forked from gsnyder206/mock-surveys
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construct_mockfields.py
395 lines (290 loc) · 13.7 KB
/
construct_mockfields.py
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import os
import sys
import numpy as np
import glob
import gfs_sublink_utils as gsu
import shutil
import math
import astropy
import astropy.io.fits as pyfits
#import matplotlib
#import matplotlib.pyplot as plt
import scipy
import scipy.ndimage
#import make_color_image
import numpy.random as random
import congrid
import tarfile
import string
import astropy.io.ascii as ascii
from astropy.convolution import *
import copy
sq_arcsec_per_sr = 42545170296.0
c = 3.0e8
filters_to_analyze = np.asarray(['hst/acs_f435w','hst/acs_f606w','hst/acs_f775w','hst/acs_f850lp',
'hst/wfc3_f105w','hst/wfc3_f125w','hst/wfc3_f160w',
'jwst/nircam_f070w', 'jwst/nircam_f090w','jwst/nircam_f115w', 'jwst/nircam_f150w',
'jwst/nircam_f200w', 'jwst/nircam_f277w', 'jwst/nircam_f356w', 'jwst/nircam_f444w',
'hst/wfc3_f140w',
'hst/wfc3_f275w', 'hst/wfc3_f336w',
'hst/acs_f814w',
'jwst/miri_F560W','jwst/miri_F770W','jwst/miri_F1000W','jwst/miri_F1130W',
'jwst/miri_F1280W','jwst/miri_F1500W','jwst/miri_F1800W','jwst/miri_F2100W','jwst/miri_F2550W'])
psf_dir = os.path.expandvars('$GFS_PYTHON_CODE/vela-yt-sunrise/kernels')
psf_names = np.asarray(['TinyTim_IllustrisPSFs/F435W_rebin.fits','TinyTim_IllustrisPSFs/F606W_rebin.fits','TinyTim_IllustrisPSFs/F775W_rebin.fits','TinyTim_IllustrisPSFs/F850LP_rebin.fits',
'TinyTim_IllustrisPSFs/F105W_rebin.fits','TinyTim_IllustrisPSFs/F125W_rebin.fits','TinyTim_IllustrisPSFs/F160W_rebin.fits',
'WebbPSF_F070W_trunc.fits','WebbPSF_F090W_trunc.fits','WebbPSF_F115W_trunc.fits','WebbPSF_F150W_trunc.fits',
'WebbPSF_F200W_trunc.fits','WebbPSF_F277W_trunc.fits','WebbPSF_F356W_trunc.fits','WebbPSF_F444W_trunc.fits',
'TinyTim_IllustrisPSFs/F140W_rebin.fits','TinyTim_IllustrisPSFs/F275W_rebin.fits','TinyTim_IllustrisPSFs/F336W_rebin.fits','TinyTim_IllustrisPSFs/F814W_rebin.fits',
'WebbPSF_F560W_trunc.fits','WebbPSF_F770W_trunc.fits','WebbPSF_F1000W_trunc.fits','WebbPSF_F1130W_trunc.fits',
'WebbPSF_F1280W_trunc.fits','WebbPSF_F1500W_trunc.fits','WebbPSF_F1800W_trunc.fits','WebbPSF_F2100W_trunc.fits','WebbPSF_F2550W_trunc.fits'])
psf_pix_arcsec = np.asarray([0.03,0.03,0.03,0.03,0.06,0.06,0.06,0.0317,0.0317,0.0317,0.0317,0.0317,0.0648,0.0648,0.0648,0.06,0.03,0.03,0.03,0.11,0.11,0.11,0.11,0.11,0.11,0.11,0.11,0.11])
psf_fwhm = np.asarray([0.10,0.11,0.12,0.13,0.14,0.17,0.20,0.11,0.11,0.11,0.11,0.12,0.15,0.18,0.25,0.18,0.07,0.08,0.13,
0.035*5.61,0.035*7.57,0.035*9.90,0.035*11.30,0.035*12.75,0.035*14.96,0.035*17.90,0.035*20.65,0.035*25.11])
#photfnu units Jy; flux in 1 ct/s
photfnu_Jy = np.asarray([1.96e-7,9.17e-8,1.97e-7,4.14e-7,
1.13e-7,1.17e-7,1.52e-7,
5.09e-8,3.72e-8,3.17e-8,2.68e-8,2.64e-8,2.25e-8,2.57e-8,2.55e-8,
9.52e-8,8.08e-7,4.93e-7,1.52e-7,
5.75e-8,3.10e-8,4.21e-8,1.39e-7,
4.65e-8,4.48e-8,5.88e-8,4.98e-8,1.15e-7])
#construct real illustris lightcones from individual images
#in parallel, produce estimated Hydro-ART surveys based on matching algorithms -- high-res?
