.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "content/examples/01-maps/plot_combo.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_content_examples_01-maps_plot_combo.py>`
        to download the full example code

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

.. _sphx_glr_content_examples_01-maps_plot_combo.py:


Maps: ComboMaps
===============

We will use an example where we want a 1D layered earth as our model,
but we want to map this to a 2D discretization to do our forward
modeling. We will also assume that we are working in log conductivity
still, so after the transformation we map to conductivity space.
To do this we will introduce the vertical 1D map
(:class:`SimPEG.maps.SurjectVertical1D`), which does the first part of
what we just described. The second part will be done by the
:class:`SimPEG.maps.ExpMap` described above.

.. code-block:: python
    :linenos:

    M = discretize.TensorMesh([7,5])
    v1dMap = maps.SurjectVertical1D(M)
    expMap = maps.ExpMap(M)
    myMap = expMap * v1dMap
    m = np.r_[0.2,1,0.1,2,2.9] # only 5 model parameters!
    sig = myMap * m

If you noticed, it was pretty easy to combine maps. What is even cooler
is that the derivatives also are made for you (if everything goes
right). Just to be sure that the derivative is correct, you should
always run the test on the mapping that you create.

.. GENERATED FROM PYTHON SOURCE LINES 29-66



.. image-sg:: /content/examples/01-maps/images/sphx_glr_plot_combo_001.png
   :alt: Model, Physical Property
   :srcset: /content/examples/01-maps/images/sphx_glr_plot_combo_001.png
   :class: sphx-glr-single-img





.. code-block:: default

    import discretize
    from SimPEG import maps
    import numpy as np
    import matplotlib.pyplot as plt


    def run(plotIt=True):

        M = discretize.TensorMesh([7, 5])
        v1dMap = maps.SurjectVertical1D(M)
        expMap = maps.ExpMap(M)
        myMap = expMap * v1dMap
        m = np.r_[0.2, 1, 0.1, 2, 2.9]  # only 5 model parameters!
        sig = myMap * m

        if not plotIt:
            return

        figs, axs = plt.subplots(1, 2)
        axs[0].plot(m, M.cell_centers_y, "b-o")
        axs[0].set_title("Model")
        axs[0].set_ylabel("Depth, y")
        axs[0].set_xlabel("Value, $m_i$")
        axs[0].set_xlim(0, 3)
        axs[0].set_ylim(0, 1)
        clbar = plt.colorbar(
            M.plot_image(sig, ax=axs[1], grid=True, grid_opts=dict(color="grey"))[0]
        )
        axs[1].set_title("Physical Property")
        axs[1].set_ylabel("Depth, y")
        clbar.set_label("$\sigma = \exp(\mathbf{P}m)$")
        plt.tight_layout()


    if __name__ == "__main__":
        run()
        plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.336 seconds)

**Estimated memory usage:**  19 MB


.. _sphx_glr_download_content_examples_01-maps_plot_combo.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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