def mass_partitioning(masses):
"""
Mass partitioning
masses: array of planet masses
"""
return LMC.D(masses / np.sum(masses))
def mass_partitioning(masses):
"""
Mass partitioning, Q
Parameters
----------
masses : array-like
planet masses [any units]
"""
return LMC.D(masses/np.sum(masses))
def gap_complexity(periods, warn=True):
"""
Gap complexity
periods: array of planet periods
warn: boolean flag to control warnings (default=True)
"""
if len(periods) < 3:
if warn:
warnings.warn('Complexity is undefined for N < 3; returning NaN')
return np.nan
elif len(periods) >= 3:
order = np.argsort(periods)
P = np.array(periods)[order]
pp = np.log(P[1:] / P[:-1]) / np.log(P.max() / P.min())
return LMC.C(pp)
def Equilibrium_LC(tau_hat, taup, alphas, T, B, G, Din, J, N, maxit, tol, VAn,
Sn, vfactor):
# initialize vectors of ex-post wage and price factors
wf0 = np.ones([N, 1])
pf0 = np.ones([J, N])
wfmax = np.array([1.0])
e = 1.0
while (e <= maxit) and (wfmax[0] > tol):
pf0, c = ph.PH(wf0, tau_hat, T, B, G, Din, J, N, maxit, tol)
# Calculating trade shares
Dinp = dinprime.Dinprime(Din, tau_hat, c, T, J, N)
Dinp_om = Dinp / taup
Fp = np.zeros((J, N))
for j in range(J):
Fp[j, :] = sum((Dinp[j * N:(j + 1) * N:1, :] /
taup[j * N:(j + 1) * N:1, :]).T)
# Expenditure matrix
PQ = expenditure.Expenditure(alphas, B, G, Dinp, taup, Fp, VAn, wf0,
Sn, J, N)
# Iterating using LMC
wf1 = LMC.lmc(PQ, Dinp_om, J, N, B, VAn)
# Excess function
ZW = (wf1 - wf0)
PQ_vec = PQ.T.reshape(1, J * N, order='F').copy().T
DP = np.zeros((J * N, N))
for n in range(N):
DP[:, n] = Dinp_om[:, n] * PQ_vec.reshape(1, J * N)
# Exports
LHS = sum(DP).T.reshape(N, 1)
# calculating RHS (Imports) trade balance
PF = PQ * Fp
# Imports
RHS = sum(PF).T.reshape(N, 1)
# Excess function (trade balance)
Snp = (RHS - LHS) + Sn
ZW2 = -(RHS - LHS + Sn) / VAn
# Itaration factor prices
wf1 = wf0 * (1 - vfactor * ZW2 / wf0)
# wfmax = sum((abs(wf1 - wf0)))
wfmax = sum(abs(Snp))
print(wfmax)
# wfmax0 = wfmax
wf0 = wf1
e += 1
return wf0, pf0, PQ, Fp, Dinp, ZW, Snp