def get_luminosity_and_flux(tbdata, xmm=False):
#
### Extract magnitude table and error table ###
flux = vari_funcs.flux5_stacks(tbdata)
flux, tbdata = vari_funcs.noneg(flux, tbdata)
flux, fluxerr, tbdata = vari_funcs.create_quad_error_array(sigtb, tbdata)
### 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.cm)
### Calculate luminosity ###
if xmm == True:
xrayF = tbdata['CR(S)'] * 0.171 * (10**(-14)
) #conversion into ergs/s/cm2
else:
xrayF = tbdata['Soft_flux'] #no conversion required in chandra
xrayL = xrayF * 4 * np.pi * (DL.value**2)
return tbdata, xrayL, 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)
### 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']
### remove any that have val of 99 ###
L[L == 99] = 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 get_luminosity_and_flux(tbdata, xmm=False):
#
### Extract magnitude table and error table ###
flux = vari_funcs.flux5_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)
### 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
return tbdata, L, fluxnorm, fluxerrnorm
def make_ensemble(tbdata, xrayL, binedge):
### Extract magnitude table and error table ###
flux = vari_funcs.flux5_stacks(tbdata)
flux, tbdata = vari_funcs.noneg(flux, tbdata)
flux, fluxerr, tbdata = vari_funcs.create_quad_error_array(sigtb, tbdata)
### Normalise ###
fluxnorm, fluxerrnorm = vari_funcs.normalise_flux_and_errors(flux, fluxerr)
### Create dicts to save data into ###
enflux = {}
enfluxerr = {}
enXrayL = {}
### loop over bins ###
for m, enmin in enumerate(binedge):
### Isolate data needed ###
mask1 = xrayL >= enmin
if m != len(binedge) - 1:
mask2 = xrayL < binedge[m + 1]
else:
mask2 = np.ones(len(mask1))
enmask = mask1 * mask2.astype(bool)
enflux[m] = fluxnorm[enmask]
enfluxerr[m] = fluxerrnorm[enmask]
enXrayL[m] = xrayL[enmask]
return enflux, enfluxerr, enXrayL
def get_and_normalise_flux(tbdata, sigtb, aper=5):
# Extract magnitude table and error table
flux = vari_funcs.flux_stacks(tbdata, aper)
flux, tbdata = vari_funcs.noneg(flux, tbdata)
flux, fluxerr, tbdata = vari_funcs.create_quad_error_array(
sigtb, tbdata, aper)
### Normalise ###
fluxnorm, fluxerrnorm = vari_funcs.normalise_flux_and_errors(flux, fluxerr)
return fluxnorm, fluxerrnorm, tbdata
def get_ensemble_sig(tbdata, sigtb, binedge, posvar, aper=5):
# Extract magnitude table and error table
flux = vari_funcs.flux_stacks(tbdata,aper)
flux, tbdata = vari_funcs.noneg(flux, tbdata)
flux, fluxerr, tbdata = vari_funcs.create_quad_error_array(sigtb, tbdata,aper)
### 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.cm)
### Calculate the luminosity ###
xrayF = tbdata['Full_flux']#[chanmask]
xrayL = xrayF*4*np.pi*(DL.value**2)
### Create dicts to save data into ###
enflux = {}
enfluxerr = {}
enxrayL = {}
sig = np.empty(size)
sigerr = np.empty(size)
meanxrayL = np.empty(size)
for m, enmin in enumerate(binedge):
### Isolate data needed ###
mask1 = xrayL >= enmin
if m != size-1:
mask2 = xrayL < binedge[m+1]
else:
mask2 = np.ones(len(mask1))
enmask = mask1*mask2.astype(bool)
enflux[m] = fluxnorm[enmask]
enfluxerr[m] = fluxerrnorm[enmask]
enxrayL[m] = xrayL[enmask]
### Combine into one flux curve per bin ###
enfluxcurve = np.ravel(enflux[m])
enfluxcurveerr = np.ravel(enfluxerr[m])
