def test_direct():
from dolo import yaml_import
from dolo.algos.dtcscc.perturbations import approximate_controls
from dolo.algos.dtcscc.time_iteration import time_iteration_direct, time_iteration
model = yaml_import("examples/models/compat/rbc_full.yaml")
# Check without complementarity conditions
dr = time_iteration_direct(model, with_complementarities=False)
ddr = time_iteration(model, with_complementarities=False)
x0 = dr(dr.grid)
x1 = ddr(dr.grid)
print(abs(x1 - x0).max()<1e-5)
# Check with complementarity conditions
dr = time_iteration_direct(model, with_complementarities=True)
ddr = time_iteration(model, with_complementarities=True)
x0 = dr(dr.grid)
x1 = ddr(dr.grid)
print(abs(x1 - x0).max()<1e-5)
def test_omega_errors():
from dolo import yaml_import
from dolo.algos.dtcscc.time_iteration import time_iteration as time_iteration
model = yaml_import('examples/models/rbc.yaml')
from dolo.algos.dtcscc.perturbations import approximate_controls
dr = approximate_controls(model)
dr_global = time_iteration(model, smolyak_order=3, verbose=False, pert_order=1)
sigma = model.covariances
model.sigma = sigma
s_0 = dr.S_bar
from dolo.algos.dtcscc.accuracy import omega
res_1 = omega( model, dr, orders=[10,10], time_discount=0.96)
res_2 = omega( model, dr_global)
print(res_1)
print(res_2)
def test_model_print():
from dolo import yaml_import
fname = 'examples/models/rbc_mfga.yaml'
model = yaml_import(fname)
print(model)
def test_denhaan_errors():
from dolo import yaml_import
from dolo.algos.fg.time_iteration import time_iteration as global_solve
model = yaml_import('examples/models/rbc.yaml')
from dolo.algos.fg.perturbations import approximate_controls
dr = approximate_controls(model)
dr_global = global_solve(model, interp_type='smolyak', smolyak_order=4, verbose=False)
sigma = model.covariances
model.sigma = sigma
from dolo.algos.fg.accuracy import denhaanerrors
denerr_1 = denhaanerrors(model, dr)
denerr_2 = denhaanerrors(model, dr_global)
print(denerr_1)
print(denerr_2)
print(denerr_2['max_errors'][0])
assert( max(denerr_2['max_errors']) < 10-7) # errors with solyak colocations at order 4 are very small
def test_denhaan_errors():
from dolo import yaml_import
from dolo.algos.time_iteration import time_iteration as global_solve
model = yaml_import('examples/models/rbc.yaml')
from dolo.algos.perturbations import approximate_controls
dr = approximate_controls(model)
dr_global = global_solve(model,
interp_type='smolyak',
smolyak_order=4,
verbose=False)
sigma = model.covariances
model.sigma = sigma
from dolo.algos.accuracy import denhaanerrors
denerr_1 = denhaanerrors(model, dr)
denerr_2 = denhaanerrors(model, dr_global)
print(denerr_1)
print(denerr_2)
print(denerr_2['max_errors'][0])
assert (max(denerr_2['max_errors']) < 10 - 7
) # errors with solyak colocations at order 4 are very small
def test_denhaan_errors():
from dolo import yaml_import
from dolo.algos.dtcscc.time_iteration import time_iteration as time_iteration
model = yaml_import('examples/models/rbc.yaml')
from dolo.algos.dtcscc.perturbations import approximate_controls
dr = approximate_controls(model)
dr_global = time_iteration(model, verbose=False)
s0 = model.calibration['states']
sigma = model.get_distribution().sigma
from dolo.algos.dtcscc.accuracy import denhaanerrors
denerr_1 = denhaanerrors(model, dr)
denerr_2 = denhaanerrors(model, dr_global)
print(denerr_1)
print(denerr_2)
