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simulation.py
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simulation.py
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from copy import copy
from cge_tools import empty_recarray
import numpy as np
from openopt import NLP
from table import Table
def unlink2(value, Value):
return value, Value;
def unlink(value):
return value;
class Simulation:
""" equations_doc = {
'UU(x)': 'utility / target',
'eqX(i)': 'household demand function'
"""
def __init__(self, calibration, parameter, debug=False):
def eqX(industry, Industry):
i, I = industry, Industry
def equation(x):
X, px, pf = x[sX:sF], x[spx:spz], x[spf:epf]
pf = np.array([float(x[spf:epf]), 1])
return X[i] - self.calibration.alpha[I] * sum([pf[h] * self.calibration.FF[H] for h, H in enumerate(parameter.factors)] / px[i])
return equation
def eqpx(i):
def equation(x):
X, Z = x[sX:sF], x[sZ:spx]
return X[i] - Z[i]
return equation
def eqZ(i):
def equation(x):
px, pz = x[spx:spz], x[spz:spf]
return px[i] - pz[i]
return equation
def eqpz(j, J):
def equation(x):
F, Z = Table.unflatten(index=parameter.factors, columns=parameter.industries, sam=x[sF:sZ]), x[sZ:spx]
return Z[j] - self.calibration.b[J] * np.prod([F[J][H] ** self.calibration.beta[J][H] for H in parameter.factors])
return equation
def eqpf(H):
def equation(x):
F = Table.unflatten(index=parameter.factors, columns=parameter.industries, sam=x[sF:sZ])
return sum(F[J][H] for J in parameter.industries) - self.calibration.FF[H]
return equation
def eqF(h, H, j, J):
def equation(x):
F, Z, pz, pf = Table.unflatten(index=parameter.factors, columns=parameter.industries, sam=x[sF:sZ]), x[sZ:spx], x[spz:spf], x[spf:epf]
pf = np.array([float(x[spf:epf]), 1])
return F[J][H] - self.calibration.beta[J][H] * pz[j] * Z[j] / pf[h]
return equation
self.calibration = copy(calibration)
self.calibration.X0 = self.calibration.X0['HH']
j = i = len(parameter.industries)
h = len(parameter.factors)
sX = 0
sF = sX + i
sZ = sF + i * h
spx = sZ + j
spz = spx + i
spf = spz + j
epf = spf + h - 1
epf_numerair = epf + 1
lb = np.array([(np.float64(0.001))] * epf)
self.x = x = np.empty(epf, dtype='f64')
x[sX:sF] = self.calibration.X0.data
x[sF:sZ] = self.calibration.F0.as_matrix().flatten()
x[sZ:spx] = self.calibration.Z0.data
x[spx:spz] = [1] * i
x[spz:spf] = [1] * j
x[spf:epf] = [1] * (epf - spf)
self.t = x[:]
if debug:
self.x = x = np.array([21.1] * epf)
print x
xnames = [] * (epf_numerair)
xnames[sX:sF] = self.calibration.X0.names
xnames[sF:sZ] = [i+h+' ' for i in parameter.industries for h in parameter.factors]
xnames[sZ:spx] = self.calibration.Z0.names
xnames[spx:spz] = parameter.industries
xnames[spz:spf] = parameter.industries
xnames[spf:epf] = parameter.factors
xnames[epf] = parameter.factors[-1]
xtypes = [] * epf_numerair
xtypes[sX:sF] = ['X0'] * len(self.calibration.X0)
xtypes[sF:sZ] = ['F'] * (len(parameter.industries) + len(parameter.factors))
xtypes[sZ:spx] = ['F0'] * len(self.calibration.F0)
xtypes[spx:spz] = ['pb'] * len(parameter.industries)
xtypes[spz:spf] = ['pz'] * len(parameter.industries)
xtypes[spf:epf] = ['pf'] * len(parameter.factors)
xtypes[epf] = ['pf']
self.xnametypes = ['%s %s' % (xnames[i], xtypes[i]) for i in range(epf - 1)]
constraints = []
for i, I in enumerate(parameter.industries):
industry, Industry = unlink2(i, I)
constraints.append(eqX(industry, Industry))
constraints.append(eqpx(industry))
constraints.append(eqZ(industry))
constraints.append(eqpz(industry, Industry))
for F in parameter.factors:
Factor = unlink(F)
constraints.append(eqpf(Factor))
for f, F in enumerate(parameter.factors):
for i, I in enumerate(parameter.industries):
industry, Industry = unlink2(i, I)
factor, Factor = unlink2(f, F)
constraints.append(eqF(factor, Factor, industry, Industry))
self.UU = UU = lambda x: - np.prod([x[i] ** self.calibration.alpha[i] for i in range(len(parameter.industries))])
p = NLP(UU, x, h=constraints, lb=lb, iprint = 50, maxIter = 10000, maxFunEvals = 1e7, name = 'NLP_1')
p.plot = debug
self.r = p.solve('ralg', plot=0)
if debug:
for i, constraint in enumerate(constraints):
print(i, '%02f' % constraint(self.r.xf))
def __str__(self):
r = self.r
ret = '%02f (%02f, %02f)\n' % (r.ff, self.UU(self.t), self.UU(self.x))
return ret + '\n'.join(['%s %f %f' % (line[0], line[1][0], line[1][1])
for line in zip(self.xnametypes,zip(self.t,r.xf))])