lcfile_cols={'col1':'snapshot',
'col2':'SubfindID',
'col3':'ra_deg',
'col4':'dec_deg',
'col5':'ra_kpc',
'col6':'dec_kpc',
'col7':'ra_kpc_inferred',
'col8':'dec_kpc_inferred',
'col9':'true_z',
'col10':'inferred_z',
'col11':'peculiar_z',
'col12':'true_kpc_per_arcsec',
'col13':'X_cmpc',
'col14':'Y_cmpc',
'col15':'Z_cmpc',
'col16':'ADD_cmpc',
'col17':'ADD_cmpc_inferred',
'col18':'snapshot_z',
'col19':'geometric_z',
'col20':'cylinder_number',
'col21':'mstar_msun_rad',
'col22':'mgas_msun_rad',
'col23':'subhalo_mass_msun',
'col24':'bhmass_msun_rad',
'col25':'mbary_msun_rad',
'col26':'sfr_msunperyr_rad',
'col27':'bhrate_code',
'col28':'camX_mpc',
'col29':'camY_mpc',
'col30':'camZ_mpc',
'col31':'g_AB_absmag',
'col32':'r_AB_absmag',
'col33':'i_AB_absmag',
'col34':'z_AB_absmag',
'col35':'v_kms_camX',
'col36':'v_kms_camY',
'col37':'v_kms_camZ',
'col38':'v_kms_hubble',
'col39':'g_AB_appmag'}
def process_single_filter(data,lcdata,filname,fil_index,output_dir,image_filelabel,image_suffix,eff_lambda_microns,lim=None,minz=None):
data=copy.copy(data)
print('Processing: ', filname)
full_npix=data['full_npix'][0]
pixsize_arcsec=data['pixsize_arcsec'][0]
n_galaxies=data['full_npix'].shape[0]
if filname.find('WFI')==0:
fn=filname[-4:]
filname='wfirst/wfidrm15_'+fn
try:
pbi= filters_to_analyze==filname
this_psf_file=os.path.join(psf_dir,psf_names[pbi][0])
this_psf_pixsize_arcsec=psf_pix_arcsec[pbi][0]
this_psf_fwhm=psf_fwhm[pbi][0]
this_photfnu_Jy=photfnu_Jy[pbi][0]
print('PSF info: ', this_psf_file, this_psf_pixsize_arcsec, this_psf_fwhm, this_photfnu_Jy)
do_psf=True
except:
print('Missing filter info, skipping PSF: ', filname)
do_psf=False
this_psf_pixsize_arcsec=pixsize_arcsec
#return None
desired_pixsize_arcsec=this_psf_pixsize_arcsec
full_fov=full_npix*pixsize_arcsec
desired_npix=full_fov/desired_pixsize_arcsec
print('Orig pix: ', full_npix, ' Desired pix: ', desired_npix)
if do_psf is True:
orig_psf_kernel = pyfits.open(this_psf_file)[0].data
#psf kernel shape must be odd for astropy.convolve??
