.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_gallery/plot_cdip.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_gallery_plot_cdip.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_gallery_plot_cdip.py:


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.

.. GENERATED FROM PYTHON SOURCE LINES 11-23

.. code-block:: Python


    # 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









.. GENERATED FROM PYTHON SOURCE LINES 24-27

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 $\eta(t)$.

.. GENERATED FROM PYTHON SOURCE LINES 27-45

.. code-block:: Python

    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]")




.. image-sg:: /auto_gallery/images/sphx_glr_plot_cdip_001.png
   :alt: plot cdip
   :srcset: /auto_gallery/images/sphx_glr_plot_cdip_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    <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




.. GENERATED FROM PYTHON SOURCE LINES 46-51

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.

.. GENERATED FROM PYTHON SOURCE LINES 51-63

.. code-block:: Python

    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": "$E(f,\\theta)\\,\\mathrm{[m^2 Hz^{-1} deg^{-1}]}$"}
    )



.. image-sg:: /auto_gallery/images/sphx_glr_plot_cdip_002.png
   :alt: plot cdip
   :srcset: /auto_gallery/images/sphx_glr_plot_cdip_002.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    <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





.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 1.203 seconds)


.. _sphx_glr_download_auto_gallery_plot_cdip.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_cdip.ipynb <plot_cdip.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_cdip.py <plot_cdip.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_cdip.zip <plot_cdip.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_