def solve_all(self, f1, trace):
""" Returns an upperset in UR. You want to project
it to R1 to use as the output. """
dp0 = self.dp1
R = dp0.get_res_space()
R1 = R[0]
UR = UpperSets(R)
# we consider a set of iterates
# we start from the bottom
trace.log('Iterating in UR = %s' % UR.__str__())
s0 = R.Us(R.get_minimal_elements())
S = [
KleeneIteration(s=s0,
s_converged=R.Us(set()),
r=upperset_project(s0, 0),
r_converged=R1.Us(set()))
]
for i in range(1, 1000000): # XXX
with trace.iteration(i) as t:
si_prev = S[-1].s
si_next, converged = solve_f_iterate(dp0, f1, R, si_prev, t)
iteration = KleeneIteration(s=si_next,
s_converged=converged,
r=upperset_project(si_next, 0),
r_converged=upperset_project(
converged, 0))
S.append(iteration)
t.log('R = %s' % UR.format(si_next))
if do_extra_checks():
try:
UR.check_leq(si_prev, si_next)
except NotLeq as e:
msg = 'Loop iteration invariant not satisfied.'
raise_wrapped(Exception,
e,
msg,
si_prev=si_prev,
si_next=si_next,
dp=self.dp1)
t.values(state=S[-1])
if UR.leq(si_next, si_prev):
t.log('Breaking because converged (iteration %s) ' % i)
#t.log(' solution is %s' % (UR.format(sip)))
# todo: add reason why interrupted
break
trace.values(type='loop2', UR=UR, R=R, dp=self, iterations=S)
res_all = S[-1].s
res_r1 = upperset_project(res_all, 0)
result = dict(res_all=res_all, res_r1=res_r1)
return result
def solve_stats(ndp):
res = {}
query = {
"endurance": "1.5 hour",
"velocity": "1 m/s",
"extra_power": " 1 W",
"extra_payload": "100 g",
"num_missions": "100 []",
}
context = Context()
f = convert_string_query(ndp=ndp, query=query, context=context)
dp0 = ndp.get_dp()
dpL, dpU = get_dp_bounds(dp0, nl=1, nu=1)
F = dp0.get_fun_space()
F.belongs(f)
from mcdp import logger
traceL = Tracer(logger=logger)
resL = dpL.solve_trace(f, traceL)
traceU = Tracer(logger=logger)
resU = dpU.solve_trace(f, traceU)
R = dp0.get_res_space()
UR = UpperSets(R)
print('resultsL: %s' % UR.format(resL))
print('resultsU: %s' % UR.format(resU))
res['traceL'] = traceL
res['traceU'] = traceU
res['resL'] = resL
res['resU'] = resU
res['nsteps'] = 100
return res
def solve_meat_solve_ftor(trace, ndp, dp, fg, intervals, max_steps, exp_advanced):
R = dp.get_res_space()
UR = UpperSets(R)
# if intervals:
# res = solver_iterative(dp, fg, trace)
# else:
if True:
# if not _exp_advanced:
res = dp.solve_trace(fg, trace)
rnames = ndp.get_rnames()
x = ", ".join(rnames)
# todo: add better formatting
if res.minimals:
trace.log('Minimal resources needed: %s = %s' % (x, UR.format(res)))
else:
trace.log('This problem is unfeasible.')
