moczarr quickstart¶

Open a morton-hive zarr store as an xarray Dataset, scope it to an area of interest through coverage arithmetic, use the xdggs ds.dggs accessor, and run a cross-resolution join — all against the small SERC fixture store committed in this repo (six order-6 shards around the NEON SERC site, ATL06-shaped synthetic data at cell order 8).

The prose version of this walk-through is the quickstart page; the store convention and the lazy index are explained on the concepts page.

Open a store¶

One call from store root to Dataset. An S3 store works the same way — pass s3://bucket/prefix (plus anonymous=True for public buckets).

In [1]:

import moczarr

STORE = "../../tests/data/serc_hive"  # ← swap this path for the public source.coop store when it lands

ds = moczarr.open_hive(STORE, decode=True)
ds

import moczarr STORE = "../../tests/data/serc_hive" # ← swap this path for the public source.coop store when it lands ds = moczarr.open_hive(STORE, decode=True) ds

Out[1]:

<xarray.Dataset> Size: 5kB
Dimensions:     (cells: 96)
Coordinates:
    cell_ids    (cells) uint64 768B 238064 238065 238066 ... 239118 239119
  * morton      (cells) uint64 768B 5340987683084697608 ... 5359547439361556488
Dimensions without coordinates: cells
Data variables:
    count       (cells) int32 384B 30 31 6 2 11 9 22 34 30 ... 0 0 0 0 0 0 0 0
    h_max       (cells) float32 384B 26.6 32.99 30.18 25.69 ... nan nan nan nan
    h_mean      (cells) float32 384B 29.92 22.49 22.39 28.04 ... nan nan nan nan
    h_min       (cells) float32 384B 25.92 30.41 31.41 22.01 ... nan nan nan nan
    h_q25       (cells) float32 384B 31.59 28.73 39.07 18.92 ... nan nan nan nan
    h_q50       (cells) float32 384B 24.4 31.03 26.63 32.05 ... nan nan nan nan
    h_q75       (cells) float32 384B 32.78 32.36 25.53 38.62 ... nan nan nan nan
    h_sigma     (cells) float32 384B 27.28 24.61 29.69 27.95 ... nan nan nan nan
    h_variance  (cells) float32 384B 31.41 28.55 34.17 25.4 ... nan nan nan nan
Indexes:
    morton   MortonIndex(level=8, kind=moc)
Attributes:
    zarr_conventions:  [{'schema_url': 'https://raw.githubusercontent.com/zar...
    dggs:              {'name': 'healpix', 'refinement_level': 8, 'indexing_s...
    morton_hive:       {'spec': 'morton-hive/1', 'cell_order': 8, 'shard_orde...

xarray.Dataset

• Dimensions:
  • cells: 96
• Coordinates: (2)
  • cell_ids
    (cells)
    uint64
    238064 238065 ... 239118 239119

    array([238064, 238065, 238066, 238067, 238068, 238069, 238070, 238071,
           238072, 238073, 238074, 238075, 238076, 238077, 238078, 238079,
           238400, 238401, 238402, 238403, 238404, 238405, 238406, 238407,
           238408, 238409, 238410, 238411, 238412, 238413, 238414, 238415,
           238416, 238417, 238418, 238419, 238420, 238421, 238422, 238423,
           238424, 238425, 238426, 238427, 238428, 238429, 238430, 238431,
           238448, 238449, 238450, 238451, 238452, 238453, 238454, 238455,
           238456, 238457, 238458, 238459, 238460, 238461, 238462, 238463,
           238752, 238753, 238754, 238755, 238756, 238757, 238758, 238759,
           238760, 238761, 238762, 238763, 238764, 238765, 238766, 238767,
           239104, 239105, 239106, 239107, 239108, 239109, 239110, 239111,
           239112, 239113, 239114, 239115, 239116, 239117, 239118, 239119],
          dtype=uint64)

  • morton
    (cells)
    uint64
    5340987683084697608 ... 53595474...

    grid_name :

    morton

    level :

