plotting_map = maps.ActiveCells(mesh, ind_active, np.nan)
plotting_model = [
true_conductivity_model_log10,
l2_conductivity_model_log10,
recovered_conductivity_model_log10,
]
for ii in range(0, 3):
ax1[ii] = fig.add_axes([0.1, 0.73 - 0.3 * ii, 0.72, 0.21])
mesh.plotImage(
plotting_map * plotting_model[ii],
ax=ax1[ii],
grid=False,
clim=(
np.min(true_conductivity_model_log10),
np.max(true_conductivity_model_log10),
),
range_x=[-700, 700],
range_y=[-600, 0],
pcolorOpts={"cmap": "viridis"},
)
ax1[ii].set_title(title_str[ii])
ax1[ii].set_xlabel("x (m)")
ax1[ii].set_ylabel("z (m)")
ax2[ii] = fig.add_axes([0.83, 0.73 - 0.3 * ii, 0.05, 0.21])
norm = mpl.colors.Normalize(
vmin=np.min(true_conductivity_model_log10),
vmax=np.max(true_conductivity_model_log10),
)
cbar = mpl.colorbar.ColorbarBase(
# The total number of cells
nC = mesh.nC
# An (nC, 2) array containing the cell-center locations
cc = mesh.gridCC
# A boolean array specifying which cells lie on the boundary
bInd = mesh.cellBoundaryInd
# The cell areas (2D "volume")
s = mesh.vol
fig = plt.figure(figsize=(6, 6))
ax = fig.add_subplot(111)
mesh.plotImage(np.log10(s), grid=True, ax=ax)
ax.set_xbound(mesh.x0[0], mesh.x0[0] + np.sum(mesh.hx))
ax.set_ybound(mesh.x0[1], mesh.x0[1] + np.sum(mesh.hy))
ax.set_title('Log of Cell Areas')
###############################################
# 3D Example
# ----------
#
# Here we show how the same approach can be used to create and extract
# properties from a 3D tree mesh.
#
dx = 5 # minimum cell width (base mesh cell width) in x
dy = 5 # minimum cell width (base mesh cell width) in y
dz = 5 # minimum cell width (base mesh cell width) in z
cbar.set_label("$S/m$", rotation=270, labelpad=15, size=12)
plt.show()
# # Plot Recovered Model
fig = plt.figure(figsize=(9, 4))
recovered_conductivity = conductivity_map * recovered_conductivity_model
recovered_conductivity[~ind_active] = np.NaN
ax1 = fig.add_axes([0.1, 0.12, 0.72, 0.8])
mesh.plotImage(
recovered_conductivity,
normal="Y",
ax=ax1,
grid=False,
range_x=[-700, 700],
range_y=[-700, 0],
pcolorOpts={"norm": norm},
)
ax1.set_title("Recovered Conductivity Model")
ax1.set_xlabel("x (m)")
ax1.set_ylabel("z (m)")
ax2 = fig.add_axes([0.83, 0.12, 0.05, 0.8])
cbar = mpl.colorbar.ColorbarBase(ax2, norm=norm, orientation="vertical")
cbar.set_label(r"$\sigma$ (S/m)", rotation=270, labelpad=15, size=12)
plt.show()
###################################################################
ax2 = fig.add_axes([0.84, 0.17, 0.03, 0.7])
cbar = mpl.colorbar.ColorbarBase(ax2, norm=norm, orientation="vertical")
cbar.set_label("$S/m$", rotation=270, labelpad=15, size=12)
plt.show()
# Plot Recovered Model
fig = plt.figure(figsize=(9, 4))
recovered_conductivity = conductivity_map * recovered_conductivity_model
recovered_conductivity[~ind_active] = np.NaN
ax1 = fig.add_axes([0.14, 0.17, 0.68, 0.7])
mesh.plotImage(
recovered_conductivity, normal="Y", ax=ax1, grid=False, pcolorOpts={"norm": norm}
)
ax1.set_xlim(-600, 600)
ax1.set_ylim(-600, 0)
ax1.set_title("Recovered Conductivity Model")
ax1.set_xlabel("x (m)")
ax1.set_ylabel("z (m)")
ax2 = fig.add_axes([0.84, 0.17, 0.03, 0.7])
cbar = mpl.colorbar.ColorbarBase(ax2, norm=norm, orientation="vertical")
