#Calculate Vertical Gradient
vel = ra.paraModel()
vertGrad = pg.solver.grad(mesh, vel)
#pr = np.array(pr)
#ind = np.where(pr<0)
#pr[ind] = 0
#ind = np.where(pr>0.5)
#pr[ind] = 0.5
#Save Results as a .vtk file (Paraview)
mesh.addData('P-wave Velocity', vel)
mesh.addData('Vertical Velocity Gradient', vertGrad)
mesh.addData('cov', ra.coverage())
mesh.addData('stdCov', ra.standardizedCoverage())
mesh.exportVTK(pickFile.split('.')[0] + '_gimliResults.vtk')
#Spatially Locate the Profile
spatiallyLocateVTKFile(
pickFile.split('.')[0] + '_gimliResults.vtk', SoL, EoL)
#******************** PLOT AND SAVE RESULTS ******************************
fig1 = plt.figure('Model Results')
fig1.set_size_inches([12, 8])
ax = fig1.add_subplot(211)
vel = ra.paraModel()
pg.show(mesh,
vel,
coverage=ra.standardizedCoverage(),
cMin=100,
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)
################################################################################
# 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 plot only the model and customize with a bunch of keywords
ax, cbar = mgr.showResult(logScale=False, cMin=500, cMax=3000, cMap="plasma_r",
coverage=mgr.standardizedCoverage())
mgr.drawRayPaths(ax=ax, color="k", lw=0.3, alpha=0.5)
# mgr.coverage() yields the ray coverage in m and standardizedCoverage as 0/1
################################################################################
# You can play around with the gradient starting model (`vTop` and `vBottom`
# arguments) and the regularization strength `lam`. You can also customize the
# mesh.