#
### Multiply all flux values in a yearstack by the correct constant ###
#fluxcorrn = vari_funcs.psf_correct_mag(fluxn, fluxn, 'median')
#fluxchancorrn = vari_funcs.psf_correct(fluxn, fluxchann, 'median')
#sfluxcorrn = vari_funcs.psf_correct(fluxn, sfluxn, 'median')
### get error arrays and correct them ###
fluxerrn = vari_funcs.magerr5_stacks(tbdata)
fluxerrchan = vari_funcs.magerr5_stacks(chandata)
sfluxerr = vari_funcs.magerr5_stacks(sdata)
#fluxerrcorrn = vari_funcs.err_correct(fluxn, fluxerrn, fluxcorrn)
fig = vari_funcs.flux_variability_plot(fluxn,
fluxchann,
'mad',
starflux=sfluxn,
stars=True)
#fig = vari_funcs.flux_variability_plot(fluxn, fluxchann, 'excess',
# fluxerr = fluxerrn, starfluxerr = sfluxerr,
# starflux=sfluxn, stars=True,
# chanerr = fluxerrchan,
# normalised=True)
#fig.canvas.mpl_connect('pick_event', vari_funcs.onpick)
#
combined.close()
chandra.close()
stars.close()
return flux, fluxerr, tbdata
### Prep data ###
flux, fluxerr, tbdata = prep_data(tbdata)
fluxchan, chanerr, chandata = prep_data(chandata)
sflux, serr, sdata = prep_data(sdata)
### reset X-ray column as messed up by stacking ###
tbdata['X-ray'][tbdata['X-ray']==70] = False
tbdata['X-ray'][tbdata['X-ray']==84] = True
### Check chisq plot looks correct ###
fig,_ = vari_funcs.flux_variability_plot(flux, fluxchan, 'chisq',
fluxerr=fluxerr, chanerr=chanerr,
starflux=sflux, starfluxerr=serr,
#normalised=True,
stars=True, scale='log')
fig.canvas.mpl_connect('pick_event', vari_funcs.onpickflux_2arcsec)
#varydata = fits.open('variable_tables/no06_variables_chi30_2arcsec_DR11data_restframe.fits')[1].data
#varydatalow = vari_funcs.flux_split(varydata, 'lower')
varydatalow = fits.open('variable_tables/no06_variables_chi30_2arcsec_spec_DR11.fits')[1].data
#varydatalow = vari_funcs.flux_split(varydata, 'lower')
varyfluxlow, varyfluxerrlow, varydatalow = prep_data(varydatalow)
varymeanlow = np.nanmean(varyfluxlow, axis=1)
varychilow = vari_funcs.my_chisquare_err(varyfluxlow, varyfluxerrlow)
plt.plot(varymeanlow, varychilow, 'kd', mfc='None')
### get error arrays and correct them ###
#fluxerr = vari_funcs.fluxerr5_stacks_corr(tbdata2)
#fluxerrchan = vari_funcs.fluxerr5_stacks_corr(chandata2)
#sfluxerr = vari_funcs.fluxerr5_stacks_corr(sdata2)
fluxerr = vari_funcs.fluxerr5_stacks(tbdata2)
fluxerrchan = vari_funcs.fluxerr5_stacks(chandata2)
sfluxerr = vari_funcs.fluxerr5_stacks(sdata2)
fig, _ = vari_funcs.flux_variability_plot(flux,
fluxchan,
'excess',
fluxerr=fluxerr,
flux2=fluxn,
fluxerr2=fluxerrn,
fluxchan2=fluxchann,
chanerr2=fluxerrchann,
comparison=True,
starfluxerr=sfluxerr,
starflux=sflux,
stars=True,
chanerr=fluxerrchan,
normalised=True)
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 ###
## 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,
chanerr = fluxerrchan,
normalised=True, scale='symlog')
plt.text(5e2, 5e4, r'$\chi^{2} = \sum{\frac{( \,{x_{i} - \bar{x}})^{2} \,}{\sigma_{i}^{2}}}$')
def my_chisquare(flux, char_var):
fluxn = vari_funcs.normalise_flux(flux)
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)
#plt.scatter(avgflux, sigma, marker='+')