    8

    array([5340987683084697608, 5341005275270742024, 5341022867456786440,
           5341040459642830856, 5341058051828875272, 5341075644014919688,
           5341093236200964104, 5341110828387008520, 5341128420573052936,
           5341146012759097352, 5341163604945141768, 5341181197131186184,
           5341198789317230600, 5341216381503275016, 5341233973689319432,
           5341251565875363848, 5346898657595621384, 5346916249781665800,
           5346933841967710216, 5346951434153754632, 5346969026339799048,
           5346986618525843464, 5347004210711887880, 5347021802897932296,
           5347039395083976712, 5347056987270021128, 5347074579456065544,
           5347092171642109960, 5347109763828154376, 5347127356014198792,
           5347144948200243208, 5347162540386287624, 5347180132572332040,
           5347197724758376456, 5347215316944420872, 5347232909130465288,
           5347250501316509704, 5347268093502554120, 5347285685688598536,
           5347303277874642952, 5347320870060687368, 5347338462246731784,
           5347356054432776200, 5347373646618820616, 5347391238804865032,
           5347408830990909448, 5347426423176953864, 5347444015362998280,
           5347743082525753352, 5347760674711797768, 5347778266897842184,
           5347795859083886600, 5347813451269931016, 5347831043455975432,
           5347848635642019848, 5347866227828064264, 5347883820014108680,
           5347901412200153096, 5347919004386197512, 5347936596572241928,
           5347954188758286344, 5347971780944330760, 5347989373130375176,
           5348006965316419592, 5353091107083255816, 5353108699269300232,
           5353126291455344648, 5353143883641389064, 5353161475827433480,
           5353179068013477896, 5353196660199522312, 5353214252385566728,
           5353231844571611144, 5353249436757655560, 5353267028943699976,
           5353284621129744392, 5353302213315788808, 5353319805501833224,
           5353337397687877640, 5353354989873922056, 5359283556570890248,
           5359301148756934664, 5359318740942979080, 5359336333129023496,
           5359353925315067912, 5359371517501112328, 5359389109687156744,
           5359406701873201160, 5359424294059245576, 5359441886245289992,
           5359459478431334408, 5359477070617378824, 5359494662803423240,
           5359512254989467656, 5359529847175512072, 5359547439361556488],
          dtype=uint64)

• Data variables: (9)
  • count
    (cells)
    int32
    30 31 6 2 11 9 22 ... 0 0 0 0 0 0 0

    array([30, 31,  6,  2, 11,  9, 22, 34, 30, 17, 22, 25, 31,  5, 31,  8,  0,
            0,  0,  0,  0, 30, 13, 27,  0,  0,  0,  0,  0,  0, 34, 34,  0,  0,
            0,  0,  0,  0,  0, 18,  0,  0,  0,  0,  0,  0,  0,  0, 30, 31,  6,
            2, 11,  9, 22, 34, 30, 17, 22, 25, 31,  5, 31,  8,  0,  0,  0,  0,
            0, 30, 13, 27,  0,  0,  0,  0,  0,  0, 34, 34,  0,  0,  0,  0,  0,
            0,  0, 18,  0,  0,  0,  0,  0,  0,  0,  0], dtype=int32)

  • h_max
    (cells)
    float32
    26.6 32.99 30.18 ... nan nan nan

    array([26.597473, 32.988308, 30.17942 , 25.688665, 40.0094  , 28.181429,
           29.4185  , 31.970823, 38.738255, 29.258081, 33.515915, 30.493977,
           22.681032, 33.959885, 35.103195, 28.800068,       nan,       nan,
                 nan,       nan,       nan, 34.477375, 29.23477 , 35.428154,
                 nan,       nan,       nan,       nan,       nan,       nan,
           40.0094  , 28.181429,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 31.535923,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan,
           26.597473, 32.988308, 30.17942 , 25.688665, 40.0094  , 28.181429,
           29.4185  , 31.970823, 38.738255, 29.258081, 33.515915, 30.493977,
           22.681032, 33.959885, 35.103195, 28.800068,       nan,       nan,
                 nan,       nan,       nan, 34.477375, 29.23477 , 35.428154,
                 nan,       nan,       nan,       nan,       nan,       nan,
           40.0094  , 28.181429,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 31.535923,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan],
          dtype=float32)

