.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "content/examples/04-dcip/plot_dc_analytic.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_content_examples_04-dcip_plot_dc_analytic.py>`
        to download the full example code.

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

.. _sphx_glr_content_examples_04-dcip_plot_dc_analytic.py:


DC Analytic Dipole
==================

Comparison of the analytic and numerical solution for a direct current
resistivity dipole in 3D.

.. GENERATED FROM PYTHON SOURCE LINES 8-67



.. image-sg:: /content/examples/04-dcip/images/sphx_glr_plot_dc_analytic_001.png
   :alt: Analytic, Computed
   :srcset: /content/examples/04-dcip/images/sphx_glr_plot_dc_analytic_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /home/vsts/work/1/s/simpeg/simulation.py:197: DefaultSolverWarning:

    Using the default solver: Pardiso. 

    If you would like to suppress this notification, add 
    warnings.filterwarnings('ignore', simpeg.utils.solver_utils.DefaultSolverWarning)
     to your script.

    /usr/share/miniconda/envs/simpeg-test/lib/python3.10/site-packages/pymatsolver/solvers.py:415: FutureWarning:

    In Future pymatsolver v0.4.0, passing a vector of shape (n, 1) to the solve method will return an array with shape (n, 1), instead of always returning a flattened array. This is to be consistent with numpy.linalg.solve broadcasting.

    0.06464333484677041






|

.. code-block:: Python


    import discretize
    from simpeg import utils
    import numpy as np
    import matplotlib.pyplot as plt
    from simpeg.electromagnetics.static import resistivity as DC


    cs = 25.0
    hx = [(cs, 7, -1.3), (cs, 21), (cs, 7, 1.3)]
    hy = [(cs, 7, -1.3), (cs, 21), (cs, 7, 1.3)]
    hz = [(cs, 7, -1.3), (cs, 20)]
    mesh = discretize.TensorMesh([hx, hy, hz], "CCN")
    sighalf = 1e-2
    sigma = np.ones(mesh.nC) * sighalf
    xtemp = np.linspace(-150, 150, 21)
    ytemp = np.linspace(-150, 150, 21)
    xyz_rxP = utils.ndgrid(xtemp - 10.0, ytemp, np.r_[0.0])
    xyz_rxN = utils.ndgrid(xtemp + 10.0, ytemp, np.r_[0.0])
    xyz_rxM = utils.ndgrid(xtemp, ytemp, np.r_[0.0])


    rx = DC.Rx.Dipole(xyz_rxP, xyz_rxN)
    src = DC.Src.Dipole([rx], np.r_[-200, 0, -12.5], np.r_[+200, 0, -12.5])
    survey = DC.Survey([src])
    sim = DC.Simulation3DCellCentered(mesh, survey=survey, sigma=sigma, bc_type="Neumann")

    data = sim.dpred()


    def DChalf(srclocP, srclocN, rxloc, sigma, I=1.0):
        rp = (srclocP.reshape([1, -1])).repeat(rxloc.shape[0], axis=0)
        rn = (srclocN.reshape([1, -1])).repeat(rxloc.shape[0], axis=0)
        rP = np.sqrt(((rxloc - rp) ** 2).sum(axis=1))
        rN = np.sqrt(((rxloc - rn) ** 2).sum(axis=1))
        return I / (sigma * 2.0 * np.pi) * (1 / rP - 1 / rN)


    data_anaP = DChalf(np.r_[-200, 0, 0.0], np.r_[+200, 0, 0.0], xyz_rxP, sighalf)
    data_anaN = DChalf(np.r_[-200, 0, 0.0], np.r_[+200, 0, 0.0], xyz_rxN, sighalf)
    data_ana = data_anaP - data_anaN
    Data_ana = data_ana.reshape((21, 21), order="F")
    Data = data.reshape((21, 21), order="F")
    X = xyz_rxM[:, 0].reshape((21, 21), order="F")
    Y = xyz_rxM[:, 1].reshape((21, 21), order="F")


    fig, ax = plt.subplots(1, 2, figsize=(12, 5))
    vmin = np.r_[data, data_ana].min()
    vmax = np.r_[data, data_ana].max()
    dat0 = ax[0].contourf(X, Y, Data_ana, 60, vmin=vmin, vmax=vmax)
    dat1 = ax[1].contourf(X, Y, Data, 60, vmin=vmin, vmax=vmax)
    plt.colorbar(dat0, orientation="horizontal", ax=ax[0])
    plt.colorbar(dat1, orientation="horizontal", ax=ax[1])
    ax[0].set_title("Analytic")
    ax[1].set_title("Computed")

    print(np.linalg.norm(data - data_ana) / np.linalg.norm(data_ana))
    plt.show()


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

   **Total running time of the script:** (0 minutes 1.356 seconds)

**Estimated memory usage:**  296 MB


.. _sphx_glr_download_content_examples_04-dcip_plot_dc_analytic.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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