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 run(circ, an_list=None):
""" Processes an analysis vector:
circ: the circuit instance
an_queue: the list of analyses to be performed, if unset defaults to those queued
with queue_analysis()
Returns: the results (in dict form)
"""
results = {}
if not an_list:
an_list = _queue
else:
if type(an_list) == tuple:
an_list = list(an_list)
elif type(an_list) == dict:
an_list = [an_list] # run(mycircuit, op1)
elif type(an_list) == list:
pass
while len(an_list):
an_item = an_list.pop(0)
an_type = an_item.pop('type')
if 'x0' in an_item and isinstance(an_item['x0'], str):
printing.print_warning("%s has x0 set to %s, unavailable. Using 'None'." %
(an_type.upper(), an_item['x0']))
an_item['x0'] = None
r = analysis[an_type](circ, **an_item)
results.update({an_type: r})
if an_type == 'op':
_x0s.update({'op': r})
_x0s.update({'op+ic': icmodified_x0(circ, r)})
_handle_netlist_ics(circ, an_list, ic_list=[])
return results
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 process_postproc(postproc_list, title, results, outfilename, remote=False):
"""Runs the post-processing operations, such as plotting.
postproc_list: list of post processing operations as returned by main()
title: the deck title
results: the results to be plotted (including the ones that are not needed)
outfilename: if the plots are saved to disk, this is the filename without extension
remote: boolean, do not show plots if True (such as ssh without X11 forwarding)
Returns: None
"""
index = 0
if outfilename == 'stdout':
printing.print_warning(
"Plotting and printing the results to stdout are incompatible options. Plotting skipped."
)
return
for postproc in postproc_list:
#print postproc["analysis"], results.keys(), results.has_key(postproc["analysis"]), results[postproc["analysis"]] is None #DEBUG
plotting.plot_results(
title, postproc["x"], postproc["l2l1"],
results[postproc["analysis"]],
"%s-%d.%s" % (outfilename, index, options.plotting_outtype))
index = index + 1
if len(postproc_list) and not remote:
plotting.show_plots()
return None
def check_step_and_points(step, points, period):
"""Sets consistently the step size and the number of points, according to the given period
Returns: (points, step)
"""
if step is None and points is None:
print "Warning: shooting had no step nor n. of points setted. Using", options.shooting_default_points, "points."
points = options.shooting_default_points
elif step is not None and points is not None:
print "Warning: shooting had both step and n. of points setted. Using", step, "step. (NA)"
points = None
if points:
step = (1.0 * period) / (points - 1)
else:
points = (1.0 * period) / step
if points % 1 != 0:
step = step + (step * (points % 1)) / int(points)
points = int((1.0 * period) / step)
printing.print_warning("adapted step is %g" % (step,))
else:
points = int(points)
points = points + \
1 # 0 - N where xN is in reality the first point of the second period!!
return (points, step)
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 check_step_and_points(step, points, period):
"""Sets consistently the step size and the number of points, according to the given period
Returns: (points, step)
"""
if step is None and points is None:
print "Warning: shooting had no step nor n. of points setted. Using", options.shooting_default_points, "points."
points = options.shooting_default_points
elif step is not None and points is not None:
print "Warning: shooting had both step and n. of points setted. Using", step, "step. (NA)"
points = None
if points:
step = (1.0 * period) / (points - 1)
else:
points = (1.0 * period) / step
if points % 1 != 0:
step = step + (step * (points % 1)) / int(points)
points = int((1.0 * period) / step)
printing.print_warning("adapted step is %g" % (step, ))
else:
points = int(points)
points = points + \
1 # 0 - N where xN is in reality the first point of the second period!!
return (points, step)
def run(circ, an_list=None):
""" Processes an analysis vector:
circ: the circuit instance
an_queue: the list of analyses to be performed, if unset defaults to those queued
with queue_analysis()
Returns: the results (in dict form)
"""
results = {}
if not an_list:
an_list = _queue
else:
if type(an_list) == tuple:
an_list = list(an_list)
elif type(an_list) == dict:
an_list = [an_list] # run(mycircuit, op1)
elif type(an_list) == list:
pass
while len(an_list):
an_item = an_list.pop(0)
an_type = an_item.pop('type')
if 'x0' in an_item and isinstance(an_item['x0'], str):
printing.print_warning(
"%s has x0 set to %s, unavailable. Using 'None'." %
(an_type.upper(), an_item['x0']))
an_item['x0'] = None
r = analysis[an_type](circ, **an_item)
results.update({an_type: r})
if an_type == 'op':
_x0s.update({'op': r})
_x0s.update({'op+ic': icmodified_x0(circ, r)})
_handle_netlist_ics(circ, an_list, ic_list=[])
return results
def add(self, atuple):
if not len(atuple) == self._width:
printing.print_warning("Attempted to add a element of wrong size to LIFO buffer. BUG?")
return False
else:
self._the_real_buffer.insert(0, atuple)
if len(self._the_real_buffer) > self._length:
self._the_real_buffer = self._the_real_buffer[:self._length]
return True
def add(self, atuple):
if not len(atuple) == self._width:
printing.print_warning(
"Attempted to add a element of wrong size to LIFO buffer. BUG?"
)
return False
else:
self._the_real_buffer.insert(0, atuple)
if len(self._the_real_buffer) > self._length:
self._the_real_buffer = self._the_real_buffer[:self._length]
return True
def plot_results(title, y2y1_list, results, outfilename):
"""Plot the results.
