def get_luminosity_and_flux(tbdata, xmm=False):
#
### Extract magnitude table and error table ###
flux = vari_funcs.flux4_stacks(tbdata)
flux, tbdata = vari_funcs.noneg(flux, tbdata)
# tbdata = tbdata[np.nanmean(flux,axis=1)>1e4]
flux, fluxerr, tbdata = vari_funcs.create_quad_error_array(sigtb,
tbdata,
aper=4)
### Normalise ###
fluxnorm, fluxerrnorm = vari_funcs.normalise_flux_and_errors(flux, fluxerr)
### Find luminosity distance ###
z = tbdata['z_spec'] #[mask]
z[z == -1] = tbdata['z_p'][z == -1]
DL = cosmo.luminosity_distance(z)
DL = DL.to(u.m)
### Get AB magnitude ###
# abmag = tbdata['KMAG_20']
### Convert to luminosity using formula worked out in lab book ###
# L = 10 ** (-abmag/2.5) * 3631 * 1e-26 * (3e8/0.34e-6) * 4 * np.pi * (DL.value**2) # gives luminosity in W
L = tbdata['M_K_z_p']
return tbdata, L, fluxnorm, fluxerrnorm
def prep_data(tbdata):
### Remove edges ###
tbdata = vari_funcs.remove_edges(tbdata)
## Create arrays of flux values ###
flux = vari_funcs.flux4_stacks(tbdata)
### remove values that are negative ###
flux, tbdata = vari_funcs.noneg(flux, tbdata)
### Get error arrays ###
flux, fluxerr, tbdata = vari_funcs.create_quad_error_array(sigtb, tbdata, aper=4)
return flux, fluxerr, tbdata
def get_luminosity_and_flux(tbdata, xmm=False):
### Extract magnitude table and error table ###
flux = vari_funcs.flux4_stacks(tbdata)
flux, tbdata = vari_funcs.noneg(flux, tbdata)
# tbdata = tbdata[np.nanmean(flux,axis=1)>1e4]
flux, fluxerr, tbdata = vari_funcs.create_quad_error_array(sigtb, tbdata, aper=4)
### Normalise ###
fluxnorm, fluxerrnorm = vari_funcs.normalise_flux_and_errors(flux, fluxerr)
### Get Luminosity ###
L = tbdata['M_K_z_p']
L[L == 99] = np.nan
mask = ~np.isnan(L)
tbdata = tbdata[mask]
L = L[mask]
fluxnorm = fluxnorm[mask]
fluxerrnorm = fluxerrnorm[mask]
return tbdata, L, fluxnorm, fluxerrnorm
def get_luminosity_and_flux(tbdata, xmm=False):
#
### Extract magnitude table and error table ###
flux = vari_funcs.flux4_stacks(tbdata)
flux, tbdata = vari_funcs.noneg(flux, tbdata)
# tbdata = tbdata[np.nanmean(flux,axis=1)>1e4]
flux, fluxerr, tbdata = vari_funcs.create_quad_error_array(sigtb,
tbdata,
aper=4)
### Normalise ###
fluxnorm, fluxerrnorm = vari_funcs.normalise_flux_and_errors(flux, fluxerr)
L = tbdata['KFLUX_20']
### remove any that have -ve val ###
L[L < 0] = np.nan # remove those with null values
# L[L > -5] = np.nan # remove faintest as seem spurious
mask = ~np.isnan(L)
# [mask]
return tbdata[mask], L[mask], fluxnorm[mask], fluxerrnorm[mask]
def run_max_likely(tbdata):
posvar = np.linspace(0,2,5000)
### Remove edges ###
tbdata = vari_funcs.remove_edges(tbdata)
sigtb = Table.read('sigma_tables/quad_epoch_sigma_table_extra_clean_no06_2arcsec.fits')
### Extract magnitude table and error table ###
flux = vari_funcs.flux4_stacks(tbdata)
flux, tbdata = vari_funcs.noneg(flux, tbdata)
# tbdata = tbdata[np.nanmean(flux,axis=1)>1e4]
flux, fluxerr, tbdata = vari_funcs.create_quad_error_array(sigtb, tbdata, aper=4)
### Normalise ###
fluxnorm, fluxerrnorm = vari_funcs.normalise_flux_and_errors(flux, fluxerr)
### Get sig values ###
numobs = np.shape(fluxnorm)[0]
meanflux = np.nanmean(fluxnorm, axis=1)
out = np.array([vari_funcs.maximum_likelihood(fluxnorm[n,:],
fluxerrnorm[n,:], meanflux[n],
posvar, n=n, printn=100) for n in range(numobs)])
