As raster imagery

A lot of packages rasterize HEALPix maps using a destination pull interpolation (most common: nearest neighbour).

Existing software:

• healpy: static image, single variable, a few projections (gnomonic, cartesian, mollweide), spherical only

• easy.gems: static image, single variable, projections via cartopy, spherical only

import xdggs

ds = xdggs.tutorial.open_dataset("air_temperature", "healpix").compute().dggs.decode()
ds

<xarray.Dataset> Size: 10MB
Dimensions:   (time: 2920, cells: 434)
Coordinates:
  * time      (time) datetime64[ns] 23kB 2013-01-01 ... 2014-12-31T18:00:00
    lon       (cells) float64 3kB 227.8 222.2 225.0 239.1 ... 272.8 267.2 270.0
    lat       (cells) float64 3kB 14.48 14.48 16.96 14.48 ... 35.69 35.69 38.68
  * cell_ids  (cells) int64 3kB 525 526 527 529 530 ... 2043 2044 2045 2046 2047
Dimensions without coordinates: cells
Data variables:
    air       (time, cells) float64 10MB nan nan 294.1 nan ... 272.6 270.8 265.0
Indexes:
    cell_ids  HealpixIndex(level=4, indexing_scheme=nested)
Attributes:
    Conventions:  COARDS
    title:        4x daily NMC reanalysis (1948)
    description:  Data is from NMC initialized reanalysis\n(4x/day).  These a...
    platform:     Model
    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...

xarray.Dataset

• Dimensions:
  • time: 2920
  • cells: 434
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    2013-01-01 ... 2014-12-31T18:00:00

    standard_name :

    time

    long_name :

    Time

    array(['2013-01-01T00:00:00.000000000', '2013-01-01T06:00:00.000000000',
           '2013-01-01T12:00:00.000000000', ..., '2014-12-31T06:00:00.000000000',
           '2014-12-31T12:00:00.000000000', '2014-12-31T18:00:00.000000000'],
          shape=(2920,), dtype='datetime64[ns]')

  • lon
    (cells)
    float64
    227.8 222.2 225.0 ... 267.2 270.0

    array([227.8125    , 222.1875    , 225.        , 239.0625    ,
           233.4375    , 236.25      , 241.875     , 244.6875    ,
           239.0625    , 241.875     , 230.625     , 233.4375    ,
           227.8125    , 230.625     , 236.25      , 239.0625    ,
           233.4375    , 236.25      , 216.5625    , 210.9375    ,
           213.75      , 219.375     , 222.1875    , 216.5625    ,
           219.375     , 208.125     , 210.9375    , 205.3125    ,
           208.125     , 213.75      , 216.5625    , 210.9375    ,
           213.75      , 225.        , 227.8125    , 222.1875    ,
           225.        , 230.625     , 233.4375    , 227.8125    ,
           230.625     , 219.375     , 222.1875    , 216.5625    ,
           219.375     , 225.        , 227.8125    , 222.1875    ,
           225.        , 247.5       , 250.3125    , 244.6875    ,
           247.5       , 253.125     , 255.9375    , 250.3125    ,
           253.125     , 241.875     , 244.6875    , 239.0625    ,
           241.875     , 247.5       , 250.3125    , 244.6875    ,
           247.5       , 258.75      , 261.5625    , 255.9375    ,
           258.75      , 264.375     , 267.1875    , 261.5625    ,
           267.        , 253.125     , 255.9375    , 250.3125    ,
           255.        , 261.        , 266.78571429, 260.35714286,
    ...
           295.        , 305.        , 309.375     , 298.125     ,
           302.14285714, 285.        , 286.875     , 275.625     ,
           276.42857143, 289.28571429, 292.5       , 277.5       ,
           279.        , 315.        , 322.5       , 307.5       ,
           315.        , 333.        , 297.        , 205.3125    ,
           199.6875    , 202.5       , 199.6875    , 295.3125    ,
           289.6875    , 292.5       , 250.3125    , 244.6875    ,
           247.5       , 272.8125    , 267.1875    , 270.        ,
           284.0625    , 278.4375    , 281.25      , 286.875     ,
           289.6875    , 284.0625    , 286.875     , 275.625     ,
           278.4375    , 272.8125    , 275.625     , 281.25      ,
           284.0625    , 278.4375    , 281.25      , 261.5625    ,
           255.9375    , 258.75      , 264.375     , 267.1875    ,
           261.5625    , 264.375     , 253.125     , 255.9375    ,
           250.3125    , 253.125     , 258.75      , 261.5625    ,
           255.9375    , 258.75      , 270.        , 272.8125    ,
           267.1875    , 270.        , 275.625     , 278.4375    ,
           272.8125    , 275.625     , 264.375     , 267.1875    ,
           261.5625    , 264.375     , 270.        , 272.8125    ,
           267.1875    , 270.        ])