#plt.xscale('log')
##plt.yscale('log')
#plt.xlabel('Mean Flux')
#plt.ylabel('sigma')
fig, _ = vari_funcs.flux_variability_plot(fluxn,
fluxchann,
### Normalise arrays ###
#fluxn = vari_funcs.normalise_flux(flux)
#fluxchann = vari_funcs.normalise_flux(fluxchan)
#sfluxn = vari_funcs.normalise_flux(sflux)
#
#
#### Multiply all flux values in a yearstack by the correct constant ###
#fluxcorrn = vari_funcs.psf_correct(fluxn, fluxn, 'median')
#fluxchancorrn = vari_funcs.psf_correct(fluxn, fluxchann, 'median')
#fluxcorr = vari_funcs.psf_correct(flux, flux, 'median')
#fluxchancorr = vari_funcs.psf_correct(flux, fluxchan, 'median')
fig, _ = vari_funcs.flux_variability_plot(flux,
fluxchan,
'mad',
starflux=sflux,
stars=True)
fig.canvas.mpl_connect('pick_event', vari_funcs.onpick)
# Find outliers ###
bins = np.array([13, 15])
bins = np.append(bins, np.arange(16, 24, 0.2))
bins = np.append(bins, [24, 25, 26])
#bins = 10**((30-bins)/2.5)
#bins = np.flip(bins, axis=0)
#outliers, tb, modz = vari_funcs.find_outliers(fluxn, tbdata, bins)
#varys = tb[outliers]
#varyflux = vari_funcs.mag5_stacks(varys)
#varymad = median_absolute_deviation(varyflux, axis=1)
newallsmag = smag
newallsmagerr = newsmagerr
else:
newallmag = np.vstack([newallmag, mag])
newallmagerr = np.vstack([newallmagerr, newmagerr])
newallchanmag = np.vstack([newallchanmag, chanmag])
newallchanmagerr = np.vstack([newallchanmagerr, newchanmagerr])
newallsmag = np.vstack([newallsmag, smag])
newallsmagerr = np.vstack([newallsmagerr, newsmagerr])
#%% plot excess variance with new arrays
fig = vari_funcs.flux_variability_plot(newallmag,
newallchanmag,
'excess',
fluxerr=newallmagerr,
starfluxerr=newallsmagerr,
starflux=newallsmag,
stars=True,
chanerr=newallchanmagerr,
normalised=True)
#%% plot errchange with magnitude
plt.figure()
plt.scatter(bins[0:44], allerrchange)
#%% save new magnitude and error arrays
#np.save('chanmagorderedarray', newallchanmag)
#np.save('channewmagerrarray', newallchanmagerr)
#np.save('smagorderedarray', newallsmag)
#np.save('snewmagerrarray', newallsmagerr)
#corrections = np.array([bins[0:44], allerrchange])
#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
### Multiply all flux values in a yearstack by the correct constant ###
fluxcorrn = vari_funcs.psf_correct(fluxn, fluxn, 'mean')
fluxchancorrn = vari_funcs.psf_correct(fluxn, fluxchann, 'mean')
avgfluxcorrn = vari_funcs.psf_correct(fluxn, avgfluxperepochn, 'mean')
### Multiply all flux values in a yearstack by the star constant ###
fluxscorrn = vari_funcs.psf_correct(sfluxn, fluxn, 'median')
fluxchanscorrn = vari_funcs.psf_correct(sfluxn, fluxchann, 'median')
avgfluxscorrn = vari_funcs.psf_correct(sfluxn, avgfluxperepochn, 'median')
### get error arrays and correct them ###
fluxerrn = vari_funcs.fluxerr5_stacks_no06(tbdata)
fluxerrcorrn = vari_funcs.err_correct(fluxn, fluxerrn, fluxcorrn)
fluxerrscorr = vari_funcs.err_correct(fluxn, fluxerrn, fluxscorrn)
fig = vari_funcs.flux_variability_plot(fluxcorrn,
fluxchancorrn,
'mad',
normalised=True)
fig.canvas.mpl_connect('pick_event', vari_funcs.onpickchanonly)