return out, tbdata
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
plt.close('all') #close any open plots
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
font = {'family' : 'DejaVu Sans',
'weight' : 'normal',
'size' : 14}
plt.rc('font', **font)
### Open the fits files and get data ###
varys = fits.open('variable_tables/no06_variables_chi30_2arcsec.fits')[1].data
sigtb = Table.read('sigma_tables/quad_epoch_sigma_table_extra_clean_no06_2arcsec.fits')
#varys = vari_funcs.chandra_only(varys)
flux = vari_funcs.flux4_stacks(varys)
flux, varys = vari_funcs.noneg(flux, varys)
flux, fluxerr, newvarys = vari_funcs.create_quad_error_array(sigtb, varys, aper=4)
#flux,fluxerr = vari_funcs.normalise_flux_and_errors(flux, fluxerr)
#set up time variable for plot
t = np.linspace(1, 8, num=8)
years = ['05B', '06B', '07B', '08B', '09B', '10B', '11B', '12B']
xcurve = [1,3,4,5,6,7,8]
### reduce to just single lightcurve ###
obnum = 62243
mask = newvarys['NUMBER_05B'] == obnum
flux = flux[mask].reshape(len(xcurve))
fluxerr = fluxerr[mask].reshape(len(xcurve))
obdata = newvarys[mask]
def get_mean_flux(tbdata):
flux = vari_funcs.flux4_stacks(tbdata)
meanflux = np.nanmean(flux, axis=1)
return meanflux
quaddata = vari_funcs.quadrants(tbdata, '05B')
chanquaddata = vari_funcs.quadrants(chandata, '05B')
squaddata = vari_funcs.quadrants(sdata, '05B')
sigsqdict = {}
sigdict = {}
finalchisq = np.array([])
galchisq = np.array([])
oldchisq = np.array([])
for m, qtbdata in enumerate(quaddata):
### get quad arrays for chan and stars ###
qchandata = chanquaddata[m]
qsdata = squaddata[m]
## Create arrays of flux values ###
fluxn = vari_funcs.flux4_stacks(qtbdata)
fluxchann = vari_funcs.flux4_stacks(qchandata)
sfluxn = vari_funcs.flux4_stacks(qsdata)
### remove values that are negative ###
fluxn, qtbdata = vari_funcs.noneg(fluxn, qtbdata)
fluxchann, qchandata = vari_funcs.noneg(fluxchann, qchandata)
sfluxn, qsdata = vari_funcs.noneg(sfluxn, qsdata)
fluxerr = vari_funcs.fluxerr4_stacks(qtbdata)
fluxerrchan = vari_funcs.fluxerr4_stacks(qchandata)
sfluxerr = vari_funcs.fluxerr4_stacks(qsdata)
print(len(fluxn) + len(sfluxn))
if m == 3:
return I1minI2, z, zlimsmask, Iuplimsmask, Ilolimsmask, tbdata[mask]
I1minI2, z, zlimsmask, Iuplimsmask, Ilolimsmask, tbdata = get_data(tbdata)
varyI1minI2, varyz, varyzlimsmask, varyIuplimsmask, varyIlolimsmask, varydata = get_data(
varydata)
noxvaryI1minI2, noxvaryz, noxvaryzlimsmask, noxvaryIuplimsmask, noxvaryIlolimsmask, noxvarydata = get_data(
noxvarydata)
xvaryI1minI2, xvaryz, xvaryzlimsmask, xvaryIuplimsmask, xvaryIlolimsmask, xvarydata = get_data(
xvarydata)
devI1minI2, devz, devzlimsmask, devIuplimsmask, devIlolimsmask, devdata = get_data(
devdata)
xI1minI2, xz, xzlimsmask, xIuplimsmask, xIlolimsmask, xdata = get_data(xdata)
### flux ###
varyL = vari_funcs.flux4_stacks(varydata)
varyL = np.nanmean(varyL, axis=1)
xvaryL = vari_funcs.flux4_stacks(xvarydata)
xvaryL = np.nanmean(xvaryL, axis=1)
noxvaryL = vari_funcs.flux4_stacks(noxvarydata)
noxvaryL = np.nanmean(noxvaryL, axis=1)
#colmap = 'cool'
#
#### M_star ###
#varyL = varydata['Mstar_z_p']
#xvaryL = xvarydata['Mstar_z_p']
#noxvaryL = noxvarydata['Mstar_z_p']
#def ignore_zeros(m, z, I1minI2, zlimsmask, Iuplimsmask, Ilolimsmask, tbdata):
# m[m==0] = np.nan
# mask = ~np.isnan(m)
# z = z[mask]