print(denerr_2['max_errors'][0])
assert( max(denerr_2['max_errors']) < 10-7) # errors with solyak colocations at order 4 are very small
def test_denhaan_errors():
from dolo import yaml_import
from dolo.algos.dtcscc.time_iteration import time_iteration as time_iteration
model = yaml_import('examples/models/rbc.yaml')
from dolo.algos.dtcscc.perturbations import approximate_controls
dr = approximate_controls(model)
dr_global = time_iteration(model, verbose=False)
s0 = model.calibration['states']
sigma = model.get_distribution().sigma
from dolo.algos.dtcscc.accuracy import denhaanerrors
denerr_1 = denhaanerrors(model, dr)
denerr_2 = denhaanerrors(model, dr_global)
print(denerr_1)
print(denerr_2)
print(denerr_2['max_errors'][0])
assert (max(denerr_2['max_errors']) < 10 - 7
) # errors with solyak colocations at order 4 are very small
def test_options():
from dolo import yaml_import
fname = 'examples/models/rbc_dtmscc.yaml'
model = yaml_import(fname)
print(model.options)
def test_direct():
from dolo import yaml_import
from dolo.algos.dtcscc.perturbations import approximate_controls
from dolo.algos.dtcscc.time_iteration import time_iteration_direct, time_iteration
model = yaml_import("examples/models/rbc_full.yaml")
# Check without complementarity conditions
dr = time_iteration_direct(model, with_complementarities=False)
ddr = time_iteration(model, with_complementarities=False)
x0 = dr(dr.grid)
x1 = ddr(dr.grid)
print(abs(x1 - x0).max() < 1e-5)
# Check with complementarity conditions
dr = time_iteration_direct(model, with_complementarities=True)
ddr = time_iteration(model, with_complementarities=True)
x0 = dr(dr.grid)
x1 = ddr(dr.grid)
print(abs(x1 - x0).max() < 1e-5)
def test_global_solution(self):
from dolo import yaml_import, global_solve
filename = 'examples/global_models/rbc.yaml'
model = yaml_import(filename)
print model.calibration['covariances']
import time
dr = global_solve(model, pert_order=1, maxit=500, smolyak_order=3, verbose=True, polish=False, method='newton')
t1 = time.time()
dr = global_solve(model, pert_order=1, maxit=5, smolyak_order=5, verbose=True, polish=False, method='newton')
t2 = time.time()
dr = global_solve(model, pert_order=1, maxit=5, interp_type='multilinear', verbose=True, polish=False, method='newton')
t3 = time.time()
print(t2-t1)
print(t3-t2)
def test_options(self):
from dolo import yaml_import
fname = 'examples/models/rbc_mfga.yaml'
model = yaml_import(fname)
print(model.options)
def test_mfg_model():
from dolo import yaml_import
# from dolo.algos.commands import time_iteration, simulate, evaluate_policy
from dolo.algos.dtmscc.time_iteration import time_iteration
from dolo.algos.dtmscc.value_iteration import evaluate_policy
from dolo.algos.dtmscc.simulations import simulate, plot_decision_rule
model = yaml_import("examples/models/sudden_stop.yaml")
mdr = time_iteration(model)
#
sim = simulate(model, mdr, 0, horizon=50, n_exp=0) # irf
assert(sim.shape==(50,6))
sim = simulate(model, mdr, 0, horizon=50, n_exp=1) # one stochastic simulation
assert(sim.shape==(50,6))
sim = simulate(model, mdr, 0, horizon=50, n_exp=10) # many stochastic simulations
assert(sim.shape==(10,50,6))
mv = evaluate_policy(model, mdr, verbose=True, maxit=500)
mv = evaluate_policy(model, mdr, verbose=True, maxit=500, initial_guess=mv)
sim_v = simulate(model, mdr, 0, drv=mv, horizon=50, n_exp=10)
sim_irf = simulate(model, mdr, 0, drv=mv, horizon=50, n_exp=0)
