def build_CMAT(mna, D, step, points, tick, n_of_var=None, verbose=3):
if n_of_var is None:
n_of_var = mna.shape[0]
printing.print_info_line(("Building CMAT (%dx%d)... " % (n_of_var*points, n_of_var*points), 5), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
(C1, C0) = implicit_euler.get_df_coeff(step)
I = numpy.mat(numpy.eye(n_of_var))
M = mna + C1*D
N = C0 * D
#Z = numpy.mat(numpy.zeros((n_of_var, n_of_var)))
CMAT = numpy.mat(numpy.zeros((n_of_var*points, n_of_var*points)))
for li in xrange(points): #li = line index
for ci in xrange(points):
if li == 0:
if ci == 0:
temp = 1.0 * I
elif ci == points - 1:
temp = -1.0 * I
else:
continue #temp = Z
else:
if ci == li:
temp = M
elif ci == li -1:
temp = N
else:
continue #temp = Z
CMAT = set_submatrix(row=li*n_of_var, col=ci*n_of_var, dest_matrix=CMAT, source_matrix=temp)
tick.step(verbose > 2)
tick.hide(verbose > 2)
printing.print_info_line(("done.", 5), verbose)
return CMAT
def build_Tass_static_vector(circ, Tf, points, step, tick, n_of_var, verbose=3):
Tass_vector = []
nv = len(circ.nodes_dict)
printing.print_info_line(("Building Tass...", 5), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
for index in xrange(0, points):
Tt = numpy.zeros((n_of_var, 1))
v_eq = 0
time = index * step
for elem in circ:
if (isinstance(elem, devices.VSource) or isinstance(elem, devices.ISource)) and elem.is_timedependent:
if isinstance(elem, devices.VSource):
Tt[nv - 1 + v_eq, 0] = -1.0 * elem.V(time)
elif isinstance(elem, devices.ISource):
if elem.n1:
Tt[elem.n1 - 1, 0] = \
Tt[elem.n1 - 1, 0] + elem.I(time)
if elem.n2:
Tt[elem.n2 - 1, 0] = \
Tt[elem.n2 - 1, 0] - elem.I(time)
if circuit.is_elem_voltage_defined(elem):
v_eq = v_eq + 1
tick.step(verbose > 2)
Tass_vector.append(Tf + Tt)
tick.hide(verbose > 2)
printing.print_info_line(("done.", 5), verbose)
return Tass_vector
def find_vde_index(self, id_wdescr, verbose=3):
"""Finds a voltage defined element MNA index.
Parameters:
id_wdescr (string): the element name, eg. 'V1'. Notice it includes
both the id ('V') and the description ('1').
verbose (int): verbosity level, from 0 (silent) to 6 (debug).
Returns:
the index (int)
"""
vde_index = 0
found = False
for elem in self:
if is_elem_voltage_defined(elem):
if elem.part_id.upper() == id_wdescr.upper():
found = True
break
else:
vde_index += 1
if not found:
printing.print_warning(
"find_vde_index(): element %s was not found. This is a bug." % (id_wdescr,))
else:
printing.print_info_line(
("%s found at index %d" % (id_wdescr, vde_index), 6), verbose)
return vde_index
def build_Tt(circ, points, step, tick, n_of_var, verbose=3):
nv = len(circ.nodes_dict)
printing.print_info_line(("Building Tt...", 5), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
Tt = numpy.zeros((points*n_of_var, 1))
for index in xrange(1, points):
v_eq = 0
time = index * step
for elem in circ.elements:
if (isinstance(elem, devices.vsource) or isinstance(elem, devices.isource)) and elem.is_timedependent:
if isinstance(elem, devices.vsource):
Tt[index*n_of_var + nv - 1 + v_eq, 0] = -1.0 * elem.V(time)
elif isinstance(elem, devices.isource):
if elem.n1:
Tt[index*n_of_var + elem.n1-1, 0] = \
Tt[index*n_of_var + elem.n1-1, 0] + elem.I(time)
if elem.n2:
Tt[index*n_of_var + elem.n2-1, 0] = \
Tt[index*n_of_var + elem.n2-1, 0] - elem.I(time)
if circuit.is_elem_voltage_defined(elem):
v_eq = v_eq +1
#print Tt[index*n_of_var:(index+1)*n_of_var]
tick.step(verbose > 2)
tick.hide(verbose > 2)
printing.print_info_line(("done.", 5), verbose)
return Tt
def build_Tt(circ, points, step, tick, n_of_var, verbose=3):
nv = len(circ.nodes_dict)
printing.print_info_line(("Building Tt...", 5), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
Tt = numpy.zeros((points * n_of_var, 1))
for index in xrange(1, points):
v_eq = 0
time = index * step
for elem in circ:
if (isinstance(elem, devices.VSource) or isinstance(
elem, devices.ISource)) and elem.is_timedependent:
if isinstance(elem, devices.VSource):
Tt[index * n_of_var + nv - 1 + v_eq, 0] = - \
1.0 * elem.V(time)
elif isinstance(elem, devices.ISource):
if elem.n1:
Tt[index * n_of_var + elem.n1 - 1, 0] = \
Tt[index * n_of_var + elem.n1 - 1, 0] + \
elem.I(time)
if elem.n2:
Tt[index * n_of_var + elem.n2 - 1, 0] = \
Tt[index * n_of_var + elem.n2 - 1, 0] - \
elem.I(time)
if circuit.is_elem_voltage_defined(elem):
v_eq = v_eq + 1
# print Tt[index*n_of_var:(index+1)*n_of_var]
tick.step(verbose > 2)
tick.hide(verbose > 2)
printing.print_info_line(("done.", 5), verbose)
return Tt
def set_next_solve_method(standard_solving,
gmin_stepping,
source_stepping,
verbose=3):
if standard_solving["enabled"]:
printing.print_info_line(("failed.", 1), verbose)
standard_solving["enabled"] = False
standard_solving["failed"] = True
elif gmin_stepping["enabled"]:
printing.print_info_line(("failed.", 1), verbose)
gmin_stepping["enabled"] = False
gmin_stepping["failed"] = True
elif source_stepping["enabled"]:
printing.print_info_line(("failed.", 1), verbose)
source_stepping["enabled"] = False
source_stepping["failed"] = True
if not standard_solving["failed"] and options.use_standard_solve_method:
standard_solving["enabled"] = True
elif not gmin_stepping["failed"] and options.use_gmin_stepping:
gmin_stepping["enabled"] = True
printing.print_info_line(
("Enabling gmin stepping convergence aid.", 3), verbose)
elif not source_stepping["failed"] and options.use_source_stepping:
source_stepping["enabled"] = True
printing.print_info_line(
("Enabling source stepping convergence aid.", 3), verbose)
return standard_solving, gmin_stepping, source_stepping
def find_vde_index(self, id_wdescr, verbose=3):
"""Finds a voltage defined element MNA index.
Parameters:
id_wdescr (string): the element name, eg. 'V1'. Notice it includes
both the id ('V') and the description ('1').
verbose (int): verbosity level, from 0 (silent) to 6 (debug).
Returns:
the index (int)
"""
vde_index = 0
found = False
for elem in self:
if is_elem_voltage_defined(elem):
if elem.part_id.upper() == id_wdescr.upper():
found = True
break
else:
vde_index += 1
if not found:
printing.print_warning(
"find_vde_index(): element %s was not found. This is a bug." %
(id_wdescr, ))
else:
printing.print_info_line(
("%s found at index %d" % (id_wdescr, vde_index), 6), verbose)
return vde_index
def build_gmin_matrix(circ, gmin, mna_size, verbose):
printing.print_info_line(("Building Gmin matrix...", 5), verbose)
Gmin_matrix = numpy.mat(numpy.zeros((mna_size, mna_size)))
for index in xrange(len(circ.nodes_dict)-1):
Gmin_matrix[index, index] = gmin
# the three missing terms of the stample matrix go on [index,0] [0,0] [0, index] but since
# we discarded the 0 row and 0 column, we simply don't need to add them
#the last lines are the KVL lines, introduced by voltage sources. Don't add gmin there.
return Gmin_matrix
def build_gmin_matrix(circ, gmin, mna_size, verbose):
printing.print_info_line(("Building Gmin matrix...", 5), verbose)
Gmin_matrix = numpy.mat(numpy.zeros((mna_size, mna_size)))
for index in xrange(len(circ.nodes_dict) - 1):
Gmin_matrix[index, index] = gmin
# the three missing terms of the stample matrix go on [index,0] [0,0] [0, index] but since
# we discarded the 0 row and 0 column, we simply don't need to add them
# the last lines are the KVL lines, introduced by voltage sources. Don't add gmin there.
return Gmin_matrix
def build_Tf(sTf, points, tick, n_of_var, verbose=3):
printing.print_info_line(("Building Tf...", 5), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
Tf = numpy.mat(numpy.zeros((points*n_of_var, 1)))
for index in xrange(1, points):
Tf = set_submatrix(row=index*n_of_var, col=0, dest_matrix=Tf, source_matrix=sTf)
tick.step(verbose > 2)
tick.hide(verbose > 2)
printing.print_info_line(("done.", 5), verbose)
return Tf
def build_Tf(sTf, points, tick, n_of_var, verbose=3):
printing.print_info_line(("Building Tf...", 5), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
Tf = numpy.mat(numpy.zeros((points * n_of_var, 1)))
for index in xrange(1, points):
Tf = set_submatrix(row=index * n_of_var,
col=0,
dest_matrix=Tf,
source_matrix=sTf)
tick.step(verbose > 2)
tick.hide(verbose > 2)
printing.print_info_line(("done.", 5), verbose)
return Tf
def build_x(mna, step, points, tick, x0=None, n_of_var=None, verbose=3):
if n_of_var is None:
n_of_var = mna.shape[0]
printing.print_info_line(("Building x...", 5), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
x = numpy.mat(numpy.zeros((points*n_of_var, 1)))
if x0 is not None:
if isinstance(x0, results.op_solution):
x0 = x0.asmatrix()
if x0.shape[0] != n_of_var:
print "Warning x0 has the wrong dimensions. Using all 0s."
else:
for index in xrange(points):
x = set_submatrix(row=index*n_of_var, col=0, dest_matrix=x, source_matrix=x0)
tick.step(verbose > 2)
tick.hide(verbose > 2)
printing.print_info_line(("done.", 5), verbose)
return x
def build_CMAT(mna, D, step, points, tick, n_of_var=None, verbose=3):
if n_of_var is None:
n_of_var = mna.shape[0]
printing.print_info_line(("Building CMAT (%dx%d)... " %
(n_of_var * points, n_of_var * points), 5),
verbose,
print_nl=False)
tick.reset()
tick.display(verbose > 2)
(C1, C0) = implicit_euler.get_df_coeff(step)
I = numpy.mat(numpy.eye(n_of_var))
M = mna + C1 * D
N = C0 * D
# Z = numpy.mat(numpy.zeros((n_of_var, n_of_var)))
CMAT = numpy.mat(numpy.zeros((n_of_var * points, n_of_var * points)))
for li in xrange(points): # li = line index
for ci in xrange(points):
if li == 0:
if ci == 0:
temp = 1.0 * I
elif ci == points - 1:
temp = -1.0 * I
else:
continue # temp = Z
else:
if ci == li:
temp = M
elif ci == li - 1:
temp = N
else:
continue # temp = Z
CMAT = set_submatrix(row=li * n_of_var,
col=ci * n_of_var,
dest_matrix=CMAT,
source_matrix=temp)
tick.step(verbose > 2)
tick.hide(verbose > 2)
printing.print_info_line(("done.", 5), verbose)
return CMAT
def set_next_solve_method(standard_solving, gmin_stepping, source_stepping, verbose=3):
if standard_solving["enabled"]:
printing.print_info_line(("failed.", 1), verbose)
standard_solving["enabled"] = False
standard_solving["failed"] = True
elif gmin_stepping["enabled"]:
printing.print_info_line(("failed.", 1), verbose)
gmin_stepping["enabled"] = False
gmin_stepping["failed"] = True
elif source_stepping["enabled"]:
printing.print_info_line(("failed.", 1), verbose)
source_stepping["enabled"] = False
source_stepping["failed"] = True
if not standard_solving["failed"] and options.use_standard_solve_method:
standard_solving["enabled"] = True
elif not gmin_stepping["failed"] and options.use_gmin_stepping:
gmin_stepping["enabled"] = True
printing.print_info_line(("Enabling gmin stepping convergence aid.", 3), verbose)
elif not source_stepping["failed"] and options.use_source_stepping:
source_stepping["enabled"] = True
printing.print_info_line(("Enabling source stepping convergence aid.", 3), verbose)
return standard_solving, gmin_stepping, source_stepping
def build_Tass_static_vector(circ,
Tf,
points,
step,
tick,
n_of_var,
verbose=3):
Tass_vector = []
nv = len(circ.nodes_dict)
printing.print_info_line(("Building Tass...", 5), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
for index in xrange(0, points):
Tt = numpy.zeros((n_of_var, 1))
v_eq = 0
time = index * step
for elem in circ.elements:
if (isinstance(elem, devices.vsource) or isinstance(
elem, devices.isource)) and elem.is_timedependent:
if isinstance(elem, devices.vsource):
Tt[nv - 1 + v_eq, 0] = -1.0 * elem.V(time)
elif isinstance(elem, devices.isource):
if elem.n1:
Tt[elem.n1-1, 0] = \
Tt[elem.n1-1, 0] + elem.I(time)
if elem.n2:
Tt[elem.n2-1, 0] = \
Tt[elem.n2-1, 0] - elem.I(time)
if circuit.is_elem_voltage_defined(elem):
v_eq = v_eq + 1
tick.step(verbose > 2)
Tass_vector.append(Tf + Tt)
tick.hide(verbose > 2)
printing.print_info_line(("done.", 5), verbose)
return Tass_vector
def build_x(mna, step, points, tick, x0=None, n_of_var=None, verbose=3):
if n_of_var is None:
n_of_var = mna.shape[0]
printing.print_info_line(("Building x...", 5), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
x = numpy.mat(numpy.zeros((points * n_of_var, 1)))
if x0 is not None:
if isinstance(x0, results.op_solution):
x0 = x0.asmatrix()
if x0.shape[0] != n_of_var:
print "Warning x0 has the wrong dimensions. Using all 0s."
else:
for index in xrange(points):
x = set_submatrix(row=index * n_of_var,
col=0,
dest_matrix=x,
source_matrix=x0)
tick.step(verbose > 2)
tick.hide(verbose > 2)
printing.print_info_line(("done.", 5), verbose)
return x
def bfpss(circ,
period,
step=None,
points=None,
autonomous=False,
x0=None,
mna=None,
Tf=None,
D=None,
outfile='stdout',
vector_norm=lambda v: max(abs(v)),
verbose=3):
"""Performs a PSS analysis.
Time step is constant, IE will be used as DF
Parameters:
circ is the circuit description class
period is the period of the solution
mna, D, Tf are not compulsory they will be computed if they're set to None
step is the time step between consecutive points
points is the number of points to be used
step and points are mutually exclusive options:
- if step is specified, the number of points will be automatically determined
- if points is set, the step will be automatically determined
- if none of them is set, options.shooting_default_points will be used as points
autonomous has to be False, autonomous circuits are not supported
x0 is the initial guess to be used. Needs work.
outfile is the output filename. Defaults to stdout.
verbose is set to zero (print errors only) if datafilename == 'stdout'.
