def time_shift(expr,n):
from dolo.misc.misc import map_function_to_expression
from dolo.symbolic.symbolic import Variable,Shock
def f(e):
if isinstance(e,(Variable,Shock)):
return e(n)
else:
return e
return map_function_to_expression(f,expr)
def time_shift(expr, n):
from dolo.misc.misc import map_function_to_expression
from dolo.symbolic.symbolic import Variable, Shock
def f(e):
if isinstance(e, (Variable, Shock)):
return e(n)
else:
return e
return map_function_to_expression(f, expr)
def tabify_expression(self,eq,for_matlab=False,for_c=True):
#from dolo.misc.calculus import map_function_to_expression
# works when all variables are expressed without lag
subs_dict = self.build_substitution_list(for_matlab,not for_c)
def f(expr):
if expr.__class__ in [Variable,Parameter]:
vname = subs_dict[expr]
return(Symbol(vname))
else:
return(expr)
res = map_function_to_expression(f,eq)
return res
def tabify_expression(self, eq, for_matlab=False, for_c=True):
#from dolo.misc.calculus import map_function_to_expression
# works when all variables are expressed without lag
subs_dict = self.build_substitution_list(for_matlab, not for_c)
def f(expr):
if expr.__class__ in [Variable, Parameter]:
vname = subs_dict[expr]
return (Symbol(vname))
else:
return (expr)
res = map_function_to_expression(f, eq)
return res
def process_output(self, solution_order=False, fname=None):
from dolo.numeric.perturbations_to_states import simple_global_representation
data = simple_global_representation(self.model,
substitute_auxiliary=True,
keep_auxiliary=True,
solve_systems=True)
# print data['a_eqs']
# print data['f_eqs']
dmodel = self.model
model = dmodel
f_eqs = data['f_eqs']
g_eqs = data['g_eqs']
g_eqs = [
map_function_to_expression(lambda x: timeshift(x, 1), eq)
for eq in g_eqs
]
# h_eqs = data['h_eqs']
auxiliaries = data['auxiliaries']
states = data['states']
controls = data['controls']
# exp_vars = data['exp_vars']
#inf_bounds = data['inf_bounds']
#sup_bounds = data['sup_bounds']
controls_f = [v(1) for v in controls]
states_f = [v(1) for v in states]
sub_list = dict()
# for i,v in enumerate(exp_vars):
# sub_list[v] = 'z(:,{0})'.format(i+1)
for i, v in enumerate(controls):
sub_list[v] = 'x(:,{0})'.format(i + 1)
sub_list[v(1)] = 'xnext(:,{0})'.format(i + 1)
for i, v in enumerate(states):
sub_list[v] = 's(:,{0})'.format(i + 1)
sub_list[v(1)] = 'snext(:,{0})'.format(i + 1)
for i, v in enumerate(dmodel.shocks):
sub_list[v] = 'e(:,{0})'.format(i + 1)
for i, v in enumerate(dmodel.parameters):
sub_list[v] = 'p({0})'.format(i + 1)
text = '''function [model] = get_model()
model = model_info;
model.f = @f;
model.g = @g;
model.a = @a;
end
function [out1,out2,out3,out4,out5] = f(s,x,snext,xnext,p)
n = size(s,1);
{eq_fun_block}
end
function [out1,out2,out3] = g(s,x,e,p)
n = size(s,1);
{state_trans_block}
end
function [out1,out2,out3] = a(s,x,p)
n = size(s,1);
{aux_block}
end
function [out1] = model_info() % informations about the model
{model_info}
end
'''
from dolo.compiler.compiler import DicPrinter
