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)
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)
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()
if args.initPhi:
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()
if args.initPhi:
# Figure out how many models and orders we have
