# We initialize the refraction manager.
mgr = TravelTimeManager()
# Alternatively, one can plot a matrix plot of apparent velocities which is the
# more general function also making sense for crosshole data.
ax, cbar = mgr.showData(data)
################################################################################
# Finally, we call the `invert` method and plot the result.The mesh is created
# based on the sensor positions on-the-fly.
mgr.invert(data,
secNodes=3,
paraMaxCellSize=5.0,
zWeight=0.2,
vTop=500,
vBottom=5000,
verbose=1)
ax, cbar = mgr.showResult(logScale=True)
mgr.drawRayPaths(ax=ax, color="w", lw=0.3, alpha=0.5)
################################################################################
# Show result and fit of measured data and model response. You may want to save your results too.
fig = mgr.showResultAndFit()
mgr.saveResult()
################################################################################
# You can play around with the gradient starting model (`vTop` and `vBottom`
# arguments) and the regularization strength `lam`. You can also customize the
# mesh.
fig1.tight_layout()
fig1.savefig('ModelResults.png', dpi=600)
fig2 = plt.figure('Ray Coverage P-wave')
vel = ra.paraModel()
fig2.set_size_inches([8, 8])
ax = fig2.add_subplot(211)
pg.show(ra.paraDomain,
vel,
coverage=ra.standardizedCoverage(),
cMin=100,
cMax=4000,
cmap='jet_r',
ax=ax)
pg.viewer.mpl.drawSensors(ax, data.sensorPositions(), diam=0.5, color="k")
ra.drawRayPaths(ax, color="k")
ax.set_xlabel('Distance (m)')
ax.set_ylabel('Elevation (m)')
ax.set_title('Ray Paths')
ax2 = fig2.add_subplot(212)
tmpCov = ra.coverage()
tmpCov[np.isneginf(tmpCov)] = 0
pg.show(mesh,
data=tmpCov,
coverage=ra.standardizedCoverage(),
cMin=0,
cMax=10,
ax=ax2,
cmap='plasma')
pg.viewer.mpl.drawSensors(ax2, data.sensorPositions(), diam=0.5, color="k")
ax2.set_xlabel('Distance (m)')