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mutils.py
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mutils.py
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'''
Functions here are:
- obswave_to_vel_2
- extract_data
- continuum_normalize
- custom_mask_J0313
- custom_mask_J1342
- custom_mask_J0038
- extract_and_norm
- qso_exclude_proximity_zone
- qso_redshift_and_pz_mask
- final_qso_pathlength
- init_onespec
- plot_onespec_pdf
- plot_allspec_pdf
- init_skewers_compute_model_grids
'''
import sys
sys.path.append('/Users/suksientie/codes/enigma')
sys.path.append('/Users/suksientie/Research/data_redux')
sys.path.append('/Users/suksientie/Research/CIV_forest')
sys.path.append('/home/sstie/codes/PypeIt') # for running on IGM cluster
import numpy as np
from astropy.cosmology import FlatLambdaCDM
from matplotlib import pyplot as plt
from matplotlib.ticker import AutoMinorLocator
from astropy.io import fits
from pypeit.core.fitting import iterfit, robust_fit
from pypeit import utils as putils
from pypeit import bspline
import scipy.interpolate as interpolate
from astropy import constants as const
from astropy.table import Table
from astropy.stats import sigma_clip, mad_std
from astropy.modeling.models import Gaussian1D
from enigma.reion_forest import utils
import compute_cf_data as ccf
import pdb
from astropy.cosmology import FlatLambdaCDM
from astropy import units as u
from pypeit.core.arc import detect_peaks
from astropy import constants
def obswave_to_vel_2(wave_arr):
# converts wavelength array from Angstrom to km/s using the following relation
# dv (km/s) = c * d(log_lambda), and log is natural log
# Input: 'wave_arr' = numpy array
# Output: velocity array
c_kms = const.c.to('km/s').value
log10_wave = np.log10(wave_arr)
diff_log10_wave = np.diff(log10_wave) # d(log10_lambda)
diff_log10_wave = np.append(diff_log10_wave, diff_log10_wave[-1]) # appending the last value twice to make array same size as wave_arr
dv = c_kms * np.log(10) * diff_log10_wave
vel = np.cumsum(dv) # first pixel is dv; vel = [dv, 2*dv, 3*dv, ....]
return vel
def extract_data(fitsfile, wavetype='wavegridmid', wavemin=None):
# 'fitsfile' = name of fitsfile containing Pypeit 1d spectrum
data = fits.open(fitsfile)[1].data
if wavetype == 'wave':
wave_arr = data['wave'].astype('float64')
elif wavetype == 'wavegridmid':
wave_arr = data['wave_grid_mid'].astype('float64') # midpoint values of wavelength bin
# hack for jwst nirspec
try:
flux_arr = data['F_lam'].astype('float64')
mask_arr = data['mask'].astype('bool')
std_arr = data['sigma_lam'].astype('float64')
ivar_arr = 1 / std_arr ** 2
except KeyError:
flux_arr = data['flux'].astype('float64')
ivar_arr = data['ivar'].astype('float64')
mask_arr = data['mask'].astype('bool')
std_arr = np.sqrt(putils.inverse(ivar_arr))
try:
tell_arr = data['telluric'].astype('float64')
except KeyError:
tell_arr = None
wave_arr = wave_arr.squeeze()
flux_arr = flux_arr.squeeze()
ivar_arr = ivar_arr.squeeze()
mask_arr = mask_arr.squeeze()
std_arr = std_arr.squeeze()
if tell_arr is not None:
tell_arr = tell_arr.squeeze()
if wavemin is not None:
wavemin_mask = wave_arr >= wavemin
wave_arr = wave_arr[wavemin_mask]
flux_arr = flux_arr[wavemin_mask]
ivar_arr = ivar_arr[wavemin_mask]
mask_arr = mask_arr[wavemin_mask]
std_arr = std_arr[wavemin_mask]
tell_arr = tell_arr[wavemin_mask]
return wave_arr, flux_arr, ivar_arr, mask_arr, std_arr, tell_arr
def continuum_normalize_new(wave_arr, flux_arr, ivar_arr, mask_arr, std_arr, nbkpt, plot=False):
# continuum normalize using breakpoint spline method in Pypeit
# nbkpt: NOT the total number of breakpoints, but instead it's placing a breakpoint at every n-th index,
# since we're using the 'everyn' argument below. I.e. if nbkpt=20, it places a breakpoint at every 20-th element.
(sset, outmask) = iterfit(wave_arr, flux_arr, invvar=ivar_arr, inmask=mask_arr, upper=3, lower=3, x2=None,
maxiter=10, nord=4, bkpt=None, fullbkpt=None, kwargs_bspline = {'everyn': nbkpt})
cont_fit, cont_fit_mask = sset.value(wave_arr)
if plot:
# plotting provided by Joe
goodbk = sset.mask
# This is approximate
yfit_bkpt = np.interp(sset.breakpoints[goodbk], wave_arr, cont_fit)
plt.figure(figsize=(12, 5))
ax = plt.gca()
was_fit_and_masked = mask_arr & np.logical_not(outmask)
print("np.sum(was_fit_and_masked)", np.sum(was_fit_and_masked))
ax.plot(wave_arr[mask_arr], flux_arr[mask_arr], color='k', marker='o', markersize=0.4, mfc='k', fillstyle='full',
linestyle='-', label='Pixels that were fit')
ax.plot(wave_arr[was_fit_and_masked], flux_arr[was_fit_and_masked], color='red', marker='x', markersize=5.0, mfc='red',