### Find max likelihood sig of curve ###
[sig[m],sigerr[m]] = vari_funcs.maximum_likelihood(enfluxcurve, enfluxcurveerr, 1, posvar)
### find mean z ###
meanxrayL[m] = np.nanmean(enxrayL[m])
return fluxnorm, fluxerrnorm, sig, sigerr, xrayL, meanxrayL
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.flux5_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)
### Normalise ###
fluxnorm, fluxerrnorm = vari_funcs.normalise_flux_and_errors(flux, fluxerr)
### Get absolute magnitude ###
L = tbdata['M_K_z_p']
return tbdata, L, fluxnorm, fluxerrnorm
def get_ensemble_sig(tbdata, sigtb, binedge, posvar, aper=5):
# Extract magnitude table and error table
flux = vari_funcs.flux_stacks(tbdata, aper)
flux, tbdata = vari_funcs.noneg(flux, tbdata)
flux, fluxerr, tbdata = vari_funcs.create_quad_error_array(
sigtb, tbdata, aper)
### Normalise ###
fluxnorm, fluxerrnorm = vari_funcs.normalise_flux_and_errors(flux, fluxerr)
### Find z ###
z = tbdata['z_spec'] #[mask]
z[z == -1] = tbdata['z_p'][z == -1]
### Create dicts to save data into ###
enflux = {}
enfluxerr = {}
enz = {}
sig = np.empty(size)
sigerr = np.empty(size)
meanz = np.empty(size)
for m, enmin in enumerate(binedge):
### Isolate data needed ###
mask1 = z >= enmin
if m != size - 1:
mask2 = z < binedge[m + 1]
else:
mask2 = z < 4.5 #np.ones(len(mask1))
enmask = mask1 * mask2.astype(bool)
enflux[m] = fluxnorm[enmask]
enfluxerr[m] = fluxerrnorm[enmask]
enz[m] = z[enmask]
### Combine into one flux curve per bin ###
enfluxcurve = np.ravel(enflux[m])
enfluxcurveerr = np.ravel(enfluxerr[m])
### Find max likelihood sig of curve ###
[sig[m],
sigerr[m]] = vari_funcs.maximum_likelihood(enfluxcurve,
enfluxcurveerr, 1, posvar)
### find mean z ###
meanz[m] = np.nanmean(enz[m])
return fluxnorm, fluxerrnorm, sig, sigerr, z, meanz
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 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 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
import numpy as np #for handling arrays
#import math
from astropy.stats import median_absolute_deviation
import vari_funcs #my module to help run code neatly
#from scipy.stats import chisquare
plt.close('all') #close any open plots
### Open the fits files and get data ###
tbdata = fits.open('mag_flux_tables/mag_flux_table_best_1519match.fits')[1].data
sdata = fits.open('mag_flux_tables/stars_mag_flux_table_1519match.fits')[1].data
### Get flux array and remove values that are negative ###
flux = vari_funcs.flux5_stacks(tbdata)
sflux = vari_funcs.flux5_stacks(sdata)
sflux, sdata = vari_funcs.noneg(sflux, sdata)
flux, tbdata = vari_funcs.noneg(flux, tbdata)
### Everything needs to be done on flux bins so create bin array ###
#bins = np.array([13, 15])
#bins = np.append(bins, np.arange(16,22.4,0.2))
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
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:
### plot flux vs err ###
plt.figure(1)
# plt.subplot(2,2,m+1)
plt.plot(fluxn[:, 6], fluxerr[:, 6], 'b+')
plt.plot(sfluxn[:, 6], sfluxerr[:, 6], 'm*')
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]
return sigma, newmagerr #output the required err change and the new array
### Open the fits files and get data ###
tbdata = fits.open('mag_flux_tables/mag_flux_table_best.fits')[1].data
