def _fastInnerProduct(self, projType, prop=None, invProp=False, invMat=False):
"""
Fast version of getFaceInnerProduct.
This does not handle the case of a full tensor prop.
:param numpy.array prop: material property (tensor properties are possible) at each cell center (nC, (1, 3, or 6))
:param str projType: 'E' or 'F'
:param bool returnP: returns the projection matrices
:param bool invProp: inverts the material property
:param bool invMat: inverts the matrix
:rtype: scipy.sparse.csr_matrix
:return: M, the inner product matrix (nF, nF)
"""
assert projType in ['F', 'E'], ("projType must be 'F' for faces or 'E'"
" for edges")
if prop is None:
prop = np.ones(self.nC)
if invProp:
prop = 1./prop
if Utils.isScalar(prop):
prop = prop*np.ones(self.nC)
# number of elements we are averaging (equals dim for regular
# meshes, but for cyl, where we use symmetry, it is 1 for edge
# variables and 2 for face variables)
if self._meshType == 'CYL':
n_elements = np.sum(getattr(self, 'vn'+projType).nonzero())
else:
n_elements = self.dim
# Isotropic? or anisotropic?
if prop.size == self.nC:
Av = getattr(self, 'ave'+projType+'2CC')
Vprop = self.vol * Utils.mkvc(prop)
M = n_elements * Utils.sdiag(Av.T * Vprop)
elif prop.size == self.nC*self.dim:
Av = getattr(self, 'ave'+projType+'2CCV')
# if cyl, then only certain components are relevant due to symmetry
# for faces, x, z matters, for edges, y (which is theta) matters
if self._meshType == 'CYL':
if projType == 'E':
prop = prop[:, 1] # this is the action of a projection mat
elif projType == 'F':
prop = prop[:, [0, 2]]
V = sp.kron(sp.identity(n_elements), Utils.sdiag(self.vol))
M = Utils.sdiag(Av.T * V * Utils.mkvc(prop))
else:
return None
if invMat:
return Utils.sdInv(M)
else:
return M
def _fastInnerProduct(self, projType, prop=None, invProp=False, invMat=False):
"""
Fast version of getFaceInnerProduct.
This does not handle the case of a full tensor prop.
:param numpy.array prop: material property (tensor properties are possible) at each cell center (nC, (1, 3, or 6))
:param str projType: 'E' or 'F'
:param bool returnP: returns the projection matrices
:param bool invProp: inverts the material property
:param bool invMat: inverts the matrix
:rtype: scipy.sparse.csr_matrix
:return: M, the inner product matrix (nF, nF)
"""
assert projType in ["F", "E"], "projType must be 'F' for faces or 'E'" " for edges"
if prop is None:
prop = np.ones(self.nC)
if invProp:
prop = 1.0 / prop
if Utils.isScalar(prop):
prop = prop * np.ones(self.nC)
# number of elements we are averaging (equals dim for regular
# meshes, but for cyl, where we use symmetry, it is 1 for edge
# variables and 2 for face variables)
if self._meshType == "CYL":
n_elements = np.sum(getattr(self, "vn" + projType).nonzero())
else:
n_elements = self.dim
# Isotropic? or anisotropic?
if prop.size == self.nC:
Av = getattr(self, "ave" + projType + "2CC")
Vprop = self.vol * Utils.mkvc(prop)
M = n_elements * Utils.sdiag(Av.T * Vprop)
elif prop.size == self.nC * self.dim:
Av = getattr(self, "ave" + projType + "2CCV")
# if cyl, then only certain components are relevant due to symmetry
# for faces, x, z matters, for edges, y (which is theta) matters
if self._meshType == "CYL":
if projType == "E":
prop = prop[:, 1] # this is the action of a projection mat
elif projType == "F":
prop = prop[:, [0, 2]]
V = sp.kron(sp.identity(n_elements), Utils.sdiag(self.vol))
M = Utils.sdiag(Av.T * V * Utils.mkvc(prop))
else:
return None
if invMat:
return Utils.sdInv(M)
else:
return M
def getError(self):
#Test function
phi = lambda x: np.cos(np.pi * x[:, 0]) * np.cos(np.pi * x[:, 1])
j_funX = lambda x: -np.pi * np.sin(np.pi * x[:, 0]) * np.cos(np.pi *
x[:, 1])
j_funY = lambda x: -np.pi * np.cos(np.pi * x[:, 0]) * np.sin(np.pi *
x[:, 1])
q_fun = lambda x: -2 * (np.pi**2) * phi(x)
xc_ana = phi(self.M.gridCC)
q_ana = q_fun(self.M.gridCC)
jX_ana = j_funX(self.M.gridFx)
jY_ana = j_funY(self.M.gridFy)
j_ana = np.r_[jX_ana, jY_ana]
#TODO: Check where our boundary conditions are CCx or Nx
# fxm,fxp,fym,fyp = self.M.faceBoundaryInd
# gBFx = self.M.gridFx[(fxm|fxp),:]
# gBFy = self.M.gridFy[(fym|fyp),:]
fxm, fxp, fym, fyp = self.M.cellBoundaryInd
gBFx = self.M.gridCC[(fxm | fxp), :]
gBFy = self.M.gridCC[(fym | fyp), :]
bc = phi(np.r_[gBFx, gBFy])
# P = sp.csr_matrix(([-1,1],([0,self.M.nF-1],[0,1])), shape=(self.M.nF, 2))
P, Pin, Pout = self.M.getBCProjWF('dirichlet')
Mc = self.M.getFaceInnerProduct()
McI = Utils.sdInv(self.M.getFaceInnerProduct())
G = -self.M.faceDiv.T * Utils.sdiag(self.M.vol)
D = self.M.faceDiv
j = McI * (G * xc_ana + P * bc)
q = D * j
# self.M.plotImage(j, 'FxFy', showIt=True)
# Rearrange if we know q to solve for x
A = D * McI * G
rhs = q_ana - D * McI * P * bc
if self.myTest == 'j':
err = np.linalg.norm((j - j_ana), np.inf)
elif self.myTest == 'q':
err = np.linalg.norm((q - q_ana), np.inf)
elif self.myTest == 'xc':
xc = Solver(A) * (rhs)
err = np.linalg.norm((xc - xc_ana), np.inf)
elif self.myTest == 'xcJ':
xc = Solver(A) * (rhs)
j = McI * (G * xc + P * bc)
err = np.linalg.norm((j - j_ana), np.inf)
return err
def getError(self):
#Test function
phi = lambda x: np.cos(0.5*np.pi*x)
j_fun = lambda x: -0.5*np.pi*np.sin(0.5*np.pi*x)
q_fun = lambda x: -0.25*(np.pi**2)*np.cos(0.5*np.pi*x)
xc_ana = phi(self.M.gridCC)
q_ana = q_fun(self.M.gridCC)
j_ana = j_fun(self.M.gridFx)
#TODO: Check where our boundary conditions are CCx or Nx
vecN = self.M.vectorNx
vecC = self.M.vectorCCx
phi_bc = phi(vecC[[0,-1]])
j_bc = j_fun(vecN[[0,-1]])
P, Pin, Pout = self.M.getBCProjWF([['dirichlet', 'neumann']])
Mc = self.M.getFaceInnerProduct()
McI = Utils.sdInv(self.M.getFaceInnerProduct())
V = Utils.sdiag(self.M.vol)
G = -Pin.T*Pin*self.M.faceDiv.T * V
D = self.M.faceDiv
