def optimalAperture(t_time,
t_fluxarr,
t_quality,
qual_cut=False,
return_qual=False,
toss_resat=False,
bg_cut=5,
skip=0):
"""
This routine determines an optimal apertures and outputs the flux (i.e. a light curve) from a TPF.
Inputs:
------------
t_time = 1D array of 'TIME' from TPF
t_fluxarr = 1D array of 'FLUX' from TPF
t_quality = 1D array of 'QUALITY' from TPF
qual_cut = exclude cadences with a non-zero quality flag; this is False by default
return_qual = if True then nothing is returned; this is True by default
toss_resat = exclude cadences where there is a wheel resaturation event; this is True by default
bg_cut = threshold to find pixels that are bg_cut * MAD above the median
skip = index of first cadence that should be used in the time series
Outputs:
------------
time = 1D array of time in BKJD
lc = 1D array of flux measured in optimal aperture
xbar = 1D array of x-coordinate of target centroid
ybar = 1D array of y-coordinate of target centroid
regnum = integer value of brightest pixel
lab = 2D array identifying pixels used in aperture
Usage:
------------
tpf,tpf_hdr = ar.getLongTpf(k2id, campaign, header=True)
tpf_time = tpf['TIME']
tpf_flux = tpf['FLUX']
tpf_quality = tpf['QUALITY']
time,lc,xbar,ybar,regnum,lab = optimalAperture(tpf_time, tpf_flux, tpf_quality, qual_cut=False, return_qual=False, toss_resat=True, bg_cut=5, skip=0)
"""
time = t_time[skip:]
fluxarr = t_fluxarr[skip:]
quality = t_quality[skip:]
if qual_cut:
time = time[quality == 0]
fluxarr = fluxarr[quality == 0, :, :]
elif toss_resat:
# cadences where there is a wheel resaturation event
time = time[quality != 32800]
fluxarr = fluxarr[quality != 32800, :, :]
#remove any nans
try:
fluxarr[fluxarr == 0] = np.nan
except ValueError:
pass
#subtract background
flux_b = bg_sub(fluxarr)
# create a median image to calculate where the pixels to use are
flatim = np.nanmedian(flux_b, axis=0)
#find pixels that are X MAD above the median
vals = flatim[np.isfinite(flatim)].flatten()
mad_cut = 1.4826 * MAD(vals) * bg_cut
flatim[np.isnan(flatim)] = 0.
region = np.where(flatim > mad_cut, 1, 0)
lab = label(region)[0]
#find the central pixel
imshape = np.shape(flatim)
centralpix = [1 + imshape[0] // 2, 1 + imshape[1] // 2]
#find brightest pix within 9x9 of central pix
#this assumes target is at center of postage stamp which I think is ok
centflatim = flatim[centralpix[0] - 2:centralpix[0] + 2,
centralpix[1] - 2:centralpix[1] + 2]
flatimfix = np.where(np.isfinite(centflatim), centflatim, 0)
brightestpix = np.unravel_index(flatimfix.argmax(), centflatim.shape)
bpixy, bpixx = brightestpix
#use all pixels in the postage stamp that are X MAD above the median
#this identifies location of brightest pixel only
regnum = lab[centralpix[0] - 2 + bpixy, centralpix[1] - 2 + bpixx]
lc = np.zeros_like(time)
xbar = np.zeros_like(time)
ybar = np.zeros_like(time)
#make a rectangular aperture for the moments thing
ymin = np.min(np.where(lab == regnum)[0])
ymax = np.max(np.where(lab == regnum)[0])
xmin = np.min(np.where(lab == regnum)[1])
xmax = np.max(np.where(lab == regnum)[1])
momlims = [ymin, ymax + 1, xmin, xmax + 1]
#loop that performs the aperture photometry
for i, fl in enumerate(flux_b):
