Plotting maps with ocean bench

from pathlib import Path
if Path('.').absolute().name == 'notebooks':
    %cd ..

/raid/localscratch/qfebvre/oceanbench

import yaml
import inspect
from IPython.display import Markdown, display
from omegaconf import OmegaConf
import hvplot
import hvplot.xarray
hvplot.extension('matplotlib')
import hydra

def pprint_cfg(cfg):
    display(Markdown("""```yaml\n\n""" +yaml.dump(OmegaConf.to_container(cfg), default_flow_style=None, indent=2)+"""\n\n```"""))

def get_cfg(cfg_path, v=True):
    with hydra.initialize('../config', version_base='1.3'):
        cfg = hydra.compose(cfg_path)
    if v: pprint_cfg(cfg)
    return cfg

task = 'osse_gf_nadir'
method = '4dvarnet'

lb_cfg = get_cfg(f'task/{task}/leaderboard', v=False)
lb_cfg.method = method
lb = hydra.utils.call(lb_cfg.outputs)

hydra.utils.call(lb_cfg.outputs.eval_ds.dataarrays.ref.inp)()

<xarray.DataArray 'ssh' (time: 365, lat: 781, lon: 1721)>
[490596865 values with dtype=float64]
Coordinates:
  * lon      (lon) float64 -79.0 -78.95 -78.9 -78.85 -78.8 ... 6.85 6.9 6.95 7.0
  * lat      (lat) float64 26.0 26.05 26.1 26.15 26.2 ... 64.85 64.9 64.95 65.0
  * time     (time) datetime64[ns] 2012-10-01T12:00:00 ... 2013-09-30T12:00:00
Attributes:
    units:          m
    standard_name:  sea_surface_height
    long_name:      Sea Surface Height

xarray.DataArray

'ssh'

• time: 365
• lat: 781
• lon: 1721

• ...

  [490596865 values with dtype=float64]

• Coordinates: (3)
  • lon
    (lon)
    float64
    -79.0 -78.95 -78.9 ... 6.9 6.95 7.0

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    Longitude

    array([-79.  , -78.95, -78.9 , ...,   6.9 ,   6.95,   7.  ])

  • lat
    (lat)
    float64
    26.0 26.05 26.1 ... 64.9 64.95 65.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    Latitude

    array([26.  , 26.05, 26.1 , ..., 64.9 , 64.95, 65.  ])

  • time
    (time)
    datetime64[ns]
    2012-10-01T12:00:00 ... 2013-09-...

    array(['2012-10-01T12:00:00.000000000', '2012-10-02T12:00:00.000000000',
           '2012-10-03T12:00:00.000000000', ..., '2013-09-28T12:00:00.000000000',
           '2013-09-29T12:00:00.000000000', '2013-09-30T12:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (3)
  • lon
    PandasIndex

    PandasIndex(Index([             -79.0,             -78.95,              -78.9,
           -78.85000000000001, -78.80000000000001, -78.75000000000001,
           -78.70000000000002, -78.65000000000002, -78.60000000000002,
           -78.55000000000003,
           ...
            6.549999999995123,  6.599999999995134,  6.649999999995146,
           6.6999999999951285, 6.7499999999951115,  6.799999999995123,
            6.849999999995134,  6.899999999995117,    6.9499999999951,
           6.9999999999951115],
          dtype='float64', name='lon', length=1721))

  • lat
    PandasIndex

    PandasIndex(Index([              26.0, 26.049999999999997, 26.099999999999994,
           26.150000000000006, 26.200000000000003,              26.25,
            26.30000000000001,  26.35000000000001, 26.400000000000006,
           26.450000000000003,
           ...
            64.55000000000055,  64.60000000000053,  64.65000000000055,
            64.70000000000056,  64.75000000000057,  64.80000000000055,
            64.85000000000053,  64.90000000000055,  64.95000000000056,
            65.00000000000057],
          dtype='float64', name='lat', length=781))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2012-10-01 12:00:00', '2012-10-02 12:00:00',
                   '2012-10-03 12:00:00', '2012-10-04 12:00:00',
                   '2012-10-05 12:00:00', '2012-10-06 12:00:00',
                   '2012-10-07 12:00:00', '2012-10-08 12:00:00',
                   '2012-10-09 12:00:00', '2012-10-10 12:00:00',
                   ...
                   '2013-09-21 12:00:00', '2013-09-22 12:00:00',
                   '2013-09-23 12:00:00', '2013-09-24 12:00:00',
                   '2013-09-25 12:00:00', '2013-09-26 12:00:00',
                   '2013-09-27 12:00:00', '2013-09-28 12:00:00',
                   '2013-09-29 12:00:00', '2013-09-30 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=365, freq=None))

