import xarray as xr
import xgcm
import numpy as np
import xroms
import matplotlib.pyplot as plt

Matplotlib is building the font cache; this may take a moment.

How to calculate with xarray and xroms

Here we demonstrate a number of calculations built into xroms, through accessors to DataArrays and Datasets.

xarray Datasets

Use an xarray accessor in xroms to easily perform calculations with syntax

ds.xroms.[method]

Importantly, the xroms accessor to a Dataset is initialized with an xgcm grid object (or you can input a previously-calculated grid object), stored at ds.xroms.xgrid, which is used to perform the basic grid calculations. More on this under “How to set up grid” below.

The built-in native calculations are properties of the xroms accessor and are not functions.

The accessor functions can take in the horizontal then vertical grid label you want the calculation to be on as options:

ds.xroms.ddz('u', hcoord='rho', scoord='s_rho')  # to make sure result is on rho horizontal grid and s_rho vertical grid, a function

or

ds.xroms.dudz  # return on native grid it is calculated on, a property

Other inputs are available for functions when the calculation involves a derivative and there is a choice for how to treat the boundary (hboundary and hfill_value for horizontal calculations and sboundary and sfill_value for vertical calculations). More information on those inputs can be found in the docs for xgcm such as under found under:

ds.xroms.xgrid.interp?

xarray DataArrays

A few of the more basic methods in xroms are available to DataArrays too. xroms methods for DataArrays require the grid object to be input:

ds.temp.xroms.to_grid(xgrid, hcoord='psi', scoord='s_w')

Attributes

xroms provides attributes as metadata to track calculations, provide context, and to be used as indicators for plots.

The option to always keep attributes in xarray is turned on in the call to xroms.

cf-xarray

Some functionality is added by using the package cf-xarray. Necessary attributes are added to datasets when the following call is run:

ds, xgrid = xroms.roms_dataset(ds)

For example, when all CF Convention attributes are available in the Dataset, you can refer to dimensions and coordinates generically, regardless of the actual variable names.

• For dimensions:

  • ds.cf[“T”], ds.cf[“Z”], ds.cf[“Y”], ds.cf[“X”]

• For coordinates:

  • ds.cf[“time], ds.cf[“vertical”], ds.cf[“latitude”], ds.cf[“longitude”]

Load in data

More information on input/output in input/output page. For model output available at , you can find your dataset with and chunks according to the dataset itself (though if none are known by the dataset this will use no chunks):

ds = xr.open_dataset(url, chunks={})

Also, an example ROMS dataset is available with xroms that we will read in for this tutorial.

ds = xroms.datasets.fetch_ROMS_example_full_grid()
ds

Downloading file 'ROMS_example_full_grid.nc' from 'https://github.com/xoceanmodel/xroms/raw/main/xroms/data/ROMS_example_full_grid.nc' to '/home/docs/.cache/xroms'.

<xarray.Dataset> Size: 62MB
Dimensions:     (eta_rho: 191, xi_rho: 300, s_rho: 30, s_w: 31, ocean_time: 2,
                 eta_u: 191, xi_u: 299, eta_v: 190, xi_v: 300)
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
    lon_u       (eta_u, xi_u) float64 457kB dask.array<chunksize=(191, 299), meta=np.ndarray>
    lat_u       (eta_u, xi_u) float64 457kB dask.array<chunksize=(191, 299), meta=np.ndarray>
    lon_v       (eta_v, xi_v) float64 456kB dask.array<chunksize=(190, 300), meta=np.ndarray>
    lat_v       (eta_v, xi_v) float64 456kB dask.array<chunksize=(190, 300), meta=np.ndarray>
Dimensions without coordinates: eta_rho, xi_rho, eta_u, xi_u, eta_v, xi_v
Data variables: (12/17)
    angle       (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    hc          float64 8B ...
    Cs_r        (s_rho) float64 240B dask.array<chunksize=(30,), meta=np.ndarray>
    zeta        (ocean_time, eta_rho, xi_rho) float32 458kB dask.array<chunksize=(2, 191, 300), meta=np.ndarray>
    h           (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    Cs_w        (s_w) float64 248B dask.array<chunksize=(31,), meta=np.ndarray>
    ...          ...
    temp        (ocean_time, s_rho, eta_rho, xi_rho) float32 14MB dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>
    salt        (ocean_time, s_rho, eta_rho, xi_rho) float32 14MB dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>
    Vtransform  int32 4B ...
    pm          (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    pn          (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    f           (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
Attributes: (12/29)
    file:              ocean_his_0150.nc
    format:            netCDF-3 classic file
    Conventions:       CF-1.4
    type:              ROMS/TOMS history file
    title:             Texas and Louisiana Shelf case (Nesting)
    rst_file:          ocean_rst.nc
    ...                ...
    compiler_command:  /g/software/openmpi/1.4.3/intel/bin/mpif90
    compiler_flags:    -heap-arrays -fp-model precise -assume 2underscores -c...
    tiling:            016x032
    history:           ROMS/TOMS, Version 3.4, Sunday - December 4, 2011 -  7...
    ana_file:          /scratch/zhangxq/projects/txla_nesting6/Functionals/an...
    CPP_options:       TXLA, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, BULK_FLUX...

xarray.Dataset

• Dimensions:
  • eta_rho: 191
  • xi_rho: 300
  • s_rho: 30
  • s_w: 31
  • ocean_time: 2
  • eta_u: 191
  • xi_u: 299
  • eta_v: 190
  • xi_v: 300
• Coordinates: (9)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    positive :

    up

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • s_w
    (s_w)
    float64
    -1.0 -0.9667 ... -0.03333 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    positive :

    up

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    array([-1.      , -0.966667, -0.933333, -0.9     , -0.866667, -0.833333,
           -0.8     , -0.766667, -0.733333, -0.7     , -0.666667, -0.633333,
           -0.6     , -0.566667, -0.533333, -0.5     , -0.466667, -0.433333,
           -0.4     , -0.366667, -0.333333, -0.3     , -0.266667, -0.233333,
           -0.2     , -0.166667, -0.133333, -0.1     , -0.066667, -0.033333,
            0.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon_u
    (eta_u, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    units :

    meters

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_u
    (eta_u, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    units :

    meters

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon_v
    (eta_v, xi_v)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    units :

    meters

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_v
    (eta_v, xi_v)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    units :

    meters

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Data variables: (17)
  • angle
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    angle between XI-axis and EAST

    units :

    radians

    field :

    angle, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • hc
    ()
    float64
    ...

    long_name :

    S-coordinate parameter, critical depth

    units :

    meter

    [1 values with dtype=float64]

  • Cs_r
    (s_rho)
    float64
    dask.array<chunksize=(30,), meta=np.ndarray>

    long_name :

    S-coordinate stretching curves at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    field :

    Cs_r, scalar

    Array Chunk Bytes 240 B 240 B Shape (30,) (30,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • zeta
    (ocean_time, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 191, 300), meta=np.ndarray>

    long_name :

    free-surface

    units :

    meter

    time :

    ocean_time

    field :

    free-surface, scalar, series

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (2, 191, 300) (2, 191, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • h
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    bathymetry at RHO-points

    units :

    meter

    field :

    bath, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Cs_w
    (s_w)
    float64
    dask.array<chunksize=(31,), meta=np.ndarray>

    long_name :

    S-coordinate stretching curves at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    field :

    Cs_w, scalar

    Array Chunk Bytes 248 B 248 B Shape (31,) (31,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    mask on RHO-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_u
    (eta_u, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    long_name :

    mask on U-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_v
    (eta_v, xi_v)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    long_name :

    mask on V-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • u
    (ocean_time, s_rho, eta_u, xi_u)
    float32
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    long_name :

    u-momentum component

    units :

    meter second-1

    time :

    ocean_time

    field :

    u-velocity, scalar, series

    Array Chunk Bytes 13.07 MiB 13.07 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • v
    (ocean_time, s_rho, eta_v, xi_v)
    float32
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    long_name :

    v-momentum component

    units :

    meter second-1

    time :

    ocean_time

    field :

    v-velocity, scalar, series

    Array Chunk Bytes 13.05 MiB 13.05 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • temp
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    potential temperature

    units :

    Celsius

    time :

    ocean_time

    field :

    temperature, scalar, series

    Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • salt
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    salinity

    time :

    ocean_time

    field :

    salinity, scalar, series

    Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • Vtransform
    ()
    int32
    ...

    long_name :

    vertical terrain-following transformation equation

    [1 values with dtype=int32]

  • pm
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    curvilinear coordinate metric in XI

    units :

    meter-1

    field :

    pm, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • pn
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    curvilinear coordinate metric in ETA

    units :

    meter-1

    field :

    pn, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • f
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    Coriolis parameter at RHO-points

    units :

    second-1

    field :

    coriolis, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

• Attributes: (29)

  file :

  ocean_his_0150.nc

  format :

  netCDF-3 classic file

  Conventions :

  CF-1.4

  type :

  ROMS/TOMS history file

  title :

  Texas and Louisiana Shelf case (Nesting)

  rst_file :

  ocean_rst.nc

  his_base :

  ocean_his

  grd_file :

  txla_grd_v4_new.nc

  ini_file :

  ocean_rst.nc

  frc_file_01 :

  txla_blk_narr_2009.nc

  frc_file_02 :

  TXLA_river_4dyes_2011.nc

  frc_file_03 :

  txla_flx_frc_9911.nc

  bry_file :

  txla_bry_2009_new.nc

  clm_file :

  txla_clm_2009_new.nc

  script_file :

  External/ocean_txla.in

  svn_url :

  https://www.myroms.org/svn/src/trunk

  svn_rev :

  exported

  code_dir :

  /scratch/zhangxq/ROMS

  header_dir :

  /scratch/zhangxq/projects/txla_nesting6

  header_file :

  txla.h

  os :

  Linux

  cpu :

  x86_64

  compiler_system :

  ifort

  compiler_command :

  /g/software/openmpi/1.4.3/intel/bin/mpif90

  compiler_flags :

  -heap-arrays -fp-model precise -assume 2underscores -convert big_endian -ip -O3 -free

  tiling :

  016x032

  history :

  ROMS/TOMS, Version 3.4, Sunday - December 4, 2011 - 7:17:45 AM

  ana_file :

  /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_btflux.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_hmixcoef.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_nudgcoef.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_stflux.h

  CPP_options :