Returns: nothing
"""
if outfile == "stdout":
verbose = 0
printing.print_info_line(("Starting periodic steady state analysis:", 3),
verbose)
printing.print_info_line(("Method: brute-force", 3), verbose)
if mna is None or Tf is None:
(mna, Tf) = dc_analysis.generate_mna_and_N(circ, verbose=verbose)
mna = utilities.remove_row_and_col(mna)
Tf = utilities.remove_row(Tf, rrow=0)
elif not mna.shape[0] == Tf.shape[0]:
printing.print_general_error(
"mna matrix and N vector have different number of rows.")
sys.exit(0)
if D is None:
D = transient.generate_D(circ, [mna.shape[0], mna.shape[0]])
D = utilities.remove_row_and_col(D)
elif not mna.shape == D.shape:
printing.print_general_error(
"mna matrix and D matrix have different sizes.")
sys.exit(0)
(points, step) = check_step_and_points(step, points, period)
n_of_var = mna.shape[0]
locked_nodes = circ.get_locked_nodes()
tick = ticker.ticker(increments_for_step=1)
CMAT = build_CMAT(mna,
D,
step,
points,
tick,
n_of_var=n_of_var,
verbose=verbose)
x = build_x(mna,
step,
points,
tick,
x0=x0,
n_of_var=n_of_var,
verbose=verbose)
Tf = build_Tf(Tf, points, tick, n_of_var=n_of_var, verbose=verbose)
# time variable component: Tt this is always the same in each iter. So we build it once for all
# this holds all time-dependent sources (both V/I).
Tt = build_Tt(circ, points, step, tick, n_of_var=n_of_var, verbose=verbose)
# Indices to differentiate between currents and voltages in the
# convergence check
nv_indices = []
ni_indices = []
nv_1 = len(circ.nodes_dict) - 1
ni = n_of_var - nv_1
for i in range(points):
nv_indices += (i * mna.shape[0] * numpy.ones(nv_1) + \
numpy.arange(nv_1)).tolist()
ni_indices += (i * mna.shape[0] * numpy.ones(ni) + \
numpy.arange(nv_1, n_of_var)).tolist()
converged = False
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
J = numpy.mat(numpy.zeros(CMAT.shape))
T = numpy.mat(numpy.zeros((CMAT.shape[0], 1)))
# td is a numpy matrix that will hold the damping factors
td = numpy.mat(numpy.zeros((points, 1)))
iteration = 0 # newton iteration counter
while True:
if iteration: # the first time are already all zeros
J[:, :] = 0
T[:, 0] = 0
td[:, 0] = 0
for index in xrange(1, points):
for elem in circ:
# build all dT(xn)/dxn (stored in J) and T(x)
if elem.is_nonlinear:
oports = elem.get_output_ports()
for opindex in range(len(oports)):
dports = elem.get_drive_ports(opindex)
v_ports = []
for dpindex in range(len(dports)):
dn1, dn2 = dports[dpindex]
v = 0 # build v: remember we trashed the 0 row and 0 col of mna -> -1
if dn1:
v = v + x[index * n_of_var + dn1 - 1, 0]
if dn2:
v = v - x[index * n_of_var + dn2 - 1, 0]
v_ports.append(v)
# all drive ports are ready.
n1, n2 = oports[opindex][0], oports[opindex][1]
if n1:
T[index * n_of_var + n1 - 1, 0] = T[index * n_of_var + n1 - 1, 0] + \
elem.i(opindex, v_ports)
if n2:
T[index * n_of_var + n2 - 1, 0] = T[index * n_of_var + n2 - 1, 0] - \
elem.i(opindex, v_ports)
for dpindex in range(len(dports)):
dn1, dn2 = dports[dpindex]
if n1:
if dn1:
J[index * n_of_var + n1 - 1, index * n_of_var + dn1 - 1] = \
J[index * n_of_var + n1 - 1, index * n_of_var + dn1 - 1] + \
elem.g(opindex, v_ports, dpindex)
if dn2:
J[index * n_of_var + n1 - 1, index * n_of_var + dn2 - 1] =\
J[index * n_of_var + n1 - 1, index * n_of_var + dn2 - 1] - 1.0 * \
elem.g(opindex, v_ports, dpindex)
if n2:
if dn1:
J[index * n_of_var + n2 - 1, index * n_of_var + dn1 - 1] = \
J[index * n_of_var + n2 - 1, index * n_of_var + dn1 - 1] - 1.0 * \
elem.g(opindex, v_ports, dpindex)
if dn2:
J[index * n_of_var + n2 - 1, index * n_of_var + dn2 - 1] =\
J[index * n_of_var + n2 - 1, index * n_of_var + dn2 - 1] + \
elem.g(opindex, v_ports, dpindex)
J = J + CMAT
residuo = CMAT * x + T + Tf + Tt
dx = -1 * (numpy.linalg.inv(J) * residuo)
# td
for index in xrange(points):
td[index, 0] = dc_analysis.get_td(dx[index * n_of_var:(index + 1) *
n_of_var, 0],
locked_nodes,
n=-1)
x = x + min(abs(td))[0, 0] * dx
# convergence check
converged = convergence_check(dx, x, nv_indices, ni_indices,
vector_norm)
if converged:
break
tick.step(verbose > 2)
if options.shooting_max_nr_iter and iteration == options.shooting_max_nr_iter:
printing.print_general_error("Hitted SHOOTING_MAX_NR_ITER (" +
str(options.shooting_max_nr_iter) +
"), iteration halted.")
converged = False
break
else:
iteration = iteration + 1
tick.hide(verbose > 2)
if converged:
printing.print_info_line(("done.", 3), verbose)
t = numpy.mat(numpy.arange(points) * step)
t = t.reshape((1, points))
x = x.reshape((points, n_of_var))
sol = results.pss_solution(circ=circ,
method="brute-force",
period=period,
outfile=outfile,
t_array=t,
x_array=x.T)
else:
print "failed."
sol = None
return sol
def bfpss(circ, period, step=None, mna=None, Tf=None, D=None, points=None, autonomous=False, x0=None, data_filename='stdout', vector_norm=lambda v: max(abs(v)), verbose=3):
"""Performs a PSS analysis.
Time step is constant, IE will be used as DF
Parameters:
circ is the circuit description class
period is the period of the solution
mna, D, Tf are not compulsory they will be computed if they're set to None
step is the time step between consecutive points
points is the number of points to be used
step and points are mutually exclusive options:
- if step is specified, the number of points will be automatically determined
- if points is set, the step will be automatically determined
- if none of them is set, options.shooting_default_points will be used as points
autonomous has to be False, autonomous circuits are not supported
x0 is the initial guess to be used. Needs work.
data_filename is the output filename. Defaults to stdout.
verbose is set to zero (print errors only) if datafilename == 'stdout'.
Returns: nothing
"""
if data_filename == "stdout":
verbose = 0
printing.print_info_line(("Starting periodic steady state analysis:",3), verbose)
printing.print_info_line(("Method: brute-force",3), verbose)
if mna is None or Tf is None:
(mna, Tf) = dc_analysis.generate_mna_and_N(circ)
mna = utilities.remove_row_and_col(mna)
Tf = utilities.remove_row(Tf, rrow=0)
elif not mna.shape[0] == Tf.shape[0]:
printing.print_general_error("mna matrix and N vector have different number of rows.")
sys.exit(0)
if D is None:
D = transient.generate_D(circ, [mna.shape[0], mna.shape[0]])
D = utilities.remove_row_and_col(D)
elif not mna.shape == D.shape:
printing.print_general_error("mna matrix and D matrix have different sizes.")
sys.exit(0)
(points, step) = check_step_and_points(step, points, period)
n_of_var = mna.shape[0]
locked_nodes = circ.get_locked_nodes()
tick = ticker.ticker(increments_for_step=1)
CMAT = build_CMAT(mna, D, step, points, tick, n_of_var=n_of_var, \
verbose=verbose)
x = build_x(mna, step, points, tick, x0=x0, n_of_var=n_of_var, \
verbose=verbose)
Tf = build_Tf(Tf, points, tick, n_of_var=n_of_var, verbose=verbose)
# time variable component: Tt this is always the same in each iter. So we build it once for all
# this holds all time-dependent sources (both V/I).
Tt = build_Tt(circ, points, step, tick, n_of_var=n_of_var, verbose=verbose)
# Indices to differentiate between currents and voltages in the convergence check
nv_indices = []
ni_indices = []
nv_1 = len(circ.nodes_dict) - 1
ni = n_of_var - nv_1
for i in range(points):
nv_indices += (i*mna.shape[0]*numpy.ones(nv_1) + numpy.arange(nv_1)).tolist()
ni_indices += (i*mna.shape[0]*numpy.ones(ni) + numpy.arange(nv_1, n_of_var)).tolist()
converged = False
printing.print_info_line(("Solving... ",3), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
J = numpy.mat(numpy.zeros(CMAT.shape))
T = numpy.mat(numpy.zeros((CMAT.shape[0], 1)))
# td is a numpy matrix that will hold the damping factors
td = numpy.mat(numpy.zeros((points, 1)))
iteration = 0 # newton iteration counter
while True:
if iteration: # the first time are already all zeros
J[:, :] = 0
T[:, 0] = 0
td[:, 0] = 0
for index in xrange(1, points):
for elem in circ.elements:
# build all dT(xn)/dxn (stored in J) and T(x)
if elem.is_nonlinear:
oports = elem.get_output_ports()
for opindex in range(len(oports)):
dports = elem.get_drive_ports(opindex)
v_ports = []
for dpindex in range(len(dports)):
dn1, dn2 = dports[dpindex]
v = 0 # build v: remember we trashed the 0 row and 0 col of mna -> -1
if dn1:
v = v + x[index*n_of_var + dn1 - 1, 0]
if dn2:
v = v - x[index*n_of_var + dn2 - 1, 0]
v_ports.append(v)
# all drive ports are ready.
n1, n2 = oports[opindex][0], oports[opindex][1]
if n1:
T[index*n_of_var + n1 - 1, 0] = T[index*n_of_var + n1 - 1, 0] + elem.i(opindex, v_ports)
if n2:
T[index*n_of_var + n2 - 1, 0] = T[index*n_of_var + n2 - 1, 0] - elem.i(opindex, v_ports)
for dpindex in range(len(dports)):
dn1, dn2 = dports[dpindex]
if n1:
if dn1:
J[index*n_of_var + n1-1, index*n_of_var + dn1-1] = \
J[index*n_of_var + n1-1, index*n_of_var + dn1-1] + elem.g(opindex, v_ports, dpindex)
if dn2:
J[index*n_of_var + n1-1, index*n_of_var + dn2-1] =\
J[index*n_of_var + n1-1, index * n_of_var + dn2-1] - 1.0*elem.g(opindex, v_ports, dpindex)
if n2:
if dn1:
J[index*n_of_var + n2-1, index*n_of_var + dn1-1] = \
J[index*n_of_var + n2-1, index*n_of_var + dn1-1] - 1.0*elem.g(opindex, v_ports, dpindex)
if dn2:
J[index*n_of_var + n2-1, index*n_of_var + dn2-1] =\
J[index*n_of_var + n2-1, index*n_of_var + dn2-1] + elem.g(opindex, v_ports, dpindex)
J = J + CMAT
residuo = CMAT*x + T + Tf + Tt
dx = -1 * (numpy.linalg.inv(J) * residuo)
#td
for index in xrange(points):
td[index, 0] = dc_analysis.get_td(dx[index*n_of_var:(index+1)*n_of_var, 0], locked_nodes, n=-1)
x = x + min(abs(td))[0, 0] * dx
# convergence check
converged = convergence_check(dx, x, nv_indices, ni_indices, vector_norm)
if converged:
break
tick.step(verbose > 2)
if options.shooting_max_nr_iter and iteration == options.shooting_max_nr_iter:
printing.print_general_error("Hitted SHOOTING_MAX_NR_ITER (" + str(options.shooting_max_nr_iter) + "), iteration halted.")
converged = False
break
else:
iteration = iteration + 1
tick.hide(verbose > 2)
if converged:
printing.print_info_line(("done.", 3), verbose)
t = numpy.mat(numpy.arange(points)*step)
t = t.reshape((1, points))
x = x.reshape((points, n_of_var))
sol = results.pss_solution(circ=circ, method="brute-force", period=period, outfile=data_filename, t_array=t, x_array=x.T)
else:
print "failed."
sol = None
return sol
def dc_analysis(circ, start, stop, step, type_descr, xguess=None, data_filename="stdout", print_int_nodes=True, guess=True, stype="LINEAR", verbose=2):
"""Performs a sweep of the value of V or I of a independent source from start
value to stop value using the provided step.
For every circuit generated, computes the op and prints it out.
This function relays on dc_analysis.op_analysis to actually solve each circuit.
circ: the circuit instance to be simulated
start: start value of the sweep source
stop: stop value of the sweep source
step: step value of the sweep source
elem_type: string, may be 'vsource' or 'isource'
elem_descr: the description of the element, used to recognize it in circ (i.e v<desc>)
data_filename: string, filename of the output file. If set to stdout, prints to screen
print_int_nodes: do it
guess: op_analysis will guess to start the first NR iteration for the first point, the previsious dc is used from then on
verbose: verbosity level
Returns:
A results.dc_solution instance, if a solution was found for at least one sweep value.
None, if an error occurred (eg invalid start/stop/step values) or there was no solution
for any sweep value.
"""
if data_filename == 'stdout':
verbose = 0
printing.print_info_line(("Starting DC analysis:", 2), verbose)
(elem_type, elem_descr) = type_descr
sweep_label = elem_type[0].upper()+elem_descr
#check step/start/stop parameters
if step == 0:
printing.print_general_error("Can't sweep with step=0 !")
sys.exit(1)
if start > stop:
printing.print_general_error("DC analysis has start > stop")
sys.exit(1)
if (stop-start)/step < 1:
printing.print_general_error("DC analysis has number of steps < 1")
sys.exit(1)
if stype == options.dc_log_step:
dc_iter = utilities.log_axis_iterator(stop, start, nsteps=int(stop-start)/step)
elif stype == options.dc_lin_step:
dc_iter = utilities.lin_axis_iterator(stop, start, nsteps=int(stop-start)/step)
else:
printing.print_general_error("Unknown sweep type: %s" % (stype,))
sys.exit(1)
if elem_type != 'vsource' and elem_type != 'isource':
printing.print_general_error("Sweeping is possible only with voltage and current sources. (" +str(elem_type)+ ")")
sys.exit(1)
source_elem = None
for index in xrange(len(circ.elements)):
if circ.elements[index].descr == elem_descr:
if elem_type == 'vsource':
if isinstance(circ.elements[index], devices.vsource):
source_elem = circ.elements[index]
break
if elem_type == 'isource':
if isinstance(circ.elements[index], devices.isource):
source_elem = circ.elements[index]
break
if not source_elem:
printing.print_general_error(elem_type + " element with descr. "+ elem_descr +" was not found.")
sys.exit(1)
if isinstance(source_elem, devices.vsource):
initial_value = source_elem.vdc
else:
initial_value = source_elem.idc
# The initial value is set to None and this IS CORRECT.
# op_analysis will attempt to do a smart guess, if called with x0 = None and guess=True
# For each iteration over the source voltage (current) value, the last result is used as x0.
# op_analysis will not attempt to guess the op if x0 is not None
x = None
sol = results.dc_solution(circ, start, stop, sweepvar=sweep_label, stype=stype, outfile=data_filename)
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
tick = ticker.ticker(1)
tick.display(verbose>2)
#sweep setup
#tarocca il generatore di tensione, avvia DC silenziosa, ritarocca etc
index = 0
for sweep_value in dc_iter:
index = index + 1
if isinstance(source_elem, devices.vsource):
source_elem.vdc = sweep_value
else:
source_elem.idc = sweep_value
#silently calculate the op
op = op_analysis(circ, x0=x, guess=guess, verbose=0)
if op is None:
tick.hide(verbose>2)
if not options.dc_sweep_skip_allowed:
print "Could't solve the circuit for sweep value:", start + index*step
solved = False
break
else:
print "Skipping sweep value:", start + index*step
continue
solved = True
sol.add_op(sweep_value, op)
if guess:
guess = False
tick.step(verbose>2)
tick.hide(verbose>2)
if solved:
printing.print_info_line(("done", 3), verbose)
# clean up
if isinstance(source_elem, devices.vsource):
source_elem.vdc = initial_value
else:
source_elem.idc = initial_value
return sol if solved else None
def dc_analysis(circ,
start,
stop,
step,
source,
sweep_type='LINEAR',
guess=True,
x0=None,
outfile="stdout",
verbose=3):
"""Performs a sweep of the value of V or I of a independent source from start
value to stop value using the provided step.