• Attributes: (3)

  units :

  m

  standard_name :

  sea_surface_height

  long_name :

  Sea Surface Height

list(lb.plots.maps)

['ssh_study',
 'ssh_ref',
 'ke_study',
 'ke_ref',
 'vort_r_study',
 'vort_r_ref',
 'strain_study',
 'strain_ref']

plot_ds = lb.plots.build_ds.maps()

    
def trim(plots, ncols=4):
    nrows = len(plots)/ncols
    trimed_plots = []
    for i, p in enumerate(plots):
        img, contour = dict(p.items()).values()
        
        img = img.opts(
            colorbar=(i%ncols) == (ncols-1),
            xticks= None if (i >= ( len(plots) - ncols)) else False,
            xlabel= None if  (i >= ( len(plots) - ncols)) else '',
            ylabel= None if  (i%ncols) ==0 else '',
            yticks= None if  (i%ncols) ==0 else False,
        )    
        trimed_plots.append((img * contour).opts(**p.opts.get().kwargs))
    return functools.reduce(operator.add, trimed_plots).opts(sublabel_format='').opts(vspace=0.1, hspace=0.1)

def plot_both(quantity):
    
    return (
        lb.plots.maps[f'{quantity}_study'](plot_ds).opts(title=method) +
         lb.plots.maps[f'{quantity}_ref'](plot_ds).opts(title='NATL60')
    ).opts(sublabel_format='')
    
plot_ds

<xarray.Dataset>
Dimensions:  (time: 42, lat: 200, lon: 200)
Coordinates:
  * time     (time) datetime64[ns] 2012-10-22 2012-10-23 ... 2012-12-02
  * lon      (lon) float64 -65.0 -64.95 -64.9 -64.85 ... -55.15 -55.1 -55.05
  * lat      (lat) float64 33.0 33.05 33.1 33.15 33.2 ... 42.8 42.85 42.9 42.95
Data variables:
    ref      (time, lat, lon) float64 nan 0.6639 0.6693 ... -0.2023 -0.2091
    study    (time, lat, lon) float64 nan 0.6685 0.6754 ... -0.1738 -0.1766

xarray.Dataset

• Dimensions:
  • time: 42
  • lat: 200
  • lon: 200
• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    2012-10-22 ... 2012-12-02

    array(['2012-10-22T00:00:00.000000000', '2012-10-23T00:00:00.000000000',
           '2012-10-24T00:00:00.000000000', '2012-10-25T00:00:00.000000000',
           '2012-10-26T00:00:00.000000000', '2012-10-27T00:00:00.000000000',
           '2012-10-28T00:00:00.000000000', '2012-10-29T00:00:00.000000000',
           '2012-10-30T00:00:00.000000000', '2012-10-31T00:00:00.000000000',
           '2012-11-01T00:00:00.000000000', '2012-11-02T00:00:00.000000000',
           '2012-11-03T00:00:00.000000000', '2012-11-04T00:00:00.000000000',
           '2012-11-05T00:00:00.000000000', '2012-11-06T00:00:00.000000000',
           '2012-11-07T00:00:00.000000000', '2012-11-08T00:00:00.000000000',
           '2012-11-09T00:00:00.000000000', '2012-11-10T00:00:00.000000000',
           '2012-11-11T00:00:00.000000000', '2012-11-12T00:00:00.000000000',
           '2012-11-13T00:00:00.000000000', '2012-11-14T00:00:00.000000000',
           '2012-11-15T00:00:00.000000000', '2012-11-16T00:00:00.000000000',
           '2012-11-17T00:00:00.000000000', '2012-11-18T00:00:00.000000000',
           '2012-11-19T00:00:00.000000000', '2012-11-20T00:00:00.000000000',
           '2012-11-21T00:00:00.000000000', '2012-11-22T00:00:00.000000000',
           '2012-11-23T00:00:00.000000000', '2012-11-24T00:00:00.000000000',
           '2012-11-25T00:00:00.000000000', '2012-11-26T00:00:00.000000000',
           '2012-11-27T00:00:00.000000000', '2012-11-28T00:00:00.000000000',
           '2012-11-29T00:00:00.000000000', '2012-11-30T00:00:00.000000000',
           '2012-12-01T00:00:00.000000000', '2012-12-02T00:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    float64
    -65.0 -64.95 -64.9 ... -55.1 -55.05

