class ScalarsHDFAnalyzer(HDFAnalyzer):
corrections = obj(
mpc = obj(ElecElec=-1,MPC=1),
kc = obj(KEcorr=1)
)
def __init__(self,exclude):
HDFAnalyzer.__init__(self)
self.info.exclude = exclude
#end def
def load_data_local(self,data=None):
if data==None:
self.error('attempted load without data')
#end if
exclude = self.info.exclude
self.data = QAHDFdata()
for var in data.keys():
if not var in exclude and not str(var)[0]=='_':
self.data[var] = data[var]
del data[var]
#end if
#end for
corrvars = ['LocalEnergy','ElecElec','MPC','KEcorr']
if set(corrvars)<set(self.data.keys()):
Ed,Ved,Vmd,Kcd = self.data.tuple(*corrvars)
E,E2 = Ed.value,Ed.value_squared
Ve,Ve2 = Ved.value,Ved.value_squared
Vm,Vm2 = Vmd.value,Vmd.value_squared
Kc,Kc2 = Kcd.value,Kcd.value_squared
self.data.LocalEnergy_mpc_kc = obj(
value = E-Ve+Vm+Kc,
value_squared = E2+Ve2+Vm2+Kc2 + 2*(E*(-Ve+Vm+Kc)-Ve*(Vm+Kc)+Vm*Kc)
)
#end if
#end def load_data_local
def analyze_local(self):
nbe = QAanalyzer.method_info.nblocks_exclude
self.info.nblocks_exclude = nbe
for varname,val in self.data.iteritems():
(mean,var,error,kappa)=simstats(val.value[nbe:,...].ravel())
self[varname] = obj(
mean = mean,
variance = val.value_squared[nbe:,...].mean()-mean**2,
sample_variance = var,
error = error,
kappa = kappa
)
#end for
self.correct('mpc','kc')
#end def analyze_local
def correct(self,*corrections):
corrkey=''
for corr in corrections:
corrkey+=corr+'_'
#end for
corrkey=corrkey[:-1]
if set(corrections)>set(self.corrections.keys()):
self.warn('correction '+corrkey+' is unknown and cannot be applied')
return
#end if
if not 'data' in self:
self.warn('correction '+corrkey+' cannot be applied because data is not present')
return
#end if
varname = 'LocalEnergy_'+corrkey
if varname in self and varname in self.data:
return
#end if
corrvars = ['LocalEnergy']
signs = [1]
for corr in corrections:
for var,sign in self.corrections[corr].iteritems():
corrvars.append(var)
signs.append(sign)
#end for
#end for
missing = list(set(corrvars)-set(self.data.keys()))
if len(missing)>0:
self.warn('correction '+corrkey+' cannot be applied because '+str(missing)+' are missing')
return
#end if
le = self.data.LocalEnergy
E,E2 = 0*le.value,0*le.value_squared
n = len(corrvars)
for i in range(n):
ed = self.data[corrvars[i]]
e,e2 = ed.value,ed.value_squared
s = signs[i]
E += s*e
E2 += e2
for j in range(i+1,n):
eo = self.data[corrvars[j]].value
so = signs[j]
E2 += 2*s*e*so*eo
#end for
#end for
val = obj(value=E,value_squared=E2)
self.data[varname] = val
nbe = self.info.nblocks_exclude
(mean,var,error,kappa)=simstats(val.value[nbe:,...].ravel())
self[varname] = obj(
mean = mean,
variance = val.value_squared[nbe:,...].mean()-mean**2,
sample_variance = var,
error = error,
kappa = kappa
)