For every circuit generated, computes the op and prints it out.
This function relays on dc_analysis.op_analysis to actually solve each circuit.
circ: the circuit instance to be simulated
start: start value of the sweep source
stop: stop value of the sweep source
step: the step size in the sweep
source: string, the name of the source to be swept
sweep_type: either options.dc_lin_step (default) or options.dc_log_step
guess: op_analysis will guess to start the first NR iteration for the first point,
the previsious dc is used from then on
outfile: string, filename of the output file. If set to 'stdout', prints to screen.
verbose: verbosity level
Returns:
* A results.dc_solution instance, if a solution was found for at least one sweep value.
* None, if an error occurred (eg invalid start/stop/step values) or there was no solution
for any sweep value.
"""
if outfile == 'stdout':
verbose = 0
printing.print_info_line(("Starting DC analysis:", 2), verbose)
elem_type, elem_descr = source[0].lower(), source.lower() # eg. 'v', 'v34'
sweep_label = elem_type[0].upper() + elem_descr[1:]
if sweep_type == options.dc_log_step and stop - start < 0:
printing.print_general_error(
"DC analysis has log sweeping and negative stepping.")
sys.exit(1)
if (stop - start) * step < 0:
raise ValueError, "Unbonded stepping in DC analysis."
points = (stop - start) / step + 1
sweep_type = sweep_type.upper()[:3]
if sweep_type == options.dc_log_step:
dc_iter = utilities.log_axis_iterator(stop, start, nsteps=points)
elif sweep_type == options.dc_lin_step:
dc_iter = utilities.lin_axis_iterator(stop, start, nsteps=points)
else:
printing.print_general_error("Unknown sweep type: %s" % (sweep_type, ))
sys.exit(1)
if elem_type != 'v' and elem_type != 'i':
printing.print_general_error(
"Sweeping is possible only with voltage and current sources. (" +
str(elem_type) + ")")
sys.exit(1)
source_elem = None
for index in xrange(len(circ)):
if circ[index].part_id.lower() == elem_descr:
if elem_type == 'v':
if isinstance(circ[index], devices.VSource):
source_elem = circ[index]
break
if elem_type == 'i':
if isinstance(circ[index], devices.ISource):
source_elem = circ[index]
break
if not source_elem:
printing.print_general_error("%s was not found." % source[0].part_id)
sys.exit(1)
if isinstance(source_elem, devices.VSource):
initial_value = source_elem.dc_value
else:
initial_value = source_elem.dc_value
# If the initial value is set to None, op_analysis will attempt a smart guess (if guess),
# Then for each iteration, the last result is used as x0, since op_analysis will not
# attempt to guess the op if x0 is not None.
x = x0
sol = results.dc_solution(circ,
start,
stop,
sweepvar=sweep_label,
stype=sweep_type,
outfile=outfile)
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
tick = ticker.ticker(1)
tick.display(verbose > 2)
# sweep setup
# tarocca il generatore di tensione, avvia DC silenziosa, ritarocca etc
index = 0
for sweep_value in dc_iter:
index = index + 1
if isinstance(source_elem, devices.VSource):
source_elem.dc_value = sweep_value
else:
source_elem.dc_value = sweep_value
# silently calculate the op
x = op_analysis(circ, x0=x, guess=guess, verbose=0)
if x is None:
tick.hide(verbose > 2)
if not options.dc_sweep_skip_allowed:
print "Could't solve the circuit for sweep value:", start + index * step
solved = False
break
else:
print "Skipping sweep value:", start + index * step
continue
solved = True
sol.add_op(sweep_value, x)
tick.step(verbose > 2)
tick.hide(verbose > 2)
if solved:
printing.print_info_line(("done", 3), verbose)
# clean up
if isinstance(source_elem, devices.VSource):
source_elem.dc_value = initial_value
else:
source_elem.dc_value = initial_value
return sol if solved else None
def dc_solve(mna,
Ndc,
circ,
Ntran=None,
Gmin=None,
x0=None,
time=None,
MAXIT=None,
locked_nodes=None,
skip_Tt=False,
verbose=3):
"""Tries to perform a DC analysis of the circuit.
The system we want to solve is:
(mna+Gmin)*x + N + T(x) = 0
mna is the reduced mna matrix with the required KVL rows
N is Ndc + Ntran
T(x) will be built.
circ is the circuit instance from which mna, N were built.
x0 is the (optional) initial guess. If not specified, the all-zeros vector will be used
This method is used ever by transient analysis. For transient analysis, time has to be set.
In "real" DC analysis, time may be left to None. See circuit.py for more info about the default
behaviour of time variant sources that also have a dc value.
MAXIT is the maximum number of NR iteration to be supplied to mdn_solver.
locked_nodes: array of tuples of nodes controlling non linear elements of the circuit.
This is generated by circ.get_locked_nodes() and will be generated that way if left to none.
However, if you are doing lots of simulations of the same circuit (a transient analysis), it's
a good idea to generate it only once.
Returns:
(x, error, converged, tot_iterations)
"""
if MAXIT == None:
MAXIT = options.dc_max_nr_iter
if locked_nodes is None:
locked_nodes = circ.get_locked_nodes()
mna_size = mna.shape[0]
nv = len(circ.nodes_dict)
tot_iterations = 0
if Gmin is None:
Gmin = 0
if Ntran is None:
Ntran = 0
# time variable component: Tt this is always the same in each iter. So we
# build it once for all.
Tt = numpy.mat(numpy.zeros((mna_size, 1)))
v_eq = 0
if not skip_Tt:
for elem in circ:
if (isinstance(elem, devices.VSource) or isinstance(
elem, devices.ISource)) and elem.is_timedependent:
if isinstance(elem, devices.VSource):
Tt[nv - 1 + v_eq, 0] = -1 * elem.V(time)
elif isinstance(elem, devices.ISource):
if elem.n1:
Tt[elem.n1 - 1, 0] = Tt[elem.n1 - 1, 0] + elem.I(time)
if elem.n2:
Tt[elem.n2 - 1, 0] = Tt[elem.n2 - 1, 0] - elem.I(time)
if circuit.is_elem_voltage_defined(elem):
v_eq = v_eq + 1
# update N to include the time variable sources
Ndc = Ndc + Tt
# initial guess, if specified, otherwise it's zero
if x0 is not None:
if isinstance(x0, results.op_solution):
x = x0.asmatrix()
else:
x = x0
else:
x = numpy.mat(numpy.zeros((mna_size, 1)))
# has n-1 rows because of discard of ^^^
converged = False
standard_solving, gmin_stepping, source_stepping = get_solve_methods()
standard_solving, gmin_stepping, source_stepping = set_next_solve_method(
standard_solving, gmin_stepping, source_stepping, verbose)
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
while (not converged):
if standard_solving["enabled"]:
mna_to_pass = mna + Gmin
N_to_pass = Ndc + Ntran * (Ntran is not None)
elif gmin_stepping["enabled"]:
# print "gmin index:", str(gmin_stepping["index"])+", gmin:", str(
# 10**(gmin_stepping["factors"][gmin_stepping["index"]]))
printing.print_info_line(
("Setting Gmin to: " +
str(10**gmin_stepping["factors"][gmin_stepping["index"]]), 6),
verbose)
mna_to_pass = build_gmin_matrix(
circ, 10**(gmin_stepping["factors"][gmin_stepping["index"]]),
mna_size, verbose) + mna
N_to_pass = Ndc + Ntran * (Ntran is not None)
elif source_stepping["enabled"]:
printing.print_info_line(("Setting sources to " + str(
source_stepping["factors"][source_stepping["index"]] * 100) +
"% of their actual value", 6), verbose)
mna_to_pass = mna + Gmin
N_to_pass = source_stepping["factors"][
source_stepping["index"]] * Ndc + Ntran * (Ntran is not None)
try:
(x, error, converged, n_iter,
convergence_by_node) = mdn_solver(x,
mna_to_pass,
circ,
T=N_to_pass,
nv=nv,
print_steps=(verbose > 0),
locked_nodes=locked_nodes,
time=time,
MAXIT=MAXIT,
debug=(verbose == 6))
tot_iterations += n_iter
except numpy.linalg.linalg.LinAlgError:
n_iter = 0
converged = False
print "failed."
printing.print_general_error("J Matrix is singular")
except OverflowError:
n_iter = 0
converged = False
print "failed."
printing.print_general_error("Overflow")
if not converged:
if verbose == 6:
for ivalue in range(len(convergence_by_node)):
if not convergence_by_node[ivalue] and ivalue < nv - 1:
print "Convergence problem node %s" % (
circ.int_node_to_ext(ivalue), )
elif not convergence_by_node[ivalue] and ivalue >= nv - 1:
e = circ.find_vde(ivalue)
print "Convergence problem current in %s" % e.part_id
if n_iter == MAXIT - 1:
printing.print_general_error("Error: MAXIT exceeded (" +
str(MAXIT) + ")")
if more_solve_methods_available(standard_solving, gmin_stepping,
source_stepping):
standard_solving, gmin_stepping, source_stepping = set_next_solve_method(
standard_solving, gmin_stepping, source_stepping, verbose)
else:
# print "Giving up."
x = None
error = None
break
else:
printing.print_info_line(("[%d iterations]" % (n_iter, ), 6),
verbose)
if (source_stepping["enabled"] and source_stepping["index"] != 9):
converged = False
source_stepping["index"] = source_stepping["index"] + 1
elif (gmin_stepping["enabled"] and gmin_stepping["index"] != 9):
gmin_stepping["index"] = gmin_stepping["index"] + 1
converged = False
else:
printing.print_info_line((" done.", 3), verbose)
return (x, error, converged, tot_iterations)
def ac_analysis(circ, start, points, stop, sweep_type, x0=None,
mna=None, AC=None, Nac=None, J=None,
outfile="stdout", verbose=3):
"""Performs an AC analysis of the circuit described by circ.
Parameters:
start (float): the start angular frequency for the AC analysis
stop (float): stop angular frequency
points (float): the number of points to be use the discretize the
[start, stop] interval.
sweep_type (string): Either 'LOG' or 'LINEAR', defaults to 'LOG'.
outfile (string): the filename of the output file where the results will be written.
'.ac' is automatically added at the end to prevent different
analyses from overwriting each-other's results.
If unset or set to None, defaults to stdout.
verbose (int): the verbosity level, from 0 (silent) to 6 (debug).
Returns: an AC results object
"""
if outfile == 'stdout':
verbose = 0
# check step/start/stop parameters
nsteps = points - 1
if start == 0:
printing.print_general_error("AC analysis has start frequency = 0")
sys.exit(5)
if start > stop:
printing.print_general_error("AC analysis has start > stop")
sys.exit(1)
if nsteps < 1:
printing.print_general_error("AC analysis has number of steps <= 1")
sys.exit(1)
if sweep_type == options.ac_log_step:
omega_iter = utilities.log_axis_iterator(stop, start, nsteps)
elif sweep_type == options.ac_lin_step:
omega_iter = utilities.lin_axis_iterator(stop, start, nsteps)
else:
printing.print_general_error("Unknown sweep type.")
sys.exit(1)
tmpstr = "Vea =", options.vea, "Ver =", options.ver, "Iea =", options.iea, "Ier =", \
options.ier, "max_ac_nr_iter =", options.ac_max_nr_iter
printing.print_info_line((tmpstr, 5), verbose)
del tmpstr
printing.print_info_line(("Starting AC analysis: ", 1), verbose)
tmpstr = "w: start = %g Hz, stop = %g Hz, %d steps" % (start, stop, nsteps)
printing.print_info_line((tmpstr, 3), verbose)
del tmpstr
# It's a good idea to call AC with prebuilt MNA matrix if the circuit is
# big
if mna is None:
(mna, N) = dc_analysis.generate_mna_and_N(circ, verbose=verbose)
del N
mna = utilities.remove_row_and_col(mna)
if Nac is None:
Nac = generate_Nac(circ)
Nac = utilities.remove_row(Nac, rrow=0)
if AC is None:
AC = generate_AC(circ, [mna.shape[0], mna.shape[0]])
AC = utilities.remove_row_and_col(AC)
if circ.is_nonlinear():
if J is not None:
pass
# we used the supplied linearization matrix
else:
if x0 is None:
printing.print_info_line(
("Starting OP analysis to get a linearization point...", 3), verbose, print_nl=False)
# silent OP
x0 = dc_analysis.op_analysis(circ, verbose=0)
if x0 is None: # still! Then op_analysis has failed!
printing.print_info_line(("failed.", 3), verbose)
printing.print_general_error(
"OP analysis failed, no linearization point available. Quitting.")
sys.exit(3)
else:
printing.print_info_line(("done.", 3), verbose)
printing.print_info_line(
("Linearization point (xop):", 5), verbose)
if verbose > 4:
x0.print_short()
printing.print_info_line(
("Linearizing the circuit...", 5), verbose, print_nl=False)
J = generate_J(xop=x0.asmatrix(), circ=circ, mna=mna,
Nac=Nac, data_filename=outfile, verbose=verbose)
printing.print_info_line((" done.", 5), verbose)
# we have J, continue
else: # not circ.is_nonlinear()
# no J matrix is required.
J = 0
printing.print_info_line(("MNA (reduced):", 5), verbose)
printing.print_info_line((str(mna), 5), verbose)
printing.print_info_line(("AC (reduced):", 5), verbose)
printing.print_info_line((str(AC), 5), verbose)
printing.print_info_line(("J (reduced):", 5), verbose)
printing.print_info_line((str(J), 5), verbose)
printing.print_info_line(("Nac (reduced):", 5), verbose)
printing.print_info_line((str(Nac), 5), verbose)
sol = results.ac_solution(circ, ostart=start, ostop=stop,
opoints=nsteps, stype=sweep_type, op=x0, outfile=outfile)
# setup the initial values to start the iteration:
nv = len(circ.nodes_dict)
j = numpy.complex('j')
Gmin_matrix = dc_analysis.build_gmin_matrix(
circ, options.gmin, mna.shape[0], verbose)
iter_n = 0 # contatore d'iterazione
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
tick = ticker.ticker(increments_for_step=1)
tick.display(verbose > 1)
x = x0
for omega in omega_iter:
(x, error, solved, n_iter) = dc_analysis.dc_solve(
mna=(mna + numpy.multiply(j * omega, AC) + J),
Ndc = Nac,
Ntran = 0,
circ = circuit.Circuit(
title="Dummy circuit for AC", filename=None),
Gmin = Gmin_matrix,
x0 = x,
time = None,
locked_nodes = None,
MAXIT = options.ac_max_nr_iter,
skip_Tt = True,
verbose = 0)
if solved:
tick.step(verbose > 1)
iter_n = iter_n + 1
# hooray!
sol.add_line(omega, x)
else:
break
tick.hide(verbose > 1)
if solved:
printing.print_info_line(("done.", 1), verbose)
ret_value = sol
else:
printing.print_info_line(("failed.", 1), verbose)
ret_value = None
return ret_value
def dc_solve(
mna, Ndc, circ, Ntran=None, Gmin=None, x0=None, time=None, MAXIT=None, locked_nodes=None, skip_Tt=False, verbose=3
):
"""Tries to perform a DC analysis of the circuit.