  TXLA, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, BULK_FLUXES, CURVGRID, DIFF_GRID, DJ_GRADPS, DOUBLE_PRECISION, EAST_FSCHAPMAN, EAST_M2FLATHER, EAST_M3NUDGING, EAST_M3RADIATION, EAST_TNUDGING, EAST_TRADIATION, EMINUSP, LONGWAVE, M3CLIMATOLOGY, M3CLM_NUDGING, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, MY25_MIXING, NONLINEAR, NONLIN_EOS, NORTHERN_WALL, N2S2_HORAVG, POWER_LAW, PROFILE, QCORRECTION, K_GSCHEME, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SOUTH_FSCHAPMAN, SOUTH_M2FLATHER, SOUTH_M3NUDGING, SOUTH_M3RADIATION, SOUTH_TNUDGING, SOUTH_TRADIATION, SPLINES, TCLIMATOLOGY, TCLM_NUDGING, THREE_GHOST, T_PASSIVE, TS_MPDATA, TS_DIF2, TS_PSOURCE, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_LOGDRAG, UV_PSOURCE, UV_VIS2, VAR_RHO_2D, VISC_GRID, WEST_FSCHAPMAN, WEST_M2FLATHER, WEST_M3NUDGING, WEST_M3RADIATION, WEST_TNUDGING, WEST_TRADIATION

ds, xgrid = xroms.roms_dataset(ds, include_cell_volume=True)

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

# add grid to xrom accessor explicitly
ds.xroms.set_grid(xgrid)

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

ds.xroms.xgrid

<xgcm.Grid>
X Axis (not periodic, boundary=None):
  * center   xi_rho --> inner
  * inner    xi_u --> center
Y Axis (not periodic, boundary=None):
  * center   eta_rho --> inner
  * inner    eta_v --> center
Z Axis (not periodic, boundary=None):
  * center   s_rho --> outer
  * outer    s_w --> center

xgcm grid and extra ROMS coordinates

How to set up grid

The package xcgm has many nice grid functions for ROMS users, however, a bit of set up is required to connect from ROMS to the xgcm standardds. This grid set up does that.

The grid object contains metrics (X, Y, Z) with distances for each grid (‘dx’, ‘dx_u’, ‘dx_v’, ‘dx_psi’, and ‘dz’, ‘dz_u’, ‘dz_v’, ‘dz_w’, ‘dz_w_u’, ‘dz_w_v’, ‘dz_psi’, ‘dz_w_psi’), and all of these as grid coordinates too.

After setting up your Dataset, you should add coordinates and other information to the dataset and set up an xgcm grid object with:

ds, xgrid = xroms.roms_dataset(ds, include_cell_volume=True)

If you want to use the xroms accessor, add the grid object explicitly with:

ds.xroms.set_grid(xgrid)

If you don’t do this step, the first time the grid object is required it will be set up, though you can’t choose which input flags to use in that case.

The xgcm grid object is then available at

ds.xroms.xgrid

Grid lengths

Distances between grid nodes on every ROMS grid can be calculated and set up in the xgcm grid object — some by default and some have to be requested by the user with optional flags.

• Horizontal grids:

• inverse distances between nodes are given in an analogous way to distance (i.e., ds.pm and ds.pn_psi)

• distances between nodes are given in meters by dx’s and dy’s stored in ds, such as: ds.dx for the rho grid and ds.dy_psi for the psi grid, calculated from inverse distances

• Vertical grids:

• There are lazily-evaluated z-coordinates for both rho and w vertical grids for each horizontal grid.

• There are also arrays of z distances between nodes, called dz’s, available for each combination of grids. For example, there is ds.dz_u for z distances on the u horizontal and rho vertical grid, and there is ds.dz_w_v for z distances on the v horizontal and w vertical grid. These are [ocean_time x s_* x eta_* x xi_*] arrays.

• Arrays of z distances relative to a sea level of 0 are also available. They have analogous names to the previous entries but with “0” on the end. They are computationally faster to use because they do not vary in time. They are also less accurate for this reason but it depends on your use as to how much that matters.

Grid areas

• Horizontal

  • rho grid ds.dA, psi grid ds.dA_psi, u grid ds.dA_u, v grid ds.dA_v

• Vertical

  • These aren’t built in but can easily be calculated. For example, for cell areas in the x direction on the rho horizontal and rho vertical grids: ds.dx * ds.dz.

Grid volumes

Time varying: All 8 combinations of 4 horizontal grids and 2 vertical grids are available if include_cell_volume==True in roms_dataset(), such as: ds.dV (rho horizontal, rho vertical), and ds.dV_w_v (w vertical, v horizontal).

A user can easily calculate the same but for time-constant dz’s, for example as:

ds['dV_w'] = ds.dx * ds.dy * ds.dz_w0  # w vertical, rho horizontal, constant in time

You can calculate the full domain volume in time with:

ds.dV.sum(('s_rho', 'eta_rho', 'xi_rho'))

Or, using cf-xarray with:

ds.dV.cf.sum(('Z', 'Y', 'X'))

ds.dV.cf.sum(('Z', 'Y', 'X'))  # with cf-xarray accessor

<xarray.DataArray 'dV' (ocean_time: 2)> Size: 16B
dask.array<sum-aggregate, shape=(2,), dtype=float64, chunksize=(2,), chunktype=numpy.ndarray>
Coordinates:
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
Attributes:
    long_name:  volume metric in XI and ETA and S on RHO/RHO grids
    units:      meter3
    field:      dV, scalar

xarray.DataArray

'dV'

• ocean_time: 2

• dask.array<chunksize=(2,), meta=np.ndarray>

  Array Chunk Bytes 16 B 16 B Shape (2,) (2,) Dask graph 1 chunks in 49 graph layers Data type float64 numpy.ndarray

• Coordinates: (1)
  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (1)
  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

• Attributes: (3)

  long_name :

  volume metric in XI and ETA and S on RHO/RHO grids

  units :

  meter3

  field :

  dV, scalar

Change grids

A ROMS user frequently needs to move between horizontal and vertical grids, so it is built into many of the function wrappers, but you can also do it as a separate function. It can also be done directly to Datasets with the xroms accessor. Here we change salinity from its default grids to be on the psi grid horizontally and the s_w grid vertically:

ds.xroms.to_grid('salt', 'psi', 's_w')

You can also use the xroms function directly instead of using the xarray accessor if you prefer to have more options. Here is the equivalent call to the accessor, using the same defaults:

xroms.to_grid(ds["salt"], xgrid,
              hcoord="psi", scoord="s_w",
              hboundary="extend", hfill_value=None,
              sboundary="extend", sfill_value=None)

ds.xroms.to_grid('salt', 'psi', 's_w')

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'salt' (ocean_time: 2, s_w: 31, eta_v: 190, xi_u: 299)> Size: 14MB
dask.array<transpose, shape=(2, 31, 190, 299), dtype=float32, chunksize=(2, 31, 190, 299), chunktype=numpy.ndarray>
Coordinates:
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_u        (xi_u) int64 2kB 0 1 2 3 4 5 6 7 ... 292 293 294 295 296 297 298
  * eta_v       (eta_v) int64 2kB 0 1 2 3 4 5 6 ... 183 184 185 186 187 188 189
    z_w_psi     (ocean_time, s_w, eta_v, xi_u) float64 28MB dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>
Attributes:
    long_name:  salinity
    time:       ocean_time
    field:      salinity, scalar, series
    name:       salt
    units:      units

xarray.DataArray

'salt'

• ocean_time: 2
• s_w: 31
• eta_v: 190
• xi_u: 299

• dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

  Array Chunk Bytes 13.44 MiB 13.44 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 19 graph layers Data type float32 numpy.ndarray

• Coordinates: (5)
  • s_w
    (s_w)
    float64
    -1.0 -0.9667 ... -0.03333 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    axis :

    Z

    array([-1.      , -0.966667, -0.933333, -0.9     , -0.866667, -0.833333,
           -0.8     , -0.766667, -0.733333, -0.7     , -0.666667, -0.633333,
           -0.6     , -0.566667, -0.533333, -0.5     , -0.466667, -0.433333,
           -0.4     , -0.366667, -0.333333, -0.3     , -0.266667, -0.233333,
           -0.2     , -0.166667, -0.133333, -0.1     , -0.066667, -0.033333,
            0.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_u
    (xi_u)
    int64
    0 1 2 3 4 5 ... 294 295 296 297 298

    axis :

    X

    array([  0,   1,   2, ..., 296, 297, 298])

  • eta_v
    (eta_v)
    int64
    0 1 2 3 4 5 ... 185 186 187 188 189

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189])

  • z_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

    long_name :

    depth of PSI-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_psi, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.87 MiB 26.87 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 37 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_u
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           289, 290, 291, 292, 293, 294, 295, 296, 297, 298],
          dtype='int64', name='xi_u', length=299))

  • eta_v
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           180, 181, 182, 183, 184, 185, 186, 187, 188, 189],
          dtype='int64', name='eta_v', length=190))

• Attributes: (5)

  long_name :

  salinity

  time :

  ocean_time

  field :

  salinity, scalar, series

  name :

  salt

  units :

  units

Dimension ordering convention

By convention, ROMS DataArrays should be in the order [‘T’, ‘Z’, ‘Y’, ‘X’], for however many of these dimensions they contain. The following function does this for you:

xroms.order(ds.temp);  # function call

ds.temp.xroms.order();  # accessor

ds.temp.xroms.order()  # accessor

<xarray.DataArray 'temp' (ocean_time: 2, s_rho: 30, eta_rho: 191, xi_rho: 300)> Size: 14MB
dask.array<open_dataset-temp, shape=(2, 30, 191, 300), dtype=float32, chunksize=(2, 30, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>
Attributes:
    long_name:  potential temperature
    units:      Celsius
    time:       ocean_time
    field:      temperature, scalar, series

xarray.DataArray

'temp'

• ocean_time: 2
• s_rho: 30
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (4)

  long_name :

  potential temperature

  units :

  Celsius

  time :

  ocean_time

  field :

  temperature, scalar, series

Basic computations

These are all functions, not properties.

xarray

Many computations are built into xarray itself. Often it is possible to input the dimension over which to perform a computation by name, such as:

arr.sum(dim="xi_rho")

or

arr.sum(dim=("xi_rho","eta_rho"))

