.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_gallery/plot_numpy_as_input.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_numpy_as_input.py>`
        to download the full example code.

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

.. _sphx_glr_auto_gallery_plot_numpy_as_input.py:


Using numpy arrays as input
===========================

You don't have a `xarray.Dataset` ready to use as input? No worries, you can use
the `.from_numpy()` class method to initialise the `Triplets` and `Arrays` classes from `numpy.ndarray` objects

.. GENERATED FROM PYTHON SOURCE LINES 12-24

.. code-block:: Python


    import numpy as np
    import xarray as xr

    from scipy.io import loadmat
    from matplotlib import pyplot as plt

    import ewdm
    from ewdm.sources import SpotterBuoysDataSource
    from ewdm.plots import plot_directional_spectrum









.. GENERATED FROM PYTHON SOURCE LINES 25-29

Testing with ewdm.Triplets
--------------------------

Let's see how this feature work with the ewdm.Triplets class

.. GENERATED FROM PYTHON SOURCE LINES 29-43

.. code-block:: Python


    spotter = SpotterBuoysDataSource("../../data/displacement.csv")
    dataset = spotter.read_dataset()

    subset = dataset.sel(time=slice("2020-08-20 10:00", "2020-08-20 10:30"))

    # extract numpy arrays from dataset
    time = subset["time"].data
    surface_elevation = subset["surface_elevation"].data
    eastward_displacement = subset["eastward_displacement"].data
    northward_displacement = subset["northward_displacement"].data
    sampling_rate = subset.attrs["sampling_rate"]









.. GENERATED FROM PYTHON SOURCE LINES 44-45

Now, we can initialise the `ewdm.Triplets` class with the numpy arrays

.. GENERATED FROM PYTHON SOURCE LINES 45-56

.. code-block:: Python

    spec = ewdm.Triplets.from_numpy(
        time = time,
        surface_elevation = surface_elevation,
        eastward_displacement = eastward_displacement,
        northward_displacement = northward_displacement,
        fs=sampling_rate,
        max_time_gap="30s"
    )
    print("Sampling rate is: ", spec.fs)
    print("Maximum allowed time gap is: ", spec.max_time_gap)





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

 .. code-block:: none

    Sampling rate is:  2.5
    Maximum allowed time gap is:  30s




.. GENERATED FROM PYTHON SOURCE LINES 57-58

Let's compute the spectra and print the output

.. GENERATED FROM PYTHON SOURCE LINES 58-62

.. code-block:: Python

    output_triplets = spec.compute()
    print(output_triplets)






.. 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) float64 576B -180.0 -175.0 ... 175.0
    Data variables:
        directional_spectrum      (frequency, direction) float64 47kB 0.0001156 ....
        directional_distribution  (frequency, direction) float64 47kB 0.001684 .....
        frequency_spectrum        (frequency) float64 648B 0.06864 ... 0.0003756




.. GENERATED FROM PYTHON SOURCE LINES 63-67

Testing with ewdm.Arrays
------------------------

It works for `ewdm.Arrays` class too.

.. GENERATED FROM PYTHON SOURCE LINES 67-82

.. code-block:: Python


    # loading matlab file
    mat_fname = "../../data/donelan_run62.mat"
    mat_data = loadmat(mat_fname, simplify_cells=True)

    sampling_rate = mat_data["fs"]
    time = np.arange(len(mat_data["eta"])) / sampling_rate
    elements = np.arange(len(mat_data["x"]))

    print("Surface elevation shape: ", mat_data["eta"].shape)
    print("Time array shape: ", time.shape)
    print("Position x shape : ", mat_data["x"].shape)
    print("Position y shape : ", mat_data["y"].shape)






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

 .. code-block:: none

    Surface elevation shape:  (4096, 6)
    Time array shape:  (4096,)
    Position x shape :  (6,)
    Position y shape :  (6,)




.. GENERATED FROM PYTHON SOURCE LINES 83-84

Now let's create spec object

.. GENERATED FROM PYTHON SOURCE LINES 84-97

.. code-block:: Python


    spec = ewdm.Arrays.from_numpy(
        time = time,
        surface_elevation = mat_data["eta"],
        position_x = mat_data["x"],
        position_y = mat_data["y"],
        fs = sampling_rate,
        max_nan_ratio = 0.05,
    )

    print("Sampling rate is: ", spec.fs)
    print("Maximum allowed nan ratio: ", spec.max_nan_ratio)





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

 .. code-block:: none

    Sampling rate is:  4
    Maximum allowed nan ratio:  0.05




.. GENERATED FROM PYTHON SOURCE LINES 98-99

Let's compute the spectra and print the output

.. GENERATED FROM PYTHON SOURCE LINES 99-103

.. code-block:: Python

    output_array = spec.compute()
    print(output_array)






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

 .. code-block:: none

    <xarray.Dataset> Size: 114kB
    Dimensions:                   (frequency: 97, direction: 72)
    Coordinates:
      * frequency                 (frequency) float64 776B 0.03125 0.03263 ... 2.0
      * direction                 (direction) float64 576B -180.0 -175.0 ... 175.0
    Data variables:
        directional_spectrum      (frequency, direction) float64 56kB 1.444e-05 ....
        directional_distribution  (frequency, direction) float64 56kB 0.003793 .....
        frequency_spectrum        (frequency) float64 776B 0.003806 ... 3.278e-05




.. GENERATED FROM PYTHON SOURCE LINES 104-108

Let's plot the results
----------------------

It's always nice to see some specral plots, isn't it?

.. GENERATED FROM PYTHON SOURCE LINES 108-122

.. code-block:: Python


    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8,4), layout="constrained")

    plot_directional_spectrum(
        output_array.directional_spectrum, ax=ax1, levels=None, colorbar=True,
        axes_kw={"rmin": 0.1, "rmax": 1.2, "rstep": 0.2, "angle": 135},
        cbar_kw={"label": ""}
    )

    plot_directional_spectrum(
        output_triplets.directional_spectrum, ax=ax2, levels=None, colorbar=True,
        axes_kw={"rmin": 0.1, "rmax": 1.2, "rstep": 0.2, "angle": 135},
        cbar_kw={"label": "$E(f,\\theta)$"}
    )



.. image-sg:: /auto_gallery/images/sphx_glr_plot_numpy_as_input_001.png
   :alt: plot numpy as input
   :srcset: /auto_gallery/images/sphx_glr_plot_numpy_as_input_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 1.364 seconds)


.. _sphx_glr_download_auto_gallery_plot_numpy_as_input.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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