lc[i] = np.sum(fl[lab == regnum])
#lc[i] = np.sum(fl[np.where(lab == 1)]
momim = fl[momlims[0]:momlims[1], momlims[2]:momlims[3]]
momim[~np.isfinite(momim)] == 0.0
xbar[i], ybar[i], cov = intertial_axis(momim)
if return_qual:
return None
else:
# TODO: think about whether this should be normalized
return (time, lc, xbar - np.nanmean(xbar), ybar - np.nanmean(ybar),
regnum, lab)
def run_C0_data_extract(fn, second_half_only=True,
qual_cut=False, return_qual=False, toss_resat=True,
bg_cut=5, skip_cads=None, skip=None):
#for C0, lets just ise the good data post safe mode
if second_half_only and skip_cads == None:
skip = 1976
elif skip is not None:
skip = skip_cads
else:
skip = 0
with pyfits.open(fn) as f:
time = f[1].data['TIME'][skip:] - f[1].data['TIME'][0]
fluxarr = f[1].data['FLUX'][skip:]
quality = f[1].data['QUALITY'][skip:]
if qual_cut:
time = time[quality == 0]
fluxarr = fluxarr[quality == 0,:,:]
elif toss_resat:
# data the cadences where there is a wheel
# resetuation event
time = time[quality != 32800]
fluxarr = fluxarr[quality != 32800,:,:]
# fix dodgy data
# the first C0 data release included zeros
# this will be changed later but we need this
# fix for now
fluxarr[fluxarr == 0] = np.nan
#subtract background
flux_b = bg_sub(fluxarr)
# create a median image to calculate where
# the pixels to use are: is this good?
# this needs to be investiagated.
flatim = np.nanmedian(flux_b,axis=0)
#fixd pixels that are X MAD above the median
vals = flatim[np.isfinite(flatim)].flatten()
mad_cut = 1.4826 * MAD(vals) * bg_cut
region = np.where(flatim > mad_cut,1,0)
lab = label(region)[0]
#find the central pixel
imshape = np.shape(flatim)
centralpix = [1+imshape[0] // 2,1+imshape[1] // 2]
#find brightest pix within 9x9 of central pix
centflatim = flatim[centralpix[0]-4:centralpix[0]+4,
centralpix[1]-4:centralpix[1]+4]
flatimfix = np.where(np.isfinite(centflatim),centflatim,0)
brightestpix = np.unravel_index(flatimfix.argmax(), centflatim.shape)
bpixy, bpixx = brightestpix
regnum = lab[centralpix[0]-4+bpixy,centralpix[1]-4+bpixx]
if regnum == 0:
print('WARNING, no star was found in light curve, \
{} light curve will be junk!'.format(fn))
lc = np.zeros_like(time)
xbar = np.zeros_like(time)
ybar = np.zeros_like(time)
#there is a loop that performs the aperture photometry
#lets also calcualte the moments of the image
#make a rectangular aperture for the moments thing
ymin = np.min(np.where(lab == regnum)[0])
ymax = np.max(np.where(lab == regnum)[0])
xmin = np.min(np.where(lab == regnum)[1])
xmax = np.max(np.where(lab == regnum)[1])
#print(np.where(lab == regnum))
momlims = [ymin,ymax+1,xmin,xmax+1]
for i,fl in enumerate(fluxarr):
lc[i] = np.sum(fl[lab == regnum])
momim = fl[momlims[0]:momlims[1],
momlims[2]:momlims[3]]
momim[~np.isfinite(momim)] == 0.0
xbar[i], ybar[i], cov = intertial_axis(momim)
if return_qual:
return None
else:
return (time,lc, xbar /
np.mean(xbar), ybar / np.mean(xbar), regnum)
def optimalAperture(t_time, t_fluxarr, t_quality, qual_cut=False, return_qual=False, toss_resat=False, bg_cut=5, skip=0):
"""
This routine determines an optimal apertures and outputs the flux (i.e. a light curve) from a TPF.
