def z_split_max_likely(tbdata):
posvar = np.linspace(0, 2, 5000)
### Remove edges ###
tbdata = vari_funcs.remove_edges(tbdata)
### Split by z ###
tbhigh, tblow = z_split(tbdata)
### Get luminosity and flux ###
tbhigh, highL, highfluxnorm, higherrnorm = get_luminosity_and_flux(tbhigh)
tblow, lowL, lowfluxnorm, lowerrnorm = get_luminosity_and_flux(tblow)
### Get sig values ###
numobs = np.shape(highfluxnorm)[0]
meanflux = np.nanmean(highfluxnorm, axis=1)
highout = np.array([
vari_funcs.maximum_likelihood(highfluxnorm[n, :],
higherrnorm[n, :],
meanflux[n],
posvar,
n=n,
printn=100) for n in range(numobs)
])
numobs = np.shape(lowfluxnorm)[0]
meanflux = np.nanmean(lowfluxnorm, axis=1)
lowout = np.array([
vari_funcs.maximum_likelihood(lowfluxnorm[n, :],
lowerrnorm[n, :],
meanflux[n],
posvar,
n=n,
printn=100) for n in range(numobs)
])
return highL, highout, lowL, lowout, tbdata
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 run_max_likely(tbdata):
posvar = np.linspace(0,2,5000)
### Remove edges ###
tbdata = vari_funcs.remove_edges(tbdata)
### Get luminosity and flux ###
tbdata, L, fluxnorm, fluxerrnorm= get_luminosity_and_flux(tbdata)
### 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 L, out, tbdata
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
from scipy import stats
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_extra_clean_no06.fits')[1].data
chandata = fits.open(
'mag_flux_tables/xray_mag_flux_table_best_extra_clean_no06.fits')[1].data
sdata = fits.open(
'mag_flux_tables/stars_mag_flux_table_extra_clean_no06.fits')[1].data
sigtb = Table.read('sigma_tables/quad_epoch_sigma_table_extra_clean_no06.fits')
### Remove edges ###
tbdata = vari_funcs.remove_edges(tbdata)
chandata = vari_funcs.remove_edges(chandata)
sdata = vari_funcs.remove_edges(sdata)
#### Limit to Chandra region ###
#tbdata = vari_funcs.chandra_only(tbdata)
#chandata = vari_funcs.chandra_only(chandata)
#sdata = vari_funcs.chandra_only(sdata)
## Create arrays of flux values ###
flux = vari_funcs.flux5_stacks(tbdata)
fluxchan = vari_funcs.flux5_stacks(chandata)
sflux = vari_funcs.flux5_stacks(sdata)
### remove values that are negative ###
flux, tbdata = vari_funcs.noneg(flux, tbdata)
### create ensemble arrays ###
enflux, enfluxerr, enL = make_ensemble(tbdata, L, binedge,
fluxnorm, fluxerrnorm)
### Get ensemble maximum likelihood ###
sig, sigerr, meanL = get_ensemble_sig(enflux, enfluxerr, enL, posvar)
#### get errors ###
xerr = find_err_values(binedge, sortedL, meanL)
return tbdata, meanL, xerr, sig, sigerr
#%% remove edges and split by z ###
tbdata = vari_funcs.remove_edges(tbdata)
#fullxray = vari_funcs.remove_edges(fullxray)
tb1, tb2, tb3, tb4, tb5 = z_split(tbdata)
#fullhigh, fulllow = z_split(fullxray)
#%% run function ###
tb1, meanL1, xerr1, sig1, sigerr1 = get_ensemble_data(tb1)
tb2, meanL2, xerr2, sig2, sigerr2 = get_ensemble_data(tb2)
tb3, meanL3, xerr3, sig3, sigerr3 = get_ensemble_data(tb3)
tb4, meanL4, xerr4, sig4, sigerr4 = get_ensemble_data(tb4)
tb5, meanL5, xerr5, sig5, sigerr5 = get_ensemble_data(tb5)
#%% run plot ###
plt.figure(figsize=[12,7])
#plt.subplot(121)
from astropy.io import fits #for handling fits
from astropy.table import Table #for handling tables
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
plt.close('all')
plt.style.use('dark_background')
### Get fits files ###
chandata = fits.open('variable_tables/no06_variables_chi30_2arcsec_chandata_DR11data_restframe.fits')[1].data
xmmdata = fits.open('variable_tables/no06_variables_chi30_2arcsec_xmmdata_DR11data_restframe.fits')[1].data
tbdata = fits.open('variable_tables/no06_variables_chi30_2arcsec_DR11data_restframe.fits')[1].data
### Remove edges ###
tbdata = vari_funcs.remove_edges(tbdata)
chandata = vari_funcs.remove_edges(chandata)
xmmdata = vari_funcs.remove_edges(xmmdata)
nontb = tbdata[~tbdata['X-ray']]
#xtb = tbdata[tbdata['X-ray']]
### Get positions ###
nonRA = nontb['ALPHA_J2000']
nonDec = nontb['DELTA_J2000']
#xRA = xtb['ALPHA_J2000']
#xDec = xtb['DELTA_J2000']
chanRA = chandata['ALPHA_J2000']
chanDec = chandata['DELTA_J2000']
xmmRA = xmmdata['ALPHA_J2000']
xmmDec = xmmdata['DELTA_J2000']
return binedge
def combine_Xray(binedge, xmmenflux, xmmenfluxerr, xmmenL,
chanenflux, chanenfluxerr, chanenL):
enflux = {}
enfluxerr = {}
enL = {}
for m, enmin in enumerate(binedge):
enflux[m] = np.vstack([xmmenflux[m],chanenflux[m]])
enfluxerr[m] = np.vstack([xmmenfluxerr[m],chanenfluxerr[m]])
enL[m] = np.append(xmmenL[m],chanenL[m])
return enflux, enfluxerr, enL
#%% remove edges ###
tbdata = vari_funcs.remove_edges(tbdata)
chandata = vari_funcs.remove_edges(chandata)
xmmdata = vari_funcs.remove_edges(xmmdata)
fullxray = vari_funcs.remove_edges(fullxray)
#%% get_luminosity_and_flux ###
tbdata, L, fluxnorm, fluxerrnorm= get_luminosity_and_flux(tbdata)
chandata, chanL, chanfluxnorm, chanerrnorm= get_luminosity_and_flux(chandata)
xmmdata, xmmL, xmmfluxnorm, xmmerrnorm = get_luminosity_and_flux(xmmdata, xmm=True)
fullxray, fullL, fullfluxnorm, fullerrnorm= get_luminosity_and_flux(fullxray)
#%% Split into bins according to nir luminosity ###
### Join xmm and chan arrays ###
xrayL = np.append(xmmL, chanL)