Note that many basic xarray calculations should be used with caution when using with ROMS output, since a ROMS grid can be stretched both horizontally and vertically. When using these functions, consider if your calculation should account for variable grid cell distances, areas, or volumes. Additionally, it is straight-forward to use basic grid functions from xarray on a ROMS time dimension (resampling, differentiation, interpolation, etc), however, be careful before using these functions on spatial dimensions for the same reasons as before.

ds.salt.mean(dim=("xi_rho","eta_rho"))

# same call but using cf-xarray
ds.salt.cf.mean(("Y","X"))

ds.salt.cf.mean(("Y","X"))

<xarray.DataArray 'salt' (ocean_time: 2, s_rho: 30)> Size: 240B
dask.array<mean_agg-aggregate, shape=(2, 30), dtype=float32, chunksize=(2, 30), chunktype=numpy.ndarray>
Coordinates:
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
Attributes:
    long_name:  salinity
    time:       ocean_time
    field:      salinity, scalar, series

xarray.DataArray

'salt'

• ocean_time: 2
• s_rho: 30

• dask.array<chunksize=(2, 30), meta=np.ndarray>

  Array Chunk Bytes 240 B 240 B Shape (2, 30) (2, 30) Dask graph 1 chunks in 4 graph layers Data type float32 numpy.ndarray

• Coordinates: (2)
  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

• Indexes: (2)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

• Attributes: (3)

  long_name :

  salinity

  time :

  ocean_time

  field :

  salinity, scalar, series

xroms grid-based metrics

Spatial metrics that account for the variable grid cell sizing in ROMS (both curvilinear horizontal and s vertical) are available by wrapping xgcm functions. These also have the additional benefit that the user can change grids and attributes are tracked. The available functions are:

• gridsum

• gridmean

Example usage:

xroms.gridsum(ds.temp, xgrid, dim)  # function call

ds['temp'].xroms.gridsum(xgrid, dim)  # accessor

where dimension names in the xgcm convention are ‘Z’, ‘Y’, or ‘X’. dim can be a string, list, or tuple of combinations of these names for dimensions to average over.

sum

uint = ds.u.xroms.gridsum(xgrid, "Z")

uint.xroms.gridsum(xgrid, 'Y')

<xarray.DataArray (ocean_time: 2, xi_u: 299)> Size: 5kB
dask.array<sum-aggregate, shape=(2, 299), dtype=float64, chunksize=(2, 299), chunktype=numpy.ndarray>
Coordinates:
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_u        (xi_u) int64 2kB 0 1 2 3 4 5 6 7 ... 292 293 294 295 296 297 298
Attributes:
    long_name:  u-momentum component
    units:      meter second-1
    time:       ocean_time
    field:      u-velocity, scalar, series

xarray.DataArray

• ocean_time: 2
• xi_u: 299

• dask.array<chunksize=(2, 299), meta=np.ndarray>

  Array Chunk Bytes 4.67 kiB 4.67 kiB Shape (2, 299) (2, 299) Dask graph 1 chunks in 58 graph layers Data type float64 numpy.ndarray

• Coordinates: (2)
  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_u
    (xi_u)
    int64
    0 1 2 3 4 5 ... 294 295 296 297 298

    axis :

    X

    array([  0,   1,   2, ..., 296, 297, 298])

• Indexes: (2)
  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_u
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           289, 290, 291, 292, 293, 294, 295, 296, 297, 298],
          dtype='int64', name='xi_u', length=299))

• Attributes: (4)

  long_name :

  u-momentum component

  units :

  meter second-1

  time :

  ocean_time

  field :

  u-velocity, scalar, series

mean

vint = ds.v.xroms.gridmean(xgrid, "Z")
vint.xroms.gridmean(xgrid, "Y")

<xarray.DataArray 'v' (ocean_time: 2, xi_rho: 300)> Size: 5kB
dask.array<truediv, shape=(2, 300), dtype=float64, chunksize=(2, 300), chunktype=numpy.ndarray>
Coordinates:
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
Attributes:
    long_name:  v-momentum component
    units:      meter second-1
    time:       ocean_time
    field:      v-velocity, scalar, series

xarray.DataArray

'v'

• ocean_time: 2
• xi_rho: 300

• dask.array<chunksize=(2, 300), meta=np.ndarray>

  Array Chunk Bytes 4.69 kiB 4.69 kiB Shape (2, 300) (2, 300) Dask graph 1 chunks in 83 graph layers Data type float64 numpy.ndarray

• Coordinates: (2)
  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

• Indexes: (2)
  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

• Attributes: (4)

  long_name :

  v-momentum component

  units :

  meter second-1

  time :

  ocean_time

  field :

  v-velocity, scalar, series

Derivatives

Vertical

Syntax is:

ds.xroms.ddz("salt")  # accessor to dataset

xroms.ddz(ds.salt, xgrid)  # No accessor

Other options:

ds.xroms.ddz('salt', hcoord='psi', scoord='s_rho', sboundary='extend', sfill_value=np.nan);  # Dataset

xroms.ddz(ds.salt, xgrid, hcoord='psi', scoord='s_rho', sboundary='extend', sfill_value=np.nan);  # No accessor

ds.xroms.ddz('salt')  # Dataset

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'dsaltdz' (ocean_time: 2, s_w: 31, eta_rho: 191, xi_rho: 300)> Size: 28MB
dask.array<truediv, shape=(2, 31, 191, 300), dtype=float64, chunksize=(2, 31, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_w         (ocean_time, s_w, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>
Attributes:
    long_name:  vertical derivative of salinity
    time:       ocean_time
    field:      salinity, scalar, series
    name:       dsaltdz
    units:      1/m * units

xarray.DataArray

'dsaltdz'

• ocean_time: 2
• s_w: 31
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 50 graph layers Data type float64 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_w
    (s_w)
    float64
    -1.0 -0.9667 ... -0.03333 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    axis :

    Z

    array([-1.      , -0.966667, -0.933333, -0.9     , -0.866667, -0.833333,
           -0.8     , -0.766667, -0.733333, -0.7     , -0.666667, -0.633333,
           -0.6     , -0.566667, -0.533333, -0.5     , -0.466667, -0.433333,
           -0.4     , -0.366667, -0.333333, -0.3     , -0.266667, -0.233333,
           -0.2     , -0.166667, -0.133333, -0.1     , -0.066667, -0.033333,
            0.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    long_name :

    depth of W-points

    time :

    ocean_time

    field :

    z_w, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (5)

  long_name :

  vertical derivative of salinity

  time :

  ocean_time

  field :

  salinity, scalar, series

  name :

  dsaltdz

  units :

  1/m * units

Horizontal

Syntax:

ds.xroms.ddxi('u');  # horizontal xi-direction gradient (accessor)

ds.xroms.ddeta('u');  #  horizontal eta-direction gradient (accessor)

dtempdxi, dtempdeta = xroms.hgrad(ds.temp, xgrid)  # both gradients simultaneously, as function

xroms.ddxi(ds.temp, xgrid)  # individual derivative, as function

xroms.ddeta(ds.temp, xgrid)  # individual derivative, as function

ds.xroms.ddxi('u')  # horizontal xi-direction gradient

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'dudxi' (ocean_time: 2, s_w: 31, eta_rho: 191, xi_rho: 300)> Size: 28MB
dask.array<sub, shape=(2, 31, 191, 300), dtype=float64, chunksize=(2, 31, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_w         (ocean_time, s_w, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>
Attributes:
    long_name:  horizontal xi derivative of u-momentum component
    units:      1/m * meter second-1
    time:       ocean_time
    field:      u-velocity, scalar, series
    name:       dudxi

xarray.DataArray

'dudxi'

• ocean_time: 2
• s_w: 31
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 121 graph layers Data type float64 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_w
    (s_w)
    float64
    -1.0 -0.9667 ... -0.03333 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    axis :

    Z

    array([-1.      , -0.966667, -0.933333, -0.9     , -0.866667, -0.833333,
           -0.8     , -0.766667, -0.733333, -0.7     , -0.666667, -0.633333,
           -0.6     , -0.566667, -0.533333, -0.5     , -0.466667, -0.433333,
           -0.4     , -0.366667, -0.333333, -0.3     , -0.266667, -0.233333,
           -0.2     , -0.166667, -0.133333, -0.1     , -0.066667, -0.033333,
            0.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    long_name :

    depth of W-points

    time :

    ocean_time

    field :

    z_w, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (5)

  long_name :

  horizontal xi derivative of u-momentum component

  units :

  1/m * meter second-1

  time :

  ocean_time

  field :

  u-velocity, scalar, series

  name :

  dudxi

Time

Use xarray directly for this.

ddt = ds.differentiate('ocean_time', datetime_unit='s')
ddt

<xarray.Dataset> Size: 734MB
Dimensions:     (eta_rho: 191, xi_rho: 300, s_rho: 30, s_w: 31, ocean_time: 2,
                 xi_u: 299, eta_v: 190)
Coordinates: (12/21)
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
    lon_u       (eta_rho, xi_u) float64 457kB dask.array<chunksize=(191, 299), meta=np.ndarray>
    ...          ...
    z_w_v       (ocean_time, s_w, eta_v, xi_rho) float64 28MB dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>
    z_w_psi     (ocean_time, s_w, eta_v, xi_u) float64 28MB dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>
    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>
    z_rho_u     (ocean_time, s_rho, eta_rho, xi_u) float64 27MB dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>
    z_rho_v     (ocean_time, s_rho, eta_v, xi_rho) float64 27MB dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>
    z_rho_psi   (ocean_time, s_rho, eta_v, xi_u) float64 27MB dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>
Data variables: (12/45)
    angle       (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    hc          float64 8B ...
    Cs_r        (s_rho) float64 240B dask.array<chunksize=(30,), meta=np.ndarray>
    zeta        (ocean_time, eta_rho, xi_rho) float32 458kB dask.array<chunksize=(2, 191, 300), meta=np.ndarray>
    h           (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    Cs_w        (s_w) float64 248B dask.array<chunksize=(31,), meta=np.ndarray>
    ...          ...
    dV_w_u      (ocean_time, s_w, eta_rho, xi_u) float64 28MB dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>
    dV_v        (ocean_time, s_rho, eta_v, xi_rho) float64 27MB dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>
    dV_w_v      (ocean_time, s_w, eta_v, xi_rho) float64 28MB dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>
    dV_psi      (ocean_time, s_rho, eta_v, xi_u) float64 27MB dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>
    dV_w_psi    (ocean_time, s_w, eta_v, xi_u) float64 28MB dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>
    rho0        int64 8B 1025
Attributes: (12/29)
    file:              ocean_his_0150.nc
    format:            netCDF-3 classic file
    Conventions:       CF-1.4
    type:              ROMS/TOMS history file
    title:             Texas and Louisiana Shelf case (Nesting)
    rst_file:          ocean_rst.nc
    ...                ...
    compiler_command:  /g/software/openmpi/1.4.3/intel/bin/mpif90
    compiler_flags:    -heap-arrays -fp-model precise -assume 2underscores -c...
    tiling:            016x032
    history:           ROMS/TOMS, Version 3.4, Sunday - December 4, 2011 -  7...
    ana_file:          /scratch/zhangxq/projects/txla_nesting6/Functionals/an...
    CPP_options:       TXLA, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, BULK_FLUX...