  • h_mean
    (cells)
    float32
    29.92 22.49 22.39 ... nan nan nan

    array([29.92282 , 22.49342 , 22.38524 , 28.0424  , 33.129665, 27.652126,
           31.460114, 34.838837, 30.478811, 32.87234 , 37.23937 , 23.76986 ,
           33.720375, 20.576576, 32.158215, 31.014973,       nan,       nan,
                 nan,       nan,       nan, 30.493977, 22.681032, 33.959885,
                 nan,       nan,       nan,       nan,       nan,       nan,
           33.129665, 27.652126,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 29.258081,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan,
           29.92282 , 22.49342 , 22.38524 , 28.0424  , 33.129665, 27.652126,
           31.460114, 34.838837, 30.478811, 32.87234 , 37.23937 , 23.76986 ,
           33.720375, 20.576576, 32.158215, 31.014973,       nan,       nan,
                 nan,       nan,       nan, 30.493977, 22.681032, 33.959885,
                 nan,       nan,       nan,       nan,       nan,       nan,
           33.129665, 27.652126,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 29.258081,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan],
          dtype=float32)

  • h_min
    (cells)
    float32
    25.92 30.41 31.41 ... nan nan nan

    array([25.918427, 30.406515, 31.407154, 22.005058, 21.346378, 31.775393,
           25.68906 , 36.041786, 31.97273 , 31.535923, 31.042213, 34.477375,
           29.23477 , 35.428154, 27.370428, 28.875011,       nan,       nan,
                 nan,       nan,       nan, 36.461933, 38.99393 , 29.86963 ,
                 nan,       nan,       nan,       nan,       nan,       nan,
           21.346378, 31.775393,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 27.099024,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan,
           25.918427, 30.406515, 31.407154, 22.005058, 21.346378, 31.775393,
           25.68906 , 36.041786, 31.97273 , 31.535923, 31.042213, 34.477375,
           29.23477 , 35.428154, 27.370428, 28.875011,       nan,       nan,
                 nan,       nan,       nan, 36.461933, 38.99393 , 29.86963 ,
                 nan,       nan,       nan,       nan,       nan,       nan,
           21.346378, 31.775393,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 27.099024,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan],
          dtype=float32)

  • h_q25
    (cells)
    float32
    31.59 28.73 39.07 ... nan nan nan

    array([31.590723, 28.72736 , 39.07039 , 18.919832, 21.328255, 27.46557 ,
           23.723913, 17.220373, 26.024658, 26.253695, 17.757254, 34.293465,
           36.49556 , 36.171505, 27.458296, 30.73806 ,       nan,       nan,
                 nan,       nan,       nan, 25.227154, 30.774246, 28.391079,
                 nan,       nan,       nan,       nan,       nan,       nan,
           21.328255, 27.46557 ,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 25.496517,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan,
           31.590723, 28.72736 , 39.07039 , 18.919832, 21.328255, 27.46557 ,
           23.723913, 17.220373, 26.024658, 26.253695, 17.757254, 34.293465,
           36.49556 , 36.171505, 27.458296, 30.73806 ,       nan,       nan,
                 nan,       nan,       nan, 25.227154, 30.774246, 28.391079,
                 nan,       nan,       nan,       nan,       nan,       nan,
           21.328255, 27.46557 ,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 25.496517,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan],
          dtype=float32)