fillstyle='full', linestyle='None', label='Pixels masked by fit')
ax.plot(wave_arr, cont_fit, color='cornflowerblue', label='B-spline fit')
ax.plot(sset.breakpoints[goodbk], yfit_bkpt, color='lawngreen', marker='o', markersize=4.0, mfc='lawngreen',
fillstyle='full', linestyle='None', label='Good B-spline breakpoints')
#ax.set_ylim((0.99 * cont_fit.min(), 1.01 * cont_fit.max()))
ax.set_ylim((0.8 * cont_fit.min(), 1.2 * cont_fit.max()))
plt.ylabel('Flux')
plt.xlabel('Wave (Ang)')
plt.legend()
plt.tight_layout()
plt.show()
return cont_fit, cont_fit_mask, sset, outmask
# Define some global constants that will be used in this module
c_light = (const.c.to('km/s')).value
from pypeit.utils import fast_running_median
from pypeit.core.wavecal import wvutils
def inverse(array):
return (array > 0.0)/(np.abs(array) + (array == 0.0))
def fit_continuum(wave, flux, ivar, gpm, dv_bkpt, upper=3.0, lower=3.0, maxiter=10, nord=4, grow=0, sticky=False,
maxrej=None, plot=False, maxdev=None, use_mad=False):
# Definte the full set of breakpoints
fullbkpt, wave_grid_mid, dsamp = wvutils.get_wave_grid(waves = [wave], gpms=[gpm], wave_method='log10', dv=dv_bkpt)
# Loop over the breakpoints and
bkpt_gpm = np.ones_like(fullbkpt, dtype=bool)
nbkpt = fullbkpt.size
n_inside = np.zeros(nbkpt-1, dtype=int)
for i in range(nbkpt-1):
n_inside[i] = np.sum((wave >= fullbkpt[i]) & (wave < fullbkpt[i+1]))
if n_inside[i] == 0:
bkpt_gpm[i] = False
fullbkpt = fullbkpt[bkpt_gpm]
kwargs_reject = dict(maxrej=maxrej, grow=grow, sticky=sticky, maxdev=maxdev, use_mad=use_mad)
sset, out_gpm = iterfit(wave, flux, invvar=ivar, inmask=gpm, upper=upper, lower=lower, x2=None,
maxiter=maxiter, nord=nord, bkpt=None, fullbkpt=fullbkpt, kwargs_reject=kwargs_reject)
wave_gpm = wave > 1.0
cont_fit = np.zeros_like(wave)
cont_fit_gpm = np.zeros_like(wave, dtype=bool)
cont_fit[wave_gpm], cont_fit_gpm[wave_gpm] = sset.value(wave[wave_gpm])
if plot:
goodbk = sset.mask
# This is approximate
yfit_bkpt = np.interp(sset.breakpoints[goodbk], wave, cont_fit)
sigma = np.sqrt(inverse(ivar))
flux_sm = fast_running_median(flux[gpm], 100)
sigma_sm = fast_running_median(sigma[gpm], 100)
plt.figure(figsize=(12, 5))
ax = plt.gca()
was_fit_and_masked = gpm & np.logical_not(out_gpm)
print("Was fit and masked ={}".format(np.sum(was_fit_and_masked)))
ax.plot(wave[gpm], flux[gpm], color='k', marker='o', markersize=0.4, mfc='k', fillstyle='full',
linestyle='-', label='Pixels that were fit')
ax.plot(wave[gpm], sigma[gpm], color='orange', drawstyle='steps-mid', linestyle='-', label='Noise')
ax.plot(wave[was_fit_and_masked], flux[was_fit_and_masked], color='red', marker='x', markersize=5.0, mfc='red',
fillstyle='full', linestyle='None', label='Pixels masked by fit')
ax.plot(wave, cont_fit, color='cornflowerblue', label='B-spline fit')
ax.plot(sset.breakpoints[goodbk], yfit_bkpt, color='lawngreen', marker='o', markersize=4.0, mfc='lawngreen',
fillstyle='full', linestyle='None', label='Good B-spline breakpoints')
#ax.set_ylim((0.99 * cont_fit.min(), 1.01 * cont_fit.max()))
ax.set_ylim((-1.0*sigma_sm.max(), 1.2*flux_sm.max()))
plt.ylabel('Flux')
plt.xlabel('Wave (Ang)')
plt.legend()
plt.tight_layout()
plt.show()
return cont_fit, cont_fit_gpm, sset, out_gpm
################## by-eye strong absorbers masks for each QSO ##################
def custom_mask_J0313(fitsfile, wavetype='wavegridmid', wavemin=None, plot=False):
wave, flux, ivar, mask, std, tell = extract_data(fitsfile, wavetype=wavetype, wavemin=wavemin)
mask_wave1 = [19815, 19825]
mask_wave2 = [19863, 19875] #[19865, 19870]
mask_wave3 = [23303, 23325]
mask_wave4 = [23370, 23387]
all_mask_wave = [mask_wave1, mask_wave2, mask_wave3, mask_wave4]
strong_abs_gpm = np.ones(wave.shape, dtype=bool) # good pixel mask accounting for strong absorbers
for mask_wave_i in all_mask_wave:
a = mask_wave_i[0] < wave
b = wave < mask_wave_i[1]
gpm = np.invert(a * b)
strong_abs_gpm *= gpm
if plot:
alpha = 0.3
plt.plot(wave, flux, c='b', drawstyle='steps-mid')
plt.plot(wave, std, c='k', drawstyle='steps-mid')
plt.axvspan(mask_wave1[0], mask_wave1[1], facecolor='r', alpha=alpha)
plt.axvspan(mask_wave2[0], mask_wave2[1], facecolor='r', alpha=alpha)
plt.axvspan(mask_wave3[0], mask_wave3[1], facecolor='r', alpha=alpha)
plt.axvspan(mask_wave4[0], mask_wave4[1], facecolor='r', alpha=alpha)
plt.show()
return strong_abs_gpm
def custom_mask_J1342(fitsfile, wavetype='wavegridmid', wavemin=None, plot=False):
wave, flux, ivar, mask, std, tell = extract_data(fitsfile, wavetype=wavetype, wavemin=wavemin)