xarray.Dataset

• Dimensions:
  • eta_rho: 191
  • xi_rho: 300
  • s_rho: 30
  • s_w: 31
  • ocean_time: 2
  • xi_u: 299
  • eta_v: 190
• Coordinates: (21)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • s_w
    (s_w)
    float64
    -1.0 -0.9667 ... -0.03333 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    axis :

    Z

    array([-1.      , -0.966667, -0.933333, -0.9     , -0.866667, -0.833333,
           -0.8     , -0.766667, -0.733333, -0.7     , -0.666667, -0.633333,
           -0.6     , -0.566667, -0.533333, -0.5     , -0.466667, -0.433333,
           -0.4     , -0.366667, -0.333333, -0.3     , -0.266667, -0.233333,
           -0.2     , -0.166667, -0.133333, -0.1     , -0.066667, -0.033333,
            0.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lon_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • xi_u
    (xi_u)
    int64
    0 1 2 3 4 5 ... 294 295 296 297 298

    axis :

    X

    array([  0,   1,   2, ..., 296, 297, 298])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • eta_v
    (eta_v)
    int64
    0 1 2 3 4 5 ... 185 186 187 188 189

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189])

  • z_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    long_name :

    depth of W-points

    time :

    ocean_time

    field :

    z_w, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

  • z_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>

    long_name :

    depth of U-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_u, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 27.01 MiB 27.01 MiB Shape (2, 31, 191, 299) (2, 31, 191, 299) Dask graph 1 chunks in 32 graph layers Data type float64 numpy.ndarray

  • z_w_v
    (ocean_time, s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>

    long_name :

    depth of V-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_v, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.96 MiB 26.96 MiB Shape (2, 31, 190, 300) (2, 31, 190, 300) Dask graph 1 chunks in 34 graph layers Data type float64 numpy.ndarray

  • z_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

    long_name :

    depth of PSI-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_psi, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.87 MiB 26.87 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 37 graph layers Data type float64 numpy.ndarray

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

  • z_rho_u
    (ocean_time, s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    long_name :

    depth of U-points on vertical RHO grid

    time :

    ocean_time

    field :

    z_rho_u, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.14 MiB 26.14 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 32 graph layers Data type float64 numpy.ndarray

  • z_rho_v
    (ocean_time, s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    long_name :

    depth of V-points on vertical RHO grid

    time :

    ocean_time

    field :

    z_rho_v, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.09 MiB 26.09 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 34 graph layers Data type float64 numpy.ndarray

  • z_rho_psi
    (ocean_time, s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>

    long_name :

    depth of PSI-points on vertical RHO grid

    time :

    ocean_time

    field :

    z_rho_psi, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.01 MiB 26.01 MiB Shape (2, 30, 190, 299) (2, 30, 190, 299) Dask graph 1 chunks in 37 graph layers Data type float64 numpy.ndarray

• Data variables: (45)
  • angle
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    angle between XI-axis and EAST

    units :

    radians

    field :

    angle, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • hc
    ()
    float64
    ...

    long_name :

    S-coordinate parameter, critical depth

    units :

    meter

    [1 values with dtype=float64]

  • Cs_r
    (s_rho)
    float64
    dask.array<chunksize=(30,), meta=np.ndarray>

    long_name :

    S-coordinate stretching curves at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    field :

    Cs_r, scalar

    Array Chunk Bytes 240 B 240 B Shape (30,) (30,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • zeta
    (ocean_time, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 191, 300), meta=np.ndarray>

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (2, 191, 300) (2, 191, 300) Dask graph 1 chunks in 7 graph layers Data type float32 numpy.ndarray

  • h
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    bathymetry at RHO-points

    units :

    meter

    field :

    bath, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • Cs_w
    (s_w)
    float64
    dask.array<chunksize=(31,), meta=np.ndarray>

    long_name :

    S-coordinate stretching curves at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    field :

    Cs_w, scalar

    Array Chunk Bytes 248 B 248 B Shape (31,) (31,) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    mask on RHO-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    long_name :

    mask on U-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • mask_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    long_name :

    mask on V-points

    flag_values :

    [0. 1.]

    flag_meanings :

    land water

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • u
    (ocean_time, s_rho, eta_rho, xi_u)
    float32
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    Array Chunk Bytes 13.07 MiB 13.07 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 7 graph layers Data type float32 numpy.ndarray

  • v
    (ocean_time, s_rho, eta_v, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    Array Chunk Bytes 13.05 MiB 13.05 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 7 graph layers Data type float32 numpy.ndarray

  • temp
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 7 graph layers Data type float32 numpy.ndarray

  • salt
    (ocean_time, s_rho, eta_rho, xi_rho)
    float32
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 7 graph layers Data type float32 numpy.ndarray

  • Vtransform
    ()
    int32
    ...

    long_name :

    vertical terrain-following transformation equation

    [1 values with dtype=int32]

  • pm
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    curvilinear coordinate metric in XI

    units :

    meter-1

    field :

    pm, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • pn
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    curvilinear coordinate metric in ETA

    units :

    meter-1

    field :

    pn, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • f
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    Coriolis parameter at RHO-points

    units :

    second-1

    field :

    coriolis, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • spherical
    ()
    int64
    1

    array(1)

  • 3d
    ()
    bool
    True

    array(True)

  • dx
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI

    units :

    meter

    field :

    dx, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

  • dx_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI on U grid

    units :

    meter

    field :

    dx_u, scalar

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 6 graph layers Data type float64 numpy.ndarray

  • dx_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI on V grid

    units :

    meter

    field :

    dx_v, scalar

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray

  • dx_psi
    (eta_v, xi_u)
    float64
    dask.array<chunksize=(190, 299), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in XI on PSI grid

    units :

    meter

    field :

    dx_psi, scalar

    Array Chunk Bytes 443.83 kiB 443.83 kiB Shape (190, 299) (190, 299) Dask graph 1 chunks in 11 graph layers Data type float64 numpy.ndarray

  • dy
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA

    units :

    meter

    field :

    dy, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 3 graph layers Data type float64 numpy.ndarray

  • dy_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA on U grid

    units :

    meter

    field :

    dy_u, scalar

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 6 graph layers Data type float64 numpy.ndarray

  • dy_v
    (eta_v, xi_rho)
    float64
    dask.array<chunksize=(190, 300), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA on V grid

    units :

    meter

    field :

    dy_v, scalar

    Array Chunk Bytes 445.31 kiB 445.31 kiB Shape (190, 300) (190, 300) Dask graph 1 chunks in 8 graph layers Data type float64 numpy.ndarray

  • dy_psi
    (eta_v, xi_u)
    float64
    dask.array<chunksize=(190, 299), meta=np.ndarray>

    long_name :

    inverse curvilinear coordinate metric in ETA on PSI grid

    units :

    meter

    field :

    dy_psi, scalar

    Array Chunk Bytes 443.83 kiB 443.83 kiB Shape (190, 299) (190, 299) Dask graph 1 chunks in 11 graph layers Data type float64 numpy.ndarray

  • dz
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 39 graph layers Data type float64 numpy.ndarray

  • dz_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 43 graph layers Data type float64 numpy.ndarray

  • dz_u
    (ocean_time, s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    Array Chunk Bytes 26.14 MiB 26.14 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 42 graph layers Data type float64 numpy.ndarray

  • dz_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>

    Array Chunk Bytes 27.01 MiB 27.01 MiB Shape (2, 31, 191, 299) (2, 31, 191, 299) Dask graph 1 chunks in 46 graph layers Data type float64 numpy.ndarray

  • dz_v
    (ocean_time, s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    Array Chunk Bytes 26.09 MiB 26.09 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 44 graph layers Data type float64 numpy.ndarray

  • dz_w_v
    (ocean_time, s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>

    Array Chunk Bytes 26.96 MiB 26.96 MiB Shape (2, 31, 190, 300) (2, 31, 190, 300) Dask graph 1 chunks in 48 graph layers Data type float64 numpy.ndarray

  • dz_psi
    (ocean_time, s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>

    Array Chunk Bytes 26.01 MiB 26.01 MiB Shape (2, 30, 190, 299) (2, 30, 190, 299) Dask graph 1 chunks in 47 graph layers Data type float64 numpy.ndarray

  • dz_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

    Array Chunk Bytes 26.87 MiB 26.87 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 51 graph layers Data type float64 numpy.ndarray

  • dA
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    long_name :

    area metric in XI and ETA on RHO grid

    units :

    meter2

    field :

    dA, scalar

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 7 graph layers Data type float64 numpy.ndarray

  • dV
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 49 graph layers Data type float64 numpy.ndarray

  • dV_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 53 graph layers Data type float64 numpy.ndarray

  • dV_u
    (ocean_time, s_rho, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 30, 191, 299), meta=np.ndarray>

    Array Chunk Bytes 26.14 MiB 26.14 MiB Shape (2, 30, 191, 299) (2, 30, 191, 299) Dask graph 1 chunks in 58 graph layers Data type float64 numpy.ndarray

  • dV_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>

    Array Chunk Bytes 27.01 MiB 27.01 MiB Shape (2, 31, 191, 299) (2, 31, 191, 299) Dask graph 1 chunks in 62 graph layers Data type float64 numpy.ndarray

  • dV_v
    (ocean_time, s_rho, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 190, 300), meta=np.ndarray>

    Array Chunk Bytes 26.09 MiB 26.09 MiB Shape (2, 30, 190, 300) (2, 30, 190, 300) Dask graph 1 chunks in 64 graph layers Data type float64 numpy.ndarray

  • dV_w_v
    (ocean_time, s_w, eta_v, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 190, 300), meta=np.ndarray>