"""
if results is None:
printing.print_warning("No results available for plotting. Skipping.")
return
fig = pylab.figure()
analysis = results.get_type().upper()
gdata = []
x, xlabel = results.get_x(), results.get_xlabel()
xunit = results.units[xlabel]
yvu = []
for y2label, y1label in y2y1_list:
if y1label is not None and y1label != '':
data1 = results[y1label]
line_label = y2label + "-" + y1label
else:
line_label = y2label
data1 = 0
data2 = results[y2label]
yvu += [(line_label, results.units[y2label])]
gdata.append((data2 - data1, line_label))
if xlabel == 'w':
xlog = True
else:
xlog = False
setup_plot(fig, title, (xlabel, xunit), yvu, xlog=xlog)
pylab.hold(True)
ymax, ymin = None, None
for y, label in gdata:
[line] = pylab.plot(x,
y,
options.plotting_style,
label=label + " (" + analysis + ")",
mfc='w',
lw=options.plotting_lw,
mew=options.plotting_lw)
line.set_mec(line.get_color()) # nice empty circles
ymax = y.max() if ymax is None or y.max() > ymax else ymax
ymin = y.min() if ymin is None or y.min() < ymin else ymin
pylab.xlim((x.min(), x.max()))
pylab.ylim((ymin - (ymax - ymin) * .01, ymax + (ymax - ymin) * .01))
pylab.hold(False)
pylab.legend()
if outfilename is not None and options.plotting_outtype is not None:
save_figure(outfilename, fig)
return
def plot_results(title, y2y1_list, results, outfilename):
"""Plot the results.
"""
if results is None:
printing.print_warning("No results available for plotting. Skipping.")
return
fig = pylab.figure()
analysis = results.get_type().upper()
gdata = []
x, xlabel = results.get_x(), results.get_xlabel()
xunit = results.units[xlabel]
yvu = []
for y2label, y1label in y2y1_list:
if y1label is not None and y1label != '':
data1 = results[y1label]
line_label = y2label + "-" + y1label
else:
line_label = y2label
data1 = 0
data2 = results[y2label]
yvu += [(line_label, results.units[y2label])]
gdata.append((data2 - data1, line_label))
if xlabel == 'w':
xlog = True
else:
xlog = False
setup_plot(fig, title, (xlabel, xunit), yvu, xlog=xlog)
pylab.hold(True)
ymax, ymin = None, None
for y, label in gdata:
[line] = pylab.plot(
x, y, options.plotting_style, label=label +
" (" + analysis + ")",
mfc='w', lw=options.plotting_lw, mew=options.plotting_lw)
line.set_mec(line.get_color()) # nice empty circles
ymax = y.max() if ymax is None or y.max() > ymax else ymax
ymin = y.min() if ymin is None or y.min() < ymin else ymin
pylab.xlim((x.min(), x.max()))
pylab.ylim((ymin - (ymax - ymin) * .01, ymax + (ymax - ymin) * .01))
pylab.hold(False)
pylab.legend()
if outfilename is not None and options.plotting_outtype is not None:
save_figure(outfilename, fig)
return
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 plot_results(title, xvarname, y2y1_list, results, outfilename):
"""Plot the results.
"""
if results is None:
printing.print_warning("No results available for plotting. Skipping.")
return
fig = pylab.figure()
analysis = results.get_type().upper()
xunit = results.units[xvarname]
gdata = []
x = results[xvarname]
yvu = []
for y2label, y1label in y2y1_list:
if y1label is not None and y1label != '':
data1 = results[y1label]
line_label = y2label + "-" + y1label
else:
line_label = y2label
data1 = 0
data2 = results[y2label]
yvu += [(line_label, results.units[y2label])]
gdata.append((data2 - data1, line_label))
if xvarname == 'w':
xlog = True
else:
xlog = False
setup_plot(fig, title, (xvarname, xunit), yvu, xlog=xlog)
pylab.hold(True)
for y, label in gdata:
# pylab wants matrices in the form: N,1, while results come as (1, N) -> Transpose
pylab.plot(
x.T,
y.T,
options.plotting_style,
label=label + " (" + analysis + ")",
)
pylab.xlim((x.min(), x.max()))
pylab.hold(False)
pylab.legend()
if outfilename is not None and options.plotting_outtype is not None:
save_figure(outfilename, fig)
return
def check_ground_paths(mna, circ, reduced_mna=True, verbose=3):
"""Checks that every node has a DC path to ground, wheather through
nonlinear or linear elements.
- This does not ensure that the circuit will have a DC solution.
- A node without DC path to ground would be rescued (likely) by GMIN
so (for the time being at least) we do *not* halt the execution.
- Also, two series capacitors always fail this check (GMIN saves us)
Bottom line: if there is no DC path to ground, there is probably a
mistake in the netlist. Print a warning.
"""
test_passed = True
if reduced_mna:
# reduced_correction
r_c = 1
else:
r_c = 0
to_be_checked_for_nonlinear_paths = []
for node in circ.nodes_dict.iterkeys():
if node == 0:
continue
# ground
if mna[node - r_c,
node - r_c] == 0 and not mna[node - r_c,
len(circ.nodes_dict) - r_c:].any():
to_be_checked_for_nonlinear_paths.append(node)
for node in to_be_checked_for_nonlinear_paths:
node_is_nl_op = False
for elem in circ:
if not elem.is_nonlinear:
continue
ops = elem.get_output_ports()
for op in ops:
if op.count(node):
node_is_nl_op = True
if not node_is_nl_op:
if verbose:
printing.print_warning("No path to ground from node " +
circ.nodes_dict[node])
test_passed = False
return test_passed
def plot_results(title, xvarname, y2y1_list, results, outfilename):
"""Plot the results.
"""
if results is None:
printing.print_warning("No results available for plotting. Skipping.")
return
fig = pylab.figure()
analysis = results.get_type().upper()
xunit = results.units[xvarname]
gdata = []
x = results[xvarname]
yvu = []
for y2label, y1label in y2y1_list:
if y1label is not None and y1label != "":
data1 = results[y1label]
line_label = y2label + "-" + y1label
else:
line_label = y2label
data1 = 0
data2 = results[y2label]
yvu += [(line_label, results.units[y2label])]
gdata.append((data2 - data1, line_label))
if xvarname == "w":
xlog = True
else:
xlog = False
setup_plot(fig, title, (xvarname, xunit), yvu, xlog=xlog)
pylab.hold(True)
for y, label in gdata:
# pylab wants matrices in the form: N,1, while results come as (1, N) -> Transpose
pylab.plot(x.T, y.T, options.plotting_style, label=label + " (" + analysis + ")")
pylab.xlim((x.min(), x.max()))
pylab.hold(False)
pylab.legend()
if outfilename is not None and options.plotting_outtype is not None:
save_figure(outfilename, fig)
return
def process_postproc(postproc_list, title, results, outfilename):
"""Runs the post-processing operations, such as plotting.
