def bootstrapMCMCprobabilityOfDeath(EmpFrame, AICFrame, N=10, MCMConPartitions = [], Delta=2, Punishment='Death'):
"""Return the high and low confidence intervals for probabiity of death base on MCMC run on partitions.
"""
#NOTE: Unfinished WIP
#Generate our own dummy data
"""
EmpFrame = oboM.changePeriod( oboM.loadCatEmp( 'Data/Raw/OBOextractNoGender1yr.csv'), Delta=2).iloc[ [30, 31, 116, 117]]
AICFrame = oboM.loadMod('Data/Partitions/AICtableSortedValuesProperly2yr.csv').iloc[[30,31,116,117],0:4]
"""
puns = 7
offs = 9
PunIndex = c2n.puncatFullUp.index(Punishment)
DFlon,DFlen = EmpFrame.shape
OffenceEstimate = oboM.generateDependentModelLaplace(oboM.combineOnOffence(EmpFrame),Delta=Delta)
#Generate MCMC data if it does not exist.
if type(MCMConPartitions) is list:
print('Generating MCMC on {} partitions'.format(N))
MCMConPartitions = mcmcOnPartitions(AICFrame, N = N, Verbose=True, Sort=False)
MCMClon,MCMClen = MCMConPartitions.shape
#Convert the partition numbers to partitions:
#print('Converting the MCMC frame of partition numbers to partitions')
#MCMCofPartitions = MCMCofPartitions.applymap(lambda x: oboP.partitioN[x])
for row in range(DFlon):
print('Calculating probabilites for row {} of {}'.format(row,DFlon))
print('Looping through every MCMC partitioning')
#Find the groupings for the counts per partition as a series
Row = MCMCofPartitions.iloc[row]
RowGrouping = Row.apply(lambda partId: [ x for x in oboP.partitioN.unrank_rgf(partId)[1:] for y in range(puns) ])
#Find the punishment groupings, with 1 for the punishment and 0 else, discarding 0:
PunishmentGrouping = Row.apply(lambda partId: [ x if y is PunIndex else 0 for x in oboP.partitioN.unrank_rgf(partId)[1:] for y in list(range(puns)) ])
#Find the percentage probability estimates for the punishment, per block:
RowCountsPerBlock = RowGrouping.apply(lambda group: EmpRow.groupby(group).sum())
PunishmentCountPerBlock = RowDeathOffGrouping.apply(lambda blah: EmpRow.groupby(blah).sum()[1:])
PunishmentEstimatesPerBlock = (PunishmentCountPerBlock/ RowGroupingCounts)*100
#Get rid of the pesky il defined infinities
PunishmentEstimatesPerBlock[np.isinf(PunishmentEstimatesPerBlock)] = 0
#for MCMC in range(MCMClen):
#print('Finding estimates for {} for row {}, partition {}'.format(Punishment, row, MCMC))
#RowDeathOffGrouping = EmpFrame.iloc[row].apply(lambda partId: [ x if y is PunIndex else 0 for x in partitioN.unrank_rgf( MCMConPartitions.iloc[row,MCMC] )[1:] for y in list(range(puns)) ])
#print(RowDeathOffGrouping)
return OffenceEstimate, MCMConPartitions