    Array Chunk Bytes 26.96 MiB 26.96 MiB Shape (2, 31, 190, 300) (2, 31, 190, 300) Dask graph 1 chunks in 68 graph layers Data type float64 numpy.ndarray

  • dV_psi
    (ocean_time, s_rho, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 30, 190, 299), meta=np.ndarray>

    Array Chunk Bytes 26.01 MiB 26.01 MiB Shape (2, 30, 190, 299) (2, 30, 190, 299) Dask graph 1 chunks in 73 graph layers Data type float64 numpy.ndarray

  • dV_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

    Array Chunk Bytes 26.87 MiB 26.87 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 77 graph layers Data type float64 numpy.ndarray

  • rho0
    ()
    int64
    1025

    array(1025)

• Indexes: (7)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • xi_u
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           289, 290, 291, 292, 293, 294, 295, 296, 297, 298],
          dtype='int64', name='xi_u', length=299))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

  • eta_v
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           180, 181, 182, 183, 184, 185, 186, 187, 188, 189],
          dtype='int64', name='eta_v', length=190))

• Attributes: (29)

  file :

  ocean_his_0150.nc

  format :

  netCDF-3 classic file

  Conventions :

  CF-1.4

  type :

  ROMS/TOMS history file

  title :

  Texas and Louisiana Shelf case (Nesting)

  rst_file :

  ocean_rst.nc

  his_base :

  ocean_his

  grd_file :

  txla_grd_v4_new.nc

  ini_file :

  ocean_rst.nc

  frc_file_01 :

  txla_blk_narr_2009.nc

  frc_file_02 :

  TXLA_river_4dyes_2011.nc

  frc_file_03 :

  txla_flx_frc_9911.nc

  bry_file :

  txla_bry_2009_new.nc

  clm_file :

  txla_clm_2009_new.nc

  script_file :

  External/ocean_txla.in

  svn_url :

  https://www.myroms.org/svn/src/trunk

  svn_rev :

  exported

  code_dir :

  /scratch/zhangxq/ROMS

  header_dir :

  /scratch/zhangxq/projects/txla_nesting6

  header_file :

  txla.h

  os :

  Linux

  cpu :

  x86_64

  compiler_system :

  ifort

  compiler_command :

  /g/software/openmpi/1.4.3/intel/bin/mpif90

  compiler_flags :

  -heap-arrays -fp-model precise -assume 2underscores -convert big_endian -ip -O3 -free

  tiling :

  016x032

  history :

  ROMS/TOMS, Version 3.4, Sunday - December 4, 2011 - 7:17:45 AM

  ana_file :

  /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_btflux.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_hmixcoef.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_nudgcoef.h, /scratch/zhangxq/projects/txla_nesting6/Functionals/ana_stflux.h

  CPP_options :

  TXLA, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, BULK_FLUXES, CURVGRID, DIFF_GRID, DJ_GRADPS, DOUBLE_PRECISION, EAST_FSCHAPMAN, EAST_M2FLATHER, EAST_M3NUDGING, EAST_M3RADIATION, EAST_TNUDGING, EAST_TRADIATION, EMINUSP, LONGWAVE, M3CLIMATOLOGY, M3CLM_NUDGING, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, MY25_MIXING, NONLINEAR, NONLIN_EOS, NORTHERN_WALL, N2S2_HORAVG, POWER_LAW, PROFILE, QCORRECTION, K_GSCHEME, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SOUTH_FSCHAPMAN, SOUTH_M2FLATHER, SOUTH_M3NUDGING, SOUTH_M3RADIATION, SOUTH_TNUDGING, SOUTH_TRADIATION, SPLINES, TCLIMATOLOGY, TCLM_NUDGING, THREE_GHOST, T_PASSIVE, TS_MPDATA, TS_DIF2, TS_PSOURCE, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_LOGDRAG, UV_PSOURCE, UV_VIS2, VAR_RHO_2D, VISC_GRID, WEST_FSCHAPMAN, WEST_M2FLATHER, WEST_M3NUDGING, WEST_M3RADIATION, WEST_TNUDGING, WEST_TRADIATION

Built-in Physical Calculations

These are all properties of the accessor, so should be called without (). Demonstrated below are the calculations using the accessor and not using the accessor.

Horizontal speed

ds.xroms.speed  # accessor

xroms.speed(ds.u, ds.v, xgrid)  # function

ds.xroms.speed

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'speed' (ocean_time: 2, s_rho: 30, eta_rho: 191, xi_rho: 300)> Size: 14MB
dask.array<sqrt, shape=(2, 30, 191, 300), dtype=float32, chunksize=(2, 30, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>
Attributes:
    long_name:  horizontal speed
    units:      m/s
    time:       ocean_time
    field:      u-velocity, scalar, series
    name:       speed

xarray.DataArray

'speed'

• ocean_time: 2
• s_rho: 30
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 24 graph layers Data type float32 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (5)

  long_name :

  horizontal speed

  units :

  m/s

  time :

  ocean_time

  field :

  u-velocity, scalar, series

  name :

  speed

Kinetic energy

ds.xroms.KE  # accessor

# without the accessor you need to manage this yourself — first calculate speed to then calculate KE
speed = xroms.speed(ds.u, ds.v, xgrid)
xroms.KE(ds.rho0, speed)

ds.xroms.KE

<xarray.DataArray 'KE' (ocean_time: 2, s_rho: 30, eta_rho: 191, xi_rho: 300)> Size: 28MB
dask.array<multiply, shape=(2, 30, 191, 300), dtype=float64, chunksize=(2, 30, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>
Attributes:
    name:       KE
    long_name:  kinetic energy
    units:      kg/(m*s^2)

xarray.DataArray

'KE'

• ocean_time: 2
• s_rho: 30
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 27 graph layers Data type float64 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (3)

  name :

  KE

  long_name :

  kinetic energy

  units :

  kg/(m*s^2)

Geostrophic velocities

ds.xroms.ug  # accessor, u component
ds.xroms.vg  # accessor, v component

ug, vg = xroms.uv_geostrophic(ds.zeta, ds.f, xgrid)  # function

ds.xroms.ug

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'ug' (ocean_time: 2, eta_rho: 191, xi_u: 299)> Size: 914kB
dask.array<truediv, shape=(2, 191, 299), dtype=float64, chunksize=(2, 191, 299), chunktype=numpy.ndarray>
Coordinates:
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
    lon_u       (eta_rho, xi_u) float64 457kB dask.array<chunksize=(191, 299), meta=np.ndarray>
    lat_u       (eta_rho, xi_u) float64 457kB dask.array<chunksize=(191, 299), meta=np.ndarray>
  * xi_u        (xi_u) int64 2kB 0 1 2 3 4 5 6 7 ... 292 293 294 295 296 297 298
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
Attributes:
    long_name:  geostrophic u velocity
    units:      m/s
    time:       ocean_time
    field:      free-surface, scalar, series
    name:       ug

xarray.DataArray

'ug'

• ocean_time: 2
• eta_rho: 191
• xi_u: 299

• dask.array<chunksize=(2, 191, 299), meta=np.ndarray>

  Array Chunk Bytes 892.33 kiB 892.33 kiB Shape (2, 191, 299) (2, 191, 299) Dask graph 1 chunks in 37 graph layers Data type float64 numpy.ndarray

• Coordinates: (5)
  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • xi_u
    (xi_u)
    int64
    0 1 2 3 4 5 ... 294 295 296 297 298

    axis :

    X

    array([  0,   1,   2, ..., 296, 297, 298])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

• Indexes: (3)
  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_u
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           289, 290, 291, 292, 293, 294, 295, 296, 297, 298],
          dtype='int64', name='xi_u', length=299))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (5)

  long_name :

  geostrophic u velocity

  units :

  m/s

  time :

  ocean_time

  field :

  free-surface, scalar, series

  name :

  ug

Eddy kinetic energy (EKE)

ds.xroms.EKE  # accessor

ug, vg = xroms.uv_geostrophic(ds.zeta, ds.f, xgrid)
xroms.EKE(ug, vg, xgrid)

ds.xroms.EKE

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'EKE' (ocean_time: 2, eta_rho: 191, xi_rho: 300)> Size: 917kB
dask.array<mul, shape=(2, 191, 300), dtype=float64, chunksize=(2, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
Attributes:
    long_name:  eddy kinetic energy
    units:      m^2/s^2
    time:       ocean_time
    field:      free-surface, scalar, series
    name:       EKE

xarray.DataArray

'EKE'

• ocean_time: 2
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 895.31 kiB 895.31 kiB Shape (2, 191, 300) (2, 191, 300) Dask graph 1 chunks in 88 graph layers Data type float64 numpy.ndarray

• Coordinates: (5)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

• Indexes: (3)
  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (5)

  long_name :

  eddy kinetic energy

  units :

  m^2/s^2

  time :

  ocean_time

  field :

  free-surface, scalar, series

  name :

  EKE

Vertical shear

Since it is a common use case, there are specific methods to return the u and v components of vertical shear on their own grids. These are just available for Datasets.

ds.xroms.dudz
ds.xroms.dvdz

xroms.dudz(ds.u, xgrid)
xroms.dvdz(ds.v, xgrid)

# already on same grid:
ds.xroms.vertical_shear

dudz = xroms.dudz(ds.u, xgrid)
dvdz = xroms.dvdz(ds.v, xgrid)
xroms.vertical_shear(dudz, dvdz, xgrid)

ds.xroms.dudz

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'dudz' (ocean_time: 2, s_w: 31, eta_rho: 191, xi_u: 299)> Size: 28MB
dask.array<truediv, shape=(2, 31, 191, 299), dtype=float64, chunksize=(2, 31, 191, 299), chunktype=numpy.ndarray>
Coordinates:
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
    lon_u       (eta_rho, xi_u) float64 457kB dask.array<chunksize=(191, 299), meta=np.ndarray>
    lat_u       (eta_rho, xi_u) float64 457kB dask.array<chunksize=(191, 299), meta=np.ndarray>
  * xi_u        (xi_u) int64 2kB 0 1 2 3 4 5 6 7 ... 292 293 294 295 296 297 298
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_w_u       (ocean_time, s_w, eta_rho, xi_u) float64 28MB dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>
Attributes:
    name:       dudz
    long_name:  u component of vertical shear
    units:      1/s

xarray.DataArray

'dudz'