if orig_psf_kernel.shape[0] % 2 == 0:
new_psf_shape = orig_psf_kernel.shape[0]-1
psf_kernel = congrid.congrid(orig_psf_kernel,(new_psf_shape,new_psf_shape))
else:
psf_kernel = orig_psf_kernel
assert( psf_kernel.shape[0] % 2 != 0)
image_cube = np.zeros((full_npix,full_npix),dtype=np.float64)
success=[]
mag=[]
#for bigger files, may need to split by filter first
index=np.arange(n_galaxies)
for pos_i,pos_j,origin_i,origin_j,run_dir,this_npix,this_z,num in zip(data['pos_i'],data['pos_j'],data['origin_i'],data['origin_j'],data['run_dir'],data['this_npix'],data['z'],index):
if lim is not None:
if num > lim:
success.append(False)
mag.append(99.0)
continue
if minz is not None:
if this_z < minz:
success.append(False)
mag.append(99.0)
continue
try:
bblist=pyfits.open(os.path.join(run_dir,'broadbandz.fits'))
this_cube = bblist['CAMERA0-BROADBAND-NONSCATTER'].data
this_mag = ((bblist['FILTERS'].data)['AB_mag_nonscatter0'])[fil_index]
bblist.close()
#if catalog and image have different npix, this is a failure somewhere
cube_npix=this_cube.shape[-1]
assert(cube_npix==this_npix)
success.append(True)
mag.append(this_mag)
except:
print('Missing file or mismatched shape, ', run_dir, this_npix)
success.append(False)
mag.append(99.0)
continue
i_tc=0
j_tc=0
i_tc1=this_npix
j_tc1=this_npix
if origin_i < 0:
i0=0
i_tc=-1*origin_i
else:
i0=origin_i
if origin_j < 0:
j0=0
j_tc=-1*origin_j
else:
j0=origin_j
if i0+this_npix > full_npix:
i1=full_npix
i_tc1= full_npix-i0 #this_npix - (i0+this_npix-full_npix)
else:
i1=i0+this_npix-i_tc
if j0+this_npix > full_npix:
j1=full_npix
j_tc1= full_npix-j0
else:
j1=j0+this_npix-j_tc
sub_cube1=image_cube[i0:i1,j0:j1]
this_subcube=this_cube[fil_index,i_tc:i_tc1,j_tc:j_tc1]
print(run_dir, this_subcube.shape)
image_cube[i0:i1,j0:j1] = sub_cube1 + this_subcube
#convolve here
#first, re-grid to desired scale
new_image=congrid.congrid(image_cube,(desired_npix,desired_npix))
new_i=data['pos_i']*desired_npix/full_npix
new_j=data['pos_j']*desired_npix/full_npix
pixel_Sr = (desired_pixsize_arcsec**2)/sq_arcsec_per_sr #pixel area in steradians: Sr/pixel
to_nJy_per_Sr = (1.0e9)*(1.0e14)*(eff_lambda_microns**2)/c #((pixscale/206265.0)^2)*
#sigma_nJy = 0.3*(2.0**(-0.5))*((1.0e9)*(3631.0/5.0)*10.0**(-0.4*self.maglim))*self.Pix_arcsec*(3.0*self.FWHM_arcsec)
to_nJy_per_pix = to_nJy_per_Sr*pixel_Sr
nopsf_im=new_image*to_nJy_per_pix
if do_psf is True:
conv_im = convolve_fft(new_image,psf_kernel,boundary='fill',fill_value=0.0,normalize_kernel=True,allow_huge=True)
final_im=conv_im*to_nJy_per_pix
outname=os.path.join(output_dir,image_filelabel+'_'+filname.replace('/','-')+'_'+image_suffix+'_v1_lightcone.fits')
print('saving:', outname)
primary_hdu=pyfits.PrimaryHDU(nopsf_im)
primary_hdu.header['FILTER']=filname.replace('/','-')
primary_hdu.header['PIXSIZE']=(desired_pixsize_arcsec,'arcsec')
primary_hdu.header['UNIT']=('nanoJanskies','per pixel')
abzp= - 2.5*(-9.0) + 2.5*np.log10(3631.0) #images in nanoJanskies
primary_hdu.header['ABZP']=(abzp, 'AB mag zeropoint')
if do_psf is True:
primary_hdu.header['PHOTFNU']=(this_photfnu_Jy,'Jy; approx flux[Jy] at 1 count/sec')
primary_hdu.header['EXTNAME']='IMAGE_NOPSF'
if do_psf is True:
psfim_hdu=pyfits.ImageHDU(final_im)
psfim_hdu.header['EXTNAME']='IMAGE_PSF'
psf_hdu = pyfits.ImageHDU(psf_kernel)