    array([-65.  , -64.95, -64.9 , -64.85, -64.8 , -64.75, -64.7 , -64.65, -64.6 ,
           -64.55, -64.5 , -64.45, -64.4 , -64.35, -64.3 , -64.25, -64.2 , -64.15,
           -64.1 , -64.05, -64.  , -63.95, -63.9 , -63.85, -63.8 , -63.75, -63.7 ,
           -63.65, -63.6 , -63.55, -63.5 , -63.45, -63.4 , -63.35, -63.3 , -63.25,
           -63.2 , -63.15, -63.1 , -63.05, -63.  , -62.95, -62.9 , -62.85, -62.8 ,
           -62.75, -62.7 , -62.65, -62.6 , -62.55, -62.5 , -62.45, -62.4 , -62.35,
           -62.3 , -62.25, -62.2 , -62.15, -62.1 , -62.05, -62.  , -61.95, -61.9 ,
           -61.85, -61.8 , -61.75, -61.7 , -61.65, -61.6 , -61.55, -61.5 , -61.45,
           -61.4 , -61.35, -61.3 , -61.25, -61.2 , -61.15, -61.1 , -61.05, -61.  ,
           -60.95, -60.9 , -60.85, -60.8 , -60.75, -60.7 , -60.65, -60.6 , -60.55,
           -60.5 , -60.45, -60.4 , -60.35, -60.3 , -60.25, -60.2 , -60.15, -60.1 ,
           -60.05, -60.  , -59.95, -59.9 , -59.85, -59.8 , -59.75, -59.7 , -59.65,
           -59.6 , -59.55, -59.5 , -59.45, -59.4 , -59.35, -59.3 , -59.25, -59.2 ,
           -59.15, -59.1 , -59.05, -59.  , -58.95, -58.9 , -58.85, -58.8 , -58.75,
           -58.7 , -58.65, -58.6 , -58.55, -58.5 , -58.45, -58.4 , -58.35, -58.3 ,
           -58.25, -58.2 , -58.15, -58.1 , -58.05, -58.  , -57.95, -57.9 , -57.85,
           -57.8 , -57.75, -57.7 , -57.65, -57.6 , -57.55, -57.5 , -57.45, -57.4 ,
           -57.35, -57.3 , -57.25, -57.2 , -57.15, -57.1 , -57.05, -57.  , -56.95,
           -56.9 , -56.85, -56.8 , -56.75, -56.7 , -56.65, -56.6 , -56.55, -56.5 ,
           -56.45, -56.4 , -56.35, -56.3 , -56.25, -56.2 , -56.15, -56.1 , -56.05,
           -56.  , -55.95, -55.9 , -55.85, -55.8 , -55.75, -55.7 , -55.65, -55.6 ,
           -55.55, -55.5 , -55.45, -55.4 , -55.35, -55.3 , -55.25, -55.2 , -55.15,
           -55.1 , -55.05])

