def survivalIntegral(self):
"""
Calculate the survival integral.
:return: float, float, StatusMessage -- survival integral, var(survival integral), status.
"""
if self.isReplicateGroup():
# here we average over the underlying replicates
si=numpy.zeros([len(self.activeChildWellIndices())])
allstatuses=StatusMessage()
statuses=StatusMessage()
i=0
for clstc in self.activeChildWells():
si[i], sivar, status = clstc.survivalIntegral()
if status is not None:
allstatuses.addStatus(status)
if status is not None and days is not None:
statuses.addStatus(status)
i+=1
idcs=numpy.isnan(si)
if numpy.all(idcs):
allstatuses.addStatus(self.initDiffStatus)
return None, None, allstatuses
simean, sivar = maskedArrayToMeanVar(si, ddof=1)
statuses.addStatus(self.initDiffStatus)
return simean, sivar, statuses
days, viability, viabilityvar, initDiffStatus=self.viability()
si=None
if viability is not None and days is not None:
si=numpy.trapz(viability,x=days)
return si,None,None
def viability(self):
"""
Calculate the viability.
:return: list(float), list(float), list(float), StatusMessage -- days, viability, var(viability), status.
"""
if self.isReplicateGroup():
# here we average over the underlying replicates
viability=numpy.zeros([len(self.activeChildWellIndices()), self.days.shape[0]])
allstatuses=StatusMessage()
statuses=StatusMessage()
i=0
for clstc in self.activeChildWells():
days, viability[i], viabilityvar, status = clstc.viability()
if status is not None:
allstatuses.addStatus(status)
if status is not None and days is not None:
statuses.addStatus(status)
i+=1
idcs=numpy.isnan(viability)
if numpy.all(idcs):
allstatuses.addStatus(self.initDiffStatus)
return None, None, None, allstatuses
viabilitymean, viabilityvar = maskedArrayToMeanVar(viability, idcs=idcs, ddof=1, axis=0)
statuses.addStatus(self.initDiffStatus)
return self.days, viabilitymean, viabilityvar, statuses
viability=numpy.zeros([len(self._odReplicates)])
viabilityvar=numpy.zeros([len(self._odReplicates)])
mu_ef, mu_ef_var, od0_ef, od0_ef_var, maxt_ef, maxt_ef_var, lag_ef__0, lag_ef_var__0, method_ef, status = self._odReplicates[0].maxGrowthrate()
if lag_ef__0 is None:
return None, None, None, StatusMessage(
key='viability:noInitialLag', severity=Severity.failed,
longmsg='lag could not be extract for first timepoint')
tcidx=-1
for tc in self._odReplicates:
tcidx+=1
if tc is None:
viability[tcidx]=None
viabilityvar[tcidx]=None
print('WARNING no Replicate defined for day '+str(self.days[tcidx])+' and sample/condition '+self._odReplicates[0].fullId())
continue
mu_ef, mu_ef_var, od0_ef, od0_ef_var, maxt_ef, maxt_ef_var, lag_ef, lag_ef_var, method_ef, status = tc.maxGrowthrate()
doublingtime, doublingtimevar = Replicate.growthrateToDoublingTime(mu_ef,mu_ef_var)
if lag_ef is not None and doublingtime is not None:
deltaT=lag_ef-lag_ef__0
# f(deltaT,doubling) = 2^(- deltaT/doubling ) [ == e^(-ln(2) deltaT/doubling) ]
viability[tcidx]=math.pow(2,-deltaT/doublingtime)
elif doublingtime is None:
# if we could not extract a doubling time we assume that there was no growth at all
viability[tcidx]=0.
viabilityvar[tcidx]=0.
else:
viability[tcidx]=None
if lag_ef_var__0 is not None and lag_ef_var is not None and doublingtimevar is not None:
deltaTvar=lag_ef_var+lag_ef_var__0
# df/ddeltaT = - ln(2)/doubling 2^(- deltaT/doubling )
# df/doubling = + ln(2) deltaT 1/doubling^2 2^(- deltaT/doubling )
viabilityvar[tcidx]=( math.pow( math.log(2)/doublingtime * math.pow(2,-deltaT/doublingtime),2 ) * doublingtimevar
+ math.pow( math.log(2)*deltaT * math.pow(doublingtime,-2) * math.pow(2,-deltaT/doublingtime),2 ) * deltaTvar)
else:
viabilityvar[tcidx]=None
return self.days, viability, viabilityvar, self.initDiffStatus