) + rmse = round(ClimaAnalysis.global_rmse(sim, obs), sigdigits = 3) + units = ClimaAnalysis.units(bias_var) + + bias_var.attributes["long_name"] *= " (RMSE: $rmse $units, Global bias: $global_bias $units)" + min_level, max_level = cmap_extrema + + # Make sure that 0 is at the center + cmap = Visualize._constrained_cmap( + Makie.cgrad(:vik).colors, + min_level, + max_level; + categorical = true, + ) + nlevels = 11 + # Offset so that it covers 0 + levels = collect(range(min_level, max_level, length = nlevels)) + offset = levels[argmin(abs.(levels))] + levels = levels .- offset + ticklabels = map(x -> string(round(x; digits = 0)), levels) + ticks = (levels, ticklabels) + + default_kwargs = Dict( + :plot => Dict( + :colormap => cmap, + :levels => levels, + :extendhigh => :auto, + :extendlow => :auto, + ), + :cb => Dict(:ticks => ticks), + ) + + # Function for recursively merging two dictionaries if the values of the dictionaries + # are dictionaries and the values of those are also dictionaries and so on + # See: https://discourse.julialang.org/t/multi-layer-dict-merge/27261/6 + recursive_merge(x::AbstractDict...) = merge(recursive_merge, x...) + recursive_merge(x...) = x[end] + default_and_more_kwargs = recursive_merge(default_kwargs, more_kwargs) + + return Visualize.contour2D_on_globe!( + place, + bias_var; + p_loc = p_loc, + plot_coastline = plot_coastline, + plot_colorbar = plot_colorbar, + mask = mask, + more_kwargs = default_and_more_kwargs, + ) +end + end diff --git a/src/Visualize.jl b/src/Visualize.jl index e2e59ae..ac770bd 100644 --- a/src/Visualize.jl +++ b/src/Visualize.jl @@ -28,4 +28,6 @@ function contour2D_on_globe! end function heatmap2D_on_globe! end +function plot_bias! end + end diff --git a/test/test_GeoMakieExt.jl b/test/test_GeoMakieExt.jl index 5f5c49c..ce5f3a8 100644 --- a/test/test_GeoMakieExt.jl +++ b/test/test_GeoMakieExt.jl @@ -100,4 +100,40 @@ using OrderedCollections output_name = joinpath(tmp_dir, "test_contours2D_globe_with_oceanmask.png") Makie.save(output_name, fig5) + + # Test plot_bias + fig6 = Makie.Figure() + + lon = collect(range(-179.5, 179.5, 360)) + lat = collect(range(-89.5, 89.5, 180)) + data = collect(reshape(-32400:32399, (360, 180))) ./ (32399.0 / 5.0) + dims = OrderedDict(["lon" => lon, "lat" => lat]) + attribs = + Dict("long_name" => "idk", "short_name" => "short", "units" => "kg") + dim_attribs = OrderedDict([ + "lon" => Dict("units" => "deg"), + "lat" => Dict("units" => "deg"), + ]) + var = ClimaAnalysis.OutputVar(attribs, dims, dim_attribs, data) + + data_zero = zeros(length(lon), length(lat)) + var_zero = ClimaAnalysis.OutputVar(attribs, dims, dim_attribs, data_zero) + + ClimaAnalysis.Visualize.plot_bias!(fig6, var, var_zero) + output_name = joinpath(tmp_dir, "plot_bias.png") + Makie.save(output_name, fig6) + + # Test plot bias with keyword arguments + fig7 = Makie.Figure() + ClimaAnalysis.Visualize.plot_bias!( + fig7, + var, + var_zero, + more_kwargs = Dict( + :axis => Dict(:title => "no title"), + :plot => Dict(:extendhigh => nothing), + ), + ) + output_name = joinpath(tmp_dir, "plot_bias_kwargs.png") + Makie.save(output_name, fig7) end