Inputs:
------------
t_time = 1D array of 'TIME' from TPF
t_fluxarr = 1D array of 'FLUX' from TPF
t_quality = 1D array of 'QUALITY' from TPF
qual_cut = exclude cadences with a non-zero quality flag; this is False by default
return_qual = if True then nothing is returned; this is True by default
toss_resat = exclude cadences where there is a wheel resaturation event; this is True by default
bg_cut = threshold to find pixels that are bg_cut * MAD above the median
skip = index of first cadence that should be used in the time series
Outputs:
------------
time = 1D array of time in BKJD
lc = 1D array of flux measured in optimal aperture
xbar = 1D array of x-coordinate of target centroid
ybar = 1D array of y-coordinate of target centroid
regnum = integer value of brightest pixel
lab = 2D array identifying pixels used in aperture
Usage:
------------
tpf,tpf_hdr = ar.getLongTpf(k2id, campaign, header=True)
tpf_time = tpf['TIME']
tpf_flux = tpf['FLUX']
tpf_quality = tpf['QUALITY']
time,lc,xbar,ybar,regnum,lab = optimalAperture(tpf_time, tpf_flux, tpf_quality, qual_cut=False, return_qual=False, toss_resat=True, bg_cut=5, skip=0)
"""
time = t_time[skip:]
fluxarr = t_fluxarr[skip:]
quality = t_quality[skip:]
if qual_cut:
time = time[quality == 0]
fluxarr = fluxarr[quality == 0,:,:]
elif toss_resat:
# cadences where there is a wheel resaturation event
time = time[quality != 32800]
fluxarr = fluxarr[quality != 32800,:,:]
#remove any nans
fluxarr[fluxarr == 0] = np.nan
#subtract background
flux_b = bg_sub(fluxarr)
# create a median image to calculate where the pixels to use are
flatim = np.nanmedian(flux_b,axis=0)
#find pixels that are X MAD above the median
vals = flatim[np.isfinite(flatim)].flatten()
mad_cut = 1.4826 * MAD(vals) * bg_cut
flatim[np.isnan(flatim)] = 0.
region = np.where(flatim > mad_cut,1,0)
lab = label(region)[0]
#find the central pixel
imshape = np.shape(flatim)
centralpix = [1+imshape[0] // 2,1+imshape[1] // 2]
#find brightest pix within 9x9 of central pix
#this assumes target is at center of postage stamp which I think is ok
centflatim = flatim[centralpix[0]-4:centralpix[0]+4,
centralpix[1]-4:centralpix[1]+4]
flatimfix = np.where(np.isfinite(centflatim),centflatim,0)
brightestpix = np.unravel_index(flatimfix.argmax(), centflatim.shape)
bpixy, bpixx = brightestpix
#use all pixels in the postage stamp that are X MAD above the median
#this identifies location of brightest pixel only
regnum = lab[centralpix[0]-4+bpixy,centralpix[1]-4+bpixx]
lc = np.zeros_like(time)
xbar = np.zeros_like(time)
ybar = np.zeros_like(time)
#make a rectangular aperture for the moments thing
ymin = np.min(np.where(lab == regnum)[0])
ymax = np.max(np.where(lab == regnum)[0])
xmin = np.min(np.where(lab == regnum)[1])
xmax = np.max(np.where(lab == regnum)[1])
momlims = [ymin,ymax+1,xmin,xmax+1]
#loop that performs the aperture photometry
for i,fl in enumerate(flux_b):
lc[i] = np.sum(fl[lab == regnum])
#lc[i] = np.sum(fl[np.where(lab == 1)]
momim = fl[momlims[0]:momlims[1],
momlims[2]:momlims[3]]
momim[~np.isfinite(momim)] == 0.0
xbar[i], ybar[i], cov = intertial_axis(momim)
if return_qual:
return None
else:
# TODO: think about whether this should be normalized
return (time,lc, xbar - np.nanmean(xbar), ybar - np.nanmean(ybar), regnum, lab)