  • h_q50
    (cells)
    float32
    24.4 31.03 26.63 ... nan nan nan

    array([24.395948, 31.032187, 26.629124, 32.049416, 28.28514 , 35.16879 ,
           34.133038, 36.194725, 21.973522, 20.901964, 32.023712, 27.114199,
           31.989145, 31.494404, 30.901075, 34.596268,       nan,       nan,
                 nan,       nan,       nan, 34.293465, 36.49556 , 36.171505,
                 nan,       nan,       nan,       nan,       nan,       nan,
           28.28514 , 35.16879 ,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 26.253695,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan,
           24.395948, 31.032187, 26.629124, 32.049416, 28.28514 , 35.16879 ,
           34.133038, 36.194725, 21.973522, 20.901964, 32.023712, 27.114199,
           31.989145, 31.494404, 30.901075, 34.596268,       nan,       nan,
                 nan,       nan,       nan, 34.293465, 36.49556 , 36.171505,
                 nan,       nan,       nan,       nan,       nan,       nan,
           28.28514 , 35.16879 ,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 26.253695,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan],
          dtype=float32)

  • h_q75
    (cells)
    float32
    32.78 32.36 25.53 ... nan nan nan

    array([32.78308 , 32.36223 , 25.530901, 38.623325, 30.633068, 35.535248,
           31.635336, 36.34683 , 30.767677, 31.088053, 30.689066, 26.456604,
           27.139917, 29.265255, 29.415092, 25.086529,       nan,       nan,
                 nan,       nan,       nan, 27.114199, 31.989145, 31.494404,
                 nan,       nan,       nan,       nan,       nan,       nan,
           30.633068, 35.535248,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 20.901964,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan,
           32.78308 , 32.36223 , 25.530901, 38.623325, 30.633068, 35.535248,
           31.635336, 36.34683 , 30.767677, 31.088053, 30.689066, 26.456604,
           27.139917, 29.265255, 29.415092, 25.086529,       nan,       nan,
                 nan,       nan,       nan, 27.114199, 31.989145, 31.494404,
                 nan,       nan,       nan,       nan,       nan,       nan,
           30.633068, 35.535248,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 20.901964,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan],
          dtype=float32)

  • h_sigma
    (cells)
    float32
    27.28 24.61 29.69 ... nan nan nan

    array([27.283327, 24.613434, 29.690845, 27.948807, 30.488108, 25.46099 ,
           32.72764 , 24.82075 , 28.478823, 28.04006 , 36.339436, 22.52153 ,
           27.92988 , 35.912304, 26.708294, 23.802645,       nan,       nan,
                 nan,       nan,       nan, 23.76986 , 33.720375, 20.576576,
                 nan,       nan,       nan,       nan,       nan,       nan,
           30.488108, 25.46099 ,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 32.87234 ,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan,
           27.283327, 24.613434, 29.690845, 27.948807, 30.488108, 25.46099 ,
           32.72764 , 24.82075 , 28.478823, 28.04006 , 36.339436, 22.52153 ,
           27.92988 , 35.912304, 26.708294, 23.802645,       nan,       nan,
                 nan,       nan,       nan, 23.76986 , 33.720375, 20.576576,
                 nan,       nan,       nan,       nan,       nan,       nan,
           30.488108, 25.46099 ,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 32.87234 ,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan],
          dtype=float32)

  • h_variance
    (cells)
    float32
    31.41 28.55 34.17 ... nan nan nan

    array([31.414207, 28.549849, 34.173775, 25.398048, 30.954596, 33.330635,
           35.178608, 24.469275, 37.118942, 25.496517, 22.437246, 25.227154,
           30.774246, 28.391079, 32.20228 , 32.478016,       nan,       nan,
                 nan,       nan,       nan, 22.52153 , 27.92988 , 35.912304,
                 nan,       nan,       nan,       nan,       nan,       nan,
           30.954596, 33.330635,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 28.04006 ,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan,
           31.414207, 28.549849, 34.173775, 25.398048, 30.954596, 33.330635,
           35.178608, 24.469275, 37.118942, 25.496517, 22.437246, 25.227154,
           30.774246, 28.391079, 32.20228 , 32.478016,       nan,       nan,
                 nan,       nan,       nan, 22.52153 , 27.92988 , 35.912304,
                 nan,       nan,       nan,       nan,       nan,       nan,
           30.954596, 33.330635,       nan,       nan,       nan,       nan,
                 nan,       nan,       nan, 28.04006 ,       nan,       nan,
                 nan,       nan,       nan,       nan,       nan,       nan],
          dtype=float32)

