dir_name = "/data/user/brelethford/AGN_Core/Plots/fast/plots/stacking/{}yr/{}/".format(len(seasons),cat)
#dir_name = "/home/brelethford/public_html/skylab/fast/plots/stacking/{}yr/{}/{}/".format(len(seasons),cat,weight)
subprocess.call(['mkdir', '-p', '{0}'.format(dir_name)])
else:
dir_name = "/data/user/brelethford/AGN_Core/Plots/fast/plots/allsky/{0}yr/dec_{1:.3f}/".format(len(seasons),np.degrees(src_dec))
#dir_name = "/home/brelethford/public_html/skylab/fast/plots/allsky/{0}yr/dec_{1:.3f}/".format(len(seasons),np.degrees(src_dec))
subprocess.call(['mkdir', '-p', '{0}'.format(dir_name)])
def get_bg_fit():
ts_parameters = fit_background(trials, dir_name + "background_trials_%s.png" % tag)
print ("Finished background fit.")
return ts_parameters
#save trials and fit params
outfile = sens_dir + 'trials.array'
trials = cache.get(outfile, get_background)
outfile = sens_dir + 'bg_fit.array'
ts_parameters = cache.get(outfile, get_bg_fit)
median_ts, eta, ndf, scale = ts_parameters
print("\nBackground only TS > 0 described by:")
print(" Median TS: %6.4f" % median_ts)
print(" PDF(TS>0): %6.4f * chi2(ts / %.4f, ndf = %.4f)" % (eta, scale, ndf))
###################################
# BACKGROUND ONLY TS DISTRIBUTION #
###################################
# BACKGROUND ONLY TS DISTRIBUTION #
###################################
print("\nRunning injection trials with {} events...".format(n_inj))
def get_trials():
t0 = time.time()
trials = llh.do_trials(args.nscramble,
injector=inj,
mean_signal=n_inj,
src_ra=src_ra,
src_dec=src_dec,
src_extension=src_ext,
src_w=src_w)
dt = time.time() - t0
print(" Completed %d trials in %.2f sec (%.2f trials/sec)" %
(trials.size, dt, trials.size / dt))
return trials
#save trials and fit params
outfile = sens_dir + 'trials_batch_{}.array'.format(batch)
trials = cache.get(outfile, get_trials)
print("\nInjection with {} events batch {} finished.".format(n_inj, batch))
###################################
# BACKGROUND ONLY TS DISTRIBUTION #
###################################
redshift = [float(catalog[i][15]) for i in range(len(catalog))]
lum = [float(catalog[i][16]) for i in range(len(catalog))]
gamma = [float(catalog[i][11]) for i in range(len(catalog))]
flux = [float(catalog[i][7]) for i in range(len(catalog))]
params = {
'ra': ra,
'dec': dec,
'redshift': redshift,
'gamma': gamma,
'flux': flux,
'lum': lum
}
return params
params = cache.get(datafolder + 'SwiftBAT70m/pickle/params.pickle',
get_SwiftBAT_params)
###Now I'll read in the background TS stuff.###
datafolder = '/data/user/brelethford/Output/stacking_sensitivity/SwiftBAT70m/flux/background_trials/'
files = [
cache.load(datafolder + file) for file in os.listdir(datafolder)
if file.endswith('.array')
]
n_inj = []
nsources = []
TS = []
beta = (0.5) #For background ts
TS_beta = [] #Calculated from the total TS median after we get all the TS.
beta_err = []
gamma = []
TS.append(file['TS'][item])
TSs = TS
eta = float(np.count_nonzero(TSs)) / len(TSs)
fit = fitfun(eta=eta, df=2.)
TSval5sig = fit.isf(norm.sf(5))
return TSval5sig
if corrected:
backfolder = '/data/user/brelethford/Output/stacking_sensitivity/{0}/{1}yr_corrected/{2}/background_trials/'.format(
catalog, str(years), llhweight)
else:
backfolder = '/data/user/brelethford/Output/stacking_sensitivity/{0}/{1}yr/{2}/background_trials/'.format(
catalog, str(years), llhweight)
TSval_median = cache.get(backfolder + 'TSval.pickle', getTSval)
TSval5sig = cache.get(backfolder + 'TSval5sig.pickle', getTSval5sig)
exp40 = np.load(datafolder + '/IC40_exp.npy')
exp59 = np.load(datafolder + '/IC59_exp.npy')
exp79 = np.load(datafolder + '/IC79b_exp.npy')
exp86 = np.load(datafolder + '/IC86_exp.npy')
if corrected:
mc40 = np.load(datafolder + '/IC40_corrected_MC.npy')
mc59 = np.load(datafolder + '/IC59_corrected_MC.npy')
mc79 = np.load(datafolder + '/IC79b_corrected_MC.npy')
mc86 = np.load(datafolder + '/IC86_corrected_MC.npy')
else:
mc40 = np.load(datafolder + '/IC40_MC.npy')
mc59 = np.load(datafolder + '/IC59_MC.npy')
mc79 = np.load(datafolder + '/IC79b_MC.npy')
###################################
# BACKGROUND ONLY TS DISTRIBUTION #
###################################
print("\nRunning background only trials ...")
