Example (Python Version)

Here is an example shows how to use OCEANLYZ. In this example, we use a provided sample file “waterpressure_5burst.csv” as input data. This sample file contains five bursts of water pressure data recorded with a pressure sensor. Sample file may be downloaded at https://github.com/akarimp/oceanlyz/releases/tag/2.0 .

Measurement properties for “waterpressure_5burst.csv” are:

Properties Value OCEANLYZ Properties
File name waterpressure_5burst.csv  
Data type Water pressure obj.InputType=’pressure’
Number of recorded burst (n_burst) 5 obj.n_burst=5
Sampling frequency (fs) 10 (Hz) obj.fs=10
Recording duration (burst_duration) 1024 (second) obj.burst_duration=1024
Pressure sensor height from bed (heightfrombed) 0.05 (m) obj.heightfrombed=0.05
Mean water depth (h) Varies in each burst  

To start using OCEANLYZ, first, we need to be import required libraries.

#Import libraries
import oceanlyz
import os
import numpy as np
import matplotlib.pyplot as plt

Next, we download water pressure dataset (“waterpressure_5burst.csv”), we unzip it and copy sample files in a desired folder.

Assume downloaded sample data file is stored in ‘C:\oceanlyz_python\Sample_Data’. Then, we load data as:

os.chdir('C:\\oceanlyz_python\\Sample_Data') #Change current folder to a folder that contains data file
water_pressure = np.genfromtxt('waterpressure_5burst.csv') #Load data

We can plot data if we need to as:

plt.xlabel('Sample points')
plt.ylabel('Water Pressure (N/m^2)')

Figure 1: Plot of input water pressure data

Then, we need to create an OCEANLYZ object as:

#Create OCEANLYZ object
ocn = oceanlyz.oceanlyz()

Next, we assign wave data to OCEANLYZ object as:

#Input data
ocn.data = water_pressure.copy()

Now, we set up OCEANLYZ properties as:

ocn.fmaxpcorrCalcMethod='auto'   #Only required if ocn.InputType='pressure'
ocn.Kpafterfmaxpcorr='constant'  #Only required if ocn.InputType='pressure'
ocn.fminpcorr=0.15               #Only required if ocn.InputType='pressure'
ocn.fmaxpcorr=0.55               #Only required if ocn.InputType='pressure'
ocn.heightfrombed=0.05           #Only required if ocn.InputType='pressure'
ocn.Rho=1024                     #Seawater density (Varies)

After all required properties are set, we can run OCEANLYZ as:


Output is stored as a structure array. Name of output is ‘oceanlyz_object.wave’. Field(s) in this structure array can be called by using ‘.’ For example oceanlyz_object.wave.Hm0 contains zero-moment wave height and oceanlyz_object.wave.Tp contains peak wave period.

Here we show how to plot zero-moment wave height:

Hm0 = ocn.wave['Hm0'] #zero-moment wave height
plt.xlabel('Burst Number')
plt.ylabel('Hm0 (m)')

Figure 2: Plot of \(H_{m0}\) versus burst number

Similarly, we can plot wave spectrum for the first burst:

f = ocn.wave['f'] #frequency of the first burst
Syy = ocn.wave['Syy'] #spectrum of the first burst
plt.xlabel('f (Hz)')
plt.ylabel('Syy (m^2/Hz)')

Figure 3: Plot of \(S_{yy}\) versus f



If data are collected in continuous mode and you need to analyze them in smaller blocks, you can analyze it in a burst mode. For that, you choose n_burst and burst_duration as follow:

The burst_duration is equal to a period of time that you want data analyzed over that. For example, if you need wave properties reported every 15 min, then the burst_duration would be 15*60 second.

the n_burst is equal to the total length of the time series divided by the burst_duration. The n_burst should be an integer. So, if the total length of the time series divided by the burst_duration leads to a decimal number, then data should be shortened to avoid that.

Welch spectrum is used to calculate a power spectral density. In all spectral calculation, a default window function with a default overlap window between segments are used.
If fmaxpcorrCalcMethod=’auto’, then OCEANLYZ calculates fmaxpcorr based on water depth and a sensor height from a seabed (refer to Applying Pressure Response Factor section). A maximum value for calculated fmaxpcorr will be limited to the value user set for fmaxpcorr.