def mese_followup(**kwargs):
livetime = 988.54
livetime += 358.402
livetime += 368.381
print("\tLoading MESE with 2 follow-up years...")
exp = np.append(np.load(os.path.join(path, "MESE_exp.npy")),
np.load(os.path.join(path, "MESE_followup_exp.npy")))
mc = np.load(os.path.join(path, "MESE_MC.npy"))
if "dist" in exp.dtype.names:
exp = drop_fields(exp, ["dist"], usemask=False)
if "dist" in mc.dtype.names:
mc = drop_fields(mc, ["dist"], usemask=False)
sinDec = kwargs.pop("sinDec", [-1., hem])
exp = exp[(exp["sinDec"] > sinDec[0]) & (exp["sinDec"] < sinDec[-1])]
mc = mc[(mc["sinDec"] > sinDec[0]) & (mc["sinDec"] < sinDec[-1])]
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.93, 4 + 1),
np.linspace(-0.93, hem, 12 + 1),
]))
dec_bins = dec_bins[(dec_bins >= sinDec[0]) & (dec_bins <= sinDec[1])]
dec_bins = np.unique(np.concatenate([sinDec, dec_bins]))
dec_bins_logE = np.linspace(-1., hem, 4 + 1)
dec_bins_logE = dec_bins_logE[(dec_bins_logE >= sinDec[0])
& (dec_bins_logE <= sinDec[1])]
dec_bins_logE = np.unique(np.concatenate([sinDec, dec_bins_logE]))
energy_bins = [
np.linspace(2., 8.5, 67 + 1),
np.linspace(-1., hem, 4 + 1),
]
mc = mc[mc["logE"] > 1.]
llh_model = EnergyLLH(twodim_bins=energy_bins, sinDec_bins=dec_bins)
if "upscale" in kwargs and kwargs["upscale"] is not None and (
kwargs["upscale"] or not type(kwargs["upscale"]) == bool):
lt = kwargs.pop("livetime", livetime)
kwargs["upscale"] = (int(kwargs.pop("upscale")), lt)
kwargs.pop("livetime", None)
kwargs.setdefault("mode", "all")
kwargs.setdefault("seed", 20101112)
llh = StackingPointSourceLLH(exp,
mc,
livetime,
llh_model=llh_model,
**kwargs)
print("{0:>80s}".format("[done]"))
return llh
def ic86_I(**kwargs):
livetime = 332.61
print("\tLoading IC86 data...")
decorr = kwargs.pop("no_mese", False)
if decorr:
print("\t\tRemove MESE from exp and MC")
exp = np.load(os.path.join(path, "IC86_noMESE_exp.npy"))
mc = np.load(os.path.join(path, "IC86_noMESE_corrected_MC.npy"))
else:
exp = np.load(os.path.join(path, "IC86_exp.npy"))
mc = np.load(os.path.join(path, "IC86_corrected_MC.npy"))
sinDec = kwargs.pop("sinDec", [-1., 1.])
exp = exp[(exp["sinDec"] > sinDec[0]) & (exp["sinDec"] < sinDec[-1])]
mc = mc[(mc["sinDec"] > sinDec[0]) & (mc["sinDec"] < sinDec[-1])]
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.2, 10 + 1),
np.linspace(-0.2, hem, 4 + 1),
np.linspace(hem, 0.2, 5 + 1),
np.linspace(0.2, 1., 10),
]))
dec_bins = dec_bins[(dec_bins >= sinDec[0]) & (dec_bins <= sinDec[1])]
dec_bins = np.unique(np.concatenate([sinDec, dec_bins]))
energy_bins = [np.linspace(1., 10., 40 + 1), dec_bins]
mc = mc[mc["logE"] > 1.]
llh_model = EnergyLLH(twodim_bins=energy_bins, sinDec_bins=dec_bins)
if "upscale" in kwargs and kwargs["upscale"] is not None and (
kwargs["upscale"] or not type(kwargs["upscale"]) == bool):
lt = kwargs.pop("livetime", livetime)
kwargs["upscale"] = (int(kwargs.pop("upscale")), lt)
kwargs.pop("livetime", None)
kwargs.setdefault("seed", 2011)
llh = StackingPointSourceLLH(exp,
mc,
livetime,
llh_model=llh_model,
**kwargs)
print("{0:>80s}".format("[done]"))
return llh
def ic86_2012_bdt(**kwargs):
livetime = 331.88
print("\tLoading IC86-II...")