cbar.set_label(r"$\sigma$ (S/m)", rotation=270, labelpad=15, size=12)
plt.show()
###################################################################
cbar.set_label("$S/m$", rotation=270, labelpad=15, size=12)
plt.show()
# # Plot Recovered Model
fig = plt.figure(figsize=(9, 4))
recovered_conductivity = conductivity_map * recovered_conductivity_model
recovered_conductivity[~ind_active] = np.NaN
ax1 = fig.add_axes([0.1, 0.12, 0.72, 0.8])
mesh.plotImage(
recovered_conductivity,
normal="Y",
ax=ax1,
grid=False,
range_x=[-700, 700],
range_y=[-700, 0],
pcolorOpts={"norm": norm},
)
ax1.set_title("Recovered Conductivity Model")
ax1.set_xlabel("x (m)")
ax1.set_ylabel("z (m)")
ax2 = fig.add_axes([0.83, 0.12, 0.05, 0.8])
cbar = mpl.colorbar.ColorbarBase(ax2, norm=norm, orientation="vertical")
cbar.set_label(r"$\sigma$ (S/m)", rotation=270, labelpad=15, size=12)
plt.show()
###################################################################
def plotVectorSectionsOctree(
mesh,
m,
normal="X",
ind=0,
vmin=None,
vmax=None,
scale=1.0,
vec="k",
axs=None,
actvMap=None,
fill=True,
):
"""
Plot section through a 3D tensor model
"""
# plot recovered model
normalInd = {"X": 0, "Y": 1, "Z": 2}[normal]
antiNormalInd = {"X": [1, 2], "Y": [0, 2], "Z": [0, 1]}[normal]
h2d = (mesh.h[antiNormalInd[0]], mesh.h[antiNormalInd[1]])
x2d = (mesh.x0[antiNormalInd[0]], mesh.x0[antiNormalInd[1]])
#: Size of the sliced dimension
szSliceDim = len(mesh.h[normalInd])
if ind is None:
ind = int(szSliceDim // 2)
cc_tensor = [None, None, None]
for i in range(3):
cc_tensor[i] = np.cumsum(np.r_[mesh.x0[i], mesh.h[i]])
cc_tensor[i] = (cc_tensor[i][1:] + cc_tensor[i][:-1]) * 0.5
slice_loc = cc_tensor[normalInd][ind]
# Create a temporary TreeMesh with the slice through
temp_mesh = TreeMesh(h2d, x2d)
level_diff = mesh.max_level - temp_mesh.max_level
XS = [None, None, None]
XS[antiNormalInd[0]], XS[antiNormalInd[1]] = np.meshgrid(
cc_tensor[antiNormalInd[0]], cc_tensor[antiNormalInd[1]])
XS[normalInd] = np.ones_like(XS[antiNormalInd[0]]) * slice_loc
loc_grid = np.c_[XS[0].reshape(-1), XS[1].reshape(-1), XS[2].reshape(-1)]
inds = np.unique(mesh._get_containing_cell_indexes(loc_grid))
grid2d = mesh.gridCC[inds][:, antiNormalInd]
levels = mesh._cell_levels_by_indexes(inds) - level_diff
temp_mesh.insert_cells(grid2d, levels)
tm_gridboost = np.empty((temp_mesh.nC, 3))
tm_gridboost[:, antiNormalInd] = temp_mesh.gridCC
tm_gridboost[:, normalInd] = slice_loc
# Interpolate values to mesh.gridCC if not 'CC'
mx = actvMap * m[:, 0]
my = actvMap * m[:, 1]
mz = actvMap * m[:, 2]
m = np.c_[mx, my, mz]
# Interpolate values from mesh.gridCC to grid2d
ind_3d_to_2d = mesh._get_containing_cell_indexes(tm_gridboost)
v2d = m[ind_3d_to_2d, :]
amp = np.sum(v2d**2.0, axis=1)**0.5
if axs is None:
axs = plt.subplot(111)
if fill:
temp_mesh.plotImage(amp, ax=axs, clim=[vmin, vmax], grid=True)
axs.quiver(
temp_mesh.gridCC[:, 0],
temp_mesh.gridCC[:, 1],
v2d[:, antiNormalInd[0]],
v2d[:, antiNormalInd[1]],
pivot="mid",
scale_units="inches",
scale=scale,
linewidths=(1, ),
edgecolors=(vec),
headaxislength=0.1,
headwidth=10,
headlength=30,
)
ind_resistor = model_builder.getIndicesSphere(np.r_[120.0, -180.0], 60.0,
mesh.gridCC)
ind_resistor = ind_resistor[ind_active]