postproc_list: list of post processing operations as returned by main()
title: the deck title
results: the results to be plotted (including the ones that are not needed)
outfilename: if the plots are saved to disk, this is the filename without extension
Returns: None
"""
index = 0
if outfilename == 'stdout':
printing.print_warning(
"Plotting and printing the results to stdout are incompatible options. Plotting skipped.")
return
for postproc in postproc_list:
plotting.plot_results(title, postproc["l2l1"], results[
postproc["analysis"]], "%s-%d.%s" % (outfilename, index, options.plotting_outtype))
index = index + 1
if len(postproc_list) and options.plotting_show_plots:
plotting.show_plots()
return None
def process_postproc(postproc_list, title, results, outfilename, remote=False):
"""Runs the post-processing operations, such as plotting.
postproc_list: list of post processing operations as returned by main()
title: the deck title
results: the results to be plotted (including the ones that are not needed)
outfilename: if the plots are saved to disk, this is the filename without extension
remote: boolean, do not show plots if True (such as ssh without X11 forwarding)
Returns: None
"""
index = 0
if outfilename == 'stdout':
printing.print_warning("Plotting and printing the results to stdout are incompatible options. Plotting skipped.")
return
for postproc in postproc_list:
#print postproc["analysis"], results.keys(), results.has_key(postproc["analysis"]), results[postproc["analysis"]] is None #DEBUG
plotting.plot_results(title, postproc["x"], postproc["l2l1"], results[postproc["analysis"]], "%s-%d.%s" % (outfilename, index, options.plotting_outtype))
index = index +1
if len(postproc_list) and not remote:
plotting.show_plots()
return None
def process_postproc(postproc_list, title, results, outfilename):
"""Runs the post-processing operations, such as plotting.
postproc_list: list of post processing operations as returned by main()
title: the deck title
results: the results to be plotted (including the ones that are not needed)
outfilename: if the plots are saved to disk, this is the filename without extension
Returns: None
"""
index = 0
if outfilename == 'stdout':
printing.print_warning(
"Plotting and printing the results to stdout are incompatible options. Plotting skipped."
)
return
for postproc in postproc_list:
plotting.plot_results(
title, postproc["l2l1"], results[postproc["analysis"]],
"%s-%d.%s" % (outfilename, index, options.plotting_outtype))
index = index + 1
if len(postproc_list) and options.plotting_show_plots:
plotting.show_plots()
return None
def check_ground_paths(mna, circ, reduced_mna=True):
"""Checks that every node has a DC path to ground, wheather through
nonlinear or linear elements.
- This does not ensure that the circuit will have a DC solution.
- A node without DC path to ground would be rescued (likely) by GMIN
so (for the time being at least) we do *not* halt the execution.
- Also, two series capacitors always fail this check (GMIN saves us)
Bottom line: if there is no DC path to ground, there is probably a
mistake in the netlist. Print a warning.
"""
test_passed = True
if reduced_mna:
# reduced_correction
r_c = 1
else:
r_c = 0
to_be_checked_for_nonlinear_paths = []
for node in circ.nodes_dict.iterkeys():
if node == 0:
continue
# ground
if mna[node - r_c, node - r_c] == 0 and not mna[node - r_c, len(circ.nodes_dict) - r_c :].any():
to_be_checked_for_nonlinear_paths.append(node)
for node in to_be_checked_for_nonlinear_paths:
node_is_nl_op = False
for elem in circ.elements:
if not elem.is_nonlinear:
continue
ops = elem.get_output_ports()
for op in ops:
if op.count(node):
node_is_nl_op = True
if not node_is_nl_op:
printing.print_warning("No path to ground from node " + circ.nodes_dict[node])
test_passed = False
return test_passed
def generate_mna_and_N(circ, opts, ac=False):
"""Generates a symbolic Modified Nodal Analysis matrix and N vector.
"""
#print options
n_of_nodes = len(circ.nodes_dict)
mna = smzeros(n_of_nodes)
N = smzeros((n_of_nodes, 1))
s = sympy.Symbol("s", complex=True)
subs_g = {}
#process_elements()
for elem in circ.elements:
#if elem.is_nonlinear and not (isinstance(elem, mosq.mosq_device) or isinstance(elem, ekv.ekv_device)):
# print "Skipped elem "+elem.letter_id.upper()+elem.descr + ": not implemented."
# continue
if isinstance(elem, devices.resistor):
# we use conductances instead of 1/R because there is a significant
# overhead handling many 1/R terms in sympy.
if elem.is_symbolic:
R = sympy.Symbol(elem.letter_id.upper()+elem.descr, real=True)
G = sympy.Symbol("G"+elem.descr, real=True)