# visually-identified strong absorbers
mask_wave1 = [21920, 21940]
mask_wave2 = [21974, 22000] #[21972, 22000] # updated with rebinned spec
mask_wave3 = [20320, 20335]
mask_wave4 = [20375, 20400]
all_mask_wave = [mask_wave1, mask_wave2, mask_wave3, mask_wave4]
strong_abs_gpm = np.ones(wave.shape, dtype=bool) # good pixel mask accounting for strong absorbers
for mask_wave_i in all_mask_wave:
a = mask_wave_i[0] < wave
b = wave < mask_wave_i[1]
gpm = np.invert(a * b)
strong_abs_gpm *= gpm
if plot:
alpha = 0.3
plt.plot(wave, flux, c='b', drawstyle='steps-mid')
plt.plot(wave, std, c='k', drawstyle='steps-mid')
plt.axvspan(mask_wave1[0], mask_wave1[1], facecolor='r', alpha=alpha)
plt.axvspan(mask_wave2[0], mask_wave2[1], facecolor='r', alpha=alpha)
plt.axvspan(mask_wave3[0], mask_wave3[1], facecolor='r', alpha=alpha)
plt.axvspan(mask_wave4[0], mask_wave4[1], facecolor='r', alpha=alpha)
plt.show()
return strong_abs_gpm
def custom_mask_J0252(fitsfile, wavetype='wavegridmid', wavemin=None, plot=False):
# no strong absorbers that I can identify by eye
# placeholder function in case want to add strong absorbers
wave, flux, ivar, mask, std, tell = extract_data(fitsfile, wavetype=wavetype, wavemin=wavemin)
strong_abs_gpm = np.ones(wave.shape, dtype=bool)
if plot:
alpha = 0.3
plt.plot(wave, flux, c='b', drawstyle='steps-mid')
plt.plot(wave, std, c='k', drawstyle='steps-mid')
plt.show()
return strong_abs_gpm
def custom_mask_J0038(fitsfile, wavetype='wavegridmid', wavemin=None, plot=False):
wave, flux, ivar, mask, std, tell = extract_data(fitsfile, wavetype=wavetype, wavemin=wavemin)
# visually-identified strong absorbers
mask_wave1 = [19777, 19804] # [19777, 19796]
mask_wave2 = [19828, 19855]
all_mask_wave = [mask_wave1, mask_wave2]
strong_abs_gpm = np.ones(wave.shape, dtype=bool)
for mask_wave_i in all_mask_wave:
a = mask_wave_i[0] < wave
b = wave < mask_wave_i[1]
gpm = np.invert(a * b)
strong_abs_gpm *= gpm
if plot:
alpha = 0.3
plt.plot(wave, flux, c='b', drawstyle='steps-mid')
plt.plot(wave, std, c='k', drawstyle='steps-mid')
plt.axvspan(mask_wave1[0], mask_wave1[1], facecolor='r', alpha=alpha)
plt.axvspan(mask_wave2[0], mask_wave2[1], facecolor='r', alpha=alpha)
plt.show()
return strong_abs_gpm
def custom_mask_J0410(fitsfile, wavetype='wavegridmid', wavemin=None, plot=False):
wave, flux, ivar, mask, std, tell = extract_data(fitsfile, wavetype=wavetype, wavemin=wavemin)
# visually-identified strong absorbers
mask_wave1 = [20638, 20653]
mask_wave2 = [20688, 20705]
mask_wave3 = [20742, 20755]
all_mask_wave = [mask_wave1, mask_wave2, mask_wave3]
strong_abs_gpm = np.ones(wave.shape, dtype=bool)
for mask_wave_i in all_mask_wave:
a = mask_wave_i[0] < wave
b = wave < mask_wave_i[1]
gpm = np.invert(a * b)
strong_abs_gpm *= gpm
if plot:
alpha = 0.3
plt.plot(wave, flux, c='b', drawstyle='steps-mid')
plt.plot(wave, std, c='k', drawstyle='steps-mid')
plt.axvspan(mask_wave1[0], mask_wave1[1], facecolor='r', alpha=alpha)
plt.axvspan(mask_wave2[0], mask_wave2[1], facecolor='r', alpha=alpha)
plt.show()
return strong_abs_gpm
def extract_and_norm(fitsfile, everyn_bkpt, qso_name, wavetype='wavegridmid', plot=False, wavemin=None):
# combine extract_data() and continuum_normalize_new()
# continuum normalize after masking strong absorbers identified by eye
wave, flux, ivar, mask, std, tell = extract_data(fitsfile, wavetype=wavetype, wavemin=wavemin)
if qso_name == 'J0313-1806':
print('using custom mask for %s' % qso_name)
strong_abs_gpm = custom_mask_J0313(fitsfile, wavetype=wavetype, wavemin=wavemin)
elif qso_name == 'J1342+0928':
print('using custom mask for %s' % qso_name)
strong_abs_gpm = custom_mask_J1342(fitsfile, wavetype=wavetype, wavemin=wavemin)
elif qso_name == 'J0252-0503':
print('using custom mask for %s' % qso_name)
strong_abs_gpm = custom_mask_J0252(fitsfile, wavetype=wavetype, wavemin=wavemin)
elif qso_name == 'J0038-1527':
print('using custom mask for %s' % qso_name)
strong_abs_gpm = custom_mask_J0038(fitsfile, wavetype=wavetype, wavemin=wavemin)
elif qso_name == 'J0410-0139':
print('using custom mask for %s' % qso_name)
strong_abs_gpm = custom_mask_J0410(fitsfile, wavetype=wavetype, wavemin=wavemin)
else:
print('using custom mask for %s' % qso_name)
strong_abs_gpm = np.ones(wave.shape, dtype=bool) # dummy mask
inmask = mask * strong_abs_gpm
fluxfit, fluxfit_mask, sset, bspline_mask = continuum_normalize_new(wave, flux, ivar, inmask, std, everyn_bkpt, plot=plot)
#fluxfit, fluxfit_mask, sset, bspline_mask = fit_continuum(wave, flux, ivar, inmask, everyn_bkpt*40)
return wave, flux, ivar, mask, std, tell, fluxfit, strong_abs_gpm