j = McI*(G*xc_ana + P*phi_bc)
q = V*D*Pin.T*Pin*j + V*D*Pout.T*j_bc
# Rearrange if we know q to solve for x
A = V*D*Pin.T*Pin*McI*G
rhs = V*q_ana - V*D*Pin.T*Pin*McI*P*phi_bc - V*D*Pout.T*j_bc
# A = D*McI*G
# rhs = q_ana - D*McI*P*phi_bc
if self.myTest == 'j':
err = np.linalg.norm((Pin*j-Pin*j_ana), np.inf)
elif self.myTest == 'q':
err = np.linalg.norm((q-V*q_ana), np.inf)
elif self.myTest == 'xc':
#TODO: fix the null space
xc, info = sp.linalg.minres(A, rhs, tol = 1e-6)
err = np.linalg.norm((xc-xc_ana), np.inf)
if info > 0:
print('Solve does not work well')
print('ACCURACY', np.linalg.norm(Utils.mkvc(A*xc) - rhs))
elif self.myTest == 'xcJ':
#TODO: fix the null space
xc, info = sp.linalg.minres(A, rhs, tol = 1e-6)
j = McI*(G*xc + P*phi_bc)
err = np.linalg.norm((Pin*j-Pin*j_ana), np.inf)
if info > 0:
print('Solve does not work well')
print('ACCURACY', np.linalg.norm(Utils.mkvc(A*xc) - rhs))
return err
def DCfun(mesh, pts):
D = mesh.faceDiv
G = D.T
sigma = 1e-2*np.ones(mesh.nC)
Msigi = mesh.getFaceInnerProduct(1./sigma)
MsigI = Utils.sdInv(Msigi)
A = D*MsigI*G
A[-1,-1] /= mesh.vol[-1] # Remove null space
rhs = np.zeros(mesh.nC)
txind = Utils.meshutils.closestPoints(mesh, pts)
rhs[txind] = np.r_[1,-1]
return A, rhs
def DCfun(mesh, pts):
D = mesh.faceDiv
G = D.T
sigma = 1e-2 * np.ones(mesh.nC)
Msigi = mesh.getFaceInnerProduct(1. / sigma)
MsigI = Utils.sdInv(Msigi)
A = D * MsigI * G
A[-1, -1] /= mesh.vol[-1] # Remove null space
rhs = np.zeros(mesh.nC)
txind = Utils.meshutils.closestPoints(mesh, pts)
rhs[txind] = np.r_[1, -1]
return A, rhs
def getError(self):
#Test function
phi = lambda x: np.cos(0.5 * np.pi * x)
j_fun = lambda x: -0.5 * np.pi * np.sin(0.5 * np.pi * x)
q_fun = lambda x: -0.25 * (np.pi**2) * np.cos(0.5 * np.pi * x)
xc_ana = phi(self.M.gridCC)
q_ana = q_fun(self.M.gridCC)
j_ana = j_fun(self.M.gridFx)
#TODO: Check where our boundary conditions are CCx or Nx
vecN = self.M.vectorNx
vecC = self.M.vectorCCx
phi_bc = phi(vecC[[0, -1]])
j_bc = j_fun(vecN[[0, -1]])
P, Pin, Pout = self.M.getBCProjWF([['dirichlet', 'neumann']])
Mc = self.M.getFaceInnerProduct()
McI = Utils.sdInv(self.M.getFaceInnerProduct())
V = Utils.sdiag(self.M.vol)
G = -Pin.T * Pin * self.M.faceDiv.T * V
D = self.M.faceDiv
j = McI * (G * xc_ana + P * phi_bc)
q = V * D * Pin.T * Pin * j + V * D * Pout.T * j_bc
# Rearrange if we know q to solve for x
A = V * D * Pin.T * Pin * McI * G
rhs = V * q_ana - V * D * Pin.T * Pin * McI * P * phi_bc - V * D * Pout.T * j_bc
# A = D*McI*G
# rhs = q_ana - D*McI*P*phi_bc
if self.myTest == 'j':
err = np.linalg.norm((Pin * j - Pin * j_ana), np.inf)
elif self.myTest == 'q':
err = np.linalg.norm((q - V * q_ana), np.inf)
elif self.myTest == 'xc':
#TODO: fix the null space
xc, info = sp.linalg.minres(A, rhs, tol=1e-6)
err = np.linalg.norm((xc - xc_ana), np.inf)
if info > 0:
print 'Solve does not work well'
print 'ACCURACY', np.linalg.norm(Utils.mkvc(A * xc) - rhs)
elif self.myTest == 'xcJ':
