def extract_flux(band, binlen, tbdata):
### first get the flux and error values for each stack
binarr = range(binlen)
shortflux = np.empty(len(binarr))
shortfluxerr = np.empty(len(binarr))
for n in binarr:
seout = fits.open('SE_outputs_shortstacks/08B_' + str(band) +
'_output_cam1_' + str(binlen) + 'bin_' + str(n) +
'.fits')[1].data
if band == 'J':
seout = seout[seout['NUMBER'] == 13363]
elif band == 'K':
seout = seout[seout['NUMBER'] == 15100]
elif band == 'H':
seout = seout[seout['NUMBER'] == 11900]
shortflux[n] = seout['FLUX_APER'][:, 4]
shortfluxerr[n] = seout['FLUXERR_APER'][:, 4]
### Average non-flare flux ###
if band == 'J':
flux = vari_funcs.jflux5_stacks(tbdata).reshape(4)
fluxerr = vari_funcs.jfluxerr5_stacks(tbdata).reshape(4)
flux[3] = np.nan
fluxerr[3] = np.nan
elif band == 'K':
flux = vari_funcs.flux5_stacks(tbdata).reshape(7)
fluxerr = vari_funcs.fluxerr5_stacks(tbdata).reshape(7)
flux[2] = np.nan
fluxerr[2] = np.nan
elif band == 'H':
flux = vari_funcs.hflux5_stacks(tbdata).reshape(7)
fluxerr = vari_funcs.hfluxerr5_stacks(tbdata).reshape(7)
flux[2] = np.nan
fluxerr[2] = np.nan
meanflux = np.nanmean(flux)
### get flare flux ###
flareflux = shortflux - meanflux #flux[0]
### Propogate errors ###
errmeanflux = np.sqrt(np.nansum(np.square(fluxerr))) / binlen
flarefluxerr = np.sqrt(np.square(shortfluxerr) + np.square(errmeanflux))
### Convert to magnitudes ###
if band == 'J':
flaremag = 30 - 2.5 * np.log10(flareflux) + 0.91
elif band == 'H':
flaremag = 30 - 2.5 * np.log10(flareflux) + 1.39
elif band == 'K':
flaremag = 30 - 2.5 * np.log10(flareflux) + 1.85
flaremagerr = 1.086 / (flareflux / flarefluxerr)
return flareflux, flarefluxerr, flaremag, flaremagerr
sfluxn = vari_funcs.flux5_stacks(sdata)
### remove values that are negative ###
fluxn, tbdata = vari_funcs.semfluxlim(fluxn, tbdata)
fluxchann, chandata = vari_funcs.semfluxlim(fluxchann, chandata)
sfluxn, sdata = vari_funcs.semfluxlim(sfluxn, sdata)
#fluxn, tbdata = vari_funcs.noneg(fluxn, tbdata)
#fluxchann, chandata = vari_funcs.noneg(fluxchann, chandata)
#sfluxn, sdata = vari_funcs.noneg(sfluxn, sdata)
### get error arrays and correct them ###
#fluxerrn = vari_funcs.fluxerr5_stacks_corr(tbdata)
#fluxerrchann = vari_funcs.fluxerr5_stacks_corr(chandata)
#sfluxerrn = vari_funcs.fluxerr5_stacks_corr(sdata)
fluxerrn = vari_funcs.fluxerr5_stacks(tbdata)
fluxerrchann = vari_funcs.fluxerr5_stacks(chandata)
sfluxerrn = vari_funcs.fluxerr5_stacks(sdata)
#
#### create an error array that is just the flux depths ###
## read in depths array
#depths = np.load('fluxdepthsconv_PSF.npy')
## create empty arrays the correct shape
#fluxerrn = np.zeros(np.shape(fluxn)) + depths[None,:]
#fluxerrchann = np.zeros(np.shape(fluxchann)) + depths[None,:]
#sfluxerrn = np.zeros(np.shape(sfluxn)) + depths[None,:]
### Open the fits files and get data ###
tbdata2 = fits.open('mag_flux_tables/mag_flux_table_best.fits')[1].data
