# ESMValTool
# recipe_seaborn.yml
---
documentation:
  title: Example recipe for the Seaborn diagnostic.

  description: >
    This recipe showcases the use of the Seaborn diagnostic that provides a
    high-level interface to Seaborn for ESMValTool recipes. For this, the input
    data is arranged into a single `pandas.DataFrame`, which is then used as
    input for the Seaborn function defined by the option `seaborn_func`. With
    the Seaborn diagnostic, arbitrary Seaborn plots can be created.

  authors:
    - schlund_manuel

  maintainer:
    - schlund_manuel

  references:
    - waskom21joss

  projects:
    - 4c
    - esm2025
    - isenes3
    - usmile


preprocessors:

  zonal_mean:
    zonal_statistics:
      operator: mean

  extract_ar6_regions:
    regrid:
      target_grid: 5x5
      scheme: linear
    extract_shape:
      shapefile: ar6
      crop: true
      decomposed: true
      ids:
        Name: &regions_to_extract
          - N.Europe
          - West&Central-Europe
          - Mediterranean
          - Equatorial.Pacific-Ocean
          - Equatorial.Atlantic-Ocean
          - Equatorial.Indic-Ocean
    convert_units:
      units: mm day-1


diagnostics:

  plot_temperature_vs_lat:
    description: Plot air temperature vs. latitude (pressure levels = colors).
    variables:
      zonal_mean_ta:
        short_name: ta
        mip: Amon
        preprocessor: zonal_mean
        project: CMIP6
        exp: historical
        timerange: '1991/2014'
    additional_datasets:
      - {dataset: CESM2-WACCM, grid: gn, ensemble: r1i1p1f1}
      - {dataset: GFDL-ESM4, grid: gr1, ensemble: r1i1p1f1}
    scripts:
      plot:
        script: seaborn_diag.py
        seaborn_func: relplot
        seaborn_kwargs:
          x: latitude
          y: zonal_mean_ta
          col: alias
          col_wrap: 2
          hue: air_pressure
          hue_norm: log
          palette: plasma
          linewidth: 0.0
          marker: o
          s: 1
        add_aux_coords: true
        data_frame_ops:
          eval: air_pressure = air_pressure / 100.0
        dropna_kwargs:
          axis: 0
          how: any
        legend_title: Pressure [hPa]
        plot_object_methods:
          set:
            xlabel: 'Latitude [°]'
            ylabel: 'Temperatute [K]'
          set_titles: '{col_name}'
        seaborn_settings:
          style: ticks
          rc:
            axes.titlepad: 15.0
        suptitle: Simulated Temperature (1991-2014)

  plot_precipitation_histograms_region:
    description: Plot precipitation histograms for different regions.
    variables:
      pr:
        mip: day
        preprocessor: extract_ar6_regions
        project: CMIP6
        exp: historical
        timerange: '2005/2014'
    additional_datasets:
      - {dataset: CESM2-WACCM, grid: gn, ensemble: r1i1p1f1}
      - {dataset: GFDL-ESM4, grid: gr1, ensemble: r1i1p1f1}
    scripts:
      plot:
        script: seaborn_diag.py
        seaborn_func: displot
        seaborn_kwargs:
          kind: hist
          stat: density
          bins: 300
          x: pr
          col: shape_id
          col_order: *regions_to_extract
          col_wrap: 3
          hue: alias
          facet_kws:
            sharey: false
        add_aux_coords: true
        dropna_kwargs:
          axis: 0
          how: any
        legend_title: Model
        plot_object_methods:
          set:
            xlabel: 'Precipitation [mm/day]'
            xlim: [0, 30]
          set_titles: '{col_name}'
        seaborn_settings:
          style: ticks
          rc:
            axes.titlepad: 15.0
        suptitle: Simulated Precipitation (2005-2014)