def newDetrendDataTask(clip):
flux = clip['cotrend.flux_frac']
flags = clip['cotrend.flags']
nPoints = clip['config.nPointsForMedianSmooth']
#When you detrend, you must do something about the gaps and bad values.
#This is the simplest possible thing. Replace all bad/missing data with
#zeros. This is a placehold. Bad data inside a transit is replaced with
#a zero, which is not what you want.
flux[flags] = 0
#Do a simple detrend.
detrend = kplrfits.medianSubtract1d(flux, nPoints)
clip['detrend'] = dict()
clip['detrend.flux_frac'] = detrend
clip['detrend.flags'] = flags
clip['detrend.source'] = "Simple Median detrend"
rollTweakAmp = noise.computeRollTweakAmplitude(detrend[~flags])
clip['detrend.rollTweakAmp'] = rollTweakAmp
cdpp6 = noise.computeSgCdpp_ppm(detrend[~flags])
clip['detrend.cdpp6_ppm'] = cdpp6
perPointScatter = noise.estimateScatterWithMarshallMethod(detrend[~flags])
clip['detrend.perPointScatter_ppm'] = 1e6*perPointScatter
assert(detrend is not None)
return clip
def newDetrendDataTask(clip):
flux = clip['cotrend.flux_frac']
flags = clip['cotrend.flags']
nPoints = clip['config.nPointsForMedianSmooth']
#When you detrend, you must do something about the gaps and bad values.
#This is the simplest possible thing. Replace all bad/missing data with
#zeros. This is a placehold. Bad data inside a transit is replaced with
#a zero, which is not what you want.
flux[flags] = 0
#Do a simple detrend.
detrend = kplrfits.medianSubtract1d(flux, nPoints)
clip['detrend'] = dict()
clip['detrend.flux_frac'] = detrend
clip['detrend.flags'] = flags
clip['detrend.source'] = "Simple Median detrend"
rollTweakAmp = noise.computeRollTweakAmplitude(detrend[~flags])
clip['detrend.rollTweakAmp'] = rollTweakAmp
cdpp6 = noise.computeSgCdpp_ppm(detrend[~flags])
clip['detrend.cdpp6_ppm'] = cdpp6
perPointScatter = noise.estimateScatterWithMarshallMethod(detrend[~flags])
clip['detrend.perPointScatter_ppm'] = 1e6 * perPointScatter
assert (detrend is not None)
return clip
def findBestPeak(blsArray, nPointsForSmooth=100, offset=0):
"""Find the index of the best peak in the bls spectrum.
The best peak is not usually the strongest one. The noise floor
of a BLS increases at long period, so this must be accounted for
before selecting the strongest amplitude.
Inputs:
-------------
blsArray
(3d numpy array)
Optional Inputs:
-----------------
nPointsForSmooth
(int) Tuning parameter. This is the number of points near a given
period used to determine what the typical background BLS value is.
offset
(int) Debugging option. Change the chosen period by this number
of indices.
Returns:
------------
A three element tuple of indices into blsArray giving the best
period, duration and phase of transit.
"""
bb = blsArray.max(axis=1)
bbb = bb.max(axis=1)
# bbb is 1d array of strongest peak at each period.
# We need to remove background trend before we decide which peak
# is the best choice
filt = kf.medianSubtract1d(bbb, nPointsForSmooth)
iPer = np.argmax(filt)
iPer += offset
subArray = blsArray[iPer,:,:]
iDur, iPhase = np.unravel_index(np.argmax(subArray), subArray.shape)
return iPer, iDur, iPhase
def getBls(clip):
bls = clip['bls.convolved_bls']
filt = kf.medianSubtract1d(bls, 100)
return filt
def getBls(clip):
bls = clip['bls.convolved_bls']
filt = kf.medianSubtract1d(bls, 100)
return filt