exp = np.load(os.path.join(path, "IC86-2012_exp.npy"))
mc = np.load(os.path.join(path, "IC86-2012_MC.npy"))
sinDec = kwargs.pop("sinDec", [-1., 1.])
exp = exp[(exp["sinDec"] > sinDec[0]) & (exp["sinDec"] < sinDec[-1])]
mc = mc[(mc["sinDec"] > sinDec[0]) & (mc["sinDec"] < sinDec[-1])]
exp = drop_fields(exp, ["BDT2", "perc"])
mc = drop_fields(mc, ["BDT2", "perc"])
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.92, 5 + 1),
np.linspace(-0.92, -0.15, 10 + 1),
np.linspace(-0.15, 0.01, 10 + 1),
np.linspace(0.01, 1., 20 + 1),
]))
dec_bins = dec_bins[(dec_bins >= sinDec[0]) & (dec_bins <= sinDec[1])]
dec_bins = np.unique(np.concatenate([sinDec, dec_bins]))
X = np.concatenate([exp["BDT"], mc["BDT"]])
bdt_bins = np.percentile(X, [0., 20., 40., 60., 80., 100.])
energy_bdt_bins = [
np.linspace(1., 10., 40 + 1),
np.concatenate([[bdt_bins[0] - (bdt_bins[1] - bdt_bins[0])], bdt_bins,
[bdt_bins[-1] + bdt_bins[-1] - bdt_bins[-2]]]),
dec_bins
]
llh_model = EnergyBDTLLH(bins=energy_bdt_bins, sinDec_bins=dec_bins)
if "upscale" in kwargs and kwargs["upscale"] is not None and (
kwargs["upscale"] or not type(kwargs["upscale"]) == bool):
lt = kwargs.pop("livetime", livetime)
kwargs["upscale"] = (int(kwargs.pop("upscale")), lt)
kwargs.pop("livetime", None)
kwargs.setdefault("seed", 2012)
llh = StackingPointSourceLLH(exp,
mc,
livetime,
llh_model=llh_model,
**kwargs)
print("{0:>80s}".format("[done]"))
return llh
def init59(energy=False, mode='all', **kwargs):
arr_exp = cache.load(filename_pickle+"IC59/exp.pickle")
arr_mc = cache.load(filename_pickle+"IC59/mc.pickle")
dec_bins = np.unique(np.concatenate([
np.linspace(-1., -0.95, 2 + 1),
np.linspace(-0.95, -0.25, 25 + 1),
np.linspace(-0.25, 0.05, 15 + 1),
np.linspace(0.05, 1., 10 + 1),
]))
dec_bins_logE = np.unique(np.concatenate([
np.linspace(-1., -0.05, 20 + 1),
np.linspace(0.05, 1., 10 + 1),
]))
energy_bins = [np.linspace(2., 9.5, 67 + 1), dec_bins_logE]
if energy:
llh_model = EnergyLLH(energy_bins,
sinDec_bins=dec_bins)
#llh_model = EnergyLLH(sinDec_bins=dec_bins(50),#min(50, Nexp // 50),
# sinDec_range=[-1., 1.],
# logE_bins=min(50, Nexp // 50), #energybins(50)
# logE_range=[[0.9 * min(arr_exp["logE"].min(),
# arr_mc["logE"].min()),
# 1.1 * max(arr_exp["logE"].max(),
# arr_mc["logE"].max())],
# [-1., 1.]])
else:
llh_model = ClassicLLH(#["logE"], min(50, Nexp // 50),
#twodim_range=[0.9 * arr_mc["logE"].min(),
# 1.1 * arr_mc["logE"].max()],
sinDec_bins=dec_bins,
sinDec_range=[-1., 1.])