The system we want to solve is:
(mna+Gmin)*x + N + T(x) = 0
mna is the reduced mna matrix with the required KVL rows
N is Ndc + Ntran
T(x) will be built.
circ is the circuit instance from which mna, N were built.
x0 is the (optional) initial guess. If not specified, the all-zeros vector will be used
This method is used ever by transient analysis. For transient analysis, time has to be set.
In "real" DC analysis, time may be left to None. See circuit.py for more info about the default
behaviour of time variant sources that also have a dc value.
MAXIT is the maximum number of NR iteration to be supplied to mdn_solver.
locked_nodes: array of tuples of nodes controlling non linear elements of the circuit.
This is generated by circ.get_locked_nodes() and will be generated that way if left to none.
However, if you are doing lots of simulations of the same circuit (a transient analysis), it's
a good idea to generate it only once.
Returns:
(x, error, converged, tot_iterations)
"""
if MAXIT == None:
MAXIT = options.dc_max_nr_iter
if locked_nodes is None:
locked_nodes = circ.get_locked_nodes()
mna_size = mna.shape[0]
nv = len(circ.nodes_dict)
tot_iterations = 0
if Gmin is None:
Gmin = 0
if Ntran is None:
Ntran = 0
# time variable component: Tt this is always the same in each iter. So we build it once for all.
Tt = numpy.mat(numpy.zeros((mna_size, 1)))
v_eq = 0
if not skip_Tt:
for elem in circ.elements:
if (isinstance(elem, devices.vsource) or isinstance(elem, devices.isource)) and elem.is_timedependent:
if isinstance(elem, devices.vsource):
Tt[nv - 1 + v_eq, 0] = -1 * elem.V(time)
elif isinstance(elem, devices.isource):
if elem.n1:
Tt[elem.n1 - 1, 0] = Tt[elem.n1 - 1, 0] + elem.I(time)
if elem.n2:
Tt[elem.n2 - 1, 0] = Tt[elem.n2 - 1, 0] - elem.I(time)
if circuit.is_elem_voltage_defined(elem):
v_eq = v_eq + 1
# update N to include the time variable sources
Ndc = Ndc + Tt
# initial guess, if specified, otherwise it's zero
if x0 is not None:
if isinstance(x0, results.op_solution):
x = x0.asmatrix()
else:
x = x0
else:
x = numpy.mat(numpy.zeros((mna_size, 1))) # has n-1 rows because of discard of ^^^
converged = False
standard_solving, gmin_stepping, source_stepping = get_solve_methods()
standard_solving, gmin_stepping, source_stepping = set_next_solve_method(
standard_solving, gmin_stepping, source_stepping, verbose
)
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
while not converged:
if standard_solving["enabled"]:
mna_to_pass = mna + Gmin
N_to_pass = Ndc + Ntran * (Ntran is not None)
elif gmin_stepping["enabled"]:
# print "gmin index:", str(gmin_stepping["index"])+", gmin:", str( 10**(gmin_stepping["factors"][gmin_stepping["index"]]))
printing.print_info_line(
("Setting Gmin to: " + str(10 ** gmin_stepping["factors"][gmin_stepping["index"]]), 6), verbose
)
mna_to_pass = (
build_gmin_matrix(circ, 10 ** (gmin_stepping["factors"][gmin_stepping["index"]]), mna_size, verbose)
+ mna
)
N_to_pass = Ndc + Ntran * (Ntran is not None)
elif source_stepping["enabled"]:
printing.print_info_line(
(
"Setting sources to "
+ str(source_stepping["factors"][source_stepping["index"]] * 100)
+ "% of their actual value",
6,
),
verbose,
)
mna_to_pass = mna + Gmin
N_to_pass = source_stepping["factors"][source_stepping["index"]] * Ndc + Ntran * (Ntran is not None)
try:
(x, error, converged, n_iter, convergence_by_node) = mdn_solver(
x,
mna_to_pass,
circ,
T=N_to_pass,
nv=nv,
print_steps=(verbose > 0),
locked_nodes=locked_nodes,
time=time,
MAXIT=MAXIT,
debug=(verbose == 6),
)
tot_iterations += n_iter
except numpy.linalg.linalg.LinAlgError:
n_iter = 0
converged = False
print "failed."
printing.print_general_error("J Matrix is singular")
except OverflowError:
n_iter = 0
converged = False
print "failed."
printing.print_general_error("Overflow")
if not converged:
if verbose == 6:
for ivalue in range(len(convergence_by_node)):
if not convergence_by_node[ivalue] and ivalue < nv - 1:
print "Convergence problem node %s" % (circ.int_node_to_ext(ivalue),)
elif not convergence_by_node[ivalue] and ivalue >= nv - 1:
e = circ.find_vde(ivalue)
print "Convergence problem current in %s%s" % (e.letter_id, e.descr)
if n_iter == MAXIT - 1:
printing.print_general_error("Error: MAXIT exceeded (" + str(MAXIT) + ")")
if more_solve_methods_available(standard_solving, gmin_stepping, source_stepping):
standard_solving, gmin_stepping, source_stepping = set_next_solve_method(
standard_solving, gmin_stepping, source_stepping, verbose
)
else:
# print "Giving up."
x = None
error = None
break
else:
printing.print_info_line(("[%d iterations]" % (n_iter,), 6), verbose)
if source_stepping["enabled"] and source_stepping["index"] != 9:
converged = False
source_stepping["index"] = source_stepping["index"] + 1
elif gmin_stepping["enabled"] and gmin_stepping["index"] != 9:
gmin_stepping["index"] = gmin_stepping["index"] + 1
converged = False
else:
printing.print_info_line((" done.", 3), verbose)
return (x, error, converged, tot_iterations)
def dc_analysis(
circ,
start,
stop,
step,
type_descr,
xguess=None,
data_filename="stdout",
print_int_nodes=True,
guess=True,
stype="LINEAR",
verbose=2,
):
"""Performs a sweep of the value of V or I of a independent source from start
value to stop value using the provided step.
For every circuit generated, computes the op and prints it out.
This function relays on dc_analysis.op_analysis to actually solve each circuit.
circ: the circuit instance to be simulated
start: start value of the sweep source
stop: stop value of the sweep source
step: step value of the sweep source
elem_type: string, may be 'vsource' or 'isource'
elem_descr: the description of the element, used to recognize it in circ (i.e v<desc>)
data_filename: string, filename of the output file. If set to stdout, prints to screen
print_int_nodes: do it
guess: op_analysis will guess to start the first NR iteration for the first point, the previsious dc is used from then on
verbose: verbosity level
Returns:
A results.dc_solution instance, if a solution was found for at least one sweep value.
None, if an error occurred (eg invalid start/stop/step values) or there was no solution
for any sweep value.
"""
if data_filename == "stdout":
verbose = 0
printing.print_info_line(("Starting DC analysis:", 2), verbose)
(elem_type, elem_descr) = type_descr
sweep_label = elem_type[0].upper() + elem_descr
# check step/start/stop parameters
if step == 0:
printing.print_general_error("Can't sweep with step=0 !")
sys.exit(1)
if start > stop:
printing.print_general_error("DC analysis has start > stop")
sys.exit(1)
if (stop - start) / step < 1:
printing.print_general_error("DC analysis has number of steps < 1")
sys.exit(1)
if stype == options.dc_log_step:
dc_iter = utilities.log_axis_iterator(stop, start, nsteps=int(stop - start) / step)
elif stype == options.dc_lin_step:
dc_iter = utilities.lin_axis_iterator(stop, start, nsteps=int(stop - start) / step)
else:
printing.print_general_error("Unknown sweep type: %s" % (stype,))
sys.exit(1)
if elem_type != "vsource" and elem_type != "isource":
printing.print_general_error(
"Sweeping is possible only with voltage and current sources. (" + str(elem_type) + ")"
)
sys.exit(1)
source_elem = None
for index in xrange(len(circ.elements)):
if circ.elements[index].descr == elem_descr:
if elem_type == "vsource":
if isinstance(circ.elements[index], devices.vsource):
source_elem = circ.elements[index]
break
if elem_type == "isource":
if isinstance(circ.elements[index], devices.isource):
source_elem = circ.elements[index]
break
if not source_elem:
printing.print_general_error(elem_type + " element with descr. " + elem_descr + " was not found.")
sys.exit(1)
if isinstance(source_elem, devices.vsource):
initial_value = source_elem.vdc
else:
initial_value = source_elem.idc
# The initial value is set to None and this IS CORRECT.
# op_analysis will attempt to do a smart guess, if called with x0 = None and guess=True
# For each iteration over the source voltage (current) value, the last result is used as x0.
# op_analysis will not attempt to guess the op if x0 is not None
x = None
sol = results.dc_solution(circ, start, stop, sweepvar=sweep_label, stype=stype, outfile=data_filename)
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
tick = ticker.ticker(1)
tick.display(verbose > 2)
# sweep setup
# tarocca il generatore di tensione, avvia DC silenziosa, ritarocca etc
index = 0
for sweep_value in dc_iter:
index = index + 1
if isinstance(source_elem, devices.vsource):
source_elem.vdc = sweep_value
else:
source_elem.idc = sweep_value
# silently calculate the op
op = op_analysis(circ, x0=x, guess=guess, verbose=0)
if op is None:
tick.hide(verbose > 2)
if not options.dc_sweep_skip_allowed:
print "Could't solve the circuit for sweep value:", start + index * step
solved = False
break
else:
print "Skipping sweep value:", start + index * step
continue
solved = True
sol.add_op(sweep_value, op)
if guess:
guess = False
tick.step(verbose > 2)
tick.hide(verbose > 2)
if solved:
printing.print_info_line(("done", 3), verbose)
# clean up
if isinstance(source_elem, devices.vsource):
source_elem.vdc = initial_value
else:
source_elem.idc = initial_value
return sol if solved else None
def solve(circ, tf_source=None, subs=None, opts=None, verbose=3):
"""Attempt a symbolic solution of the circuit.
circ: the circuit instance to be simulated.
tf_source: the name (string) of the source to be used as input for the transfer
function. If None, no transfer function is evaluated.
subs: a dictionary of sympy Symbols to be substituted. It makes solving the circuit
easier. Eg. {R1:R2} - replace R1 with R2. It can be generated with
parse_substitutions()
opts: dict of 'option':boolean to be taken into account in simulation.
currently 'r0s' and 'ac' are the only options considered.
verbose: verbosity level 0 (silent) to 6 (painful).
Returns: a dictionary with the solutions.
"""
if opts is None:
# load the defaults
opts = {'r0s':True, 'ac':False}
if not 'r0s' in opts.keys():
opts.update({'r0s':True})
if not 'ac' in opts.keys():
opts.update({'ac':False})
if subs is None:
subs = {} # no subs by default
if not opts['ac']:
printing.print_info_line(("Starting symbolic DC...", 1), verbose)
else:
printing.print_info_line(("Starting symbolic AC...", 1), verbose)
printing.print_info_line(("Building symbolic MNA, N and x...", 2), verbose, print_nl=False)
mna, N, subs_g = generate_mna_and_N(circ, opts, opts['ac'])
x = get_variables(circ)
mna = mna[1:, 1:]
N = N[1:, :]
printing.print_info_line((" done.", 2), verbose)
printing.print_info_line(("Performing variable substitutions...", 5), verbose)
mna, N = apply_substitutions(mna, N, subs)
printing.print_info_line(("MNA matrix (reduced):", 5), verbose)
if verbose > 5: print sympy.sstr(mna)
printing.print_info_line(("N matrix (reduced):", 5), verbose)
if verbose > 5: print sympy.sstr(N)
printing.print_info_line(("Building equations...", 2), verbose)
eq = []
for i in to_real_list(mna * x + N):
eq.append(sympy.Eq(i, 0))
x = to_real_list(x)
if verbose > 4:
printing.print_symbolic_equations(eq)
print "To be solved for:"
print x
#print "Matrix is singular: ", (mna.det() == 0)
#print -1.0*mna.inv()*N #too heavy
#print sympy.solve_linear_system(mna.row_join(-N), x)
printing.print_info_line(("Performing auxiliary simplification...", 2), verbose)
eq, x, sol_h = help_the_solver(eq, x)
if len(eq):
if verbose > 3:
print "Symplified sytem:"
printing.print_symbolic_equations(eq)
print "To be solved for:"
print x
printing.print_info_line(("Solving...", 2), verbose)
if options.symb_internal_solver:
sol = local_solve(eq, x)
else:
sol = sympy.solve(eq, x, manual=options.symb_sympy_manual_solver, simplify=True)
if sol is not None:
sol.update(sol_h)
else:
sol = sol_h
else:
printing.print_info_line(("Auxiliary simplification solved the problem.", 3), verbose)
sol = sol_h
for ks in sol.keys():
sol.update({ks:sol[ks].subs(subs_g)})
#sol = sol_to_dict(sol, x)
if sol == {}:
printing.print_warning("No solutions. Check the netlist.")
else:
printing.print_info_line(("Success!", 2), verbose)
if verbose > 1:
print "Results:"
printing.print_symbolic_results(sol)
if tf_source is not None:
src = sympy.Symbol(tf_source.upper(), real=True)
printing.print_info_line(("Calculating small-signal symbolic transfer functions (%s))..."%(str(src),), 2), verbose, print_nl=False)
tfs = calculate_gains(sol, src)
printing.print_info_line(("done.", 2), verbose)
printing.print_info_line(("Small-signal symbolic transfer functions:", 1), verbose)
printing.print_symbolic_transfer_functions(tfs)
else:
tfs = None
return sol, tfs
def transient_analysis(circ, tstart, tstep, tstop, method=TRAP, x0=None, mna=None, N=None, \
D=None, data_filename="stdout", use_step_control=True, return_req_dict=None, verbose=3):
"""Performs a transient analysis of the circuit described by circ.
Important parameters:
- tstep is the maximum step to be allowed during simulation.
- print_step_and_lte is a boolean value. Is set to true, the step and the LTE of the first
element of x will be printed out to step_and_lte.graph in the current directory.
