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

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

.. _sphx_glr_auto_examples_simple_simulations_two_gaussians.py:


Simple simulation (2 Gaussians)
===============================

Creates a 2D hyperspectrum consisting of two Gaussians and plots it.

This example can serve as starting point to test other functionalities on the
simulated hyperspectrum.

.. GENERATED FROM PYTHON SOURCE LINES 11-66



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


    *

      .. image-sg:: /auto_examples/simple_simulations/images/sphx_glr_two_gaussians_001.png
         :alt:  from fitted model Navigator
         :srcset: /auto_examples/simple_simulations/images/sphx_glr_two_gaussians_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/simple_simulations/images/sphx_glr_two_gaussians_002.png
         :alt:  from fitted model Signal
         :srcset: /auto_examples/simple_simulations/images/sphx_glr_two_gaussians_002.png
         :class: sphx-glr-multi-img





.. code-block:: Python

    import numpy as np
    import hyperspy.api as hs
    import matplotlib.pyplot as plt


    # Create an empty spectrum
    s = hs.signals.Signal1D(np.zeros((32, 32, 1024)))

    # Generate some simple data: two Gaussians with random centers and area

    # First we create a model
    m = s.create_model()

    # Define the first gaussian
    gs1 = hs.model.components1D.Gaussian()
    # Add it to the model
    m.append(gs1)

    # Set the parameters
    m.set_parameters_value('sigma', 10, component_list=[gs1])
    # Make the center vary in the -5,5 range around 128
    gs1.centre.map['values'][:] = 256 + (np.random.random((32, 32)) - 0.5) * 10
    gs1.centre.map['is_set'][:] = True

    # Make the area vary between 0 and 10000
    gs1.A.map['values'][:] = 10000 * np.random.random((32, 32))
    gs1.A.map['is_set'][:] = True

    # Second gaussian
    gs2 = hs.model.components1D.Gaussian()
    # Add it to the model
    m.append(gs2)

    # Set the parameters
    m.set_parameters_value('sigma', 20, component_list=[gs2])

    # Make the center vary in the -10,10 range around 768
    gs2.centre.map['values'][:] = 768 + (np.random.random((32, 32)) - 0.5) * 20
    gs2.centre.map['is_set'][:] = True

    # Make the area vary between 0 and 20000
    gs2.A.map['values'][:] = 20000 * np.random.random((32, 32))
    gs2.A.map['is_set'][:] = True

    # Create the dataset
    s_model = m.as_signal()

    # Add noise
    s_model.set_signal_origin("simulation")
    s_model.add_poissonian_noise()

    # Plot the result
    s_model.plot()

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.829 seconds)


.. _sphx_glr_download_auto_examples_simple_simulations_two_gaussians.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: two_gaussians.zip <two_gaussians.zip>`


.. only:: html

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

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