• ocean_time: 2
• s_w: 31
• eta_rho: 191
• xi_u: 299

• dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>

  Array Chunk Bytes 27.01 MiB 27.01 MiB Shape (2, 31, 191, 299) (2, 31, 191, 299) Dask graph 1 chunks in 53 graph layers Data type float64 numpy.ndarray

• Coordinates: (7)
  • s_w
    (s_w)
    float64
    -1.0 -0.9667 ... -0.03333 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    axis :

    Z

    array([-1.      , -0.966667, -0.933333, -0.9     , -0.866667, -0.833333,
           -0.8     , -0.766667, -0.733333, -0.7     , -0.666667, -0.633333,
           -0.6     , -0.566667, -0.533333, -0.5     , -0.466667, -0.433333,
           -0.4     , -0.366667, -0.333333, -0.3     , -0.266667, -0.233333,
           -0.2     , -0.166667, -0.133333, -0.1     , -0.066667, -0.033333,
            0.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_u
    (eta_rho, xi_u)
    float64
    dask.array<chunksize=(191, 299), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 446.16 kiB 446.16 kiB Shape (191, 299) (191, 299) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • xi_u
    (xi_u)
    int64
    0 1 2 3 4 5 ... 294 295 296 297 298

    axis :

    X

    array([  0,   1,   2, ..., 296, 297, 298])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_w_u
    (ocean_time, s_w, eta_rho, xi_u)
    float64
    dask.array<chunksize=(2, 31, 191, 299), meta=np.ndarray>

    long_name :

    depth of U-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_u, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 27.01 MiB 27.01 MiB Shape (2, 31, 191, 299) (2, 31, 191, 299) Dask graph 1 chunks in 32 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_u
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           289, 290, 291, 292, 293, 294, 295, 296, 297, 298],
          dtype='int64', name='xi_u', length=299))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (3)

  name :

  dudz

  long_name :

  u component of vertical shear

  units :

  1/s

The magnitude of the vertical shear is also a built-in derived variable for the xroms accessor:

ds.xroms.vertical_shear

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'shear' (ocean_time: 2, s_w: 31, eta_rho: 191, xi_rho: 300)> Size: 28MB
dask.array<sqrt, shape=(2, 31, 191, 300), dtype=float64, chunksize=(2, 31, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_w         (ocean_time, s_w, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>
Attributes:
    name:       shear
    long_name:  vertical shear
    units:      1/s

xarray.DataArray

'shear'

• ocean_time: 2
• s_w: 31
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 90 graph layers Data type float64 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_w
    (s_w)
    float64
    -1.0 -0.9667 ... -0.03333 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    axis :

    Z

    array([-1.      , -0.966667, -0.933333, -0.9     , -0.866667, -0.833333,
           -0.8     , -0.766667, -0.733333, -0.7     , -0.666667, -0.633333,
           -0.6     , -0.566667, -0.533333, -0.5     , -0.466667, -0.433333,
           -0.4     , -0.366667, -0.333333, -0.3     , -0.266667, -0.233333,
           -0.2     , -0.166667, -0.133333, -0.1     , -0.066667, -0.033333,
            0.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    long_name :

    depth of W-points

    time :

    ocean_time

    field :

    z_w, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (3)

  name :

  shear

  long_name :

  vertical shear

  units :

  1/s

Vertical vorticity

ds.xroms.vort

xroms.relative_vorticity(ds.u, ds.v, xgrid)

ds.xroms.vort

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'vort' (ocean_time: 2, s_w: 31, eta_v: 190, xi_u: 299)> Size: 28MB
dask.array<sub, shape=(2, 31, 190, 299), dtype=float64, chunksize=(2, 31, 190, 299), chunktype=numpy.ndarray>
Coordinates:
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_u        (xi_u) int64 2kB 0 1 2 3 4 5 6 7 ... 292 293 294 295 296 297 298
  * eta_v       (eta_v) int64 2kB 0 1 2 3 4 5 6 ... 183 184 185 186 187 188 189
    z_w_psi     (ocean_time, s_w, eta_v, xi_u) float64 28MB dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>
Attributes:
    long_name:  vertical component of vorticity
    units:      1/s
    time:       ocean_time
    field:      v-velocity, scalar, series
    name:       vort

xarray.DataArray

'vort'

• ocean_time: 2
• s_w: 31
• eta_v: 190
• xi_u: 299

• dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

  Array Chunk Bytes 26.87 MiB 26.87 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 201 graph layers Data type float64 numpy.ndarray

• Coordinates: (5)
  • s_w
    (s_w)
    float64
    -1.0 -0.9667 ... -0.03333 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    axis :

    Z

    array([-1.      , -0.966667, -0.933333, -0.9     , -0.866667, -0.833333,
           -0.8     , -0.766667, -0.733333, -0.7     , -0.666667, -0.633333,
           -0.6     , -0.566667, -0.533333, -0.5     , -0.466667, -0.433333,
           -0.4     , -0.366667, -0.333333, -0.3     , -0.266667, -0.233333,
           -0.2     , -0.166667, -0.133333, -0.1     , -0.066667, -0.033333,
            0.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_u
    (xi_u)
    int64
    0 1 2 3 4 5 ... 294 295 296 297 298

    axis :

    X

    array([  0,   1,   2, ..., 296, 297, 298])

  • eta_v
    (eta_v)
    int64
    0 1 2 3 4 5 ... 185 186 187 188 189

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189])

  • z_w_psi
    (ocean_time, s_w, eta_v, xi_u)
    float64
    dask.array<chunksize=(2, 31, 190, 299), meta=np.ndarray>

    long_name :

    depth of PSI-points on vertical W grid

    time :

    ocean_time

    field :

    z_w_psi, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.87 MiB 26.87 MiB Shape (2, 31, 190, 299) (2, 31, 190, 299) Dask graph 1 chunks in 37 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_u
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           289, 290, 291, 292, 293, 294, 295, 296, 297, 298],
          dtype='int64', name='xi_u', length=299))

  • eta_v
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           180, 181, 182, 183, 184, 185, 186, 187, 188, 189],
          dtype='int64', name='eta_v', length=190))

• Attributes: (5)

  long_name :

  vertical component of vorticity

  units :

  1/s

  time :

  ocean_time

  field :

  v-velocity, scalar, series

  name :

  vort

Horizontal convergence

Horizontal component of the currents convergence.

ds.xroms.convergence

xroms.convergence(ds.u, ds.v, xgrid)

ds.xroms.convergence

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'convergence' (ocean_time: 2, s_rho: 30, eta_rho: 191,
                                 xi_rho: 300)> Size: 28MB
dask.array<add, shape=(2, 30, 191, 300), dtype=float64, chunksize=(2, 30, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>
Attributes:
    long_name:  horizontal convergence
    units:      1/s
    time:       ocean_time
    field:      u-velocity, scalar, series
    name:       convergence

xarray.DataArray

'convergence'

• ocean_time: 2
• s_rho: 30
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 227 graph layers Data type float64 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (5)

  long_name :

  horizontal convergence

  units :

  1/s

  time :

  ocean_time

  field :

  u-velocity, scalar, series

  name :

  convergence

Normalized surface convergence

Horizontal component of the currents convergence at the surface, normalized by $f$. This is only available through the accessor.

ds.xroms.convergence_norm

ds.xroms.convergence_norm

<xarray.DataArray 'convergence_norm' (ocean_time: 2, eta_rho: 191, xi_rho: 300)> Size: 917kB
dask.array<truediv, shape=(2, 191, 300), dtype=float64, chunksize=(2, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
Attributes:
    name:       convergence_norm
    long_name:  normalized surface horizontal convergence
    units:      

xarray.DataArray

'convergence_norm'

• ocean_time: 2
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 895.31 kiB 895.31 kiB Shape (2, 191, 300) (2, 191, 300) Dask graph 1 chunks in 232 graph layers Data type float64 numpy.ndarray

• Coordinates: (5)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

• Indexes: (3)
  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (3)

  name :

  convergence_norm

  long_name :

  normalized surface horizontal convergence

  units :

Ertel potential vorticity

The accessor assumes you want the Ertel potential vorticity of the buoyancy:

ds.xroms.ertel

sig0 = xroms.potential_density(ds.temp, ds.salt)
buoyancy = xroms.buoyancy(sig0, rho0=ds.rho0)
xroms.ertel(buoyancy, ds.u, ds.v, ds.f, xgrid, scoord='s_w')

Alternatively, the user can access the original function and use a different tracer for this calculation (in this example, “dye_01”), and can return the result on a different vertical grid, for example:

xroms.ertel(ds.dye_01, ds.u, ds.v, ds.f, xgrid, scoord='s_w')

ds.xroms.ertel

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'ertel' (ocean_time: 2, s_rho: 30, eta_rho: 191, xi_rho: 300)> Size: 28MB
dask.array<add, shape=(2, 30, 191, 300), dtype=float64, chunksize=(2, 30, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>
Attributes:
    name:       ertel
    long_name:  ertel potential vorticity
    units:      tracer/(m*s)

xarray.DataArray

'ertel'

• ocean_time: 2
• s_rho: 30
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 518 graph layers Data type float64 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (3)

  name :

  ertel

  long_name :

  ertel potential vorticity

  units :

  tracer/(m*s)

Density

ds.xroms.rho

xroms.density(ds.temp, ds.salt)

ds.xroms.rho

<xarray.DataArray 'rho' (ocean_time: 2, s_rho: 30, eta_rho: 191, xi_rho: 300)> Size: 28MB
dask.array<truediv, shape=(2, 30, 191, 300), dtype=float64, chunksize=(2, 30, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>
Attributes:
    long_name:  density
    units:      kg/m^3
    time:       ocean_time
    field:      temperature, scalar, series
    name:       rho

xarray.DataArray

'rho'

• ocean_time: 2
• s_rho: 30
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 144 graph layers Data type float64 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (5)

  long_name :

  density

  units :

  kg/m^3

  time :

  ocean_time

  field :

  temperature, scalar, series

  name :

  rho

Potential density

ds.xroms.sig0

xroms.potential_density(ds.temp, ds.salt)

ds.xroms.sig0

<xarray.DataArray 'sig0' (ocean_time: 2, s_rho: 30, eta_rho: 191, xi_rho: 300)> Size: 14MB
dask.array<truediv, shape=(2, 30, 191, 300), dtype=float32, chunksize=(2, 30, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>
Attributes:
    long_name:  potential density
    units:      kg/m^3
    time:       ocean_time
    field:      temperature, scalar, series
    name:       sig0

xarray.DataArray

'sig0'