# else:
# try:
# trace = generic_solve(dp, f=fg, max_steps=max_steps)
# trace.log('Iteration result: %s' % trace.result)
# ss = trace.get_s_sequence()
# S = trace.S
# trace.log('Fixed-point iteration converged to: %s'
# % S.format(ss[-1]))
# R = trace.dp.get_res_space()
# UR = UpperSets(R)
# sr = trace.get_r_sequence()
# rnames = ndp.get_rnames()
# x = ", ".join(rnames)
# trace.log('Minimal resources needed: %s = %s'
# % (x, UR.format(sr[-1])))
# except:
# raise
return res, trace
def solve_meat_solve(trace, ndp, dp, fg, intervals, max_steps, exp_advanced):
R = dp.get_res_space()
UR = UpperSets(R)
# if intervals:
# res = solver_iterative(dp, fg, trace)
# else:
if True:
# if not _exp_advanced:
res = dp.solve_trace(fg, trace)
rnames = ndp.get_rnames()
x = ", ".join(rnames)
# todo: add better formatting
trace.log("Minimal resources needed: %s = %s" % (x, UR.format(res)))
# else:
# try:
# trace = generic_solve(dp, f=fg, max_steps=max_steps)
# trace.log('Iteration result: %s' % trace.result)
# ss = trace.get_s_sequence()
# S = trace.S
# trace.log('Fixed-point iteration converged to: %s'
# % S.format(ss[-1]))
# R = trace.dp.get_res_space()
# UR = UpperSets(R)
# sr = trace.get_r_sequence()
# rnames = ndp.get_rnames()
# x = ", ".join(rnames)
# trace.log('Minimal resources needed: %s = %s'
# % (x, UR.format(sr[-1])))
# except:
# raise
return res, trace
def process(self, request, string, nl, nu):
l = self.get_library(request)
parsed = l.parse_constant(string)
space = parsed.unit
value = parsed.value
model_name = self.get_model_name(request)
library = self.get_current_library_name(request)
ndp, dp = self._get_ndp_dp(library, model_name)
F = dp.get_fun_space()
UR = UpperSets(dp.get_res_space())
tu = get_types_universe()
tu.check_leq(parsed.unit, F)
f = express_value_in_isomorphic_space(parsed.unit, parsed.value, F)
print('query: %s ...' % F.format(f))
from mocdp import logger
tracer = Tracer(logger=logger)
dpl, dpu = get_dp_bounds(dp, nl, nu)
intervals = False
max_steps = 10000
result_l, _trace = solve_meat_solve(tracer, ndp, dpl, f,
intervals, max_steps, False)
result_u, trace = solve_meat_solve(tracer, ndp, dpu, f,
intervals, max_steps, False)
key = (string, nl, nu)
res = dict(result_l=result_l, result_u=result_u, dpl=dpl, dpu=dpu)
self.solutions[key] = res
res = {}
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_result'] = 'Lower: %s\nUpper: %s' % (UR.format(result_l),
UR.format(result_u))
res['output_trace'] = str(trace)
encoded = "nl=%s&nu=%s&string=%s" % (nl, nu, string)
res['output_image'] = 'display.png?' + encoded
res['ok'] = True
return res
def check_lang_singlespace3():
ndp1 = parse_ndp("""
mcdp {
provides power [ S(electric_power) x W ]
requires heat [ S(heat) x W ]
efficiency = 0.9 []
r_heat = take(required heat, 1)
f_power = take(provided power, 1)
r_heat >= f_power / efficiency
}
""")
ndp2 = parse_ndp("""
mcdp {
provides power [ S(electric_power) x W ]
requires heat [ S(heat) x W ]
efficiency = 0.9 []
f_power = take(provided power, 1)
heat >= <S(heat):*, f_power / efficiency>
}
""")
dp1 = ndp1.get_dp()
dp2 = ndp2.get_dp()
R = dp1.get_res_space()
print type(R), R
UR = UpperSets(R)
res1 = dp1.solve(('electric_power', 10.0))
res2 = dp2.solve(('electric_power', 10.0))
print UR.format(res1)
print UR.format(res2)
def check_lang_singlespace3():
ndp1 = parse_ndp("""
mcdp {
provides power [ S(electric_power) x W ]
requires heat [ S(heat) x W ]
efficiency = 0.9 []
r_heat = take(required heat, 1)
f_power = take(provided power, 1)
r_heat >= f_power / efficiency
}
""")
ndp2 = parse_ndp("""
mcdp {
provides power [ S(electric_power) x W ]
requires heat [ S(heat) x W ]
efficiency = 0.9 []
f_power = take(provided power, 1)
heat >= <S(heat):*, f_power / efficiency>
