Note

Click here to download the full example code

# Mesh: Plotting with defining rangeΒΆ

When using a large Mesh with the cylindrical code, it is advantageous
to define a `range_x`

and `range_y`

when plotting with
vectors. In this case, only the region inside of the range is
interpolated. In particular, you often want to ignore padding cells.

Out:

```
Minimum skin depth (in sphere): 1.58e+00 m
Maximum skin depth (in background): 1.58e+05 m
The maximum skin depth is (in background): 1.58e+05 m. Does the mesh go sufficiently past that?
There are 2 sources (same as the number of frequencies - 2). Each source has 2 receivers sampling the resulting b-fields
/Users/lindseyjh/git/simpeg/simpeg/examples/02-mesh/plot_view_bounds.py:267: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
plt.show()
```

```
import numpy as np
import matplotlib.pyplot as plt
from scipy.constants import mu_0
from SimPEG import Mesh, Utils, Maps
from SimPEG.EM import FDEM
# Try importing PardisoSolver from pymatsolver otherwise, use SimPEG.SolverLU
try:
from pymatsolver import PardisoSolver as Solver
except ImportError:
from SimPEG import SolverLU as Solver
def run(plotIt=True):
# ## Model Parameters
#
# We define a
# - resistive halfspace and
# - conductive sphere
# - radius of 30m
# - center is 50m below the surface
# electrical conductivities in S/m
sig_halfspace = 1e-6
sig_sphere = 1e0
sig_air = 1e-8
# depth to center, radius in m
sphere_z = -50.
sphere_radius = 30.
# ## Survey Parameters
#
# - Transmitter and receiver 20m above the surface
# - Receiver offset from transmitter by 8m horizontally
# - 25 frequencies, logaritmically between $10$ Hz and $10^5$ Hz
boom_height = 20.
rx_offset = 8.
freqs = np.r_[1e1, 1e5]
# source and receiver location in 3D space
src_loc = np.r_[0., 0., boom_height]
rx_loc = np.atleast_2d(np.r_[rx_offset, 0., boom_height])
# print the min and max skin depths to make sure mesh is fine enough and
# extends far enough
def skin_depth(sigma, f):
return 500./np.sqrt(sigma * f)
print(
'Minimum skin depth (in sphere): {:.2e} m '.format(
skin_depth(sig_sphere, freqs.max())
)
)
print(
'Maximum skin depth (in background): {:.2e} m '.format(
skin_depth(sig_halfspace, freqs.min())
)
)
# ## Mesh
#
# Here, we define a cylindrically symmetric tensor mesh.
#
# ### Mesh Parameters
#
# For the mesh, we will use a cylindrically symmetric tensor mesh. To
# construct a tensor mesh, all that is needed is a vector of cell widths in
# the x and z-directions. We will define a core mesh region of uniform cell
# widths and a padding region where the cell widths expand "to infinity".
# x-direction
csx = 2 # core mesh cell width in the x-direction
ncx = np.ceil(1.2*sphere_radius/csx) # number of core x-cells (uniform mesh slightly beyond sphere radius)
npadx = 50 # number of x padding cells
# z-direction
csz = 1 # core mesh cell width in the z-direction
ncz = np.ceil(1.2*(boom_height - (sphere_z-sphere_radius))/csz) # number of core z-cells (uniform cells slightly below bottom of sphere)
npadz = 52 # number of z padding cells
# padding factor (expand cells to infinity)
pf = 1.3
# cell spacings in the x and z directions
hx = Utils.meshTensor([(csx, ncx), (csx, npadx, pf)])
hz = Utils.meshTensor([(csz, npadz, -pf), (csz, ncz), (csz, npadz, pf)])
# define a SimPEG mesh
mesh = Mesh.CylMesh([hx, 1, hz], x0 = np.r_[0.,0., -hz.sum()/2.-boom_height])
# ### Plot the mesh
#
# Below, we plot the mesh. The cyl mesh is rotated around x=0. Ensure that
# each dimension extends beyond the maximum skin depth.
#
# Zoom in by changing the xlim and zlim.
# X and Z limits we want to plot to. Try
xlim = np.r_[0., 2.5e6]
zlim = np.r_[-2.5e6, 2.5e6]
fig, ax = plt.subplots(1, 1)
mesh.plotGrid(ax=ax)
ax.set_title('Simulation Mesh')
ax.set_xlim(xlim)
ax.set_ylim(zlim)
print(
'The maximum skin depth is (in background): {:.2e} m. '
'Does the mesh go sufficiently past that?'.format(
skin_depth(sig_halfspace, freqs.min())
)
)