chandata = fits.open('mag_flux_tables/xray_mag_flux_table_best.fits')[1].data
sdata = fits.open('mag_flux_tables/stars_mag_flux_table.fits')[1].data
### extract magnitude arrays ###
allmag = vari_funcs.flux5_stacks(tbdata)
allchanmag = vari_funcs.flux5_stacks(
chandata) # for chandra non-stellar objects
allsmag = vari_funcs.flux5_stacks(sdata)
### remove 99s ###
allmag, tbdata = vari_funcs.noneg(allmag, tbdata)
allchanmag, chandata = vari_funcs.noneg(allchanmag, chandata)
allsmag, sdata = vari_funcs.noneg(allsmag, sdata)
# need to run on all fluxbins, like in 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 = 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)
#mag, bindata = vari_funcs.fluxbin(bins[25], bins[26], allmag, tbdata)
# remove stars in new table
mag = alldataconv['MAG_APER_' + sem][:, 4]
mask1 = mag > 15 #removes saturated
mask2 = mag < 19 #removes very faint stars
mask = mask1 * mask2
alldataconv = alldataconv[mask]
avgfwhmconv[n] = np.median(alldataconv[colnames]) * 3600
# Create flux stack
allflux = vari_funcs.flux5_stacks(alldata)
allfluxconv = vari_funcs.flux5_stacks(alldataconv)
#allflux, alldata = vari_funcs.semfluxlim(allflux, alldata)
#allfluxconv, alldataconv = vari_funcs.semfluxlim(allfluxconv, alldataconv)
allflux, alldata = vari_funcs.noneg(allflux, alldata)
allfluxconv, alldataconv = vari_funcs.noneg(allfluxconv, alldataconv)
allfluxerr = vari_funcs.fluxerr1_stacks(alldata)
allfluxconverr = vari_funcs.fluxerr1_stacks(alldataconv)
#depths = np.load('fluxdepths.npy')
#allfluxerr = np.zeros(np.shape(allflux)) + depths[None,:]
#depthsconv = np.load('fluxdepthsconv_PSF.npy')
#allfluxconverr = np.zeros(np.shape(allfluxconv)) + depthsconv[None,:]
# Normalise
allflux, allfluxerr = vari_funcs.normalise_flux_and_errors(allflux, allfluxerr)
allfluxconv, allfluxconverr = vari_funcs.normalise_flux_and_errors(
allfluxconv, allfluxconverr)
import numpy as np #for handling arrays
from astropy.stats import median_absolute_deviation
import vari_funcs #my module to help run code neatly
plt.close('all') #close any open plots
#from numpy.lib.recfunctions import append_fields
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
)[1].data
tbdata = fits.open(
'variable_tables/no06_variables_chi30_DR11data_restframe.fits')[1].data
fullxray = fits.open(
'mag_flux_tables/novarys_chanDR11data_restframe_mag_flux_table_best_extra_clean_no06.fits'
)[1].data
sigtb = Table.read('sigma_tables/quad_epoch_sigma_table_extra_clean_no06.fits')
#### Limit to Chandra region for simplicity ###
#tbdata = vari_funcs.chandra_only(tbdata)
#fullxray = vari_funcs.chandra_only(fullxray)
#chandata = vari_funcs.chandra_only(chandata)
# Extract magnitude table and error table
flux = vari_funcs.flux5_stacks(tbdata)
flux, tbdata = vari_funcs.noneg(flux, tbdata)
flux, fluxerr, tbdata = vari_funcs.create_quad_error_array(sigtb, tbdata)
chanflux = vari_funcs.flux5_stacks(chandata)
chanflux, chandata = vari_funcs.noneg(chanflux, chandata)
chanflux, chanerr, chandata = vari_funcs.create_quad_error_array(
sigtb, chandata)
fullflux = vari_funcs.flux5_stacks(fullxray)
fullflux, fulldata = vari_funcs.noneg(fullflux, fullxray)
fullflux, fullerr, fullxray = vari_funcs.create_quad_error_array(
sigtb, fullxray)
