def lnprob_all(p):
pars1 = ThetaParam(grid=p[0:2], vz=p[2], vsini=p[3], logOmega=p[4])
model.update_Theta(pars1)
# hard code npoly=3 (for fixc0 = True with npoly=4)
pars2 = PhiParam(0, 0, True, p[5:8], p[8], p[9], p[10])
model.update_Phi(pars2)
draw = model.draw_save()
return draw
def lnlike(p):
try:
pars1 = ThetaParam(grid=p[0:2], vz=p[2], vsini=p[3], logOmega=p[4])
model.update_Theta(pars1)
# hard code npoly=3 (for fixc0 = True with chebyshev polynomials turned off)
pars2 = PhiParam(0, 0, True, [0.0, 0.0, 0.0], p[5], p[6], p[7])
model.update_Phi(pars2)
lnp = model.evaluate()
return lnp
except C.ModelError:
model.logger.debug("ModelError in stellar parameters, sending back -np.inf {}".format(p))
return -np.inf
def lnprob(p):
pars = ThetaParam(grid=p[0:3], vz=p[3], vsini=p[4], logOmega=p[5])
#Distribute the calculation to each process
for ((spectrum_id, order_id), pconn) in pconns.items():
pconn.send(("LNPROB", pars))
#Collect the answer from each process
lnps = np.empty((len(Starfish.data["orders"]), ))
for i, pconn in enumerate(pconns.values()):
lnps[i] = pconn.recv()
result = np.sum(lnps) # + lnprior
print("proposed:", p, result)
return result
def fprob(p):
# Assume p is [temp, logg, Z, vz, vsini, logOmega]
pars = ThetaParam(grid=p[0:3], vz=p[3], vsini=p[4], logOmega=p[5])
#Distribute the calculation to each process
for ((spectrum_id, order_id), pconn) in pconns.items():
#Parse the parameters into what needs to be sent to each Model here.
pconn.send(("LNPROB", pars))
#Collect the answer from each process
lnps = np.empty((len(Starfish.data["orders"]), ))
for i, pconn in enumerate(pconns.values()):
lnps[i] = pconn.recv()
s = np.sum(lnps)
print(pars, "lnp:", s)
if s == -np.inf:
return 1e99
else:
return -s
print(pars, "lnp:", s)
if s == -np.inf:
return 1e99
else:
return -s
start = Starfish.config["Theta"]
p0 = np.array(start["grid"] +
[start["vz"], start["vsini"], start["logOmega"]])
from scipy.optimize import fmin
p = fmin(fprob, p0, maxiter=10000, maxfun=10000)
print(p)
pars = ThetaParam(grid=p[0:3], vz=p[3], vsini=p[4], logOmega=p[5])
pars.save()
# Kill all of the orders
for pconn in pconns.values():
pconn.send(("FINISH", None))
pconn.send(("DIE", None))
# Join on everything and terminate
for p in ps.values():
p.join()
p.terminate()
import sys
sys.exit()
def setup_class(self):
self.thetaparam = ThetaParam(grid=np.array([4000., 4.32, -0.2]),
vz=10.,
vsini=4.0,
logOmega=-0.2,
Av=0.3)
