Visualize RiOMAR dggs-transformed data with xddgs and lonboard

Context

Purpose

The goal is to visualize the DGGS transformed RiOMar data with xdggs and lonboard.

Description

In this notebook, we will:

• Open DGGS RiOMar dataset

• Visualize it using a colorblind-friendly colormap

Contributions

Notebook

• Anne Fouilloux, Simula Research Laboratory (Norway), @annefou

Bibliography and other interesting resources

• xdggs

• lonboard

Install Libraries

• We need to use a newer version of xdggs

pip install git+https://github.com/xarray-contrib/xdggs cmcrameri

import xdggs
import xarray as xr
from cmcrameri import cm

Open file with xdggs

nside = 4096
dout = xr.open_zarr("https://data-fair2adapt.ifremer.fr/riomar/small_healpix.zarr")
dout = (
    dout.load()
    .drop_vars(["latitude", "longitude"])
    .stack(cell=["x", "y"], create_index=False)
)
dout

<xarray.Dataset> Size: 391MB
Dimensions:       (cells: 242076, s_rho: 40, time_counter: 5)
Coordinates:
    cell_ids      (cells) uint64 2MB 11190229 11190230 ... 57954657 57954658
    resolution    float64 8B 0.0002498
  * s_rho         (s_rho) float32 160B -0.9875 -0.9625 ... -0.0375 -0.0125
  * time_counter  (time_counter) datetime64[ns] 40B 2004-01-01T00:58:30 ... 2...
    time_instant  (time_counter) datetime64[ns] 40B 2004-01-01T00:58:30 ... 2...
Dimensions without coordinates: cells
Data variables:
    ocean_mask    (cells) float64 2MB nan nan nan nan nan ... 1.0 1.0 1.0 1.0
    temp          (time_counter, s_rho, cells) float64 387MB nan nan ... 10.47
Attributes:
    grid_type:  healpix
    lat:        0
    level:      12
    lon:        0
    nside:      4096
    rot_lat:    0
    rot_lon:    0

xarray.Dataset

• Dimensions:
  • cells: 242076
  • s_rho: 40
  • time_counter: 5
• Coordinates: (5)
  • cell_ids
    (cells)
    uint64
    11190229 11190230 ... 57954658

    grid_name :

    healpix

    level :

    12

    nest :

    True

    array([11190229, 11190230, 11190231, ..., 57954656, 57954657, 57954658],
          dtype=uint64)

  • resolution
    ()
    float64
    0.0002498

    units :

    rad

    array(0.00024984)

  • s_rho
    (s_rho)
    float32
    -0.9875 -0.9625 ... -0.0375 -0.0125

    name :

    s_rho

    array([-0.9875, -0.9625, -0.9375, -0.9125, -0.8875, -0.8625, -0.8375, -0.8125,
           -0.7875, -0.7625, -0.7375, -0.7125, -0.6875, -0.6625, -0.6375, -0.6125,
           -0.5875, -0.5625, -0.5375, -0.5125, -0.4875, -0.4625, -0.4375, -0.4125,
           -0.3875, -0.3625, -0.3375, -0.3125, -0.2875, -0.2625, -0.2375, -0.2125,
           -0.1875, -0.1625, -0.1375, -0.1125, -0.0875, -0.0625, -0.0375, -0.0125],
          dtype=float32)

  • time_counter
    (time_counter)
    datetime64[ns]
    2004-01-01T00:58:30 ... 2004-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array(['2004-01-01T00:58:30.000000000', '2004-01-01T01:58:30.000000000',
           '2004-01-01T02:58:30.000000000', '2004-01-01T03:58:30.000000000',
           '2004-01-01T04:58:30.000000000'], dtype='datetime64[ns]')

  • time_instant
    (time_counter)
    datetime64[ns]
    2004-01-01T00:58:30 ... 2004-01-...

    bounds :

    time_instant_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array(['2004-01-01T00:58:30.000000000', '2004-01-01T01:58:30.000000000',
           '2004-01-01T02:58:30.000000000', '2004-01-01T03:58:30.000000000',
           '2004-01-01T04:58:30.000000000'], dtype='datetime64[ns]')

• Data variables: (2)
  • ocean_mask
    (cells)
    float64
    nan nan nan nan ... 1.0 1.0 1.0 1.0

    array([nan, nan, nan, ...,  1.,  1.,  1.])