# but we keep track of which is which and substitute back after solving.
subs_g.update({G:1/R})
else:
R = elem.R
G = 1.0/R
#mna[elem.n1, elem.n1] = mna[elem.n1, elem.n1] + 1/R
#mna[elem.n1, elem.n2] = mna[elem.n1, elem.n2] - 1/R
#mna[elem.n2, elem.n1] = mna[elem.n2, elem.n1] - 1/R
#mna[elem.n2, elem.n2] = mna[elem.n2, elem.n2] + 1/R
mna[elem.n1, elem.n1] = mna[elem.n1, elem.n1] + G
mna[elem.n1, elem.n2] = mna[elem.n1, elem.n2] - G
mna[elem.n2, elem.n1] = mna[elem.n2, elem.n1] - G
mna[elem.n2, elem.n2] = mna[elem.n2, elem.n2] + G
elif isinstance(elem, devices.capacitor):
if ac:
if elem.is_symbolic:
capa = sympy.Symbol(elem.letter_id.upper()+elem.descr, real=True)
else:
capa = elem.C
mna[elem.n1, elem.n1] = mna[elem.n1, elem.n1] + s*capa
mna[elem.n1, elem.n2] = mna[elem.n1, elem.n2] - s*capa
mna[elem.n2, elem.n2] = mna[elem.n2, elem.n2] + s*capa
mna[elem.n2, elem.n1] = mna[elem.n2, elem.n1] - s*capa
else:
pass
elif isinstance(elem, devices.inductor):
pass
elif isinstance(elem, devices.gisource):
if elem.is_symbolic:
alpha = sympy.Symbol(elem.letter_id+elem.descr, real=True)
else:
alpha = elem.alpha
mna[elem.n1, elem.sn1] = mna[elem.n1, elem.sn1] + alpha
mna[elem.n1, elem.sn2] = mna[elem.n1, elem.sn2] - alpha
mna[elem.n2, elem.sn1] = mna[elem.n2, elem.sn1] - alpha
mna[elem.n2, elem.sn2] = mna[elem.n2, elem.sn2] + alpha
elif isinstance(elem, devices.isource):
if elem.is_symbolic:
IDC = sympy.Symbol(elem.letter_id.upper()+elem.descr, real=True)
else:
IDC = elem.idc
N[elem.n1, 0] = N[elem.n1, 0] + IDC
N[elem.n2, 0] = N[elem.n2, 0] - IDC
elif isinstance(elem, mosq.mosq_device) or isinstance(elem, ekv.ekv_device):
gm = sympy.Symbol('gm_'+elem.letter_id+elem.descr, real=True)
mna[elem.n1, elem.ng] = mna[elem.n1, elem.ng] + gm
mna[elem.n1, elem.n2] = mna[elem.n1, elem.n2] - gm
mna[elem.n2, elem.ng] = mna[elem.n2, elem.ng] - gm
mna[elem.n2, elem.n2] = mna[elem.n2, elem.n2] + gm
if opts['r0s']:
r0 = sympy.Symbol('r0_'+elem.letter_id+elem.descr, real=True)
mna[elem.n1, elem.n1] = mna[elem.n1, elem.n1] + 1/r0
mna[elem.n1, elem.n2] = mna[elem.n1, elem.n2] - 1/r0
mna[elem.n2, elem.n1] = mna[elem.n2, elem.n1] - 1/r0
mna[elem.n2, elem.n2] = mna[elem.n2, elem.n2] + 1/r0
elif isinstance(elem, diode.diode):
gd = sympy.Symbol("g"+elem.letter_id+elem.descr)
mna[elem.n1, elem.n1] = mna[elem.n1, elem.n1] + gd
mna[elem.n1, elem.n2] = mna[elem.n1, elem.n2] - gd
mna[elem.n2, elem.n1] = mna[elem.n2, elem.n1] - gd
mna[elem.n2, elem.n2] = mna[elem.n2, elem.n2] + gd
elif isinstance(elem, devices.inductor_coupling):
pass
# this is taken care of within the inductors
elif circuit.is_elem_voltage_defined(elem):
pass
#we'll add its lines afterwards
else:
printing.print_warning("Skipped elem %s: not implemented." % (elem.letter_id.upper()+elem.descr,))
pre_vde = mna.shape[0]
for elem in circ.elements:
if circuit.is_elem_voltage_defined(elem):
index = mna.shape[0] #get_matrix_size(mna)[0]
mna = expand_matrix(mna, add_a_row=True, add_a_col=True)
N = expand_matrix(N, add_a_row=True, add_a_col=False)
# KCL
mna[elem.n1, index] = +1
mna[elem.n2, index] = -1
# KVL
mna[index, elem.n1] = +1
mna[index, elem.n2] = -1
if isinstance(elem, devices.vsource):
if elem.is_symbolic:
VDC = sympy.Symbol(elem.letter_id.upper() + elem.descr, real=True)
else:
VDC = elem.vdc
N[index, 0] = -VDC
elif isinstance(elem, devices.evsource):
if elem.is_symbolic:
alpha = sympy.Symbol(elem.letter_id.upper() + elem.descr, real=True)
else:
alpha = elem.alpha
mna[index, elem.sn1] = -alpha
mna[index, elem.sn2] = +alpha
elif isinstance(elem, devices.inductor):
if ac:
if elem.is_symbolic:
L = sympy.Symbol(elem.letter_id.upper() + elem.descr, real=True)
else:
L = elem.L
mna[index, index] = -s*L
else:
pass
# already so: commented out
# N[index,0] = 0
elif isinstance(elem, devices.hvsource):
printing.print_warning("symbolic.py: BUG - hvsources are not implemented yet.")
sys.exit(33)
for elem in circ.elements:
if circuit.is_elem_voltage_defined(elem):
if isinstance(elem, devices.inductor):
if ac:
# find its index to know which column corresponds to its current
this_index = circ.find_vde_index("L"+elem.descr, verbose=0)
for cd in elem.coupling_devices:
if cd.is_symbolic:
M = sympy.Symbol("M" + cd.descr, real=True)
else:
M = cd.K
# get id+descr of the other inductor (eg. "L32")
other_id_wdescr = cd.get_other_inductor("L"+elem.descr)
# find its index to know which column corresponds to its current
other_index = circ.find_vde_index(other_id_wdescr, verbose=0)
# add the term.
#print "other_index: "+str(other_index)
#print "this_index: "+str(this_index)
mna[pre_vde+this_index,pre_vde+other_index] += -s*M
#print mna
else:
pass
# already so: commented out
# N[index,0] = 0
#all done
return (mna, N, subs_g)
def mdn_solver(x,
mna,
circ,
T,
MAXIT,
nv,
locked_nodes,
time=None,
print_steps=False,
vector_norm=lambda v: max(abs(v)),
debug=True):
"""
Solves a problem like F(x) = 0 using the Newton Algorithm with a variable damping td.
Where:
F(x) = mna*x + T + T(x)
mna is the Modified Network Analysis matrix of the circuit
T(x) is the contribute of nonlinear elements to KCL
T -> independent sources, time invariant and invariant
x is the initial guess.
Every x is given by:
x = x + td*dx
Where td is a damping coefficient to avoid overflow in non-linear components and
excessive oscillation in the very first iteration. Afterwards td=1
To calculate td, an array of locked nodes is needed.
The convergence check is done this way:
if (vector_norm(dx) < alpha*vector_norm(x) + beta) and (vector_norm(residuo) < alpha*vector_norm(x) + beta):
beta should be the machine precision, alpha 1e-(N+1) where N is the number of the significative
digits you wish to have.