#Why a different sindec binning? Curious...
llh = StackingPointSourceLLH(arr_exp, arr_mc, livetime=livetime_IC59, llh_model=llh_model,
mode=mode, #hemispheres=dict(Full=[-np.inf, np.inf]),
#nsource=Nexp / 100.,
#nsource_bounds=(-Nexp / 2., Nexp / 2.)
# if not energy else (0., Nexp / 2.),
seed=np.random.randint(2**32),
**kwargs)
return llh
def init86I(energy=False, mode='all', **kwargs):
arr_exp = cache.load(filename_pickle+"IC86I/exp.pickle")
arr_mc = cache.load(filename_pickle+"IC86I/mc.pickle")
#This hem... not sure about what it means, but stefan uses it in load.py.
#Obviously it's for the hemisphere line - but why doesn't he use it for every year?
hem = np.sin(np.radians(-5.))
dec_bins = np.unique(np.concatenate([
np.linspace(-1., -0.2, 10 + 1),
np.linspace(-0.2, hem, 4 + 1),
np.linspace(hem, 0.2, 5 + 1),
np.linspace(0.2, 1., 10),
]))
#check this later, BEN!
energy_bins = [np.linspace(1., 10., 67 + 1), dec_bins]
if energy:
llh_model = EnergyLLH(energy_bins,
sinDec_bins=dec_bins)
#llh_model = EnergyLLH(sinDec_bins=dec_bins(50),#min(50, Nexp // 50),
# sinDec_range=[-1., 1.],
# logE_bins=min(50, Nexp // 50), #energybins(50)
# logE_range=[[0.9 * min(arr_exp["logE"].min(),
# arr_mc["logE"].min()),
# 1.1 * max(arr_exp["logE"].max(),
# arr_mc["logE"].max())],
# [-1., 1.]])
else:
llh_model = ClassicLLH(#["logE"], min(50, Nexp // 50),
#twodim_range=[0.9 * arr_mc["logE"].min(),
# 1.1 * arr_mc["logE"].max()],
sinDec_bins=dec_bins,
sinDec_range=[-1., 1.])
#Why a different sindec binning? Curious...
llh = StackingPointSourceLLH(arr_exp, arr_mc, livetime=livetime_IC86I, llh_model=llh_model,
mode=mode, #hemisphere=dict(Full=[-np.inf, np.inf]),
#nsource=Nexp / 100.,
#nsource_bounds=(-Nexp / 2., Nexp / 2.)
# if not energy else (0., Nexp / 2.),
seed=np.random.randint(2**32),
**kwargs)
return llh
def ic40(**kwargs):
livetime = 375.539
print("\tLoading IC40 data...")
exp = np.load(os.path.join(path, "IC40_exp.npy"))
mc = np.load(os.path.join(path, "IC40_MC.npy"))
#dec_bins = np.linspace(-1., 1., 25 + 1)
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.25, 10 + 1),
np.linspace(-0.25, 0.0, 5 + 1),
np.linspace(0.0, 1., 10 + 1),
]))
dec_bins_logE = np.unique(
np.concatenate([
np.linspace(-1., -0.25, 10 + 1),
np.linspace(-0.25, 0.0, 10 + 1),
np.linspace(0.0, 1., 10 + 1),
]))
energy_bins = [np.linspace(1., 10., 35 + 1), dec_bins_logE]
mc = mc[mc["logE"] > 1.]
llh_model = EnergyLLH(twodim_bins=energy_bins, sinDec_bins=dec_bins)
if "upscale" in kwargs and kwargs["upscale"] is not None and (
kwargs["upscale"] or not type(kwargs["upscale"]) == bool):
lt = kwargs.pop("livetime", livetime)
kwargs["upscale"] = (int(kwargs.pop("upscale")), lt)
kwargs.pop("livetime", None)
kwargs.setdefault("seed", 2008)
llh = StackingPointSourceLLH(exp,
mc,
livetime,
llh_model=llh_model,
**kwargs)
print("{0:>80s}".format("[done]"))
return llh
def ic79(**kwargs):
livetime = 315.506
print("\tLoading IC79 data...")