sim_dr = plot_decision_rule(model, mdr, 'l')
print(sim_dr)
def test_ergo_mc():
from dolo import yaml_import, improved_time_iteration, ergodic_distribution
model = yaml_import("examples/models/rbc_mc.yaml")
sol = improved_time_iteration(model)
Π, μ = ergodic_distribution(model, sol.dr)
assert (μ.ndim == 2)
def test_division(self):
import yaml
with open('examples/models/rbc.yaml') as f:
txt = f.read()
yaml_content = yaml.load(txt)
yaml_content['equations']['transition'][0] += ' + 1/2 - 0.5' # modify first transition equation
yaml_content['calibration']['alpha'] = '1/3'
txt = yaml.dump(yaml_content)
from dolo import yaml_import
print(txt)
model = yaml_import('nofile', txt=txt)
alpha = model.get_calibration('alpha')
print(alpha)
assert( abs(alpha - 1.0/3.0) < 0.00000001)
res = model.residuals()
print( abs(res['transition'][0] ) )
assert( abs(res['transition'][0]) < 0.00000001)
def test_dtcscc_model():
# test a model defined with auxiliary variables
from dolo import yaml_import
model = yaml_import('examples/models/rbc_full.yaml')
s = model.calibration['states']
x = model.calibration['controls']
X = x
y = model.calibration['auxiliaries']
E = model.calibration['shocks']
V = model.calibration['values']
p = model.calibration['parameters']
S = model.functions['transition'](s,x,E,p)
r = model.functions['arbitrage'](s,x,E,S,X,p)
y = model.functions['auxiliary'](s,x,p)
v = model.functions['value'](s,x,S,X,V,p)
z = model.functions['expectation'](S, X, p)
x1 = model.functions['direct_response'](s,z, p)
print('x1')
print(x)
print(x1)
assert(abs(x-x1).max()<1e-12)
def test_global_solution(self):
from dolo import yaml_import, global_solve
filename = 'examples/global_models/rbc.yaml'
model = yaml_import(filename)
import time
dr = global_solve(model, pert_order=2, maxit=5, smolyak_order=3, verbose=True, polish=True)
t1 = time.time()
dr = global_solve(model, pert_order=1, maxit=5, smolyak_order=5, verbose=True, polish=False)
t2 = time.time()
dr = global_solve(model, pert_order=1, maxit=5, interp_type='multilinear', verbose=True, polish=False)
t3 = time.time()
print(t2-t1)
print(t3-t2)
def test_options(self):
from dolo import yaml_import
fname = 'examples/models/rbc_mfga.yaml'
model = yaml_import(fname)
print( model.options )
def test_options():
from dolo import yaml_import
fname = 'examples/models/rbc_dtmscc.yaml'
model = yaml_import(fname)
print( model.options )
def test_mfg_model():
from dolo import yaml_import
# from dolo.algos.commands import time_iteration, simulate, evaluate_policy
from dolo.algos.dtmscc.time_iteration import time_iteration
from dolo.algos.dtmscc.value_iteration import evaluate_policy
from dolo.algos.dtmscc.simulations import simulate, plot_decision_rule
model = yaml_import("examples/models/compat/sudden_stop.yaml")
print(model.exogenous)
print(model.is_dtmscc())
mdr = time_iteration(model)
#
sim = simulate(model, mdr, 0, horizon=50, n_exp=0) # irf
assert (sim.shape == (50, 6))
sim = simulate(model, mdr, 0, horizon=50,
n_exp=1) # one stochastic simulation
assert (sim.shape == (50, 6))
sim = simulate(model, mdr, 0, horizon=50,
n_exp=10) # many stochastic simulations
assert (sim.shape == (10, 50, 6))
mv = evaluate_policy(model, mdr, verbose=True, maxit=500)
mv = evaluate_policy(model, mdr, verbose=True, maxit=500, initial_guess=mv)
sim_v = simulate(model, mdr, 0, drv=mv, horizon=50, n_exp=10)