Parameters:
x: the initial guess. If set to None, it will be initialized to all zeros. Specifying a initial guess
may improve the convergence time of the algorithm and determine which solution (if any)
is found if there are more than one.
mna: the Modified Network Analysis matrix of the circuit, reduced, see above
element_list:
T: see above.
MAXIT: Maximum iterations that the method may perform.
nv: number of nodes in the circuit (counting the ref, 0)
locked_nodes: see get_td() and dc_solve(), generated by circ.get_locked_nodes()
time: the value of time to be passed to non_linear _and_ time variant elements.
print_steps: show a progress indicator
vector_norm:
Returns a tuple with:
the solution,
the remaining error,
a boolean that is true whenever the method exits because of a successful convergence check
the number of NR iterations performed
"""
# OLD COMMENT: FIXME REWRITE: solve through newton
# problem is F(x)= mna*x +H(x) = 0
# H(x) = N + T(x)
# lets say: J = dF/dx = mna + dT(x)/dx
# J*dx = -1*(mna*x+N+T(x))
# dT/dx � lo jacobiano -> g_eq (o gm)
# print_steps = False
# locked_nodes = get_locked_nodes(element_list)
mna_size = mna.shape[0]
nonlinear_circuit = circ.is_nonlinear()
tick = ticker.ticker(increments_for_step=1)
tick.display(print_steps)
if x is None:
x = numpy.mat(numpy.zeros((mna_size, 1)))
# if no guess was specified, its all zeros
else:
if not x.shape[0] == mna_size:
raise Exception, "x0s size is different from expected: " + \
str(x.shape[0]) + " " + str(mna_size)
if T is None:
printing.print_warning(
"dc_analysis.mdn_solver called with T==None, setting T=0. BUG or no sources in circuit?"
)
T = numpy.mat(numpy.zeros((mna_size, 1)))
converged = False
iteration = 0L
while iteration < MAXIT: # newton iteration counter
iteration += 1
tick.step(print_steps)
if nonlinear_circuit:
# build dT(x)/dx (stored in J) and Tx(x)
J, Tx = build_J_and_Tx(x, mna_size, circ, time)
J = J + mna
else:
J = mna
Tx = 0
residuo = mna * x + T + Tx
dx = numpy.linalg.inv(J) * (-1 * residuo)
x = x + get_td(dx, locked_nodes, n=iteration) * dx
if not nonlinear_circuit:
converged = True
break
elif convergence_check(x, dx, residuo, nv - 1)[0]:
converged = True
break
# if vector_norm(dx) == numpy.nan: #Overflow
# raise OverflowError
tick.hide(print_steps)
if debug and not converged:
# re-run the convergence check, only this time get the results
# by node, so we can show to the users which nodes are misbehaving.
converged, convergence_by_node = convergence_check(x,
dx,
residuo,
nv - 1,
debug=True)
else:
convergence_by_node = []
return (x, residuo, converged, iteration, convergence_by_node)
def process_analysis(an_list,
circ,
outfile,
verbose,
cli_tran_method=None,
guess=True,
disable_step_control=False):
""" Processes an analysis vector:
an_list: the list of analysis to be performed, as returned by netlist_parser
circ: the circuit instance, returned by netlist_parser
outfile: a filename. Results will be written to it. If set to stdout, prints to stdout
verbose: verbosity level
cli_tran_method: force the specified method in each tran analysis (see transient.py)
guess: use the builtin method get_dc_guess to guess x0
Returns: None
"""
x0_op = None
x0_ic_dict = {}
results = {}
for directive in [x for x in an_list if x["type"] == "ic"]:
x0_ic_dict.update({
directive["name"]:\
dc_analysis.build_x0_from_user_supplied_ic(circ, voltages_dict=directive["vdict"], currents_dict=directive["cdict"])
})
for an in an_list:
if outfile != 'stdout':
data_filename = outfile + "." + an["type"]
else:
data_filename = outfile
if an["type"] == "ic":
continue
if an["type"] == "op":
if not an.has_key('guess_label') or an["guess_label"] is None:
x0_op = dc_analysis.op_analysis(circ,
guess=guess,
data_filename=data_filename,
verbose=verbose)
else:
if not an["guess_label"] in x0_ic_dict:
printing.print_warning(
"op: guess is set but no matching .ic directive was found."
)
printing.print_warning(
"op: using built-in guess method: " + str(guess))
x0_op = dc_analysis.op_analysis(circ,
guess=guess,
verbose=verbose)
else:
x0_op = dc_analysis.op_analysis(
circ,
guess=False,
x0=x0_ic_dict[an["guess_label"]],
verbose=verbose)
sol = x0_op
elif an["type"] == "dc":
if an["source_name"][0].lower() == "v":
elem_type = "vsource"
elif an["source_name"][0].lower() == "i":
elem_type = "isource"
else:
printing.print_general_error(
"Type of sweep source is unknown: " + an[1][0])
sys.exit(1)
sol = dc_analysis.dc_analysis(
circ, start=an["start"], stop=an["stop"], step=an["step"], \
type_descr=(elem_type, an["source_name"][1:]),
xguess=x0_op, data_filename=data_filename, guess=guess,
stype=an['stype'], verbose=verbose)
#{"type":"tran", "tstart":tstart, "tstop":tstop, "tstep":tstep, "uic":uic, "method":method, "ic_label":ic_label}
elif an["type"] == "tran":
if cli_tran_method is not None:
tran_method = cli_tran_method.upper()
elif an["method"] is not None:
tran_method = an["method"].upper()
else:
tran_method = options.default_tran_method
# setup the initial condition (t=0) according to uic
# uic = 0 -> all node voltages and currents are zero
# uic = 1 -> node voltages and currents are those computed in the last OP analysis
# uic = 2 -> node voltages and currents are those computed in the last OP analysis
# combined with the ic=XX directive found in capacitors and inductors
# uic = 3 -> use a .ic directive defined by the user
uic = an["uic"]
if uic == 0:
x0 = None
elif uic == 1:
if x0_op is None:
printing.print_general_error(
"uic is set to 1, but no op has been calculated yet.")
sys.exit(51)
x0 = x0_op
elif uic == 2:
if x0_op is None:
printing.print_general_error(
"uic is set to 2, but no op has been calculated yet.")
sys.exit(51)
x0 = dc_analysis.modify_x0_for_ic(circ, x0_op)
elif uic == 3:
if an["ic_label"] is None:
printing.print_general_error(
"uic is set to 3, but param ic=<ic_label> was not defined."