decorr = kwargs.pop("no_mese", False)
if decorr:
print("\t\tRemove MESE from exp and MC")
exp = np.load(os.path.join(path, "IC79_noMESE_exp.npy"))
mc = np.load(os.path.join(path, "IC79_noMESE_corrected_MC.npy"))
else:
exp = np.load(os.path.join(path, "IC79_exp.npy"))
mc = np.load(os.path.join(path, "IC79_corrected_MC.npy"))
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.75, 10 + 1),
np.linspace(-0.75, 0., 15 + 1),
np.linspace(0., 1., 20 + 1)
]))
energy_bins = [np.linspace(2., 9., 40 + 1), dec_bins]
mc = mc[mc["logE"] > 1.]
llh_model = EnergyLLH(twodim_bins=energy_bins, sinDec_bins=dec_bins)
if "upscale" in kwargs and kwargs["upscale"] is not None and (
kwargs["upscale"] or not type(kwargs["upscale"]) == bool):
lt = kwargs.pop("livetime", livetime)
kwargs["upscale"] = (int(kwargs.pop("upscale")), lt)
kwargs.pop("livetime", None)
kwargs.setdefault("seed", 2010)
llh = StackingPointSourceLLH(exp,
mc,
livetime,
llh_model=llh_model,
**kwargs)
print("{0:>80s}".format("[done]"))
return llh
def init40(energy=False, mode='all', **kwargs):
arr_exp = cache.load(filename_pickle+"IC40/exp.pickle")
arr_mc = cache.load(filename_pickle+"IC40/mc.pickle")
dec_bins = np.unique(np.concatenate([
np.linspace(-1., -0.25, 10 + 1),
np.linspace(-0.25, 0.0, 5 + 1),
np.linspace(0.0, 1., 10 + 1),
]))
dec_bins_logE = np.unique(np.concatenate([
np.linspace(-1., -0.25, 10 + 1),
np.linspace(-0.25, 0.0, 10 + 1),
np.linspace(0.0, 1., 10 + 1),
]))
#These binnings are done, year specifically, in load.py from stefan.
energy_bins = [np.linspace(2., 9., 75 + 1), dec_bins_logE]
if energy:
llh_model = EnergyLLH(energy_bins,
sinDec_bins=dec_bins)
else:
llh_model = ClassicLLH(#["logE"], min(50, Nexp // 50),
#twodim_range=[0.9 * arr_mc["logE"].min(),
# 1.1 * arr_mc["logE"].max()],
sinDec_bins=dec_bins,
sinDec_range=[-1., 1.])
#Why a different sindec binning? Curious...
llh = StackingPointSourceLLH(arr_exp, arr_mc, livetime=livetime_IC40, llh_model=llh_model,
mode=mode, #hemispheres=dict(Full=[-np.inf, np.inf]),
#nsource=Nexp / 100.,
#nsource_bounds=(-Nexp / 2., Nexp / 2.)
# if not energy else (0., Nexp / 2.),
seed=np.random.randint(2**32),
**kwargs)
return llh
def mese(**kwargs):
livetime = 988.54
print("\tLoading MESE...")
exp = np.load(os.path.join(path, "MESE_exp.npy"))
mc = np.load(os.path.join(path, "MESE_MC.npy"))
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.92, 3 + 1),
np.linspace(-0.92, hem, 10 + 1),
]))
energy_bins = [
np.linspace(2., 8.5, 40 + 1),
np.linspace(-1., hem, 4 + 1),
]
mc = mc[mc["logE"] > 1.]
llh_model = EnergyLLH(twodim_bins=energy_bins, sinDec_bins=dec_bins)
if "upscale" in kwargs and kwargs["upscale"] is not None and (
kwargs["upscale"] or not type(kwargs["upscale"]) == bool):
lt = kwargs.pop("livetime", livetime)
kwargs["upscale"] = (int(kwargs.pop("upscale")), lt)
kwargs.pop("livetime", None)
kwargs.setdefault("mode", "all")
kwargs.setdefault("seed", 20101112)
llh = StackingPointSourceLLH(exp,
mc,
livetime,
llh_model=llh_model,
**kwargs)
print("{0:>80s}".format("[done]"))
return llh
def ic86_2012(dataset=[2012, 2013], **kwargs):
lt12 = 331.358
lt13 = 359.768
lt14 = 367.209
livetime = 0.