  • lat
    (lat)
    float64
    33.0 33.05 33.1 ... 42.9 42.95

    array([33.  , 33.05, 33.1 , 33.15, 33.2 , 33.25, 33.3 , 33.35, 33.4 , 33.45,
           33.5 , 33.55, 33.6 , 33.65, 33.7 , 33.75, 33.8 , 33.85, 33.9 , 33.95,
           34.  , 34.05, 34.1 , 34.15, 34.2 , 34.25, 34.3 , 34.35, 34.4 , 34.45,
           34.5 , 34.55, 34.6 , 34.65, 34.7 , 34.75, 34.8 , 34.85, 34.9 , 34.95,
           35.  , 35.05, 35.1 , 35.15, 35.2 , 35.25, 35.3 , 35.35, 35.4 , 35.45,
           35.5 , 35.55, 35.6 , 35.65, 35.7 , 35.75, 35.8 , 35.85, 35.9 , 35.95,
           36.  , 36.05, 36.1 , 36.15, 36.2 , 36.25, 36.3 , 36.35, 36.4 , 36.45,
           36.5 , 36.55, 36.6 , 36.65, 36.7 , 36.75, 36.8 , 36.85, 36.9 , 36.95,
           37.  , 37.05, 37.1 , 37.15, 37.2 , 37.25, 37.3 , 37.35, 37.4 , 37.45,
           37.5 , 37.55, 37.6 , 37.65, 37.7 , 37.75, 37.8 , 37.85, 37.9 , 37.95,
           38.  , 38.05, 38.1 , 38.15, 38.2 , 38.25, 38.3 , 38.35, 38.4 , 38.45,
           38.5 , 38.55, 38.6 , 38.65, 38.7 , 38.75, 38.8 , 38.85, 38.9 , 38.95,
           39.  , 39.05, 39.1 , 39.15, 39.2 , 39.25, 39.3 , 39.35, 39.4 , 39.45,
           39.5 , 39.55, 39.6 , 39.65, 39.7 , 39.75, 39.8 , 39.85, 39.9 , 39.95,
           40.  , 40.05, 40.1 , 40.15, 40.2 , 40.25, 40.3 , 40.35, 40.4 , 40.45,
           40.5 , 40.55, 40.6 , 40.65, 40.7 , 40.75, 40.8 , 40.85, 40.9 , 40.95,
           41.  , 41.05, 41.1 , 41.15, 41.2 , 41.25, 41.3 , 41.35, 41.4 , 41.45,
           41.5 , 41.55, 41.6 , 41.65, 41.7 , 41.75, 41.8 , 41.85, 41.9 , 41.95,
           42.  , 42.05, 42.1 , 42.15, 42.2 , 42.25, 42.3 , 42.35, 42.4 , 42.45,
           42.5 , 42.55, 42.6 , 42.65, 42.7 , 42.75, 42.8 , 42.85, 42.9 , 42.95])

• Data variables: (2)
  • ref
    (time, lat, lon)
    float64
    nan 0.6639 ... -0.2023 -0.2091

    array([[[        nan,  0.6638808 ,  0.66934322, ...,  0.59784135,
              0.59457589,  0.59104008],
            [        nan,  0.6492447 ,  0.6558567 , ...,  0.60204099,
              0.59884316,  0.59534992],
            [        nan,  0.63267455,  0.63993128, ...,  0.60578643,
              0.60269106,  0.59925064],
            ...,
            [        nan, -0.04433141, -0.04059621, ..., -0.00150743,
             -0.00760931, -0.01490989],
            [        nan, -0.04789889, -0.04469836, ..., -0.00525826,
             -0.01125153, -0.01826751],
            [        nan, -0.05263614, -0.04857453, ..., -0.00976781,
             -0.01591676, -0.02285879]],
    
           [[        nan,  0.6706636 ,  0.67620504, ...,  0.59118519,
              0.58802493,  0.58458904],
            [        nan,  0.65540734,  0.66249362, ...,  0.59570264,
              0.59264381,  0.58921157],
            [        nan,  0.63790289,  0.64589037, ...,  0.59986898,
              0.59692651,  0.59358696],
    ...
            [        nan,  0.01334187,  0.01341996, ..., -0.18653382,
             -0.19552841, -0.20208921],
            [        nan,  0.01600266,  0.01390729, ..., -0.19176892,
             -0.1992824 , -0.20521918],
            [        nan,  0.01684615,  0.01653204, ..., -0.19488474,
             -0.20128802, -0.20725713]],
    
           [[        nan,  0.44462685,  0.42664687, ...,  0.6212859 ,
              0.62483907,  0.62940615],
            [        nan,  0.44325273,  0.4248378 , ...,  0.62688544,
              0.63223637,  0.63604384],
            [        nan,  0.44357916,  0.42492603, ...,  0.6344688 ,
              0.63941621,  0.64200644],
            ...,
            [        nan,  0.02853629,  0.02477731, ..., -0.19185824,
             -0.19734416, -0.20159991],
            [        nan,  0.03389804,  0.03045671, ..., -0.19515703,
             -0.20009082, -0.20529056],
            [        nan,  0.0350364 ,  0.03516656, ..., -0.19669334,
             -0.20228467, -0.2090678 ]]])

