def replay(self, figlist="default", speed=np.inf, t1=0, t2=None, **kwargs):
"""Replay LivePlot with what's been stored in 'self'.
- t1, t2: time window to plot.
- 'figlist' and 'speed': See LivePlot's doc.
.. note:: `store_u` (whether to store non-obs-time stats) must
have been `True` to have smooth graphs as in the actual LivePlot.
.. note:: Ensembles are generally not stored in the stats
and so cannot be replayed.
"""
# Time settings
tseq = self.HMM.tseq
if t2 is None:
t2 = t1 + tseq.Tplot
# Ens does not get stored in stats, so we cannot replay that.
# If the LPs are initialized with P0!=None, then they will avoid ens plotting.
# TODO 4: This system for switching from Ens to stats must be replaced.
# It breaks down when M is very large.
try:
P0 = np.full_like(self.HMM.X0.C.full, np.nan)
except AttributeError: # e.g. if X0 is defined via sampling func
P0 = np.eye(self.HMM.Nx)
LP = liveplotting.LivePlot(self,
figlist,
P=P0,
speed=speed,
Tplot=t2 - t1,
replay=True,
**kwargs)
plt.pause(.01) # required when speed=inf
# Remember: must use progbar to unblock read1.
# Let's also make a proper description.
desc = self.xp.da_method + " (replay)"
# Play through assimilation cycles
for k, ko, t, _dt in progbar(tseq.ticker, desc):
if t1 <= t <= t2:
if ko is not None:
LP.update((k, ko, 'f'), None, None)
LP.update((k, ko, 'a'), None, None)
LP.update((k, ko, 'u'), None, None)
# Pause required when speed=inf.
# On Mac, it was also necessary to do it for each fig.
if LP.any_figs:
for _name, updater in LP.figures.items():
if plt.fignum_exists(_name) and getattr(
updater, 'is_active', 1):
plt.figure(_name)
plt.pause(0.01)
def assess(self,
k,
kObs=None,
faus=None,
E=None,
w=None,
mu=None,
Cov=None):
"""Common interface for both assess_ens and _ext.
The _ens assessment function gets called if E is not None,
and _ext if mu is not None.
faus: One or more of ['f',' a', 'u'], indicating
that the result should be stored in (respectively)
the forecast/analysis/universal attribute.
Default: 'u' if kObs is None else 'au' ('a' and 'u').
"""
# Initial consistency checks.
if k == 0:
if kObs is not None:
raise KeyError("DAPPER convention: no obs at t=0."
" Helps avoid bugs.")
if faus is None:
faus = 'u'
if self._is_ens == True:
if E is None:
raise TypeError("Expected ensemble input but E is None")
if mu is not None:
raise TypeError(
"Expected ensemble input but mu/Cov is not None")
else:
if E is not None:
raise TypeError("Expected mu/Cov input but E is not None")
if mu is None:
raise TypeError("Expected mu/Cov input but mu is None")
# Default. Don't add more defaults. It just gets confusing.
if faus is None:
faus = 'u' if kObs is None else 'au'
# Select assessment call and arguments
if self._is_ens:
_assess = self.assess_ens
_prms = {'E': E, 'w': w}
else:
_assess = self.assess_ext
_prms = {'mu': mu, 'P': Cov}
for sub in faus:
# Skip assessment if ('u' and stats not stored or plotted)
if k != 0 and kObs == None:
if not (self.store_u or self.LP_instance.any_figs):
continue
# Silence repeat warnings caused by zero variance
with np.errstate(divide='call', invalid='call'):
np.seterrcall(warn_zero_variance)
# Assess
stats_now = Avrgs()
_assess(stats_now, self.xx[k], **_prms)
self.derivative_stats(stats_now)
self.summarize_marginals(stats_now)
# Write current stats to series
for name, val in stats_now.items():
stat = struct_tools.deep_getattr(self, name)
isFaust = isinstance(stat, series.FAUSt)
stat[(k, kObs, sub) if isFaust else kObs] = val
# LivePlot -- Both init and update must come after the assessment.
try:
self.LP_instance.update((k, kObs, sub), E, Cov)
except AttributeError:
self.LP_instance = liveplotting.LivePlot(
self, self.liveplots, (k, kObs, sub), E, Cov)
def assess(self, k, ko=None, faus=None, E=None, w=None, mu=None, Cov=None):
"""Common interface for both `Stats.assess_ens` and `Stats.assess_ext`.
The `_ens` assessment function gets called if `E is not None`,
and `_ext` if `mu is not None`.
faus: One or more of `['f',' a', 'u', 's']`, indicating
that the result should be stored in (respectively)
the forecast/analysis/universal attribute.
Default: `'u' if ko is None else 'au' ('a' and 'u')`.
"""
# Initial consistency checks.
if k == 0:
if ko is not None:
raise KeyError(
"DAPPER convention: no obs at t=0. Helps avoid bugs.")
if self._is_ens == True:
if E is None:
raise TypeError("Expected ensemble input but E is None")
if mu is not None:
raise TypeError(
"Expected ensemble input but mu/Cov is not None")
else:
if E is not None:
raise TypeError("Expected mu/Cov input but E is not None")
if mu is None:
raise TypeError("Expected mu/Cov input but mu is None")
# Default. Don't add more defaults. It just gets confusing.
if faus is None:
faus = 'u' if ko is None else 'au'
# TODO 4: for faus="au" (e.g.) we don't need to re-**compute** stats,
# merely re-write them?
for sub in faus:
# Skip assessment if ('u' and stats not stored or plotted)
if k != 0 and ko == None:
if not (self.store_u or self.LP_instance.any_figs):
continue
# Silence repeat warnings caused by zero variance
with np.errstate(divide='call', invalid='call'):
np.seterrcall(warn_zero_variance)
# Assess
stats_now = Avrgs()
if self._is_ens:
self.assess_ens(stats_now, self.xx[k], E, w)
else:
self.assess_ext(stats_now, self.xx[k], mu, Cov)
self.derivative_stats(stats_now)
self.summarize_marginals(stats_now)
self.write(stats_now, k, ko, sub)
# LivePlot -- Both init and update must come after the assessment.
try:
self.LP_instance.update((k, ko, sub), E, Cov)
except AttributeError:
self.LP_instance = liveplotting.LivePlot(
self, self.liveplots, (k, ko, sub), E, Cov)