def __init__(self,
countdown=2,
statistics=5,
improve_factor=0.5,
worsen_factor=1.5,
max_factor=1.0,
min_factor=1.0e-4,
log_size=50):
self.improve_factor = improve_factor
self.worsen_factor = worsen_factor
self.max_factor = max_factor
self.min_factor = min_factor
self.statistics = statistics
self.countdown_initial = countdown
self._reset()
self.log = CircularList(log_size)
def __init__(self, countdown=2, statistics=5,
improve_factor=0.5, worsen_factor=1.5,
max_factor=1.0, min_factor=1.0e-4,
log_size=50):
self.improve_factor = improve_factor
self.worsen_factor = worsen_factor
self.max_factor = max_factor
self.min_factor = min_factor
self.statistics = statistics
self.countdown_initial = countdown
self._reset()
self.log = CircularList(log_size)
class Convergence:
def __init__(self,
countdown=2,
statistics=5,
improve_factor=0.5,
worsen_factor=1.5,
max_factor=1.0,
min_factor=1.0e-4,
log_size=50):
self.improve_factor = improve_factor
self.worsen_factor = worsen_factor
self.max_factor = max_factor
self.min_factor = min_factor
self.statistics = statistics
self.countdown_initial = countdown
self._reset()
self.log = CircularList(log_size)
def _reset(self):
self.num = 0
self.sum_dev_max_dmdt = 0.0
self.sum_osc_max_dmdt = 0.0
self.previous_max_dmdt = None
self.countdown = self.countdown_initial
def _quality(self, max_dmdt):
convergence_quality = None
if self.previous_max_dmdt != None:
dev = max_dmdt - self.previous_max_dmdt
osc = abs(dev)
self.sum_osc_max_dmdt += osc
self.sum_dev_max_dmdt += dev
self.num += 1
if self.num >= self.statistics and self.sum_osc_max_dmdt > 0.0:
convergence_quality = \
abs(self.sum_dev_max_dmdt/self.sum_osc_max_dmdt)
self.num = 0
self.sum_dev_max_dmdt = 0.0
self.sum_osc_max_dmdt = 0.0
self.previous_max_dmdt = max_dmdt
return convergence_quality
def check(self, step, max_dm_dt, stopping_dm_dt, tol_factor):
"""
Returns True when convergence has been reached.
"""
log.debug("Entering is_converged()")
#print "max_dm_dt=%s < %s=stopping_dm_dt ? %s" \
# % (max_dm_dt, stopping_dm_dt, max_dm_dt < stopping_dm_dt)
if max_dm_dt < stopping_dm_dt:
self.countdown -= 1
if self.countdown < 1:
log.debug("is_converged(): True")
# Since we are converging, we don't want to interfere
# and change the tolerances now! We therefore return
# new_tol_factor = None
self._reset()
return True, None
log.debug("is_converged(): Trueish (%d times converged but need "
"%d times to stop)" % (self.countdown, self.countdown))
return False, None
self.countdown = self.countdown_initial
log.debug("is_converged(): False")
convergence_quality = self._quality(max_dm_dt)
self.log.append((step, max_dm_dt, stopping_dm_dt, convergence_quality))
if convergence_quality == None:
return False, None
else:
new_tol_factor = None
if convergence_quality == 1.0:
new_tol_factor = tol_factor * self.worsen_factor
elif convergence_quality < 0.5:
new_tol_factor = tol_factor * self.improve_factor
if new_tol_factor != None:
new_tol_factor = min(self.max_factor,
max(self.min_factor, new_tol_factor))
if new_tol_factor == tol_factor: new_tol_factor = None
return False, new_tol_factor
def get_log(self):
l = self.log.get_list()
s = "# Convergence log\n"
s += "# step, ax dm/dt, stoppping dm/dt, conv. quality\n"
for step, max_dm_dt, stopping_dm_dt, conv_quality in l:
s += "%s %s %s %s\n" % (step, max_dm_dt, stopping_dm_dt,
conv_quality)
return s
class Convergence:
def __init__(self, countdown=2, statistics=5,
improve_factor=0.5, worsen_factor=1.5,
max_factor=1.0, min_factor=1.0e-4,
log_size=50):
self.improve_factor = improve_factor
self.worsen_factor = worsen_factor
self.max_factor = max_factor
self.min_factor = min_factor
self.statistics = statistics
self.countdown_initial = countdown
self._reset()
self.log = CircularList(log_size)
def _reset(self):
self.num = 0
self.sum_dev_max_dmdt = 0.0
self.sum_osc_max_dmdt = 0.0
self.previous_max_dmdt = None
self.countdown = self.countdown_initial
def _quality(self, max_dmdt):
convergence_quality = None
if self.previous_max_dmdt != None:
dev = max_dmdt - self.previous_max_dmdt
osc = abs(dev)
self.sum_osc_max_dmdt += osc
self.sum_dev_max_dmdt += dev
self.num += 1
if self.num >= self.statistics and self.sum_osc_max_dmdt > 0.0:
convergence_quality = \
abs(self.sum_dev_max_dmdt/self.sum_osc_max_dmdt)
self.num = 0
self.sum_dev_max_dmdt = 0.0
self.sum_osc_max_dmdt = 0.0
self.previous_max_dmdt = max_dmdt
return convergence_quality
def check(self, step, max_dm_dt, stopping_dm_dt, tol_factor):
"""
Returns True when convergence has been reached.
"""
log.debug("Entering is_converged()")
#print "max_dm_dt=%s < %s=stopping_dm_dt ? %s" \
# % (max_dm_dt, stopping_dm_dt, max_dm_dt < stopping_dm_dt)
if max_dm_dt < stopping_dm_dt:
self.countdown -= 1
if self.countdown < 1:
log.debug("is_converged(): True")
# Since we are converging, we don't want to interfere
# and change the tolerances now! We therefore return
# new_tol_factor = None
self._reset()
return True, None
log.debug("is_converged(): Trueish (%d times converged but need "
"%d times to stop)" % (self.countdown, self.countdown))
return False, None
self.countdown = self.countdown_initial
log.debug("is_converged(): False")
convergence_quality = self._quality(max_dm_dt)
self.log.append((step, max_dm_dt, stopping_dm_dt, convergence_quality))
if convergence_quality == None:
return False, None
else:
new_tol_factor = None
if convergence_quality == 1.0:
new_tol_factor = tol_factor*self.worsen_factor
elif convergence_quality < 0.5:
new_tol_factor = tol_factor*self.improve_factor
if new_tol_factor != None:
new_tol_factor = min(self.max_factor,
max(self.min_factor, new_tol_factor))
if new_tol_factor == tol_factor: new_tol_factor = None
return False, new_tol_factor
def get_log(self):
l = self.log.get_list()
s = "# Convergence log\n"
s += "# step, ax dm/dt, stoppping dm/dt, conv. quality\n"
for step, max_dm_dt, stopping_dm_dt, conv_quality in l:
s += "%s %s %s %s\n" % (step, max_dm_dt, stopping_dm_dt,
conv_quality)
return s