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 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()
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()
class TestPhiParam:
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 test_save(self):
self.phiparam.save(fname="phi_test.json")
def test_load(self):
load = PhiParam.load(Starfish.specfmt.format(0, 22) + "phi_test.json")
print(load.spectrum_id)
print(load.order)
print(load.fix_c0)
print(load.cheb)
print(load.sigAmp)
print(load.logAmp)
print(load.l)
print(load.regions)
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()
class TestPhiParam:
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 test_save(self):
self.phiparam.save(fname="phi_test.json")
def test_load(self):
load = PhiParam.load(Starfish.specfmt.format(0, 22) + "phi_test.json")
print(load.spectrum_id)
print(load.order)
print(load.fix_c0)
print(load.cheb)
print(load.sigAmp)
print(load.logAmp)
print(load.l)
print(load.regions)
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
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))
# 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_linear.OptimizeTheta(debug=True)
# Now that the different processes have been forked, initialize them
pconns, cconns, ps = parallel_linear.initialize(model)
# Initialize to the basics
pars = ThetaParam.from_dict(Starfish.config["Theta"])
#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))
