def generate_threshold(self):
"""
This function generates a histogram to calculate the lnB values at a given confidence level.
The histogram is then stored in the ``threshold`` folder.
"""
import pickle
import glob
import pylab as pl
B = self.B
files = glob.glob(self.folderpath+'/f*.pickle')
filesp= glob.glob(self.folderpath+'/p*.pickle')
data = np.array([])
datap= np.array([])
if len(files) < 1000 :
print("[Note] The thresholding values must be calculated for this quarter, as they do not appear to exist. This can take several hours.")
repeats = 1000 - len(files)
dt = B.lightcurve.dt()
length = (B.lightcurve.cts[-1] - B.lightcurve.cts[0] )
cadence = B.lightcurve.cadence
pf.mkdir(self.folderpath)
hours = 3600
M = pf.Flare(B.lightcurve.cts, amp=10)
M.set_taus_gauss(0, 2*hours, 3)
M.set_taus_exp(.5*hours, 5*hours, 30)
P = pf.Transit(B.lightcurve.cts, amp=10)
P.set_tauf(0.5*hours, 6*hours, 11)
for i in np.arange(repeats-len(files)):
print("[Note] Iteration ", 1+i, " of ", repeats, "(", ((float(i))/float(repeats))*100,"% completed)")
A = pf.SimLightcurve(dt=dt, length=length, cadence=cadence)
# Run the detection statistic for the flare model
B = pf.Bayes(A, M)
B.bayes_factors()
C = B.marginalise_full()
D = pf.Bayes(A, P)
D.bayes_factors()
E = D.marginalise_full()
F = C + E
pickle.dump(F.lnBmargAmp, open(self.folderpath+'/f'+str(i)+'.pickle', 'wb'))
for file in files:
data = np.append(data,pickle.load(open(file, 'rb')))
counts, bin_edges = np.histogram(data[np.isfinite(data)], bins=10000, density=True)
cdf = np.cumsum(counts)/np.sum(counts)
mkdir(self.thrfolder)
pickle.dump(cdf, open(self.thrfolder+'/cdf.pickle', 'wb'))
pickle.dump(bin_edges, open(self.thrfolder+'/binedge.pickle', 'wb'))
print("[Note] The thresholding files have been found, and the histogram has been constructed")
if kfile == '':
print >> sys.stderr, "Error... no light curve file found for KID%d" % kids
sys.exit(0)
try:
flarelc = bf.Lightcurve(curve=kfile)
except:
print >> sys.stderr, "Error... could not open light curve file %s" % kfile
sys.exit(0)
ts = np.copy(flarelc.cts)
# inject flare
if opts.injflare:
tmi = flarelc.cts - flarelc.cts[0]
Mfi = bf.Flare(tmi, amp=1.)
if opts.t0 == -1.:
t0 = tmi[int(len(tmi) / 2)]
else:
t0 = opts.t0
# create flare
injdata = np.copy(Mfi.model(opts.injamp, opts.taug, opts.taue, t0,
tmi))
if opts.oinj:
if oinj != None:
oinj = oinj + injdata
else:
oinj = np.copy(injdata)
# output the transit parameters
if outd:
fparams.write("%f\t%f\t%f\n" % (snrs[i], sigmags[i], taufs[i]))
del Mti
elif opts.injimpulse: # create a delta-function impulse
Mii = bf.Impulse(flarelc.cts, amp=1)
injdata = np.copy(Mii.model(impsign, t0, flarelc.cts))
# output the impulse parameters
if outd is not None:
fparams.write("%f\t%d\n" % (snrs[i], impsign))
del Mii
else:
Mfi = bf.Flare(flarelc.cts, amp=1)
injdata = np.copy(
Mfi.model(1., taugausss[i], tauexps[i], t0, flarelc.cts))
# output the flare parameters
if outd is not None:
fparams.write("%d\t%d\t%f\t%f\t%f" %
(i, idxt0, snrs[i], taugausss[i], tauexps[i]))
if dosinusoids:
fparams.write("\t%e\t%f" % (freqs[i], amps[i]))
fparams.write("\n")
del Mfi