Using real time CDIP data

This example shows how to compute the directional wave spectrum from waverider wave buoys data obtained from CDIP dataset in real time.

# import the dependencies
import numpy as np
import xarray as xr

from matplotlib import pyplot as plt

import ewdm
from ewdm.sources import CDIPDataSourceRealTime
from ewdm.plots import plot_directional_spectrum

Let’s download 30-min time series from Ocean Station Papa Buoy - 166p1. In this case, the buoy captures the wave-induced hortizonal $x(t)$, $y(t)$, and vertical displacements $eta(t)$.

cdip =  CDIPDataSourceRealTime(166)
dataset = cdip.read_dataset(
    time_start="2024-06-09T08:30", time_end="2024-06-09T09:00"
)
print(dataset)

# plot time series of the first 5 minutes
fig, (ax1, ax2, ax3) = plt.subplots(
    3,1, figsize=(6,6), sharex=True, sharey=True
)
subset = dataset.sel(time=slice("2024-06-09T08:30", "2024-06-09T08:35"))
subset["surface_elevation"].plot(ax=ax1)
subset["eastward_displacement"].plot(ax=ax2)
subset["northward_displacement"].plot(ax=ax3)
_ = ax1.set(xlabel="", ylabel="$\\eta(t)$ [m]")
_ = ax2.set(xlabel="", ylabel="$x(t)$ [m]")
_ = ax3.set(xlabel="", ylabel="$y(t)$ [m]")

<xarray.Dataset> Size: 46kB
Dimensions:                 (time: 2304)
Coordinates:
  * time                    (time) datetime64[ns] 18kB 2024-06-09T08:30:00 .....
Data variables:
    eastward_displacement   (time) float32 9kB -1.49 -1.27 -0.53 ... 0.33 0.5
    northward_displacement  (time) float32 9kB 1.05 0.66 0.08 ... 0.05 0.42 0.77
    surface_elevation       (time) float32 9kB 0.03 -0.73 -1.41 ... 0.87 1.35
Attributes: (12/71)
    sampling_rate:                    1.28
    naming_authority:                 edu.ucsd.cdip
    keywords_vocabulary:              Global Change Master Directory (GCMD) E...
    date_created:                     2024-06-10T21:31:12Z
    date_issued:                      2024-06-10T21:31:12Z
    date_modified:                    2024-06-10T21:31:12Z
    ...                               ...
    platform_vocabulary:              http://mmisw.org/ont/ioos/platform
    platform_name:                    OCEAN STATION PAPA BUOY - 166p1
    DODS.strlen:                      0
    DODS.dimName:                     metaStationNameLength
    DODS_EXTRA.Unlimited_Dimension:   xyzCount
    EXTRA_DIMENSION.metaBoundsCount:  2

Now, let’s compute the directional spectra using the ewdm.Triplet class. The input parameters omin, omax and nvoice control the frequency resolution, it means that a frequency array ranging from 2^-5 to 2^0 with 16 subvisions per octave will be created in this case. The parameters dd and kappa control the directional resolution and smoothness, respectively.

spec = ewdm.Triplets(dataset)
output = spec.compute(
    omin=-5, omax=0, nvoice=16, dd=5, kappa=36, use="displacements"
)
print(output)

# plot the results
fig, ax = plt.subplots(figsize=(6,5))
plot_directional_spectrum(
    output.directional_spectrum, ax=ax, levels=None, colorbar=True,
    cbar_kw={"label": "$E(f,\\theta)\\,\\mathrm{[m^2 Hz^{-1} deg^{-1}]}$"}
)

<xarray.Dataset> Size: 95kB
Dimensions:                   (frequency: 81, direction: 72)
Coordinates:
  * frequency                 (frequency) float64 648B 0.03125 0.03263 ... 1.0
  * direction                 (direction) int64 576B -180 -175 -170 ... 170 175
Data variables:
    directional_spectrum      (frequency, direction) float64 47kB 7.109e-06 ....
    directional_distribution  (frequency, direction) float64 47kB 0.0011 ... ...
    frequency_spectrum        (frequency) float64 648B 0.00646 ... 8.578e-06