Gridded data manipulation

import lenapy
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt

data=xr.open_dataset('../../data/ecco.nc',engine='lenapyNetcdf')

Ocean mean temperature and salinity

data.lngeo.mean(['latitude','longitude','depth'],weights=['latitude','depth']).compute()

<xarray.Dataset> Size: 24B
Dimensions:  ()
Coordinates:
    time     datetime64[ns] 8B 2010-01-16T12:00:00
Data variables:
    PT       float64 8B 3.497
    psal     float64 8B 34.73

xarray.Dataset

• Dimensions:
• Coordinates: (1)
  • time
    ()
    datetime64[ns]
    2010-01-16T12:00:00

    array('2010-01-16T12:00:00.000000000', dtype='datetime64[ns]')

• Data variables: (2)
  • PT
    ()
    float64
    3.497

    array(3.49716971)

  • psal
    ()
    float64
    34.73

    array(34.72637315)

• Indexes: (0)
• Attributes: (0)

Mean zonal potential temperature

data.lngeo.mean(['longitude','depth'],weights=['depth']).PT.plot()

[<matplotlib.lines.Line2D at 0x14beb69a2110>]

Depth of the 10°C isotherm - isosurface is applied on a Dataset

data.PT.lngeo.isosurface(10,'depth').plot()

<matplotlib.collections.QuadMesh at 0x14beb6934750>

Salinity at the depth of the 10°C isotherm - isosurface is applied to a DataArray

data.lngeo.isosurface(dict(PT=10),'depth').psal.plot()

<matplotlib.collections.QuadMesh at 0x14beb607af50>

Resampling

ds_out = xr.Dataset(
    {
        "latitude":(["latitude"],np.arange(-89.5,90,5.)),
        "longitude":(["longitude"],np.arange(-179.5,180,5.))
    }
)

reg=data.lngeo.regridder(ds_out,method="bilinear")
data.PT.isel(depth=0).lngeo.regrid(reg).plot()

<matplotlib.collections.QuadMesh at 0x14beb500fad0>