dp = DicPrinter(sub_list)
def write_eqs(eq_l, outname='out1', ntabs=0):
eq_block = ' ' * ntabs + '{0} = zeros(n,{1});'.format(
outname, len(eq_l))
for i, eq in enumerate(eq_l):
eq_block += '\n' + ' ' * ntabs + '{0}(:,{1}) = {2};'.format(
outname, i + 1, dp.doprint_matlab(eq, vectorize=True))
return eq_block
def write_der_eqs(eq_l, v_l, lhs, ntabs=0):
eq_block = ' ' * ntabs + '{lhs} = zeros(n,{0},{1});'.format(
len(eq_l), len(v_l), lhs=lhs)
eq_l_d = eqdiff(eq_l, v_l)
for i, eqq in enumerate(eq_l_d):
for j, eq in enumerate(eqq):
s = dp.doprint_matlab(eq, vectorize=True)
eq_block += '\n' + ' ' * ntabs + '{lhs}(:,{0},{1}) = {2}; % d eq_{eq_n} w.r.t. {vname}'.format(
i + 1,
j + 1,
s,
lhs=lhs,
eq_n=i + 1,
vname=str(v_l[j]))
return eq_block
# eq_bounds_block = write_eqs(inf_bounds,ntabs=2)
# eq_bounds_block += '\n'
# eq_bounds_block += write_eqs(sup_bounds,'out2',ntabs=2)
eq_f_block = '''
% f
{0}
if nargout >= 2
% df/ds
{1}
% df/dx
{2}
% df/dsnext
{3}
% df/dxnext
{4}
end
'''.format(
write_eqs(f_eqs, 'out1', 3),
write_der_eqs(f_eqs, states, 'out2', 3),
write_der_eqs(f_eqs, controls, 'out3', 3),
write_der_eqs(f_eqs, states_f, 'out4', 3),
write_der_eqs(f_eqs, controls_f, 'out5', 3),
# write_der_eqs(f_eqs,exp_vars,'out4',3)
)
eq_g_block = '''
% g
{0}
if nargout >=2
% dg/ds
{1}
% dg/dx
{2}
end
'''.format(write_eqs(g_eqs, 'out1', 3),
write_der_eqs(g_eqs, states, 'out2', 3),
write_der_eqs(g_eqs, controls, 'out3', 3))
if 'a_eqs' in data:
a_eqs = data['a_eqs']
eq_a_block = '''
% a
{0}
if nargout >=2
% da/ds
{1}
% da/dx
{2}
end
'''.format(write_eqs(a_eqs, 'out1', 3),
write_der_eqs(a_eqs, states, 'out2', 3),
write_der_eqs(a_eqs, controls, 'out3', 3))
else:
eq_a_block = ''
# if not with_param_names:
# eq_h_block = 's=snext;\nx=xnext;\n'+eq_h_block
# param_def = 'p = [ ' + str.join(',',[p.name for p in dmodel.parameters]) + '];'
from dolo.misc.matlab import value_to_mat
# read model informations
[y, x, params_values] = model.read_calibration()
#params_values = '[' + str.join( ',', [ str( p ) for p in params] ) + '];'
vvs = model.variables
s_ss = [y[vvs.index(v)] for v in model['variables_groups']['states']]
x_ss = [y[vvs.index(v)] for v in model['variables_groups']['controls']]
model_info = '''
mod = struct;
mod.states = {states};
mod.controls = {controls};
mod.auxiliaries = {auxiliaries};
mod.parameters = {parameters};
mod.shocks = {shocks};
mod.s_ss = {s_ss};
mod.x_ss = {x_ss};
mod.params = {params_values};
'''.format(states='{{ {} }}'.format(
str.join(',', ["'{}'".format(v) for v in states])),
controls='{{ {} }}'.format(
str.join(',', ["'{}'".format(v) for v in controls])),
auxiliaries='{{ {} }}'.format(
str.join(',', ["'{}'".format(v) for v in auxiliaries])),
parameters='{{ {} }}'.format(
str.join(',', ["'{}'".format(v) for v in model.parameters])),
shocks='{{ {} }}'.format(
str.join(',', ["'{}'".format(v) for v in model.shocks])),
s_ss=value_to_mat(s_ss),
x_ss=value_to_mat(x_ss),
params_values=value_to_mat(params_values))
