def find_number(tb):
### Get flux ###
flux = vari_funcs.flux4_stacks(tb)
bins = np.array([13, 15])
bins = np.append(bins, np.arange(16, 24, 0.2))
bins = np.append(bins, [24])
bins = 10**((30 - bins) / 2.5)
bins = np.flip(bins, axis=0)
#bins = bins[16:44] #because of flux limit
num = np.zeros(len(bins))
### Bin data ###
for n, binedge in enumerate(bins):
# print(binedge)
if n == np.size(bins) - 1:
break
mag, _ = vari_funcs.fluxbin(binedge, bins[n + 1], flux, tb)
num[n] = len(mag)
return num, bins
bins = np.arange(13,22.4,0.2)
bins = np.append(bins, [24])
bins = 10**((30-bins)/2.5)
bins = np.flip(bins, axis=0)
### set up time variable ###
t = np.arange(8)
semesters = ['05B', '06B', '07B', '08B', '09B', '10B', '11B', '12B']
lastind = len(bins)-1
sigsq = np.empty([lastind,8])
sigsqdict = {}
sigdict = {}
oldnum = 0
for n, binedge in enumerate(bins[0:lastind]):
sbinflux, bindata = vari_funcs.fluxbin(binedge, bins[n+1], sflux, sdata) #bin data
print(str(binedge) + ' '+ str(len(sbinflux)))
### find average flux of stars ###
avgflux = np.nanmean(sbinflux, axis=1)
### Find sigma^2 ###
diff = sbinflux - avgflux[:,None]
top = diff**2
bot = len(avgflux)
sigsq[n,:] = np.nansum(top/bot, axis=0)
sigsqdict[binedge] = np.nansum(top/bot, axis=0)
sigdict[binedge] = np.sqrt(sigsqdict[binedge])
# ### Plot average lightcurve for bin ###
# plt.figure()
bins = np.array([13, 15])
bins = np.append(bins, np.arange(16, 24, 0.2))
bins = np.append(bins, [24])
bins = 10**((30 - bins) / 2.5)
bins = np.flip(bins, axis=0)
bins = bins[16:44] #because of flux limit
### Bin data ###
allmedexcess = np.array([])
allmedexcesscorr = 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], flux,
tbdata2) #bindata
magcorr, bincorr = vari_funcs.fluxbin(binedge, bins[n + 1], fluxn,
tbdata) #bindata
# magerrcorr = vari_funcs.fluxerr5_stacks_corr(bincorr) #make error array
magerrcorr = vari_funcs.fluxerr5_stacks(bincorr) #make error array
# magerr = vari_funcs.fluxerr5_stacks_corr(bindata) #make error array
magerr = vari_funcs.fluxerr5_stacks(bindata) #make error array
nmag, nmagerr = vari_funcs.normalise_flux_and_errors(mag, magerr)
nmagcorr, nmagerrcorr = vari_funcs.normalise_flux_and_errors(
magcorr, magerrcorr)
binexess = vari_funcs.normsigmasq(nmag, nmagerr)
binexesscorr = vari_funcs.normsigmasq(nmagcorr, nmagerrcorr)
medexcess = np.nanmedian(binexess)
allmedexcess = np.append(allmedexcess, medexcess)
medexcesscorr = np.nanmedian(binexesscorr)
allmedexcesscorr = np.append(allmedexcesscorr, medexcesscorr)
chi = np.nansum(top/char_var, axis=1)
return chi
def my_chisquare_charanderr(flux, fluxerr, char_var):
fluxn, fluxerrn = vari_funcs.normalise_flux_and_errors(flux, fluxerr)
meanflux = np.nanmean(fluxn, axis=1)
top = np.square(fluxn-meanflux[:,None])
bot = np.square(fluxerrn) + char_var
chi = np.nansum(top/bot, axis=1)
return chi
### Put characteristic variance into chisquare ###
newmedvar = np.empty(np.shape(medvar))
finalmed = np.empty(np.shape(medvar))
for n, binedge in enumerate(bins[0:np.size(bins)-1]):
flux, bindata = vari_funcs.fluxbin(binedge, bins[n+1], fluxn, tbdata) #bindata
fluxchan, binchan = vari_funcs.fluxbin(binedge, bins[n+1], fluxchann, chandata) #bindata
sflux, sbindata = vari_funcs.fluxbin(binedge, bins[n+1], sfluxn, sdata) #bindata
# get errors
fluxerr = vari_funcs.fluxerr5_stacks(bindata)
fluxchanerr = vari_funcs.fluxerr5_stacks(binchan)
sfluxerr = vari_funcs.fluxerr5_stacks(sbindata)
meanflux = np.nanmean(flux, axis=1)
meanchan = np.nanmean(fluxchan, axis=1)
meansflux = np.nanmean(sflux, axis=1)
chisq = my_chisquare(flux, medvar[n])
chisqchan = my_chisquare(fluxchan, medvar[n])
schisq = my_chisquare(sflux, medvar[n])
### plot ###
bins = np.array(sigtb.colnames)
binarr = np.empty(int(len(bins) / 4))
for k, bin in enumerate(bins):
if bin[0] == '1':
binarr[k] = int(bin[2:])
k += 1
binarr = binarr.astype(int)
binsizes = np.empty(int(len(binarr)))
binmean = np.empty(int(len(binarr)))
for n, binedge in enumerate(binarr):
if binedge == binarr[-1]:
binupp = np.nanmax(flux)
else:
binupp = binarr[n + 1]
binflux, bindata = vari_funcs.fluxbin(binedge, binupp, flux,
tbdata) #bindata
# binsflux, binsdata = vari_funcs.fluxbin(binedge, binupp, sflux, sdata)
binsizes[n] = len(bindata) #+ len(binsdata)
plt.vlines(binedge, 1e-2, 1e4, zorder=4)
binmean[n] = np.nanmean(binflux)
# if binsizes[n] < 9:
# continue
### get chi values within bin ###
# binflux, binfluxerr, bindata = vari_funcs.create_quad_error_array(sigtb, bindata)
# binchisq = vari_funcs.my_chisquare_err(binflux, binfluxerr)
#
### P value of 30 with dof=6 is 0.00003931 ###
### therefore false positives = binsize * P ###
P = 0.00003931 #0.001
numfalpos = binsizes * P
plt.figure(figsize=[8, 5])
bins = np.append(bins, np.arange(16, 24, 0.2))
bins = np.append(bins, [24, 25, 26])
### 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.magerr5_stacks(bindata) #make error array
errchange, newmagerr = add_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.magerr5_stacks(chanbin)
newchanmagerr = np.sqrt(np.square(chanmagerr) +
np.square(errchange)) #change error
smag, sbin = vari_funcs.fluxbin(binedge, bins[n + 1], allsmag, sdata)
smagerr = vari_funcs.magerr5_stacks(sbin)
newsmagerr = np.sqrt(np.square(smagerr) +
np.square(errchange)) #change error