chandata2 = fits.open('mag_flux_tables/xray_mag_flux_table_best.fits')[1].data
sdata2 = fits.open('mag_flux_tables/stars_mag_flux_table.fits')[1].data
### Open the fits files and get data ###
tbdata = fits.open('mag_flux_tables/mag_flux_table_best_1519match.fits')[1].data
chandata = fits.open('mag_flux_tables/xray_mag_flux_table_best_1519match.fits')[1].data
sdata = fits.open('mag_flux_tables/stars_mag_flux_table_1519match.fits')[1].data
## Create arrays of flux values ###
fluxn = vari_funcs.flux5_stacks(tbdata)
fluxchann = vari_funcs.flux5_stacks(chandata)
sfluxn = vari_funcs.flux5_stacks(sdata)
### remove values that are negative ###
fluxn, tbdata = vari_funcs.noneg(fluxn, tbdata)
fluxchann, chandata = vari_funcs.noneg(fluxchann, chandata)
sfluxn, sdata = vari_funcs.noneg(sfluxn, sdata)
fluxerr = vari_funcs.fluxerr5_stacks(tbdata)
fluxerrchan = vari_funcs.fluxerr5_stacks(chandata)
sfluxerr = vari_funcs.fluxerr5_stacks(sdata)
vari_funcs.flux_variability_plot(fluxn, fluxchann, 'var', starflux=sfluxn,
stars=True, normalised=True, scale='symlog')
#bins, medvar = vari_funcs.plot_median_line(fluxn, tbdata, statistic='var')
bins, medvar = vari_funcs.plot_median_line_stars(fluxn, tbdata, sfluxn, sdata, statistic='var')
#
vari_funcs.flux_variability_plot(fluxn, fluxchann, 'chisq',
fluxerr = fluxerr,
starfluxerr = sfluxerr,
starflux=sfluxn, stars=True,
chandata = fits.open(
'mag_flux_tables/xray_mag_flux_table_best_1519match.fits')[1].data
sdata = fits.open(
'mag_flux_tables/stars_mag_flux_table_1519match.fits')[1].data
## Create arrays of flux values ###
fluxn = vari_funcs.flux5_stacks(tbdata)
fluxchann = vari_funcs.flux5_stacks(chandata)
sfluxn = vari_funcs.flux5_stacks(sdata)
### remove values that are negative ###
fluxn, tbdata = vari_funcs.noneg(fluxn, tbdata)
fluxchann, chandata = vari_funcs.noneg(fluxchann, chandata)
sfluxn, sdata = vari_funcs.noneg(sfluxn, sdata)
fluxerr = vari_funcs.fluxerr5_stacks(tbdata)
fluxerrchan = vari_funcs.fluxerr5_stacks(chandata)
sfluxerr = vari_funcs.fluxerr5_stacks(sdata)
fig = vari_funcs.flux_variability_plot(fluxn,
fluxchann,
'mad',
starflux=sfluxn,
stars=True,
normalised=True,
scale='log')
#fig = plt.figure(figsize=[9,6])
#avgflux = np.nanmean(fluxn, axis=1)
#fluxnorm = vari_funcs.normalise_flux(fluxn)
#sigma = np.std(fluxnorm, axis=1)
avgmag = np.nanmean(smag, axis=1)
mask1 = avgmag > 15
mask2 = avgmag <= 19
### Remove any variable stars from sample ###
mad = median_absolute_deviation(sfluxn, axis=1)
mask3 = mad < 0.01
### Apply mask ###
mask = mask1 * mask2 * mask3
mask = mask.astype('bool')
sflux = sflux[mask]
sfluxn = sfluxn[mask]
sdata = sdata[mask]
smag = smag[mask]
sfluxerr = vari_funcs.fluxerr5_stacks(sdata)
### Generate a random interger to look at flux and FWHM curves of ###
obnum = np.random.randint(0, 1599)
obID = sdata['NUMBER_05B'][obnum]
obflux = sflux[obnum, :]
obfwhm = np.array([
sdata['FWHM_WORLD_05B'][obnum], sdata['FWHM_WORLD_06B'][obnum],