• Indexes: (1)
  • morton
    MortonIndex(level=8, kind=moc)

    <MortonIndex(level=8, kind=moc)>

• Attributes: (3)

  zarr_conventions :

  [{'schema_url': 'https://raw.githubusercontent.com/zarr-conventions/dggs/refs/tags/v1/schema.json', 'spec_url': 'https://github.com/zarr-conventions/dggs/blob/v1/README.md', 'uuid': '7b255807-140c-42ca-97f6-7a1cfecdbc38', 'name': 'dggs', 'description': 'Discrete Global Grid Systems convention for zarr'}]

  dggs :

  {'name': 'healpix', 'refinement_level': 8, 'indexing_scheme': 'nested', 'spatial_dimension': 'cells', 'ellipsoid': {'name': 'WGS84', 'semimajor_axis': 6378137.0, 'inverse_flattening': 298.257223563}, 'coordinate': 'cell_ids', 'compression': 'none'}

  morton_hive :

  {'spec': 'morton-hive/1', 'cell_order': 8, 'shard_order': 6, 'dataset': {'short_name': 'ATL06', 'version': '007'}}

morton is the packed uint64 morton word (the stored cell identity); cell_ids is the HEALPix NESTED view, fabricated exactly from the words when the store does not carry it. decode=True (the [xdggs] extra) registers the "morton" grid so the ds.dggs accessor works below.

Scope to an AOI¶

An AOI is a morton cover — packed words or decimal strings, mixed orders allowed. A decimal prefix is a spatial ancestor, so one order-5 id covers its whole subtree; shards outside the cover are rejected arithmetically (their leaves are never opened) and rows are trimmed exactly inside partially-covered shards.

In [2]:

sub = moczarr.open_hive(STORE, aoi=["433142"])
dict(sub.sizes)

sub = moczarr.open_hive(STORE, aoi=["433142"]) dict(sub.sizes)

Out[2]:

{'cells': 48}

The lazy index¶

The default index posture (index_kind="moc") holds the row domain as an interval set built from the same coverage arithmetic that selected the leaves. The on-disk morton/cell_ids chunks are never read — the coordinate is fabricated on demand, and sel/isel/alignment run as rank arithmetic on intervals. The result is value-identical to the materialized (index_kind="pandas") open — one kwarg away when a workflow needs it (e.g. xr.concat across two opens, which the interval index does not support).

In [3]:

lazy = moczarr.open_hive(STORE)   # moc is the default
lazy.xindexes["morton"]

lazy = moczarr.open_hive(STORE) # moc is the default lazy.xindexes["morton"]

Out[3]:

<MortonMocIndex(level=8, ranges=5, size=96)>

Accessor ops: point lookup and cell boundaries¶

sel_latlon bins points to cells and selects the containing rows; cell_boundaries returns one shapely polygon per cell.

In [4]:

near = ds.dggs.sel_latlon([37.356], [-75.937])
near[["count", "h_mean"]]

near = ds.dggs.sel_latlon([37.356], [-75.937]) near[["count", "h_mean"]]

Out[4]:

<xarray.Dataset> Size: 24B
Dimensions:   (cells: 1)
Coordinates:
    cell_ids  (cells) uint64 8B 238064
  * morton    (cells) uint64 8B 5340987683084697608
Dimensions without coordinates: cells
Data variables:
    count     (cells) int32 4B 30
    h_mean    (cells) float32 4B 29.92
Indexes:
    morton   MortonMocIndex(ranges=1, size=1)
Attributes:
    zarr_conventions:  [{'schema_url': 'https://raw.githubusercontent.com/zar...
    dggs:              {'name': 'healpix', 'refinement_level': 8, 'indexing_s...
    morton_hive:       {'spec': 'morton-hive/1', 'cell_order': 8, 'shard_orde...

xarray.Dataset

• Dimensions:
  • cells: 1
• Coordinates: (2)
  • cell_ids
    (cells)
    uint64
    238064

    array([238064], dtype=uint64)