#TODO: fix the null space
xc, info = sp.linalg.minres(A, rhs, tol=1e-6)
j = McI * (G * xc + P * phi_bc)
err = np.linalg.norm((Pin * j - Pin * j_ana), np.inf)
if info > 0:
print 'Solve does not work well'
print 'ACCURACY', np.linalg.norm(Utils.mkvc(A * xc) - rhs)
return err
def getError(self):
#Test function
phi = lambda x: np.cos(np.pi*x[:,0])*np.cos(np.pi*x[:,1])
j_funX = lambda x: -np.pi*np.sin(np.pi*x[:,0])*np.cos(np.pi*x[:,1])
j_funY = lambda x: -np.pi*np.cos(np.pi*x[:,0])*np.sin(np.pi*x[:,1])
q_fun = lambda x: -2*(np.pi**2)*phi(x)
xc_ana = phi(self.M.gridCC)
q_ana = q_fun(self.M.gridCC)
jX_ana = j_funX(self.M.gridFx)
jY_ana = j_funY(self.M.gridFy)
j_ana = np.r_[jX_ana,jY_ana]
#TODO: Check where our boundary conditions are CCx or Nx
# fxm,fxp,fym,fyp = self.M.faceBoundaryInd
# gBFx = self.M.gridFx[(fxm|fxp),:]
# gBFy = self.M.gridFy[(fym|fyp),:]
fxm,fxp,fym,fyp = self.M.cellBoundaryInd
gBFx = self.M.gridCC[(fxm|fxp),:]
gBFy = self.M.gridCC[(fym|fyp),:]
bc = phi(np.r_[gBFx,gBFy])
# P = sp.csr_matrix(([-1,1],([0,self.M.nF-1],[0,1])), shape=(self.M.nF, 2))
P, Pin, Pout = self.M.getBCProjWF('dirichlet')
Mc = self.M.getFaceInnerProduct()
McI = Utils.sdInv(self.M.getFaceInnerProduct())
G = -self.M.faceDiv.T * Utils.sdiag(self.M.vol)
D = self.M.faceDiv
j = McI*(G*xc_ana + P*bc)
q = D*j
# self.M.plotImage(j, 'FxFy', showIt=True)
# Rearrange if we know q to solve for x
A = D*McI*G
rhs = q_ana - D*McI*P*bc
if self.myTest == 'j':
err = np.linalg.norm((j-j_ana), np.inf)
elif self.myTest == 'q':
err = np.linalg.norm((q-q_ana), np.inf)
elif self.myTest == 'xc':
xc = Solver(A) * (rhs)
err = np.linalg.norm((xc-xc_ana), np.inf)
elif self.myTest == 'xcJ':
xc = Solver(A) * (rhs)
j = McI*(G*xc + P*bc)
err = np.linalg.norm((j-j_ana), np.inf)
return err
def getError(self):
#Test function
phi = lambda x: np.cos(np.pi*x)
j_fun = lambda x: -np.pi*np.sin(np.pi*x)
q_fun = lambda x: -(np.pi**2)*np.cos(np.pi*x)
xc_ana = phi(self.M.gridCC)
q_ana = q_fun(self.M.gridCC)
j_ana = j_fun(self.M.gridFx)
#TODO: Check where our boundary conditions are CCx or Nx
# vec = self.M.vectorNx
vec = self.M.vectorCCx
phi_bc = phi(vec[[0,-1]])
j_bc = j_fun(vec[[0,-1]])
P, Pin, Pout = self.M.getBCProjWF([['dirichlet', 'dirichlet']])
Mc = self.M.getFaceInnerProduct()
McI = Utils.sdInv(self.M.getFaceInnerProduct())
V = Utils.sdiag(self.M.vol)
G = -Pin.T*Pin*self.M.faceDiv.T * V
D = self.M.faceDiv
j = McI*(G*xc_ana + P*phi_bc)
q = V*D*Pin.T*Pin*j + V*D*Pout.T*j_bc
# Rearrange if we know q to solve for x
A = V*D*Pin.T*Pin*McI*G
rhs = V*q_ana - V*D*Pin.T*Pin*McI*P*phi_bc - V*D*Pout.T*j_bc
# A = D*McI*G
# rhs = q_ana - D*McI*P*phi_bc
if self.myTest == 'j':
err = np.linalg.norm((j-j_ana), np.inf)
elif self.myTest == 'q':
err = np.linalg.norm((q-V*q_ana), np.inf)
elif self.myTest == 'xc':
#TODO: fix the null space
solver = SolverCG(A, maxiter=1000)
xc = solver * (rhs)
print('ACCURACY', np.linalg.norm(Utils.mkvc(A*xc) - rhs))
err = np.linalg.norm((xc-xc_ana), np.inf)