def qso_redshift_and_pz_mask(wave, qso_z, exclude_rest=2800-2728.62, mg2forest_wavemin=19500):
# Bosman exclude_rest = 1216-1185 for Lya
c_kms = constants.c.to('km/s').value
mg2_wave = 2800
pz_velsize = exclude_rest/mg2_wave * c_kms
#print("PZ size is %0.2f km/s" % pz_velsize)
redshift_mask = (wave <= (mg2_wave * (1 + qso_z))) * (wave >= mg2forest_wavemin)
obs_wave_max = (mg2_wave - exclude_rest) * (1 + qso_z)
pz_mask = wave < obs_wave_max
return redshift_mask, pz_mask, obs_wave_max
def telluric_mask(wave):
#wave_bad_start = [21628, 21778, 22153, 22307, 22745, 22799, 23156, 23394, 23553]
#wave_bad_end = [21640, 21788, 22169, 22326, 22764, 22815, 23185, 23409, 23566]
#wave_bad_start = [19791, 19864, 20000, 21628, 22153, 22307, 22745, 22799, 23156, 23394, 23553]
#wave_bad_end = [19808, 19874, 20060, 21640, 22169, 22326, 22764, 22815, 23185, 23409, 23566]
wave_bad_start = [20000] #, 20556, 21489]
wave_bad_end = [20060]#, 20571, 21512]
telluric_gpm = np.ones(wave.shape, dtype=bool)
for i in range(len(wave_bad_start)):
bpm_a = wave_bad_start[i] < wave
bpm_b = wave < wave_bad_end[i]
bpm = bpm_a * bpm_b
telluric_gpm *= np.invert(bpm)
return telluric_gpm
def init_onespec(iqso, redshift_bin, datapath='/Users/suksientie/Research/MgII_forest/rebinned_spectra2/', \
wavetype='wavegridmid', wavemin=19500):
fitsfile_list = [datapath + 'J0411-0907_coadd_dv40_tellcorr.fits', \
datapath + 'J0319-1008_NIRES_coadd_SST_tellcorr.fits', \
datapath + 'J0410-0139_coadd_dv40_tellcorr.fits', \
datapath + 'J0038-0653_coadd_dv40_tellcorr.fits', \
datapath + 'J0313-1806_coadd_dv40_abcd_tellcorr.fits', \
datapath + 'J0038-1527_coadd_dv40_tellcorr.fits', \
datapath + 'J0252-0503_coadd_dv40_ab_tellcorr.fits', \
datapath + 'J1342+0928_coadd_dv40_abc_tellcorr.fits', \
datapath + 'J1007+2115_NIRES_coadd_SST_tellcorr.fits', \
datapath + 'J1120+0641_XShooter_NIR_coadd_SST_tellcorr.fits']
qso_namelist = ['J0411-0907', 'J0319-1008', 'J0410-0139', 'J0038-0653', 'J0313-1806', 'J0038-1527', 'J0252-0503', \
'J1342+0928', 'J1007+2115', 'J1120+0641']
qso_zlist = [6.826, 6.8275, 7.0, 7.1, 7.642, 7.034, 7.001, 7.541, 7.515, 7.085]
qso_median_snr = [9.29, 5.50, 3.95, 8.60, 11.42, 14.28, 13.07, 8.72] # from Table 1 in current draft (12/6/2022)
everyn_break_list = (np.ones(len(qso_namelist)) * 60).astype('int')
exclude_restwave = 2800 - 2728.62
median_z = 6.469 #6.519 # for 10 qso; #6.500 (8qso) see allqso_pathlength_snr.py
mg2_wave = 2800 # approximated to be midpoint of blue (2796 A) and red (2804 A)
fitsfile = fitsfile_list[iqso]
#if iqso == 9:
# print("==== switching ====")
# fitsfile = '/Users/suksientie/Research/MgII_forest/rebinned_spectra/J1120+0641_dv40_coadd_tellcorr.fits'
# wavetype = 'wave'
wave, flux, ivar, mask, std, tell, fluxfit, strong_abs_gpm = extract_and_norm(fitsfile, everyn_break_list[iqso], qso_namelist[iqso], wavetype=wavetype, wavemin=wavemin)
redshift_mask, pz_mask, obs_wave_max = qso_redshift_and_pz_mask(wave, qso_zlist[iqso], exclude_rest=exclude_restwave)
telluric_gpm = telluric_mask(wave)
if redshift_bin == 'low':
zbin_mask = wave < (mg2_wave * (1 + median_z))
elif redshift_bin == 'high':
zbin_mask = wave >= (mg2_wave * (1 + median_z))
elif redshift_bin == 'all':
zbin_mask = np.ones_like(wave, dtype=bool)
# master mask for measuring 2PCF
master_mask = mask * redshift_mask * pz_mask * zbin_mask * telluric_gpm
# masked arrays
good_wave = wave[master_mask]
good_flux = flux[master_mask]
good_ivar = ivar[master_mask]
good_std = std[master_mask]
good_vel_data = obswave_to_vel_2(good_wave)
vel_data = obswave_to_vel_2(wave)
norm_good_flux = good_flux / fluxfit[master_mask]
norm_good_std = good_std / fluxfit[master_mask]
raw_data_out = wave, flux, ivar, mask, std, tell, fluxfit
masked_data_out = good_wave, good_flux, good_ivar, good_std, good_vel_data, norm_good_flux, norm_good_std
all_masks_out = strong_abs_gpm, redshift_mask, pz_mask, obs_wave_max, zbin_mask, telluric_gpm, master_mask
return raw_data_out, masked_data_out, all_masks_out
######################################################
# determining the correction factors for each QSO
def plot_onespec_pdf(iqso, seed=None, title=None):
raw_out, masked_out, masks_out = init_onespec(iqso, 'all')
wave, flux, ivar, mask, std, tell, fluxfit = raw_out
strong_abs_gpm, redshift_mask, pz_mask, obs_wave_max, zbin_mask, telluric_gpm, master_mask = masks_out
norm_flux = flux/fluxfit
norm_std = std/fluxfit
norm_flux = norm_flux[master_mask]
norm_std = norm_std[master_mask]
chi = (1 - norm_flux) / norm_std # expected to be a Gaussian with unit variance
corr_factor = mad_std(chi) # median absolute std
