def init(Nexp, NMC, energy=True, **kwargs):
Nsrc = kwargs.pop("Nsrc", 0)
arr_exp = exp(Nexp - Nsrc)
arr_mc = MC(NMC)
if Nsrc > 0:
inj = PointSourceInjector(2, sinDec_bandwidth=1, seed=0)
inj.fill(0., arr_mc, 333.)
source = inj.sample(Nsrc, poisson=False).next()[1]
arr_exp = np.append(arr_exp, source)
if energy:
llh_model = PowerLawLLH(["logE"], min(50, Nexp // 50),
range=[[0.9 * arr_mc["logE"].min(),
1.1 * arr_mc["logE"].max()]],
sinDec_bins=min(50, Nexp // 50),
sinDec_range=[-1., 1.],
bounds=(0, 5))
else:
llh_model = UniformLLH(sinDec_bins=max(3, Nexp // 200),
sinDec_range=[-1., 1.])
llh = PointSourceLLH(arr_exp, arr_mc, 365., llh_model=llh_model,
mode="all", hemispheres=dict(Full=[-np.inf, np.inf]),
nsource=25, scramble=False,
nsource_bounds=(-Nexp / 2., Nexp / 2.)
if not energy else (0., Nexp / 2.),
seed=np.random.randint(2**32),
**kwargs)
return llh
def init(Nexp, NMC, energy=True, **kwargs):
Nsrc = kwargs.pop("Nsrc", 0)
arr_exp = exp(Nexp - Nsrc)
arr_mc = MC(NMC)
if Nsrc > 0:
inj = PointSourceInjector(2, sinDec_bandwidth=1, seed=0)
inj.fill(0., arr_mc, 333.)
source = inj.sample(Nsrc, poisson=False).next()[1]
arr_exp = np.append(arr_exp, source)
if energy:
llh_model = PowerLawLLH(
["logE"],
min(50, Nexp // 50),
range=[[0.9 * arr_mc["logE"].min(), 1.1 * arr_mc["logE"].max()]],
sinDec_bins=min(50, Nexp // 50),
sinDec_range=[-1., 1.],
bounds=(0, 5))
else:
llh_model = UniformLLH(sinDec_bins=max(3, Nexp // 200),
sinDec_range=[-1., 1.])
llh = PointSourceLLH(arr_exp,
arr_mc,
365.,
llh_model=llh_model,
mode="all",
hemispheres=dict(Full=[-np.inf, np.inf]),
nsource=25,
scramble=False,
nsource_bounds=(-Nexp / 2.,
Nexp / 2.) if not energy else
(0., Nexp / 2.),
seed=np.random.randint(2**32),
**kwargs)
return llh
results = numpy.empty( [3, nstep+1], dtype=float )
# step through ns values within [ns_min, ns_max]
for step in range(0,nstep+1):
# compute signal for this step
ns = ns_min + step*delta
# compute average TS from 5 samples
nsample = 5
avgTS = 0
for i in range(0,nsample):
# get event sample for source
ni, sample = inj.sample(src_ra, ns, poisson=False).next()
# compute TS
TS, Xmin = psllh.fit_source(src_ra,src_dec,inject=sample,scramble = True)
# add to average
avgTS = avgTS + TS/nsample
# END for (i)
# compute average significance
sigma = numpy.sqrt(avgTS)
print " [%3d/%d] ns %7.2f, sigma %6.2f" % (step+1, nstep+1, ns, sigma)
results[0][step] = ns # x
print("\tgamma =", Gamma)
# init a injector class sampling events at a point source
inj = PointSourceInjector(Gamma, sinDec_bandwidth=1., seed=0)
inj.fill(0., mc, 333.)
n = 25
gamma = np.linspace(*llh.par_bounds[1], num=n)
ntrials = 100
T = list()
nsources = np.linspace(0.5 * mu, 2. * mu, 2 * n)
for i in range(ntrials):
# Inject always the same number of events for this plot
n_inj, inject = inj.sample(mu, poisson=False).next()
TS, xmin = llh.fit_source(np.pi, 0., inject=inject)
X, Y = np.meshgrid(nsources[:-1], gamma[:-1])
X = X.ravel()
Y = Y.ravel()
Z = np.empty_like(X)
for i, (x, y) in enumerate(zip(X, Y)):
Z[i] = llh.llh(nsources=x, gamma=y)[0]
Z = -2. * (Z - Z.max())