if solution_order:
from dolo.numeric.perturbations_to_states import approximate_controls
ZZ = approximate_controls(self.model,
order=solution_order,
return_dr=False)
n_c = len(controls)
ZZ = [np.array(e) for e in ZZ]
ZZ = [e[:n_c, ...] for e in ZZ
] # keep only control vars. (x) not expectations (h)
solution = " mod.X = cell({0},1);\n".format(len(ZZ))
for i, zz in enumerate(ZZ):
solution += " mod.X{{{0}}} = {1};\n".format(
i + 1, value_to_mat(zz.real))
model_info += solution
model_info += ' out1 = mod;\n'
text = text.format(
# eq_bounds_block = eq_bounds_block,
mfname=fname if fname else 'mf_' + model.fname,
eq_fun_block=eq_f_block,
state_trans_block=eq_g_block,
aux_block=eq_a_block,
# exp_fun_block=eq_h_block,
# solution = solution,
model_info=model_info)
return text
def process_output(self, solution_order=False, fname=None):
from dolo.numeric.perturbations_to_states import simple_global_representation
data = simple_global_representation(self.model, substitute_auxiliary=True, keep_auxiliary=True, solve_systems=True)
# print data['a_eqs']
# print data['f_eqs']
dmodel = self.model
model = dmodel
f_eqs = data['f_eqs']
g_eqs = data['g_eqs']
g_eqs = [map_function_to_expression(lambda x: timeshift(x,1),eq) for eq in g_eqs]
# h_eqs = data['h_eqs']
auxiliaries = data['auxiliaries']
states = data['states']
controls = data['controls']
# exp_vars = data['exp_vars']
#inf_bounds = data['inf_bounds']
#sup_bounds = data['sup_bounds']
controls_f = [v(1) for v in controls]
states_f = [v(1) for v in states]
sub_list = dict()
# for i,v in enumerate(exp_vars):
# sub_list[v] = 'z(:,{0})'.format(i+1)
for i,v in enumerate(controls):
sub_list[v] = 'x(:,{0})'.format(i+1)
sub_list[v(1)] = 'xnext(:,{0})'.format(i+1)
for i,v in enumerate(states):
sub_list[v] = 's(:,{0})'.format(i+1)
sub_list[v(1)] = 'snext(:,{0})'.format(i+1)
for i,v in enumerate(dmodel.shocks):
sub_list[v] = 'e(:,{0})'.format(i+1)
for i,v in enumerate(dmodel.parameters):
sub_list[v] = 'p({0})'.format(i+1)
text = '''function [model] = get_model()
model = model_info;
model.f = @f;
model.g = @g;
model.a = @a;
end
function [out1,out2,out3,out4,out5] = f(s,x,snext,xnext,p)
n = size(s,1);
{eq_fun_block}
end
function [out1,out2,out3] = g(s,x,e,p)
n = size(s,1);
{state_trans_block}
end
function [out1,out2,out3] = a(s,x,p)
n = size(s,1);
{aux_block}
end
function [out1] = model_info() % informations about the model
{model_info}
end
'''
from dolo.compiler.compiler import DicPrinter
dp = DicPrinter(sub_list)
def write_eqs(eq_l,outname='out1',ntabs=0):
eq_block = ' ' * ntabs + '{0} = zeros(n,{1});'.format(outname,len(eq_l))
for i,eq in enumerate(eq_l):
eq_block += '\n' + ' ' * ntabs + '{0}(:,{1}) = {2};'.format( outname, i+1, dp.doprint_matlab(eq,vectorize=True) )
return eq_block
def write_der_eqs(eq_l,v_l,lhs,ntabs=0):
eq_block = ' ' * ntabs + '{lhs} = zeros(n,{0},{1});'.format(len(eq_l),len(v_l),lhs=lhs)
eq_l_d = eqdiff(eq_l,v_l)
for i,eqq in enumerate(eq_l_d):
for j,eq in enumerate(eqq):
