def compute_err(self, svals, dvals, tol_type):
"""Compute the diff between svals and dvals for the given tol_type"""
if tol_type not in ("abs", "rel"):
raise ValueError("Invalid tol_type '%s'" % (tol_type, ))
diff = svals - dvals
if tol_type == "abs":
err = diff
else:
# relative: divide by current value of svals
old_settings = numpy.seterr(divide='ignore', invalid='ignore')
err = diff / svals
numpy.seterr(**old_settings)
# isnan means 0/0 so diff is 0
err[numpy.isnan(err)] = 0
# isinf means diff/0, so just use the diff
if any(numpy.isinf(err)):
for i in range(len(err)):
if numpy.isinf(err[i]):
err[i] = diff[i]
return err
def solve_tear_wegstein(self, G, order, function, tears, outEdges, iterLim,
tol, tol_type, report_diffs, accel_min, accel_max):
"""
Use Wegstein to solve tears. If multiple tears are given
they are solved simultaneously.
Arguments
---------
order
List of lists of order in which to calculate nodes
tears
List of tear edge indexes
iterLim
Limit on the number of iterations to run
tol
Tolerance at which iteration can be stopped
accel_min
Minimum value for Wegstein acceleration factor
accel_max
Maximum value for Wegstein acceleration factor
tol_type
Type of tolerance value, either "abs" (absolute) or
"rel" (relative to current value)
Returns
-------
list
List of lists of diff history, differences between input and
output values at each iteration
"""
hist = [] # diff at each iteration in every variable
if not len(tears):
# no need to iterate just run the calculations
self.run_order(G, order, function, tears)
return hist
logger.info("Starting Wegstein tear convergence")
itercount = 0
ignore = tears + outEdges
gofx = self.generate_gofx(G, tears)
x = self.generate_first_x(G, tears)
err = self.compute_err(gofx, x, tol_type)
hist.append(err)
if report_diffs:
print("Diff matrix:\n%s" % err)
# check if it's already solved
if numpy.max(numpy.abs(err)) < tol:
logger.info("Wegstein converged in %s iterations" % itercount)
return hist
# if not solved yet do one direct step
x_prev = x
gofx_prev = gofx
x = gofx
self.pass_tear_wegstein(G, tears, gofx)
while True:
itercount += 1
logger.info("Running Wegstein iteration %s" % itercount)
self.run_order(G, order, function, ignore)
gofx = self.generate_gofx(G, tears)
err = self.compute_err(gofx, x, tol_type)
hist.append(err)
if report_diffs:
print("Diff matrix:\n%s" % err)
if numpy.max(numpy.abs(err)) < tol:
break
if itercount > iterLim:
logger.warning("Wegstein failed to converge in %s iterations" %
iterLim)
return hist
denom = x - x_prev
# this will divide by 0 at some points but we handle that below,
# so ignore division warnings
old_settings = numpy.seterr(divide='ignore', invalid='ignore')
slope = numpy.divide((gofx - gofx_prev), denom)
numpy.seterr(**old_settings)
# if isnan or isinf then x and x_prev were the same,
# so just do direct sub for those elements
slope[numpy.isnan(slope)] = 0
slope[numpy.isinf(slope)] = 0
accel = slope / (slope - 1)
accel[accel < accel_min] = accel_min
accel[accel > accel_max] = accel_max
x_prev = x
gofx_prev = gofx
x = accel * x_prev + (1 - accel) * gofx_prev
self.pass_tear_wegstein(G, tears, x)
self.pass_edges(G, outEdges)
logger.info("Wegstein converged in %s iterations" % itercount)
return hist