  • morton
    (cells)
    uint64
    5340987683084697608

    grid_name :

    morton

    level :

    8

    array([5340987683084697608], dtype=uint64)

• Data variables: (2)
  • count
    (cells)
    int32
    30

    array([30], dtype=int32)

  • h_mean
    (cells)
    float32
    29.92

    array([29.92282], dtype=float32)

• Indexes: (1)
  • morton
    MortonMocIndex(ranges=1, size=1)

    <MortonMocIndex(level=8, ranges=1, size=1)>

• Attributes: (3)

  zarr_conventions :

  [{'schema_url': 'https://raw.githubusercontent.com/zarr-conventions/dggs/refs/tags/v1/schema.json', 'spec_url': 'https://github.com/zarr-conventions/dggs/blob/v1/README.md', 'uuid': '7b255807-140c-42ca-97f6-7a1cfecdbc38', 'name': 'dggs', 'description': 'Discrete Global Grid Systems convention for zarr'}]

  dggs :

  {'name': 'healpix', 'refinement_level': 8, 'indexing_scheme': 'nested', 'spatial_dimension': 'cells', 'ellipsoid': {'name': 'WGS84', 'semimajor_axis': 6378137.0, 'inverse_flattening': 298.257223563}, 'coordinate': 'cell_ids', 'compression': 'none'}

  morton_hive :

  {'spec': 'morton-hive/1', 'cell_order': 8, 'shard_order': 6, 'dataset': {'short_name': 'ATL06', 'version': '007'}}

In [5]:

boundaries = ds.dggs.cell_boundaries()
boundaries.values[:2]

boundaries = ds.dggs.cell_boundaries() boundaries.values[:2]

Out[5]:

array([<POLYGON ((-75.938 37.544, -76.113 37.356, -75.938 37.169, -75.762 37.356, -...>,
       <POLYGON ((-75.762 37.733, -75.938 37.544, -75.762 37.356, -75.586 37.544, -...>],
      dtype=object)

Cross-resolution: aggregate up, join down¶

Morton nesting makes cross-resolution work a lookup. Fine → coarse is plain xarray groupby over the fabricated parent coordinate (parent_cells); coarse → fine is join_coarse — each fine cell looks up its containing coarse cell, and the coarse variables land on the fine cells dimension. The coarse side can equally be a second, coarser store.

In [6]:

parents = moczarr.parent_cells(ds, 6)  # DataArray "parent_o6"
coarse = ds.groupby(parents).mean().rename({parents.name: "morton"})

both = moczarr.join_coarse(ds, coarse, variables=["h_mean"], suffix="_o6")
anomaly = (both["h_mean"] - both["h_mean_o6"]).rename("h_mean_anomaly")
anomaly

parents = moczarr.parent_cells(ds, 6) # DataArray "parent_o6" coarse = ds.groupby(parents).mean().rename({parents.name: "morton"}) both = moczarr.join_coarse(ds, coarse, variables=["h_mean"], suffix="_o6") anomaly = (both["h_mean"] - both["h_mean_o6"]).rename("h_mean_anomaly") anomaly

Out[6]:

<xarray.DataArray 'h_mean_anomaly' (cells: 96)> Size: 384B
array([ 0.4381237, -6.991276 , -7.099455 , -1.4422951,  3.64497  ,
       -1.8325691,  1.9754181,  5.354141 ,  0.9941158,  3.3876457,
        7.754673 , -5.714836 ,  4.2356796, -8.908119 ,  2.6735191,
        1.5302773,        nan,        nan,        nan,        nan,
              nan,  0.9106388, -6.9023056,  4.376547 ,        nan,
              nan,        nan,        nan,        nan,        nan,
        3.5463276, -1.9312115,        nan,        nan,        nan,
              nan,        nan,        nan,        nan,  0.       ,
              nan,        nan,        nan,        nan,        nan,
              nan,        nan,        nan,  0.4381237, -6.991276 ,
       -7.099455 , -1.4422951,  3.64497  , -1.8325691,  1.9754181,
        5.354141 ,  0.9941158,  3.3876457,  7.754673 , -5.714836 ,
        4.2356796, -8.908119 ,  2.6735191,  1.5302773,        nan,
              nan,        nan,        nan,        nan,  0.9106388,
       -6.9023056,  4.376547 ,        nan,        nan,        nan,
              nan,        nan,        nan,  3.5463276, -1.9312115,
              nan,        nan,        nan,        nan,        nan,
              nan,        nan,  0.       ,        nan,        nan,
              nan,        nan,        nan,        nan,        nan,
              nan], dtype=float32)
Coordinates:
    cell_ids  (cells) uint64 768B 238064 238065 238066 ... 239117 239118 239119
  * morton    (cells) uint64 768B 5340987683084697608 ... 5359547439361556488
Dimensions without coordinates: cells
Indexes:
    morton   MortonIndex(level=8, kind=moc)