  • temp
    (time_counter, s_rho, cells)
    float64
    nan nan nan ... 10.46 10.46 10.47

    array([[[        nan,         nan,         nan, ...,  8.40665577,
              7.5196235 ,  7.48556392],
            [        nan,         nan,         nan, ...,  8.92685265,
              8.30246179,  8.24298586],
            [        nan,         nan,         nan, ...,  9.41522822,
              9.00516139,  9.02782893],
            ...,
            [        nan,         nan,         nan, ..., 10.49678732,
             10.48821423, 10.49680822],
            [        nan,         nan,         nan, ..., 10.49376149,
             10.48523489, 10.49383764],
            [        nan,         nan,         nan, ..., 10.48795527,
             10.47952207, 10.48813372]],
    
           [[        nan,         nan,         nan, ...,  8.38582531,
              7.44468721,  7.45043894],
            [        nan,         nan,         nan, ...,  8.99761958,
              8.35295066,  8.31064068],
            [        nan,         nan,         nan, ...,  9.43605236,
              9.01841053,  9.03612531],
    ...
            [        nan,         nan,         nan, ..., 10.47658909,
             10.47104691, 10.47895002],
            [        nan,         nan,         nan, ..., 10.47302394,
             10.46746534, 10.47539515],
            [        nan,         nan,         nan, ..., 10.46676472,
             10.46118368, 10.46914509]],
    
           [[        nan,         nan,         nan, ...,  8.3069679 ,
              7.37576034,  7.42223623],
            [        nan,         nan,         nan, ...,  9.22446874,
              8.6901802 ,  8.677034  ],
            [        nan,         nan,         nan, ...,  9.59169247,
              9.28155471,  9.25587101],
            ...,
            [        nan,         nan,         nan, ..., 10.47298555,
             10.46860344, 10.47630086],
            [        nan,         nan,         nan, ..., 10.46968863,
             10.46530817, 10.47301467],
            [        nan,         nan,         nan, ..., 10.46390068,
             10.45951691, 10.46722661]]])

• Indexes: (2)
  • s_rho
    PandasIndex

    PandasIndex(Index([   -0.987500011920929,   -0.9624999761581421,               -0.9375,
             -0.9125000238418579,    -0.887499988079071,    -0.862500011920929,
             -0.8374999761581421,               -0.8125,   -0.7875000238418579,
              -0.762499988079071,    -0.737500011920929,   -0.7124999761581421,
                         -0.6875,   -0.6625000238418579,    -0.637499988079071,
              -0.612500011920929,   -0.5874999761581421,               -0.5625,
             -0.5375000238418579,    -0.512499988079071,  -0.48750001192092896,
             -0.4625000059604645,               -0.4375,   -0.4124999940395355,
            -0.38749998807907104,  -0.36250001192092896,   -0.3375000059604645,
                         -0.3125,   -0.2874999940395355,  -0.26249998807907104,
            -0.23749999701976776,  -0.21250000596046448,               -0.1875,
            -0.16249999403953552,  -0.13750000298023224,  -0.11249999701976776,
            -0.08749999850988388,               -0.0625,  -0.03750000149011612,
           -0.012500000186264515],
          dtype='float32', name='s_rho'))

  • time_counter
    PandasIndex

    PandasIndex(DatetimeIndex(['2004-01-01 00:58:30', '2004-01-01 01:58:30',
                   '2004-01-01 02:58:30', '2004-01-01 03:58:30',
                   '2004-01-01 04:58:30'],
                  dtype='datetime64[ns]', name='time_counter', freq=None))

• Attributes: (7)

  grid_type :

  healpix

  lat :

  0

  level :

  12

  lon :

  0

  nside :

  4096

  rot_lat :

  0

  rot_lon :

  0

dout_idx = dout.dggs.decode(
    {
        "grid_name": "healpix",
        "nside": nside,
        "nest": True,
    }
)
cell_centers = dout_idx.dggs.cell_centers()
dout_idx = dout_idx.assign_coords(
    cell_centers.coords
)
dout_idx