sim_irf = simulate(model, mdr, 0, drv=mv, horizon=50, n_exp=0)
sim_dr = plot_decision_rule(model, mdr, 'l')
print(sim_dr)
def test_omega_errors():
from dolo import yaml_import
from dolo.algos.time_iteration import time_iteration as global_solve
model = yaml_import('examples/models/rbc.yaml')
from dolo.algos.perturbations import approximate_controls
dr = approximate_controls(model)
dr_global = global_solve(model,
smolyak_order=3,
verbose=False,
pert_order=1)
sigma = model.covariances
model.sigma = sigma
s_0 = dr.S_bar
from dolo.algos.accuracy import omega
res_1 = omega(model, dr, orders=[10, 10], time_discount=0.96)
res_2 = omega(model, dr_global)
print(res_1)
print(res_2)
def solve_something(name, args):
from dolo import yaml_import
from dolo.algos.time_iteration import time_iteration as global_solve
model = yaml_import('examples/models/{}.yaml'.format(name))
dr = global_solve(model, **args)
return dr
def test_web_import():
from dolo import yaml_import
model = yaml_import(
"https://raw.githubusercontent.com/EconForge/dolo/master/examples/models/rbc.yaml"
)
assert (len(model.symbols['states']) == 2)
def solve_something(name, args):
from dolo import yaml_import
from dolo.algos.time_iteration import time_iteration as global_solve
model = yaml_import('examples/models/{}.yaml'.format(name))
dr = global_solve(model, **args)
return dr
def test_egm_iid():
from dolo import yaml_import
from dolo.algos.egm import egm
import numpy as np
model = yaml_import("examples/models/consumption_savings_iid.yaml")
sol = egm(model, a_grid=np.linspace(0.1, 10, 10) ** 2, verbose=True)
def process(txt):
# model = yaml_import(None, txt=txt)
# s = model._repr_html_()
try:
s = yaml_import(None, txt=txt, check_only=True)
s = "<div>"+s+"</div>"
except:
s = "Failed"
return HTML(s)
def test_time_iteration_smolyak():
from dolo import yaml_import, time_iteration
filename = 'examples/models/rbc.yaml'
model = yaml_import(filename)
import time
dr = time_iteration(model, pert_order=1, maxit=500, verbose=True)
def test_irf():
from dolo import yaml_import, perturb, response
import numpy as np
model = yaml_import("examples/models/rbc.yaml")
dr = perturb(model).dr
irf = response(model, dr, "e_z")
print(irf)
def test_time_iteration_smolyak():
from dolo import yaml_import, time_iteration
filename = 'examples/models/rbc.yaml'
model = yaml_import(filename)
import time
dr = time_iteration(model, pert_order=1, maxit=500, smolyak_order=3, verbose=True)
def test_time_iteration_spline():
import time
from dolo import yaml_import, time_iteration
filename = 'examples/models/rbc.yaml'
model = yaml_import(filename)
print(model.__class__)
dr = time_iteration(model, pert_order=1, maxit=5, verbose=True)
def test_vfi():
from dolo.algos.value_iteration import value_iteration as value_iteration, evaluate_policy
from dolo import yaml_import
from dolo.algos.time_iteration import time_iteration
model_iid = yaml_import('examples/models/rbc_iid.yaml')
# mdr = time_iteration(model_iid, with_complementarities=False, verbose=False)
# drv = evaluate_policy(model_iid, mdr)
dr_iid = value_iteration(model_iid, verbose=True, maxit=5)
def test_dtcscc__functions():
# test a model defined without auxiliary variables
from dolo import yaml_import
model = yaml_import('examples/models/rbc_fg.yaml')
s = model.calibration['states']
x = model.calibration['controls']
e = model.calibration['shocks']
p = model.calibration['parameters']
