def __call__(self,X,Y,Z,data,update=True):
if update:
# The Update the cell index using GridSearch class
if not self.__dict__.has_key('cellind'):
GridSearch.__call__(self,X,Y)
else:
if np.sum(np.abs(X-self.xpt))>0+1e-8:
self.updatexy(X,Y)
# Return the nearest data point (... for now)
if self.method == 'nearest':
dataout = data[self.cellind]
#if self.method == 'linear':
# dataout = self.lininterp(X,Y,Z,data,kind)
# Mask bogey points
dataout[self.cellind==-1]=0.0
return dataout
def __call__(self,X,Y,Z,data,update=True):
if update:
# The Update the cell index using GridSearch class
if 'cellind' not in self.__dict__:
GridSearch.__call__(self,X,Y)
else:
if np.sum(np.abs(X-self.xpt))>0+1e-8:
self.updatexy(X,Y)
# Return the nearest data point (... for now)
if self.method == 'nearest':
dataout = data[self.cellind]
#if self.method == 'linear':
# dataout = self.lininterp(X,Y,Z,data,kind)
# Mask bogey points
dataout[self.cellind==-1]=0.0
return dataout
def __call__(self,X,Y,Z,data,update=True):
if update:
# The Update the cell index using TriSearch class
if not self.__dict__.has_key('cellind'):
#print ' Finding initial particle index...'
GridSearch.__call__(self,X,Y)
else:
if np.sum(np.abs(X-self.xpt))>0+1e-8:
#print ' updating location index...'
self.updatexy(X,Y)
# Find the k-index
kind=self.z.searchsorted(Z)
kind = self.Nkmax - kind
kind[kind>=self.Nkmax-1] = self.Nkmax-1
ind = self.maskindex[kind,self.cellind]
#maskpts = self.mask3d[kind,self.cellind]
#if np.sum(maskpts==False)>10:
# pdb.set_trace()
#
#ind[maskpts == False] = 0
# Return the nearest data point (... for now)
if self.method == 'nearest':
dataout = data[ind]
if self.method == 'linear':
dataout = self.lininterp(X,Y,Z,data,kind)
# Mask bogey points
#dataout[maskpts==False]=0.0
dataout[ind==-1]=0 # these are the masked points
dataout[self.cellind==-1]=0.0
return dataout
def __call__(self,X,Y,Z,data,update=True):
if update:
# The Update the cell index using TriSearch class
if 'cellind' not in self.__dict__:
#print ' Finding initial particle index...'
GridSearch.__call__(self,X,Y)
else:
if np.sum(np.abs(X-self.xpt))>0+1e-8:
#print ' updating location index...'
self.updatexy(X,Y)
# Find the k-index
kind=self.z.searchsorted(Z)
kind = self.Nkmax - kind
kind[kind>=self.Nkmax-1] = self.Nkmax-1
ind = self.maskindex[kind,self.cellind]
#maskpts = self.mask3d[kind,self.cellind]
#if np.sum(maskpts==False)>10:
# pdb.set_trace()
#
#ind[maskpts == False] = 0
# Return the nearest data point (... for now)
if self.method == 'nearest':
dataout = data[ind]
if self.method == 'linear':
dataout = self.lininterp(X,Y,Z,data,kind)
# Mask bogey points
#dataout[maskpts==False]=0.0
dataout[ind==-1]=0 # these are the masked points
dataout[self.cellind==-1]=0.0
return dataout
