def analyze_local(self):
from spacegrid import SpaceGrid
nbe = QAanalyzer.method_info.nblocks_exclude
self.info.nblocks_exclude = nbe
data = self.data
#why is this called 3 times?
print nbe
#transfer hdf data
sg_pattern = re.compile(r'spacegrid\d*')
nspacegrids=0
# add simple data first
for k,v in data._iteritems():
if not sg_pattern.match(k):
self._add_attribute(k,v)
else:
nspacegrids+=1
#end if
#end for
# add spacegrids second
opts = QAobject()
opts.points = self.reference_points
opts.nblocks_exclude = nbe
self.spacegrids=[]
if nspacegrids==0:
self.spacegrids.append(SpaceGrid(data.spacegrid,opts))
else:
for ig in range(nspacegrids):
sg=SpaceGrid(data['spacegrid'+str(ig+1)],opts)
self.spacegrids.append(sg)
#end for
#end if
#reorder atomic data to match input file for Voronoi grids
if self.run_info.type=='bundled':
self.info.reordered=True
#end if
if not self.info.reordered:
self.reorder_atomic_data()
#end if
#convert quantities outside all spacegrids
outside = QAobject()
iD,iT,iV = tuple(range(3))
outside.D = QAobject()
outside.T = QAobject()
outside.V = QAobject()
outside.E = QAobject()
outside.P = QAobject()
value = self.outside.value.transpose()[...,nbe:]
#mean,error = simplestats(value)
mean,var,error,kappa = simstats(value)
outside.D.mean = mean[iD]
outside.D.error = error[iD]
outside.T.mean = mean[iT]
outside.T.error = error[iT]
outside.V.mean = mean[iV]
outside.V.error = error[iV]
E = value[iT,:]+value[iV,:]
#mean,error = simplestats(E)
mean,var,error,kappa = simstats(E)
outside.E.mean = mean
outside.E.error = error
P = 2./3.*value[iT,:]+1./3.*value[iV,:]
#mean,error = simplestats(P)
mean,var,error,kappa = simstats(P)
outside.P.mean = mean
outside.P.error = error
self.outside = outside
self.outside.data = obj(
D = value[iD,:],
T = value[iT,:],
V = value[iV,:],
E = E,
P = P
)
return
