from readOF import convertOFMeshToImage,convertOFMeshToImage_StructuredMesh
from sklearn.metrics import mean_squared_error as calMSE
import Ofpp
h=0.01
OFBCCoord=Ofpp.parse_boundary_field('TemplateCase/30/C')
OFLOWC=OFBCCoord[b'low'][b'value']
OFUPC=OFBCCoord[b'up'][b'value']
OFLEFTC=OFBCCoord[b'left'][b'value']
OFRIGHTC=OFBCCoord[b'right'][b'value']
leftX=OFLEFTC[:,0];leftY=OFLEFTC[:,1]
lowX=OFLOWC[:,0];lowY=OFLOWC[:,1]
rightX=OFRIGHTC[:,0];rightY=OFRIGHTC[:,1]
upX=OFUPC[:,0];upY=OFUPC[:,1]
ny=len(leftX);nx=len(lowX)
myMesh=hcubeMesh(leftX,leftY,rightX,rightY,
lowX,lowY,upX,upY,h,True,True,
tolMesh=1e-10,tolJoint=1)
batchSize=1
NvarInput=2
NvarOutput=1
nEpochs=1500
lr=0.001
Ns=1
nu=0.01
model=USCNN(h,nx,ny,NvarInput,NvarOutput).to('cuda')
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(),lr=lr)
padSingleSide=1
udfpad=nn.ConstantPad2d([padSingleSide,padSingleSide,padSingleSide,padSingleSide],0)
MeshList=[]
MeshList.append(myMesh)
leftX = []
rightX = []
for i in leftY:
if i > -l / 2 and i < l / 2:
leftX.append(+np.cos(2 * np.pi * i) * scalar - R)
rightX.append(-np.cos(2 * np.pi * i) * scalar + R)
else:
leftX.append(-R)
rightX.append(R)
leftX = np.asarray(leftX)
rightX = np.asarray(rightX)
lowX = np.linspace(-R, R, nx)
lowY = lowX * 0 - l / 2 - L / 2
upX = lowX
upY = lowY + l + L
myMesh = hcubeMesh(leftX, leftY, rightX, rightY, lowX, lowY, upX, upY, h,
False, True, './Mesh' + str(scalar) + '.pdf')
MeshList.append(myMesh)
OFLF = np.zeros([nyOF, 3])
OFLB = np.zeros([nyOF, 3])
OFRF = np.zeros([nyOF, 3])
OFRB = np.zeros([nyOF, 3])
OFLF[:, 2] = OFRF[:, 2] = 0
OFLB[:, 2] = OFRB[:, 2] = 0.01
OFLF[:, 0] = leftX[idy]
OFLF[:, 1] = leftY[idy]
OFLB[:, 0] = leftX[idy]
OFLB[:, 1] = leftY[idy]
OFRF[:, 0] = rightX[idy]
OFRF[:, 1] = rightY[idy]
OFRB[:, 0] = rightX[idy]
OFRB[:, 1] = rightY[idy]