<xarray.Dataset> Size: 395MB
Dimensions:       (cells: 242076, s_rho: 40, time_counter: 5)
Coordinates:
  * cell_ids      (cells) uint64 2MB 11190229 11190230 ... 57954657 57954658
    resolution    float64 8B 0.0002498
  * s_rho         (s_rho) float32 160B -0.9875 -0.9625 ... -0.0375 -0.0125
  * time_counter  (time_counter) datetime64[ns] 40B 2004-01-01T00:58:30 ... 2...
    time_instant  (time_counter) datetime64[ns] 40B 2004-01-01T00:58:30 ... 2...
    latitude      (cells) float64 2MB 43.31 43.31 43.32 ... 50.83 50.84 50.84
    longitude     (cells) float64 2MB 0.1698 0.1471 0.1472 ... 352.1 352.1 352.1
Dimensions without coordinates: cells
Data variables:
    ocean_mask    (cells) float64 2MB nan nan nan nan nan ... 1.0 1.0 1.0 1.0
    temp          (time_counter, s_rho, cells) float64 387MB nan nan ... 10.47
Indexes:
    cell_ids  HealpixIndex(level=12, indexing_scheme=nested)
Attributes:
    grid_type:  healpix
    lat:        0
    level:      12
    lon:        0
    nside:      4096
    rot_lat:    0
    rot_lon:    0

xarray.Dataset

• Dimensions:
  • cells: 242076
  • s_rho: 40
  • time_counter: 5
• Coordinates: (7)
  • cell_ids
    (cells)
    uint64
    11190229 11190230 ... 57954658

    grid_name :

    healpix

    nside :

    4096

    nest :

    True

    array([11190229, 11190230, 11190231, ..., 57954656, 57954657, 57954658],
          dtype=uint64)

  • resolution
    ()
    float64
    0.0002498

    units :

    rad

    array(0.00024984)

  • s_rho
    (s_rho)
    float32
    -0.9875 -0.9625 ... -0.0375 -0.0125

    name :

    s_rho

    array([-0.9875, -0.9625, -0.9375, -0.9125, -0.8875, -0.8625, -0.8375, -0.8125,
           -0.7875, -0.7625, -0.7375, -0.7125, -0.6875, -0.6625, -0.6375, -0.6125,
           -0.5875, -0.5625, -0.5375, -0.5125, -0.4875, -0.4625, -0.4375, -0.4125,
           -0.3875, -0.3625, -0.3375, -0.3125, -0.2875, -0.2625, -0.2375, -0.2125,
           -0.1875, -0.1625, -0.1375, -0.1125, -0.0875, -0.0625, -0.0375, -0.0125],
          dtype=float32)

  • time_counter
    (time_counter)
    datetime64[ns]
    2004-01-01T00:58:30 ... 2004-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array(['2004-01-01T00:58:30.000000000', '2004-01-01T01:58:30.000000000',
           '2004-01-01T02:58:30.000000000', '2004-01-01T03:58:30.000000000',
           '2004-01-01T04:58:30.000000000'], dtype='datetime64[ns]')

  • time_instant
    (time_counter)
    datetime64[ns]
    2004-01-01T00:58:30 ... 2004-01-...

    bounds :

    time_instant_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    2000-01-01 00:00:00

    array(['2004-01-01T00:58:30.000000000', '2004-01-01T01:58:30.000000000',
           '2004-01-01T02:58:30.000000000', '2004-01-01T03:58:30.000000000',
           '2004-01-01T04:58:30.000000000'], dtype='datetime64[ns]')

  • latitude
    (cells)
    float64
    43.31 43.31 43.32 ... 50.84 50.84

    array([43.30735844, 43.30735844, 43.31979798, ..., 50.83179309,
           50.84391549, 50.84391549])

  • longitude
    (cells)
    float64
    0.1698 0.1471 ... 352.1 352.1

    array([1.69768612e-01, 1.47132797e-01, 1.47169811e-01, ...,
           3.52091882e+02, 3.52116300e+02, 3.52089530e+02])

• Data variables: (2)
  • ocean_mask
    (cells)
    float64
    nan nan nan nan ... 1.0 1.0 1.0 1.0

    array([nan, nan, nan, ...,  1.,  1.,  1.])