s = dp.doprint_matlab( eq, vectorize=True )
eq_block += '\n' + ' ' * ntabs + '{lhs}(:,{0},{1}) = {2}; % d eq_{eq_n} w.r.t. {vname}'.format(i+1,j+1,s,lhs=lhs,eq_n=i+1,vname=str(v_l[j]) )
return eq_block
# eq_bounds_block = write_eqs(inf_bounds,ntabs=2)
# eq_bounds_block += '\n'
# eq_bounds_block += write_eqs(sup_bounds,'out2',ntabs=2)
eq_f_block = '''
% f
{0}
if nargout >= 2
% df/ds
{1}
% df/dx
{2}
% df/dsnext
{3}
% df/dxnext
{4}
end
'''.format( write_eqs(f_eqs,'out1',3),
write_der_eqs(f_eqs,states,'out2',3),
write_der_eqs(f_eqs,controls,'out3',3),
write_der_eqs(f_eqs,states_f,'out4',3),
write_der_eqs(f_eqs,controls_f,'out5',3),
# write_der_eqs(f_eqs,exp_vars,'out4',3)
)
eq_g_block = '''
% g
{0}
if nargout >=2
% dg/ds
{1}
% dg/dx
{2}
end
'''.format( write_eqs(g_eqs,'out1',3),
write_der_eqs(g_eqs,states,'out2',3),
write_der_eqs(g_eqs,controls,'out3',3)
)
if 'a_eqs' in data:
a_eqs = data['a_eqs']
eq_a_block = '''
% a
{0}
if nargout >=2
% da/ds
{1}
% da/dx
{2}
end
'''.format( write_eqs(a_eqs,'out1',3),
write_der_eqs(a_eqs,states,'out2',3),
write_der_eqs(a_eqs,controls,'out3',3)
)
else:
eq_a_block = ''
# if not with_param_names:
# eq_h_block = 's=snext;\nx=xnext;\n'+eq_h_block
# param_def = 'p = [ ' + str.join(',',[p.name for p in dmodel.parameters]) + '];'
from dolo.misc.matlab import value_to_mat
# read model informations
[y,x,params_values] = model.read_calibration()
#params_values = '[' + str.join( ',', [ str( p ) for p in params] ) + '];'
vvs = model.variables
s_ss = [ y[vvs.index(v)] for v in model['variables_groups']['states'] ]
x_ss = [ y[vvs.index(v)] for v in model['variables_groups']['controls'] ]
model_info = '''
mod = struct;
mod.states = {states};
mod.controls = {controls};
mod.auxiliaries = {auxiliaries};
mod.parameters = {parameters};
mod.shocks = {shocks};
mod.s_ss = {s_ss};
mod.x_ss = {x_ss};
mod.params = {params_values};
'''.format(
states = '{{ {} }}'.format(str.join(',', ["'{}'".format(v) for v in states])),
controls = '{{ {} }}'.format(str.join(',', ["'{}'".format(v) for v in controls])),
auxiliaries = '{{ {} }}'.format(str.join(',', ["'{}'".format(v) for v in auxiliaries])),
parameters = '{{ {} }}'.format(str.join(',', ["'{}'".format(v) for v in model.parameters])),
shocks = '{{ {} }}'.format(str.join(',', ["'{}'".format(v) for v in model.shocks])),
s_ss = value_to_mat(s_ss),
x_ss = value_to_mat(x_ss),
params_values = value_to_mat(params_values)
)
if solution_order:
from dolo.numeric.perturbations_to_states import approximate_controls
ZZ = approximate_controls(self.model,order=solution_order, return_dr=False)
n_c = len(controls)
ZZ = [np.array(e) for e in ZZ]
ZZ = [e[:n_c,...] for e in ZZ] # keep only control vars. (x) not expectations (h)
solution = " mod.X = cell({0},1);\n".format(len(ZZ))
for i,zz in enumerate(ZZ):
solution += " mod.X{{{0}}} = {1};\n".format(i+1,value_to_mat(zz.real))
model_info += solution
model_info += ' out1 = mod;\n'
text = text.format(
# eq_bounds_block = eq_bounds_block,
mfname = fname if fname else 'mf_' + model.fname,
