flarelcinj = bf.Lightcurve()
flarelcinj.clc = np.copy(oinj)
flarelcinj.cts = np.copy(ts)
flarelcinj.cle = np.zeros(len(ts))
flarelcinj.cadence = 'long'
flarelc.clc = flarelc.clc + injdata
if opts.onoise:
noiseest = opts.noisemethod
tmpcurve = copy(flarelc)
tmpcurve.detrend(opts.bglen, opts.bgorder)
if noiseest == 'powerspectrum':
sig = bf.estimate_noise_ps(tmpcurve, estfrac=opts.psest)[0]
elif noiseest == 'tailveto':
sig = bf.estimate_noise_tv(tmpcurve.clc, sigma=opts.tvsigma)[0]
else:
print "Error... noise estimation method %s not recognised." % noiseest
sys.exit(0)
detrendedsk, f = tmpcurve.psd()
# convert back to one-sided power spectral density
sig = 2. * (sig**2) / flarelc.fs()
# set matplotlib defaults
mplparams = { \
'text.usetex': True, # use LaTeX for all text
'axes.linewidth': 0.5, # set axes linewidths to 0.5
'axes.grid': True, # add a grid
'grid.linewidth': 0.5,
flarelc.cts = np.copy(ts)
flarelc.cle = np.zeros(len(ts))
flarelc.cadence = 'long'
if dosinusoids: # add sinusoid
flarelc.clc = flarelc.clc + amps[i] * np.sin(
2. * np.pi * freqs[i] * ts + phase[i])
#pl.plot(flarelc.cts, flarelc.clc)
#pl.show()
# get noise standard deviation from a detrended lightcurve
tmpcurve = copy(flarelc)
tmpcurve.detrend(bglen, bgorder)
#sk = bf.estimate_noise_ps(tmpcurve, estfrac=0.5)[0] # noise standard deviation
sk = bf.estimate_noise_tv(tmpcurve.clc, sigma=1.0)[0]
del tmpcurve
# set central time of the injection randomly (but not within bglen/2 of data edges)
idxt0 = np.random.randint(int(bglen / 2), len(ts) - int(bglen / 2) - 1)
t0 = flarelc.cts[idxt0]
if outd:
fparams = open(paramout,
'a') # open output parameter file for appending
# create injections
if opts.injtransit: # create a transit
Mti = bf.Transit(flarelc.cts, amp=1)
injdata = np.copy(
Mti.model(1., sigmags[i], taufs[i], t0, flarelc.cts))