)
sys.exit(53)
elif not an["ic_label"] in x0_ic_dict:
printing.print_general_error("uic is set to 3, but no .ic directive named %s was found." \
%(str(an["ic_label"]),))
sys.exit(54)
x0 = x0_ic_dict[an["ic_label"]]
sol = transient.transient_analysis(circ, \
tstart=an["tstart"], tstep=an["tstep"], tstop=an["tstop"], \
x0=x0, mna=None, N=None, verbose=verbose, data_filename=data_filename, \
use_step_control=(not disable_step_control), method=tran_method)
elif an["type"] == "shooting":
if an["method"] == "brute-force":
sol = bfpss.bfpss(circ, period=an["period"], step=an["step"], mna=None, Tf=None, \
D=None, points=an["points"], autonomous=an["autonomous"], x0=x0_op, \
data_filename=data_filename, verbose=verbose)
elif an["method"] == "shooting":
sol = shooting.shooting(circ, period=an["period"], step=an["step"], mna=None, \
Tf=None, D=None, points=an["points"], autonomous=an["autonomous"], \
data_filename=data_filename, verbose=verbose)
elif an["type"] == "symbolic":
if not 'subs' in an.keys():
an.update({'subs': None})
sol = symbolic.solve(circ,
an['source'],
opts={'ac': an['ac']},
subs=an['subs'],
verbose=verbose)
elif an["type"] == "ac":
sol = ac.ac_analysis(circ=circ, start=an['start'], nsteps=an['nsteps'], \
stop=an['stop'], step_type='LOG', xop=x0_op, mna=None,\
data_filename=data_filename, verbose=verbose)
elif an["type"] == "temp":
constants.T = utilities.Celsius2Kelvin(an['temp'])
results.update({an["type"]: sol})
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...", 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 set_temperature(T):
T = float(T)
if T > 300:
printing.print_warning(u"The temperature will be set to %f \xB0 C.")
constants.T = utilities.Celsius2Kelvin(T)
def process_analysis(an_list, circ, outfile, verbose, cli_tran_method=None, guess=True, disable_step_control=False):
""" Processes an analysis vector:
an_list: the list of analysis to be performed, as returned by netlist_parser
circ: the circuit instance, returned by netlist_parser
outfile: a filename. Results will be written to it. If set to stdout, prints to stdout
verbose: verbosity level
cli_tran_method: force the specified method in each tran analysis (see transient.py)
guess: use the builtin method get_dc_guess to guess x0
Returns: None
"""
x0_op = None
x0_ic_dict = {}
results = {}
for directive in [ x for x in an_list if x["type"] == "ic" ]:
x0_ic_dict.update({
directive["name"]:\
dc_analysis.build_x0_from_user_supplied_ic(circ, voltages_dict=directive["vdict"], currents_dict=directive["cdict"])
})
for an in an_list:
if outfile != 'stdout':
data_filename = outfile + "." + an["type"]
else:
data_filename = outfile
if an["type"] == "ic":
continue
if an["type"] == "op":
if not an.has_key('guess_label') or an["guess_label"] is None:
x0_op = dc_analysis.op_analysis(circ, guess=guess, data_filename=data_filename, verbose=verbose)
else:
if not an["guess_label"] in x0_ic_dict:
printing.print_warning("op: guess is set but no matching .ic directive was found.")
printing.print_warning("op: using built-in guess method: "+str(guess))
x0_op = dc_analysis.op_analysis(circ, guess=guess, verbose=verbose)
else:
x0_op = dc_analysis.op_analysis(circ, guess=False, x0=x0_ic_dict[an["guess_label"]], verbose=verbose)
sol = x0_op
elif an["type"] == "dc":
if an["source_name"][0].lower() == "v":
elem_type = "vsource"
elif an["source_name"][0].lower() == "i":
elem_type = "isource"
else:
printing.print_general_error("Type of sweep source is unknown: " + an[1][0])
sys.exit(1)
sol = dc_analysis.dc_analysis(
circ, start=an["start"], stop=an["stop"], step=an["step"], \
type_descr=(elem_type, an["source_name"][1:]),
xguess=x0_op, data_filename=data_filename, guess=guess,
stype=an['stype'], verbose=verbose)
#{"type":"tran", "tstart":tstart, "tstop":tstop, "tstep":tstep, "uic":uic, "method":method, "ic_label":ic_label}
elif an["type"] == "tran":
if cli_tran_method is not None:
tran_method = cli_tran_method.upper()
elif an["method"] is not None:
tran_method = an["method"].upper()
else:
tran_method = options.default_tran_method
# setup the initial condition (t=0) according to uic
# uic = 0 -> all node voltages and currents are zero
# uic = 1 -> node voltages and currents are those computed in the last OP analysis
# uic = 2 -> node voltages and currents are those computed in the last OP analysis
# combined with the ic=XX directive found in capacitors and inductors
# uic = 3 -> use a .ic directive defined by the user
uic = an["uic"]
if uic == 0:
x0 = None
elif uic == 1:
if x0_op is None:
printing.print_general_error("uic is set to 1, but no op has been calculated yet.")
sys.exit(51)
x0 = x0_op
elif uic == 2:
if x0_op is None:
printing.print_general_error("uic is set to 2, but no op has been calculated yet.")
sys.exit(51)
x0 = dc_analysis.modify_x0_for_ic(circ, x0_op)
elif uic == 3:
if an["ic_label"] is None:
printing.print_general_error("uic is set to 3, but param ic=<ic_label> was not defined.")