print(corr_factor)
rand = np.random.RandomState(seed) if seed != None else np.random.RandomState()
gaussian_data = rand.normal(np.median(norm_flux), norm_std * corr_factor)
plt.figure(figsize=(8, 5))
bins = np.arange(-0.2, 1.5, 0.03)
plt.hist(norm_flux, bins=bins, histtype='step', color='k')
plt.hist(gaussian_data, bins=bins, histtype='step', color='r')
plt.tight_layout()
plt.show()
plt.figure(figsize=(8, 5))
if title != None:
plt.suptitle(title, fontsize=18)
##### plot (1 - F) PDF #####
nbins, oneminf_min, oneminf_max = 71, 1e-5, 1.0
flux_bins, flux_pdf_data = utils.pdf_calc(1.0 - norm_flux, oneminf_min, oneminf_max, nbins)
flux_bins, flux_pdf_data_symm = utils.pdf_calc(- (1 - norm_flux), oneminf_min, oneminf_max, nbins)
flux_bins, flux_pdf_gaussian = utils.pdf_calc(1 - gaussian_data, oneminf_min, oneminf_max, nbins)
plt.plot(flux_bins, flux_pdf_data, drawstyle='steps-mid', alpha=1.0, lw=2, label='1 - F')
plt.plot(flux_bins, flux_pdf_data_symm, drawstyle='steps-mid', alpha=1.0, lw=2, label='F - 1')
plt.plot(flux_bins, flux_pdf_gaussian, drawstyle='steps-mid', alpha=1.0, lw=1, \
label=r'gaussian ($\sigma = \sigma_{\rm{ipix}}$ * corr), corr=%0.2f' % corr_factor)
plt.legend()
plt.xscale('log')
plt.yscale('log')
plt.ylabel('PDF')
plt.tight_layout()
plt.show()
def plot_allspec_pdf(cgm_fit_gpm=None, seed_list=[None, None, None, None, None, None, None, None, None, None], plot=False):
qso_namelist = ['J0411-0907', 'J0319-1008', 'J0410-0139', 'J0038-0653', 'J0313-1806', 'J0038-1527', 'J0252-0503', 'J1342+0928', 'J1007+2115', 'J1120+0641']
if plot:
plt.figure(figsize=(10, 18))
corr_all = []
for iqso in range(len(qso_namelist)):
seed = seed_list[iqso]
raw_out, masked_out, masks_out = init_onespec(iqso, 'all')
wave, flux, ivar, mask, std, tell, fluxfit = raw_out
strong_abs_gpm, redshift_mask, pz_mask, obs_wave_max, zbin_mask, telluric_mask, master_mask = masks_out
norm_flux = flux/fluxfit
norm_std = std/fluxfit
if cgm_fit_gpm is None:
norm_flux = norm_flux[mask * redshift_mask * pz_mask * zbin_mask * telluric_mask]
norm_std = norm_std[mask * redshift_mask * pz_mask * zbin_mask * telluric_mask]
else:
norm_flux = norm_flux[mask * redshift_mask * pz_mask * zbin_mask * telluric_mask * cgm_fit_gpm[iqso]]
norm_std = norm_std[mask * redshift_mask * pz_mask * zbin_mask * telluric_mask * cgm_fit_gpm[iqso]]
chi = (1 - norm_flux) / norm_std
corr_factor = mad_std(chi)
print(corr_factor)
corr_all.append(np.round(corr_factor, 3))
rand = np.random.RandomState(seed) if seed != None else np.random.RandomState()
gaussian_data = rand.normal(np.median(norm_flux), norm_std * corr_factor)
if plot:
plt.subplot(10, 2, iqso+1)
plt.title(qso_namelist[iqso])
nbins, oneminf_min, oneminf_max = 71, 1e-5, 1.0
flux_bins, flux_pdf_data = utils.pdf_calc(1.0 - norm_flux, oneminf_min, oneminf_max, nbins)
flux_bins, flux_pdf_data_symm = utils.pdf_calc(- (1 - norm_flux), oneminf_min, oneminf_max, nbins)
flux_bins, flux_pdf_gaussian = utils.pdf_calc(1 - gaussian_data, oneminf_min, oneminf_max, nbins)
plt.plot(flux_bins, flux_pdf_data, drawstyle='steps-mid', alpha=1.0, lw=2, label='1 - F')
plt.plot(flux_bins, flux_pdf_data_symm, drawstyle='steps-mid', alpha=1.0, lw=2, label='F - 1')
plt.plot(flux_bins, flux_pdf_gaussian, drawstyle='steps-mid', alpha=1.0, lw=1, \
label=r'gaussian ($\sigma = \sigma_{\rm{ipix}}$ * corr), corr=%0.2f' % corr_factor) # + '\n' + r'corr = mad_std(1 - $F_{\rm{ipix}}$)/$\sigma_{\rm{ipix}}$')
plt.legend()
plt.xscale('log')
plt.yscale('log')
plt.ylabel('PDF')
if plot:
plt.tight_layout()
plt.show()
return corr_all
######################################################
def lya_spikes(fitsfile, zlow, zhigh):
# fitsfile = '/Users/suksientie/Research/highz_absorbers/J0313m1806_fire_mosfire_nires_tellcorr_contfit.fits'
# lyb @ 1026
data = fits.open(fitsfile)[1].data
wave_arr = data['wave_grid_mid'].astype('float64') # midpoint values of wavelength bin
flux_arr = data['flux'].astype('float64')
ivar_arr = data['ivar'].astype('float64')
mask_arr = data['mask'].astype('bool')
std_arr = np.sqrt(putils.inverse(ivar_arr))
low = (1216 * (1 + zlow)) < wave_arr
high = wave_arr < 1216 * (1 + zhigh)
lowhigh = low * high
print(np.mean(flux_arr[lowhigh]))
plt.plot(wave_arr[lowhigh], flux_arr[lowhigh], 'k', drawstyle='steps-mid')
plt.plot(wave_arr[lowhigh], std_arr[lowhigh], 'r', drawstyle='steps-mid')
#plt.plot(wave_arr[lowhigh], 5*std_arr[lowhigh], 'b', alpha=0.5, drawstyle='steps-mid')
plt.ylabel('Normalized flux')
plt.xlabel('Observed wavelength')
plt.grid()
plt.show()
def abspath(z1, z2, cosmo=None):
"""
calculate pathlength between z1 and z2.
