.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/sparse/plot_0_1D_sparse.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_sparse_plot_0_1D_sparse.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_sparse_plot_0_1D_sparse.py:


Sparse along one dimension, 2D{1,1} dataset
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

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The following is an example [#f2]_ of a 2D{1,1} sparse dataset with two-dimensions,
:math:`d=2`, and two, :math:`p=2`, sparse single-component dependent-variables,
where the component is sparsely sampled along one dimension. The following is an
example of a hypercomplex acquisition of the NMR dataset.

Let's import the CSD model data-file.

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.. code-block:: Python

    import csdmpy as cp

    filename = "https://www.ssnmr.org/sites/default/files/CSDM/sparse/iglu_1d.csdf"
    sparse_1d = cp.load(filename)








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There are two linear dimensions and two single-component sparse dependent variables.
The tuple of the dimension and the dependent variable instances are

.. GENERATED FROM PYTHON SOURCE LINES 20-23

.. code-block:: Python

    x = sparse_1d.dimensions
    y = sparse_1d.dependent_variables








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The coordinates, viewed only for the first ten coordinates, are

.. GENERATED FROM PYTHON SOURCE LINES 25-27

.. code-block:: Python

    print(x[0].coordinates[:10])





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

 .. code-block:: none

    [   0.  192.  384.  576.  768.  960. 1152. 1344. 1536. 1728.] us




.. GENERATED FROM PYTHON SOURCE LINES 28-30

.. code-block:: Python

    print(x[1].coordinates[:10])





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

 .. code-block:: none

    [   0.  192.  384.  576.  768.  960. 1152. 1344. 1536. 1728.] us




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Converting the coordinates to `ms`.

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.. code-block:: Python

    x[0].to("ms")
    x[1].to("ms")








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**Visualizing the dataset**

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.. code-block:: Python

    import matplotlib.pyplot as plt

    # split the CSDM object with two dependent variables into two CSDM objects with single
    # dependent variables.

    cos, sin = sparse_1d.split()

    # cosine data
    plt.figure(figsize=(5, 3.5))
    ax = plt.subplot(projection="csdm")
    cb = ax.contourf(cos.real)
    plt.colorbar(cb, ax=ax)
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/sparse/images/sphx_glr_plot_0_1D_sparse_001.png
   :alt: cos
   :srcset: /auto_examples/sparse/images/sphx_glr_plot_0_1D_sparse_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 53-62

.. code-block:: Python


    # sine data
    plt.figure(figsize=(5, 3.5))
    ax = plt.subplot(projection="csdm")
    cb = ax.contourf(sin.real)
    plt.colorbar(cb, ax=ax)
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/sparse/images/sphx_glr_plot_0_1D_sparse_002.png
   :alt: sin
   :srcset: /auto_examples/sparse/images/sphx_glr_plot_0_1D_sparse_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 63-68

.. rubric:: Citation

.. [#f2] Balsgart NM, Vosegaard T., Fast Forward Maximum entropy reconstruction
         of sparsely sampled data., J Magn Reson. 2012, 223, 164-169.
         doi: 10.1016/j.jmr.2012.07.002


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

   **Total running time of the script:** (0 minutes 1.140 seconds)


.. _sphx_glr_download_auto_examples_sparse_plot_0_1D_sparse.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_0_1D_sparse.ipynb <plot_0_1D_sparse.ipynb>`

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

      :download:`Download Python source code: plot_0_1D_sparse.py <plot_0_1D_sparse.py>`


.. only:: html

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

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