}
""")
dp1 = ndp1.get_dp()
dp2 = ndp2.get_dp()
R = dp1.get_res_space()
print type(R), R
UR = UpperSets(R)
res1 = dp1.solve(('electric_power', 10.0))
res2 = dp2.solve(('electric_power', 10.0))
print UR.format(res1)
print UR.format(res2)
def check_coproducts1():
parse_wrap_check('choose(a:a, b:b)', Syntax.ndpt_dp_rvalue)
s = """
mcdp {
provides energy [J]
requires budget [$]
a = mcdp {
provides energy [J]
requires budget [$]
budget >= 5 $
energy <= 10 J
}
b = mcdp {
provides energy [J]
requires budget [$]
budget >= 5 $
energy <= 50 J
}
c = instance choose(optionA:a, optionB:b)
energy <= c.energy
budget >= c.budget
}
"""
ndp = parse_ndp(s)
dp = ndp.get_dp()
R = dp.get_res_space()
I = dp.get_imp_space()
print('R: %s' % R)
print('I: %s' % I)
UR = UpperSets(R)
res = dp.solve(0.0)
print UR.format(res)
imps = dp.get_implementations_f_r(f=0.0, r=R.get_top())
print imps
def solve_all(self, f1, trace):
""" Returns an upperset in UR. You want to project
it to R1 to use as the output. """
dp0 = self.dp1
R = dp0.get_res_space()
R1 = R[0]
UR = UpperSets(R)
# we consider a set of iterates
# we start from the bottom
trace.log('Iterating in UR = %s' % UR.__str__())
s0 = R.Us(R.get_minimal_elements())
S = [KleeneIteration(s=s0, s_converged=R.Us(set()),
r=upperset_project(s0, 0),
r_converged=R1.Us(set()))]
for i in range(1, 1000000): # XXX
with trace.iteration(i) as t:
si_prev = S[-1].s
si_next, converged = solve_f_iterate(dp0, f1, R, si_prev, t)
iteration = KleeneIteration(s=si_next,
s_converged=converged,
r=upperset_project(si_next, 0),
r_converged=upperset_project(converged, 0))
S.append(iteration)
t.log('R = %s' % UR.format(si_next))
if do_extra_checks():
try:
UR.check_leq(si_prev, si_next)
except NotLeq as e:
msg = 'Loop iteration invariant not satisfied.'
raise_wrapped(Exception, e, msg, si_prev=si_prev,
si_next=si_next, dp=self.dp1)
t.values(state=S[-1])
if UR.leq(si_next, si_prev):
t.log('Breaking because converged (iteration %s) ' % i)
#t.log(' solution is %s' % (UR.format(sip)))
# todo: add reason why interrupted
break
trace.values(type='loop2', UR=UR, R=R, dp=self, iterations=S)
res_all = S[-1].s
res_r1 = upperset_project(res_all, 0)
result = dict(res_all=res_all, res_r1=res_r1)
return result
def check_coproducts1():
parse_wrap_check('choose(a:a, b:b)', Syntax.ndpt_dp_rvalue)
s = """
mcdp {
provides energy [J]
requires budget [$]
a = mcdp {
provides energy [J]
requires budget [$]
budget >= 5 $
energy <= 10 J
}
b = mcdp {
provides energy [J]
requires budget [$]
budget >= 5 $
energy <= 50 J
}
c = instance choose(optionA:a, optionB:b)
energy <= c.energy
budget >= c.budget
}
"""
ndp = parse_ndp(s)
dp = ndp.get_dp()
R = dp.get_res_space()
I = dp.get_imp_space()
print('R: %s' % R)
print('I: %s' % I)
UR = UpperSets(R)
res = dp.solve(0.0)
print UR.format(res)
imps = dp.get_implementations_f_r(f=0.0, r=R.get_top())
print imps
def test_conversion(id_ndp, ndp):
if '_inf' in id_ndp:
# plusinvnat3b_inf
print(
'Assuming that the suffix "_inf" in %r means that this will not converge'
% (id_ndp))
print('Skipping this test')
return
try:
ndp.check_fully_connected()
except NotConnected:
print('Skipping test_conversion because %r not connected.' % id_ndp)
return
dp = ndp.get_dp()
F = dp.get_fun_space()
R = dp.get_res_space()
fs = F.get_minimal_elements()
max_elements = 5
if len(fs) >= max_elements:
fs = list(fs)[:max_elements]
UR = UpperSets(R)
for f in fs:
try:
res = dp.solve(f)
print('%s -> %s' % (F.format(f), UR.format(res)))
for r in res.minimals:
imps = dp.get_implementations_f_r(f, r)
print(imps)
except NotSolvableNeedsApprox:
break
def test_conversion(id_ndp, ndp):
if '_inf' in id_ndp:
# plusinvnat3b_inf
print('Assuming that the suffix "_inf" in %r means that this will not converge'