xarray.DataArray

'h_mean_anomaly'

• cells: 96

• 0.4381 -6.991 -7.099 -1.442 3.645 -1.833 ... nan nan nan nan nan nan

  array([ 0.4381237, -6.991276 , -7.099455 , -1.4422951,  3.64497  ,
         -1.8325691,  1.9754181,  5.354141 ,  0.9941158,  3.3876457,
          7.754673 , -5.714836 ,  4.2356796, -8.908119 ,  2.6735191,
          1.5302773,        nan,        nan,        nan,        nan,
                nan,  0.9106388, -6.9023056,  4.376547 ,        nan,
                nan,        nan,        nan,        nan,        nan,
          3.5463276, -1.9312115,        nan,        nan,        nan,
                nan,        nan,        nan,        nan,  0.       ,
                nan,        nan,        nan,        nan,        nan,
                nan,        nan,        nan,  0.4381237, -6.991276 ,
         -7.099455 , -1.4422951,  3.64497  , -1.8325691,  1.9754181,
          5.354141 ,  0.9941158,  3.3876457,  7.754673 , -5.714836 ,
          4.2356796, -8.908119 ,  2.6735191,  1.5302773,        nan,
                nan,        nan,        nan,        nan,  0.9106388,
         -6.9023056,  4.376547 ,        nan,        nan,        nan,
                nan,        nan,        nan,  3.5463276, -1.9312115,
                nan,        nan,        nan,        nan,        nan,
                nan,        nan,  0.       ,        nan,        nan,
                nan,        nan,        nan,        nan,        nan,
                nan], dtype=float32)

• Coordinates: (2)
  • cell_ids
    (cells)
    uint64
    238064 238065 ... 239118 239119

    array([238064, 238065, 238066, 238067, 238068, 238069, 238070, 238071,
           238072, 238073, 238074, 238075, 238076, 238077, 238078, 238079,
           238400, 238401, 238402, 238403, 238404, 238405, 238406, 238407,
           238408, 238409, 238410, 238411, 238412, 238413, 238414, 238415,
           238416, 238417, 238418, 238419, 238420, 238421, 238422, 238423,
           238424, 238425, 238426, 238427, 238428, 238429, 238430, 238431,
           238448, 238449, 238450, 238451, 238452, 238453, 238454, 238455,
           238456, 238457, 238458, 238459, 238460, 238461, 238462, 238463,
           238752, 238753, 238754, 238755, 238756, 238757, 238758, 238759,
           238760, 238761, 238762, 238763, 238764, 238765, 238766, 238767,
           239104, 239105, 239106, 239107, 239108, 239109, 239110, 239111,
           239112, 239113, 239114, 239115, 239116, 239117, 239118, 239119],
          dtype=uint64)

  • morton
    (cells)
    uint64
    5340987683084697608 ... 53595474...

    grid_name :

    morton

    level :