• ocean_time: 2
• s_rho: 30
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 13.11 MiB 13.11 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 113 graph layers Data type float32 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (5)

  long_name :

  potential density

  units :

  kg/m^3

  time :

  ocean_time

  field :

  temperature, scalar, series

  name :

  sig0

Buoyancy

ds.xroms.buoyancy

sig0 = xroms.potential_density(ds.temp, ds.salt);
xroms.buoyancy(sig0)

ds.xroms.buoyancy

<xarray.DataArray 'buoyancy' (ocean_time: 2, s_rho: 30, eta_rho: 191,
                              xi_rho: 300)> Size: 28MB
dask.array<truediv, shape=(2, 30, 191, 300), dtype=float64, chunksize=(2, 30, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_rho       (ocean_time, s_rho, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>
Attributes:
    long_name:  buoyancy
    units:      m/s^2
    time:       ocean_time
    field:      temperature, scalar, series
    name:       buoyancy

xarray.DataArray

'buoyancy'

• ocean_time: 2
• s_rho: 30
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 116 graph layers Data type float64 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_rho
    (ocean_time, s_rho, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 30, 191, 300), meta=np.ndarray>

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 26.23 MiB 26.23 MiB Shape (2, 30, 191, 300) (2, 30, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (5)

  long_name :

  buoyancy

  units :

  m/s^2

  time :

  ocean_time

  field :

  temperature, scalar, series

  name :

  buoyancy

Buoyancy frequency

Also called vertical buoyancy gradient.

ds.xroms.N2

rho = xroms.density(ds.temp, ds.salt)  # calculate rho if not in output
xroms.N2(rho, xgrid)

ds.xroms.N2

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'N2' (ocean_time: 2, s_w: 31, eta_rho: 191, xi_rho: 300)> Size: 28MB
dask.array<truediv, shape=(2, 31, 191, 300), dtype=float64, chunksize=(2, 31, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_w         (ocean_time, s_w, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>
Attributes:
    long_name:  buoyancy frequency squared, or vertical buoyancy gradient
    units:      1/s^2
    time:       ocean_time
    field:      temperature, scalar, series
    name:       N2

xarray.DataArray

'N2'

• ocean_time: 2
• s_w: 31
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 166 graph layers Data type float64 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_w
    (s_w)
    float64
    -1.0 -0.9667 ... -0.03333 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    axis :

    Z

    array([-1.      , -0.966667, -0.933333, -0.9     , -0.866667, -0.833333,
           -0.8     , -0.766667, -0.733333, -0.7     , -0.666667, -0.633333,
           -0.6     , -0.566667, -0.533333, -0.5     , -0.466667, -0.433333,
           -0.4     , -0.366667, -0.333333, -0.3     , -0.266667, -0.233333,
           -0.2     , -0.166667, -0.133333, -0.1     , -0.066667, -0.033333,
            0.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    long_name :

    depth of W-points

    time :

    ocean_time

    field :

    z_w, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (5)

  long_name :

  buoyancy frequency squared, or vertical buoyancy gradient

  units :

  1/s^2

  time :

  ocean_time

  field :

  temperature, scalar, series

  name :

  N2

Horizontal buoyancy gradient

ds.xroms.M2

rho = xroms.density(ds.temp, ds.salt)  # calculate rho if not in output
xroms.M2(rho, xgrid)

ds.xroms.M2

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid_ufunc.py:832: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.
  out_dim: grid._ds.dims[out_dim] for arg in out_core_dims for out_dim in arg

<xarray.DataArray 'M2' (ocean_time: 2, s_w: 31, eta_rho: 191, xi_rho: 300)> Size: 28MB
dask.array<truediv, shape=(2, 31, 191, 300), dtype=float64, chunksize=(2, 31, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_w         (s_w) float64 248B -1.0 -0.9667 -0.9333 ... -0.03333 0.0
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    z_w         (ocean_time, s_w, eta_rho, xi_rho) float64 28MB dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>
Attributes:
    long_name:  horizontal buoyancy gradient
    units:      1/s^2
    time:       ocean_time
    field:      temperature, scalar, series
    name:       M2

xarray.DataArray

'M2'

• ocean_time: 2
• s_w: 31
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 282 graph layers Data type float64 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_w
    (s_w)
    float64
    -1.0 -0.9667 ... -0.03333 0.0

    long_name :

    S-coordinate at W-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_w C: Cs_w eta: zeta depth: h depth_c: hc

    field :

    s_w, scalar

    axis :

    Z

    array([-1.      , -0.966667, -0.933333, -0.9     , -0.866667, -0.833333,
           -0.8     , -0.766667, -0.733333, -0.7     , -0.666667, -0.633333,
           -0.6     , -0.566667, -0.533333, -0.5     , -0.466667, -0.433333,
           -0.4     , -0.366667, -0.333333, -0.3     , -0.266667, -0.233333,
           -0.2     , -0.166667, -0.133333, -0.1     , -0.066667, -0.033333,
            0.      ])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • z_w
    (ocean_time, s_w, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 31, 191, 300), meta=np.ndarray>

    long_name :

    depth of W-points

    time :

    ocean_time

    field :

    z_w, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 27.10 MiB 27.10 MiB Shape (2, 31, 191, 300) (2, 31, 191, 300) Dask graph 1 chunks in 29 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • s_w
    PandasIndex

    PandasIndex(Index([                -1.0,  -0.9666666666666667,  -0.9333333333333333,
                           -0.9,  -0.8666666666666667,  -0.8333333333333334,
                           -0.8,  -0.7666666666666666,  -0.7333333333333333,
                           -0.7,  -0.6666666666666666,  -0.6333333333333333,
                           -0.6,  -0.5666666666666667,  -0.5333333333333333,
                           -0.5,  -0.4666666666666667, -0.43333333333333335,
                           -0.4, -0.36666666666666664,  -0.3333333333333333,
                           -0.3, -0.26666666666666666, -0.23333333333333334,
                           -0.2, -0.16666666666666666, -0.13333333333333333,
                           -0.1, -0.06666666666666667, -0.03333333333333333,
                            0.0],
          dtype='float64', name='s_w'))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (5)

  long_name :

  horizontal buoyancy gradient

  units :

  1/s^2

  time :

  ocean_time

  field :

  temperature, scalar, series

  name :

  M2

Mixed layer depth

This is not a property since the threshold is a parameter and needs to be input.

ds.xroms.mld(thresh=0.03)

sig0 = xroms.potential_density(ds.temp, ds.salt);
xroms.mld(sig0, xgrid, ds.h, ds.mask_rho, thresh=0.03)

ds.xroms.mld(thresh=0.03)

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`
  warnings.warn(
/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xgcm/grid.py:989: FutureWarning: From version 0.8.0 the Axis computation methods will be removed, in favour of using the Grid computation methods instead. i.e. use `Grid.transform` instead of `Axis.transform`
  warnings.warn(

<xarray.DataArray 'mld' (ocean_time: 2, eta_rho: 191, xi_rho: 300)> Size: 917kB
dask.array<abs, shape=(2, 191, 300), dtype=float64, chunksize=(2, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * ocean_time  (ocean_time) datetime64[ns] 16B 2009-11-19T12:00:00 2009-11-1...
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
    sig0        float64 8B 0.0
    z_rho       (ocean_time, eta_rho, xi_rho) float64 917kB dask.array<chunksize=(2, 191, 300), meta=np.ndarray>
Attributes:
    long_name:      mixed layer depth
    time:           ocean_time
    field:          z_rho, scalar, series
    units:          m
    positive:       up
    standard_name:  depth
    name:           mld

xarray.DataArray

'mld'

• ocean_time: 2
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(2, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 895.31 kiB 895.31 kiB Shape (2, 191, 300) (2, 191, 300) Dask graph 1 chunks in 164 graph layers Data type float64 numpy.ndarray

• Coordinates: (7)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00 2009-11-19T1...

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000', '2009-11-19T16:00:00.000000000'],
          dtype='datetime64[ns]')

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • sig0
    ()
    float64
    0.0

    array(0.)

  • z_rho
    (ocean_time, eta_rho, xi_rho)
    float64
    dask.array<chunksize=(2, 191, 300), meta=np.ndarray>

    axis :

    Z

    long_name :

    depth of RHO-points

    time :

    ocean_time

    field :

    z_rho, scalar, series

    units :

    m

    positive :

    up

    standard_name :

    depth

    Array Chunk Bytes 895.31 kiB 895.31 kiB Shape (2, 191, 300) (2, 191, 300) Dask graph 1 chunks in 154 graph layers Data type float64 numpy.ndarray

• Indexes: (3)
  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00', '2009-11-19 16:00:00'], dtype='datetime64[ns]', name='ocean_time', freq=None))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

• Attributes: (7)

  long_name :

  mixed layer depth

  time :

  ocean_time

  field :

  z_rho, scalar, series

  units :

  m

  positive :

  up

  standard_name :

  depth

  name :

  mld

Other calculations

Rotations

If your ROMS grid is curvilinear, you’ll need to rotate your u and v velocities from along the grid axes to being eastward and northward. You can do this with

ds.xroms.east

ds.xroms.north

Additionally, if you want to rotate your velocity to be a different orientation, for example to be along-channel, you can do that with

ds.xroms.east_rotated(angle)

ds.xroms.north_rotated(angle)

Time-based calculations including climatologies

Rolling averages in time

Here is an example of computing a rolling average in time. Nothing happens in this example because we only have two time steps to use, however, it does demonstrate the syntax. If more time steps were available we would update ds.salt.rolling(ocean_time=1) to include more time steps to average over in a rolling sense.

