.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_tutorials_generating_dataset_plot_1.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_tutorials_generating_dataset_plot_1.py:


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)








To convert this array into a csdm object, use the :meth:`~csdmpy.as_csdm`
method,


.. code-block:: default

    data_csdm = cp.as_csdm(data)
    print(data_csdm.dimensions)





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

 Out:

 .. code-block:: none

    [LinearDimension(count=256, increment=1.0),
    LinearDimension(count=32, increment=1.0)]




This generates a 2D{1} dataset, that is, a two-dimensional dataset with a
single one-component dependent variable. The two dimensions are, by default,
set as the LinearDimensions of the unit interval.

You may set the proper dimensions by generating the appropriate Dimension
objects and replacing the default dimensions in the ``data_csdm`` object.


.. 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.

Now, replace the dimension objects in ``data_csdm`` with the new ones.


.. code-block:: default

    data_csdm.dimensions[0] = d0
    data_csdm.dimensions[1] = d1









.. code-block:: default

    print(data_csdm.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(data_csdm)
    plt.tight_layout()
    plt.show()




.. image:: /auto_tutorials/generating_dataset/images/sphx_glr_plot_1_001.png
    :alt: plot 1
    :class: sphx-glr-single-img





To serialize the file, use the save method.


.. code-block:: default

    data_csdm.save("filename.csdf")








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

   **Total running time of the script:** ( 0 minutes  0.207 seconds)


.. _sphx_glr_download_auto_tutorials_generating_dataset_plot_1.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.py <plot_1.py>`



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

     :download:`Download Jupyter notebook: plot_1.ipynb <plot_1.ipynb>`


.. only:: html

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

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