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

.. only:: html

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

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

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

.. _sphx_glr_examples_gaussian_beam.py:


Gaussian beam
=============

Basic example of computation of the field emitted by a coherent Gaussian beam.

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Import required libraries

.. GENERATED FROM PYTHON SOURCE LINES 10-16

.. code-block:: default


    import matplotlib.pyplot as plt
    import numpy as np

    import pysrw as srw








.. GENERATED FROM PYTHON SOURCE LINES 17-19

Starts creating a coherent Gaussian beam with a waist of 20 um.
We leave the default initial position and angle of the source (all zero)

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.. code-block:: default

    sigma = 20e-6
    gsnBeam = srw.emitters.GaussianBeam(xSigma=sigma, ySigma=sigma)








.. GENERATED FROM PYTHON SOURCE LINES 23-25

Create a 10 mm x 10 mm observation mesh, with a resolution of 100 um, 
placed 1 m downstream of the source

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.. code-block:: default

    observer = srw.wavefronts.Observer(centerCoord=[0, 0, 1],
                                       obsXextension=10e-3, 
                                       obsYextension=10e-3,
                                       obsXres=100e-6,
                                       obsYres=100e-6)








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Compute the wavefront at the observation wavelength of 600 nm.
The multiprocessing version is reported for comparison

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.. code-block:: default

    wl = 600
    wfr = srw.computeGsnBeamWfr(gsnBeam, observer, wavelength=wl)
    wfr_mp = srw.computeGsnBeamWfrMultiProcess(4, gsnBeam, observer, wavelength=600)








.. GENERATED FROM PYTHON SOURCE LINES 39-40

Compute the corresponding optical intensity

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.. code-block:: default

    intensity = wfr.getWfrI()
    intensity_mp = wfr_mp.getWfrI()








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Plot the 2D intensity distribution

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.. code-block:: default

    srw.plotI(intensity)
    srw.plotI(intensity_mp)




.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /examples/images/sphx_glr_gaussian_beam_001.png
         :alt: gaussian beam
         :srcset: /examples/images/sphx_glr_gaussian_beam_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/images/sphx_glr_gaussian_beam_002.png
         :alt: gaussian beam
         :srcset: /examples/images/sphx_glr_gaussian_beam_002.png
         :class: sphx-glr-multi-img





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Extract cross-cut using :py:func:`~pysrw.tools.extractCrossCuts`

.. GENERATED FROM PYTHON SOURCE LINES 50-54

.. code-block:: default

    crosscuts = srw.extractCrossCuts(intensity)
    xax = crosscuts["xax"]
    xcut = crosscuts["xdata"]








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Compare the intensity profile with the theoretical distribution.
For a Gaussian beam :math:`\sigma \cdot \sigma' = \lambda / 4 \pi`.
We omit the spherical attenuation as it's negligible with respect to the 
decay due to the finite divergence of the beam

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.. code-block:: default

    sigma_p = wl*1e-9 / (4 * np.pi * sigma) #rad
    xcut_theo = srw.gaussian1p(xax, sigma_p * observer.zPos)








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Summary plot of the intensity cuts

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.. code-block:: default

    fig, ax = plt.subplots()
    ax.plot(xax * 1e3, xcut, label="sim")
    ax.plot(xax * 1e3, np.max(xcut) * xcut_theo, linestyle="--", label="theo")
    ax.set_xlabel("x position [mm]")
    ax.set_ylabel("Intensity [ph/(s mm^2 0.1%BW)]")
    ax.legend()
    plt.show()


.. image-sg:: /examples/images/sphx_glr_gaussian_beam_003.png
   :alt: gaussian beam
   :srcset: /examples/images/sphx_glr_gaussian_beam_003.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 1.796 seconds)

**Estimated memory usage:**  10 MB


.. _sphx_glr_download_examples_gaussian_beam.py:

.. only:: html

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




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

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

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

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


.. only:: html

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

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