gsw-xarray 0.3.0

Usage

import gsw_xarray as gsw

# Create a xarray.Dataset
import numpy as np
import xarray as xr
ds = xr.Dataset()
id = np.arange(3)
ds['id'] = xr.DataArray(id, coords={'id':id})
ds['CT'] = ds['id'] * 10
ds['CT'].attrs = {'standard_name':'sea_water_conservative_temperature'}
ds['SA'] = ds['id'] * 0.1 + 34
ds['SA'].attrs = {'standard_name':'sea_water_absolute_salinity'}

# Apply gsw functions
sigma0 = gsw.sigma0(SA=ds['SA'], CT=ds['CT'])
print(sigma0.attrs)

Outputs

{'standard_name': 'sea_water_sigma_t', 'units': 'kg/m^3'}

Don’t worry about usage with non xarray array objects, just use in all places you would the upstream library:

sigma0 = gsw.sigma0(id * 10, id * 0.1 + 34)
print(type(sigma0), sigma0)

Outputs

<class 'numpy.ndarray'> [-5.08964499  2.1101098   9.28348219]

We support (and convert the unit if necessary) the usage of pint.Quantities and the usage of xarray wrapped Quantities. Support for pint requires the installation of two optional dependencies: pint and pint-xarray. If all the inputs to a gsw function are Quantities, the returned object will also be a Quantity belonging to the same UnitRegistry.

import pint_xarray
import gsw_xarray as gsw

# Create a xarray.Dataset
import numpy as np
import xarray as xr
ds = xr.Dataset()
id = np.arange(3)
ds['id'] = xr.DataArray(id, coords={'id':id})
ds['CT'] = ds['id'] * 10
# make sure there are unit attrs this time
ds['CT'].attrs = {'standard_name':'sea_water_conservative_temperature', 'units': 'degC'}
ds['SA'] = ds['id'] * 0.1 + 34
ds['SA'].attrs = {'standard_name':'sea_water_absolute_salinity', 'units': 'g/kg'}

# use the pint accessor to quantify things
ds = ds.pint.quantify()

# Apply gsw functions
sigma0 = gsw.sigma0(SA=ds['SA'], CT=ds['CT'])
# outputs are now quantities!
print(sigma0)

Outputs

<xarray.DataArray 'sigma0' (id: 3)>
<Quantity([27.17191038 26.12820162 24.03930887], 'kilogram / meter ** 3')>
Coordinates:
  * id       (id) int64 0 1 2
Attributes:
    standard_name:  sea_water_sigma_t

The usage of xarray wrapped Quantities is not required, you can use pint directly (though the pint-xarray dep still needs to be installed).

import gsw_xarray as gsw
import pint
ureg = pint.UnitRegistry()
SA = ureg.Quantity(35, ureg("g/kg"))
CT = ureg.Quantity(10, ureg.degC)
sigma0 = gsw.sigma0(SA=SA, CT=CT)
print(sigma0)

Outputs

26.824644457868317 kilogram / meter ** 3

As gsw-xarray converts arguments to the proper unit when Quantities are used, we can e.g. use the temperature in Kelvin:

CT = ureg.Quantity(10, ureg.degC).to('kelvin')
sigma0 = gsw.sigma0(SA=SA, CT=CT)
print(sigma0)

Outputs

26.824644457868317 kilogram / meter ** 3

Installation

pip install gsw-xarray

Contributor guide

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. If you notice a bug or are missing a feature, fell free to open an issue in the GitHub issues page.

In order to contribute to gsw-xarray, please fork the repository and submit a pull request. A good step by step tutorial for starting with git can be found in the xarray contributor guide. A main difference is that we do not use conda as python environment, but poetry.

cd /path/to/your/gsw-xarray
poetry install

poetry shell

pytest gsw_xarray/tests