def get_avg_flux(tbdata):
flux = vari_funcs.flux1_stacks(tbdata)
flux = vari_funcs.normalise_flux(flux)
return np.nanmedian(flux, axis=0)
mag = sdata['MAG_APER_' + sem][:, 4]
mask1 = mag > 15 #removes saturated
mask2 = mag < 20 #removes very faint stars
mask = mask1 * mask2
tempsdata = sdata[mask]
avgFWHM[n] = np.median(tempsdata[colnames]) #* 3600
avgflux[n] = np.median(tempsdata['FLUX_APER_' + sem][:, 4])
if sem == '10B':
psf[sem] = fits.getdata('PSFs/small_' + sem + '_K_PSF.fits')
else:
psf[sem] = fits.getdata('PSFs/matched_' + sem + '_K_PSF.fits')
# psf[sem] = fits.open('PSFs/small_'+sem+'_K_PSF.fits')[0].data
## get flux curve
flux = vari_funcs.flux5_stacks(tempsdata)
flux = vari_funcs.normalise_flux(flux)
### Find maximum FWHM as this is what all the others willl become ###
aimind = np.argmax(avgFWHM)
aimsem = semesters[aimind]
aimpsf = psf[aimsem]
### Convert FWHM into a sigma ###
sigmaold = np.array([FWHM2sigma(fwhm, const) for fwhm in avgFWHM])
sigmabroad = sigmaold[aimind]
plt.figure(4)
plt.imshow(np.log(psf['10B']))
#plt.imshow(psf['10B'])
phot = {}
flux = {}
def my_chisquare(flux, char_var):
fluxn = vari_funcs.normalise_flux(flux)
meanflux = np.nanmean(fluxn, axis=1)
top = np.square(fluxn-meanflux[:,None])
chi = np.nansum(top/char_var, axis=1)
return chi
plt.figure(3,figsize=[8,8])
plt.plot(meanflux, chisq, 'b+',zorder=2)
plt.plot(meanchan, chisqchan, 'ro', zorder=3, mfc='None', markersize=10)
plt.plot(meansflux, schisq, 'm*', zorder=1, mfc='None', markersize=10)
plt.yscale('log')
plt.xscale('log')
plt.ylabel('Chi Squared')
plt.xlabel('Mean Flux')
plt.title('1st iteration')
plt.text(5e2, 1e3, r'$\chi^{2} = \sum{\frac{( \,{x_{i} - \bar{x}})^{2} \,}{\sigma_{noise}^{2}}}$')
### remove any with chisq > 70 ###
newgflux = flux[chisq<50,:]
newsflux = sflux[schisq<50,:]
newflux = np.vstack((newgflux, newsflux))
newfluxn = vari_funcs.normalise_flux(newflux)
vary = np.var(newfluxn, axis=1, ddof=1)
newmedvar[n] = np.nanmedian(vary)
newchisq = my_chisquare(flux, newmedvar[n])
newchisqchan = my_chisquare(fluxchan, newmedvar[n])
newschisq = my_chisquare(sflux, newmedvar[n])
### plot new ###
plt.figure(4, figsize=[8,8])
plt.plot(meanflux, newchisq, 'b+',zorder=2)
plt.plot(meanchan, newchisqchan, 'ro', zorder=3, mfc='None', markersize=10)
plt.plot(meansflux, newschisq, 'm*', zorder=1, mfc='None', markersize=10)
plt.yscale('log')
plt.xscale('log')
plt.ylabel('Chi Squared')
#varymad = median_absolute_deviation(varyflux, axis=1)
#varymean = np.mean(varyflux, axis=1)
#plt.figure(1)
#plt.plot(varymean, varymad, 'md', mfc='none', markersize=10)
outliers2, tb2, modz2 = vari_funcs.find_outliers(flux,
tbdata,
bins,
threshold=5)
tb2['X-ray'][tb2['X-ray'] == 70] = False
tb2['X-ray'][tb2['X-ray'] == 84] = True
### Find plotting values for the new flux table ###
flux2 = vari_funcs.flux5_stacks(tb2)
flux2, tb2 = vari_funcs.noneg(flux2, tb2)
avgfluxperob2 = np.nanmean(flux2, axis=1) #for UDS
flux2 = vari_funcs.normalise_flux(flux2)
vary2 = median_absolute_deviation(flux2, axis=1)
### create table of varying ###
magmask = avgfluxperob2 < 10**((30 - 21) / 2.5)
outliers2 = outliers2 * magmask
varydata = tb2[outliers2]
#cols = fits.ColDefs(varydata)
#hdu = fits.BinTableHDU.from_columns(cols)
#hdu.writeto('variable_tables/variable_mag_flux_table_with06B_mag21.fits')
varyfluxcorr = flux2[outliers2]
varymadcorr = vary2[outliers2]
varymeancorr = avgfluxperob2[outliers2]
varymodz = modz2[outliers2]
#plt.plot(varymeancorr, varymadcorr, 'kd', mfc='none', markersize=10)
from photutils import CircularAperture, aperture_photometry
hdr08B = fits.getheader('Images/UDS_08B_K.fits') # random year (same in all)
const = -hdr08B['CD1_1'] # constant that defines unit conversion for FWHM
semesters = ['05B', '06B', '07B', '08B', '09B', '10B', '11B', '12B']
### get data and create flux and errors array ###
sdata = fits.open('mag_flux_tables/stars_mag_flux_table.fits')[1].data
sflux = vari_funcs.flux5_stacks(sdata)
smag = vari_funcs.mag5_stacks(sdata)
sflux, sdata = vari_funcs.noneg(sflux, sdata)
smag = vari_funcs.mag5_stacks(sdata)
### set up normalised flux ###
sfluxn = np.copy(sflux)
sfluxn = vari_funcs.normalise_flux(sfluxn)
### Limit to same magnitude range as PSFS ###
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]