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
"""
# Linear contribution
self.iVec[:] = self.Gp * xVec
# Nonlinear contribution
self._mbase = self._mbaseLin
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_i(self.iVec, elem.nD_qpos, elem.nD_qneg,
self.im.a0 * outV[len(elem.csOutPorts):])
self._set_Jac(self.Jaccoo, outJac, *elem.nD_csidx)
qJac = self.im.a0 * outJac[len(elem.csOutPorts):, :]
self._set_Jac(self.Jaccoo, qJac, *elem.nD_qsidx)
return (self.iVec, self.Jaccoo)
def _create_tape(self, xVec):
"""
Generate main CppAD tape
Normally there is no need to call this function manually as tapes
are generated as needed.
"""
# Create derivative vector
a_xVec = ad.independent(xVec)
# perform actual calculation
# Linear contribution
a_out = np.dot(self.G, a_xVec)
xin = np.zeros(len(xVec), dtype = type(a_out[0]))
# Nonlinear contribution
for elem in self.ckt.nD_nlinElem:
# first have to retrieve port voltages from a_xVec
xin[:len(elem.controlPorts)] = 0.
#import pdb; pdb.set_trace()
set_xin(xin, elem.nD_vpos, elem.nD_vneg, a_xVec)
(outV, qVec) = elem.eval_cqs(xin)
# Update iVec. outV may have extra charge elements but
# they are not used in the following
set_i(a_out, elem.nD_cpos, elem.nD_cneg, outV)
# Save main function tape
self._func = ad.adfun(a_xVec, a_out)
# optimize main function tape
self._func.optimize()
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 get_i(self, xVec):
"""
Calculate total current
returns iVec = G' xVec + i'(xVec)
xVec: input vector of nodal voltages.
iVec: output vector of currents
"""
# Linear contribution
self.iVec[:] = self.Gp * xVec
dcounter = 0
# Nonlinear contribution
for elem in self.ckt.nD_nlinElem:
# first have to retrieve port voltages from xVec
xin = np.zeros(elem.nD_nxin)
set_xin(xin, elem.nD_vpos, elem.nD_vneg, xVec)
if elem.nDelays:
# Apply delay to port voltages
for i in xrange(-elem.nDelays, 0):
(xin[i], dc) = delay_interp(elem.nD_delay[i], xin[i],
self.im.h, self._dpsteps,
self.tdVecList[dcounter][i])
dcounter += 1
outV = elem.eval(xin)
# Update iVec. 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_i(self.iVec, elem.nD_qpos, elem.nD_qneg,
self.im.a0 * outV[len(elem.csOutPorts):])
return self.iVec
def get_i(self, xVec):
"""
Calculate total current
returns iVec = G' xVec + i'(xVec)
xVec: input vector of nodal voltages.
iVec: output vector of currents
"""
# Linear contribution
self.iVec[:] = self.Gp * xVec
dcounter = 0
# Nonlinear contribution
for elem in self.ckt.nD_nlinElem:
# first have to retrieve port voltages from xVec
xin = np.zeros(elem.nD_nxin)
set_xin(xin, elem.nD_vpos, elem.nD_vneg, xVec)
if elem.nDelays:
# Apply delay to port voltages
for i in xrange(-elem.nDelays, 0):
(xin[i], dc) = delay_interp(elem.nD_delay[i],
xin[i], self.im.h,
self._dpsteps,
self.tdVecList[dcounter][i])
dcounter += 1
outV = elem.eval(xin)
# Update iVec. 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_i(self.iVec, elem.nD_qpos, elem.nD_qneg,
self.im.a0 * outV[len(elem.csOutPorts):])
return self.iVec
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
"""
# Linear contribution
self.iVec[:] = self.G * xVec
# Nonlinear contribution
self._mbase = self._mbaseLin
for elem in self.ckt.nD_nlinElem:
# first have to retrieve port voltages from xVec
xin = np.zeros(elem.nD_nxin)
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)
self.set_Jac(self.Jaccoo, outJac, *elem.nD_csidx)
return (self.iVec, self.Jaccoo)
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 _create_tape(self, xVec):
"""
Generate main CppAD tape
Normally there is no need to call this function manually as tapes
are generated as needed.
"""
# Create derivative vector
a_xVec = ad.independent(xVec)
# perform actual calculation
# Linear contribution
a_out = np.dot(self.G, a_xVec)
xin = np.zeros(len(xVec), dtype=type(a_out[0]))
# Nonlinear contribution
for elem in self.ckt.nD_nlinElem:
# first have to retrieve port voltages from a_xVec
xin[:len(elem.controlPorts)] = 0.