  • temp
    (time_counter, s_rho, cells)
    float64
    nan nan nan ... 10.46 10.46 10.47

    array([[[        nan,         nan,         nan, ...,  8.40665577,
              7.5196235 ,  7.48556392],
            [        nan,         nan,         nan, ...,  8.92685265,
              8.30246179,  8.24298586],
            [        nan,         nan,         nan, ...,  9.41522822,
              9.00516139,  9.02782893],
            ...,
            [        nan,         nan,         nan, ..., 10.49678732,
             10.48821423, 10.49680822],
            [        nan,         nan,         nan, ..., 10.49376149,
             10.48523489, 10.49383764],
            [        nan,         nan,         nan, ..., 10.48795527,
             10.47952207, 10.48813372]],
    
           [[        nan,         nan,         nan, ...,  8.38582531,
              7.44468721,  7.45043894],
            [        nan,         nan,         nan, ...,  8.99761958,
              8.35295066,  8.31064068],
            [        nan,         nan,         nan, ...,  9.43605236,
              9.01841053,  9.03612531],
    ...
            [        nan,         nan,         nan, ..., 10.47658909,
             10.47104691, 10.47895002],
            [        nan,         nan,         nan, ..., 10.47302394,
             10.46746534, 10.47539515],
            [        nan,         nan,         nan, ..., 10.46676472,
             10.46118368, 10.46914509]],
    
           [[        nan,         nan,         nan, ...,  8.3069679 ,
              7.37576034,  7.42223623],
            [        nan,         nan,         nan, ...,  9.22446874,
              8.6901802 ,  8.677034  ],
            [        nan,         nan,         nan, ...,  9.59169247,
              9.28155471,  9.25587101],
            ...,
            [        nan,         nan,         nan, ..., 10.47298555,
             10.46860344, 10.47630086],
            [        nan,         nan,         nan, ..., 10.46968863,
             10.46530817, 10.47301467],
            [        nan,         nan,         nan, ..., 10.46390068,
             10.45951691, 10.46722661]]])

• Indexes: (3)
  • s_rho
    PandasIndex

    PandasIndex(Index([   -0.987500011920929,   -0.9624999761581421,               -0.9375,
             -0.9125000238418579,    -0.887499988079071,    -0.862500011920929,
             -0.8374999761581421,               -0.8125,   -0.7875000238418579,
              -0.762499988079071,    -0.737500011920929,   -0.7124999761581421,
                         -0.6875,   -0.6625000238418579,    -0.637499988079071,
              -0.612500011920929,   -0.5874999761581421,               -0.5625,
             -0.5375000238418579,    -0.512499988079071,  -0.48750001192092896,
             -0.4625000059604645,               -0.4375,   -0.4124999940395355,
            -0.38749998807907104,  -0.36250001192092896,   -0.3375000059604645,
                         -0.3125,   -0.2874999940395355,  -0.26249998807907104,
            -0.23749999701976776,  -0.21250000596046448,               -0.1875,
            -0.16249999403953552,  -0.13750000298023224,  -0.11249999701976776,
            -0.08749999850988388,               -0.0625,  -0.03750000149011612,
           -0.012500000186264515],
          dtype='float32', name='s_rho'))

  • time_counter
    PandasIndex

    PandasIndex(DatetimeIndex(['2004-01-01 00:58:30', '2004-01-01 01:58:30',
                   '2004-01-01 02:58:30', '2004-01-01 03:58:30',
                   '2004-01-01 04:58:30'],
                  dtype='datetime64[ns]', name='time_counter', freq=None))

  • cell_ids
    HealpixIndex(level=12, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x71e47babecc0>

• Attributes: (7)

  grid_type :

  healpix

  lat :

  0

  level :

  12

  lon :

  0

  nside :

  4096

  rot_lat :

  0

  rot_lon :

  0

display(dout_idx.temp.min(), dout_idx.temp.max())

<xarray.DataArray 'temp' ()> Size: 8B
array(2.00370111)
Coordinates:
    resolution  float64 8B 0.0002498

xarray.DataArray

'temp'

• 2.004

  array(2.00370111)

• Coordinates: (1)
  • resolution
    ()
    float64
    0.0002498

    units :

    rad

    array(0.00024984)

• Indexes: (0)
• Attributes: (0)

<xarray.DataArray 'temp' ()> Size: 8B
array(13.97993374)
Coordinates:
    resolution  float64 8B 0.0002498

xarray.DataArray

'temp'

• 13.98

  array(13.97993374)

• Coordinates: (1)
  • resolution
    ()
    float64
    0.0002498

    units :

    rad

    array(0.00024984)

• Indexes: (0)
• Attributes: (0)

dout_idx.temp.isel(time_counter=0, s_rho=0).dggs.explore(center=7, cmap=cm.oleron, alpha=0.8)