% (id_ndp))
print('Skipping this test')
return
try:
ndp.check_fully_connected()
except NotConnected:
print('Skipping test_conversion because %r not connected.' % id_ndp)
return
dp = ndp.get_dp()
F = dp.get_fun_space()
R = dp.get_res_space()
fs = F.get_minimal_elements()
max_elements = 5
if len(fs) >= max_elements:
fs = list(fs)[:max_elements]
UR = UpperSets(R)
for f in fs:
try:
res = dp.solve(f)
print('%s -> %s' % (F.format(f), UR.format(res)))
for r in res.minimals:
imps = dp.get_implementations_f_r(f, r)
print(imps)
except NotSolvableNeedsApprox:
break
def check_power8(): # TODO: move to ther files
ndp = parse_ndp("""
mcdp {
requires a [dimensionless]
requires b [dimensionless]
provides c [dimensionless]
a + b >= c
}
""")
dp = ndp.get_dp()
print(dp.repr_long())
nl = 5
nu = 5
dpL, dpU = get_dp_bounds(dp, nl, nu)
print(dpL.repr_long())
print(dpU.repr_long())
f = 10.0
UR = UpperSets(dp.get_res_space())
Rl = dpL.solve(f)
Ru = dpU.solve(f)
assert_equal(len(Rl.minimals), nl)
assert_equal(len(Ru.minimals), nu)
print('Rl: %s' % UR.format(Rl))
print('Ru: %s' % UR.format(Ru))
UR.check_leq(Rl, Ru)
import numpy as np
for x in np.linspace(0, f, 100):
y = f - x
p = (x, y)
Rl.belongs(p)
def solve_stats(ndp, n, algo):
res = {}
query = {
"travel_distance": " 2 km",
"carry_payload": "100 g",
"num_missions": "100 []",
}
context = Context()
f = convert_string_query(ndp=ndp, query=query, context=context)
dp0 = ndp.get_dp()
dpL, dpU = get_dp_bounds(dp0, nl=n, nu=n)
F = dp0.get_fun_space()
F.belongs(f)
logger = None
InvMult2.ALGO = algo
traceL = Tracer(logger=logger)
resL = dpL.solve_trace(f, traceL)
traceU = Tracer(logger=logger)
resU = dpU.solve_trace(f, traceU)
R = dp0.get_res_space()
UR = UpperSets(R)
print('resultsL: %s' % UR.format(resL))
print('resultsU: %s' % UR.format(resU))
res['traceL'] = traceL
res['traceU'] = traceU
res['resL'] = resL
res['resU'] = resU
res['n'] = n
res['query'] = query
return res
def solve_stats(ndp):
res = {}
query = {
"endurance": "1.5 hour",
"velocity": "1 m/s",
"extra_power": " 1 W",
"extra_payload": "100 g",
"num_missions": "100 []",
}
context = Context()
f = convert_string_query(ndp=ndp, query=query, context=context)
dp0 = ndp.get_dp()
dpL, dpU = get_dp_bounds(dp0, nl=1, nu=1)
F = dp0.get_fun_space()
F.belongs(f)
from mocdp import logger
traceL = Tracer(logger=logger)
resL = dpL.solve_trace(f, traceL)
traceU = Tracer(logger=logger)
resU = dpU.solve_trace(f, traceU)
R = dp0.get_res_space()
UR = UpperSets(R)
print('resultsL: %s' % UR.format(resL))
print('resultsU: %s' % UR.format(resU))
res['traceL'] = traceL
res['traceU'] = traceU
res['resL'] = resL
res['resU'] = resU
res['nsteps'] = 100
return 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
def friendly_solve(ndp, query, result_like='dict(str:str)', upper=None, lower=None):
"""
query = dict(power=(100,"W"))
result_like = dict(power="W")
s = solve
"""
#print('friendly_solve(upper=%s, lower=%s)' % (upper, lower))
# TODO: replace with convert_string_query(ndp, query, context):
fnames = ndp.get_fnames()
rnames = ndp.get_rnames()
if not len(rnames) >= 1:
raise NotImplementedError()
value = []
for fname in fnames:
if not fname in query:
msg = 'Missing function'
raise_desc(ValueError, msg, fname=fname, query=query, fnames=fnames)
F = ndp.get_ftype(fname)
q, qs = query[fname]
s = '%s %s' % (q, qs)
try:
val = interpret_params_1string(s, F=F)
except NotLeq as e:
raise_wrapped(ValueError, e, 'wrong type', fname=fname)
value.append(val)
if len(fnames) == 1:
value = value[0]
else:
value = tuple(value)
if hasattr(ndp, '_cache_dp0'):
dp0 = ndp._cache_dp0
else:
dp0 = ndp.get_dp()
ndp._cache_dp0 = dp0
if upper is not None:
_, dp = get_dp_bounds(dp0, nl=1, nu=upper)