elif self.myTest == 'xcJ':
#TODO: fix the null space
xc = Solver(A) * (rhs)
print(np.linalg.norm(Utils.mkvc(A*xc) - rhs))
j = McI*(G*xc + P*phi_bc)
err = np.linalg.norm((j-j_ana), np.inf)
return err
def getError(self):
#Test function
phi = lambda x: np.cos(np.pi * x)
j_fun = lambda x: -np.pi * np.sin(np.pi * x)
q_fun = lambda x: -(np.pi**2) * np.cos(np.pi * x)
xc_ana = phi(self.M.gridCC)
q_ana = q_fun(self.M.gridCC)
j_ana = j_fun(self.M.gridFx)
#TODO: Check where our boundary conditions are CCx or Nx
# vec = self.M.vectorNx
vec = self.M.vectorCCx
phi_bc = phi(vec[[0, -1]])
j_bc = j_fun(vec[[0, -1]])
P, Pin, Pout = self.M.getBCProjWF([['dirichlet', 'dirichlet']])
Mc = self.M.getFaceInnerProduct()
McI = Utils.sdInv(self.M.getFaceInnerProduct())
V = Utils.sdiag(self.M.vol)
G = -Pin.T * Pin * self.M.faceDiv.T * V
D = self.M.faceDiv
j = McI * (G * xc_ana + P * phi_bc)
q = V * D * Pin.T * Pin * j + V * D * Pout.T * j_bc
# Rearrange if we know q to solve for x
A = V * D * Pin.T * Pin * McI * G
rhs = V * q_ana - V * D * Pin.T * Pin * McI * P * phi_bc - V * D * Pout.T * j_bc
# A = D*McI*G
# rhs = q_ana - D*McI*P*phi_bc
if self.myTest == 'j':
err = np.linalg.norm((j - j_ana), np.inf)
elif self.myTest == 'q':
err = np.linalg.norm((q - V * q_ana), np.inf)
elif self.myTest == 'xc':
#TODO: fix the null space
solver = SolverCG(A, maxiter=1000)
xc = solver * (rhs)
print 'ACCURACY', np.linalg.norm(Utils.mkvc(A * xc) - rhs)
err = np.linalg.norm((xc - xc_ana), np.inf)
elif self.myTest == 'xcJ':
#TODO: fix the null space
xc = Solver(A) * (rhs)
print np.linalg.norm(Utils.mkvc(A * xc) - rhs)
j = McI * (G * xc + P * phi_bc)
err = np.linalg.norm((j - j_ana), np.inf)
return err
def _fastInnerProduct(self,
projType,
prop=None,
invProp=False,
invMat=False):
"""
Fast version of getFaceInnerProduct.
This does not handle the case of a full tensor prop.
:param numpy.array prop: material property (tensor properties are possible) at each cell center (nC, (1, 3, or 6))
:param str projType: 'E' or 'F'
:param bool returnP: returns the projection matrices
:param bool invProp: inverts the material property
:param bool invMat: inverts the matrix
:rtype: scipy.csr_matrix
:return: M, the inner product matrix (nF, nF)
"""
assert projType in [
'F', 'E'
], "projType must be 'F' for faces or 'E' for edges"
if prop is None:
prop = np.ones(self.nC)
if invProp:
prop = 1. / prop
if Utils.isScalar(prop):
prop = prop * np.ones(self.nC)
if prop.size == self.nC:
Av = getattr(self, 'ave' + projType + '2CC')
Vprop = self.vol * Utils.mkvc(prop)
M = self.dim * Utils.sdiag(Av.T * Vprop)
elif prop.size == self.nC * self.dim:
Av = getattr(self, 'ave' + projType + '2CCV')
V = sp.kron(sp.identity(self.dim), Utils.sdiag(self.vol))
M = Utils.sdiag(Av.T * V * Utils.mkvc(prop))
else:
return None
if invMat:
return Utils.sdInv(M)
else:
return M
def _fastInnerProduct(self, projType, prop=None, invProp=False, invMat=False):
"""
Fast version of getFaceInnerProduct.
