def check_eq(self):
"""
Check the variables and equations and set the limiter flags.
Reset differential equation values based on limiter flags.
Notes
-----
The current implementation reallocates memory for `self.x_set` in each call.
Consider improving for speed. (TODO)
"""
if not self.no_upper:
self.zu[:] = np.logical_and(
np.greater_equal(self.u.v, self.upper.v),
np.greater_equal(self.state.e, 0))
if not self.no_lower:
self.zl[:] = np.logical_and(np.less_equal(self.u.v, self.lower.v),
np.less_equal(self.state.e, 0))
self.zi[:] = np.logical_not(np.logical_or(self.zu, self.zl))
# must flush the `x_set` list at the beginning
self.x_set = list()
if not np.all(self.zi):
idx = np.where(self.zi == 0)
self.state.e[:] = self.state.e * self.zi
self.state.v[:] = self.state.v * self.zi + self.upper.v * self.zu + self.lower.v * self.zl
self.x_set.append((self.state.a[idx], self.state.v[idx],
0)) # (address, var. values, eqn. values)
def check_eq(self):
"""
Check the variables and equations and set the limiter flags.
"""
self.zu[:] = np.logical_and(np.greater_equal(self.u.v, self.upper.v),
np.greater_equal(self.state.e, 0))
self.zl[:] = np.logical_and(np.less_equal(self.u.v, self.lower.v),
np.less_equal(self.state.e, 0))
self.zi[:] = np.logical_not(np.logical_or(self.zu, self.zl))
def check_eq(self):
"""
Check the variables and equations and set the limiter flags.
"""
self.zu[:] = np.greater(self.u.v, self.upper.v)
self.zl[:] = np.less(self.u.v, self.lower.v)
self.zi[:] = np.logical_not(np.logical_or(self.zu, self.zl))
self.zu[:] = np.logical_and(self.zu, np.greater(self.state.e, 0)).astype(np.float64)
self.zl[:] = np.logical_and(self.zl, np.less(self.state.e, 0)).astype(np.float64)
self.zi[:] = np.logical_not(
np.logical_or(self.zu.astype(np.bool),
self.zl.astype(np.bool))).astype(np.float64)
def check_var(self, *args, **kwargs):
if not self.enable:
return
super().check_var()
if self.n_select is not None and self.n_select > 0:
asc = np.argsort(self.u.v - self.lower.v) # ascending order
desc = np.argsort(self.upper.v - self.u.v)
lowest_n = asc[:self.n_select]
highest_n = desc[:self.n_select]
reset_in = np.ones(self.u.v.shape)
reset_in[lowest_n] = 0
reset_in[highest_n] = 0
reset_out = 1 - reset_in
self.zi[:] = np.logical_or(reset_in, self.zi)
self.zl[:] = np.logical_and(reset_out, self.zl)
self.zu[:] = np.logical_and(reset_out, self.zu)
