def solve_it(**cc):
tree = DeterministicTree(N)
for s in tree.nodes:
tree.values[s] = zbar
model = import_tree_model('models.yaml', key='optimal', tree=tree)
model.calibration.update(cc)
sol = model.solve(verbose=True)
df = numpy.concatenate( [get_ts(tree, sol, varname)[:,None] for varname in ['e','f', 'Gamma']], axis=1 )
df = pandas.DataFrame(df, columns=['e','f','Gamma'])
return df
def perfect_foresight(calib):
from calibrations import list_of_calibrations
calibration = calib.copy()
from bttt.trees import DeterministicTree, get_ts
import numpy
import pandas
import copy
from bttt.model import import_tree_model
# R0 = calibration['Rbar']
N = calibration['N']
shock = calibration['zbar']
model = calibration['model']
# cc = copy.copy(calibration)
# cc['Rbar'] = R0
# solve deterministic case
etree = DeterministicTree(N)
for s in etree.nodes:
etree.values[s] = shock
model = import_tree_model('models.yaml', key=model, tree=etree)
model.calibration.update(**calib)
sol = model.solve(verbose=True, solver_options=dict(eps2=1e-6))
columns = ['Gamma', 'e', 'f']
tab = [get_ts(etree, sol, c) for c in columns]
tab = numpy.row_stack(tab).T
df = pandas.DataFrame(tab, columns=columns)
return df
from bttt.model import import_tree_model
from bttt.trees import get_ts, DeterministicTree
N = 40
etree = DeterministicTree(N)
for n in etree.nodes:
etree.values[n] = 0.1
model = import_tree_model('example.yaml', tree=etree)
sol = model.solve()
sim = get_ts(etree, sol, 'e')
print(sim)
N = 25
T = 25
calib0 = calib.copy()
beta = calib0['beta']
a = calib0['a']
p = calib0['p']
zbar = calib0['zbar']
max_R=5
tree = DeterministicTree(N)
for s in tree.nodes:
tree.values[s] = zbar
model = import_tree_model('models.yaml', key='stochastic', tree=tree)
def solve_it(**cc):
tree = DeterministicTree(N)
for s in tree.nodes:
tree.values[s] = zbar
model = import_tree_model('models.yaml', key='optimal', tree=tree)
model.calibration.update(cc)
sol = model.solve(verbose=True)
df = numpy.concatenate( [get_ts(tree, sol, varname)[:,None] for varname in ['e','f', 'Gamma']], axis=1 )
df = pandas.DataFrame(df, columns=['e','f','Gamma'])
return df
from collections import OrderedDict
from bttt.model import import_tree_model
from bttt.trees import get_ts, DeterministicTree
N = 40
etree = DeterministicTree(N)
for n in etree.nodes:
etree.values[n] = 0.1
model = import_tree_model('example.yaml', tree=etree)
sol = model.solve()
sim = get_ts(etree, sol, 'e')
print(sim)
if beta < 0.82:
max_R = 4
else:
max_R = 4
r = calib['Rbar']
# sol = solve_time_consistent(max_R=2, **v)
sol = solve_time_consistent(max_R=max_R, order=100, **v)
sim_tc = simulate(r, sol, T)
results[c] = sim_tc
else:
print(p, c[1])
# if p == 1:
tree = DeterministicTree(N)
for s in tree.nodes:
tree.values[s] = zbar
model = import_tree_model('models.yaml', key=c[1], tree=tree)
model.calibration.update(calib)
sol = model.solve(verbose=True)
df = numpy.concatenate([
get_ts(tree, sol, varname)[:, None]
for varname in ['e', 'f', 'Gamma']
],
axis=1)
df = pandas.DataFrame(df, columns=['e', 'f', 'Gamma'])
results[c] = df
def Gamma(t, e, parm):
tot = 0
estar = parm['estar']
beta = parm['beta']
else:
etree.values[s] = 0.0
def get_ts(etree, sol, vname, terminal_state=None, ts_ind=0):
import numpy
if terminal_state is None:
terminal_states = [e for e in etree.nodes if len(e) == len(etree)]
terminal_state = terminal_states[ts_ind]
history = etree.history(terminal_state)
his_inds = [etree.nodes.index(e) for e in history]
vals = [sol["{}_{}".format(vname, h)] for h in his_inds]
return numpy.array(vals).astype(dtype=float)
model = import_tree_model("models.yaml", tree=etree, key="stochastic")
model.calibration["Rbar"] = 1.0
model.calibration["beta"] = 0.8 / (0.8 + 0.15)
model.calibration["a"] = 0.8
model.calibration["c"] = 0.15
sol = model.solve(verbose=True,
linear=True,
solver_options={
"presteps": 2,
'tol': 1e-5
})
from numpy import *
from matplotlib import pyplot as mplt
from pandas import DataFrame