eq_fun_block=eq_f_block,
state_trans_block=eq_g_block,
aux_block=eq_a_block,
# exp_fun_block=eq_h_block,
# solution = solution,
model_info = model_info
)
return text
def read_model(self):
if self.__transformed_model__:
return self.__transformed_model__
dmodel = Model(**self.model) # copy the model
dmodel.check_consistency(auto_remove_variables=False)
def_eqs = [
eq for eq in dmodel.equations
if eq.tags['eq_type'] in ('def', 'auxiliary')
]
from dolo.misc.misc import map_function_to_expression
from dolo.symbolic.symbolic import Variable
def timeshift(v, n):
if isinstance(v, Variable):
return v(n)
else:
return v
import sympy
#### build substitution dict
def_dict = {}
for eq in def_eqs:
v = eq.lhs
rhs = sympy.sympify(eq.rhs)
def_dict[v] = rhs
def_dict[v(1)] = map_function_to_expression(
lambda x: timeshift(x, 1), rhs)
new_equations = []
tbr = []
for i, eq in enumerate(dmodel.equations):
if not ('def' == eq.tags['eq_type']):
lhs = sympy.sympify(eq.lhs).subs(def_dict)
rhs = sympy.sympify(eq.rhs).subs(def_dict)
neq = Equation(lhs, rhs).tag(**eq.tags)
new_equations.append(neq)
dmodel['equations'] = new_equations
dmodel.check_consistency()
f_eqs = [
eq for eq in dmodel.equations
if eq.tags['eq_type'] in ('f', 'arbitrage')
]
g_eqs = [
eq for eq in dmodel.equations
if eq.tags['eq_type'] in ('g', 'transition')
]
h_eqs = [
eq for eq in dmodel.equations
if eq.tags['eq_type'] in ('h', 'expectation')
]
states_vars = [eq.lhs for eq in g_eqs]
exp_vars = [eq.lhs for eq in h_eqs]
controls = set(dmodel.variables) - set(states_vars + exp_vars)
controls = list(controls)
states_vars = [v for v in dmodel.variables if v in states_vars]
exp_vars = [v for v in dmodel.variables if v in exp_vars]
controls = [v for v in dmodel.variables if v in controls]
# now we remove the left side of equations
f_eqs = [eq.gap for eq in f_eqs]
g_eqs = [eq.rhs for eq in g_eqs]
h_eqs = [eq.rhs for eq in h_eqs]
g_eqs = [
map_function_to_expression(lambda x: timeshift(x, 1), eq)
for eq in g_eqs
]
#h_eqs = [map_function_to_expression(lambda x: timeshift(x,-1),eq) for eq in h_eqs] #no
# sub_list[v] = v.name
# read complementarity conditions
compcond = {}
of_eqs = [
eq for eq in dmodel.equations
if eq.tags['eq_type'] in ('f', 'arbitrage')
]
locals = {}
import sympy
locals['inf'] = sympy.Symbol('inf')
locals['log'] = sympy.log # this should be more generic
locals['exp'] = sympy.exp
for v in dmodel.variables + dmodel.parameters:
locals[v.name] = v
import re
compregex = re.compile('(.*)<=(.*)<=(.*)')
for eq in of_eqs:
tg = eq.tags['complementarity']
[lhs, mhs, rhs] = compregex.match(tg).groups()
[lhs, mhs, rhs] = [dmodel.eval_string(x) for x in [lhs, mhs, rhs]]
compcond[mhs] = (lhs, rhs)
complementarities = [compcond[v] for v in controls]
inf_bounds = [c[0] for c in complementarities]
sup_bounds = [c[1] for c in complementarities]
data = {
'f_eqs': f_eqs,
'g_eqs': g_eqs,
'h_eqs': h_eqs,
'controls': controls,
'states_vars': states_vars,
'exp_vars': exp_vars,
'inf_bounds': inf_bounds,