  • lat
    (cells)
    float64
    14.48 14.48 16.96 ... 35.69 38.68

    array([14.47751219, 14.47751219, 16.9577633 , 14.47751219, 14.47751219,
           16.9577633 , 16.9577633 , 19.47122063, 19.47122063, 22.02431284,
           16.9577633 , 19.47122063, 19.47122063, 22.02431284, 22.02431284,
           24.62431835, 24.62431835, 27.27961274, 14.47751219, 14.47751219,
           16.9577633 , 16.9577633 , 19.47122063, 19.47122063, 22.02431284,
           16.9577633 , 19.47122063, 19.47122063, 22.02431284, 22.02431284,
           24.62431835, 24.62431835, 27.27961274, 22.02431284, 24.62431835,
           24.62431835, 27.27961274, 27.27961274, 30.        , 30.        ,
           32.7971683 , 27.27961274, 30.        , 30.        , 32.7971683 ,
           32.7971683 , 35.68533471, 35.68533471, 38.68218745, 22.02431284,
           24.62431835, 24.62431835, 27.27961274, 27.27961274, 30.        ,
           30.        , 32.7971683 , 27.27961274, 30.        , 30.        ,
           32.7971683 , 32.7971683 , 35.68533471, 35.68533471, 38.68218745,
           32.7971683 , 35.68533471, 35.68533471, 38.68218745, 38.68218745,
           41.8103149 , 41.8103149 , 44.99388015, 38.68218745, 41.8103149 ,
           41.8103149 , 44.99388015, 44.99388015, 48.14120779, 48.14120779,
           51.25580695, 32.7971683 , 35.68533471, 35.68533471, 38.68218745,
           38.68218745, 41.8103149 , 41.8103149 , 44.99388015, 38.68218745,
           41.8103149 , 41.8103149 , 44.99388015, 44.99388015, 48.14120779,
           48.14120779, 51.25580695, 44.99388015, 48.14120779, 48.14120779,
    ...
           54.3409123 , 54.3409123 , 57.39952354, 51.25580695, 54.3409123 ,
           54.3409123 , 57.39952354, 57.39952354, 60.43443884, 60.43443884,
           63.44828368, 57.39952354, 60.43443884, 63.44828368, 66.44353569,
           66.44353569, 69.42254649, 72.38756093, 57.39952354, 60.43443884,
           60.43443884, 63.44828368, 63.44828368, 66.44353569, 66.44353569,
           69.42254649, 63.44828368, 66.44353569, 66.44353569, 69.42254649,
           69.42254649, 72.38756093, 72.38756093, 75.34073424, 69.42254649,
           72.38756093, 72.38756093, 75.34073424, 75.34073424, 75.34073424,
           14.47751219, 14.47751219, 16.9577633 , 19.47122063, 14.47751219,
           14.47751219, 16.9577633 , 14.47751219, 14.47751219, 16.9577633 ,
           14.47751219, 14.47751219, 16.9577633 , 14.47751219, 14.47751219,
           16.9577633 , 16.9577633 , 19.47122063, 19.47122063, 22.02431284,
           16.9577633 , 19.47122063, 19.47122063, 22.02431284, 22.02431284,
           24.62431835, 24.62431835, 27.27961274, 14.47751219, 14.47751219,
           16.9577633 , 16.9577633 , 19.47122063, 19.47122063, 22.02431284,
           16.9577633 , 19.47122063, 19.47122063, 22.02431284, 22.02431284,
           24.62431835, 24.62431835, 27.27961274, 22.02431284, 24.62431835,
           24.62431835, 27.27961274, 27.27961274, 30.        , 30.        ,
           32.7971683 , 27.27961274, 30.        , 30.        , 32.7971683 ,
           32.7971683 , 35.68533471, 35.68533471, 38.68218745])

  • cell_ids
    (cells)
    int64
    525 526 527 529 ... 2045 2046 2047

    grid_name :

    healpix

    level :

    4

    indexing_scheme :

    nested

    array([ 525,  526,  527, ..., 2045, 2046, 2047], shape=(434,))