    8

    array([5340987683084697608, 5341005275270742024, 5341022867456786440,
           5341040459642830856, 5341058051828875272, 5341075644014919688,
           5341093236200964104, 5341110828387008520, 5341128420573052936,
           5341146012759097352, 5341163604945141768, 5341181197131186184,
           5341198789317230600, 5341216381503275016, 5341233973689319432,
           5341251565875363848, 5346898657595621384, 5346916249781665800,
           5346933841967710216, 5346951434153754632, 5346969026339799048,
           5346986618525843464, 5347004210711887880, 5347021802897932296,
           5347039395083976712, 5347056987270021128, 5347074579456065544,
           5347092171642109960, 5347109763828154376, 5347127356014198792,
           5347144948200243208, 5347162540386287624, 5347180132572332040,
           5347197724758376456, 5347215316944420872, 5347232909130465288,
           5347250501316509704, 5347268093502554120, 5347285685688598536,
           5347303277874642952, 5347320870060687368, 5347338462246731784,
           5347356054432776200, 5347373646618820616, 5347391238804865032,
           5347408830990909448, 5347426423176953864, 5347444015362998280,
           5347743082525753352, 5347760674711797768, 5347778266897842184,
           5347795859083886600, 5347813451269931016, 5347831043455975432,
           5347848635642019848, 5347866227828064264, 5347883820014108680,
           5347901412200153096, 5347919004386197512, 5347936596572241928,
           5347954188758286344, 5347971780944330760, 5347989373130375176,
           5348006965316419592, 5353091107083255816, 5353108699269300232,
           5353126291455344648, 5353143883641389064, 5353161475827433480,
           5353179068013477896, 5353196660199522312, 5353214252385566728,
           5353231844571611144, 5353249436757655560, 5353267028943699976,
           5353284621129744392, 5353302213315788808, 5353319805501833224,
           5353337397687877640, 5353354989873922056, 5359283556570890248,
           5359301148756934664, 5359318740942979080, 5359336333129023496,
           5359353925315067912, 5359371517501112328, 5359389109687156744,
           5359406701873201160, 5359424294059245576, 5359441886245289992,
           5359459478431334408, 5359477070617378824, 5359494662803423240,
           5359512254989467656, 5359529847175512072, 5359547439361556488],
          dtype=uint64)

• Indexes: (1)
  • morton
    MortonIndex(level=8, kind=moc)

    <MortonIndex(level=8, kind=moc)>

Plot the cells¶

A small map of the fixture's 96 cells: each polygon is one order-8 cell boundary, filled by its h_mean (single-hue sequential ramp; cells with no observations stay gray).

In [7]:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from matplotlib.patches import Polygon as MplPolygon

patches = [MplPolygon(np.asarray(p.exterior.coords)) for p in boundaries.values]
pc = PatchCollection(patches, edgecolor="white", linewidth=0.8, cmap="Blues")
pc.set_array(ds["h_mean"].values)
pc.cmap.set_bad("#d9d9d9")

fig, ax = plt.subplots(figsize=(7, 5))
ax.add_collection(pc)
ax.autoscale()
ax.set_aspect("equal")
ax.set_xlabel("longitude")
ax.set_ylabel("latitude")
ax.set_title("SERC fixture: mean height per order-8 cell")
fig.colorbar(pc, ax=ax, label="h_mean (m)")
plt.show()

import numpy as np import matplotlib.pyplot as plt from matplotlib.collections import PatchCollection from matplotlib.patches import Polygon as MplPolygon patches = [MplPolygon(np.asarray(p.exterior.coords)) for p in boundaries.values] pc = PatchCollection(patches, edgecolor="white", linewidth=0.8, cmap="Blues") pc.set_array(ds["h_mean"].values) pc.cmap.set_bad("#d9d9d9") fig, ax = plt.subplots(figsize=(7, 5)) ax.add_collection(pc) ax.autoscale() ax.set_aspect("equal") ax.set_xlabel("longitude") ax.set_ylabel("latitude") ax.set_title("SERC fixture: mean height per order-8 cell") fig.colorbar(pc, ax=ax, label="h_mean (m)") plt.show()

Where next¶

• Quickstart (prose) — the same flow with AOI/empty-coverage semantics spelled out.
• Concepts — the hive tree, coverage tiers, and how the lazy index works, with pointers to the normative specs.
• API reference.