def solve_homotopy_gmin2(self, x0, sV):
"""Newton's method with gmin stepping (nonlinear ports)"""
# Adds gmin in parallel with nonlinear element (external) ports
# Create Gmin matrix (structure is fixed)
Gonesll = pysparse.spmatrix.ll_mat(self.ckt.nD_dimension,
self.ckt.nD_dimension)
for elem in self.ckt.nD_nlinElem:
Gadd = np.eye(len(elem.nD_extPorts))
# import pdb; pdb.set_trace()
set_Jac(Gonesll, elem.nD_epos, elem.nD_eneg,
elem.nD_epos, elem.nD_eneg, Gadd)
Gones = Gonesll.to_csr()
tmpVec = np.empty(self.ckt.nD_dimension)
def get_deltax(xVec):
(iVec, Jac) = self.get_i_Jac(xVec)
Gones.matvec(xVec, tmpVec)
iVec += self.gmin * tmpVec
Jac.shift(self.gmin, Gonesll)
return self._get_deltax(iVec - sV, Jac)
def f_eval(xVec):
iVec = self.get_i(xVec)
self.Gones.matvec(xVec, tmpVec)
iVec += self.gmin * tmpVec
return iVec - sV
(x, res, iterations, success) = \
self._homotopy(0.5, self._set_gmin, x0, get_deltax, f_eval)
if success:
return (x, res, iterations)
else:
raise NoConvergenceError('gmin stepping did not converge')
def get_i_Jac(self, xVec):
"""
Calculate total current and Jacobian
Returns (iVec, Jac)::
iVec = G xVec + i(xVec)
Jac = G + (di/dx)(xVec)
xVec: input vector of nodal voltages.
iVec: output vector of currents
Jac: system Jacobian
"""
# Erase sparse matrix
self.Jac.scale(0.)
# Linear contribution
self.G.matvec(xVec, self.iVec)
self.Jac.shift(1., self.Gll)
# Nonlinear contribution
for elem in self.ckt.nD_nlinElem:
# first have to retrieve port voltages from xVec
xin = np.zeros(len(elem.controlPorts))
set_xin(xin, elem.nD_vpos, elem.nD_vneg, xVec)
(outV, outJac) = elem.eval_and_deriv(xin)
# Update iVec and Jacobian now. outV may have extra charge
# elements but they are not used in the following
set_i(self.iVec, elem.nD_cpos, elem.nD_cneg, outV)
set_Jac(self.Jac, elem.nD_cpos, elem.nD_cneg,
elem.nD_vpos, elem.nD_vneg, outJac)
return (self.iVec, self.Jac)
def solve_homotopy_gmin2(self, x0, sV):
"""Newton's method with gmin stepping (nonlinear ports)"""
# Adds gmin in parallel with nonlinear element (external) ports
# Create Gmin matrix (structure is fixed)
Gonesll = pysparse.spmatrix.ll_mat(self.ckt.nD_dimension,
self.ckt.nD_dimension)
for elem in self.ckt.nD_nlinElem:
Gadd = np.eye(len(elem.nD_extPorts))
# import pdb; pdb.set_trace()
set_Jac(Gonesll, elem.nD_epos, elem.nD_eneg, elem.nD_epos,
elem.nD_eneg, Gadd)
Gones = Gonesll.to_csr()
tmpVec = np.empty(self.ckt.nD_dimension)
def get_deltax(xVec):
(iVec, Jac) = self.get_i_Jac(xVec)
Gones.matvec(xVec, tmpVec)
iVec += self.gmin * tmpVec
Jac.shift(self.gmin, Gonesll)
return self._get_deltax(iVec - sV, Jac)
def f_eval(xVec):
iVec = self.get_i(xVec)
self.Gones.matvec(xVec, tmpVec)
iVec += self.gmin * tmpVec
return iVec - sV
(x, res, iterations, success) = \
self._homotopy(0.5, self._set_gmin, x0, get_deltax, f_eval)
if success:
return (x, res, iterations)
else:
raise NoConvergenceError('gmin stepping did not converge')
def get_i_Jac(self, xVec):
"""
Calculate total current and Jacobian
Returns (iVec, Jac)::
iVec = G xVec + i(xVec)
Jac = G + (di/dx)(xVec)
xVec: input vector of nodal voltages.
iVec: output vector of currents
Jac: system Jacobian
"""
# Erase sparse matrix
self.Jac.scale(0.)
# Linear contribution
self.G.matvec(xVec, self.iVec)
self.Jac.shift(1., self.Gll)
# Nonlinear contribution
for elem in self.ckt.nD_nlinElem:
# first have to retrieve port voltages from xVec
xin = np.zeros(len(elem.controlPorts))
set_xin(xin, elem.nD_vpos, elem.nD_vneg, xVec)
(outV, outJac) = elem.eval_and_deriv(xin)
# Update iVec and Jacobian now. outV may have extra charge
# elements but they are not used in the following
set_i(self.iVec, elem.nD_cpos, elem.nD_cneg, outV)
set_Jac(self.Jac, elem.nD_cpos, elem.nD_cneg, elem.nD_vpos,
elem.nD_vneg, outJac)
return (self.iVec, self.Jac)
def refresh(self):
"""
Re-generate linear matrices
Used for parameter sweeps
"""
self.Gll = pysparse.spmatrix.ll_mat(self.ckt.nD_dimension,
self.ckt.nD_dimension)
# Generate G matrix (never changes)
for elem in self.ckt.nD_elemList:
# All elements have nD_linVCCS (perhaps empty)
for vccs in elem.nD_linVCCS:
set_quad(self.Gll, *vccs)
# Frequency-defined elements
for elem in self.ckt.nD_freqDefinedElem:
set_Jac(self.Gll, elem.nD_fpos, elem.nD_fneg,
elem.nD_fpos, elem.nD_fneg, elem.get_G_matrix())
# Free unused memory
self.Gll.compress()
# Create G in csr form for efficient matrix-vector multiplication
self.G = self.Gll.to_csr()
def refresh(self):
"""
Re-generate linear matrices
Used for parameter sweeps
"""
self.Gll = pysparse.spmatrix.ll_mat(self.ckt.nD_dimension,
self.ckt.nD_dimension)
# Generate G matrix (never changes)
for elem in self.ckt.nD_elemList:
# All elements have nD_linVCCS (perhaps empty)
for vccs in elem.nD_linVCCS:
set_quad(self.Gll, *vccs)
# Frequency-defined elements
for elem in self.ckt.nD_freqDefinedElem:
set_Jac(self.Gll, elem.nD_fpos, elem.nD_fneg, elem.nD_fpos,
elem.nD_fneg, elem.get_G_matrix())
# Free unused memory
self.Gll.compress()
# Create G in csr form for efficient matrix-vector multiplication
self.G = self.Gll.to_csr()