r = model.functions['arbitrage'](s,x,e,s,x,p)
def test_markov_chain():
from dolo import yaml_import
fname = 'examples/models/rbc_mfga.yaml'
model = yaml_import(fname)
from dolo.numeric.discretization import multidimensional_discretization
import numpy
sigma = numpy.array([[0.01]])
rho = 0.01
[P,Q] = multidimensional_discretization(rho, sigma, 3)
print(model.markov_chain)
def test_global_solution(self):
from dolo import yaml_import, global_solve
filename = 'examples/global_models/rbc.yaml'
model = yaml_import(filename)
import time
t1 = time.time()
dr = global_solve(model, pert_order=2, maxit=5, interp_type='spline', verbose=True)
def test_time_iteration_spline():
import time
from dolo import yaml_import, time_iteration
filename = 'examples/models/rbc.yaml'
model = yaml_import(filename)
print(model.__class__)
dr = time_iteration(model, pert_order=1, maxit=5, verbose=True)
def test_global_solution(self):
from dolo import yaml_import, time_iteration
filename = 'examples/models/rbc.yaml'
model = yaml_import(filename)
import time
t1 = time.time()
dr = time_iteration(model, pert_order=1, maxit=5, verbose=True)
def test_fga_higher_order_perturbations():
from dolo import yaml_import
from dolo.algos.dtcscc.perturbations_higher_order import approximate_controls
model = yaml_import('examples/models/rbc.yaml')
# for i in [1,2,3]:
dr1 = approximate_controls(model, order=1)
dr2 = approximate_controls(model, order=2)
dr3 = approximate_controls(model, order=3)
assert(dr1.order==1)
assert(dr1.X_s.ndim==2)
assert(dr3.X_ss.ndim==3)
def finite_time_iteration():
from dolo import yaml_import, time_iteration
from dolo.numeric.decision_rule import CustomDR
import matplotlib.pyplot as plt
model = yaml_import("examples/models/consumption_savings_iid.yaml")
# in the near future this will become:
# """c[t] = w[t]"""
values = {"c": "w"}
edr = CustomDR(values, model)
sol = time_iteration(model, dr0=edr, maxit=5, trace=True)
def test_fga_higher_order_perturbations():
from dolo import yaml_import
from dolo.algos.dtcscc.perturbations_higher_order import approximate_controls
model = yaml_import('examples/models/rbc.yaml')
# for i in [1,2,3]:
dr1 = approximate_controls(model, order=1)
dr2 = approximate_controls(model, order=2)
dr3 = approximate_controls(model, order=3)
assert (dr1.order == 1)
assert (dr1.X_s.ndim == 2)
assert (dr3.X_ss.ndim == 3)
def test_global_solution(self):
from dolo import yaml_import, time_iteration
filename = 'examples/models/rbc.yaml'
model = yaml_import(filename)
import time
t1 = time.time()
dr = time_iteration(model, pert_order=1, maxit=5, verbose=True)
def test_parameterized_expectations():
from dolo import yaml_import
from dolo.algos.dtcscc.parameterized_expectations import parameterized_expectations
from dolo.algos.dtcscc.time_iteration import time_iteration
model = yaml_import("examples/models/compat/rbc_full.yaml")
dr_ti = time_iteration(model)
dr_pea = parameterized_expectations(model, direct=False)
x_ti = dr_ti(dr_ti.grid)
x_pea = dr_pea(dr_ti.grid)
print(abs(x_ti - x_pea).max() < 1e-5)
def test_parameterized_expectations():
from dolo import yaml_import
from dolo.algos.dtcscc.parameterized_expectations import parameterized_expectations
from dolo.algos.dtcscc.time_iteration import time_iteration
model = yaml_import("examples/models/compat/rbc_full.yaml")
dr_ti = time_iteration(model)
dr_pea = parameterized_expectations(model, direct=False)