"""
if data_filename == "stdout":
verbose = 0
_debug = False
if _debug:
print_step_and_lte = True
else:
print_step_and_lte = False
HMAX = tstep
#check parameters
if tstart > tstop:
printing.print_general_error("tstart > tstop")
sys.exit(1)
if tstep < 0:
printing.print_general_error("tstep < 0")
sys.exit(1)
if verbose > 4:
tmpstr = "Vea = %g Ver = %g Iea = %g Ier = %g max_time_iter = %g HMIN = %g" % \
(options.vea, options.ver, options.iea, options.ier, options.transient_max_time_iter, options.hmin)
printing.print_info_line((tmpstr, 5), verbose)
locked_nodes = circ.get_locked_nodes()
if print_step_and_lte:
flte = open("step_and_lte.graph", "w")
flte.write("#T\tStep\tLTE\n")
printing.print_info_line(("Starting transient analysis: ", 3), verbose)
printing.print_info_line(("Selected method: %s" % (method,), 3), verbose)
#It's a good idea to call transient with prebuilt MNA and N matrix
#the analysis will be slightly faster (long netlists).
if mna is None or N is None:
(mna, N) = dc_analysis.generate_mna_and_N(circ)
mna = utilities.remove_row_and_col(mna)
N = utilities.remove_row(N, rrow=0)
elif not mna.shape[0] == N.shape[0]:
printing.print_general_error("mna matrix and N vector have different number of columns.")
sys.exit(0)
if D is None:
# if you do more than one tran analysis, output streams should be changed...
# this needs to be fixed
D = generate_D(circ, [mna.shape[0], mna.shape[0]])
D = utilities.remove_row_and_col(D)
# setup x0
if x0 is None:
printing.print_info_line(("Generating x(t=%g) = 0" % (tstart,), 5), verbose)
x0 = numpy.matrix(numpy.zeros((mna.shape[0], 1)))
opsol = results.op_solution(x=x0, error=x0, circ=circ, outfile=None)
else:
if isinstance(x0, results.op_solution):
opsol = x0
x0 = x0.asmatrix()
else:
opsol = results.op_solution(x=x0, error=numpy.matrix(numpy.zeros((mna.shape[0], 1))), circ=circ, outfile=None)
printing.print_info_line(("Using the supplied op as x(t=%g)." % (tstart,), 5), verbose)
if verbose > 4:
print "x0:"
opsol.print_short()
# setup the df method
printing.print_info_line(("Selecting the appropriate DF ("+method+")... ", 5), verbose, print_nl=False)
if method == IMPLICIT_EULER:
import implicit_euler as df
elif method == TRAP:
import trap as df
elif method == GEAR1:
import gear as df
df.order = 1
elif method == GEAR2:
import gear as df
df.order = 2
elif method == GEAR3:
import gear as df
df.order = 3
elif method == GEAR4:
import gear as df
df.order = 4
elif method == GEAR5:
import gear as df
df.order = 5
elif method == GEAR6:
import gear as df
df.order = 6
else:
df = import_custom_df_module(method, print_out=(data_filename != "stdout"))
# df is none if module is not found
if df is None:
sys.exit(23)
if not df.has_ff() and use_step_control:
printing.print_warning("The chosen DF does not support step control. Turning off the feature.")
use_step_control = False
#use_aposteriori_step_control = False
printing.print_info_line(("done.", 5), verbose)
# setup the data buffer
# if you use the step control, the buffer has to be one point longer.
# That's because the excess point is used by a FF in the df module to predict the next value.
printing.print_info_line(("Setting up the buffer... ", 5), verbose, print_nl=False)
((max_x, max_dx), (pmax_x, pmax_dx)) = df.get_required_values()
if max_x is None and max_dx is None:
printing.print_general_error("df doesn't need any value?")
sys.exit(1)
if use_step_control:
thebuffer = dfbuffer(length=max(max_x, max_dx, pmax_x, pmax_dx) + 1, width=3)
else:
thebuffer = dfbuffer(length=max(max_x, max_dx) + 1, width=3)
thebuffer.add((tstart, x0, None)) #setup the first values
printing.print_info_line(("done.", 5), verbose) #FIXME
#setup the output buffer
if return_req_dict:
output_buffer = dfbuffer(length=return_req_dict["points"], width=1)
output_buffer.add((x0,))
else:
output_buffer = None
# import implicit_euler to be used in the first iterations
# this is because we don't have any dx when we start, nor any past point value
if (max_x is not None and max_x > 0) or max_dx is not None:
import implicit_euler
printing.print_info_line(("MNA (reduced):", 5), verbose)
printing.print_info_line((str(mna), 5), verbose)
printing.print_info_line(("D (reduced):", 5), verbose)
printing.print_info_line((str(D), 5), verbose)
# setup the initial values to start the iteration:
x = None
time = tstart
nv = len(circ.nodes_dict)
Gmin_matrix = dc_analysis.build_gmin_matrix(circ, options.gmin, mna.shape[0], verbose)
# lo step viene generato automaticamente, ma non superare mai quello fornito.
if use_step_control:
#tstep = min((tstop-tstart)/9999.0, HMAX, 100.0 * options.hmin)
tstep = min((tstop-tstart)/9999.0, HMAX)
printing.print_info_line(("Initial step: %g"% (tstep,), 5), verbose)
if max_dx is None:
max_dx_plus_1 = None
else:
max_dx_plus_1 = max_dx +1
if pmax_dx is None:
pmax_dx_plus_1 = None
else:
pmax_dx_plus_1 = pmax_dx +1
# setup error vectors
aerror = numpy.mat(numpy.zeros((x0.shape[0], 1)))
aerror[:nv-1, 0] = options.vea
aerror[nv-1:, 0] = options.vea
rerror = numpy.mat(numpy.zeros((x0.shape[0], 1)))
rerror[:nv-1, 0] = options.ver
rerror[nv-1:, 0] = options.ier
iter_n = 0 # contatore d'iterazione
lte = None
sol = results.tran_solution(circ, tstart, tstop, op=x0, method=method, outfile=data_filename)
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
tick = ticker.ticker(increments_for_step=1)
tick.display(verbose > 1)
while time < tstop:
if iter_n < max(max_x, max_dx_plus_1):
x_coeff, const, x_lte_coeff, prediction, pred_lte_coeff = \
implicit_euler.get_df((thebuffer.get_df_vector()[0],), tstep, \
predict=(use_step_control and (iter_n >= max(pmax_x, pmax_dx_plus_1))))
else:
[x_coeff, const, x_lte_coeff, prediction, pred_lte_coeff] = \
df.get_df(thebuffer.get_df_vector(), tstep, predict=use_step_control)
if options.transient_prediction_as_x0 and use_step_control and prediction is not None:
x0 = prediction
elif x is not None:
x0 = x
(x1, error, solved, n_iter) = dc_analysis.dc_solve(mna=(mna + numpy.multiply(x_coeff, D)) , Ndc=N, Ntran=D*const, circ=circ, Gmin=Gmin_matrix, x0=x0, time=(time + tstep), locked_nodes=locked_nodes, MAXIT=options.transient_max_nr_iter, verbose=0)
if solved:
old_step = tstep #we will modify it, if we're using step control otherwise it's the same
# step control (yeah)
if use_step_control:
if x_lte_coeff is not None and pred_lte_coeff is not None and prediction is not None:
# this is the Local Truncation Error :)
lte = abs((x_lte_coeff / (pred_lte_coeff - x_lte_coeff)) * (prediction - x1))
# it should NEVER happen that new_step > 2*tstep, for stability
new_step_coeff = 2
for index in xrange(x.shape[0]):
if lte[index, 0] != 0:
new_value = ((aerror[index, 0] + rerror[index, 0]*abs(x[index, 0])) / lte[index, 0]) \
** (1.0 / (df.order+1))
if new_value < new_step_coeff:
new_step_coeff = new_value
#print new_value
new_step = tstep * new_step_coeff
if options.transient_use_aposteriori_step_control and new_step < options.transient_aposteriori_step_threshold * tstep:
#don't recalculate a x for a small change
tstep = check_step(new_step, time, tstop, HMAX)
#print "Apost. (reducing) step = "+str(tstep)
continue
tstep = check_step(new_step, time, tstop, HMAX) # used in the next iteration
#print "Apriori tstep = "+str(tstep)
else:
#print "LTE not calculated."
lte = None
if print_step_and_lte and lte is not None:
#if you wish to look at the step. We print just a lte
flte.write(str(time)+"\t"+str(old_step)+"\t"+str(lte.max())+"\n")
# if we get here, either aposteriori_step_control is
# disabled, or it's enabled and the error is small
# enough. Anyway, the result is GOOD, STORE IT.
time = time + old_step
x = x1
iter_n = iter_n + 1
sol.add_line(time, x)
dxdt = numpy.multiply(x_coeff, x) + const
thebuffer.add((time, x, dxdt))
if output_buffer is not None:
output_buffer.add((x, ))
tick.step(verbose > 1)
else:
# If we get here, Newton failed to converge. We need to reduce the step...
if use_step_control:
tstep = tstep/5.0
tstep = check_step(tstep, time, tstop, HMAX)
printing.print_info_line(("At %g s reducing step: %g s (convergence failed)" % (time, tstep), 5), verbose)
else: #we can't reduce the step
printing.print_general_error("Can't converge with step "+str(tstep)+".")
printing.print_general_error("Try setting --t-max-nr to a higher value or set step to a lower one.")
solved = False
break
if options.transient_max_time_iter and iter_n == options.transient_max_time_iter:
printing.print_general_error("MAX_TIME_ITER exceeded ("+str(options.transient_max_time_iter)+"), iteration halted.")
solved = False
break
if print_step_and_lte:
flte.close()
tick.hide(verbose > 1)
if solved:
printing.print_info_line(("done.", 3), verbose)
printing.print_info_line(("Average time step: %g" % ((tstop - tstart)/iter_n,), 3), verbose)
if output_buffer:
ret_value = output_buffer.get_as_matrix()
else:
ret_value = sol
else:
print "failed."
ret_value = None
return ret_value
def shooting(circ, period, step=None, x0=None, points=None, autonomous=False,
mna=None, Tf=None, D=None, outfile='stdout', vector_norm=lambda v: max(abs(v)), verbose=3):
"""Performs a periodic steady state analysis based on the algorithm described in
Brambilla, A.; D'Amore, D., "Method for steady-state simulation of
strongly nonlinear circuits in the time domain," Circuits and
Systems I: Fundamental Theory and Applications, IEEE Transactions on,
vol.48, no.7, pp.885-889, Jul 2001
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=933329&isnumber=20194
The results have been computed again by me, the formulas are not exactly the
same, but the idea behind the shooting algorithm is.
This method allows us to have a period with many points without having to
invert a huge matrix (and being limited to the maximum matrix size).
A tran is performed to initialize the solver.
We compute the change in the last point, calculating several matrices in
the process.
From that, with the same matrices we calculate the changes in all points,
starting from 0 (which is the same as the last one), then 1, ...
Key points:
- Only not autonomous circuits are supported.
- The time step is constant
- Implicit euler is used as DF
Parameters:
circ is the circuit description class
period is the period of the solution
mna, D, Tf are not compulsory they will be computed if they're set to None
step is the time step between consecutive points
points is the number of points to be used
step and points are mutually exclusive options:
- if step is specified, the number of points will be automatically determined
- if points is set, the step will be automatically determined
- if none of them is set, options.shooting_default_points will be used as points
autonomous has to be False, autonomous circuits are not supported
outfile is the output filename. Defaults to stdout.
verbose is set to zero (print errors only) if datafilename == 'stdout'.
Returns: nothing
"""
if outfile == "stdout":
verbose = 0
printing.print_info_line(
("Starting periodic steady state analysis:", 3), verbose)
printing.print_info_line(("Method: shooting", 3), verbose)
if isinstance(x0, results.op_solution):
x0 = x0.asmatrix()
if mna is None or Tf is None:
(mna, Tf) = dc_analysis.generate_mna_and_N(circ, verbose=verbose)
mna = utilities.remove_row_and_col(mna)
Tf = utilities.remove_row(Tf, rrow=0)
elif not mna.shape[0] == Tf.shape[0]:
printing.print_general_error(
"mna matrix and N vector have different number of rows.")
sys.exit(0)
if D is None:
D = transient.generate_D(circ, [mna.shape[0], mna.shape[0]])
D = utilities.remove_row_and_col(D)
elif not mna.shape == D.shape:
printing.print_general_error(
"mna matrix and D matrix have different sizes.")
sys.exit(0)
(points, step) = check_step_and_points(step, points, period)
print "points", points
print "step", step
n_of_var = mna.shape[0]
locked_nodes = circ.get_locked_nodes()
printing.print_info_line(
("Starting transient analysis for algorithm init: tstop=%g, tstep=%g... " % (10 * points * step, step), 3), verbose, print_nl=False)
xtran = transient.transient_analysis(
circ=circ, tstart=0, tstep=step, tstop=10 * points * step, method="TRAP", x0=None, mna=mna, N=Tf,
D=D, use_step_control=False, outfile=outfile + ".tran", return_req_dict={"points": points}, verbose=0)
if xtran is None:
print "failed."
return None
printing.print_info_line(("done.", 3), verbose)
x = []
for index in range(points):
x.append(xtran[index * n_of_var:(index + 1) * n_of_var, 0])
tick = ticker.ticker(increments_for_step=1)
MAass_static, MBass = build_static_MAass_and_MBass(mna, D, step)
# This contains
# the time invariant part, Tf
# time variable component: Tt this is always the same, since the time interval is the same
# this holds all time-dependent sources (both V/I).
Tass_static_vector = build_Tass_static_vector(
circ, Tf, points, step, tick, n_of_var, verbose)
converged = False
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
iteration = 0 # newton iteration counter
conv_counter = 0
while True:
dx = []
Tass_variable_vector = []
MAass_variable_vector = []
for index in range(points):
if index == 0:
xn_minus_1 = x[points - 1]
else:
xn_minus_1 = x[index - 1]
MAass_variable, Tass_variable = get_variable_MAass_and_Tass(
circ, x[index], xn_minus_1, mna, D, step, n_of_var)
MAass_variable_vector.append(MAass_variable + MAass_static)
Tass_variable_vector.append(
Tass_variable + Tass_static_vector[index])
dxN = compute_dxN(circ, MAass_variable_vector, MBass,
Tass_variable_vector, n_of_var, points, verbose=verbose)
td = dc_analysis.get_td(dxN, locked_nodes, n=-1)
x[points - 1] = td * dxN + x[points - 1]
for index in range(points - 1):
if index == 0:
dxi_minus_1 = dxN
else:
dxi_minus_1 = dx[index - 1]
dx.append(
compute_dx(MAass_variable_vector[index], MBass, Tass_variable_vector[index], dxi_minus_1))
td = dc_analysis.get_td(dx[index], locked_nodes, n=-1)
x[index] = td * dx[index] + x[index]
dx.append(dxN)
if (vector_norm_wrapper(dx, vector_norm) < min(options.ver, options.ier) * vector_norm_wrapper(x, vector_norm) + min(options.vea, options.iea)): # \
# and (dc_analysis.vector_norm(residuo) <
# options.er*dc_analysis.vector_norm(x) + options.ea):
if conv_counter == 3:
converged = True
break
else:
conv_counter = conv_counter + 1
elif vector_norm(dx[points - 1]) is numpy.nan: # needs work fixme
raise OverflowError
# break
else:
conv_counter = 0
tick.step(verbose > 2)
if options.shooting_max_nr_iter and iteration == options.shooting_max_nr_iter:
printing.print_general_error(
"Hitted SHOOTING_MAX_NR_ITER (" + str(options.shooting_max_nr_iter) + "), iteration halted.")
converged = False
break
else:
iteration = iteration + 1
tick.hide(verbose > 2)
if converged:
printing.print_info_line(("done.", 3), verbose)
t = numpy.mat(numpy.arange(points) * step)
t = t.reshape((1, points))
xmat = x[0]
for index in xrange(1, points):
xmat = numpy.concatenate((xmat, x[index]), axis=1)
sol = results.pss_solution(
circ=circ, method="shooting", period=period, outfile=outfile, t_array=t, x_array=xmat)
# print_results(circ, x, fdata, points, step)
else:
print "failed."
sol = None
return sol
def main(filename, outfile="stdout", tran_method=transient.TRAP.lower(), no_step_control=False, dc_guess='guess', print_circuit=False, remote=True, verbose=3):
"""This method allows calling ahkab from a Python script.
