scientimate.wavefromsurfaceelevpsd#

Hm0, fp, Tp, Tm01, Tm02, f, Syy, m0 = scientimate.wavefromsurfaceelevpsd(Eta, fs, fcL=0, fcH=None, nfft=None, SegmentSize=256, OverlapSize=128, dispout='no')

Description#

Calculate wave properties from water surface elevation power spectral density

Inputs#

Eta: Water surface elevation time series data in (m)
fs: Sampling frequency that data collected at in (Hz)
fcL=0: Low cut-off frequency, between 0*fs to 0.5*fs (Hz)
fcH=fs/2: High cut-off frequency, between 0*fs to 0.5*fs (Hz)
nfft=length(Eta): Total number of points between 0 and fs that spectrum reports at is (nfft+1)
SegmentSize=256: Segment size, data are divided into the segments each has a total element equal to SegmentSize
OverlapSize=128: Number of data points that are overlaped with data in previous segments
dispout=’no’: Define to display outputs or not (‘yes’: display, ‘no’: not display)

Outputs#

Hm0: Zero-Moment Wave Height (m)
fp: Peak wave frequency (Hz)
Tp: Peak wave period (second)
Tm01: Wave Period from m01 (second), Mean Wave Period
Tm02: Wave Period from m02 (second), Mean Zero Crossing Period
f: Frequency (Hz)
Syy: Power spectral density (m^2/Hz)
m0: Zero-Moment of the power spectral density (m^2)

Examples#

import scientimate as sm
import numpy as np
import scipy as sp
from scipy import signal

fs=2 #Sampling frequency
duration=1024 #Duration of the data
N=fs*duration #Total number of points
df=fs/N #Frequency difference
dt=1/fs #Time difference, dt=1/fs
t=np.linspace(0,duration-dt,N) #Time
Eta=sp.signal.detrend(0.5*np.cos(2*np.pi*0.2*t)+(-0.1+(0.1-(-0.1)))*np.random.rand(N))
Hm0,fp,Tp,Tm01,Tm02,f,Syy,m0=sm.wavefromsurfaceelevpsd(Eta,fs,0,fs/2,N,256,128,'yes')

References#

Welch, P. (1967). The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Transactions on audio and electroacoustics, 15(2), 70-73.

scientimate.wavefromsurfaceelevpsd

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