sys.exit(53)
elif not an["ic_label"] in x0_ic_dict:
printing.print_general_error("uic is set to 3, but no .ic directive named %s was found." \
%(str(an["ic_label"]),))
sys.exit(54)
x0 = x0_ic_dict[an["ic_label"]]
sol = transient.transient_analysis(circ, \
tstart=an["tstart"], tstep=an["tstep"], tstop=an["tstop"], \
x0=x0, mna=None, N=None, verbose=verbose, data_filename=data_filename, \
use_step_control=(not disable_step_control), method=tran_method)
elif an["type"] == "shooting":
if an["method"]=="brute-force":
sol = bfpss.bfpss(circ, period=an["period"], step=an["step"], mna=None, Tf=None, \
D=None, points=an["points"], autonomous=an["autonomous"], x0=x0_op, \
data_filename=data_filename, verbose=verbose)
elif an["method"]=="shooting":
sol = shooting.shooting(circ, period=an["period"], step=an["step"], mna=None, \
Tf=None, D=None, points=an["points"], autonomous=an["autonomous"], \
data_filename=data_filename, verbose=verbose)
elif an["type"] == "symbolic":
if not 'subs' in an.keys():
an.update({'subs':None})
sol = symbolic.solve(circ, an['source'], opts={'ac':an['ac']}, subs=an['subs'], verbose=verbose)
elif an["type"] == "ac":
sol = ac.ac_analysis(circ=circ, start=an['start'], nsteps=an['nsteps'], \
stop=an['stop'], step_type='LOG', xop=x0_op, mna=None,\
data_filename=data_filename, verbose=verbose)
elif an["type"] == "temp":
constants.T = utilities.Celsius2Kelvin(an['temp'])
results.update({an["type"]:sol})
return results
def generate_mna_and_N(circ, opts, ac=False, verbose=3):
"""Generates a symbolic Modified Nodal Analysis matrix and N vector.
"""
# print options
n_of_nodes = len(circ.nodes_dict)
mna = smzeros(n_of_nodes)
N = smzeros((n_of_nodes, 1))
s = sympy.Symbol('s', complex=True)
subs_g = {}
for elem in circ:
if isinstance(elem, devices.Resistor):
# we use conductances instead of 1/R because there is a significant
# overhead handling many 1/R terms in sympy.
if elem.is_symbolic:
R = sympy.Symbol(elem.part_id.upper(),
real=True,
positive=True)
G = sympy.Symbol('G' + elem.part_id[1:],
real=True,
positive=True)
# but we keep track of which is which and substitute back after
# solving.
subs_g.update({G: 1 / R})
else:
R = elem.value
G = 1.0 / R
mna[elem.n1, elem.n1] = mna[elem.n1, elem.n1] + G
mna[elem.n1, elem.n2] = mna[elem.n1, elem.n2] - G
mna[elem.n2, elem.n1] = mna[elem.n2, elem.n1] - G
mna[elem.n2, elem.n2] = mna[elem.n2, elem.n2] + G
elif isinstance(elem, devices.Capacitor):
if ac:
if elem.is_symbolic:
capa = sympy.Symbol(elem.part_id.upper(),
real=True,
positive=True)
else:
capa = elem.value
mna[elem.n1, elem.n1] = mna[elem.n1, elem.n1] + s * capa
mna[elem.n1, elem.n2] = mna[elem.n1, elem.n2] - s * capa
mna[elem.n2, elem.n2] = mna[elem.n2, elem.n2] + s * capa
mna[elem.n2, elem.n1] = mna[elem.n2, elem.n1] - s * capa
else:
pass
elif isinstance(elem, devices.Inductor):
pass
elif isinstance(elem, devices.GISource):
if elem.is_symbolic:
alpha = sympy.Symbol(elem.part_id.upper(), real=True)
else:
alpha = elem.value
mna[elem.n1, elem.sn1] = mna[elem.n1, elem.sn1] + alpha
mna[elem.n1, elem.sn2] = mna[elem.n1, elem.sn2] - alpha
mna[elem.n2, elem.sn1] = mna[elem.n2, elem.sn1] - alpha
mna[elem.n2, elem.sn2] = mna[elem.n2, elem.sn2] + alpha
elif isinstance(elem, devices.ISource):
if elem.is_symbolic:
IDC = sympy.Symbol(elem.part_id.upper(), real=True)
else:
IDC = elem.dc_value
N[elem.n1, 0] = N[elem.n1, 0] + IDC
N[elem.n2, 0] = N[elem.n2, 0] - IDC
elif isinstance(elem, mosq.mosq_device) or isinstance(
elem, ekv.ekv_device):
gm = sympy.Symbol('gm_' + elem.part_id, real=True, positive=True)
mna[elem.n1, elem.ng] = mna[elem.n1, elem.ng] + gm
mna[elem.n1, elem.n2] = mna[elem.n1, elem.n2] - gm
mna[elem.n2, elem.ng] = mna[elem.n2, elem.ng] - gm
mna[elem.n2, elem.n2] = mna[elem.n2, elem.n2] + gm
if opts['r0s']:
r0 = sympy.Symbol('r0_' + elem.part_id,
real=True,
positive=True)
mna[elem.n1, elem.n1] = mna[elem.n1, elem.n1] + 1 / r0
mna[elem.n1, elem.n2] = mna[elem.n1, elem.n2] - 1 / r0
mna[elem.n2, elem.n1] = mna[elem.n2, elem.n1] - 1 / r0
mna[elem.n2, elem.n2] = mna[elem.n2, elem.n2] + 1 / r0
elif isinstance(elem, diode.diode):
gd = sympy.Symbol("g" + elem.part_id, positive=True)
mna[elem.n1, elem.n1] = mna[elem.n1, elem.n1] + gd
mna[elem.n1, elem.n2] = mna[elem.n1, elem.n2] - gd
mna[elem.n2, elem.n1] = mna[elem.n2, elem.n1] - gd
mna[elem.n2, elem.n2] = mna[elem.n2, elem.n2] + gd
elif isinstance(elem, devices.InductorCoupling):
pass
# this is taken care of within the inductors
elif circuit.is_elem_voltage_defined(elem):
pass
# we'll add its lines afterwards
elif verbose:
printing.print_warning("Skipped elem %s: not implemented." %
(elem.part_id.upper(), ))
pre_vde = mna.shape[0]
for elem in circ:
if circuit.is_elem_voltage_defined(elem):
index = mna.shape[0] # get_matrix_size(mna)[0]
mna = expand_matrix(mna, add_a_row=True, add_a_col=True)
N = expand_matrix(N, add_a_row=True, add_a_col=False)
# KCL
mna[elem.n1, index] = +1
mna[elem.n2, index] = -1
# KVL
mna[index, elem.n1] = +1
mna[index, elem.n2] = -1
if isinstance(elem, devices.VSource):
if elem.is_symbolic:
VDC = sympy.Symbol(elem.part_id.upper(), real=True)
else:
VDC = elem.dc_value
N[index, 0] = -VDC
elif isinstance(elem, devices.EVSource):
if elem.is_symbolic:
alpha = sympy.Symbol(elem.part_id.upper(), real=True)
else:
alpha = elem.alpha
mna[index, elem.sn1] = -alpha
mna[index, elem.sn2] = +alpha
elif isinstance(elem, devices.Inductor):
if ac:
if elem.is_symbolic:
L = sympy.Symbol(elem.part_id.upper(),
real=True,
positive=True)
else:
L = elem.L
mna[index, index] = -s * L
else:
pass
# already so: commented out
# N[index,0] = 0
elif isinstance(elem, devices.HVSource):
printing.print_warning(
"symbolic.py: BUG - hvsources are not implemented yet.")
