wavespectra 3.4

Ocean wave spectra tools

Python library for ocean wave spectra.

Main contents:

• SpecArray: extends xarray’s DataArray with methods to manipulate wave spectra and calculate spectral statistics.

• SpecDataset: wrapper around SpecArray with methods for saving spectra in different formats.

Documentation:

The documentation is hosted on ReadTheDocs at https://wavespectra.readthedocs.io/en/docs/.

Install:

Where to get it

The source code is currently hosted on GitHub at: https://github.com/metocean/wavespectra

Binary installers for the latest released version are available at the Python package index.

Install from pypi

# Default install, miss some dependencies and functionality
pip install wavespectra

# Complete install
pip install wavespectra[extra]

Install from sources

Install requirements. Navigate to the base root of wavespectra and execute:

# Default install, miss some dependencies and functionality
pip install -r requirements/default.txt

# Also, for complete install
pip install -r requirements/extra.txt

# Also, for testing requirements
pip install -r requirements/test.txt

Then install wavespectra:

python setup.py install

# Run pytest integration
python setup.py test

Alternatively, to install in development mode:

pip install -e .

Code structure:

The two main classes SpecArray and SpecDataset are defined as xarray accessors. The accessors are registered on xarray’s DataArray and Dataset respectively as a new namespace called spec.

To use methods in the accessor classes simply import the classes into your code and they will be available to your xarray.Dataset or xarray.DataArray instances through the spec attribute, e.g.

import datetime
import numpy as np
import xarray as xr

from wavespectra.specarray import SpecArray
from wavespectra.specdataset import SpecDataset

coords = {'time': [datetime.datetime(2017,01,n+1) for n in range(2)],
          'freq': [0.05,0.1],
          'dir': np.arange(0,360,120)}
efth = xr.DataArray(data=np.random.rand(2,2,3),
                    coords=coords,
                    dims=('time','freq', 'dir'),
                    name='efth')

In [1]: efth
Out[1]:
<xarray.DataArray (time: 2, freq: 2, dir: 3)>
array([[[ 0.100607,  0.328229,  0.332708],
        [ 0.532   ,  0.665938,  0.177731]],

       [[ 0.469371,  0.002963,  0.627179],
        [ 0.004523,  0.682717,  0.09766 ]]])
Coordinates:
  * freq     (freq) float64 0.05 0.1
  * dir      (dir) int64 0 120 240
  * time     (time) datetime64[ns] 2017-01-01 2017-01-02

In [2]: efth.spec
Out[2]:
<SpecArray (time: 2, freq: 2, dir: 3)>
array([[[ 0.100607,  0.328229,  0.332708],
        [ 0.532   ,  0.665938,  0.177731]],

       [[ 0.469371,  0.002963,  0.627179],
        [ 0.004523,  0.682717,  0.09766 ]]])
Coordinates:
  * freq     (freq) float64 0.05 0.1
  * dir      (dir) int64 0 120 240
  * time     (time) datetime64[ns] 2017-01-01 2017-01-02

In [3]: efth.spec.hs()
Out[3]:
<xarray.DataArray 'hs' (time: 2)>
array([ 10.128485,   9.510618])
Coordinates:
  * time     (time) datetime64[ns] 2017-01-01 2017-01-02
Attributes:
    standard_name: sea_surface_wave_significant_height
    units: m

SpecDataset provides a wrapper around the methods in SpecArray. For instance, these produce same result:

In [4]: dset = efth.to_dataset(name='efth')

In [5]: tm01 = dset.spec.tm01()

In [6]: tm01.identical(dset.efth.spec.tm01())
Out[6]: True

Data requirements:

SpecArray methods require DataArray to have the following attributes:

• wave frequency coordinate in \(Hz\) named as freq (required).

• wave direction coordinate in \(degree\) (coming from) named as dir (optional for 1D, required for 2D spectra).

• wave energy density data in \(m2/Hz/degree\) (2D) or \(m2/Hz\) (1D) named as efth

SpecDataset methods require xarray’s Dataset to have the following attributes:

• spectra DataArray named as efth, complying with the above specifications

Examples:

Define and plot spectra history from example SWAN spectra file:

from wavespectra import read_swan

dset = read_swan('/source/wavespectra/tests/manus.spec')
spec_hist = dset.isel(lat=0, lon=0).sel(freq=slice(0.05,0.2)).spec.oned().T
spec_hist.plot.contourf(levels=10)