This does not handle the case of a full tensor prop.
:param numpy.array prop: material property (tensor properties are possible) at each cell center (nC, (1, 3, or 6))
:param str projType: 'E' or 'F'
:param bool returnP: returns the projection matrices
:param bool invProp: inverts the material property
:param bool invMat: inverts the matrix
:rtype: scipy.csr_matrix
:return: M, the inner product matrix (nF, nF)
"""
assert projType in ['F', 'E'], "projType must be 'F' for faces or 'E' for edges"
if prop is None:
prop = np.ones(self.nC)
if invProp:
prop = 1./prop
if Utils.isScalar(prop):
prop = prop*np.ones(self.nC)
if prop.size == self.nC:
Av = getattr(self, 'ave'+projType+'2CC')
Vprop = self.vol * Utils.mkvc(prop)
M = self.dim * Utils.sdiag(Av.T * Vprop)
elif prop.size == self.nC*self.dim:
Av = getattr(self, 'ave'+projType+'2CCV')
V = sp.kron(sp.identity(self.dim), Utils.sdiag(self.vol))
M = Utils.sdiag(Av.T * V * Utils.mkvc(prop))
else:
return None
if invMat:
return Utils.sdInv(M)
else:
return M
def getError(self):
#Test function
phi = lambda x: np.cos(0.5 * np.pi * x[:, 0]) * np.cos(0.5 * np.pi *
x[:, 1])
j_funX = lambda x: -0.5 * np.pi * np.sin(
0.5 * np.pi * x[:, 0]) * np.cos(0.5 * np.pi * x[:, 1])
j_funY = lambda x: -0.5 * np.pi * np.cos(
0.5 * np.pi * x[:, 0]) * np.sin(0.5 * np.pi * x[:, 1])
q_fun = lambda x: -2 * ((0.5 * np.pi)**2) * phi(x)
xc_ana = phi(self.M.gridCC)
q_ana = q_fun(self.M.gridCC)
jX_ana = j_funX(self.M.gridFx)
jY_ana = j_funY(self.M.gridFy)
j_ana = np.r_[jX_ana, jY_ana]
#TODO: Check where our boundary conditions are CCx or Nx
cxm, cxp, cym, cyp = self.M.cellBoundaryInd
fxm, fxp, fym, fyp = self.M.faceBoundaryInd
gBFx = self.M.gridFx[(fxm | fxp), :]
gBFy = self.M.gridFy[(fym | fyp), :]
gBCx = self.M.gridCC[(cxm | cxp), :]
gBCy = self.M.gridCC[(cym | cyp), :]
phi_bc = phi(np.r_[gBCx, gBCy])
j_bc = np.r_[j_funX(gBFx), j_funY(gBFy)]
# P = sp.csr_matrix(([-1,1],([0,self.M.nF-1],[0,1])), shape=(self.M.nF, 2))
P, Pin, Pout = self.M.getBCProjWF([['dirichlet', 'neumann'],
['dirichlet', 'neumann']])
Mc = self.M.getFaceInnerProduct()
McI = Utils.sdInv(self.M.getFaceInnerProduct())
V = Utils.sdiag(self.M.vol)
G = -Pin.T * Pin * self.M.faceDiv.T * V
D = self.M.faceDiv
j = McI * (G * xc_ana + P * phi_bc)
q = V * D * Pin.T * Pin * j + V * D * Pout.T * j_bc
# Rearrange if we know q to solve for x
A = V * D * Pin.T * Pin * McI * G
rhs = V * q_ana - V * D * Pin.T * Pin * McI * P * phi_bc - V * D * Pout.T * j_bc
if self.myTest == 'j':
err = np.linalg.norm((Pin * j - Pin * j_ana), np.inf)
elif self.myTest == 'q':