elif lower is not None:
dp, _ = get_dp_bounds(dp0, nl=lower, nu=1)
else:
dp = dp0
F = dp.get_fun_space()
F.belongs(value)
from mcdp import logger
trace = Tracer(logger=logger)
res = dp.solve_trace(value, trace)
R = dp.get_res_space()
UR = UpperSets(R)
print('value: %s' % F.format(value))
print('results: %s' % UR.format(res))
ares = []
implementations = []
for r in res.minimals:
rnames = ndp.get_rnames()
fr = dict()
for rname, sunit in result_like.items():
if not rname in rnames:
msg = 'Could not find resource %r.' % rname
raise_desc(ValueError, msg, rnames=rnames)
i = rnames.index(rname)
unit = interpret_string_as_space(sunit)
Ri = ndp.get_rtype(rname)
if len(rnames) > 1:
ri = r[i]
else:
assert i == 0
ri = r
v = express_value_in_isomorphic_space(S1=Ri, s1=ri, S2=unit)
fr[rname] = v
ares.append(fr)
ms = dp.get_implementations_f_r(value, r)
implementations.append(ms)
return ares, implementations
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 process_ftor(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)
F = dp.get_fun_space()
UR = UpperSets(dp.get_res_space())
try:
f = parsed.cast_value(F)
except NotLeq:
msg = 'Space %s cannot be converted to %s' % (parsed.unit, F)
raise DPSemanticError(msg)
logger.info('query rtof: %s ...' % F.format(f))
tracer = Tracer(logger=logger)
intervals = False
max_steps = 10000
res = {}
if do_approximations:
dpl, dpu = get_dp_bounds(dp, nl, nu)
result_l, _trace = solve_meat_solve_ftor(tracer, ndp, dpl, f,
intervals, max_steps,
False)
result_u, trace = solve_meat_solve_ftor(tracer, ndp, dpu, f,
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' % (
UR.format(result_l), UR.format(result_u))
else:
try:
result, trace = solve_meat_solve_ftor(tracer, ndp, dp, f,
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'] = UR.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 friendly_solve(ndp, query, result_like='dict(str:str)', upper=None, lower=None):
"""
query = dict(power=(100,"W"))
result_like = dict(power="W")
s = solve
"""
#print('friendly_solve(upper=%s, lower=%s)' % (upper, lower))
# TODO: replace with convert_string_query(ndp, query, context):
fnames = ndp.get_fnames()
rnames = ndp.get_rnames()
if not len(rnames) >= 1:
raise NotImplementedError()
value = []
for fname in fnames:
if not fname in query:
msg = 'Missing function'
raise_desc(ValueError, msg, fname=fname, query=query, fnames=fnames)
F = ndp.get_ftype(fname)
q, qs = query[fname]
s = '%s %s' % (q, qs)
try:
val = interpret_params_1string(s, F=F)
except NotLeq as e:
raise_wrapped(ValueError, e, 'wrong type', fname=fname)
value.append(val)
if len(fnames) == 1:
value = value[0]
else:
value = tuple(value)
if hasattr(ndp, '_cache_dp0'):
dp0 = ndp._cache_dp0
else:
dp0 = ndp.get_dp()
ndp._cache_dp0 = dp0
if upper is not None:
_, dp = get_dp_bounds(dp0, nl=1, nu=upper)
elif lower is not None:
dp, _ = get_dp_bounds(dp0, nl=lower, nu=1)
else:
dp = dp0
F = dp.get_fun_space()
F.belongs(value)
from mocdp import logger
trace = Tracer(logger=logger)
res = dp.solve_trace(value, trace)
R = dp.get_res_space()
UR = UpperSets(R)
print('value: %s' % F.format(value))
print('results: %s' % UR.format(res))
ares = []
implementations = []
for r in res.minimals:
rnames = ndp.get_rnames()
fr = dict()
for rname, sunit in result_like.items():
if not rname in rnames:
msg = 'Could not find resource %r.' % rname
raise_desc(ValueError, msg, rnames=rnames)
i = rnames.index(rname)
unit = interpret_string_as_space(sunit)
Ri = ndp.get_rtype(rname)
if len(rnames) > 1:
ri = r[i]
else:
assert i == 0
ri = r
v = express_value_in_isomorphic_space(S1=Ri, s1=ri, S2=unit)
fr[rname] = v
ares.append(fr)
ms = dp.get_implementations_f_r(value, r)
implementations.append(ms)
return ares, implementations