x_ti = dr_ti(dr_ti.grid)
x_pea = dr_pea(dr_ti.grid)
print(abs(x_ti - x_pea).max() < 1e-5)
def test_direct():
from dolo import yaml_import
from dolo.algos.dtcscc.perturbations import approximate_controls
from dolo.algos.dtcscc.time_iteration import time_iteration_direct, time_iteration
model = yaml_import("examples/models/rbc_full.yaml")
dr = time_iteration_direct(model)
ddr = time_iteration(model)
x0 = dr(dr.grid)
x1 = ddr(dr.grid)
print(abs(x1 - x0).max() < 1e-5)
def test_direct():
from dolo import yaml_import
from dolo.algos.dtcscc.perturbations import approximate_controls
from dolo.algos.dtcscc.time_iteration import time_iteration_direct, time_iteration
model = yaml_import("examples/models/rbc_full.yaml")
dr = time_iteration_direct(model)
ddr = time_iteration(model)
x0 = dr(dr.grid)
x1 = ddr(dr.grid)
print(abs(x1 - x0).max()<1e-5)
def test_eval_formula():
from dolo.compiler.eval_formula import eval_formula
from dolo import yaml_import, approximate_controls, simulate
model = yaml_import('examples/models/rbc.yaml')
dr = approximate_controls(model)
sim = simulate(model, dr)
rr = eval_formula("delta*k-i", sim, context=model.calibration)
rr = eval_formula("y(1) - y", sim, context=model.calibration)
sim['diff'] = model.eval_formula("delta*k-i", sim)
model.eval_formula("y(1) - y", sim)
sim['ddiff'] = model.eval_formula("diff(1)-diff(-1)", sim)
def test_vfi():
from dolo.algos.value_iteration import value_iteration as value_iteration, evaluate_policy
from dolo import yaml_import
from dolo.algos.time_iteration import time_iteration
model_iid = yaml_import('examples/models/rbc_dtcc_iid_ar1.yaml')
endo_grid = model_iid.get_grid()
exo_grid = model_iid.exogenous.discretize()
mdr = time_iteration(model_iid)
drv = evaluate_policy(model_iid, mdr)
dr_iid = value_iteration(model_iid, verbose=True, maxit=5)
def test_eval_formula():
from dolo.compiler.eval_formula import eval_formula
from dolo import yaml_import, time_iteration, simulate
model = yaml_import('examples/models/rbc.yaml')
dr = time_iteration(model)
sim = simulate(model, dr)
sim = sim.sel(N=0)
sim = sim.to_pandas()
rr = eval_formula("delta*k-i", sim, context=model.calibration)
rr = eval_formula("y(1) - y", sim, context=model.calibration)
sim['diff'] = model.eval_formula("delta*k-i", sim)
model.eval_formula("y(1) - y", sim)
sim['ddiff'] = model.eval_formula("diff(1)-diff(-1)", sim)
def test_big_system():
from dolo import yaml_import
from dolo.algos.dtcscc.nonlinearsystem import nonlinear_system
from dolo.algos.dtcscc.time_iteration import time_iteration
model = yaml_import('examples/models/compat/rbc.yaml')
dr = time_iteration(model, grid={'type': 'smolyak', 'mu': 3}, verbose=True)
sol = nonlinear_system(model, grid={'type': 'smolyak', 'mu': 3})
diff = (sol.__values__) - dr.__values__
assert(abs(diff).max() < 1e-6)
sol_high_precision = nonlinear_system(model, grid={'type': 'smolyak', 'mu': 5}, initial_dr=sol)
assert(sol_high_precision.grid.shape == (145, 2))
def test_big_system():
from dolo import yaml_import
from dolo.algos.dtcscc.nonlinearsystem import nonlinear_system
from dolo.algos.dtcscc.time_iteration import time_iteration
model = yaml_import("examples/models/rbc.yaml")
dr = time_iteration(model, grid={"type": "smolyak", "mu": 3}, verbose=True)
sol = nonlinear_system(model, grid={"type": "smolyak", "mu": 3})
diff = (sol.__values__) - dr.__values__
assert abs(diff).max() < 1e-6
