def check(self):
"""
Check the bounds and equality conditions.
"""
if not self.enable:
return
def _not_all_close(a, b):
return np.logical_not(np.isclose(a, b))
if self._v is None:
self._v = np.zeros_like(self.u.v)
checks = [(self.lower, np.less_equal, "violation of the lower limit",
"limit"),
(self.upper, np.greater_equal,
"violation of the upper limit", "limit"),
(self.equal, _not_all_close, 'should be equal', "expected"),
(self.not_equal, np.equal, 'should not be equal',
"not expected")]
for check in checks:
limit = check[0]
func = check[1]
text = check[2]
text2 = check[3]
if limit is None:
continue
self.v[:] = np.logical_or(self.v, func(self.u.v, limit.v))
pos = np.argwhere(func(self.u.v, limit.v)).ravel()
if len(pos) == 0:
continue
idx = [self.owner.idx.v[i] for i in pos]
lim_v = limit.v * np.ones(self.n)
title = f'{self.owner.class_name} {self.info} {text}.'
err_dict = OrderedDict([
('idx', idx),
('values', self.u.v[pos]),
(f'{text2}', lim_v[pos]),
])
data = list(map(list, zip(*err_dict.values())))
tab = Tab(title=title, data=data, header=list(err_dict.keys()))
if self.error_out:
logger.error(tab.draw())
else:
logger.warning(tab.draw())
self.v[:] = np.logical_not(self.v)
def warn_init_limit(self):
"""
Warn if initialized at limits.
"""
if self.no_warn:
return
for f, limit in self.warn_flags:
if f not in self.export_flags:
logger.error(f'warn_flags contain unknown flag {f}')
continue
pos = np.argwhere(np.not_equal(self.__dict__[f], 0)).ravel()
if not len(pos):
continue
err_msg = f'{self.owner.class_name}.{self.name} {self.__dict__[limit].name} at limits'
if isinstance(self.__dict__[limit].v, np.ndarray):
lim_value = self.__dict__[limit].v[pos]
else:
lim_value = self.__dict__[limit].v
err_data = {
'idx': [self.owner.idx.v[i] for i in pos],
'Flag': [f] * len(pos),
'Input Value': self.u.v[pos],
'Limit': lim_value * np.ones_like(pos),
}
tab = Tab(title=err_msg,
header=err_data.keys(),
data=list(map(list, zip(*err_data.values()))))
logger.warning(tab.draw())
def supported_models(self, export='plain'):
"""
Return the support group names and model names in a table.
Returns
-------
str
A table-formatted string for the groups and models
"""
pairs = list()
for g in self.groups:
models = list()
for m in self.groups[g].models:
models.append(m)
if len(models) > 0:
pairs.append((g, ', '.join(models)))
tab = Tab(
title='Supported Groups and Models',
header=['Group', 'Models'],
data=pairs,
export=export,
)
return tab.draw()
def check(self):
"""
Check the bounds and equality conditions.
"""
if not self.enable:
return
if self._v is None:
self._v = np.zeros_like(self.u.v)
for check in self.checks:
limit = check[0]
func = check[1]
text = check[2]
text2 = check[3]
if limit is None:
continue
self.v[:] = np.logical_or(self.v, func(self.u.v, limit.v))
pos = np.argwhere(func(self.u.v, limit.v)).ravel()
if len(pos) == 0:
continue
idx = [self.owner.idx.v[i] for i in pos]
lim_v = limit.v * np.ones(self.n)
title = f'{self.owner.class_name} {self.info} {text}.'
err_dict = OrderedDict([
('idx', idx),
('values', self.u.v[pos]),
(f'{text2}', lim_v[pos]),
])
data = list(map(list, zip(*err_dict.values())))
tab = Tab(title=title, data=data, header=list(err_dict.keys()))
if self.error_out:
logger.error(tab.draw())
else:
logger.warning(tab.draw())
self.v[:] = np.logical_not(self.v)
def test_init(self):
"""
Test if the TDS initialization is successful.
This function update ``dae.f`` and ``dae.g`` and checks if the residuals
are zeros.
"""
system = self.system
# fg_update is called in TDS.init()
system.j_update(models=system.exist.pflow_tds)
# reset diff. RHS where `check_init == False`
system.dae.f[system.no_check_init] = 0.0
# warn if variables are initialized at limits
if system.config.warn_limits:
for model in system.exist.pflow_tds.values():
for item in model.discrete.values():
item.warn_init_limit()
if np.max(np.abs(system.dae.fg)) < self.config.tol:
logger.debug('Initialization tests passed.')
return True
# otherwise, show suspect initialization error
fail_idx = np.ravel(np.where(abs(system.dae.fg) >= self.config.tol))
nan_idx = np.ravel(np.where(np.isnan(system.dae.fg)))
bad_idx = np.hstack([fail_idx, nan_idx])
fail_names = [system.dae.xy_name[int(i)] for i in fail_idx]
nan_names = [system.dae.xy_name[int(i)] for i in nan_idx]
bad_names = fail_names + nan_names
title = 'Suspect initialization issue! Simulation may crash!'
err_data = {
'Name': bad_names,
'Var. Value': system.dae.xy[bad_idx],
'Eqn. Mismatch': system.dae.fg[bad_idx],
}
tab = Tab(
title=title,
header=err_data.keys(),
data=list(map(list, zip(*err_data.values()))),
)
logger.error(tab.draw())
if system.options.get('verbose') == 1:
breakpoint()
system.exit_code += 1
return False
def test_init(self):
"""
Update f and g to see if initialization is successful.
"""
system = self.system
self.fg_update(system.exist.pflow_tds)
system.j_update(models=system.exist.pflow_tds)
# reset diff. RHS where `check_init == False`
system.dae.f[system.no_check_init] = 0.0
# warn if variables are initialized at limits
if system.config.warn_limits:
for model in system.exist.pflow_tds.values():
for item in model.discrete.values():
item.warn_init_limit()
if np.max(np.abs(system.dae.fg)) < self.config.tol:
logger.debug('Initialization tests passed.')
return True
# otherwise, show suspect initialization error
fail_idx = np.where(abs(system.dae.fg) >= self.config.tol)
fail_names = [system.dae.xy_name[int(i)] for i in np.ravel(fail_idx)]
title = 'Suspect initialization issue! Simulation may crash!'
err_data = {
'Name': fail_names,
'Var. Value': system.dae.xy[fail_idx],
'Eqn. Mismatch': system.dae.fg[fail_idx],
}
tab = Tab(
title=title,
header=err_data.keys(),
data=list(map(list, zip(*err_data.values()))),
)
logger.error(tab.draw())
if system.options.get('verbose') == 1:
breakpoint()
system.exit_code += 1
return False