"""
printing.print_info_line(("This is ahkab %s running with:" %(__version__),6), verbose)
printing.print_info_line((" Python %s" % (sys.version.split('\n')[0],),6), verbose)
printing.print_info_line((" Numpy %s" % (numpy.__version__),6), verbose)
printing.print_info_line((" Sympy %s" % (sympy.__version__),6), verbose)
printing.print_info_line((" Matplotlib %s" % (matplotlib.__version__),6), verbose)
utilities._set_execution_lock()
read_netlist_from_stdin = (filename is None or filename == "-")
(circ, directives, postproc_direct) = netlist_parser.parse_circuit(filename, read_netlist_from_stdin)
check, reason = dc_analysis.check_circuit(circ)
if not check:
printing.print_general_error(reason)
printing.print_circuit(circ)
sys.exit(3)
if verbose > 3 or print_circuit:
print "Parsed circuit:"
printing.print_circuit(circ)
elif verbose > 1:
print circ.title.upper()
an_list = netlist_parser.parse_analysis(circ, directives)
postproc_list = netlist_parser.parse_postproc(circ, an_list, postproc_direct)
if len(an_list) > 0:
printing.print_info_line(("Requested an.:", 4), verbose)
if verbose >= 4:
map(printing.print_analysis, an_list)
else:
if verbose:
printing.print_warning("No analysis requested.")
if len(an_list) > 0:
results = process_analysis(an_list, circ, outfile, verbose, guess=dc_guess.lower()=="guess", \
cli_tran_method=tran_method, disable_step_control=no_step_control)
else:
printing.print_warning("Nothing to do. Quitting.")
if len(an_list) > 0 and len(postproc_list) > 0 and len(results):
process_postproc(postproc_list, circ.title, results, outfile, remote)
utilities._unset_execution_lock()
return results
def op_analysis(circ, x0=None, guess=True, data_filename=None, verbose=3):
"""Runs an Operating Point (OP) analysis
circ: the circuit instance on which the simulation is run
x0: is the initial guess to be used to start the NR mdn_solver
guess: if set to True and x0 is None, it will generate a 'smart' guess
verbose: verbosity level from 0 (silent) to 6 (debug).
Returns a Operation Point result, if successful, None otherwise.
"""
# use_gmin = True
# solved=False
# x0 = numpy.mat(numpy.zeros((1,2)))
(mna, N) = generate_mna_and_N(circ)
printing.print_info_line(("MNA matrix and constant term (complete):", 4), verbose)
printing.print_info_line((str(mna), 4), verbose)
printing.print_info_line((str(N), 4), verbose)
# lets trash the unneeded col & row
printing.print_info_line(("Removing unneeded row and column...", 4), verbose)
mna = utilities.remove_row_and_col(mna)
N = utilities.remove_row(N, rrow=0)
printing.print_info_line(("Starting op analysis:", 2), verbose)
if x0 is None and guess:
x0 = dc_guess.get_dc_guess(circ, verbose=verbose)
# if x0 is not None, use that
printing.print_info_line(("Solving with Gmin:", 4), verbose)
Gmin_matrix = build_gmin_matrix(circ, options.gmin, mna.shape[0], verbose - 2)
(x1, error1, solved1, n_iter1) = dc_solve(mna, N, circ, Gmin=Gmin_matrix, x0=x0, verbose=verbose)
# We'll check the results now. Recalculate them without Gmin (using previsious solution as initial guess)
# and check that differences on nodes and current do not exceed the tolerances.
if solved1:
op1 = results.op_solution(x1, error1, circ, outfile=data_filename, iterations=n_iter1)
printing.print_info_line(("Solving without Gmin:", 4), verbose)
(x2, error2, solved2, n_iter2) = dc_solve(mna, N, circ, Gmin=None, x0=x1, verbose=verbose)
if not solved2:
printing.print_general_error("Can't solve without Gmin.")
if verbose:
print "Displaying latest valid results."
op1.write_to_file(filename="stdout")
opsolution = op1
else:
op2 = results.op_solution(x2, error2, circ, outfile=data_filename, iterations=n_iter1 + n_iter2)
op2.gmin = 0
badvars = results.op_solution.gmin_check(op2, op1)
printing.print_result_check(badvars, verbose=verbose)
check_ok = not (len(badvars) > 0)
if not check_ok and verbose:
print "Solution with Gmin:"
op1.write_to_file(filename="stdout")
print "Solution without Gmin:"
if verbose:
op2.write_to_file(filename="stdout")
opsolution = op2
if data_filename != "stdout" and data_filename is not None:
opsolution.write_to_file()
else:
printing.print_general_error("Couldn't solve the circuit. Giving up.")
opsolution = None
return opsolution
def main(filename,
outfile="stdout",
tran_method=transient.TRAP.lower(),
no_step_control=False,
dc_guess='guess',
print_circuit=False,
remote=True,
verbose=3):
"""This method allows calling ahkab from a Python script.
"""
printing.print_info_line(
("This is ahkab %s running with:" % (__version__), 6), verbose)
printing.print_info_line(
(" Python %s" % (sys.version.split('\n')[0], ), 6), verbose)
printing.print_info_line((" Numpy %s" % (numpy.__version__), 6), verbose)
printing.print_info_line((" Sympy %s" % (sympy.__version__), 6), verbose)
printing.print_info_line((" Matplotlib %s" % (matplotlib.__version__), 6),
verbose)
utilities._set_execution_lock()
read_netlist_from_stdin = (filename is None or filename == "-")
(circ, directives,
postproc_direct) = netlist_parser.parse_circuit(filename,
read_netlist_from_stdin)
check, reason = dc_analysis.check_circuit(circ)
if not check:
printing.print_general_error(reason)
printing.print_circuit(circ)
sys.exit(3)
if verbose > 3 or print_circuit:
print "Parsed circuit:"
printing.print_circuit(circ)
elif verbose > 1:
print circ.title.upper()
an_list = netlist_parser.parse_analysis(circ, directives)
postproc_list = netlist_parser.parse_postproc(circ, an_list,
postproc_direct)
if len(an_list) > 0:
printing.print_info_line(("Requested an.:", 4), verbose)
if verbose >= 4:
map(printing.print_analysis, an_list)
else:
if verbose:
printing.print_warning("No analysis requested.")
if len(an_list) > 0:
results = process_analysis(an_list, circ, outfile, verbose, guess=dc_guess.lower()=="guess", \
cli_tran_method=tran_method, disable_step_control=no_step_control)
else:
printing.print_warning("Nothing to do. Quitting.")
if len(an_list) > 0 and len(postproc_list) > 0 and len(results):
process_postproc(postproc_list, circ.title, results, outfile, remote)
utilities._unset_execution_lock()
return results
def solve(circ, tf_source=None, subs=None, opts=None, verbose=3):
"""Attempt a symbolic solution of the circuit.
circ: the circuit instance to be simulated.
tf_source: the name (string) of the source to be used as input for the transfer
function. If None, no transfer function is evaluated.
subs: a dictionary of sympy Symbols to be substituted. It makes solving the circuit
easier. Eg. {R1:R2} - replace R1 with R2. It can be generated with
parse_substitutions()
opts: dict of 'option':boolean to be taken into account in simulation.
currently 'r0s' and 'ac' are the only options considered.
verbose: verbosity level 0 (silent) to 6 (painful).
Returns: a dictionary with the solutions.
"""
if opts is None:
# load the defaults
opts = {'r0s':True, 'ac':False}
if not 'r0s' in opts.keys():
opts.update({'r0s':True})
if not 'ac' in opts.keys():
opts.update({'ac':False})
if subs is None:
subs = {} # no subs by default
if not opts['ac']:
printing.print_info_line(("Starting symbolic DC analysis...", 1), verbose)
else:
printing.print_info_line(("Starting symbolic AC analysis...", 1), verbose)
printing.print_info_line(("Building symbolic MNA, N and x...", 3), verbose, print_nl=False)
mna, N, subs_g = generate_mna_and_N(circ, opts, opts['ac'])
x = get_variables(circ)
mna = mna[1:, 1:]
N = N[1:, :]
printing.print_info_line((" done.", 3), verbose)
printing.print_info_line(("Performing variable substitutions...", 5), verbose)
mna, N = apply_substitutions(mna, N, subs)
printing.print_info_line(("MNA matrix (reduced):", 5), verbose)
if verbose > 5: print sympy.sstr(mna)
printing.print_info_line(("N matrix (reduced):", 5), verbose)
if verbose > 5: print sympy.sstr(N)
printing.print_info_line(("Building equations...", 3), verbose)
eq = []
for i in to_real_list(mna * x + N):
eq.append(sympy.Eq(i, 0))
x = to_real_list(x)
if verbose > 4:
printing.print_symbolic_equations(eq)
print "To be solved for:"
print x
#print "Matrix is singular: ", (mna.det() == 0)
#print -1.0*mna.inv()*N #too heavy
#print sympy.solve_linear_system(mna.row_join(-N), x)
printing.print_info_line(("Performing auxiliary simplification...", 3), verbose)
eq, x, sol_h = help_the_solver(eq, x)
if len(eq):
if verbose > 3:
print "Symplified sytem:"
printing.print_symbolic_equations(eq)
print "To be solved for:"
print x
printing.print_info_line(("Solving...", 1), verbose)
if options.symb_internal_solver:
sol = local_solve(eq, x)
else:
sol = sympy.solve(eq, x, manual=options.symb_sympy_manual_solver, simplify=True)
if sol is not None:
sol.update(sol_h)
else:
sol = sol_h
else:
printing.print_info_line(("Auxiliary simplification solved the problem.", 3), verbose)
sol = sol_h
for ks in sol.keys():
sol.update({ks:sol[ks].subs(subs_g)})
#sol = sol_to_dict(sol, x)
if sol == {}:
printing.print_warning("No solutions. Check the netlist.")
else:
printing.print_info_line(("Success!", 2), verbose)
if verbose > 1:
print "Results:"
printing.print_symbolic_results(sol)
if tf_source is not None:
src = sympy.Symbol(tf_source.upper(), real=True)
printing.print_info_line(("Calculating small-signal symbolic transfer functions (%s))..."%(str(src),), 2), verbose, print_nl=False)
tfs = calculate_gains(sol, src)
printing.print_info_line(("done.", 2), verbose)
printing.print_info_line(("Small-signal symbolic transfer functions:", 1), verbose)
printing.print_symbolic_transfer_functions(tfs)
else:
tfs = None
# convert to a results instance
sol = results.symbolic_solution(sol, subs, circ)
return sol, tfs
def ac_analysis(circ, start, nsteps, stop, step_type, xop=None, mna=None,\
AC=None, Nac=None, J=None, data_filename="stdout", verbose=3):
"""Performs an AC analysis of the circuit (described by circ).
"""
if data_filename == 'stdout':
verbose = 0
#check step/start/stop parameters
if start == 0:
printing.print_general_error("AC analysis has start frequency = 0")
sys.exit(5)
if start > stop:
printing.print_general_error("AC analysis has start > stop")
sys.exit(1)
if nsteps < 1:
printing.print_general_error("AC analysis has number of steps <= 1")
sys.exit(1)
if step_type == options.ac_log_step:
omega_iter = utilities.log_axis_iterator(stop, start, nsteps)
elif step_type == options.ac_lin_step:
omega_iter = utilities.lin_axis_iterator(stop, start, nsteps)
else:
printing.print_general_error("Unknown sweep type.")
sys.exit(1)
tmpstr = "Vea =", options.vea, "Ver =", options.ver, "Iea =", options.iea, "Ier =", \
options.ier, "max_ac_nr_iter =", options.ac_max_nr_iter
printing.print_info_line((tmpstr, 5), verbose)
del tmpstr
printing.print_info_line(("Starting AC analysis: ", 1), verbose)
tmpstr = "w: start = %g Hz, stop = %g Hz, %d steps" % (start, stop, nsteps)
printing.print_info_line((tmpstr, 3), verbose)
del tmpstr
#It's a good idea to call AC with prebuilt MNA matrix if the circuit is big
if mna is None:
(mna, N) = dc_analysis.generate_mna_and_N(circ)
del N
mna = utilities.remove_row_and_col(mna)
if Nac is None:
Nac = generate_Nac(circ)
Nac = utilities.remove_row(Nac, rrow=0)
if AC is None:
AC = generate_AC(circ, [mna.shape[0], mna.shape[0]])
AC = utilities.remove_row_and_col(AC)
if circ.is_nonlinear():
if J is not None:
pass
# we used the supplied linearization matrix
else:
if xop is None:
printing.print_info_line(
("Starting OP analysis to get a linearization point...",
3),
verbose,
print_nl=False)
#silent OP
xop = dc_analysis.op_analysis(circ, verbose=0)
if xop is None: #still! Then op_analysis has failed!
printing.print_info_line(("failed.", 3), verbose)
printing.print_general_error(
"OP analysis failed, no linearization point available. Quitting."
)
sys.exit(3)
else:
printing.print_info_line(("done.", 3), verbose)
printing.print_info_line(("Linearization point (xop):", 5),
verbose)
if verbose > 4: xop.print_short()
printing.print_info_line(("Linearizing the circuit...", 5),
verbose,
print_nl=False)
J = generate_J(xop=xop.asmatrix(),
circ=circ,
mna=mna,
Nac=Nac,
data_filename=data_filename,
verbose=verbose)
printing.print_info_line((" done.", 5), verbose)
# we have J, continue
else: #not circ.is_nonlinear()
# no J matrix is required.
J = 0
printing.print_info_line(("MNA (reduced):", 5), verbose)
printing.print_info_line((str(mna), 5), verbose)
printing.print_info_line(("AC (reduced):", 5), verbose)
printing.print_info_line((str(AC), 5), verbose)
printing.print_info_line(("J (reduced):", 5), verbose)
printing.print_info_line((str(J), 5), verbose)
printing.print_info_line(("Nac (reduced):", 5), verbose)
printing.print_info_line((str(Nac), 5), verbose)
sol = results.ac_solution(circ,
ostart=start,
ostop=stop,
opoints=nsteps,
stype=step_type,
op=xop,
outfile=data_filename)
# setup the initial values to start the iteration:
nv = len(circ.nodes_dict)
j = numpy.complex('j')
Gmin_matrix = dc_analysis.build_gmin_matrix(circ, options.gmin,
mna.shape[0], verbose)
iter_n = 0 # contatore d'iterazione
#printing.print_results_header(circ, fdata, print_int_nodes=options.print_int_nodes, print_omega=True)
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
tick = ticker.ticker(increments_for_step=1)
tick.display(verbose > 1)
x = xop
for omega in omega_iter:
(x, error, solved, n_iter) = dc_analysis.dc_solve(mna=(mna + numpy.multiply(j*omega, AC) + J), \
Ndc=Nac, Ntran=0, circ=circuit.circuit(title="Dummy circuit for AC", filename=None), Gmin=Gmin_matrix, x0=x, \
time=None, locked_nodes=None, MAXIT=options.ac_max_nr_iter, skip_Tt=True, verbose=0)
if solved:
tick.step(verbose > 1)
iter_n = iter_n + 1
# hooray!
sol.add_line(omega, x)
else:
break
tick.hide(verbose > 1)
if solved:
printing.print_info_line(("done.", 1), verbose)
ret_value = sol
else:
printing.print_info_line(("failed.", 1), verbose)
ret_value = None
return ret_value
def transient_analysis(circ, tstart, tstep, tstop, method=TRAP, x0=None, mna=None, N=None, \
D=None, data_filename="stdout", use_step_control=True, return_req_dict=None, verbose=3):
"""Performs a transient analysis of the circuit described by circ.
Important parameters:
- tstep is the maximum step to be allowed during simulation.
- print_step_and_lte is a boolean value. Is set to true, the step and the LTE of the first
element of x will be printed out to step_and_lte.graph in the current directory.