'sup_bounds': sup_bounds
}
self.__transformed_model__ = data # cache computation
return data
def read_model(self):
if self.__transformed_model__:
return self.__transformed_model__
dmodel = Model(**self.model) # copy the model
dmodel.check_consistency(auto_remove_variables=False)
def_eqs = [eq for eq in dmodel.equations if eq.tags['eq_type'] in ('def', 'auxiliary')]
from dolo.misc.misc import map_function_to_expression
from dolo.symbolic.symbolic import Variable
def timeshift(v,n):
if isinstance(v,Variable):
return v(n)
else:
return v
import sympy
#### build substitution dict
def_dict = {}
for eq in def_eqs:
v = eq.lhs
rhs = sympy.sympify( eq.rhs )
def_dict[v] = rhs
def_dict[v(1)] = map_function_to_expression( lambda x: timeshift(x,1), rhs)
new_equations = []
tbr = []
for i,eq in enumerate(dmodel.equations) :
if not ('def' == eq.tags['eq_type']):
lhs = sympy.sympify( eq.lhs ).subs(def_dict)
rhs = sympy.sympify( eq.rhs ).subs(def_dict)
neq = Equation(lhs,rhs).tag(**eq.tags)
new_equations.append( neq )
dmodel['equations'] = new_equations
dmodel.check_consistency()
f_eqs = [eq for eq in dmodel.equations if eq.tags['eq_type'] in ('f','arbitrage')]
g_eqs = [eq for eq in dmodel.equations if eq.tags['eq_type'] in ('g','transition')]
h_eqs = [eq for eq in dmodel.equations if eq.tags['eq_type'] in ('h','expectation')]
states_vars = [eq.lhs for eq in g_eqs]
exp_vars = [eq.lhs for eq in h_eqs]
controls = set(dmodel.variables) - set(states_vars + exp_vars)
controls = list(controls)
states_vars = [v for v in dmodel.variables if v in states_vars]
exp_vars = [v for v in dmodel.variables if v in exp_vars]
controls = [v for v in dmodel.variables if v in controls]
# now we remove the left side of equations
f_eqs = [eq.gap for eq in f_eqs]
g_eqs = [eq.rhs for eq in g_eqs]
h_eqs = [eq.rhs for eq in h_eqs]
g_eqs = [map_function_to_expression(lambda x: timeshift(x,1),eq) for eq in g_eqs]
#h_eqs = [map_function_to_expression(lambda x: timeshift(x,-1),eq) for eq in h_eqs] #no
# sub_list[v] = v.name
# read complementarity conditions
compcond = {}
of_eqs = [eq for eq in dmodel.equations if eq.tags['eq_type'] in ('f','arbitrage')]
locals = {}
import sympy
locals['inf'] = sympy.Symbol('inf')
locals['log'] = sympy.log # this should be more generic
locals['exp'] = sympy.exp
for v in dmodel.variables + dmodel.parameters:
locals[v.name] = v
import re
compregex = re.compile('(.*)<=(.*)<=(.*)')
for eq in of_eqs:
tg = eq.tags['complementarity']
[lhs,mhs,rhs] = compregex.match(tg).groups()
[lhs,mhs,rhs] = [dmodel.eval_string(x) for x in [lhs,mhs,rhs] ]
compcond[mhs] = (lhs,rhs)
complementarities = [compcond[v] for v in controls]
inf_bounds = [c[0] for c in complementarities]
sup_bounds = [c[1] for c in complementarities]
data = {
'f_eqs': f_eqs,
'g_eqs': g_eqs,
'h_eqs': h_eqs,
'controls': controls,
'states_vars': states_vars,
'exp_vars': exp_vars,
'inf_bounds': inf_bounds,
'sup_bounds': sup_bounds
}
self.__transformed_model__ = data # cache computation
return data
