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.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/04_Tools/plot_02_using_numpy_functions.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_04_Tools_plot_02_using_numpy_functions.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_04_Tools_plot_02_using_numpy_functions.py:


.. _plot_02_using_numpy_functions:

===============================================
Using common NumPy functions on dnpdata objects
===============================================

This example demonstrates how to use common numpy functions to manipulate dnpdata objects.

Note, this is still an experimental feature.

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===================================================
How numpy array functions are operating on DNPData
===================================================

Many numpy functions can directly be used on DNPData. What the function returns depends on the result:

* when the result is a scalar a salar is returned
* when the result is a ndarray a DNPData object is returned

when the axis keyword is support by the numpy function one can provide the dimension (e.g. np.sum(mydata, axis='f2'))
The corresponding axis is consumed and no longer in the returned DNPData object.
The following example shows how this can be conveniently used.

To get started, first, setup the python environment:

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.. code-block:: Python


    import dnplab as dnp
    import numpy as np
    import matplotlib.pyplot as plt








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Let's load some example data
the data consists of 4 fid that are phase cycled (0-90-180-270)

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.. code-block:: Python


    data = dnp.load("../../data/prospa/water_phase_cycled/data.2d")








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we are interested in the spectra

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.. code-block:: Python


    data = dnp.fourier_transform(data)








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since we don't know what the spectra is made of, we want to have a quick look at the magnitude:

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.. code-block:: Python


    data_magn = np.abs(data)








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and lets plot the magnitude spectrum for all 4 cycles

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.. code-block:: Python


    dnp.fancy_plot(data_magn)




.. image-sg:: /auto_examples/04_Tools/images/sphx_glr_plot_02_using_numpy_functions_001.png
   :alt: plot 02 using numpy functions
   :srcset: /auto_examples/04_Tools/images/sphx_glr_plot_02_using_numpy_functions_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    [<matplotlib.lines.Line2D object at 0x7f83a993ecd0>, <matplotlib.lines.Line2D object at 0x7f8387d3e760>, <matplotlib.lines.Line2D object at 0x7f8387d3e520>, <matplotlib.lines.Line2D object at 0x7f8387d3e040>]



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since the spectra are phase cycled the mean of the real part of the spectrum should be 0.
Let's check and plot that:

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.. code-block:: Python


    mean_real_spectrum = np.real(np.mean(data, axis="Average"))
    total_mean = np.mean(mean_real_spectrum)
    average_in_dims = "Average" in mean_real_spectrum.dims
    print("The sum of the mean spectrum is {0} ".format(total_mean))
    print("Average in mean_real_spectrum.dims: {0}".format(average_in_dims))
    dnp.fancy_plot(mean_real_spectrum)




.. image-sg:: /auto_examples/04_Tools/images/sphx_glr_plot_02_using_numpy_functions_002.png
   :alt: plot 02 using numpy functions
   :srcset: /auto_examples/04_Tools/images/sphx_glr_plot_02_using_numpy_functions_002.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    The sum of the mean spectrum is -1.037494719028472 
    Average in mean_real_spectrum.dims: False

    [<matplotlib.lines.Line2D object at 0x7f838cf2f340>]



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the total mean could also be calculated directly using np.mean() without the axis keyword (up to numerical precision)

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.. code-block:: Python

    print(np.mean(np.real(data)), "~=", total_mean)
    dnp.plt.show()




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

 .. code-block:: none

    -1.037494719028473 ~= -1.037494719028472





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

   **Total running time of the script:** (0 minutes 0.251 seconds)


.. _sphx_glr_download_auto_examples_04_Tools_plot_02_using_numpy_functions.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_02_using_numpy_functions.ipynb <plot_02_using_numpy_functions.ipynb>`

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

      :download:`Download Python source code: plot_02_using_numpy_functions.py <plot_02_using_numpy_functions.py>`


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