# ## Put Model on Mesh
#
# Now that the model parameters and mesh are defined, we can define
# electrical conductivity on the mesh.
#
# The electrical conductivity is defined at cell centers when using the
# finite volume method. So here, we define a vector that contains an
# electrical conductivity value for every cell center.
# create a vector that has one entry for every cell center
sigma = sig_air*np.ones(mesh.nC) # start by defining the conductivity of the air everwhere
sigma[mesh.gridCC[:, 2] < 0.] = sig_halfspace # assign halfspace cells below the earth
# indices of the sphere (where (x-x0)**2 + (z-z0)**2 <= R**2)
sphere_ind =(
(mesh.gridCC[:,0]**2 + (mesh.gridCC[:,2] - sphere_z)**2) <=
sphere_radius**2
)
sigma[sphere_ind] = sig_sphere # assign the conductivity of the sphere
# Plot a cross section of the conductivity model
fig, ax = plt.subplots(1, 1)
cb = plt.colorbar(mesh.plotImage(np.log10(sigma), ax=ax, mirror=True)[0])
# plot formatting and titles
cb.set_label('$\log_{10}\sigma$', fontsize=13)
ax.axis('equal')
ax.set_xlim([-120., 120.])
ax.set_ylim([-100., 30.])
ax.set_title('Conductivity Model')
# ## Set up the Survey
#
# Here, we define sources and receivers. For this example, the receivers
# are magnetic flux recievers, and are only looking at the secondary field
# (eg. if a bucking coil were used to cancel the primary). The source is a
# vertical magnetic dipole with unit moment.
# Define the receivers, we will sample the real secondary magnetic flux
# density as well as the imaginary magnetic flux density
bz_r = FDEM.Rx.Point_bSecondary(
locs=rx_loc, orientation='z', component='real'
) # vertical real b-secondary
bz_i = FDEM.Rx.Point_b(
locs=rx_loc, orientation='z', component='imag'
) # vertical imag b (same as b-secondary)
rxList = [bz_r, bz_i] # list of receivers
# Define the list of sources - one source for each frequency. The source is
# a point dipole oriented in the z-direction
srcList = [
FDEM.Src.MagDipole(rxList, f, src_loc, orientation='z') for f in freqs
]
print(
'There are {nsrc} sources (same as the number of frequencies - {nfreq}). '
'Each source has {nrx} receivers sampling the resulting b-fields'.format(
nsrc = len(srcList),
nfreq = len(freqs),
nrx = len(rxList)
)
)
# ## Set up Forward Simulation
#
# A forward simulation consists of a paired SimPEG problem and Survey.
# For this example, we use the E-formulation of Maxwell's equations,
# solving the second-order system for the electric field, which is defined
# on the cell edges of the mesh. This is the `prob` variable below. The
# `survey` takes the source list which is used to construct the RHS for the
# problem. The source list also contains the receiver information, so the
# `survey` knows how to sample fields and fluxes that are produced by
# solving the `prob`.
# define a problem - the statement of which discrete pde system we want to
# solve
prob = FDEM.Problem3D_e(mesh, sigmaMap=Maps.IdentityMap(mesh))
prob.solver = Solver
survey = FDEM.Survey(srcList)
# tell the problem and survey about each other - so the RHS can be
# constructed for the problem and the
# resulting fields and fluxes can be sampled by the receiver.
prob.pair(survey)
# ### Solve the forward simulation
#
# Here, we solve the problem for the fields everywhere on the mesh.
fields = prob.fields(sigma)
# ### Plot the fields
#
# Lets look at the physics!
# log-scale the colorbar
from matplotlib.colors import LogNorm
# sphinx_gallery_thumbnail_number = 3
fig, ax = plt.subplots(1, 2, figsize=(12, 6))
def plotMe(field, ax):
plt.colorbar(mesh.plotImage(
field, vType='F', view='vec',
range_x=[-100., 100.], range_y=[-180., 60.],
pcolorOpts={
'norm': LogNorm(), 'cmap': plt.get_cmap('viridis')
},
streamOpts={'color': 'k'},
ax=ax, mirror=True
)[0], ax=ax)
plotMe(fields[srcList[0], 'bSecondary'].real, ax[0])
ax[0].set_title('Real B-Secondary, {}Hz'.format(freqs[0]))
plotMe(fields[srcList[1], 'bSecondary'].real, ax[1])
ax[1].set_title('Real B-Secondary, {}Hz'.format(freqs[1]))
plt.tight_layout()
if plotIt:
plt.show()
if __name__ == '__main__':
run(plotIt=True)
```

**Total running time of the script:** ( 0 minutes 2.141 seconds)