geo-parameters 0.5.1

Metadata of geophysical (especially wave) parameters

Geo-parameters

Metadata of geophysical (especially wave) parameters. The main idea is to be able to use standard CF-names without having to remember or check the names.

Quick Installation

To get started with geo-parameters, you can install it with pip or conda:

$ pip install geo-parameters

or

$ conda install conda-forge geo-parameters

Names of parameters

The parameters are classes in sub-modules according to the physical property (wave, ocean, wind)

from geo_parameters.wave import Hs, Tp

These classes have information about the parameter:

Hs.standard_name()
'sea_surface_wave_significant_height'

Every parameters has a pre-defined name

Hs.name
'hs'

If you want to define some other names for the parameters, then you can create an instance. By default the name of the instance is the short name, but this can be changed:

hsig = Hs()
hsig.name
'hs'

hsig = Hs(name='hsig')
hsig.name
'hsig'

The instance still contains the standard name (and some long name that doesn't follow any standard):

hsig.standard_name() 
'sea_surface_wave_significant_height'

hsig.long_name() 
'significant_wave_height'

If the parameter has several standard names, you can use the alias-keyword

hsig.standard_name(alias=True) 
'significant_height_of_wind_and_swell_waves'

If the parameter has only one standard name, alias does nothing:

Tp.standard_name()
'sea_surface_wave_period_at_variance_spectral_density_maximum'

Tp.standard_name(alias=True)
'sea_surface_wave_period_at_variance_spectral_density_maximum'

Finding parameters

The right class can be found from a name using the get function:

from geo_parameters import get as gpget

The parameter can then be fetched using the standard name, the alias or the short name:

gpget("sea_surface_wave_significant_height")
<class 'geo_parameters.geo_parameters.wave.Hs'>

gpget("significant_height_of_wind_and_swell_waves")
<class 'geo_parameters.geo_parameters.wave.Hs'>

gpget("hs")
<class 'geo_parameters.geo_parameters.wave.Hs'>

A dict of all available parameters can also be compiled:

from geo_parameters import dict_of_parameters

dop = dict_of_parameters()
dop = dict_of_parameters(alias=True)
dop = dict_of_parameters(short=True)

where the keywords define what string will serve as key in the dictonary (the value is always the parameter class).

If an xarray Dataset has standard_name attributes, the correct key can be found using the class-method:

ds
<xarray.Dataset> Size: 1kB
Dimensions:              (lat: 11, lon: 6)
Coordinates:
  * lon                  (lon) float64 48B 5.0 6.0 7.0 8.0 9.0 10.0
  * lat                  (lat) float64 88B 50.0 51.0 52.0 ... 58.0 59.0 60.0
Data variables:
    interesting_hs_name  (lat, lon) float64 528B 0.0 0.0 0.0 ... 0.0 0.0 0.0
    peak_period          (lat, lon) float64 528B 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0

ds.interesting_hs_name
<xarray.DataArray 'interesting_hs_name' (lat: 11, lon: 6)> Size: 528B
array([[0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.],
        ...
       [0., 0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0., 0.]])
Coordinates:
  * lon      (lon) float64 48B 5.0 6.0 7.0 8.0 9.0 10.0
  * lat      (lat) float64 88B 50.0 51.0 52.0 53.0 54.0 ... 57.0 58.0 59.0 60.0
Attributes:
    standard_name:  sea_surface_wave_significant_height

Hs.find_me_in_ds(ds)
['interesting_hs_name']

Tp.find_me_in_ds(ds)
['peak_period']

Tp.find_me_in_ds(ds, return_first=True)
'peak_period'