.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "content/examples/06-tdem/plot_0_tdem_analytic.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_content_examples_06-tdem_plot_0_tdem_analytic.py: Simulation with Analytic TDEM Solutions ======================================= Here, the module *simpeg.electromagnetics.analytics.TDEM* is used to simulate transient electric and magnetic field for both electric and magnetic dipole sources in a wholespace. .. GENERATED FROM PYTHON SOURCE LINES 13-16 Import modules -------------- .. GENERATED FROM PYTHON SOURCE LINES 16-28 .. code-block:: Python import numpy as np from simpeg import utils from simpeg.electromagnetics.analytics.TDEM import ( TransientElectricDipoleWholeSpace, TransientMagneticDipoleWholeSpace, ) import matplotlib.pyplot as plt from matplotlib.colors import LogNorm .. GENERATED FROM PYTHON SOURCE LINES 29-37 Magnetic Fields for a Transient Magnetic Dipole Source ------------------------------------------------------ Here, we compute the magnetic field and its time-derivative for a transient magnetic dipole source in the z direction. Based on the geometry of the problem, we expect magnetic fields in the x and z directions, but none in the y direction. .. GENERATED FROM PYTHON SOURCE LINES 37-118 .. code-block:: Python # Defining electric dipole location and frequency source_location = np.r_[0, 0, 0] t = [1e-4, 1e-3, 1e-2] # Defining observation locations (avoid placing observation at source) x = np.arange(-201, 201, step=2.0) y = np.r_[0] z = x observation_locations = utils.ndgrid(x, y, z) # Define wholespace conductivity sig = 1e0 # Plot the magnetic field fig = plt.figure(figsize=(14, 3)) ax = 3 * [None] cb = 3 * [None] pc = 3 * [None] for ii in range(0, 3): # Compute the fields Hx, Hy, Hz = TransientMagneticDipoleWholeSpace( observation_locations, source_location, sig, t[ii], moment="Z", fieldType="h", mu_r=1, ) hxplt = Hx.reshape(x.size, z.size) hzplt = Hz.reshape(x.size, z.size) ax[ii] = fig.add_axes([0.1 + 0.28 * ii, 0.1, 0.2, 0.8]) absH = np.sqrt(Hx**2 + Hy**2 + Hz**2) pc[ii] = ax[ii].pcolor(x, z, absH.reshape(x.size, z.size), norm=LogNorm()) ax[ii].streamplot(x, z, hxplt.real, hzplt.real, color="k", density=1) ax[ii].set_xlim([x.min(), x.max()]) ax[ii].set_ylim([z.min(), z.max()]) ax[ii].set_title("H at t = {} s".format(t[ii])) ax[ii].set_xlabel("x") ax[ii].set_ylabel("z") cb[ii] = plt.colorbar(pc[ii], ax=ax[ii]) cb[ii].set_label("H (A/m)") # Plot the time-derivative fig = plt.figure(figsize=(14, 3)) ax = 3 * [None] cb = 3 * [None] pc = 3 * [None] for ii in range(0, 3): # Compute the fields dHdtx, dHdty, dHdtz = TransientMagneticDipoleWholeSpace( observation_locations, source_location, sig, t[ii], moment="Z", fieldType="dhdt", mu_r=1, ) dhdtxplt = dHdtx.reshape(x.size, z.size) dhdtzplt = dHdtz.reshape(x.size, z.size) ax[ii] = fig.add_axes([0.1 + 0.28 * ii, 0.1, 0.2, 0.8]) absdHdt = np.sqrt(dHdtx**2 + dHdty**2 + dHdtz**2) pc[ii] = ax[ii].pcolor(x, z, absdHdt.reshape(x.size, z.size), norm=LogNorm()) ax[ii].streamplot(x, z, dhdtxplt.real, dhdtzplt.real, color="k", density=1) ax[ii].set_xlim([x.min(), x.max()]) ax[ii].set_ylim([z.min(), z.max()]) ax[ii].set_title("dH/dt at t = {} s".format(t[ii])) ax[ii].set_xlabel("x") ax[ii].set_ylabel("z") cb[ii] = plt.colorbar(pc[ii], ax=ax[ii]) cb[ii].set_label("dH/dt (A/m*s)") .. rst-class:: sphx-glr-horizontal * .. image-sg:: /content/examples/06-tdem/images/sphx_glr_plot_0_tdem_analytic_001.png :alt: H at t = 0.0001 s, H at t = 0.001 s, H at t = 0.01 s :srcset: /content/examples/06-tdem/images/sphx_glr_plot_0_tdem_analytic_001.png :class: sphx-glr-multi-img * .. image-sg:: /content/examples/06-tdem/images/sphx_glr_plot_0_tdem_analytic_002.png :alt: dH/dt at t = 0.0001 s, dH/dt at t = 0.001 s, dH/dt at t = 0.01 s :srcset: /content/examples/06-tdem/images/sphx_glr_plot_0_tdem_analytic_002.png :class: sphx-glr-multi-img .. GENERATED FROM PYTHON SOURCE LINES 119-127 Electric Field from a Transient