xmmflux = vari_funcs.flux5_stacks(xmmdata)
xmmflux, xmmdata = vari_funcs.noneg(xmmflux, xmmdata)
xmmflux, xmmerr, xmmdata = vari_funcs.create_quad_error_array(sigtb, xmmdata)
### Normalise ###
fluxnorm, fluxerrnorm = vari_funcs.normalise_flux_and_errors(flux, fluxerr)
import vari_funcs #my module to help run code neatly
#from scipy.stats import chisquare
plt.close('all') #close any open plots
### 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')
#
#from astropy.table import Table #for handling tables
import numpy as np #for handling arrays
import matplotlib.gridspec as gridspec # to nest subplots
#from astropy.stats import median_absolute_deviation
import vari_funcs #my module to help run code neatly
plt.close('all') #close any open plots
### Read in fits files ###
imdata = fits.getdata(
'UDS-DR11-K.mef.fits') ### SHOULD CHANGE TO THE DR11 IMAGE
varydata = fits.getdata(
'variable_tables/no06_variables_chi30_DR11data_restframe.fits', 1)
### stick to those above 1e4 ###
flux = vari_funcs.flux5_stacks(varydata)
flux, varydata = vari_funcs.noneg(flux, varydata)
meanflux = np.nanmean(flux, axis=1)
varydata = varydata[meanflux >= 1e4]
#imthresh = 200 #fits.getheader('UDS_05B_K.fits')['SATURATE']
#imdata[imdata>imthresh] = imthresh
### Create mask array ###
maskx = np.zeros(imdata.shape[1]).astype(bool)
masky = np.zeros(imdata.shape[0]).astype(bool)
### Find coordinates of objects ###
x = varydata['X_IMAGE']
x = x.astype(int)
y = varydata['Y_IMAGE']
y = y.astype(int)
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_DR11data_restframe.fits')[1].data
tbdata = fits.open('mag_flux_tables/mag_flux_table_best_extra_clean_no06.fits')[1].data
sigtb = Table.read('sigma_tables/quad_epoch_sigma_table_extra_clean_no06.fits')
#varys = vari_funcs.chandra_only(varys)
flux = vari_funcs.flux5_stacks(tbdata)
flux, tbdata = vari_funcs.noneg(flux, tbdata)
flux, fluxerr, newtbdata = vari_funcs.create_quad_error_array(sigtb, tbdata)
#flux,fluxerr = vari_funcs.normalise_flux_and_errors(flux, fluxerr)
mag = 30 - 2.5*np.log10(flux)
magerr = 1.086/(flux/fluxerr) # taken from http://faculty.virginia.edu/skrutskie/astr3130.s16/notes/astr3130_lec12.pdf?fbclid=IwAR0fe6lNYH8Azj1iVqusb5l-z3xeECx7JBv23ACDV0Xjdq04FHJPD3nPlxE
#set up time variable for plot
t = np.linspace(1, 8, num=8)
years = ['05B', '06B', '07B', '08B', '09B', '10B', '11B', '12B']
x = [1,3,4,5,6,7,8]
#### reduce to just single lightcurve ###
#obnum = 94904
#mask = newvarys['NUMBER_05B'] == obnum
#flux = flux[mask].reshape(len(x))
tbdata = combined[1].data
chandra = fits.open('mag_flux_tables/xray_mag_flux_table_best.fits')
chandata = chandra[1].data
stars = fits.open('mag_flux_tables/stars_mag_flux_table.fits')
sdata = stars[1].data
### Restrict objects to those in the Chandra field ###
#tbdata = vari_funcs.chandra_only(tbdata)
## Create arrays of flux values but without 06B ###
flux = vari_funcs.flux5_stacks(tbdata)
fluxchan = vari_funcs.flux5_stacks(chandata) # for chandra non-stellar objects
sflux = vari_funcs.flux5_stacks(sdata)
### remove values that are +/-99 ###
flux, tbdata = vari_funcs.noneg(flux, tbdata)
fluxchan, chandata = vari_funcs.noneg(fluxchan, chandata)
sflux, sdata = vari_funcs.noneg(sflux, sdata)
### 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')