"""
if data_filename == "stdout":
verbose = 0
_debug = False
if _debug:
print_step_and_lte = True
else:
print_step_and_lte = False
HMAX = tstep
#check parameters
if tstart > tstop:
printing.print_general_error("tstart > tstop")
sys.exit(1)
if tstep < 0:
printing.print_general_error("tstep < 0")
sys.exit(1)
if verbose > 4:
tmpstr = "Vea = %g Ver = %g Iea = %g Ier = %g max_time_iter = %g HMIN = %g" % \
(options.vea, options.ver, options.iea, options.ier, options.transient_max_time_iter, options.hmin)
printing.print_info_line((tmpstr, 5), verbose)
locked_nodes = circ.get_locked_nodes()
if print_step_and_lte:
flte = open("step_and_lte.graph", "w")
flte.write("#T\tStep\tLTE\n")
printing.print_info_line(("Starting transient analysis: ", 3), verbose)
printing.print_info_line(("Selected method: %s" % (method, ), 3), verbose)
#It's a good idea to call transient with prebuilt MNA and N matrix
#the analysis will be slightly faster (long netlists).
if mna is None or N is None:
(mna, N) = dc_analysis.generate_mna_and_N(circ)
mna = utilities.remove_row_and_col(mna)
N = utilities.remove_row(N, rrow=0)
elif not mna.shape[0] == N.shape[0]:
printing.print_general_error(
"mna matrix and N vector have different number of columns.")
sys.exit(0)
if D is None:
# if you do more than one tran analysis, output streams should be changed...
# this needs to be fixed
D = generate_D(circ, [mna.shape[0], mna.shape[0]])
D = utilities.remove_row_and_col(D)
# setup x0
if x0 is None:
printing.print_info_line(("Generating x(t=%g) = 0" % (tstart, ), 5),
verbose)
x0 = numpy.matrix(numpy.zeros((mna.shape[0], 1)))
opsol = results.op_solution(x=x0, error=x0, circ=circ, outfile=None)
else:
if isinstance(x0, results.op_solution):
opsol = x0
x0 = x0.asmatrix()
else:
opsol = results.op_solution(x=x0,
error=numpy.matrix(
numpy.zeros((mna.shape[0], 1))),
circ=circ,
outfile=None)
printing.print_info_line(
("Using the supplied op as x(t=%g)." % (tstart, ), 5), verbose)
if verbose > 4:
print "x0:"
opsol.print_short()
# setup the df method
printing.print_info_line(
("Selecting the appropriate DF (" + method + ")... ", 5),
verbose,
print_nl=False)
if method == IMPLICIT_EULER:
import implicit_euler as df
elif method == TRAP:
import trap as df
elif method == GEAR1:
import gear as df
df.order = 1
elif method == GEAR2:
import gear as df
df.order = 2
elif method == GEAR3:
import gear as df
df.order = 3
elif method == GEAR4:
import gear as df
df.order = 4
elif method == GEAR5:
import gear as df
df.order = 5
elif method == GEAR6:
import gear as df
df.order = 6
else:
df = import_custom_df_module(method,
print_out=(data_filename != "stdout"))
# df is none if module is not found
if df is None:
sys.exit(23)
if not df.has_ff() and use_step_control:
printing.print_warning(
"The chosen DF does not support step control. Turning off the feature."
)
use_step_control = False
#use_aposteriori_step_control = False
printing.print_info_line(("done.", 5), verbose)
# setup the data buffer
# if you use the step control, the buffer has to be one point longer.
# That's because the excess point is used by a FF in the df module to predict the next value.
printing.print_info_line(("Setting up the buffer... ", 5),
verbose,
print_nl=False)
((max_x, max_dx), (pmax_x, pmax_dx)) = df.get_required_values()
if max_x is None and max_dx is None:
printing.print_general_error("df doesn't need any value?")
sys.exit(1)
if use_step_control:
thebuffer = dfbuffer(length=max(max_x, max_dx, pmax_x, pmax_dx) + 1,
width=3)
else:
thebuffer = dfbuffer(length=max(max_x, max_dx) + 1, width=3)
thebuffer.add((tstart, x0, None)) #setup the first values
printing.print_info_line(("done.", 5), verbose) #FIXME
#setup the output buffer
if return_req_dict:
output_buffer = dfbuffer(length=return_req_dict["points"], width=1)
output_buffer.add((x0, ))
else:
output_buffer = None
# import implicit_euler to be used in the first iterations
# this is because we don't have any dx when we start, nor any past point value
if (max_x is not None and max_x > 0) or max_dx is not None:
import implicit_euler
printing.print_info_line(("MNA (reduced):", 5), verbose)
printing.print_info_line((str(mna), 5), verbose)
printing.print_info_line(("D (reduced):", 5), verbose)
printing.print_info_line((str(D), 5), verbose)
# setup the initial values to start the iteration:
x = None
time = tstart
nv = len(circ.nodes_dict)
Gmin_matrix = dc_analysis.build_gmin_matrix(circ, options.gmin,
mna.shape[0], verbose)
# lo step viene generato automaticamente, ma non superare mai quello fornito.
if use_step_control:
#tstep = min((tstop-tstart)/9999.0, HMAX, 100.0 * options.hmin)
tstep = min((tstop - tstart) / 9999.0, HMAX)
printing.print_info_line(("Initial step: %g" % (tstep, ), 5), verbose)
if max_dx is None:
max_dx_plus_1 = None
else:
max_dx_plus_1 = max_dx + 1
if pmax_dx is None:
pmax_dx_plus_1 = None
else:
pmax_dx_plus_1 = pmax_dx + 1
# setup error vectors
aerror = numpy.mat(numpy.zeros((x0.shape[0], 1)))
aerror[:nv - 1, 0] = options.vea
aerror[nv - 1:, 0] = options.vea
rerror = numpy.mat(numpy.zeros((x0.shape[0], 1)))
rerror[:nv - 1, 0] = options.ver
rerror[nv - 1:, 0] = options.ier
iter_n = 0 # contatore d'iterazione
lte = None
sol = results.tran_solution(circ,
tstart,
tstop,
op=x0,
method=method,
outfile=data_filename)
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
tick = ticker.ticker(increments_for_step=1)
tick.display(verbose > 1)
while time < tstop:
if iter_n < max(max_x, max_dx_plus_1):
x_coeff, const, x_lte_coeff, prediction, pred_lte_coeff = \
implicit_euler.get_df((thebuffer.get_df_vector()[0],), tstep, \
predict=(use_step_control and (iter_n >= max(pmax_x, pmax_dx_plus_1))))
else:
[x_coeff, const, x_lte_coeff, prediction, pred_lte_coeff] = \
df.get_df(thebuffer.get_df_vector(), tstep, predict=use_step_control)
if options.transient_prediction_as_x0 and use_step_control and prediction is not None:
x0 = prediction
elif x is not None:
x0 = x
(x1, error, solved,
n_iter) = dc_analysis.dc_solve(mna=(mna + numpy.multiply(x_coeff, D)),
Ndc=N,
Ntran=D * const,
circ=circ,
Gmin=Gmin_matrix,
x0=x0,
time=(time + tstep),
locked_nodes=locked_nodes,
MAXIT=options.transient_max_nr_iter,
verbose=0)
if solved:
old_step = tstep #we will modify it, if we're using step control otherwise it's the same
# step control (yeah)
if use_step_control:
if x_lte_coeff is not None and pred_lte_coeff is not None and prediction is not None:
# this is the Local Truncation Error :)
lte = abs((x_lte_coeff / (pred_lte_coeff - x_lte_coeff)) *
(prediction - x1))
# it should NEVER happen that new_step > 2*tstep, for stability
new_step_coeff = 2
for index in xrange(x.shape[0]):
if lte[index, 0] != 0:
new_value = ((aerror[index, 0] + rerror[index, 0]*abs(x[index, 0])) / lte[index, 0]) \
** (1.0 / (df.order+1))
if new_value < new_step_coeff:
new_step_coeff = new_value
#print new_value
new_step = tstep * new_step_coeff
if options.transient_use_aposteriori_step_control and new_step < options.transient_aposteriori_step_threshold * tstep:
#don't recalculate a x for a small change
tstep = check_step(new_step, time, tstop, HMAX)
#print "Apost. (reducing) step = "+str(tstep)
continue
tstep = check_step(new_step, time, tstop,
HMAX) # used in the next iteration
#print "Apriori tstep = "+str(tstep)
else:
#print "LTE not calculated."
lte = None
if print_step_and_lte and lte is not None:
#if you wish to look at the step. We print just a lte
flte.write(
str(time) + "\t" + str(old_step) + "\t" + str(lte.max()) +
"\n")
# if we get here, either aposteriori_step_control is
# disabled, or it's enabled and the error is small
# enough. Anyway, the result is GOOD, STORE IT.
time = time + old_step
x = x1
iter_n = iter_n + 1
sol.add_line(time, x)
dxdt = numpy.multiply(x_coeff, x) + const
thebuffer.add((time, x, dxdt))
if output_buffer is not None:
output_buffer.add((x, ))
tick.step(verbose > 1)
else:
# If we get here, Newton failed to converge. We need to reduce the step...
if use_step_control:
tstep = tstep / 5.0
tstep = check_step(tstep, time, tstop, HMAX)
printing.print_info_line(
("At %g s reducing step: %g s (convergence failed)" %
(time, tstep), 5), verbose)
else: #we can't reduce the step
printing.print_general_error("Can't converge with step " +
str(tstep) + ".")
printing.print_general_error(
"Try setting --t-max-nr to a higher value or set step to a lower one."
)
solved = False
break
if options.transient_max_time_iter and iter_n == options.transient_max_time_iter:
printing.print_general_error("MAX_TIME_ITER exceeded (" +
str(options.transient_max_time_iter) +
"), iteration halted.")
solved = False
break
if print_step_and_lte:
flte.close()
tick.hide(verbose > 1)
if solved:
printing.print_info_line(("done.", 3), verbose)
printing.print_info_line(
("Average time step: %g" % ((tstop - tstart) / iter_n, ), 3),
verbose)
if output_buffer:
ret_value = output_buffer.get_as_matrix()
else:
ret_value = sol
else:
print "failed."
ret_value = None
return ret_value
def main(filename, outfile="stdout", verbose=3):
"""This method allows calling ahkab from a Python script.
"""
printing.print_info_line(
("This is ahkab %s running with:" % (__version__), 6), verbose)
printing.print_info_line(
(" Python %s" % (sys.version.split('\n')[0],), 6), verbose)
printing.print_info_line((" Numpy %s" % (numpy.__version__), 6), verbose)
printing.print_info_line((" Sympy %s" % (sympy.__version__), 6), verbose)
printing.print_info_line(
(" Matplotlib %s" % (matplotlib.__version__), 6), verbose)
read_netlist_from_stdin = (filename is None or filename == "-")
(circ, directives, postproc_direct) = netlist_parser.parse_circuit(
filename, read_netlist_from_stdin)
check, reason = dc_analysis.check_circuit(circ)
if not check:
printing.print_general_error(reason)
printing.print_circuit(circ)
sys.exit(3)
if verbose > 3 or _print:
print "Parsed circuit:"
printing.print_circuit(circ)
ic_list = netlist_parser.parse_ics(directives)
_handle_netlist_ics(circ, an_list=[], ic_list=ic_list)
results = {}
for an in netlist_parser.parse_analysis(circ, directives):
if 'outfile' not in an.keys() or not an['outfile']:
an.update(
{'outfile': outfile + ("." + an['type']) * (outfile != 'stdout')})
if 'verbose' in an.keys() and (an['verbose'] is None or an['verbose'] < verbose) \
or not 'verbose' in an.keys():
an.update({'verbose': verbose})
_handle_netlist_ics(circ, [an], ic_list=[])
if verbose >= 4:
printing.print_info_line(("Requested an.:", 4), verbose)
printing.print_analysis(an)
results.update(run(circ, [an]))
postproc_list = netlist_parser.parse_postproc(circ, postproc_direct)
if len(postproc_list) > 0 and len(results):
process_postproc(postproc_list, circ.title, results, outfile)
return results
def shooting(circ,
period,
step=None,
mna=None,
Tf=None,
D=None,
points=None,
autonomous=False,
data_filename='stdout',
vector_norm=lambda v: max(abs(v)),
verbose=3):
"""Performs a periodic steady state analysis based on the algorithm described in
Brambilla, A.; D'Amore, D., "Method for steady-state simulation of
strongly nonlinear circuits in the time domain," Circuits and
Systems I: Fundamental Theory and Applications, IEEE Transactions on,
vol.48, no.7, pp.885-889, Jul 2001
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=933329&isnumber=20194
The results have been computed again by me, the formulas are not exactly the
same, but the idea behind the shooting algorithm is.
This method allows us to have a period with many points without having to
invert a huge matrix (and being limited to the maximum matrix size).
A tran is performed to initialize the solver.
We compute the change in the last point, calculating several matrices in
the process.
From that, with the same matrices we calculate the changes in all points,
starting from 0 (which is the same as the last one), then 1, ...
Key points:
- Only not autonomous circuits are supported.
- The time step is constant
- Implicit euler is used as DF
Parameters:
circ is the circuit description class
period is the period of the solution
mna, D, Tf are not compulsory they will be computed if they're set to None
step is the time step between consecutive points
points is the number of points to be used
step and points are mutually exclusive options:
- if step is specified, the number of points will be automatically determined
- if points is set, the step will be automatically determined
- if none of them is set, options.shooting_default_points will be used as points
autonomous has to be False, autonomous circuits are not supported
data_filename is the output filename. Defaults to stdout.
verbose is set to zero (print errors only) if datafilename == 'stdout'.
Returns: nothing
"""
if data_filename == "stdout":
verbose = 0
printing.print_info_line(("Starting periodic steady state analysis:", 3),
verbose)
printing.print_info_line(("Method: shooting", 3), verbose)
if mna is None or Tf is None:
(mna, Tf) = dc_analysis.generate_mna_and_N(circ)
mna = utilities.remove_row_and_col(mna)
Tf = utilities.remove_row(Tf, rrow=0)
elif not mna.shape[0] == Tf.shape[0]:
printing.print_general_error(
"mna matrix and N vector have different number of rows.")
sys.exit(0)
if D is None:
D = transient.generate_D(circ, [mna.shape[0], mna.shape[0]])
D = utilities.remove_row_and_col(D)
elif not mna.shape == D.shape:
printing.print_general_error(
"mna matrix and D matrix have different sizes.")
sys.exit(0)
(points, step) = check_step_and_points(step, points, period)
print "points", points
print "step", step
n_of_var = mna.shape[0]
locked_nodes = circ.get_locked_nodes()
printing.print_info_line((
"Starting transient analysis for algorithm init: tstop=%g, tstep=%g... "
% (10 * points * step, step), 3),
verbose,
print_nl=False)
xtran = transient.transient_analysis(circ=circ, tstart=0, tstep=step, tstop=10*points*step, method="TRAP", x0=None, mna=mna, N=Tf, \
D=D, use_step_control=False, data_filename=data_filename+".tran", return_req_dict={"points":points}, verbose=0)
if xtran is None:
print "failed."
return None
printing.print_info_line(("done.", 3), verbose)
x = []
for index in range(points):
x.append(xtran[index * n_of_var:(index + 1) * n_of_var, 0])
tick = ticker.ticker(increments_for_step=1)
MAass_static, MBass = build_static_MAass_and_MBass(mna, D, step)
# This contains
# the time invariant part, Tf
# time variable component: Tt this is always the same, since the time interval is the same
# this holds all time-dependent sources (both V/I).
Tass_static_vector = build_Tass_static_vector(circ, Tf, points, step, tick,
n_of_var, verbose)
converged = False
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
tick.reset()
tick.display(verbose > 2)
iteration = 0 # newton iteration counter
conv_counter = 0
while True:
dx = []
Tass_variable_vector = []
MAass_variable_vector = []
for index in range(points):
if index == 0:
xn_minus_1 = x[points - 1]
else:
xn_minus_1 = x[index - 1]
MAass_variable, Tass_variable = get_variable_MAass_and_Tass(
circ, x[index], xn_minus_1, mna, D, step, n_of_var)
MAass_variable_vector.append(MAass_variable + MAass_static)
Tass_variable_vector.append(Tass_variable +
Tass_static_vector[index])
dxN = compute_dxN(circ,
MAass_variable_vector,
MBass,
Tass_variable_vector,
n_of_var,
points,
verbose=verbose)
td = dc_analysis.get_td(dxN, locked_nodes, n=-1)
x[points - 1] = td * dxN + x[points - 1]
for index in range(points - 1):
if index == 0:
dxi_minus_1 = dxN
else:
dxi_minus_1 = dx[index - 1]
dx.append(
compute_dx(MAass_variable_vector[index], MBass,
Tass_variable_vector[index], dxi_minus_1))
td = dc_analysis.get_td(dx[index], locked_nodes, n=-1)
x[index] = td * dx[index] + x[index]
dx.append(dxN)
if (vector_norm_wrapper(dx, vector_norm) <
min(options.ver, options.ier) * vector_norm_wrapper(
x, vector_norm) + min(options.vea, options.iea)): #\
#and (dc_analysis.vector_norm(residuo) < options.er*dc_analysis.vector_norm(x) + options.ea):
if conv_counter == 3:
converged = True
break
else:
conv_counter = conv_counter + 1
elif vector_norm(dx[points - 1]) is numpy.nan: #needs work fixme
raise OverflowError
#break
else:
conv_counter = 0
tick.step(verbose > 2)
if options.shooting_max_nr_iter and iteration == options.shooting_max_nr_iter:
printing.print_general_error("Hitted SHOOTING_MAX_NR_ITER (" +
str(options.shooting_max_nr_iter) +
"), iteration halted.")
converged = False
break
else:
iteration = iteration + 1
tick.hide(verbose > 2)
if converged:
printing.print_info_line(("done.", 3), verbose)
t = numpy.mat(numpy.arange(points) * step)
t = t.reshape((1, points))
xmat = x[0]
for index in xrange(1, points):
xmat = numpy.concatenate((xmat, x[index]), axis=1)
sol = results.pss_solution(circ=circ,
method="shooting",
period=period,
outfile=data_filename,
t_array=t,
x_array=xmat)
#print_results(circ, x, fdata, points, step)
else:
print "failed."
sol = None
return sol
def op_analysis(circ, x0=None, guess=True, outfile=None, verbose=3):
"""Runs an Operating Point (OP) analysis
circ: the circuit instance on which the simulation is run
x0: is the initial guess to be used to start the NR mdn_solver
guess: if set to True and x0 is None, it will generate a 'smart' guess
verbose: verbosity level from 0 (silent) to 6 (debug).