def get_background():
t0 = time.time()
trials = llh.do_trials(args.nscramble,
src_ra=src_ra,
src_dec=src_dec,
src_extension=src_ext,
src_w=src_w)
dt = time.time() - t0
print(" Completed %d trials in %.2f sec (%.2f trials/sec)" %
(trials.size, dt, trials.size / dt))
return trials
#save trials and fit params
outfile = sens_dir + 'trials_batch_{}.array'.format(batch)
trials = cache.get(outfile, get_background)
print("\nBackground batch {} finished.".format(batch))
###################################
# BACKGROUND ONLY TS DISTRIBUTION #
###################################
redshift = [float(catalog[i][15]) for i in range(len(catalog))]
lum = [float(catalog[i][16]) for i in range(len(catalog))]
gamma = [float(catalog[i][11]) for i in range(len(catalog))]
flux = [float(catalog[i][7]) for i in range(len(catalog))]
params = {
'ra': [ra[0]],
'dec': [dec[0]],
'redshift': [redshift[0]],
'gamma': [gamma[0]],
'flux': [flux[0]],
'lum': [lum[0]]
}
return params
params = cache.get(datafolder + 'SwiftBAT70m/pickle/onesource.pickle',
get_SwiftBAT_params)
###Now I'll read in the background TS stuff.###
datafolder = '/data/user/brelethford/Output/stacking_sensitivity/SwiftBAT70m/{}/background_trials/'.format(
llhweight)
files = [
cache.load(datafolder + file) for file in os.listdir(datafolder)
if file.endswith('.array')
]
n_inj = []
nsources = []
TS = []
beta = (0.5) #For background ts
TS_beta = [] #Calculated from the total TS median after we get all the TS.
beta_err = []
MultiPointSourceLLH.weighted_sensitivity(llhmodel,
src_ra=np.atleast_1d(0.),
src_dec=np.atleast_1d(src_dec[i]),
alpha=.5,
beta=.9,
inj=inj,
trials={
'n_inj': [],
'TS': [],
'nsources': [],
'gamma': []
},
bckg_trials=bckg_trials,
eps=10.99,
n_iter=1)
flux_array.append(inj.mu2flux(1) * 1000)
for i in range(len(src_dec)):
print('for declination = ' + str(src_dec[i]) + ', flux = ' +
str(flux_array[i]))
def get_results():
results = {'dec': src_dec, 'flux': flux_array}
return results
results = cache.get(
'/data/user/brelethford/Output/stacking_sensitivity/testing/onesource.pickle',
get_results)
backfolder = '/data/user/brelethford/Output/stacking_sensitivity/{0}/bstacking_{1}yr/{2}/schatto79_background_trials/'.format(catalog, str(years), llhweight)
def getTSval():
files = [cache.load(backfolder+file) for file in os.listdir(backfolder) if file.endswith('.array')]
fitfun = delta_chi2
TS=[]
for file in files:
for item in range(len(file['n_inj'])):
if file['n_inj'][item] ==0:
TS.append(file['TS'][item])
TSs=TS
fit = fitfun(TSs, df=2., floc=0., fscale=1.)
TSval = np.asscalar(fit.isf(norm.sf(5)))
return TSval
TSval = cache.get(backfolder + 'TSval5sig.pickle', getTSval)
print ('TSval = ' + str(TSval))
#Now I've defined all the possible modelweights for each catalog (I can add new ones later). Now I'll read in the years of data.
if catalog == '4yr_Starburst':
import starburst_data_schatto79 as datascript
else:
import new_data_multi as datascript
##Here I add an argument for the sole purpose of having separate background files differently named. ##
## We'll assign the proper weighting scheme for the search, then use it to calculate and cache the associated bckg trials: ##
llh40= datascript.init40(energy=True,mode='all')
llh59= datascript.init59(energy=True,mode='all')
llh79 = datascript.init79(energy=True,mode='all')
llh86I= datascript.init86I(energy=True,mode='all')
def getting(filename, *a, **kw):
prush('<-> {0} ...'.format(filename))
return cache.get(filename, *a, **kw)
muex86I_data = getBoth('SplineMPEMuEXDifferential','energy',fdata_86_1)
#Doing ra and dec calculations take a while - pickle?
mjd86I_sim=getBoth('SplineMPE','time',fsim_86_1)
mjd86I_data = getBoth('SplineMPE','time',fdata_86_1)
#I'll recalculate the ras, since we need to unblind before using the actual ras.
def get_coords():
ra86I_sim,dec86I_sim=astro.dir_to_equa(zen86I_sim,azi86I_sim,mjd86I_sim)
ra86I_data,dec86I_data=astro.dir_to_equa(zen86I_data,azi86I_data,mjd86I_data)
#scramble RA! We can't know this until unblinding. (Should I fix these in place, or renew them each time?)
ra86I_sim = np.random.random(len(ra86I_sim))*2*np.pi
ra86I_data = np.random.random(len(ra86I_data))*2*np.pi
return ra86I_sim, dec86I_sim , ra86I_data, dec86I_data
ra86I_sim, dec86I_sim , ra86I_data, dec86I_data = cache.get (filename_pickle+"coords.pickle", get_coords)
#Let's now make a histogram of the sin(decs). In order to hist a MC set, we should first weight it with...
honda = NewNuFlux.makeFlux('honda2006')
honda.knee_reweighting_model = 'gaisserH3a_elbert'
flux = honda.getFlux
#Weighting fcn for MC. Later on we'll need to limit the data to a certain dec band, so let's make the masking optional. To pass it a mask, we'll need to specify the dec band (i.e., pass the function [bandmask_sim[bandnum]]).
def simweight(fsim,gamma,mask=None):
if mask == None:
mask=np.cast[np.bool](np.ones_like(getBoth('I3MCWeightDict','OneWeight',fsim)))
OneWeight=getBoth('I3MCWeightDict','OneWeight',fsim)[:][mask]
zen_sim = getBoth('MCPrimary1','zenith',fsim)[mask]
energy_sim=getBoth('MCPrimary1','energy',fsim)[mask]