"""
if cosmo is None:
#f = 'ran_skewers_z75_OVT_xHI_0.50_tau.fits'
#par = Table.read(f, hdu=1)
#lit_h, m0, b0, l0 = par['lit_h'][0], par['Om0'][0], par['Ob0'][0], par['Ode0'][0]
# using the params explicitly, but basically pulling these values from the file above
lit_h = 0.670386
m0 = 0.319181
b0 = 0.049648
l0 = 0.680819
cosmo = FlatLambdaCDM(H0=lit_h*100, Om0=m0, Ob0=b0, Tcmb0=2.725)
return cosmo.absorption_distance(z1)-cosmo.absorption_distance(z2)
######################################################
def cf_lags_to_mask():
given_bins = np.array(ccf.custom_cf_bin4(dv1=80))
v_lo, v_hi = given_bins
vel_mid = (v_hi + v_lo) / 2
lag_mask = np.ones_like(vel_mid, dtype=bool) # Boolean array
#ibad = np.array([7, 9, 11, 14, 18]) # corresponding to lags 610, 770, 930, 1170, 1490
ibad = np.array([4, 5, 14, 18]) # 370.0, 450.0, 1170.0, 1490.0
lag_mask[ibad] = 0
return lag_mask, ibad
def cf_lags_to_mask_highz():
given_bins = np.array(ccf.custom_cf_bin4(dv1=80))
v_lo, v_hi = given_bins
vel_mid = (v_hi + v_lo) / 2
lag_mask = np.ones_like(vel_mid, dtype=bool) # Boolean array
#ibad = np.array([0]) # corresponding to lags 50
ibad = np.array([0, 13]) # 50.0, 1090.0
lag_mask[ibad] = 0
return lag_mask, ibad
def cf_lags_to_mask_lowz():
given_bins = np.array(ccf.custom_cf_bin4(dv1=80))
v_lo, v_hi = given_bins
vel_mid = (v_hi + v_lo) / 2
lag_mask = np.ones_like(vel_mid, dtype=bool) # Boolean array
#ibad = np.array([9, 11, 18]) # corresponding to lags 770, 930, 1490
ibad = np.array([0, 1, 3, 4, 5, 9, 11, 18]) #50.0, 130.0, 290.0, 370.0, 450.0, 770.0, 930.0, 1490.0
lag_mask[ibad] = 0
return lag_mask, ibad
import numpy.ma as ma
def extract_subarr(lag_mask, xi_model_array, xi_mock_array, covar_array):
# mask xi_model, xi_mock, and covar arrays according to lag_mask
nhi, nlogZ, nmock, nbin = xi_mock_array.shape
# masking xi_model
lag_mask_tile = np.tile(lag_mask, (nhi, nlogZ, 1))
xi_model_array_masked = np.reshape(xi_model_array[lag_mask_tile], (nhi, nlogZ, np.sum(lag_mask)))
# masking xi_mock
lag_mask_tile = np.tile(lag_mask, (nhi, nlogZ, nmock, 1))
xi_mock_array_masked = np.reshape(xi_mock_array[lag_mask_tile], (nhi, nlogZ, nmock, np.sum(lag_mask)))
# masking covar_array
m1 = lag_mask.astype(int)
m1 = np.tile(m1, (1, 1))
m2 = np.transpose(m1)
lag_mask_2d = np.matmul(m2, m1)
lag_mask_tile = np.tile(lag_mask_2d, (nhi, nlogZ, 1, 1))
mx = ma.masked_array(covar_array, mask=lag_mask_tile)
#mx = covar_array * lag_mask_tile
new_covar_array = np.zeros((nhi, nlogZ, np.sum(lag_mask), np.sum(lag_mask)))
for ihi in range(nhi):
for ilogZ in range(nlogZ):
tmp = []
for ibin in range(nbin):
d = mx[ihi][ilogZ][ibin].data
m = mx[ihi][ilogZ][ibin].mask
if np.sum(m) > 0:
tmp.append(d[m])
new_covar_array[ihi, ilogZ, :, :] = tmp
sign, new_lndet_array = np.linalg.slogdet(new_covar_array)
return xi_model_array_masked , xi_mock_array_masked, new_covar_array, new_lndet_array
def xi_err_master(mcmc_fits_full, mcmc_fits_subarr, saveout=None):
mcmc_full = fits.open(mcmc_fits_full)
mcmc_subarr = fits.open(mcmc_fits_subarr)
vel_corr_full = mcmc_full['vel_corr'].data
vel_corr_subarr = mcmc_subarr['vel_corr'].data
xi_err_full = mcmc_full['xi_err'].data
xi_err_subarr = mcmc_subarr['xi_err'].data
xi_err_out = []
for i in range(len(vel_corr_full)):
if vel_corr_full[i] in vel_corr_subarr:
j = np.argwhere(vel_corr_subarr == vel_corr_full[i]).squeeze()
xi_err_out.append(xi_err_subarr[j])
else:
xi_err_out.append(xi_err_full[i])
if saveout is not None:
np.save(saveout, xi_err_out)
return xi_err_out
def order_paper_table(list_to_order):
# order the input "list_to_order" according to Table 1 of paper,
# since the analysis ordering is different from the paper ordering
here_qso_namelist = ['J0411-0907', 'J0319-1008', 'J0410-0139', 'J0038-0653', 'J0313-1806', 'J0038-1527', 'J0252-0503', 'J1342+0928', 'J1007+2115', 'J1120+0641']
table_qso_namelist = ['J0313-1806', 'J1342+0928', 'J1007+2115', 'J1120+0641', 'J0252-0503', 'J0038-1527', 'J0411-0907', 'J0319-1008', 'J0410-0139', 'J0038-0653']
i = [here_qso_namelist.index(qso) for qso in table_qso_namelist]
assert np.array_equal(np.array(here_qso_namelist)[i], np.array(table_qso_namelist))
return np.array(list_to_order)[i]
def pz_Mpc():
cosmo = FlatLambdaCDM(H0=100.0 * 0.6704, Om0=0.3192, Ob0=0.04964) # Nyx values
qso_zlist = [6.826, 6.8275, 7.0, 7.1, 7.642, 7.034, 7.001, 7.541, 7.515, 7.085]
zmean_qso = np.mean(qso_zlist)
Hz_mean = cosmo.H(zmean_qso)
dlambda_pz_bosman = 31
c = constants.c.to('km/s').value
dv_pz_bosman = (dlambda_pz_bosman/1216 * c) * u.km / u.s
pz_Mpc = dv_pz_bosman / Hz_mean # proper Mpc
print(pz_Mpc, dv_pz_bosman)
import astropy.cosmology.units as cu
zmean_qso = np.mean(qso_zlist) * cu.redshift
d_comov = zmean_qso.to(u.Mpc, cu.redshift_distance(cosmo, kind="comoving"))
d_comov_end = d_comov - (pz_Mpc * (1 + zmean_qso))
z_pz_end = d_comov_end.to(cu.redshift, cu.redshift_distance(cosmo, kind="comoving"))
print(z_pz_end, zmean_qso)
def get_wave_grid_min(wave, want_wave_grid_min, dv):