Returns a Operation Point result, if successful, None otherwise.
"""
if outfile == 'stdout':
verbose = 0 # silent mode, print out results only.
if not options.dc_use_guess:
guess = False
(mna, N) = generate_mna_and_N(circ, verbose=verbose)
printing.print_info_line(("MNA matrix and constant term (complete):", 4),
verbose)
printing.print_info_line((str(mna), 4), verbose)
printing.print_info_line((str(N), 4), verbose)
# lets trash the unneeded col & row
printing.print_info_line(("Removing unneeded row and column...", 4),
verbose)
mna = utilities.remove_row_and_col(mna)
N = utilities.remove_row(N, rrow=0)
printing.print_info_line(("Starting op analysis:", 2), verbose)
if x0 is None and guess:
x0 = dc_guess.get_dc_guess(circ, verbose=verbose)
# if x0 is not None, use that
printing.print_info_line(("Solving with Gmin:", 4), verbose)
Gmin_matrix = build_gmin_matrix(circ, options.gmin, mna.shape[0],
verbose - 2)
(x1, error1, solved1, n_iter1) = dc_solve(mna,
N,
circ,
Gmin=Gmin_matrix,
x0=x0,
verbose=verbose)
# We'll check the results now. Recalculate them without Gmin (using previsious solution as initial guess)
# and check that differences on nodes and current do not exceed the
# tolerances.
if solved1:
op1 = results.op_solution(x1,
error1,
circ,
outfile=outfile,
iterations=n_iter1)
printing.print_info_line(("Solving without Gmin:", 4), verbose)
(x2, error2, solved2, n_iter2) = dc_solve(mna,
N,
circ,
Gmin=None,
x0=x1,
verbose=verbose)
else:
solved2 = False
if solved1 and not solved2:
printing.print_general_error("Can't solve without Gmin.")
if verbose:
print "Displaying latest valid results."
op1.write_to_file(filename='stdout')
opsolution = op1
elif solved1 and solved2:
op2 = results.op_solution(x2,
error2,
circ,
outfile=outfile,
iterations=n_iter1 + n_iter2)
op2.gmin = 0
badvars = results.op_solution.gmin_check(op2, op1)
printing.print_result_check(badvars, verbose=verbose)
check_ok = not (len(badvars) > 0)
if not check_ok and verbose:
print "Solution with Gmin:"
op1.write_to_file(filename='stdout')
print "Solution without Gmin:"
op2.write_to_file(filename='stdout')
opsolution = op2
else: # not solved1
printing.print_general_error("Couldn't solve the circuit. Giving up.")
opsolution = None
if opsolution and outfile != 'stdout' and outfile is not None:
opsolution.write_to_file()
if opsolution and verbose > 2 and options.cli:
opsolution.write_to_file(filename='stdout')
return opsolution
def ac_analysis(circ, start, nsteps, stop, step_type, xop=None, mna=None,\
AC=None, Nac=None, J=None, data_filename="stdout", verbose=3):
"""Performs an AC analysis of the circuit (described by circ).
"""
if data_filename == 'stdout':
verbose = 0
#check step/start/stop parameters
if start == 0:
printing.print_general_error("AC analysis has start frequency = 0")
sys.exit(5)
if start > stop:
printing.print_general_error("AC analysis has start > stop")
sys.exit(1)
if nsteps < 1:
printing.print_general_error("AC analysis has number of steps <= 1")
sys.exit(1)
if step_type == options.ac_log_step:
omega_iter = utilities.log_axis_iterator(stop, start, nsteps)
elif step_type == options.ac_lin_step:
omega_iter = utilities.lin_axis_iterator(stop, start, nsteps)
else:
printing.print_general_error("Unknown sweep type.")
sys.exit(1)
tmpstr = "Vea =", options.vea, "Ver =", options.ver, "Iea =", options.iea, "Ier =", \
options.ier, "max_ac_nr_iter =", options.ac_max_nr_iter
printing.print_info_line((tmpstr, 5), verbose)
del tmpstr
printing.print_info_line(("Starting AC analysis: ", 1), verbose)
tmpstr = "w: start = %g Hz, stop = %g Hz, %d steps" % (start, stop, nsteps)
printing.print_info_line((tmpstr, 3), verbose)
del tmpstr
#It's a good idea to call AC with prebuilt MNA matrix if the circuit is big
if mna is None:
(mna, N) = dc_analysis.generate_mna_and_N(circ)
del N
mna = utilities.remove_row_and_col(mna)
if Nac is None:
Nac = generate_Nac(circ)
Nac = utilities.remove_row(Nac, rrow=0)
if AC is None:
AC = generate_AC(circ, [mna.shape[0], mna.shape[0]])
AC = utilities.remove_row_and_col(AC)
if circ.is_nonlinear():
if J is not None:
pass
# we used the supplied linearization matrix
else:
if xop is None:
printing.print_info_line(("Starting OP analysis to get a linearization point...", 3), verbose, print_nl=False)
#silent OP
xop = dc_analysis.op_analysis(circ, verbose=0)
if xop is None: #still! Then op_analysis has failed!
printing.print_info_line(("failed.", 3), verbose)
printing.print_general_error("OP analysis failed, no linearization point available. Quitting.")
sys.exit(3)
else:
printing.print_info_line(("done.", 3), verbose)
printing.print_info_line(("Linearization point (xop):", 5), verbose)
if verbose > 4: xop.print_short()
printing.print_info_line(("Linearizing the circuit...", 5), verbose, print_nl=False)
J = generate_J(xop=xop.asmatrix(), circ=circ, mna=mna, Nac=Nac, data_filename=data_filename, verbose=verbose)
printing.print_info_line((" done.", 5), verbose)
# we have J, continue
else: #not circ.is_nonlinear()
# no J matrix is required.
J = 0
printing.print_info_line(("MNA (reduced):", 5), verbose)
printing.print_info_line((str(mna), 5), verbose)
printing.print_info_line(("AC (reduced):", 5), verbose)
printing.print_info_line((str(AC), 5), verbose)
printing.print_info_line(("J (reduced):", 5), verbose)
printing.print_info_line((str(J), 5), verbose)
printing.print_info_line(("Nac (reduced):", 5), verbose)
printing.print_info_line((str(Nac), 5), verbose)
sol = results.ac_solution(circ, ostart=start, ostop=stop, opoints=nsteps, stype=step_type, op=xop, outfile=data_filename)
# setup the initial values to start the iteration:
nv = len(circ.nodes_dict)
j = numpy.complex('j')
Gmin_matrix = dc_analysis.build_gmin_matrix(circ, options.gmin, mna.shape[0], verbose)
iter_n = 0 # contatore d'iterazione
#printing.print_results_header(circ, fdata, print_int_nodes=options.print_int_nodes, print_omega=True)
printing.print_info_line(("Solving... ", 3), verbose, print_nl=False)
tick = ticker.ticker(increments_for_step=1)
tick.display(verbose > 1)
x = xop
for omega in omega_iter:
(x, error, solved, n_iter) = dc_analysis.dc_solve(mna=(mna + numpy.multiply(j*omega, AC) + J), \
Ndc=Nac, Ntran=0, circ=circuit.circuit(title="Dummy circuit for AC", filename=None), Gmin=Gmin_matrix, x0=x, \
time=None, locked_nodes=None, MAXIT=options.ac_max_nr_iter, skip_Tt=True, verbose=0)
if solved:
tick.step(verbose > 1)
iter_n = iter_n + 1
# hooray!
sol.add_line(omega, x)
else:
break
tick.hide(verbose > 1)
if solved:
printing.print_info_line(("done.", 1), verbose)
ret_value = sol
else:
printing.print_info_line(("failed.", 1), verbose)
ret_value = None
return ret_value
def symbolic_analysis(circ,
source=None,
ac_enable=True,
r0s=False,
subs=None,
outfile=None,
verbose=3):
"""Attempt a symbolic solution of the circuit.
circ: the circuit instance to be simulated.
source: the name (string) of the source to be used as input for the transfer
function. If None, no transfer function is evaluated.
ac_enable: take frequency dependency into consideration (default: True)
r0s: take transistors' output impedance into consideration (default: False)
subs: a dictionary of sympy Symbols to be substituted. It makes solving the circuit
easier. Eg. {R1:R2} - replace R1 with R2. It can be generated with
parse_substitutions()
outfile: output filename ('stdout' means print to stdout).
verbose: verbosity level 0 (silent) to 6 (painful).
Returns: a dictionary with the solutions.
"""
if subs is None:
subs = {} # no subs by default
if not ac_enable:
printing.print_info_line(("Starting symbolic DC analysis...", 1),
verbose)
else:
printing.print_info_line(("Starting symbolic AC analysis...", 1),
verbose)
printing.print_info_line(("Building symbolic MNA, N and x...", 3),
verbose,
print_nl=False)
mna, N, subs_g = generate_mna_and_N(circ,
opts={'r0s': r0s},
ac=ac_enable,
verbose=verbose)
x = get_variables(circ)
mna = mna[1:, 1:]
N = N[1:, :]
printing.print_info_line((" done.", 3), verbose)
printing.print_info_line(("Performing variable substitutions...", 5),
verbose)
mna, N = apply_substitutions(mna, N, subs)
printing.print_info_line(("MNA matrix (reduced):", 5), verbose)
printing.print_info_line((sympy.sstr(mna), 5), verbose)
printing.print_info_line(("N matrix (reduced):", 5), verbose)
printing.print_info_line((sympy.sstr(N), 5), verbose)
printing.print_info_line(("Building equations...", 3), verbose)
eq = []
for i in to_real_list(mna * x + N):
eq.append(sympy.Eq(i, 0))
x = to_real_list(x)
if verbose > 4:
printing.print_symbolic_equations(eq)
print "To be solved for:"
print x
# print "Matrix is singular: ", (mna.det() == 0)
# print -1.0*mna.inv()*N #too heavy
# print sympy.solve_linear_system(mna.row_join(-N), x)
printing.print_info_line(("Performing auxiliary simplification...", 3),
verbose)
eq, x, sol_h = help_the_solver(eq, x)
if len(eq):
printing.print_info_line(("Symplified sytem:", 3), verbose)
if verbose > 3:
printing.print_symbolic_equations(eq)
printing.print_info_line(("To be solved for:", 3), verbose)
printing.print_info_line((str(x), 3), verbose)
printing.print_info_line(("Solving...", 1), verbose)
if options.symb_internal_solver:
sol = local_solve(eq, x)
else:
sol = sympy.solve(eq,
x,
manual=options.symb_sympy_manual_solver,
simplify=True)
if sol is not None:
sol.update(sol_h)
else:
sol = sol_h
else:
printing.print_info_line(
("Auxiliary simplification solved the problem.", 3), verbose)
sol = sol_h
for ks in sol.keys():
sol.update({ks: sol[ks].subs(subs_g)})
# sol = sol_to_dict(sol, x)
if sol == {}:
printing.print_warning("No solutions. Check the netlist.")
else:
printing.print_info_line(("Success!", 2), verbose)
printing.print_info_line(("Results:", 1), verbose)
if options.cli:
printing.print_symbolic_results(sol)
if source is not None:
src = sympy.Symbol(source.upper(), real=True)
printing.print_info_line(
("Calculating small-signal symbolic transfer functions (%s))..." %
(str(src), ), 2),
verbose,
print_nl=False)
tfs = calculate_gains(sol, src)
printing.print_info_line(("done.", 2), verbose)
printing.print_info_line(
("Small-signal symbolic transfer functions:", 1), verbose)
if options.cli:
printing.print_symbolic_transfer_functions(tfs)
else:
tfs = None
# convert to a results instance
sol = results.symbolic_solution(sol, subs, circ, outfile)
if tfs:
if outfile and outfile != 'stdout':
outfile += ".tfs"
tfs = results.symbolic_solution(tfs, subs, circ, outfile, tf=True)
return sol, tfs
def main(filename, outfile="stdout", verbose=3):
"""This method allows calling ahkab from a Python script.
"""
printing.print_info_line(
("This is ahkab %s running with:" % (__version__), 6), verbose)
printing.print_info_line(
(" Python %s" % (sys.version.split('\n')[0], ), 6), verbose)
printing.print_info_line((" Numpy %s" % (numpy.__version__), 6), verbose)
printing.print_info_line((" Sympy %s" % (sympy.__version__), 6), verbose)
printing.print_info_line((" Matplotlib %s" % (matplotlib.__version__), 6),
verbose)
read_netlist_from_stdin = (filename is None or filename == "-")
(circ, directives,
postproc_direct) = netlist_parser.parse_circuit(filename,
read_netlist_from_stdin)
check, reason = dc_analysis.check_circuit(circ)
if not check:
printing.print_general_error(reason)
printing.print_circuit(circ)
sys.exit(3)
if verbose > 3 or _print:
print "Parsed circuit:"
printing.print_circuit(circ)
ic_list = netlist_parser.parse_ics(directives)
_handle_netlist_ics(circ, an_list=[], ic_list=ic_list)
results = {}
for an in netlist_parser.parse_analysis(circ, directives):
if 'outfile' not in an.keys() or not an['outfile']:
an.update({
'outfile':
outfile + ("." + an['type']) * (outfile != 'stdout')
})
if 'verbose' in an.keys() and (an['verbose'] is None or an['verbose'] < verbose) \
or not 'verbose' in an.keys():
an.update({'verbose': verbose})
_handle_netlist_ics(circ, [an], ic_list=[])
if verbose >= 4:
printing.print_info_line(("Requested an.:", 4), verbose)
printing.print_analysis(an)
results.update(run(circ, [an]))
postproc_list = netlist_parser.parse_postproc(circ, postproc_direct)
if len(postproc_list) > 0 and len(results):
process_postproc(postproc_list, circ.title, results, outfile)
return results