def solve_meat_solve_rtof(trace, ndp, dp, r, intervals, max_steps, exp_advanced):
F = dp.get_fun_space()
LF = LowerSets(F)
res = dp.solve_r(r)
fnames = ndp.get_fnames()
x = ", ".join(fnames)
# todo: add better formatting
trace.log('Maximal functionality possible: %s = %s' % (x, LF.format(res)))
return res, trace
def dual01_chain(id_dp, dp):
try:
with primitive_dp_test(id_dp, dp):
print('Starting testing with %r' % id_dp)
# get a chain of resources
F = dp.get_fun_space()
R = dp.get_res_space()
# try to solve
try:
dp.solve(F.witness())
dp.solve_r(R.witness())
except NotSolvableNeedsApprox:
print('NotSolvableNeedsApprox - doing lower bound ')
n = 5
dpL, dpU = get_dp_bounds(dp, nl=n, nu=n)
dual01_chain(id_dp+'_L%s'%n, dpL)
dual01_chain(id_dp+'_U%s'%n, dpU)
return
LF = LowerSets(F)
UR = UpperSets(R)
rchain = R.get_test_chain(n=8)
poset_check_chain(R, rchain)
try:
lfchain = list(map(dp.solve_r, rchain))
for lf in lfchain:
LF.belongs(lf)
except NotSolvableNeedsApprox as e:
print('skipping because %s' % e)
return
try:
poset_check_chain(LF, list(reversed(lfchain)))
except ValueError as e:
msg = 'The results of solve_r() are not a chain.'
raise_wrapped(Exception, e, msg, chain=rchain, lfchain=lfchain)
# now, for each functionality f,
# we know that the corresponding resource should be feasible
for lf, r in zip(lfchain, rchain):
print('')
print('r: %s' % R.format(r))
print('lf = h*(r) = %s' % LF.format(lf))
for f in lf.maximals:
print(' f = %s' % F.format(f))
f_ur = dp.solve(f)
print(' f_ur = h(f) = %s' % UR.format(f_ur))
try:
f_ur.belongs(r)
except NotBelongs as e:
try:
Rcomp.tolerate_numerical_errors = True
f_ur.belongs(r)
logger.info('In this case, there was a numerical error')
logger.info('Rcomp.tolerate_numerical_errors = True solved the problem')
except:
msg = ''
raise_wrapped(AssertionError, e, msg,
lf=lf, r=r, f_ur=f_ur,
r_repr=r.__repr__(),
f_ur_minimals=f_ur.minimals.__repr__())
finally:
Rcomp.tolerate_numerical_errors = False
def repr_h_map(self):
LF = LowerSets(self.F)
return 'f ⟼ {⟨⟩} if f ∈ %s, else ø' % LF.format(self.limit)
def process_rtof(self, e, string, do_approximations, nl, nu):
mcdp_library = library_from_env(e)
parsed = mcdp_library.parse_constant(string)
space = parsed.unit
value = parsed.value
ndp, dp = self.get_ndp_dp_e(e)
R = dp.get_res_space()
LF = LowerSets(dp.get_fun_space())
try:
r = parsed.cast_value(R)
except NotLeq:
msg = 'Space %s cannot be converted to %s' % (parsed.unit, R)
raise DPSemanticError(msg)
logger.info('query rtof: %s ...' % R.format(r))
tracer = Tracer(logger=logger)
max_steps = 10000
intervals = False
res = {}
if do_approximations:
dpl, dpu = get_dp_bounds(dp, nl, nu)
result_l, _trace = solve_meat_solve_rtof(tracer, ndp, dpl, r,
intervals, max_steps,
False)
result_u, trace = solve_meat_solve_rtof(tracer, ndp, dpu, r,
intervals, max_steps,
False)
data = dict(result_l=result_l, result_u=result_u, dpl=dpl, dpu=dpu)
res['output_result'] = 'Lower: %s\nUpper: %s' % (
LF.format(result_l), LF.format(result_u))
else:
try:
result, trace = solve_meat_solve_rtof(tracer, ndp, dp, r,
intervals, max_steps,
False)
except NotSolvableNeedsApprox:
msg = 'The design problem has infinite antichains. Please use approximations.'
raise NeedsApprox(msg)
data = dict(result=result, dp=dp)
res['output_result'] = LF.format(result)
e = cgi.escape
res['output_space'] = e(space.__repr__() + '\n' + str(type(space)))
res['output_raw'] = e(value.__repr__() + '\n' + str(type(value)))
res['output_formatted'] = e(space.format(value))
res['output_trace'] = str(trace)
return data, res
def dual01_chain(id_dp, dp):
try:
with primitive_dp_test(id_dp, dp):
print('Starting testing with %r' % id_dp)
# get a chain of resources
F = dp.get_fun_space()
R = dp.get_res_space()
# try to solve
try:
dp.solve(F.witness())
dp.solve_r(R.witness())
except NotSolvableNeedsApprox:
print('NotSolvableNeedsApprox - doing lower bound ')
n = 5
dpL, dpU = get_dp_bounds(dp, nl=n, nu=n)
dual01_chain(id_dp+'_L%s'%n, dpL)
dual01_chain(id_dp+'_U%s'%n, dpU)
return
LF = LowerSets(F)
UR = UpperSets(R)
rchain = R.get_test_chain(n=8)
poset_check_chain(R, rchain)
try:
lfchain = list(map(dp.solve_r, rchain))
for lf in lfchain:
LF.belongs(lf)
except NotSolvableNeedsApprox as e:
print('skipping because %s' % e)
return
try:
poset_check_chain(LF, list(reversed(lfchain)))
except ValueError as e:
msg = 'The results of solve_r() are not a chain.'
raise_wrapped(Exception, e, msg, chain=rchain, lfchain=lfchain)
# now, for each functionality f,
# we know that the corresponding resource should be feasible
for lf, r in zip(lfchain, rchain):
print('')
print('r: %s' % R.format(r))
print('lf = h*(r) = %s' % LF.format(lf))
for f in lf.maximals:
print(' f = %s' % F.format(f))
f_ur = dp.solve(f)
print(' f_ur = h(f) = %s' % UR.format(f_ur))
try:
f_ur.belongs(r)
except NotBelongs as e:
try:
Rcomp.tolerate_numerical_errors = True
f_ur.belongs(r)
logger.info('In this case, there was a numerical error')
logger.info('Rcomp.tolerate_numerical_errors = True solved the problem')
except:
msg = ''
raise_wrapped(AssertionError, e, msg,
lf=lf, r=r, f_ur=f_ur,
r_repr=r.__repr__(),
f_ur_minimals=f_ur.minimals.__repr__())
finally:
Rcomp.tolerate_numerical_errors = False