err = np.linalg.norm((q - V * q_ana), np.inf)
elif self.myTest == 'xc':
#TODO: fix the null space
xc, info = sp.linalg.minres(A, rhs, tol=1e-6)
err = np.linalg.norm((xc - xc_ana), np.inf)
if info > 0:
print 'Solve does not work well'
print 'ACCURACY', np.linalg.norm(Utils.mkvc(A * xc) - rhs)
elif self.myTest == 'xcJ':
#TODO: fix the null space
xc, info = sp.linalg.minres(A, rhs, tol=1e-6)
j = McI * (G * xc + P * phi_bc)
err = np.linalg.norm((Pin * j - Pin * j_ana), np.inf)
if info > 0:
print 'Solve does not work well'
print 'ACCURACY', np.linalg.norm(Utils.mkvc(A * xc) - rhs)
return err
def getError(self):
#Test function
phi = lambda x: np.cos(0.5*np.pi*x[:,0])*np.cos(0.5*np.pi*x[:,1])
j_funX = lambda x: -0.5*np.pi*np.sin(0.5*np.pi*x[:,0])*np.cos(0.5*np.pi*x[:,1])
j_funY = lambda x: -0.5*np.pi*np.cos(0.5*np.pi*x[:,0])*np.sin(0.5*np.pi*x[:,1])
q_fun = lambda x: -2*((0.5*np.pi)**2)*phi(x)
xc_ana = phi(self.M.gridCC)
q_ana = q_fun(self.M.gridCC)
jX_ana = j_funX(self.M.gridFx)
jY_ana = j_funY(self.M.gridFy)
j_ana = np.r_[jX_ana,jY_ana]
#TODO: Check where our boundary conditions are CCx or Nx
cxm,cxp,cym,cyp = self.M.cellBoundaryInd
fxm,fxp,fym,fyp = self.M.faceBoundaryInd
gBFx = self.M.gridFx[(fxm|fxp),:]
gBFy = self.M.gridFy[(fym|fyp),:]
gBCx = self.M.gridCC[(cxm|cxp),:]
gBCy = self.M.gridCC[(cym|cyp),:]
phi_bc = phi(np.r_[gBCx,gBCy])
j_bc = np.r_[j_funX(gBFx), j_funY(gBFy)]
# P = sp.csr_matrix(([-1,1],([0,self.M.nF-1],[0,1])), shape=(self.M.nF, 2))
P, Pin, Pout = self.M.getBCProjWF([['dirichlet', 'neumann'], ['dirichlet', 'neumann']])
Mc = self.M.getFaceInnerProduct()
McI = Utils.sdInv(self.M.getFaceInnerProduct())
V = Utils.sdiag(self.M.vol)
G = -Pin.T*Pin*self.M.faceDiv.T * V
D = self.M.faceDiv
j = McI*(G*xc_ana + P*phi_bc)
q = V*D*Pin.T*Pin*j + V*D*Pout.T*j_bc
# Rearrange if we know q to solve for x
A = V*D*Pin.T*Pin*McI*G
rhs = V*q_ana - V*D*Pin.T*Pin*McI*P*phi_bc - V*D*Pout.T*j_bc
if self.myTest == 'j':
err = np.linalg.norm((Pin*j-Pin*j_ana), np.inf)
elif self.myTest == 'q':
err = np.linalg.norm((q-V*q_ana), np.inf)
elif self.myTest == 'xc':
#TODO: fix the null space
xc, info = sp.linalg.minres(A, rhs, tol = 1e-6)
err = np.linalg.norm((xc-xc_ana), np.inf)
if info > 0:
print('Solve does not work well')
print('ACCURACY', np.linalg.norm(Utils.mkvc(A*xc) - rhs))
elif self.myTest == 'xcJ':
#TODO: fix the null space
xc, info = sp.linalg.minres(A, rhs, tol = 1e-6)
j = McI*(G*xc + P*phi_bc)
err = np.linalg.norm((Pin*j-Pin*j_ana), np.inf)
if info > 0:
print('Solve does not work well')
print('ACCURACY', np.linalg.norm(Utils.mkvc(A*xc) - rhs))
return err
