def preconstrain(self, x):
source = self.get_source(x)
if any(self.distance_min > source.distance_to(t)
for t in self.targets):
raise Forbidden()
return num.array(x, dtype=num.float)
def preconstrain(self, x):
d = self.get_parameter_dict(x)
m6 = num.array([d.rmnn, d.rmee, d.rmdd, d.rmne, d.rmnd, d.rmed],
dtype=num.float)
m9 = mtm.symmat6(*m6)
if self.mt_type == 'deviatoric':
trace_m = num.trace(m9)
m_iso = num.diag([trace_m / 3., trace_m / 3., trace_m / 3.])
m9 -= m_iso
elif self.mt_type == 'dc':
mt = mtm.MomentTensor(m=m9)
m9 = mt.standard_decomposition()[1][2]
m0_unscaled = math.sqrt(num.sum(m9.A**2)) / math.sqrt(2.)
m9 /= m0_unscaled
m6 = mtm.to6(m9)
d.rmnn, d.rmee, d.rmdd, d.rmne, d.rmnd, d.rmed = m6
x = self.get_parameter_array(d)
source = self.get_source(x)
for t in self.targets:
if (self.distance_min > num.asarray(t.distance_to(source))).any():
raise Forbidden()
return x