def fast_odf(self,s):
odf = np.zeros(self.odfn)
Eq=np.zeros((self.sz,self.sz,self.sz))
#for i in range(self.dn):
# Eq[self.q[i][0],self.q[i][1],self.q[i][2]]+=s[i]/s[0]
Eq[self.q[:,0],self.q[:,1],self.q[:,2]]=s[:]/np.float(s[0])
#self.Eqs.append(Eq)
if self.operator=='laplacian':
LEq=laplace(Eq)
sign=-1
if self.operator=='laplap':
LEq=laplace(laplace(Eq))
sign=1
if self.operator=='signal':
LEq=Eq
sign=1
#LEs=map_coordinates(LEq,self.Ys.T,order=1)
#"""
LEs=np.zeros(self.Ysn)
strides=np.array(LEq.strides,'i8')
map_coordinates_trilinear_iso(LEq,self.Ys,
strides,self.Ysn, LEs)
#LEs=map_coordinates(LEq,self.zoom*self.Ys,order=1)
LEs=LEs.reshape(self.odfn,self.radiusn)
LEs=LEs*self.radius
#LEs=LEs*self.radius*self.zoom
LEsum=np.sum(LEs,axis=1)
#This is what the following code is doing
#for i in xrange(self.odfn):
# odf[i]=np.sum(LEsum[self.eqinds[i]])/self.eqinds_len[i]
#odf2=odf.copy()
LES=LEsum[self.eqinds_com]
sum_on_blocks_1d(LES,self.eqinds_len,odf,self.odfn)
odf=odf/self.eqinds_len
return self.angular_weighting(sign*odf)
def fast_odf(self, s):
odf = np.zeros(self.odfn)
Eq = np.zeros((self.sz, self.sz, self.sz))
#for i in range(self.dn):
# Eq[self.q[i][0],self.q[i][1],self.q[i][2]]+=s[i]/s[0]
Eq[self.q[:, 0], self.q[:, 1], self.q[:, 2]] = s[:] / np.float(s[0])
#self.Eqs.append(Eq)
if self.operator == 'laplacian':
LEq = laplace(Eq)
sign = -1
if self.operator == 'laplap':
LEq = laplace(laplace(Eq))
sign = 1
if self.operator == 'signal':
LEq = Eq
sign = 1
#LEs=map_coordinates(LEq,self.Ys.T,order=1)
#"""
LEs = np.zeros(self.Ysn)
strides = np.array(LEq.strides, 'i8')
map_coordinates_trilinear_iso(LEq, self.Ys, strides, self.Ysn, LEs)
#LEs=map_coordinates(LEq,self.zoom*self.Ys,order=1)
LEs = LEs.reshape(self.odfn, self.radiusn)
LEs = LEs * self.radius
#LEs=LEs*self.radius*self.zoom
LEsum = np.sum(LEs, axis=1)
#This is what the following code is doing
#for i in xrange(self.odfn):
# odf[i]=np.sum(LEsum[self.eqinds[i]])/self.eqinds_len[i]
#odf2=odf.copy()
LES = LEsum[self.eqinds_com]
sum_on_blocks_1d(LES, self.eqinds_len, odf, self.odfn)
odf = odf / self.eqinds_len
return self.angular_weighting(sign * odf)
def fast_odf(self,s):
odf = np.zeros(self.odfn)
Eq=np.zeros((self.sz,self.sz,self.sz))
#for i in range(self.dn):
# Eq[self.q[i][0],self.q[i][1],self.q[i][2]]+=s[i]/s[0]
Eq[self.q[:,0],self.q[:,1],self.q[:,2]]=s[:]/s[0]
if self.operator=='2laplacian':
LEq=self.eit_operator(Eq,2)
sign=-1
if self.operator=='laplacian':
LEq=laplace(Eq)
sign=-1
if self.operator=='laplap':
LEq=laplace(laplace(Eq))
sign=1
if self.operator=='signal':
LEq=Eq
sign=1
LEs=map_coordinates(LEq,self.Ys,order=1)
LEs=LEs.reshape(self.odfn,self.radiusn)
LEs=LEs*self.radius
LEsum=np.sum(LEs,axis=1)
#This is what the following code is doing
#for i in xrange(self.odfn):