c_kms = constants.c.to('km/s').value
dloglam_pix = dv / c_kms / np.log(10.0)
dist_dloglam = np.log10(want_wave_grid_min) - np.log10(wave.min())
ngrid = np.round(dist_dloglam/dloglam_pix)
wm = 10 ** (np.log10(want_wave_grid_min) - ngrid * dloglam_pix)
return wm
def check_wgm():
datapath = '/Users/suksientie/Research/MgII_forest/rebinned_spectra2/'
fitsfile_list = [datapath + 'J0411-0907_coadd_dv40_tellcorr.fits', \
datapath + 'J0319-1008_NIRES_coadd_SST_tellcorr.fits', \
datapath + 'J0410-0139_coadd_dv40_tellcorr.fits', \
datapath + 'J0038-0653_coadd_dv40_tellcorr.fits', \
datapath + 'J0313-1806_coadd_dv40_abcd_tellcorr.fits', \
datapath + 'J0038-1527_coadd_dv40_tellcorr.fits', \
datapath + 'J0252-0503_coadd_dv40_ab_tellcorr.fits', \
datapath + 'J1342+0928_coadd_dv40_abc_tellcorr.fits', \
datapath + 'J1007+2115_NIRES_coadd_SST_tellcorr.fits', \
datapath + 'J1120+0641_XShooter_NIR_coadd_SST_tellcorr.fits']
qso_namelist = ['J0411-0907', 'J0319-1008', 'J0410-0139', 'J0038-0653', 'J0313-1806', 'J0038-1527', 'J0252-0503', \
'J1342+0928', 'J1007+2115', 'J1120+0641']
qso_zlist = [6.826, 6.8275, 7.0, 7.1, 7.642, 7.034, 7.001, 7.541, 7.515, 7.085]
outwave = []
vel = []
for iqso, fitsfile in enumerate(fitsfile_list):
wave, flux, ivar, mask, std, tell, fluxfit, strong_abs_gpm = extract_and_norm(fitsfile, 60,
qso_namelist[iqso],
wavetype='wavegridmid')
outwave.append(wave[wave >= 19500])
vel.append(obswave_to_vel_2(wave))
return outwave, vel
def plot_new_old_cf():
cf_allz_old = fits.open('save_cf/paper/xi_mean_mask_10qso_everyn60_corr.fits')
cf_lowz_old = fits.open('save_cf/paper/xi_mean_mask_10qso_everyn60_lowz.fits')
cf_highz_old = fits.open('save_cf/paper/xi_mean_mask_10qso_everyn60_highz.fits')
cf_allz = fits.open('save_cf/paper_new/xi_10qso_everyn60_corr_allz.fits')
cf_lowz = fits.open('save_cf/paper_new/xi_10qso_everyn60_corr_lowz.fits')
cf_highz = fits.open('save_cf/paper_new/xi_10qso_everyn60_corr_highz.fits')
vel_mid = cf_allz_old['vel_mid'].data
xi_unmask_allz_old = cf_allz_old['xi_mean_unmask'].data
xi_unmask_lowz_old = cf_lowz_old['xi_mean_unmask'].data
xi_unmask_highz_old = cf_highz_old['xi_mean_unmask'].data
xi_mask_allz_old = cf_allz_old['xi_mean_mask'].data
xi_mask_lowz_old = cf_lowz_old['xi_mean_mask'].data
xi_mask_highz_old = cf_highz_old['xi_mean_mask'].data
xi_unmask_allz = cf_allz['xi_mean_unmask'].data
xi_unmask_lowz = cf_lowz['xi_mean_unmask'].data
xi_unmask_highz = cf_highz['xi_mean_unmask'].data
xi_mask_allz = cf_allz['xi_mean_mask'].data
xi_mask_lowz = cf_lowz['xi_mean_mask'].data
xi_mask_highz = cf_highz['xi_mean_mask'].data
plt.figure(figsize=(12, 6))
plt.suptitle('All z')
plt.subplot(211)
plt.plot(vel_mid, xi_unmask_allz_old, 'ko')
plt.plot(vel_mid, xi_unmask_allz, 'rx', label='new')
plt.axhline(0, linestyle='--')
plt.legend()
plt.subplot(212)
plt.plot(vel_mid, xi_mask_allz_old, 'ko')
plt.plot(vel_mid, xi_mask_allz, 'rx', label='new')
plt.axhline(0, linestyle='--')
plt.legend()
plt.figure(figsize=(12, 6))
plt.suptitle('Low z')
plt.subplot(211)
plt.plot(vel_mid, xi_unmask_lowz_old, 'ko')
plt.plot(vel_mid, xi_unmask_lowz, 'rx', label='new')
plt.axhline(0, linestyle='--')
plt.legend()
plt.subplot(212)
plt.plot(vel_mid, xi_mask_lowz_old, 'ko')
plt.plot(vel_mid, xi_mask_lowz, 'rx', label='new')
plt.axhline(0, linestyle='--')
plt.legend()
plt.figure(figsize=(12, 6))
plt.suptitle('High z')
plt.subplot(211)
plt.plot(vel_mid, xi_unmask_highz_old, 'ko')
plt.plot(vel_mid, xi_unmask_highz, 'rx', label='new')
plt.axhline(0, linestyle='--')
plt.legend()
plt.subplot(212)
plt.plot(vel_mid, xi_mask_highz_old, 'ko')
plt.plot(vel_mid, xi_mask_highz, 'rx', label='new')
plt.axhline(0, linestyle='--')
plt.legend()
plt.show()
####################################### OLD stuffs
def old_init_onespec(iqso, redshift_bin, datapath='/Users/suksientie/Research/data_redux/'):
# initialize all needed quantities for one qso, included all masks, for all subsequent analyses
# important to make sure fits files and global variables are up to dates
# datapath = '/Users/suksientie/Research/data_redux/' # path on laptop
# datapath = '/mnt/quasar/sstie/MgII_forest/z75/' # path on IGM server
# not using the padded fits for J0038-1527 (as of 4/25/2022)
fitsfile_list = [datapath + 'wavegrid_vel/J0313-1806/vel1234_coadd_tellcorr.fits', \
datapath + 'wavegrid_vel/J1342+0928/vel123_coadd_tellcorr.fits', \
datapath + 'wavegrid_vel/J0252-0503/vel12_coadd_tellcorr.fits', \
datapath + 'wavegrid_vel/J0038-1527/vel1_tellcorr.fits', \
datapath + 'wavegrid_vel/J0038-0653/vel1_tellcorr.fits']
qso_namelist = ['J0313-1806', 'J1342+0928', 'J0252-0503', 'J0038-1527', 'J0038-0653']
qso_zlist = [7.642, 7.541, 7.001, 7.034, 7.1]
everyn_break_list = [20, 20, 20, 20, 20]
exclude_restwave = 1216 - 1185
median_z = 6.554 # 6.573 # median pixel redshift of measurement (excluding proximity zones)
fitsfile = fitsfile_list[iqso]
