def modify_x0_for_ic(circ, x0):
"""Modifies a supplied x0.
"""
if isinstance(x0, results.op_solution):
x0 = x0.asmatrix()
return_obj = True
else:
return_obj = False
nv = len(circ.nodes_dict) # number of voltage variables
voltage_defined_elements = [
x for x in circ if circuit.is_elem_voltage_defined(x)
]
# setup voltages this may _not_ work properly
for elem in circ:
if isinstance(elem, devices.Capacitor) and elem.ic or \
isinstance(elem, diode.diode) and elem.ic:
x0[elem.n1 - 1, 0] = x0[elem.n2 - 1, 0] + elem.ic
# setup the currents
for elem in voltage_defined_elements:
if isinstance(elem, devices.Inductor) and elem.ic:
x0[nv - 1 + voltage_defined_elements.index(elem), 0] = elem.ic
if return_obj:
xnew = results.op_solution(x=x0, \
error=numpy.mat(numpy.zeros(x0.shape)), circ=circ, outfile=None)
xnew.netlist_file = None
xnew.netlist_title = "Self-generated OP to be used as tran IC"
else:
xnew = x0
return xnew
def modify_x0_for_ic(circ, x0):
"""Modifies a supplied x0.
"""
if isinstance(x0, results.op_solution):
x0 = x0.asmatrix()
return_obj = True
else:
return_obj = False
nv = len(circ.nodes_dict) # number of voltage variables
voltage_defined_elements = [x for x in circ.elements if circuit.is_elem_voltage_defined(x)]
# setup voltages this may _not_ work properly
for elem in circ.elements:
if isinstance(elem, devices.capacitor) and elem.ic or isinstance(elem, devices.diode) and elem.ic:
x0[elem.n1 - 1, 0] = x0[elem.n2 - 1, 0] + elem.ic
# setup the currents
for elem in voltage_defined_elements:
if isinstance(elem, devices.inductor) and elem.ic:
x0[nv - 1 + voltage_defined_elements.index(elem), 0] = elem.ic
if return_obj:
xnew = results.op_solution(x=x0, error=numpy.mat(numpy.zeros(x0.shape)), circ=circ, outfile=None)
xnew.netlist_file = None
xnew.netlist_title = "Self-generated OP to be used as tran IC"
else:
xnew = x0
return xnew
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 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 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 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