# odf[i]=np.sum(LEsum[self.eqinds[i]])/self.eqinds_len[i]
#odf2=odf.copy()
LES=LEsum[self.eqinds_com]
sum_on_blocks_1d(LES,self.eqinds_len,odf,self.odfn)
odf=odf/self.eqinds_len
return sign*odf
def fast_odf(self,s):
odf = np.zeros(self.odfn)
Eq=np.zeros((self.sz,self.sz,self.sz))
#for i in range(self.dn):
# Eq[self.q[i][0],self.q[i][1],self.q[i][2]]+=s[i]/s[0]
Eq[self.q[:,0],self.q[:,1],self.q[:,2]]=s[:]/s[0]
#self.Eqs.append(Eq)
if self.operator=='2laplacian':
LEq=self.eit_operator(Eq,2)
sign=-1
if self.operator=='laplacian':
#ZEq=zoom(Eq,zoom=5,order=self.order,mode=self.mode,cval=0.0,prefilter=True)
#self.ZEqs.append(ZEq)
#ZLEq=laplace(ZEq)
#self.ZLEqs.append(ZLEq)
#LEq=zoom(ZLEq,zoom=.2,order=self.order,mode=self.mode,cval=0.0,prefilter=True)
#LEq=laplace(Eq)
#if self.zoom>1:
# ZEq=zoom(Eq,zoom=self.zoom,order=self.order,mode=self.mode)
# LEq=laplace(ZEq)
#else:
LEq=laplace(Eq)
sign=-1
if self.operator=='laplap':
LEq=laplace(laplace(Eq))
sign=1
if self.operator=='signal':
LEq=Eq
sign=1
LEs=map_coordinates(LEq,self.Ys,order=1)
#LEs=map_coordinates(LEq,self.zoom*self.Ys,order=1)
LEs=LEs.reshape(self.odfn,self.radiusn)
LEs=LEs*self.radius
#LEs=LEs*self.radius*self.zoom
LEsum=np.sum(LEs,axis=1)
#This is what the following code is doing
#for i in xrange(self.odfn):
# odf[i]=np.sum(LEsum[self.eqinds[i]])/self.eqinds_len[i]
#odf2=odf.copy()
LES=LEsum[self.eqinds_com]
sum_on_blocks_1d(LES,self.eqinds_len,odf,self.odfn)
odf=odf/self.eqinds_len
return sign*odf
def fast_odf(self, s):
odf = np.zeros(self.odfn)
Eq = np.zeros((self.sz, self.sz, self.sz))
#for i in range(self.dn):
# Eq[self.q[i][0],self.q[i][1],self.q[i][2]]+=s[i]/s[0]
Eq[self.q[:, 0], self.q[:, 1], self.q[:, 2]] = s[:] / s[0]
#self.Eqs.append(Eq)
if self.operator == '2laplacian':
LEq = self.eit_operator(Eq, 2)
sign = -1
if self.operator == 'laplacian':
#ZEq=zoom(Eq,zoom=5,order=self.order,mode=self.mode,cval=0.0,prefilter=True)
#self.ZEqs.append(ZEq)
#ZLEq=laplace(ZEq)
#self.ZLEqs.append(ZLEq)
#LEq=zoom(ZLEq,zoom=.2,order=self.order,mode=self.mode,cval=0.0,prefilter=True)
#LEq=laplace(Eq)
#if self.zoom>1:
# ZEq=zoom(Eq,zoom=self.zoom,order=self.order,mode=self.mode)
# LEq=laplace(ZEq)
#else:
LEq = laplace(Eq)
sign = -1
if self.operator == 'laplap':
LEq = laplace(laplace(Eq))
sign = 1
if self.operator == 'signal':
LEq = Eq
sign = 1
LEs = map_coordinates(LEq, self.Ys, order=1)
#LEs=map_coordinates(LEq,self.zoom*self.Ys,order=1)
LEs = LEs.reshape(self.odfn, self.radiusn)
LEs = LEs * self.radius
#LEs=LEs*self.radius*self.zoom
LEsum = np.sum(LEs, axis=1)
#This is what the following code is doing
#for i in xrange(self.odfn):
# odf[i]=np.sum(LEsum[self.eqinds[i]])/self.eqinds_len[i]
#odf2=odf.copy()
LES = LEsum[self.eqinds_com]
sum_on_blocks_1d(LES, self.eqinds_len, odf, self.odfn)
odf = odf / self.eqinds_len
return sign * odf
