.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/correlated_examples/plot_0_0D11_dataset.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_correlated_examples_plot_0_0D11_dataset.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_correlated_examples_plot_0_0D11_dataset.py:


Scatter, 0D{1,1} dataset
^^^^^^^^^^^^^^^^^^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 6-15

We start with a 0D{1,1} correlated dataset, that is, a dataset
without a coordinate grid. A 0D{1,1} dataset has no dimensions, d = 0, and
two single-component dependent variables.
In the following example [#f3]_, the two `correlated` dependent variables are
the :math:`^{29}\text{Si}` - :math:`^{29}\text{Si}` nuclear spin couplings,
:math:`^2J`, across a Si-O-Si linkage, and the `s`-character product on the
O and two Si along the Si-O bond across the Si-O-Si linkage.

Let's import the dataset.

.. GENERATED FROM PYTHON SOURCE LINES 15-21

.. code-block:: Python

    import csdmpy as cp

    domain = "https://www.ssnmr.org/sites/default/files/CSDM"
    filename = f"{domain}/correlatedDataset/0D_dataset/J_vs_s.csdf"
    zero_d_dataset = cp.load(filename)








.. GENERATED FROM PYTHON SOURCE LINES 22-25

Since the dataset has no dimensions, the value of the
:attr:`~csdmpy.CSDM.dimensions` attribute of the :attr:`~csdmpy.CSDM`
class is an empty tuple,

.. GENERATED FROM PYTHON SOURCE LINES 25-27

.. code-block:: Python

    print(zero_d_dataset.dimensions)





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

 .. code-block:: none

    []




.. GENERATED FROM PYTHON SOURCE LINES 28-31

The :attr:`~csdmpy.CSDM.dependent_variables` attribute, however, holds
two dependent-variable objects. The data structure from the two dependent
variables is

.. GENERATED FROM PYTHON SOURCE LINES 31-33

.. code-block:: Python

    print(zero_d_dataset.dependent_variables[0].data_structure)





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

 .. code-block:: none

    {
      "type": "internal",
      "name": "Gaussian computed J-couplings",
      "unit": "Hz",
      "quantity_name": "frequency",
      "numeric_type": "float32",
      "quantity_type": "scalar",
      "component_labels": [
        "J-coupling"
      ],
      "components": [
        [
          "-1.87378, -1.42918, ..., 25.1742, 26.0608"
        ]
      ]
    }




.. GENERATED FROM PYTHON SOURCE LINES 34-35

and

.. GENERATED FROM PYTHON SOURCE LINES 35-37

.. code-block:: Python

    print(zero_d_dataset.dependent_variables[1].data_structure)





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

 .. code-block:: none

    {
      "type": "internal",
      "name": "product of s-characters",
      "unit": "%",
      "numeric_type": "float32",
      "quantity_type": "scalar",
      "component_labels": [
        "s-character product"
      ],
      "components": [
        [
          "0.8457453, 0.8534185, ..., 1.5277092, 1.5289451"
        ]
      ]
    }




.. GENERATED FROM PYTHON SOURCE LINES 38-39

respectively.

.. GENERATED FROM PYTHON SOURCE LINES 41-45

**Visualizing the dataset**

The correlation plot of the dependent-variables from the dataset is
shown below.

.. GENERATED FROM PYTHON SOURCE LINES 45-56

.. code-block:: Python

    import matplotlib.pyplot as plt

    y0 = zero_d_dataset.dependent_variables[0]
    y1 = zero_d_dataset.dependent_variables[1]

    plt.scatter(y1.components[0], y0.components[0], s=2, c="k")
    plt.xlabel(y1.axis_label[0])
    plt.ylabel(y0.axis_label[0])
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/correlated_examples/images/sphx_glr_plot_0_0D11_dataset_001.png
   :alt: plot 0 0D11 dataset
   :srcset: /auto_examples/correlated_examples/images/sphx_glr_plot_0_0D11_dataset_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 57-62

.. rubric:: Citation

.. [#f3] Srivastava DJ, Florian P, Baltisberger JH, Grandinetti PJ. Correlating
      geminal couplings to structure in framework silicates. Phys Chem Chem Phys.
      2018;20:562–571. DOI:10.1039/C7CP06486A


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

   **Total running time of the script:** (0 minutes 0.269 seconds)


.. _sphx_glr_download_auto_examples_correlated_examples_plot_0_0D11_dataset.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_0D11_dataset.ipynb <plot_0_0D11_dataset.ipynb>`

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

      :download:`Download Python source code: plot_0_0D11_dataset.py <plot_0_0D11_dataset.py>`


.. only:: html

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

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