.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "signal_processor/plot_5_tophat.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_signal_processor_plot_5_tophat.py>`
        to download the full example code

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

.. _sphx_glr_signal_processor_plot_5_tophat.py:


Top-Hat Apodization
^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 7-27

In this example, we will use the
:py:class:`~mrsimulator.signal_processor.apodization.TopHat` class to apply a
point-wise top hat apodization on the Fourier transform of an example dataset. The
function is defined as follows

.. math::

  f(x) = \begin{cases}
    1, \tt{rising\_edge} \leq x \leq \tt{falling\_edge} \\
    0, \text{otherwise}
    \end{cases}

where ``rising_edge`` is the start of the window and ``falling_edge``
is the end of the window.

When ``falling_edge`` is undefined, all points after ``rising_edge`` will be 1.
Similarly, when ``rising_edge`` is undefined, all points before ``falling_edge``
are 1.

Below we import the necessary modules

.. GENERATED FROM PYTHON SOURCE LINES 27-33

.. code-block:: default

    import csdmpy as cp
    import matplotlib.pyplot as plt
    import numpy as np
    from mrsimulator import signal_processor as sp









.. GENERATED FROM PYTHON SOURCE LINES 35-41

First we create ``processor``, and instance of the
:py:class:`~mrsimulator.signal_processor.SignalProcessor` class. The required
attribute of the SignalProcessor class, *operations*, is a list of operations to which
we add a :py:class:`~mrsimulator.signal_processor.apodization.TopHat` object
sandwiched between two Fourier transformations. Here the window is between
1 and 9 seconds.

.. GENERATED FROM PYTHON SOURCE LINES 41-49

.. code-block:: default

    processor = sp.SignalProcessor(
        operations=[
            sp.IFFT(),
            sp.apodization.TopHat(rising_edge="1 s", falling_edge="9 s"),
            sp.FFT(),
        ]
    )








.. GENERATED FROM PYTHON SOURCE LINES 50-53

Next we create a CSDM object with a test dataset which our signal processor will
operate on. Here, the dataset is a delta function centered at 0 Hz with a some
applied Gaussian line broadening.

.. GENERATED FROM PYTHON SOURCE LINES 53-60

.. code-block:: default

    test_data = np.zeros(500)
    test_data[250] = 1
    csdm_object = cp.CSDM(
        dependent_variables=[cp.as_dependent_variable(test_data)],
        dimensions=[cp.LinearDimension(count=500, increment="0.1 Hz", complex_fft=True)],
    )








.. GENERATED FROM PYTHON SOURCE LINES 61-64

To apply the previously defined signal processor, we use the
:py:meth:`~mrsimulator.signal_processor.SignalProcessor.apply_operations` method as
as follows

.. GENERATED FROM PYTHON SOURCE LINES 64-66

.. code-block:: default

    processed_dataset = processor.apply_operations(dataset=csdm_object).real








.. GENERATED FROM PYTHON SOURCE LINES 67-69

To see the results of the top hat apodization, we create a simple plot using the
``matplotlib`` library.

.. GENERATED FROM PYTHON SOURCE LINES 69-77

.. code-block:: default

    fig, ax = plt.subplots(1, 2, figsize=(8, 3.5), subplot_kw={"projection": "csdm"})
    ax[0].plot(csdm_object, color="black", linewidth=1)
    ax[0].set_title("Before")
    ax[1].plot(processed_dataset.real, color="black", linewidth=1)
    ax[1].set_title("After")
    plt.tight_layout()
    plt.show()




.. image-sg:: /signal_processor/images/sphx_glr_plot_5_tophat_001.png
   :alt: Before, After
   :srcset: /signal_processor/images/sphx_glr_plot_5_tophat_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 78-80

Below are plots showing how the apodization functions when only ``rising_edge`` or
``falling_edge`` are defined.

.. GENERATED FROM PYTHON SOURCE LINES 80-105

.. code-block:: default

    rising_edge_processor = sp.SignalProcessor(
        operations=[sp.apodization.TopHat(rising_edge="2 s")]
    )
    falling_edge_processor = sp.SignalProcessor(
        operations=[sp.apodization.TopHat(falling_edge="8 s")]
    )

    constant_csdm = cp.CSDM(
        dependent_variables=[cp.as_dependent_variable(np.ones(100))],
        dimensions=[cp.LinearDimension(100, increment="0.1 s")],
    )
    rising_dataset = rising_edge_processor.apply_operations(
        dataset=constant_csdm.copy()
    ).real
    falling_dataset = falling_edge_processor.apply_operations(
        dataset=constant_csdm.copy()
    ).real

    fig, ax = plt.subplots(1, 2, figsize=(8, 3.5), subplot_kw={"projection": "csdm"})
    ax[0].plot(rising_dataset, color="black", linewidth=1)
    ax[0].set_title("rising_edge")
    ax[1].plot(falling_dataset, color="black", linewidth=1)
    ax[1].set_title("falling_edge")
    plt.tight_layout()
    plt.show()



.. image-sg:: /signal_processor/images/sphx_glr_plot_5_tophat_002.png
   :alt: rising_edge, falling_edge
   :srcset: /signal_processor/images/sphx_glr_plot_5_tophat_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  0.717 seconds)


.. _sphx_glr_download_signal_processor_plot_5_tophat.py:


.. only :: html

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



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

     :download:`Download Python source code: plot_5_tophat.py <plot_5_tophat.py>`



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

     :download:`Download Jupyter notebook: plot_5_tophat.ipynb <plot_5_tophat.ipynb>`


.. only:: html

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

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