sol_high_precision = nonlinear_system(model, grid={"type": "smolyak", "mu": 5}, initial_dr=sol)
assert sol_high_precision.grid.shape == (145, 2)
def test_global_solution(self):
from dolo import yaml_import, global_solve
filename = 'examples/models/rbc.yaml'
model = yaml_import(filename)
import time
t1 = time.time()
print('ok')
dr = global_solve(model,
pert_order=1,
maxit=5,
interp_type='spline',
verbose=True)
def test_global_solution(self):
import time
from dolo import yaml_import, global_solve
filename = 'examples/models/rbc.yaml'
model = yaml_import(filename)
print(model.__class__)
t3 = time.time()
dr = global_solve(model, pert_order=1, maxit=5, interp_type='spline', verbose=True, interp_orders=[100,100])
t4 = time.time()
print(t4-t3)
def model_evaluation(compiler='numpy', data_layout='columns'):
from dolo import yaml_import
#model = yaml_import('examples/models/rbc_fg.yaml', compiler=compiler, order=data_layout)
model = yaml_import('examples/models/rbc_fg.yaml')
s0 = model.calibration['states']
x0 = model.calibration['controls']
e0 = model.calibration['shocks']
p = model.calibration['parameters']
assert(s0.ndim == 1)
assert(x0.ndim == 1)
assert(p.ndim == 1)
N = 10
f = model.functions['arbitrage']
if data_layout == 'rows':
ss = numpy.ascontiguousarray( numpy.tile(s0, (N,1)).T )
xx = numpy.ascontiguousarray( numpy.tile(x0, (N,1)).T )
ee = e0[:,None].repeat(N,axis=1)
else:
ss = ( numpy.tile(s0, (N,1)) )
xx = ( numpy.tile(x0, (N,1)) )
ee = e0[None,:].repeat(N,axis=0)
ss = s0[None,:].repeat(N,axis=0)
xx = x0[None,:].repeat(N,axis=0)
ee = e0[None,:].repeat(N,axis=0)
vec_res = f(ss,xx,ee,ss,xx,p)
res = f(s0, x0, e0, s0, x0, p)
assert(res.ndim==1)
d = 0
for i in range(N):
d += abs(vec_res[i,:] - res).max()
assert(d == 0)
def test_lint_all():
from dolo import yaml_import
import os
import re
examples_dir = 'examples/models/'
examples = os.listdir(examples_dir)
regex = re.compile("(.*).yaml")
for f in examples:
if not regex.match(f):
continue
print("Checking: {}".format(f))
check = yaml_import(examples_dir + f, check_only=True)
# TODO: need to fix Dynare lint before reenabling
# if len(check)>1:
# assert("Linter Error" not in check[0]) # assert there is no linter error
print()
def test_omega_errors():
from dolo import yaml_import
from dolo.algos.dtcscc.time_iteration import time_iteration as time_iteration
model = yaml_import('examples/models/compat/rbc.yaml')
from dolo.algos.dtcscc.perturbations import approximate_controls
dr = approximate_controls(model)
dr_global = time_iteration(model, verbose=False, pert_order=1)
sigma = model.get_distribution().sigma
s_0 = dr.S_bar
from dolo.algos.dtcscc.accuracy import omega
res_1 = omega(model, dr, grid=dict(orders=[10, 10]), time_discount=0.96)
res_2 = omega(model, dr_global)
print(res_1)
print(res_2)
def test_eval_formula():
from dolo.compiler.eval_formula import eval_formula
from dolo import yaml_import, time_iteration, simulate
import dolo.config
from dolo import perturb
model = yaml_import('examples/models/rbc.yaml')
dr = perturb(model)
sim = simulate(model, dr)
sim = sim.sel(N=0)
sim = sim.to_pandas()
print(sim.columns)
rr = eval_formula("delta*k(0)-i(0)", sim, context=model.calibration)
rr = eval_formula("y(1) - y(0)", sim, context=model.calibration)
sim['diff'] = model.eval_formula("delta*k(0)-i(0)", sim)
model.eval_formula("y(1) - y(0)", sim)
sim['ddiff'] = model.eval_formula("diff(1)-diff(-1)", sim)
