interim /= interimcount
# Generate interior model without nans
interim = np.nan_to_num(interim)
# Create selection array
selector = interimcount != 0
# Generate output grids
X, Z = np.mgrid[0:nx, 0:nz]
X = X * cellsize + outbox[0]
Z = outbox[3] - Z * cellsize
P = np.array([X.ravel(), Z.ravel()]).T
#Generate initial grids
x0 = X[selector]
z0 = Z[selector]
pos0 = np.array([x0.ravel(), z0.ravel()]).T
val = interim[selector]
printnow('Extrapolating')
# Extrapolate using nearest neighbour
outim = griddata(pos0, val, P, method='nearest').reshape((nx, nz))
printnow('Writing output')
# Write FAST model
outdims = (nx, 1, nz)
writefast(outfile, outim.reshape(outdims))
# y-axis, normalized by the total number of rays in that x,z location
avg2d = np.nan_to_num(((1. * hitcount * model3d) / hitcount3dsum).sum(axis=1))
# Create mask (value of 1 where there are no rays)
selector = hitcount2dsum > 0
baseblank = 1 - selector
if (options.extrap):
nx, nz = avg2d.shape
X, Z = np.mgrid[0:nx, 0:nz]
P = np.array([X.ravel(), Z.ravel()]).T
x0 = X[selector]
z0 = Z[selector]
pos0 = np.array([x0.ravel(), z0.ravel()]).T
val = avg2d[selector]
model2d = griddata(pos0, val, P, method='nearest').reshape((nx, nz))
else:
# Create an empty (2D) model in which to store the result
# Copy the 2D average model into the masked region
model2d = avg2d.copy() + model2davg.copy() * baseblank
if (options.direct2d):
writefast(outfile, model2d.reshape(pseudodims))
else:
newmodel3d = model3d.copy()
newmodel3d[:] = model2d.reshape(pseudodims)
writefast(outfile, newmodel3d)
# y-axis, normalized by the total number of rays in that x,z location avg2d = np.nan_to_num(((1. * hitcount * model3d) / hitcount3dsum).sum(axis=1)) # Create mask (value of 1 where there are no rays) selector = hitcount2dsum > 0 baseblank = 1 - selector if (options.extrap): nx, nz = avg2d.shape X, Z = np.mgrid[0:nx, 0:nz] P = np.array([X.ravel(),Z.ravel()]).T x0 = X[selector] z0 = Z[selector] pos0 = np.array([x0.ravel(),z0.ravel()]).T val = avg2d[selector] model2d = griddata(pos0, val, P, method='nearest').reshape((nx,nz)) else: # Create an empty (2D) model in which to store the result # Copy the 2D average model into the masked region model2d = avg2d.copy() + model2davg.copy() * baseblank if (options.direct2d): writefast(outfile, model2d.reshape(pseudodims)) else: newmodel3d = model3d.copy() newmodel3d[:] = model2d.reshape(pseudodims) writefast(outfile, newmodel3d)
interim /= interimcount
# Generate interior model without nans
interim = np.nan_to_num(interim)
# Create selection array
selector = interimcount != 0
# Generate output grids
X, Z = np.mgrid[0:nx, 0:nz]
X = X*cellsize + outbox[0]
Z = outbox[3] - Z*cellsize
P = np.array([X.ravel(),Z.ravel()]).T
#Generate initial grids
x0 = X[selector]
z0 = Z[selector]
pos0 = np.array([x0.ravel(),z0.ravel()]).T
val = interim[selector]
printnow('Extrapolating')
# Extrapolate using nearest neighbour
outim = griddata(pos0, val, P, method='nearest').reshape((nx,nz))
printnow('Writing output')
# Write FAST model
outdims = (nx, 1, nz)
writefast(outfile, outim.reshape(outdims))