exp = list()
decorr = kwargs.pop("no_mese", False)
if 2012 in dataset:
livetime += lt12
print("\tLoading IC86-2012 ...")
if decorr:
print("\t\tRemove MESE from IC86-2012 exp")
exp.append(np.load(os.path.join(path, "IC86-2012_noMESE_exp.npy")))
else:
exp.append(np.load(os.path.join(path, "IC86-2012_exp.npy")))
if 2013 in dataset:
livetime += lt13
print("\tLoading IC86-2013 ...")
exp.append(np.load(os.path.join(path, "IC86-2013_exp.npy")))
if 2014 in dataset:
livetime += lt14
print("\tLoading IC86-2014 ...")
exp.append(np.load(os.path.join(path, "IC86-2014_exp.npy")))
exp = np.concatenate(exp)
if decorr:
print("\t\tRemove MESE from IC86-2012 MC")
mc = np.load(os.path.join(path, "IC86-2012_noMESE_MC.npy"))
else:
mc = np.load(os.path.join(path, "IC86-2012_MC.npy"))
sinDec = kwargs.pop("sinDec", [-1., 1.])
exp = exp[(exp["sinDec"] > sinDec[0]) & (exp["sinDec"] < sinDec[-1])]
mc = mc[(mc["sinDec"] > sinDec[0]) & (mc["sinDec"] < sinDec[-1])]
exp = drop_fields(exp, ["BDT"])
mc = drop_fields(mc, ["BDT"])
dec_bins = np.unique(
np.concatenate([
np.linspace(-1., -0.93, 4 + 1),
np.linspace(-0.93, -0.3, 10 + 1),
np.linspace(-0.3, 0.05, 9 + 1),
np.linspace(0.05, 1., 18 + 1),
]))
dec_bins = dec_bins[(dec_bins >= sinDec[0]) & (dec_bins <= sinDec[1])]
dec_bins = np.unique(np.concatenate([sinDec, dec_bins]))
energy_bins = [np.linspace(1., 9.5, 50 + 1), dec_bins]
llh_model = EnergyLLH(twodim_bins=energy_bins, sinDec_bins=dec_bins)
if "upscale" in kwargs and kwargs["upscale"] is not None and (
kwargs["upscale"] or not type(kwargs["upscale"]) == bool):
lt = kwargs.pop("livetime", livetime)
kwargs["upscale"] = (int(kwargs.pop("upscale")), lt)
kwargs.pop("livetime", None)
kwargs.setdefault("seed", 2012)
llh = StackingPointSourceLLH(exp,
mc,
livetime,
llh_model=llh_model,
**kwargs)
print("{0:>80s}".format("[done]"))
return llh
def ic86_2012_precut(**kwargs):
livetime = 331.88
print("\tLoading IC86-II with precut applied...")
exp = np.load(os.path.join(path, "IC86-2012-precut_exp.npy"))
mc = np.load(os.path.join(path, "IC86-2012-precut_MC.npy"))
sinDec = kwargs.pop("sinDec", [-1., 1.])
exp = exp[(exp["sinDec"] > sinDec[0]) & (exp["sinDec"] <= sinDec[-1])]
mc = mc[(mc["sinDec"] > sinDec[0]) & (mc["sinDec"] <= sinDec[-1])]
if "perc" in kwargs:
perc = kwargs.pop("perc")
print("\tCutting on percentage {0:8.3%}".format(perc))
nbins = 400
h, bins = np.histogram(exp["sinDec"], bins=nbins, range=sinDec)
NpB = perc * np.sum(h, dtype=np.float) / nbins
eps = np.zeros_like(h, dtype=np.float)
eps[h > 0] = NpB / h[h > 0]
eps[eps > 1] = 1.