#import pdb; pdb.set_trace()
set_xin(xin, elem.nD_vpos, elem.nD_vneg, a_xVec)
(outV, qVec) = elem.eval_cqs(xin)
# Update iVec. outV may have extra charge elements but
# they are not used in the following
set_i(a_out, elem.nD_cpos, elem.nD_cneg, outV)
# Save main function tape
self._func = ad.adfun(a_xVec, a_out)
# optimize main function tape
self._func.optimize()
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
"""
# Linear contribution
self.iVec[:] = self.Gp * xVec
dcounter = 0
# Nonlinear contribution
self._mbase = self._mbaseLin
for elem in self.ckt.nD_nlinElem:
# first have to retrieve port voltages from xVec
xin = np.zeros(elem.nD_nxin)
set_xin(xin, elem.nD_vpos, elem.nD_vneg, xVec)
if elem.nDelays:
# Derivative coefficients
dc = np.empty_like(elem.nD_delay)
# Apply delay to port voltages
for i in xrange(-elem.nDelays, 0):
(xin[i], dc[i]) = delay_interp(elem.nD_delay[i],
xin[i], self.im.h,
self._dpsteps,
self.tdVecList[dcounter][i])
dcounter += 1
(outV, outJac) = elem.eval_and_deriv(xin)
# Multiply Jacobian columns by derivative factors
for i in xrange(-elem.nDelays, 0):
outJac[:,i] *= dc[i]
else:
(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_i(self.iVec, elem.nD_qpos, elem.nD_qneg,
self.im.a0 * outV[len(elem.csOutPorts):])
self.set_Jac(self.Jaccoo, outJac, *elem.nD_csidx)
qJac = self.im.a0 * outJac[len(elem.csOutPorts):,:]
self.set_Jac(self.Jaccoo, qJac, *elem.nD_qsidx)
return (self.iVec, self.Jaccoo)
def update_q(self, xVec):
"""
Recalculate qVec for a given value of xVec
"""
# Calculate total q vector
# Calculate linear charges first
self.qVec[:] = self.C * xVec
for elem in self.ckt.nD_nlinElem:
# first have to retrieve port voltages from xVec
xin = np.zeros(elem.nD_nxin)
set_xin(xin, elem.nD_vpos, elem.nD_vneg, xVec)
outV = elem.eval(xin)
set_i(self.qVec, elem.nD_qpos, elem.nD_qneg, outV[len(elem.csOutPorts) :])
return self.qVec
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
"""
# Linear contribution
self.iVec[:] = self.Gp * xVec
dcounter = 0
# Nonlinear contribution
self._mbase = self._mbaseLin
for elem in self.ckt.nD_nlinElem:
# first have to retrieve port voltages from xVec
xin = np.zeros(elem.nD_nxin)
set_xin(xin, elem.nD_vpos, elem.nD_vneg, xVec)
if elem.nDelays:
# Derivative coefficients
dc = np.empty_like(elem.nD_delay)
# Apply delay to port voltages
for i in xrange(-elem.nDelays, 0):
(xin[i], dc[i]) = delay_interp(elem.nD_delay[i], xin[i],
self.im.h, self._dpsteps,
self.tdVecList[dcounter][i])
dcounter += 1
(outV, outJac) = elem.eval_and_deriv(xin)
# Multiply Jacobian columns by derivative factors
for i in xrange(-elem.nDelays, 0):
outJac[:, i] *= dc[i]
else:
(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_i(self.iVec, elem.nD_qpos, elem.nD_qneg,
self.im.a0 * outV[len(elem.csOutPorts):])
self.set_Jac(self.Jaccoo, outJac, *elem.nD_csidx)
qJac = self.im.a0 * outJac[len(elem.csOutPorts):, :]
self.set_Jac(self.Jaccoo, qJac, *elem.nD_qsidx)
return (self.iVec, self.Jaccoo)
def update_q(self, xVec):
"""
Recalculate qVec for a given value of xVec
"""
# Calculate total q vector
# Calculate linear charges first
self.qVec[:] = self.C * xVec
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 = elem.eval(xin)
set_i(self.qVec, elem.nD_qpos, elem.nD_qneg,
outV[len(elem.csOutPorts):])
return self.qVec
def get_i(self, xVec):
"""
Calculate total current
returns iVec = G xVec + i(xVec)
xVec: input vector of nodal voltages.
iVec: output vector of currents
"""
# Linear contribution
self.iVec[:] = self.G * xVec
# 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 = elem.eval(xin)
# Update iVec. 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)
return self.iVec
def get_i(self, xVec):
"""
Calculate total current
returns iVec = G xVec + i(xVec)
xVec: input vector of nodal voltages.