wave, flux, ivar, mask, std, tell, fluxfit, strong_abs_gpm = extract_and_norm(fitsfile, everyn_break_list[iqso], qso_namelist[iqso])
redshift_mask, pz_mask, obs_wave_max = qso_redshift_and_pz_mask(wave, qso_zlist[iqso], exclude_restwave)
if redshift_bin == 'low':
zbin_mask = wave < (2800 * (1 + median_z))
elif redshift_bin == 'high':
zbin_mask = wave >= (2800 * (1 + median_z))
elif redshift_bin == 'all':
zbin_mask = np.ones_like(wave, dtype=bool)
#master_mask = mask * strong_abs_gpm * redshift_mask * pz_mask * zbin_mask
master_mask = mask * redshift_mask * pz_mask * zbin_mask
# masked arrays
good_wave = wave[master_mask]
good_flux = flux[master_mask]
good_ivar = ivar[master_mask]
good_std = std[master_mask]
good_vel_data = obswave_to_vel_2(good_wave)
vel_data = obswave_to_vel_2(wave)
norm_good_flux = good_flux / fluxfit[master_mask]
norm_good_std = good_std / fluxfit[master_mask]
#norm_snr = flux/std # numbers used in group meeting slide
#print(np.median(norm_snr))
raw_data_out = wave, flux, ivar, mask, std, tell, fluxfit
masked_data_out = good_wave, good_flux, good_ivar, good_std, good_vel_data, norm_good_flux, norm_good_std
all_masks_out = strong_abs_gpm, redshift_mask, pz_mask, obs_wave_max, zbin_mask, master_mask
#return vel_data, master_mask, std, fluxfit, outmask, norm_good_std, norm_std, norm_good_flux, good_vel_data, good_ivar, norm_flux, ivar
return raw_data_out, masked_data_out, all_masks_out
def old_init_onespec_fluxfit(iqso):
datapath = '/Users/suksientie/Research/MgII_forest/rebinned_spectra/'
fitsfile_list = [datapath + 'J0411-0907_dv40_coadd_tellcorr.fits', \
datapath + 'J0319-1008_dv40_coadd_tellcorr.fits', \
datapath + 'J0410-0139_dv40_coadd_tellcorr.fits', \
datapath + 'J0038-0653_dv40_coadd_tellcorr.fits', \
datapath + 'J0313-1806_dv40_coadd_tellcorr.fits', \
datapath + 'J0038-1527_dv40_coadd_tellcorr.fits', \
datapath + 'J0252-0503_dv40_coadd_tellcorr.fits', \
datapath + 'J1342+0928_dv40_coadd_tellcorr.fits', \
datapath + 'J1007+2115_dv40_coadd_tellcorr.fits', \
datapath + 'J1120+0641_dv40_coadd_tellcorr.fits']
qso_namelist = ['J0411-0907', 'J0319-1008', 'J0410-0139', 'J0038-0653', 'J0313-1806', 'J0038-1527', 'J0252-0503', \
'J1342+0928', 'J1007+2115', 'J1120+0641']
everyn_ls = np.array([10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
fluxfit_ls = []
for everyn in everyn_ls:
wave, flux, ivar, mask, std, tell, fluxfit, strong_abs_gpm = extract_and_norm(fitsfile_list[iqso], everyn, qso_namelist[iqso])
fluxfit_ls.append(fluxfit)
mean_fluxfit = np.mean(fluxfit_ls, axis=0)
np.save('mean_fluxfit_' + qso_namelist[iqso] + '.npy', mean_fluxfit)
return mean_fluxfit
def old_qso_exclude_proximity_zone(fitsfile, qso_z, qso_name, exclude_rest=1216-1185, plot=False):
# BAO lyaf: 1040 A < lambda_rest < 1200 A
# Bosman+2021: lambda_rest < 1185 A
# the default exclude_rest value uses Bosman cut off
wave, flux, ivar, mask, std, tell, fluxfit, strong_abs_gpm = extract_and_norm(fitsfile, 20, qso_name)
redshift_mask = wave <= (2800 * (1 + qso_z)) # removing spectral region beyond qso redshift
master_mask = mask * strong_abs_gpm * redshift_mask
good_wave = wave[master_mask]
obs_wave_max = (2800 - exclude_rest)*(1 + qso_z)
npix_out = len(np.where(good_wave > obs_wave_max)[0])
print(npix_out/len(good_wave), obs_wave_max, good_wave.max())
if plot:
plt.figure()
plt.plot(wave, flux)
plt.axvline(obs_wave_max, color='r', label='obs wave max')
plt.axvline(2800 * (1 + qso_z), color='k', label='qso redshift')
plt.legend()
plt.show()
def plot_allspec_pdf_try(cgm_fit_gpm, seed_list=[None, None, None, None, None, None, None, None, None, None], plot=False):
qso_namelist = ['J0411-0907', 'J0319-1008', 'newqso1', 'newqso2', 'J0313-1806', 'J0038-1527', 'J0252-0503', 'J1342+0928', 'J1007+2115', 'J1120+0641']
if plot:
plt.figure(figsize=(10, 18))
corr_all = []
n_all = []
d_all = []
for iqso in range(len(qso_namelist)):
seed = seed_list[iqso]
#redshift_bin = 'high'
raw_out, masked_out, masks_out = init_onespec(iqso, 'all')
wave, flux, ivar, mask, std, tell, fluxfit = raw_out
#strong_abs_gpm, redshift_mask, pz_mask, obs_wave_max, zbin_mask, master_mask = masks_out
strong_abs_gpm, redshift_mask, pz_mask, obs_wave_max, zbin_mask, telluric_mask, master_mask = masks_out
norm_flux = flux/fluxfit
norm_std = std/fluxfit
norm_flux = norm_flux[mask * redshift_mask * pz_mask * zbin_mask * telluric_mask]
norm_std = norm_std[mask * redshift_mask * pz_mask * zbin_mask * telluric_mask]
chi = (1 - norm_flux) / norm_std
corr_factor = mad_std(chi)
plt.figure()
plt.title(qso_namelist[iqso])
plt.hist(chi, bins=50)#np.arange(-3, 3, 0.1))
norm_flux = flux / fluxfit
norm_std = std / fluxfit
norm_flux = norm_flux[mask * redshift_mask * pz_mask * zbin_mask * telluric_mask * cgm_fit_gpm[iqso]]
norm_std = norm_std[mask * redshift_mask * pz_mask * zbin_mask * telluric_mask * cgm_fit_gpm[iqso]]
chi = (1 - norm_flux) / norm_std
corr_factor = mad_std(chi)
plt.hist(chi, bins=50, histtype='step', lw=2)#np.arange(-3, 3, 0.1), histtype='step', lw=2)