  • study
    (time, lat, lon)
    float64
    nan 0.6685 ... -0.1738 -0.1766

    array([[[            nan,  6.68533847e-01,  6.75355218e-01, ...,
              5.90600759e-01,  5.88206992e-01,  5.85220657e-01],
            [            nan,  6.53351195e-01,  6.60618186e-01, ...,
              5.95323607e-01,  5.92412591e-01,  5.89891359e-01],
            [            nan,  6.36736020e-01,  6.44427374e-01, ...,
              5.98553926e-01,  5.96705407e-01,  5.93788661e-01],
            ...,
            [            nan, -1.13245584e-02, -7.26694241e-03, ...,
             -2.23759264e-02, -2.79701576e-02, -3.30549218e-02],
            [            nan, -1.41635761e-02, -9.73928347e-03, ...,
             -2.76025310e-02, -3.27964500e-02, -3.74009907e-02],
            [            nan, -1.86466910e-02, -1.30523406e-02, ...,
             -3.40565965e-02, -3.75329033e-02, -4.24349830e-02]],
    
           [[            nan,  6.77092729e-01,  6.81345900e-01, ...,
              5.89319789e-01,  5.86470660e-01,  5.84185786e-01],
            [            nan,  6.63466121e-01,  6.68174195e-01, ...,
              5.93423943e-01,  5.91232779e-01,  5.88808983e-01],
            [            nan,  6.48047358e-01,  6.52931970e-01, ...,
              5.97403962e-01,  5.94932206e-01,  5.92748411e-01],
    ...
            [            nan, -2.98093384e-03, -3.83675742e-03, ...,
             -1.64142699e-01, -1.68548063e-01, -1.72136402e-01],
            [            nan, -1.82992341e-03, -1.51305050e-03, ...,
             -1.66401223e-01, -1.70590551e-01, -1.73752568e-01],
            [            nan, -1.21544172e-03, -4.59349615e-04, ...,
             -1.68582100e-01, -1.72550086e-01, -1.75735065e-01]],
    
           [[            nan,  4.31401919e-01,  4.16137284e-01, ...,
              6.36655353e-01,  6.39078727e-01,  6.41552135e-01],
            [            nan,  4.28765648e-01,  4.14751815e-01, ...,
              6.40156390e-01,  6.43174487e-01,  6.46329166e-01],
            [            nan,  4.27548779e-01,  4.14947811e-01, ...,
              6.43787023e-01,  6.47602192e-01,  6.50812574e-01],
            ...,
            [            nan, -8.88191069e-03, -8.58047114e-03, ...,
             -1.66413086e-01, -1.70660161e-01, -1.74019082e-01],
            [            nan, -7.33254236e-03, -6.62526012e-03, ...,
             -1.68560750e-01, -1.72044861e-01, -1.75294410e-01],
            [            nan, -7.01673000e-03, -6.08301783e-03, ...,
             -1.70023280e-01, -1.73761769e-01, -1.76554003e-01]]])

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2012-10-22', '2012-10-23', '2012-10-24', '2012-10-25',
                   '2012-10-26', '2012-10-27', '2012-10-28', '2012-10-29',
                   '2012-10-30', '2012-10-31', '2012-11-01', '2012-11-02',
                   '2012-11-03', '2012-11-04', '2012-11-05', '2012-11-06',
                   '2012-11-07', '2012-11-08', '2012-11-09', '2012-11-10',
                   '2012-11-11', '2012-11-12', '2012-11-13', '2012-11-14',
                   '2012-11-15', '2012-11-16', '2012-11-17', '2012-11-18',
                   '2012-11-19', '2012-11-20', '2012-11-21', '2012-11-22',
                   '2012-11-23', '2012-11-24', '2012-11-25', '2012-11-26',
                   '2012-11-27', '2012-11-28', '2012-11-29', '2012-11-30',
                   '2012-12-01', '2012-12-02'],
                  dtype='datetime64[ns]', name='time', freq='D'))