sys.exit(33)
for elem in circ:
if circuit.is_elem_voltage_defined(elem):
if isinstance(elem, devices.Inductor):
if ac:
# find its index to know which column corresponds to its
# current
this_index = circ.find_vde_index(elem.part_id, verbose=0)
for cd in elem.coupling_devices:
if cd.is_symbolic:
M = sympy.Symbol(cd.part_id,
real=True,
positive=True)
else:
M = cd.K
# get `part_id` of the other inductor (eg. "L32")
other_id_wdescr = cd.get_other_inductor(elem.part_id)
# find its index to know which column corresponds to
# its current
other_index = circ.find_vde_index(other_id_wdescr,
verbose=0)
# add the term.
mna[pre_vde + this_index,
pre_vde + other_index] += -s * M
else:
pass
# all done
return (mna, N, subs_g)
def mdn_solver(
x,
mna,
circ,
T,
MAXIT,
nv,
locked_nodes,
time=None,
print_steps=False,
vector_norm=lambda v: max(abs(v)),
debug=True,
):
"""
Solves a problem like F(x) = 0 using the Newton Algorithm with a variable damping td.
Where:
F(x) = mna*x + T + T(x)
mna is the Modified Network Analysis matrix of the circuit
T(x) is the contribute of nonlinear elements to KCL
T -> independent sources, time invariant and invariant
x is the initial guess.
Every x is given by:
x = x + td*dx
Where td is a damping coefficient to avoid overflow in non-linear components and
excessive oscillation in the very first iteration. Afterwards td=1
To calculate td, an array of locked nodes is needed.
The convergence check is done this way:
if (vector_norm(dx) < alpha*vector_norm(x) + beta) and (vector_norm(residuo) < alpha*vector_norm(x) + beta):
beta should be the machine precision, alpha 1e-(N+1) where N is the number of the significative
digits you wish to have.
Parameters:
x: the initial guess. If set to None, it will be initialized to all zeros. Specifying a initial guess
may improve the convergence time of the algorithm and determine which solution (if any)
is found if there are more than one.
mna: the Modified Network Analysis matrix of the circuit, reduced, see above
element_list:
T: see above.
MAXIT: Maximum iterations that the method may perform.
nv: number of nodes in the circuit (counting the ref, 0)
locked_nodes: see get_td() and dc_solve(), generated by circ.get_locked_nodes()
time: the value of time to be passed to non_linear _and_ time variant elements.
print_steps: show a progress indicator
vector_norm:
Returns a tuple with:
the solution,
the remaining error,
a boolean that is true whenever the method exits because of a successful convergence check
the number of NR iterations performed
"""
# OLD COMMENT: FIXME REWRITE: solve through newton
# problem is F(x)= mna*x +H(x) = 0
# H(x) = N + T(x)
# lets say: J = dF/dx = mna + dT(x)/dx
# J*dx = -1*(mna*x+N+T(x))
# dT/dx � lo jacobiano -> g_eq (o gm)
# print_steps = False
# locked_nodes = get_locked_nodes(element_list)
mna_size = mna.shape[0]
nonlinear_circuit = circ.is_nonlinear()
tick = ticker.ticker(increments_for_step=1)
tick.display(print_steps)
if x is None:
x = numpy.mat(numpy.zeros((mna_size, 1))) # if no guess was specified, its all zeros
else:
if not x.shape[0] == mna_size:
raise Exception, "x0s size is different from expected: " + str(x.shape[0]) + " " + str(mna_size)
if T is None:
printing.print_warning("dc_analysis.mdn_solver called with T==None, setting T=0. BUG or no sources in circuit?")
T = numpy.mat(numpy.zeros((mna_size, 1)))
converged = False
iteration = 0
for iteration in xrange(MAXIT): # newton iteration counter
tick.step(print_steps)
if nonlinear_circuit:
# build dT(x)/dx (stored in J) and Tx(x)
J, Tx = build_J_and_Tx(x, mna_size, circ.elements, time)
J = J + mna
else:
J = mna
Tx = 0
residuo = mna * x + T + Tx
dx = numpy.linalg.inv(J) * (-1 * residuo)
x = x + get_td(dx, locked_nodes, n=iteration) * dx
if iteration > 0:
if convergence_check(x, dx, residuo, nv - 1)[0]:
converged = True
break
if vector_norm(dx) is numpy.nan: # Overflow
raise OverflowError
tick.hide(print_steps)
if debug and not converged:
convergence_by_node = convergence_check(x, dx, residuo, nv - 1, debug=True)[1]
else:
convergence_by_node = []
return (x, residuo, converged, iteration + 1, convergence_by_node)
def set_temperature(T):
T = float(T)
if T > 300:
printing.print_warning(u"The temperature will be set to %f \xB0 C.")
constants.T = utilities.Celsius2Kelvin(T)
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 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 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 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