Electric Current Dipole Source -------------------------------------------------------------- Here, we compute the electric fields for a transient electric current dipole source in the z direction. Based on the geometry of the problem, we expect electric fields in the x and z directions, but none in the y direction. .. GENERATED FROM PYTHON SOURCE LINES 127-174 .. code-block:: Python # Defining electric dipole location and frequency source_location = np.r_[0, 0, 0] t = [1e-4, 1e-3, 1e-2] # Defining observation locations (avoid placing observation at source) x = np.arange(-201, 201, step=2.0) y = np.r_[0] z = x observation_locations = utils.ndgrid(x, y, z) # Define wholespace conductivity sig = 1e0 fig = plt.figure(figsize=(14, 3)) ax = 3 * [None] cb = 3 * [None] pc = 3 * [None] for ii in range(0, 3): # Compute the fields Ex, Ey, Ez = TransientElectricDipoleWholeSpace( observation_locations, source_location, sig, t[ii], moment="Z", fieldType="e", mu_r=1, ) explt = Ex.reshape(x.size, z.size) ezplt = Ez.reshape(x.size, z.size) ax[ii] = fig.add_axes([0.1 + 0.28 * ii, 0.1, 0.2, 0.8]) absE = np.sqrt(Ex**2 + Ey**2 + Ez**2) pc[ii] = ax[ii].pcolor(x, z, absE.reshape(x.size, z.size), norm=LogNorm()) ax[ii].streamplot(x, z, explt.real, ezplt.real, color="k", density=1) ax[ii].set_xlim([x.min(), x.max()]) ax[ii].set_ylim([z.min(), z.max()]) ax[ii].set_title("E at t = {} s".format(t[ii])) ax[ii].set_xlabel("x") ax[ii].set_ylabel("z") cb[ii] = plt.colorbar(pc[ii], ax=ax[ii]) cb[ii].set_label("E (V/m)") .. image-sg:: /content/examples/06-tdem/images/sphx_glr_plot_0_tdem_analytic_003.png :alt: E at t = 0.0001 s, E at t = 0.001 s, E at t = 0.01 s :srcset: /content/examples/06-tdem/images/sphx_glr_plot_0_tdem_analytic_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 175-183 Magnetic Field from a Transient Electric Dipole Source ------------------------------------------------------ Here, we compute the magnetic fields for a transient electric current dipole source in the y direction. Based on the geometry of the problem, we expect rotational magnetic fields in the x and z directions, but no fields in the y direction. .. GENERATED FROM PYTHON SOURCE LINES 183-228 .. code-block:: Python # Defining electric dipole location and frequency source_location = np.r_[0, 0, 0] t = [1e-4, 1e-3, 1e-2] # Defining observation locations (avoid placing observation at source) x = np.arange(-201, 201, step=2.0) y = np.r_[0] z = x observation_locations = utils.ndgrid(x, y, z) # Define wholespace conductivity sig = 1e0 fig = plt.figure(figsize=(14, 3)) ax = 3 * [None] cb = 3 * [None] pc = 3 * [None] for ii in range(0, 3): # Compute the fields Hx, Hy, Hz = TransientElectricDipoleWholeSpace( observation_locations, source_location, sig, t[ii], moment="Y", fieldType="h", mu_r=1, ) hxplt = Hx.reshape(x.size, z.size) hzplt = Hz.reshape(x.size, z.size) ax[ii] = fig.add_axes([0.1 + 0.28 * ii, 0.1, 0.2, 0.8]) absH = np.sqrt(Hx**2 + Hy**2 + Hz**2) pc[ii] = ax[ii].pcolor(x, z, absH.reshape(x.size, z.size), norm=LogNorm()) ax[ii].streamplot(x, z, hxplt.real, hzplt.real, color="k", density=1) ax[ii].set_xlim([x.min(), x.max()]) ax[ii].set_ylim([z.min(), z.max()]) ax[ii].set_title("H at t = {} s".format(t[ii])) ax[ii].set_xlabel("x") ax[ii].set_ylabel("z") cb[ii] = plt.colorbar(pc[ii], ax=ax[ii]) cb[ii].set_label("H (A/m)") .. image-sg:: /content/examples/06-tdem/images/sphx_glr_plot_0_tdem_analytic_004.png :alt: H at t = 0.0001 s, H at t = 0.001 s, H at t = 0.01 s :srcset: /content/examples/06-tdem/images/sphx_glr_plot_0_tdem_analytic_004.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 11.746 seconds) **Estimated memory usage:** 414 MB .. _sphx_glr_download_content_examples_06-tdem_plot_0_tdem_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_0_tdem_analytic.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_0_tdem_analytic.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_0_tdem_analytic.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_