sdata['FWHM_WORLD_07B'][obnum], sdata['FWHM_WORLD_08B'][obnum],
sdata['FWHM_WORLD_09B'][obnum], sdata['FWHM_WORLD_10B'][obnum],
sdata['FWHM_WORLD_11B'][obnum], sdata['FWHM_WORLD_12B'][obnum]
]) * 3600
obfr = np.array([
sdata['FLUX_RADIUS_05B'][obnum][1], sdata['FLUX_RADIUS_06B'][obnum][1],
sdata['FLUX_RADIUS_07B'][obnum][1], sdata['FLUX_RADIUS_08B'][obnum][1],
sdata['FLUX_RADIUS_09B'][obnum][1], sdata['FLUX_RADIUS_10B'][obnum][1],
### Bin data ###
allerrchange = np.array([])
newallmag = np.array([])
newallmagerr = np.array([])
newallchanmag = np.array([])
newallchanmagerr = np.array([])
newallsmag = np.array([])
newallsmagerr = np.array([])
for n, binedge in enumerate(bins):
print(binedge)
if n == np.size(bins) - 1:
break
mag, bindata = vari_funcs.fluxbin(binedge, bins[n + 1], allmag,
tbdata) #bindata
magerr = vari_funcs.fluxerr5_stacks(bindata) #make error array
meanflux = np.mean(mag, axis=1)
mag, magerr = vari_funcs.normalise_flux_and_errors(mag, magerr)
errchange, newmagerr = times_chi_err(mag, magerr) #find correction
allerrchange = np.append(allerrchange,
errchange) #create array of corrections
### Apply correction tp stars and X-ray ###
chanmag, chanbin = vari_funcs.fluxbin(binedge, bins[n + 1], allchanmag,
chandata)
chanmagerr = vari_funcs.fluxerr5_stacks(chanbin)
chanmeanmag = np.mean(chanmag, axis=1)
chanmag, chanmagerr = vari_funcs.normalise_flux_and_errors(
chanmag, chanmagerr)
avgfluxchanperepoch = np.mean(fluxchan, axis=0) #for non-stellar chandra
savgfluxperepoch = np.median(
sflux, axis=0) #median so randomly bright star shouldn't skew it
### Multiply all flux values in a yearstack by the correct constant ###
fluxcorr = vari_funcs.psf_correct(flux, flux, 'mean')
fluxchancorr = vari_funcs.psf_correct(flux, fluxchan, 'mean')
avgfluxcorr = vari_funcs.psf_correct(flux, avgfluxperepoch, 'mean')
### Multiply all flux values in a yearstack by the star constant ###
fluxscorr = vari_funcs.psf_correct(sflux, flux, 'median')
fluxchanscorr = vari_funcs.psf_correct(sflux, fluxchan, 'median')
avgfluxscorr = vari_funcs.psf_correct(sflux, avgfluxperepoch, 'median')
### get error arrays and correct them ###
fluxerr = vari_funcs.fluxerr5_stacks(tbdata)
fluxerrcorr = vari_funcs.err_correct(flux, fluxerr, fluxcorr)
fluxerrscorr = vari_funcs.err_correct(flux, fluxerr, fluxscorr)
## Create arrays of flux values but without 06B ###
fluxn = vari_funcs.flux5_stacks_no06(tbdata)
fluxchann = vari_funcs.flux5_stacks_no06(
chandata) # for chandra non-stellar objects
#fluxpstar = vari_funcs.flux5_stacks(pstardata) # for chandra objects identified as stars
sfluxn = vari_funcs.flux5_stacks_no06(sdata)
### Calculate the average flux for each year within each flux bin ###
avgfluxperepochn = np.mean(fluxn, axis=0) #for UDS
avgfluxchanperepochn = np.mean(fluxchann, axis=0) #for non-stellar chandra
savgfluxperepochn = np.median(
sfluxn, axis=0) #median so randomly bright star shouldn't skew it