iVec: output vector of currents
"""
# Linear contribution
self.G.matvec(xVec, self.iVec)
# 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 = elem.eval(xin)
# Update iVec. 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)
return self.iVec
def get_i_derivatives(device, parList, xVec, force=False):
"""
Returns DC current derivatives with respect to given parameters
Inputs:
device (linear or nonlinear)
parList: list of parameters as returned by param.get_float_attributes()
xVec: nodal vector
force: if True, force tape re-generation
Outputs:
output currents Jacobian
Possible side effect: operating point attributes lost in internal
terminals if tape is created
"""
# Create input vector from parameter values
pVec = np.array([val for name, val in parList])
inVec = pVec
if device.isNonlinear:
# Add controlling voltages to input: AD tape can be reused for
# different operating points
xin = np.zeros(device.nD_nxin)
nd.set_xin(xin, device.nD_vpos, device.nD_vneg, xVec)
#assert(np.max(xin - device.nD_xOP) == 0)
inVec = np.concatenate((pVec, xin), axis=0)
# Use taped function if it exists
if (not hasattr(device, '_sensF')) or force:
device._sensF = create_sensitivity_tape(device, parList, inVec)
f = device._sensF
# Generate current derivatives: must use forward call only because
# reverse creates problems with calculations using condassign in
# the wrong way (no longer needed, but forward still OK because no
# need to calculate derivatives respect to nonlinear control
# voltages.
f.forward(0, inVec)
w = np.zeros_like(inVec)
Ji = np.zeros((len(xVec), len(pVec)), dtype=float)
for i in range(len(pVec)):
# In the future w[i] can be set equal to delta_p then forward
# returns the delta_i
w[i] = 1.
doutdp = f.forward(1, w)
#print(f.forward(1,w))
# Combine derivatives in output Jacobian
if device.linearVCCS:
for k, vccs in enumerate(device.nD_linVCCS):
# Controlling voltage: xVec[col1] - xVec[col2]
vc = 0.
col = vccs[1]
if col >= 0:
vc = xVec[col]
col = vccs[3]
if col >= 0:
vc -= xVec[col]
# Output current derivative = dg/dp * vc
di = vc * doutdp[k]
row = vccs[0]
if row != -1:
Ji[row, i] += di
row = vccs[2]
if row != -1:
Ji[row, i] -= di
if device.isDCSource:
row = device.sourceOutput
k = len(device.nD_linVCCS)
if row[0] >= 0:
Ji[row[0], i] -= doutdp[k]
if row[1] >= 0:
Ji[row[1], i] += doutdp[k]
if device.isNonlinear:
diNL = doutdp[-len(device.csOutPorts):]
nd.set_i(Ji[:, i], device.nD_cpos, device.nD_cneg, diNL)
# Unselect column
w[i] = 0.
#print(f.jacobian(inVec))
return Ji
def get_i_derivatives(device, parList, xVec, force=False):
"""
Returns DC current derivatives with respect to given parameters
Inputs:
device (linear or nonlinear)
parList: list of parameters as returned by param.get_float_attributes()
xVec: nodal vector
force: if True, force tape re-generation
Outputs:
output currents Jacobian
Possible side effect: operating point attributes lost in internal
terminals if tape is created
"""
# Create input vector from parameter values
pVec = np.array([val for name, val in parList])
inVec = pVec
if device.isNonlinear:
# Add controlling voltages to input: AD tape can be reused for
# different operating points
xin = np.zeros(device.nD_nxin)
nd.set_xin(xin, device.nD_vpos, device.nD_vneg, xVec)
#assert(np.max(xin - device.nD_xOP) == 0)
inVec = np.concatenate((pVec, xin), axis=0)
# Use taped function if it exists
if (not hasattr(device, '_sensF')) or force:
device._sensF = create_sensitivity_tape(device, parList, inVec)
f = device._sensF
# Generate current derivatives: must use forward call only because
# reverse creates problems with calculations using condassign in
# the wrong way (no longer needed, but forward still OK because no
# need to calculate derivatives respect to nonlinear control
# voltages.
f.forward(0, inVec)
w = np.zeros_like(inVec)
Ji = np.zeros((len(xVec), len(pVec)), dtype = float)
for i in range(len(pVec)):
# In the future w[i] can be set equal to delta_p then forward
# returns the delta_i
w[i] = 1.
doutdp = f.forward(1, w)
#print(f.forward(1,w))
# Combine derivatives in output Jacobian
if device.linearVCCS:
for k,vccs in enumerate(device.nD_linVCCS):
# Controlling voltage: xVec[col1] - xVec[col2]
vc = 0.
col = vccs[1]
if col >= 0:
vc = xVec[col]
col = vccs[3]
if col >= 0:
vc -= xVec[col]
# Output current derivative = dg/dp * vc
di = vc * doutdp[k]
row = vccs[0]
if row != -1:
Ji[row, i] += di
row = vccs[2]
if row != -1:
Ji[row, i] -= di
if device.isDCSource:
row = device.sourceOutput
k = len(device.nD_linVCCS)
if row[0] >= 0:
Ji[row[0], i] -= doutdp[k]
if row[1] >= 0:
Ji[row[1], i] += doutdp[k]
if device.isNonlinear:
diNL = doutdp[-len(device.csOutPorts):]
nd.set_i(Ji[:,i], device.nD_cpos, device.nD_cneg, diNL)
# Unselect column
w[i] = 0.
#print(f.jacobian(inVec))
return Ji