xdec = np.array([])
ybdt = np.array([])
for eps_i, lbin, ubin in zip(eps, bins[:-1], bins[1:]):
m = (exp["sinDec"] > lbin) & (exp["sinDec"] < ubin)
if not np.any(m):
print("No events in", lbin, ubin)
continue
bdt_cut = np.percentile(exp["BDT"][m], 100. * (1. - eps_i))
np.set_printoptions(precision=4)
xdec = np.append(xdec, (lbin + ubin) / 2.)
ybdt = np.append(ybdt, bdt_cut)
pol = np.polyfit(xdec, ybdt, 6)
N1 = len(exp)
polexp = np.polyval(pol, exp["sinDec"])
polmc = np.polyval(pol, mc["sinDec"])
mexp = exp["BDT"] > polexp
mmc = mc["BDT"] > polmc
exp = exp[mexp]
mc = mc[mmc]
N2 = len(exp)
print("\tCut: {0:7.2%}".format(float(N2) / N1))
elif "bdt" in kwargs and "bdt2" in kwargs:
comp = kwargs.pop("comp", np.logical_or)
bdt = kwargs.pop("bdt")
if not callable(bdt):
print("\tCutting on bdt {0:5.3f}".format(bdt))
bdt_val = bdt
bdt = lambda cz: bdt_val * np.ones_like(cz)
else:
print(
"\tCutting bdt from {0:5.3f} to {1:5.3f} in dec range".format(
*bdt(np.asarray(sinDec))))
bdt2 = kwargs.pop("bdt2")
if not callable(bdt2):
print("\tCutting on bdt2 {0:5.3f}".format(bdt2))
bdt_val2 = bdt2
bdt2 = lambda cz: bdt_val2 * np.ones_like(cz)
else:
print(
"\tCutting bdt2 from {0:5.3f} to {1:5.3f} in dec range".format(
*bdt2(np.asarray(sinDec))))
exp = exp[comp(exp["BDT"] > bdt(exp["sinDec"]),
exp["BDT2"] > bdt2(exp["sinDec"]))]
mc = mc[comp(mc["BDT"] > bdt(mc["sinDec"]),
mc["BDT2"] > bdt2(mc["sinDec"]))]
elif "bdt" in kwargs:
bdt = kwargs.pop("bdt")
if not callable(bdt):
print("\tCutting on bdt {0:5.3f}".format(bdt))
bdt_val = bdt
bdt = lambda cz: bdt_val * np.ones_like(cz)
else:
print(
"\tCutting bdt from {0:5.3f} to {1:5.3f} in dec range".format(
*bdt(np.asarray(sinDec))))
exp = exp[exp["BDT"] > bdt(exp["sinDec"])]
mc = mc[mc["BDT"] > bdt(mc["sinDec"])]
elif "bdt2" in kwargs:
bdt = kwargs.pop("bdt2")
if not callable(bdt):
print("\tCutting on bdt2 {0:5.3f}".format(bdt))
bdt_val = bdt
bdt = lambda cz: bdt_val * np.ones_like(cz)
else:
print(
"\tCutting bdt from {0:5.3f} to {1:5.3f} in dec range".format(
*bdt(np.asarray(sinDec))))
exp = exp[exp["BDT2"] > bdt(exp["sinDec"])]
mc = mc[mc["BDT2"] > bdt(mc["sinDec"])]
nmin = 10
nbins = 40
dec_bins = np.linspace(*sinDec, num=nbins)
while np.any(np.histogram(exp["sinDec"], bins=dec_bins)[0] < nmin):
print("Found close-to-empty bins, reduce binsize")
h, b = np.histogram(exp["sinDec"], bins=dec_bins)
b = np.array([b[1:], b[:-1]])
print(b[:, h < nmin])
if nbins < 2:
raise ValueError("Need more than 2 bins!")
