# package. Here, we represent the data as apparent conductivities.
#
# Extract 3D survey and observed data
survey_3d = data_3d.survey
dobs_3d = data_3d.dobs
# Convert predicted data to apparent conductivities
apparent_conductivity_3d = 1 / apparent_resistivity_from_voltage(
survey_3d, dobs_3d, space_type="half space"
)
if has_plotly:
fig = plot_3d_pseudosection(
survey_3d, apparent_conductivity_3d, scale="log", units="S/m",
)
fig.update_layout(
title_text="Apparent Conductivity",
title_x=0.5,
title_font_size=24,
width=650,
height=500,
scene_camera=dict(
center=dict(x=0, y=0, z=-0.4), eye=dict(x=1.6, y=-1.6, z=1.8)
),
)
plotly.io.show(fig)
# Here we plot the observed DC data in 3D pseudosection.
# To use this utility, you must have Python's *plotly* package.
# Here, we represent the DC data as apparent conductivities.
#
# Convert predicted data to apparent conductivities
apparent_conductivity = 1 / apparent_resistivity_from_voltage(
dc_data.survey,
dc_data.dobs,
)
if has_plotly:
# Plot DC Data
fig = plot_3d_pseudosection(dc_data.survey,
apparent_conductivity,
scale="log",
units="S/m")
fig.update_layout(
title_text="Apparent Conductivity",
title_x=0.5,
title_font_size=24,
width=650,
height=500,
scene_camera=dict(center=dict(x=0, y=0, z=-0.4),
eye=dict(x=1.5, y=-1.5, z=1.8)),
)
plotly.io.show(fig)
else:
# To use this utility, you must have Python's *plotly* package.
# Here, we represent the DC data as apparent conductivities
# and the IP data as apparent chargeabilities.
#
# Convert predicted data to apparent conductivities
apparent_conductivity = 1 / apparent_resistivity_from_voltage(
dc_data.survey,
dc_data.dobs,
)
if has_plotly:
fig = plot_3d_pseudosection(dc_data.survey,
apparent_conductivity,
scale='log',
units='S/m',
plane_distance=15)
fig.update_layout(title_text='Apparent Conductivity',
title_x=0.5,
title_font_size=24,
width=650,
height=500,
scene_camera=dict(center=dict(x=0, y=0, z=-0.4),
eye=dict(x=1.5, y=-1.5, z=1.8)))
plotly.io.show(fig)
else:
print("INSTALL 'PLOTLY' TO VISUALIZE 3D PSEUDOSECTIONS")
# To plot ALL of the data, simply remove the keyword argument *plane_points*
# when calling *plot_3d_pseudosection*.
plane_points = []
p1, p2, p3 = np.array([-1000, 0, 0]), np.array([1000, 0, 0]), np.array([0, 0, -1000])
plane_points.append([p1, p2, p3])
p1, p2, p3 = np.array([-350, -1000, 0]), np.array([-350, 1000, 0]), np.array([-350, 0, -1000])
plane_points.append([p1, p2, p3])
p1, p2, p3 = np.array([350, -1000, 0]), np.array([350, 1000, 0]), np.array([350, 0, -1000])
plane_points.append([p1, p2, p3])
if has_plotly:
fig = plot_3d_pseudosection(
dc_survey,
apparent_conductivity,
scale='log',
units='S/m',
plane_points=plane_points,
plane_distance=15
)
fig.update_layout(
title_text='Apparent Conductivity',
title_x=0.5,
title_font_size=24,
width=650,
height=500,
scene_camera=dict(center=dict(x=0.05, y=0, z=-0.4))
)
plotly.io.show(fig)
np.array([-350, 0, -1000]),
)
plane_points.append([p1, p2, p3])
p1, p2, p3 = (
np.array([350, -1000, 0]),
np.array([350, 1000, 0]),
np.array([350, 0, -1000]),
)
plane_points.append([p1, p2, p3])
if has_plotly:
fig = plot_3d_pseudosection(
dc_survey,
apparent_conductivity,
scale="log",
units="S/m",
plane_points=plane_points,
plane_distance=15,
)
fig.update_layout(
title_text="Apparent Conductivity",
title_x=0.5,
title_font_size=24,
width=650,
height=500,
scene_camera=dict(center=dict(x=0.05, y=0, z=-0.4)),
)
plotly.io.show(fig)
p1, p2, p3 = np.array([-1000, 0, 0]), np.array([1000, 0,
0]), np.array([0, 0, -1000])
plane_points.append([p1, p2, p3])
p1, p2, p3 = np.array([-350, -1000,
0]), np.array([-350, 1000,
0]), np.array([-350, 0, -1000])
plane_points.append([p1, p2, p3])
p1, p2, p3 = np.array([350, -1000,
0]), np.array([350, 1000, 0]), np.array([350, 0, -1000])
plane_points.append([p1, p2, p3])
if has_plotly:
fig = plot_3d_pseudosection(survey,
apparent_conductivity,
scale='log',
units='S/m',
plane_points=plane_points,
plane_distance=15)
fig.update_layout(title_text='Apparent Conductivity',
title_x=0.5,
title_font_size=24,
width=650,
height=500,
scene_camera=dict(center=dict(x=0.05, y=0, z=-0.4)))
plotly.io.show(fig)
else:
print("INSTALL 'PLOTLY' TO VISUALIZE 3D PSEUDOSECTIONS")
np.array([-350, 0, -1000]),
)
plane_points.append([p1, p2, p3])
p1, p2, p3 = (
np.array([350, -1000, 0]),
np.array([350, 1000, 0]),
np.array([350, 0, -1000]),
)
plane_points.append([p1, p2, p3])
if has_plotly:
fig = plot_3d_pseudosection(
survey,
apparent_conductivity,
scale="log",
units="S/m",
plane_points=plane_points,
plane_distance=15,
)
fig.update_layout(
title_text="Apparent Conductivity",
title_x=0.5,
title_font_size=24,
width=650,
height=500,
scene_camera=dict(center=dict(x=0.05, y=0, z=-0.4)),
)
plotly.io.show(fig)
azim=-45,
elev=45)
cax1 = fig.add_axes([0.83, 0.55, 0.02, 0.4])
cax2 = fig.add_axes([0.83, 0.1, 0.02, 0.4])