• Data variables: (1)
  • air
    (time, cells)
    float64
    nan nan 294.1 ... 272.6 270.8 265.0

    long_name :

    4xDaily Air temperature at sigma level 995

    units :

    degK

    precision :

    2

    GRIB_id :

    11

    GRIB_name :

    TMP

    var_desc :

    Air temperature

    dataset :

    NMC Reanalysis

    level_desc :

    Surface

    statistic :

    Individual Obs

    parent_stat :

    Other

    actual_range :

    [185.16 322.1 ]

    array([[         nan,          nan, 294.09582422, ..., 277.99414761,
            276.1433741 , 273.00766605],
           [         nan,          nan, 294.23858547, ..., 277.25335889,
            278.52337462, 271.37902224],
           [         nan,          nan, 293.43640272, ..., 279.14409431,
            273.5720547 , 266.53868763],
           ...,
           [         nan,          nan, 297.41167492, ..., 268.35817947,
            266.94037395, 261.15816568],
           [         nan,          nan, 297.46829944, ..., 269.34263412,
            268.10151995, 261.95275866],
           [         nan,          nan, 297.41167492, ..., 272.56912575,
            270.81063558, 264.95275866]], shape=(2920, 434))

• Indexes: (2)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00',
                   '2013-01-01 12:00:00', '2013-01-01 18:00:00',
                   '2013-01-02 00:00:00', '2013-01-02 06:00:00',
                   '2013-01-02 12:00:00', '2013-01-02 18:00:00',
                   '2013-01-03 00:00:00', '2013-01-03 06:00:00',
                   ...
                   '2014-12-29 12:00:00', '2014-12-29 18:00:00',
                   '2014-12-30 00:00:00', '2014-12-30 06:00:00',
                   '2014-12-30 12:00:00', '2014-12-30 18:00:00',
                   '2014-12-31 00:00:00', '2014-12-31 06:00:00',
                   '2014-12-31 12:00:00', '2014-12-31 18:00:00'],
                  dtype='datetime64[ns]', name='time', length=2920, freq=None))

  • cell_ids
    HealpixIndex(level=4, indexing_scheme=nested)

    <xdggs.healpix.HealpixIndex object at 0x757c5253d550>

• Attributes: (5)

  Conventions :

  COARDS

  title :

  4x daily NMC reanalysis (1948)

  description :

  Data is from NMC initialized reanalysis (4x/day). These are the 0.9950 sigma level values.

  platform :

  Model

  references :

  http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html

plotting with easy.gems

Code | Documentation | Cookbook

The easygems package comes with the healpix_show function that will first interpolate the data onto a regular grid and then use the standard regular grid visualization functionality of matplotlib / cartopy:

import cartopy.crs as ccrs
import cartopy.feature as cf
import easygems.healpix as egh
import matplotlib.pyplot as plt

# special format needed for the cell ids coordinate / the cells dimension
var = (
    ds.assign_coords(
        crs=lambda ds: (
            (),
            0,
            {
                "grid_mapping_name": "healpix",
                "healpix_nside": 2**ds.dggs.grid_info.level,
                "healpix_nest": "nest",
            },
        )
    )
    .drop_indexes("cell_ids")
    .rename_dims({"cells": "cell"})
    .rename_vars({"cell_ids": "cell"})
    .isel(time=0)
    .set_xindex("cell")
    .get("air")
)

projection = ccrs.Robinson()
fig, ax = plt.subplots(
    figsize=(8, 4), subplot_kw={"projection": projection}, constrained_layout=True
)
ax.set_extent([200, 350, 14, 80], crs=ccrs.PlateCarree())
egh.healpix_show(var, ax=ax)
ax.add_feature(cf.COASTLINE, linewidth=0.8)
ax.add_feature(cf.BORDERS, linewidth=0.4)

<cartopy.mpl.feature_artist.FeatureArtist at 0x757c4d6b0f50>

/home/docs/checkouts/readthedocs.org/user_builds/grid4earth-tools-inventory/checkouts/latest/.pixi/envs/default/lib/python3.13/site-packages/cartopy/io/__init__.py:242: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)

/home/docs/checkouts/readthedocs.org/user_builds/grid4earth-tools-inventory/checkouts/latest/.pixi/envs/default/lib/python3.13/site-packages/cartopy/io/__init__.py:242: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_cultural/ne_110m_admin_0_boundary_lines_land.zip
  warnings.warn(f'Downloading: {url}', DownloadWarning)