conductivity_model[ind_resistor] = resistor_conductivity
# Plot Conductivity Model
fig = plt.figure(figsize=(8.5, 4))
plotting_map = maps.InjectActiveCells(mesh, ind_active, np.nan)
log_mod = np.log10(conductivity_model)
ax1 = fig.add_axes([0.1, 0.12, 0.73, 0.78])
mesh.plotImage(
plotting_map * log_mod,
ax=ax1,
grid=False,
clim=(np.log10(resistor_conductivity), np.log10(conductor_conductivity)),
pcolor_opts={"cmap": "viridis"},
)
ax1.set_title("Conductivity Model")
ax1.set_xlabel("x (m)")
ax1.set_ylabel("z (m)")
ax2 = fig.add_axes([0.85, 0.12, 0.05, 0.78])
norm = mpl.colors.Normalize(vmin=np.log10(resistor_conductivity),
vmax=np.log10(conductor_conductivity))
cbar = mpl.colorbar.ColorbarBase(ax2,
norm=norm,
cmap=mpl.cm.viridis,
orientation="vertical",
format="$10^{%.1f}$")
ind_resistor = model_builder.getIndicesSphere(np.r_[120.0, -180.0], 60.0,
mesh.gridCC)
ind_resistor = ind_resistor[ind_active]
conductivity_model[ind_resistor] = resistor_conductivity
# Plot Conductivity Model
fig = plt.figure(figsize=(8.5, 4))
plotting_map = maps.InjectActiveCells(mesh, ind_active, np.nan)
log_mod = np.log10(conductivity_model)
ax1 = fig.add_axes([0.1, 0.12, 0.73, 0.78])
mesh.plotImage(
plotting_map * log_mod,
ax=ax1,
grid=False,
clim=(np.log10(resistor_conductivity), np.log10(conductor_conductivity)),
pcolorOpts={"cmap": "viridis"},
)
ax1.set_title("Conductivity Model")
ax1.set_xlabel("x (m)")
ax1.set_ylabel("z (m)")
ax2 = fig.add_axes([0.85, 0.12, 0.05, 0.78])
norm = mpl.colors.Normalize(vmin=np.log10(resistor_conductivity),
vmax=np.log10(conductor_conductivity))
cbar = mpl.colorbar.ColorbarBase(ax2,
norm=norm,
cmap=mpl.cm.viridis,
orientation="vertical",
format="$10^{%.1f}$")
# Load true conductivity model
true_conductivity_model = np.loadtxt(str(true_conductivity_filename))
true_conductivity_model_log10 = np.log10(true_conductivity_model[ind_active])
# Plot True Model
fig = plt.figure(figsize=(9, 4))
plotting_map = maps.ActiveCells(mesh, ind_active, np.nan)
ax1 = fig.add_axes([0.1, 0.12, 0.72, 0.8])
mesh.plotImage(
plotting_map * true_conductivity_model_log10,
ax=ax1,
grid=False,
clim=(np.min(true_conductivity_model_log10),
np.max(true_conductivity_model_log10)),
range_x=[-700, 700],
range_y=[-700, 0],
pcolor_opts={"cmap": "viridis"},
)
ax1.set_title("True Conductivity Model")
ax1.set_xlabel("x (m)")
ax1.set_ylabel("z (m)")
ax2 = fig.add_axes([0.83, 0.12, 0.05, 0.8])
norm = mpl.colors.Normalize(
vmin=np.min(true_conductivity_model_log10),
vmax=np.max(true_conductivity_model_log10),
)
cbar = mpl.colorbar.ColorbarBase(ax2,
norm=norm,
xx = M.vectorNx
yy = np.zeros(nbcx + 1) #vetor linha(poderia ser uma função ou pontos)
pts = np.c_[matutils.mkvc(xx), matutils.mkvc(yy)]
def refine(cell):
if np.sqrt(((np.r_[cell.center] + [xc, yc])**2).sum()) < Raio_length:
return 10
return 6
M.refine(refine)
M.finalize() # Must finalize tree mesh before use
#M.plotGrid(showIt=True)
print("\n the mesh has {} cells".format(M))
ccMesh = M.gridCC
print('indices:', np.size(ccMesh))
conds = [1e-2, 1]
sig = simpeg.Utils.ModelBuilder.defineBlock(M.gridCC, [-5000, -2200],
[5000, -4200], conds)
#
sig[M.gridCC[:, 1] > 0] = 1e-8
sigBG = np.zeros(M.nC) + conds[1]
sigBG[M.gridCC[:, 1] > 0] = 1e-8
M.plotImage(np.log(sig), grid=True)