# Plot the single East-West line. A list containing 3 points [p1, p2, p3] is
# used to define the plane near which we would like to plot the 3D data.
vlim = [apparent_conductivity.min(), apparent_conductivity.max()]
p1, p2, p3 = np.array([-1000, 0, 0]), np.array([1000, 0,
0]), np.array([1000, 0, -1000])
plane_points = [p1, p2, p3]
ax1 = plot_3d_pseudosection(survey,
apparent_conductivity,
s=80,
ax=ax1,
scale='log',
vlim=vlim,
cax=cax1,
plane_points=plane_points,
plane_distance=40.,
units='$S/m$')
ax1.set_xlim([-1000., 1000.])
ax1.set_ylim([-1000., 1000.])
ax1.set_xlabel('X [m]', labelpad=15)
ax1.set_ylabel('Y [m]', labelpad=15)
ax1.set_zlabel('Z [m]', labelpad=10)
ax1.set_title('Apparent Conductivity (East-West)', pad=20)
# Plot both North-South lines. For multiple planes, make a list of of plane
# points.
vlim = [apparent_conductivity.min(), apparent_conductivity.max()]
plane_points = []
# To use this utility, you must have Python's *plotly* package.
# Here, we represent the DC data as apparent conductivities
# and the IP data as apparent chargeabilities.
#
# Convert predicted data to apparent conductivities
apparent_conductivity = 1 / apparent_resistivity_from_voltage(
dc_data.survey, dc_data.dobs,
)
if has_plotly:
fig = plot_3d_pseudosection(
dc_data.survey,
apparent_conductivity,
scale="log",
units="S/m",
plane_distance=15,
)
fig.update_layout(
title_text="Apparent Conductivity",
title_x=0.5,
title_font_size=24,
width=650,
height=500,
scene_camera=dict(
center=dict(x=0, y=0, z=-0.4), eye=dict(x=1.5, y=-1.5, z=1.8)
),
)
elev=45)
cax1 = fig.add_axes([0.83, 0.55, 0.02, 0.4])
cax2 = fig.add_axes([0.83, 0.1, 0.02, 0.4])
# Plot the single East-West line. A list containing 3 points [p1, p2, p3] is
# used to define the plane near which we would like to plot the 3D data.
vlim = [apparent_conductivity.min(), apparent_conductivity.max()]
p1, p2, p3 = np.array([-1000, 0, 0]), np.array([1000, 0,
0]), np.array([1000, 0, -1000])
plane_points = [p1, p2, p3]
ax1 = plot_3d_pseudosection(
dc_survey,
apparent_conductivity,
s=80,
ax=ax1,
scale="log",
vlim=vlim,
cax=cax1,
plane_points=plane_points,
plane_distance=40.0,
units="$S/m$",
)
ax1.set_xlim([-1000.0, 1000.0])
ax1.set_ylim([-1000.0, 1000.0])
ax1.set_xlabel("X [m]", labelpad=15)
ax1.set_ylabel("Y [m]", labelpad=15)
ax1.set_zlabel("Z [m]", labelpad=10)
ax1.set_title("Apparent Conductivity (East-West)", pad=20)
# Plot both North-South lines. For multiple planes, make a list of of plane
# points.
vlim = [apparent_conductivity.min(), apparent_conductivity.max()]
# Here we plot the observed DC data in 3D pseudosection.
# To use this utility, you must have Python's *plotly* package.
# Here, we represent the DC data as apparent conductivities.
#
# Convert predicted data to apparent conductivities
apparent_conductivity = 1 / apparent_resistivity_from_voltage(
dc_data.survey,
dc_data.dobs,
)
if has_plotly:
# Plot DC Data
fig = plot_3d_pseudosection(dc_data.survey,
apparent_conductivity,
scale='log',
units='S/m')
fig.update_layout(title_text='Apparent Conductivity',
title_x=0.5,
title_font_size=24,
width=650,
height=500,
scene_camera=dict(center=dict(x=0, y=0, z=-0.4),
eye=dict(x=1.5, y=-1.5, z=1.8)))
plotly.io.show(fig)
else:
print("INSTALL 'PLOTLY' TO VISUALIZE 3D PSEUDOSECTIONS")