def update_d(self, u, dd):
# print('inside_update_d ushape',u.shape)
# print('inside_update_d fre grad ushape',freq_gradient(u).shape)
out_dd = ()
for jj in range(0, len(dd)):
if jj < 3: # derivative y
# tmp_d =get_Diff(u,jj)
out_dd = out_dd + (CsSolver.get_Diff(u, jj),)
elif jj == 3: # rho
tmpu = numpy.copy(u)
tmpu = scipy.fftpack.fftn(tmpu, axes=(2,))
# tmpu[:,:,0,:] = tmpu[:,:,0,:]*0.0
out_dd = out_dd + (tmpu,)
elif jj == 4:
average_u = numpy.sum(u, 2)
tmpu = numpy.copy(u)
# for jj in range(0,u.shape[2]):
# tmpu[:,:,jj,:]= tmpu[:,:,jj,:] - average_u
out_dd = out_dd + (tmpu,)
# elif jj == 3:
# out_dd = out_dd + (freq_gradient(u),)
return out_dd
def update_d(self, u, dd):
# print('inside_update_d ushape',u.shape)
# print('inside_update_d fre grad ushape',freq_gradient(u).shape)
out_dd = ()
for jj in range(0, len(dd)):
if jj < 3: # derivative y
#tmp_d =get_Diff(u,jj)
out_dd = out_dd + (CsSolver.get_Diff(u, jj), )
elif jj == 3: # rho
tmpu = numpy.copy(u)
tmpu = scipy.fftpack.fftn(tmpu, axes=(2, ))
# tmpu[:,:,0,:] = tmpu[:,:,0,:]*0.0
out_dd = out_dd + (tmpu, )
elif jj == 4:
average_u = numpy.sum(u, 2)
tmpu = numpy.copy(u)
# for jj in range(0,u.shape[2]):
# tmpu[:,:,jj,:]= tmpu[:,:,jj,:] - average_u
out_dd = out_dd + (tmpu, )
# elif jj == 3:
# out_dd = out_dd + (freq_gradient(u),)
return out_dd
