def _initialize_bcs(self, bcs="damp"):
# Create dampening field as symbol `damp`
if self.nbl == 0:
self.damp = 1 if bcs == "mask" else 0
return
# First initialization
init = self.damp is None
# Get current Function if alread yinitialized
self.damp = self.damp or Function(name="damp", grid=self.grid)
if callable(bcs):
bcs(self.damp, self.nbl)
else:
re_init = ((bcs == "mask" and mmin(self.damp) == 0)
or (bcs == "damp" and mmax(self.damp) == 1))
if init or re_init:
if re_init and not init:
bcs_o = "damp" if bcs == "mask" else "mask"
warning("Re-initializing damp profile from %s to %s" %
(bcs_o, bcs))
warning("Model has to be created with `bcs=\"%s\"`"
"for this WaveSolver" % bcs)
initialize_damp(self.damp,
self.padsizes,
self.spacing,
abc_type=bcs,
fs=self.fs)
self._physical_parameters.update(['damp'])
def test_min_max_sparse(self):
"""
Test that mmin/mmax work on SparseFunction
"""
grid = Grid((101, 101), extent=(1000., 1000.))
nrec = 101
rec0 = SparseTimeFunction(name='rec0', grid=grid, nt=1001, npoint=nrec)
rec0.data[:, :] = 1 + np.random.randn(*rec0.shape).astype(grid.dtype)
term1 = np.min(rec0.data)
term2 = mmin(rec0)
assert np.isclose(term1 / term2 - 1, 0.0, rtol=0.0, atol=1e-5)
term1 = np.max(rec0.data)
term2 = mmax(rec0)
assert np.isclose(term1 / term2 - 1, 0.0, rtol=0.0, atol=1e-5)
def _scale(self):
# Update scale for tti
if 'epsilon' in self._physical_parameters:
return np.sqrt(1 + 2 * mmax(self.epsilon))
return 1
def _max_vp(self):
if 'vp' in self._physical_parameters:
return mmax(self.vp)
else:
return np.sqrt(mmin(self.b) * (mmax(self.lam) + 2 * mmax(self.mu)))
def _max_vp(self):
return mmax(self.vp)