psf_hdu.header['EXTNAME']='MODELPSF'
psf_hdu.header['PIXSIZE']=(desired_pixsize_arcsec,'arcsec')
if np.sum(np.asarray(data.colnames)=='success')==0:
newcol=astropy.table.column.Column(data=success,name='success')
data.add_column(newcol)
if np.sum(np.asarray(data.colnames)=='new_i')==0:
newicol=astropy.table.column.Column(data=new_i,name='new_i')
newjcol=astropy.table.column.Column(data=new_j,name='new_j')
data.add_column(newicol)
data.add_column(newjcol)
magcol=astropy.table.column.Column(data=mag,name='AB_absmag_'+filname.replace('/','-'))
data.add_column(magcol)
data_df=data.to_pandas()
lc_df = lcdata.to_pandas()
lc_df.rename(columns=lcfile_cols,inplace=True)
assert(lc_df.shape[0]==data_df.shape[0])
new_df=lc_df.join(data_df)
failures = new_df.where(new_df['success']==False).dropna()
successes=new_df.drop(failures.index)
print('N successes: ', successes.shape[0])
new_data=astropy.table.Table.from_pandas(successes)
table_hdu = pyfits.table_to_hdu(new_data)
table_hdu.header['EXTNAME']='Catalog'
if do_psf is True:
output_list=pyfits.HDUList([primary_hdu,psfim_hdu,psf_hdu,table_hdu])
else:
output_list=pyfits.HDUList([primary_hdu,table_hdu])
tempfile=os.path.join(os.path.expandvars('/scratch/$USER/$SLURM_JOBID'),os.path.basename(outname))
print('saving to scratch first.. , ', tempfile)
output_list.writeto(tempfile,overwrite=True)
output_list.close()
shutil.copy(tempfile,output_dir)
return success
def build_lightcone_images(image_info_file,lightcone_file,run_type='images',lim=None,minz=None,image_filelabel='hlsp_misty_illustris',jwst_only=False):
data=ascii.read(image_info_file)
print(data)
#get expected shape
test_file=os.path.join(data['run_dir'][0],'broadbandz.fits')
tfo =pyfits.open(test_file)
print(tfo.info())
try:
cube=tfo['CAMERA0-BROADBAND-NONSCATTER'].data
cubeshape=cube.shape
except:
square=tfo['CAMERA0-PARAMETERS'].data
nf=tfo['FILTERS'].data.shape[0]
cubeshape=(nf,square.shape[0],square.shape[0])
print(cubeshape)
auxcube=tfo['CAMERA0-AUX'].data
filters_hdu = tfo['FILTERS']
lightcone_dir=os.path.abspath(os.path.dirname(image_info_file))
print('Constructing lightcone data from: ', lightcone_dir)
output_dir = os.path.join(lightcone_dir,os.path.basename(image_info_file).rstrip('.txt'))
print('Saving lightcone outputs in: ', output_dir)
if not os.path.lexists(output_dir):
os.mkdir(output_dir)
image_suffix=os.path.basename(image_info_file).rstrip('_images.txt')
success_catalog=os.path.join(output_dir,os.path.basename(image_info_file).rstrip('.txt')+'_success.txt')
N_filters = cubeshape[0]
#N_aux=auxcube.shape[0]
#aux_cube = np.zeros((N_aux,full_npix,full_npix),dtype=np.float64)
lcdata=ascii.read(lightcone_file)
filters_data=filters_hdu.data
print(filters_data.columns)
lambda_eff_microns = filters_data['lambda_eff']*1.0e6
for i,filname in enumerate(filters_data['filter']):
if jwst_only is True:
if filname.find('jwst')==-1:
continue
success=process_single_filter(data,lcdata,filname,i,output_dir,image_filelabel,image_suffix,lambda_eff_microns[i],lim=lim,minz=minz)
if i==0:
success=np.asarray(success)
#newcol=astropy.table.column.Column(data=success,name='success')
#data.add_column(newcol)
ascii.write(data,output=success_catalog,overwrite=True)
#convert units before saving.. or save both?
return