wavespectra2dsplitfit 0.4.2

Ocean wave 2D spectrum partitioning and fitting JONSWAP spectrum

Ocean Wave Spectra 2D Splitting/Fitting

Introduction

The main purpose of this package is to find parameters of JONSWAP wave spectra with spreading that, when recombined, best match the input 2D frequency direction wave spectra. Given a 2D wave spectrum S(f,theta), the package finds parameters of multiple JONSWAP partitions including wave spreading (i.e. Hs, Tp, Gamma, Tail exponent, ThetaP).

The aim of the package is to provide an industry wide approach to derive usable wave spectral parameters that provide the best possible reconstruction of the input wave spectrum. The method is designed to be tunable, but robust in the default configuration. A large number of observed and numerically modelled datasets have been tested during the creation and validation of the method.

It is the intention that the package will be used by consultants and weather forecastors to improve the descriptions of the ocean wave partitions for use in operations and engineering applications. It provides the metocean engineer with a robust way to separate swells and wind seas.

Usage

Import the waveSpec class

# A. Make up the spectrum S(f,th)
from wavespectra2dsplitfit import waveSpec
import numpy as np
spec = waveSpec()
spec.f = np.arange(0.04,0.4,0.005)     # units in Hz
spec.th = np.arange(0,15,360)          # units in deg
spec.S = np.zeros((len(spec.f),len(spec.th))

# B. User to fill spec.S with real data with units m^2/(Hz.deg)
# spec.S = <user to assign 2D numpy array>
spec.autoCorrect()

# Setup fitting configuration - simple example with no wind (also usually best setup with no wind)
tConfig = {
    'maxPartitions': 3,
    'useClustering': True,
    'useWind': False,
    'useFittedWindSea': False,
    'useWindSeaInClustering': False,
    'doPlot': True,
    'saveFigFilename': "test.png"
}
specParms, fitStatus = spec.fit2DSpectrum(tConfig)
print(specParms, fitStatus)

Example Result

Check out the test.py script as an example with data.

$ python test.py
running reducePeaksClustering 6,3
Optimization terminated successfully.
         Current function value: 0.082135
         Iterations: 1082
         Function evaluations: 1733
[[0.5859285326910995, 4.716981132075468, 1.0000053476007895, 0.07, 0.09, -4.234276488479486, 300.0, 4.716981132075468], [0.6129423521749234, 7.812499999999995, 5.970526837658344, 0.07, 0.09, -5.140143260428807, 290.0, 7.812499999999995], [0.4047506936099149, 10.869565217391298, 1.0000041524068202, 0.07, 0.09, -15.401874257914326, 240.0, 10.869565217391298]] [True, 0.08213522716322981, 1733]
===== PARTITION =====
Hs =  0.5859285326910995
Tp =  4.716981132075468
Gamma =  1.0000053476007895
Sigma A =  0.07
Sigma B =  0.09
Tail Exp =  -4.234276488479486
ThetaP =  300.0
===== PARTITION =====
Hs =  0.6129423521749234
Tp =  7.812499999999995
Gamma =  5.970526837658344
Sigma A =  0.07
Sigma B =  0.09
Tail Exp =  -5.140143260428807
ThetaP =  290.0
===== PARTITION =====
Hs =  0.4047506936099149
Tp =  10.869565217391298
Gamma =  1.0000041524068202
Sigma A =  0.07
Sigma B =  0.09
Tail Exp =  -15.401874257914326
ThetaP =  240.0
===== FITTING OUTCOME =====
Fitting successful:  True
RMS error of fit:  0.08213522716322981
Number of function evalutions:  1733

An example of the input and output reconstructed spectrum are shown in the image below.