More information about rolling operations is available.

roll = ds.salt.rolling(ocean_time=1, center=True, min_periods=1).mean()
roll.isel(s_rho=-1, eta_rho=10, xi_rho=20).plot(alpha=0.5, lw=2)
ds.salt.isel(s_rho=-1, eta_rho=10, xi_rho=20).plot(ls=":", lw=2)

[<matplotlib.lines.Line2D at 0x7f93b8594d00>]

Resampling in time

Can’t have any chunks in the time dimension to do this. More info: http://xarray.pydata.org/en/stable/generated/xarray.Dataset.resample.html

Upsample

Upsample to a higher resolution in time. Makes sense to interpolate to fill in data when upsampling, but can also forward or backfill, or just add nan’s.

dstest = ds.resample(ocean_time='30min', restore_coord_dims=True).interpolate()

Plot to visually inspect results

ds.salt.cf.isel(Y=30, X=20, Z=-1).plot(marker='o')
dstest.salt.cf.isel(Y=30, X=20, Z=-1).plot(marker='x')

[<matplotlib.lines.Line2D at 0x7f93b82d6290>]

Downsample

Resample down to lower resolution in time. This requires appending a method to aggregate the extra data, such as a mean. Note that other options can be used to shift the result within the interval of aggregation in various ways. Just the syntax is shown here since we only have two time steps to work with.

dstest = ds.resample(ocean_time='6H').mean()
ds.salt.cf.isel(Y=30, X=20, Z=-1).plot(marker='o')
dstest.salt.cf.isel(Y=30, X=20, Z=-1).plot(marker='x')

Seasonal average, over time

This is an example of resampling.

da.cf.resample({'T': [time frequency string]}).reduce([aggregation function])

For example, calculate the mean temperature every quarter in time with the following:

ds.temp.cf.resample({'T': 'QS'}).reduce(np.mean)

or the aggregation function can be appended on the end directly with:

ds.temp.cf.resample({'T': 'QS'}).mean()

The result of this calculation is a time series of downsampled chunks of output in time, the frequency of which is selected by input “time frequency string”, and aggregated by input “aggregation function”.

Examples of the time frequency string are:

• “QS”: quarters, starting in January of each year and averaging three months.

  • Also available are selections like “QS-DEC”, quarters but starting with December to better align with seasons. Other months are input options as well.

• “MS”: monthly

• “D”: daily

• “H”: hourly

• Many more options are given here.

Examples of aggregation functions are:

• np.mean

• np.max

• np.min

• np.sum

• np.std

Result of downsampling a 4D salt array from hourly to 6-hourly, for example, gives: [ocean_time x s_rho x eta_rho x xi_rho], where ocean_time has about 1/6 of the number of entries reflecting the aggregation in time.

ds.temp.cf.resample(indexer={'T': '6H'}).reduce(np.mean)

/home/docs/checkouts/readthedocs.org/user_builds/xroms/conda/latest/lib/python3.10/site-packages/xarray/core/groupby.py:532: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.
  index_grouper = pd.Grouper(

<xarray.DataArray 'temp' (ocean_time: 1, s_rho: 30, eta_rho: 191, xi_rho: 300)> Size: 7MB
dask.array<stack, shape=(1, 30, 191, 300), dtype=float32, chunksize=(1, 30, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho     (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho       (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * xi_rho      (xi_rho) int64 2kB 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299
  * eta_rho     (eta_rho) int64 2kB 0 1 2 3 4 5 6 ... 185 186 187 188 189 190
  * ocean_time  (ocean_time) datetime64[ns] 8B 2009-11-19T12:00:00
Attributes:
    long_name:  potential temperature
    units:      Celsius
    time:       ocean_time
    field:      temperature, scalar, series

xarray.DataArray

'temp'

• ocean_time: 1
• s_rho: 30
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(1, 30, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 6.56 MiB 6.56 MiB Shape (1, 30, 191, 300) (1, 30, 191, 300) Dask graph 1 chunks in 5 graph layers Data type float32 numpy.ndarray

• Coordinates: (6)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • ocean_time
    (ocean_time)
    datetime64[ns]
    2009-11-19T12:00:00

    long_name :

    time since initialization

    field :

    time, scalar, series

    axis :

    T

    standard_name :

    time

    array(['2009-11-19T12:00:00.000000000'], dtype='datetime64[ns]')

• Indexes: (4)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

  • ocean_time
    PandasIndex

    PandasIndex(DatetimeIndex(['2009-11-19 12:00:00'], dtype='datetime64[ns]', name='ocean_time', freq='6h'))

• Attributes: (4)

  long_name :

  potential temperature

  units :

  Celsius

  time :

  ocean_time

  field :

  temperature, scalar, series

Seasonal mean over all available time

This is how to average over the full dataset period by certain time groupings using xarray groupby which is like pandas version. In this case we show the seasonal mean averaged across the full model time period. The syntax for this is:

da.salt.cf.groupby('T.[time string]').reduce([aggregation function])

For example, to average salt by season:

da.salt.cf.groupby('T.season').reduce(np.mean)

or

da.salt.cf.groupby('T.season').mean()

Options for the time string include:

• ‘season’

• ‘year’

• ‘month’

• ‘day’

• ‘hour’

• ‘minute’

• ‘second’

• ‘dayofyear’

• ‘week’

• ‘dayofweek’

• ‘weekday’

• ‘quarter’

More information about options for time (including “derived” datetime coordinates) is here.

Examples of aggregation functions are:

• np.mean

• np.max

• np.min

• np.sum

• np.std

Result of averaging over seasons for a 4D salt array returns, for example: [season x s_rho x eta_rho x xi_rho], where season has 4 entries, each covering 3 months of the year.

# this example has only 1 season because it is a short example file
ds.temp.cf.groupby('T.season').mean()

<xarray.DataArray 'temp' (season: 1, s_rho: 30, eta_rho: 191, xi_rho: 300)> Size: 7MB
dask.array<stack, shape=(1, 30, 191, 300), dtype=float32, chunksize=(1, 30, 191, 300), chunktype=numpy.ndarray>
Coordinates:
    lon_rho  (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
    lat_rho  (eta_rho, xi_rho) float64 458kB dask.array<chunksize=(191, 300), meta=np.ndarray>
  * s_rho    (s_rho) float64 240B -0.9833 -0.95 -0.9167 ... -0.05 -0.01667
  * xi_rho   (xi_rho) int64 2kB 0 1 2 3 4 5 6 7 ... 293 294 295 296 297 298 299
  * eta_rho  (eta_rho) int64 2kB 0 1 2 3 4 5 6 7 ... 184 185 186 187 188 189 190
  * season   (season) object 8B 'SON'
Attributes:
    long_name:  potential temperature
    units:      Celsius
    time:       ocean_time
    field:      temperature, scalar, series

xarray.DataArray

'temp'

• season: 1
• s_rho: 30
• eta_rho: 191
• xi_rho: 300

• dask.array<chunksize=(1, 30, 191, 300), meta=np.ndarray>

  Array Chunk Bytes 6.56 MiB 6.56 MiB Shape (1, 30, 191, 300) (1, 30, 191, 300) Dask graph 1 chunks in 6 graph layers Data type float32 numpy.ndarray

• Coordinates: (6)
  • lon_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    longitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • lat_rho
    (eta_rho, xi_rho)
    float64
    dask.array<chunksize=(191, 300), meta=np.ndarray>

    units :

    meters

    standard_name :

    latitude

    Array Chunk Bytes 447.66 kiB 447.66 kiB Shape (191, 300) (191, 300) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • s_rho
    (s_rho)
    float64
    -0.9833 -0.95 ... -0.05 -0.01667

    long_name :

    S-coordinate at RHO-points

    valid_min :

    -1.0

    valid_max :

    0.0

    standard_name :

    ocean_s_coordinate_g1

    formula_terms :

    s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc

    field :

    s_rho, scalar

    axis :

    Z

    array([-0.983333, -0.95    , -0.916667, -0.883333, -0.85    , -0.816667,
           -0.783333, -0.75    , -0.716667, -0.683333, -0.65    , -0.616667,
           -0.583333, -0.55    , -0.516667, -0.483333, -0.45    , -0.416667,
           -0.383333, -0.35    , -0.316667, -0.283333, -0.25    , -0.216667,
           -0.183333, -0.15    , -0.116667, -0.083333, -0.05    , -0.016667])

  • xi_rho
    (xi_rho)
    int64
    0 1 2 3 4 5 ... 295 296 297 298 299

    axis :

    X

    array([  0,   1,   2, ..., 297, 298, 299])

  • eta_rho
    (eta_rho)
    int64
    0 1 2 3 4 5 ... 186 187 188 189 190

    axis :

    Y

    array([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11,  12,  13,
            14,  15,  16,  17,  18,  19,  20,  21,  22,  23,  24,  25,  26,  27,
            28,  29,  30,  31,  32,  33,  34,  35,  36,  37,  38,  39,  40,  41,
            42,  43,  44,  45,  46,  47,  48,  49,  50,  51,  52,  53,  54,  55,
            56,  57,  58,  59,  60,  61,  62,  63,  64,  65,  66,  67,  68,  69,
            70,  71,  72,  73,  74,  75,  76,  77,  78,  79,  80,  81,  82,  83,
            84,  85,  86,  87,  88,  89,  90,  91,  92,  93,  94,  95,  96,  97,
            98,  99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111,
           112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125,
           126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139,
           140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153,
           154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167,
           168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
           182, 183, 184, 185, 186, 187, 188, 189, 190])

  • season
    (season)
    object
    'SON'

    array(['SON'], dtype=object)

• Indexes: (4)
  • s_rho
    PandasIndex

    PandasIndex(Index([  -0.9833333333333333,                 -0.95,   -0.9166666666666666,
             -0.8833333333333333,                 -0.85,   -0.8166666666666667,
             -0.7833333333333333,                 -0.75,   -0.7166666666666667,
             -0.6833333333333333,                 -0.65,   -0.6166666666666667,
             -0.5833333333333334,                 -0.55,   -0.5166666666666666,
            -0.48333333333333334,                 -0.45,   -0.4166666666666667,
             -0.3833333333333333,                 -0.35,  -0.31666666666666665,
             -0.2833333333333333,                 -0.25,  -0.21666666666666667,
            -0.18333333333333332,                 -0.15,  -0.11666666666666667,
            -0.08333333333333333,                 -0.05, -0.016666666666666666],
          dtype='float64', name='s_rho'))

  • xi_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           290, 291, 292, 293, 294, 295, 296, 297, 298, 299],
          dtype='int64', name='xi_rho', length=300))

  • eta_rho
    PandasIndex

    PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
           ...
           181, 182, 183, 184, 185, 186, 187, 188, 189, 190],
          dtype='int64', name='eta_rho', length=191))

  • season
    PandasIndex

    PandasIndex(Index(['SON'], dtype='object', name='season'))

• Attributes: (4)

  long_name :

  potential temperature

  units :

  Celsius

  time :

  ocean_time

  field :

  temperature, scalar, series