class TestThetaParam:
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)
def test_save(self):
self.thetaparam.save(fname="theta_test.json")
def test_load(self):
load = ThetaParam.load("theta_test.json")
print(load.grid)
print(load.vz)
print(load.vsini)
print(load.logOmega)
print(load.Av)
def test_load(self):
load = ThetaParam.load("theta_test.json")
print(load.grid)
print(load.vz)
print(load.vsini)
print(load.logOmega)
print(load.Av)
def test_load(self):
load = ThetaParam.load("theta_test.json")
print(load.grid)
print(load.vz)
print(load.vsini)
print(load.logOmega)
print(load.Av)
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
class TestThetaParam:
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)
def test_save(self):
self.thetaparam.save(fname="theta_test.json")
def test_load(self):
load = ThetaParam.load("theta_test.json")
print(load.grid)
print(load.vz)
print(load.vsini)
print(load.logOmega)
print(load.Av)
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
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)
import numpy as np
import Starfish
from Starfish import parallel
from Starfish.parallel import args
from Starfish.model import ThetaParam, PhiParam
print("THIS IS WORKING")
if args.generate:
model = parallel.OptimizeTheta(debug=True)
# Now that the different processes have been forked, initialize them
pconns, cconns, ps = parallel.initialize(model)
pars = ThetaParam.from_dict(Starfish.config["Theta"])
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))
pconn.recv() # Receive and discard the answer so we can send the save
pconn.send(("SAVE", None))
# 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()
# All of the argument parsing is done in the `parallel.py` module.
import numpy as np
import Starfish
from Starfish import parallel_linear
from Starfish.parallel_linear import args
from Starfish.model import ThetaParam, PhiParam
if args.generate:
model = parallel_linear.OptimizeTheta(debug=True)
# Now that the different processes have been forked, initialize them
pconns, cconns, ps = parallel_linear.initialize(model)
pars = ThetaParam.from_dict(Starfish.config["Theta"])
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))
pconn.recv() # Receive and discard the answer so we can send the save
pconn.send(("SAVE", None))
# 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()
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)