nbins -= 1
dec_bins = np.linspace(*sinDec, num=nbins)
energy_bins = [np.linspace(0., 9.5, 30 + 1), dec_bins]
llh_model = EnergyLLH(twodim_bins=energy_bins, sinDec_bins=dec_bins)
if "upscale" in kwargs and kwargs["upscale"] is not None and (
kwargs["upscale"] or not type(kwargs["upscale"]) == bool):
lt = kwargs.pop("livetime", 332.61) #livetime)
kwargs["upscale"] = (int(kwargs.pop("upscale")), lt)
kwargs.pop("livetime", None)
kwargs.setdefault("seed", 2012)
llh = StackingPointSourceLLH(exp,
mc,
livetime,
llh_model=llh_model,
**kwargs)
if "pol" in locals():
llh.pol = pol
print("{0:>80s}".format("[done]"))
return llh
## Time to define my modelweights in a dictionary. ##
modelweights = {'flux':flux, 'redshift': list(np.power(redshift,-2)), 'uniform':list(np.ones_like(redshift))}
##Remember, weighted sensitivity requires src dec in radians.#
src_dec= np.radians(src_dec)
src_ra = np.radians(src_ra)
##Now, I'll input my injection weighting scheme from the commandline. ##
if injweight == 'uniform':
inj = PointSourceInjector(Gamma, sinDec_bandwidth=.05, src_dec= src_dec, seed=0)
else:
inj = PointSourceInjector(Gamma, sinDec_bandwidth=.05, src_dec= src_dec, theo_weight = modelweights['{}'.format(injweight)], seed=0)
sensitivity = StackingPointSourceLLH.weighted_sensitivity(llh86I,src_ra=src_ra,src_dec=src_dec,alpha=.5,beta=.9,inj=inj,trials={'n_inj':[],'TS':[],'nsources':[],'gamma':[]},longrun=True,bckg_trials=bckg_trials,eps=0.02,n_iter=250, w_theo=None)
print sensitivity
#discovery = PointSourceLLH.weighted_sensitivity(llhmodel,src_ra=src_ra,src_dec=src_dec,alpha=2.867e-7,beta=.5,inj=inj,trials={'n_inj':[],'TS':[],'nsources':[],'gamma':[]},bckg_trials=bckg_trials,eps=0.01,n_iter=250)
#print discovery
#choose an output dir, and make sure it exists
this_dir = os.path.dirname(os.path.abspath (__file__))
sens_dir = misc.ensure_dir ('/data/user/brelethford/Output/stacking_sensitivity/SwiftBAT70m/{0}yr/{1}/{2}_inj/sensitivity/'.format(str(n),llhweight, injweight))
# save the output
outfile_sens = sens_dir + 'gamma{}.array'.format(Gamma)
print 'Saving', outfile_sens, '...'
cache.save(sensitivity, outfile_sens)
#cache.save(discovery, outfile_disc)
##Remember, weighted sensitivity requires src dec in radians.#
src_dec = np.radians(src_dec)
src_ra = np.radians(src_ra)
inj = StackingPointSourceInjector(Gamma,
sinDec_bandwidth=.05,
src_dec=src_dec,
seed=0)
#CURRENT PROBLEM -never goes to the 'Estimation sens in region' phase. problem of too many events? wrong n_iter variable?
sensitivity = StackingPointSourceLLH.weighted_sensitivity(
llhmodel,
src_ra=src_ra,
src_dec=src_dec,
alpha=.5,
beta=.9,
inj=inj,
mc=MC,
TSval=TSval,
w_theoMC=modelweights['{}'.format(llhweight)],
w_theo=modelweights['{}'.format(injweight)],
w_theo_fit=modelweights['{}'.format(injweight)],
eps=0.05,
n_iter=100)
print sensitivity
#discovery = PointSourceLLH.weighted_sensitivity(llhmodel,src_ra=src_ra,src_dec=src_dec,alpha=2.867e-7,beta=.5,inj=inj,trials={'n_inj':[],'TS':[],'nsources':[],'gamma':[]},bckg_trials=bckg_trials,eps=0.01,n_iter=250)
#print discovery
#choose an output dir, and make sure it exists
this_dir = os.path.dirname(os.path.abspath(__file__))
sens_dir = misc.ensure_dir(
'/data/user/brelethford/Output/stacking_sensitivity/4yr_Starburst/{0}yr/{1}_mhuber_git/{2}_inj/sensitivity/'