#%%
m = 2
d = 2
device = -1
mesh_bound = zeros((2,m))
# mesh_bound[0] = arange(m)-1
# mesh_bound[1] = arange(m)+1
mesh_bound[0] = 0
mesh_bound[1] = 1
mesh_size = array([40,]*m)
I = LagrangeInterp(m,d,mesh_bound,mesh_size)
I.double()
if device>=0:
I.cuda(device)
mesh_bound[1] += 1/1000
dataset = meshgen(mesh_bound, [1001,1001])
dataset = torch.from_numpy(dataset)
dataset = I.interp_coe.data.new(dataset.size()).copy_(dataset)
dataset = Variable(dataset)
mesh_bound[1] -= 1/1000
IFixInputs = LagrangeInterpFixInputs(dataset,m,d,mesh_bound,mesh_size)
IFixInputs.double()
if device>=0:
IFixInputs.cuda(device)
ax = plt.figure().add_subplot(1,1,1)
ax.imshow(I(dataset).data.cpu().numpy())
#%%
nfi = NumpyFunctionInterface([I.interp_coe,],forward=lambda :forward(I,dataset))
nfi.flat_param = random.randn(nfi.numel())
x,f,d = lbfgsb(nfi.f,nfi.flat_param,nfi.fprime,m=1000,factr=1,pgtol=1e-14,iprint=10)
infe,infe_true = compare(I, dataset)
#%%
m = 3
d = 2
device = -1
mesh_bound = zeros((2, m))
# mesh_bound[0] = arange(m)-1
# mesh_bound[1] = arange(m)+1
mesh_bound[0] = 0
mesh_bound[1] = 1
mesh_size = array([
40,
] * m)
I = Interp(m, d, mesh_bound, mesh_size, device=device)
mesh_bound[1] += 1 / 200
dataset = meshgen(mesh_bound, [201, 201, 201])
dataset = torch.from_numpy(dataset).clone()
dataset = I.interp_coe.data.new(dataset.size()).copy_(dataset)
nfi = NumpyFunctionInterface([
I.interp_coe,
],
forward=lambda: I.forward(dataset))
nfi.flat_param = random.randn(nfi.numel())
x0 = nfi.flat_param
#%%
inputs_shape = [50, 50, 50]
IN, JN, KN = int(200 / inputs_shape[0]), int(200 / inputs_shape[1]), int(
200 / inputs_shape[2])
indx = zeros((IN * JN * KN, 3), dtype=int32)
idx = 0
for i in range(IN):