def sort_assignments_topologically(
assignments: Sequence[Union[Assignment, Node]]
) -> List[Union[Assignment, Node]]:
"""Sorts assignments in topological order, such that symbols used on rhs occur first on a lhs"""
edges = []
for c1, e1 in enumerate(assignments):
if hasattr(e1, 'lhs') and hasattr(e1, 'rhs'):
symbols = [e1.lhs]
elif isinstance(e1, Node):
symbols = e1.symbols_defined
else:
raise NotImplementedError(
f"Cannot sort topologically. Object of type {type(e1)} cannot be handled."
)
for lhs in symbols:
for c2, e2 in enumerate(assignments):
if isinstance(e2, Assignment) and lhs in e2.rhs.free_symbols:
edges.append((c1, c2))
elif isinstance(e2, Node) and lhs in e2.undefined_symbols:
edges.append((c1, c2))
return [
assignments[i]
for i in sp.topological_sort((range(len(assignments)), edges))
]
def fix_parameters(self, **kwargs):
"""Takes an estimated parameter from a DSGESelf
and converts it to a calibrated one."""
for para, value in kwargs.items():
para = Parameter(para)
self['par_ordering'].remove(para)
self['other_para'].append(para)
self['para_func'][str(para)] = value
context_tuple = ([(p, Parameter(p)) for p in self.parameters]
+ [(p.name, p) for p in self['other_para']])
context = dict(context_tuple)
context['exp'] = sympy.exp
context['log'] = sympy.log
to_replace = {}
for p in self['other_para']:
to_replace[p] = eval(str(self['para_func'][p.name]), context)
to_replace = list(to_replace.items())
from itertools import combinations, permutations
edges = [(i,j) for i,j in permutations(to_replace,2) if type(i[1]) not in [float,int] and i[1].has(j[0])]
from sympy import default_sort_key, topological_sort
edges = [(v[0],dep) for v in to_replace for dep in sympy.sympify(v[1]).atoms(Parameter) if dep in self['other_para']]
para_func = topological_sort([self['other_para'], edges], default_sort_key)[::-1]
self['other_para'] = para_func
return self
def transformation(self, prob: Problem):
if not prob.sympified:
raise ValueError('Problem must be sympified.')
if len(prob.quantities) == 0:
return prob
quantities = prob.quantities
dependencies = []
for quantity_i, quantity_j in permutations(quantities, 2):
if quantity_i.expr.has(quantity_j.sym):
dependencies.append((quantity_i, quantity_j))
try:
ordered_quantities = sympy.topological_sort(
(quantities, dependencies), key=lambda _q: _q.name)
except ValueError:
raise ValueError('Cycle found in dependencies in quantities.')
for quantity in ordered_quantities:
prob.subs_all(quantity.sym, quantity.expr)
prob.quantities.remove(quantity)
# TODO add quantity calculation to Trajectory class
return prob
def smc(model, t0=0):
import sympy
from sympy.printing import fcode
cmodel = model.compile_model()
template = fortran_model #open('fortran_model.f90').read()
write_prior_file(cmodel.prior, '.')
system_matrices = model.python_sims_matrices(matrix_format='symbolic')
npara = len(model.parameters)
para = sympy.IndexedBase('para', shape=(npara + 1, ))
fortran_subs = dict(
zip([sympy.symbols('garbage')] + model.parameters, para))
fortran_subs[0] = 0.0
fortran_subs[1] = 1.0
fortran_subs[100] = 100.0
fortran_subs[2] = 2.0
fortran_subs[400] = 400.0
fortran_subs[4] = 4.0
context = dict([(p.name, p)
for p in model.parameters + model['other_para']])
context['exp'] = sympy.exp
context['log'] = sympy.log
to_replace = {}
for p in model['other_para']:
to_replace[p] = eval(str(model['para_func'][p.name]), context)
to_replace = list(to_replace.items())
print(to_replace)
from itertools import combinations, permutations
edges = [(i, j) for i, j in permutations(to_replace, 2)
if type(i[1]) not in [float, int] and i[1].has(j[0])]
from sympy import default_sort_key, topological_sort
para_func = topological_sort([to_replace, edges], default_sort_key)
to_write = [
'GAM0', 'GAM1', 'PSI', 'PPI', 'self%QQ', 'DD2', 'self%ZZ', 'self%HH'
]
fmats = [
fcode((mat.subs(para_func)).subs(fortran_subs),
assign_to=n,
source_format='free',
standard=95,
contract=False) for mat, n in zip(system_matrices, to_write)
]
sims_mat = '\n\n'.join(fmats)
template = template.format(model=model,
yy=cmodel.yy,
p0='',
t0=t0,
sims_mat=sims_mat)
return template
def smc(model, t0=0):
import sympy
from sympy.printing import fcode
cmodel = model.compile_model()
template = fortran_model # open('fortran_model.f90').read()
write_prior_file(cmodel.prior, ".")
system_matrices = model.python_sims_matrices(matrix_format="symbolic")
npara = len(model.parameters)
para = sympy.IndexedBase("para", shape=(npara + 1,))
from .symbols import Parameter
fortran_subs = dict(
zip(
[sympy.symbols("garbage")] + [Parameter(px) for px in model.parameters],
para,
)
)
fortran_subs[0] = 0.0
fortran_subs[1] = 1.0
fortran_subs[100] = 100.0
fortran_subs[2] = 2.0
fortran_subs[400] = 400.0
fortran_subs[4] = 4.0
context_tuple = [(p, Parameter(p)) for p in model.parameters] + [
(p.name, p) for p in model["other_para"]
]
context = dict(context_tuple)
context["exp"] = sympy.exp
context["log"] = sympy.log
to_replace = {}
for p in model["other_para"]:
to_replace[p] = eval(str(model["para_func"][p.name]), context)
to_replace = list(to_replace.items())
print(to_replace)
from itertools import combinations, permutations
edges = [
(i, j)
for i, j in permutations(to_replace, 2)
if type(i[1]) not in [float, int] and i[1].has(j[0])
]
from sympy import default_sort_key, topological_sort
para_func = topological_sort([to_replace, edges], default_sort_key)
to_write = ["GAM0", "GAM1", "PSI", "PPI", "self%QQ", "DD2", "self%ZZ", "self%HH"]
fmats = [
fcode(
(mat.subs(para_func)).subs(fortran_subs),
assign_to=n,
source_format="free",
standard=95,
contract=False,
)
for mat, n in zip(system_matrices, to_write)
]
sims_mat = "\n\n".join(fmats)
template = template.format(
model=model, yy=cmodel.yy, p0="", t0=t0, sims_mat=sims_mat
)
return template