.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_tutorials_2D_datasets_plot_1_2D.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_tutorials_2D_datasets_plot_1_2D.py:


2D{1} dataset with linear and monotonic dimensions
--------------------------------------------------

In the following example, we illustrate how one can covert a Numpy array into
a CSDM object. Start by importing the Numpy and csdmpy libraries.


.. code-block:: default

    import matplotlib.pyplot as plt
    import numpy as np

    import csdmpy as cp








Let's generate a 2D NumPy array of random numbers as our dataset.


.. code-block:: default

    data = np.random.rand(8192).reshape(32, 256)









Create the DependentVariable object from the numpy object.


.. code-block:: default

    dv = cp.as_dependent_variable(data, unit="J/(mol K)")








Create the two Dimension objects.


.. code-block:: default

    d0 = cp.LinearDimension(
        count=256, increment="15.23 µs", coordinates_offset="-1.95 ms", label="t1"
    )








Here, ``d0`` is a LinearDimension with 256 points and 15.23 µs increment. You
may similarly set the second dimension as a LinearDimension, however, in this
example, let's set it as a MonotonicDimension.



.. code-block:: default

    array = 10 ** (np.arange(32) / 8)
    d1 = cp.as_dimension(array, unit="µs", label="t2")








The variable ``array`` is a NumPy array that is uniformly sampled on a log
scale. To convert this array into a Dimension object, we use the
:meth:`~csdmpy.as_dimension` method.

Creating the CSDM object.


.. code-block:: default

    csdm_object = cp.CSDM(dependent_variables=[dv], dimensions=[d0, d1])
    print(csdm_object.dimensions)





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

 Out:

 .. code-block:: none

    [LinearDimension(count=256, increment=15.23 µs, coordinates_offset=-1.95 ms, quantity_name=time, label=t1, reciprocal={'quantity_name': 'frequency'}),
    MonotonicDimension(coordinates=[1.00000000e+00 1.33352143e+00 1.77827941e+00 2.37137371e+00
     3.16227766e+00 4.21696503e+00 5.62341325e+00 7.49894209e+00
     1.00000000e+01 1.33352143e+01 1.77827941e+01 2.37137371e+01
     3.16227766e+01 4.21696503e+01 5.62341325e+01 7.49894209e+01
     1.00000000e+02 1.33352143e+02 1.77827941e+02 2.37137371e+02
     3.16227766e+02 4.21696503e+02 5.62341325e+02 7.49894209e+02
     1.00000000e+03 1.33352143e+03 1.77827941e+03 2.37137371e+03
     3.16227766e+03 4.21696503e+03 5.62341325e+03 7.49894209e+03] us, quantity_name=time, label=t2, reciprocal={'quantity_name': 'frequency'})]




Plot of the dataset.


.. code-block:: default

    plt.figure(figsize=(5, 3.5))
    cp.plot(csdm_object)
    plt.tight_layout()
    plt.show()




.. image:: /auto_tutorials/2D_datasets/images/sphx_glr_plot_1_2D_001.png
    :alt: plot 1 2D
    :class: sphx-glr-single-img





To serialize the file, use the save method.


.. code-block:: default

    csdm_object.save("2D_1_dataset.csdf")








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

   **Total running time of the script:** ( 0 minutes  0.192 seconds)


.. _sphx_glr_download_auto_tutorials_2D_datasets_plot_1_2D.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_1_2D.py <plot_1_2D.py>`



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

     :download:`Download Jupyter notebook: plot_1_2D.ipynb <plot_1_2D.ipynb>`


.. only:: html

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

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