def optimize_Cheb(self, *args):
'''
Keeping the current Theta parameters fixed and assuming white noise,
optimize the Chebyshev parameters
'''
if self.chebyshevSpectrum.fix_c0:
p0 = np.zeros((self.npoly - 1))
self.fix_c0 = True
else:
p0 = np.zeros((self.npoly))
self.fix_c0 = False
def fprob(p):
self.chebyshevSpectrum.update(p)
lnp = self.evaluate()
print(self.order, p, lnp)
if lnp == -np.inf:
return 1e99
else:
return -lnp
from scipy.optimize import fmin
result = fmin(fprob, p0, maxiter=10000, maxfun=10000)
print(self.order, result)
# Due to a JSON bug, np.int64 type objects will get read twice,
# and cause this routine to fail. Therefore we have to be careful
# to convert these to ints.
phi = PhiParam(spectrum_id=int(self.spectrum_id), order=int(self.order), fix_c0=self.chebyshevSpectrum.fix_c0, cheb=result)
phi.save()
def lnfunc(p):
# Convert p array into a PhiParam object
ind = self.npoly
#if self.chebyshevSpectrum.fix_c0:
# ind -= 1
ind=0
#cheb = p[0:ind]
cheb = [0, 0, 0]
sigAmp = p[ind]
ind+=1
logAmp = p[ind]
ind+=1
l = p[ind]
par = PhiParam(self.spectrum_id, self.order, self.chebyshevSpectrum.fix_c0, cheb, sigAmp, logAmp, l)
self.update_Phi(par)
# sigAmp must be positive (this is effectively a prior)
# See https://github.com/iancze/Starfish/issues/26
if not (0.0 < sigAmp):
self.lnprob_last = self.lnprob
lnp = -np.inf
self.logger.debug("sigAmp was negative, returning -np.inf")
self.lnprob = lnp # Same behavior as self.evaluate()
else:
lnp = self.evaluate()
self.logger.debug("Evaluated Phi parameters: {} {}".format(par, lnp))
return lnp
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 setup_class(self):
self.phiparam = PhiParam(spectrum_id=0,
order=22,
fix_c0=True,
cheb=np.zeros((4, )),
sigAmp=1.0,
logAmp=-5.0,
l=20.,
regions=np.ones((4, 3)))
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 lnfunc(p):
# Convert p array into a PhiParam object
ind = self.npoly
if self.chebyshevSpectrum.fix_c0:
ind -= 1
cheb = p[0:ind]
sigAmp = p[ind]
ind+=1
logAmp = p[ind]
ind+=1
l = p[ind]
phi = PhiParam(self.spectrum_id, self.order, self.chebyshevSpectrum.fix_c0, cheb, sigAmp, logAmp, l)
self.update_Phi(phi)
lnp = self.evaluate()
self.logger.debug("Evaluated Phi parameters: {} {}".format(phi, lnp))
return lnp
def optimize_Cheb(self, *args):
'''
Keeping the current Theta parameters fixed and assuming white noise,
optimize the Chebyshev parameters
'''
# self.fix_c0 = True if index == (len(DataSpectrum.wls) - 1) else False #Fix the last c0
# This is necessary if we want to update just a single order.
if self.chebyshevSpectrum.fix_c0 & len(self.dataSpectrum.wls) > 1:
p0 = np.zeros((self.npoly - 1))
else:
self.chebyshevSpectrum.fix_c0 = False
p0 = np.zeros((self.npoly))
def fprob(p):
self.chebyshevSpectrum.update(p)
lnp = self.evaluate()
print(self.order, p, lnp)
if lnp == -np.inf:
return 1e99
else:
return -lnp
from scipy.optimize import fmin
result = fmin(fprob, p0, maxiter=10000, maxfun=10000)
print(self.order, result)
# Due to a JSON bug, np.int64 type objects will get read twice,
# and cause this routine to fail. Therefore we have to be careful
# to convert these to ints.
phi = PhiParam(spectrum_id=int(self.spectrum_id),
order=int(self.order),
fix_c0=self.chebyshevSpectrum.fix_c0,
cheb=result)
phi.save()
if args.initPhi:
# Figure out how many models and orders we have
i_last = len(Starfish.data["orders"]) - 1
for spec_id in range(len(Starfish.data["files"])):
for i, order in enumerate(Starfish.data["orders"]):
fix_c0 = True if i == i_last else False
if fix_c0:
cheb = np.zeros((Starfish.config["cheb_degree"] - 1, ))
else:
cheb = np.zeros((Starfish.config["cheb_degree"], ))
# For each order, create a Phi with these values
# Automatically reads all of the Phi parameters from config.yaml
phi = PhiParam(spectrum_id=spec_id,
order=int(order),
fix_c0=fix_c0,
cheb=cheb)
# Write to CWD using predetermined format string
phi.save()
if args.optimize == "Cheb":
model = parallel.OptimizeCheb(debug=True)
# Now that the different processes have been forked, initialize them
pconns, cconns, ps = parallel.initialize(model)
# Initialize to the basics
pars = ThetaParam.from_dict(Starfish.config["Theta"])
#Distribute the calculation to each process