  • lon
    PandasIndex

    PandasIndex(Index([              -65.0,              -64.95,               -64.9,
            -64.85000000000001,  -64.80000000000001,  -64.75000000000001,
            -64.70000000000002,  -64.65000000000002,  -64.60000000000002,
            -64.55000000000003,
           ...
            -55.50000000000054,  -55.45000000000054, -55.400000000000546,
            -55.35000000000055,  -55.30000000000055, -55.250000000000554,
            -55.20000000000056,  -55.15000000000056,  -55.10000000000056,
           -55.050000000000566],
          dtype='float64', name='lon', length=200))

  • lat
    PandasIndex

    PandasIndex(Index([              33.0,              33.05, 33.099999999999994,
            33.14999999999999,  33.19999999999999, 33.249999999999986,
            33.29999999999998,  33.34999999999998,  33.39999999999998,
           33.449999999999974,
           ...
            42.49999999999946,  42.54999999999946, 42.599999999999454,
            42.64999999999945,  42.69999999999945, 42.749999999999446,
            42.79999999999944,  42.84999999999944,  42.89999999999944,
           42.949999999999434],
          dtype='float64', name='lat', length=200))

• Attributes: (0)

quantity = 'ssh'
plot_both(quantity).opts(title=f"Quantity: {quantity}   Task: {task}")

quantity = 'ke'
plot_both(quantity).opts(title=f"Quantity: {quantity}   Task: {task}")

quantity = 'vort_r'
plot_both(quantity).opts(title=f"Quantity: {quantity}   Task: {task}")

quantity = 'strain'
plot_both(quantity).opts(title=f"Quantity: {quantity}   Task: {task}")

list(lb_cfg.results.outputs.plots.maps.methods)

['duacs',
 'bfn',
 'miost',
 'dymost',
 'nerf_ffn',
 'nerf_siren',
 'nerf_mlp',
 '4dvarnet']

quantity = 'vort_r'
plots=[]
for method in list(lb_cfg.results.outputs.plots.maps.methods):
    print(method)    
    lb_cfg = get_cfg(f'task/{task}/leaderboard', v=False)
    lb_cfg.method = method
    lb = hydra.utils.call(lb_cfg.outputs)
    plot_ds = lb.plots.build_ds.maps()
    plots.append(lb.plots.maps[f'{quantity}_study'](plot_ds).opts(title=method))

duacs
bfn
miost
dymost
nerf_ffn
nerf_siren
nerf_mlp
4dvarnet

import functools
import operator
functools.reduce(operator.add, plots).opts(sublabel_format='')

    
def trim(plots, ncols=4):
    nrows = len(plots)/ncols
    trimed_plots = []
    for i, p in enumerate(plots):
        img, contour = dict(p.items()).values()
        
        img = img.opts(
            colorbar=(i%ncols) == (ncols-1),
            xticks= None if (i >= ( len(plots) - ncols)) else False,
            xlabel= None if  (i >= ( len(plots) - ncols)) else '',
            ylabel= None if  (i%ncols) ==0 else '',
            yticks= None if  (i%ncols) ==0 else False,
        )    
        trimed_plots.append((img * contour).opts(**p.opts.get().kwargs))
    return functools.reduce(operator.add, trimed_plots).opts(sublabel_format='').opts(vspace=0.1, hspace=0.1)

trim(plots, ncols=4).opts(title="Different methods reconstructions on a single task")

quantity = 'vort_r'
method = '4dvarnet'
plots=[]
for task in [
    'osse_gf_nadir',
    'osse_gf_nadirswot',
    'osse_gf_nadir_sst',
    'ose_gf'
]:
    print(task)    
    lb_cfg = get_cfg(f'task/{task}/leaderboard', v=False)
    lb_cfg.method = method
    lb = hydra.utils.call(lb_cfg.outputs)
    plot_ds = lb.plots.build_ds.maps()
    plots.append(lb.plots.maps[f'{quantity}_study'](plot_ds).opts(title=task))

osse_gf_nadir
osse_gf_nadirswot
osse_gf_nadir_sst
ose_gf

trim(plots, ncols=4).opts(title="4dvarnet method reconstructions on different tasks")