oinj = np.copy(injdata)
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
injdata = np.copy(Mfi.model(pdict))
if opts.oinj:
if oinj != None:
oinj = oinj + injdata
else:
oinj = np.copy(injdata)
flarelc.clc = flarelc.clc + injdata
# output different noise estimates
noiseest = opts.noisemethod
tmpcurve = copy(flarelc)
tmpcurve.detrend(method='savitzkygolay', nbins=bglen, order=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)
print "Noise estimate with '%s' method = %f" % (noiseest, sig)
if not opts.lconly:
# get the odds ratio
Or = bf.OddsRatioDetector( flarelc,
bglen=bglen,
bgorder=bgorder,
nsinusoids=nsinusoids,
noiseestmethod=noiseest,
segodds = [] # odds ratios for all above threshold segments
segtimes = [] # times of all above threshold segments
segidxs = [] # indices of all above threshold segments
for (starts, ends) in flarelist:
sidxs = np.arange(starts, ends)
segidxs.append(sidxs.tolist())
segtimes.append(ts[sidxs].tolist())
segodds.append(np.copy(lnO)[sidxs].tolist())
# get the noise estimate
tmpcurve = copy(flarelc)
tmpcurve.detrend(method='savitzkygolay',
nbins=odds.bglen,
order=odds.bgorder)
if odds.noiseestmethod == 'powerspectrum':
sk = bf.estimate_noise_ps(tmpcurve, estfrac=odds.psestfrac)[0]
elif odds.noiseestmethod == 'tailveto':
sk = bf.estimate_noise_tv(tmpcurve.clc, sigma=odds.tvsigma)[0]
else:
print "Error... Noise estimation method must be 'powerspectrum' or 'tailveto'"
sys.exit(0)
datadict = {
"Kepler ID": kids[i]["kepid"],
"No. flares": Nflares,
"Teff": kids[i]["teff"],
"log(g)": kids[i]["logg"],
"Max. odds ratios": maxL,
"Max. indices": maxIdx,
"Max. times": maxT,
"Segment odds ratios": segodds,
maxT.append(ts[maxidx]) # time of maximum
segodds = [] # odds ratios for all above threshold segments
segtimes = [] # times of all above threshold segments
segidxs = [] # indices of all above threshold segments
for (starts, ends) in flarelist:
sidxs = np.arange(starts, ends)
segidxs.append(sidxs.tolist())
segtimes.append(ts[sidxs].tolist())
segodds.append(np.copy(lnO)[sidxs].tolist())
# get the noise estimate
tmpcurve = copy(flarelc)
tmpcurve.detrend(method='savitzkygolay', odds.bglen, odds.bgorder)
if odds.noiseestmethod == 'powerspectrum':
sk = bf.estimate_noise_ps(tmpcurve, estfrac=odds.psestfrac)[0]
elif odds.noiseestmethod == 'tailveto':
sk = bf.estimate_noise_tv(tmpcurve.clc, sigma=odds.tvsigma)[0]
else:
print "Error... Noise estimation method must be 'powerspectrum' or 'tailveto'"
sys.exit(0)
datadict = {
"Kepler ID": kids[i]["kepid"],
"No. flares": Nflares,
"Teff": kids[i]["teff"],
"log(g)": kids[i]["logg"],
"Max. odds ratios": maxL,
"Max. indices": maxIdx,
"Max. times": maxT,
"Segment odds ratios": segodds,
