init

assemb_BT.py

+# -*- coding: utf-8 -*- 
+#this code assembles original BT-Settl SED into fits files.
+from __future__ import print_function
+import os, random, glob
+#import fortranformat as ff
+import time
+import scipy
+from scipy.ndimage.filters import gaussian_filter1d 
+#from scipy import integrate
+import sys
+#import subprocess
+import numpy as np
+import astropy
+from astropy.io import fits #ascii, fits
+from astropy.table import Table, Column
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_pdf import PdfPages
+from scipy.signal import savgol_filter
+
+from spectres import spectres
+
+import extinction
+
+#wave in Angstrom
+#flam in log
+#The original BT SED are computed with parallel computation (4 cpus). So the wave grids needs to be sorted.
+#Different SEDs may have different wave grids. Actually, it might be just that some of them are missing some wave points.
+#I may pre-define a wave grid, like the Kurucz one, and interpolate them onto this grid.
+
+
+Cspeed=2.997950e18 #A/s
+SB=5.66961e-5 #Stefan-Boltzmann constant in cgs
+Nmax=999 #fits maximumm column
+Check=True
+DF= -8.0
+
+def assemb_BT(wv_grid=None, lib_root_dir='/dev/null'):
+    print('By comparing Phoenix with Kurucz, the original Phoenix spectra should be normalized!')
+    NORM=True
+    BT_pm=['+0.5_a+0.0','+0.3_a+0.0','-0.0_a+0.0','-0.5_a+0.2','-1.0_a+0.4','-1.5_a+0.4','-2.0_a+0.4','-2.5_a+0.4','-3.0_a+0.4','-3.5_a+0.4','-4.0_a+0.4'] #['-0.0_a+0.0']
+
+    checkf=open('check.txt','w')
+    checkf.write("#BT Teffi loggi ratio\n")
+    #lib_root_dir='/data4/SED/work/stellar_spectra_lib/BT-Settl/AGSS2009'
+    #lib_root_dir='/home/chen/datashare/BT-Settl/AGSS2009'
+    #if (not os.path.isdir(lib_root_dir)): lib_root_dir=lib_root_dir.replace('/media/chen/','/run/media/cycy/')
+    dir_str='BT-Settl_MZZ_hot'
+    ext_str='lte*ZZ.BT-Settl.7.gz' #lte037-4.5-0.0a+0.0.BT-Settl.7.gz
+
+    wv0=[] #all spectra to be interpolated onto the same
+    if ((wv_grid != None) and ('Regular' not in wv_grid)):
+        wv0=open(wv_grid,'r').readlines()[1:]
+        wv0=' '.join(wv0).replace('\n','').split()
+        wv0=np.array([float(i) for i in wv0])
+        print('min&max of wv0:', min(wv0), max(wv0))
+        logwv0=np.log10(wv0)
+        
+    if ('Regular' in wv_grid):
+        wv_start,wv_end,wv_R,tmp = wv_grid.split(' ')
+        #logwv_start=np.log10(float(wv_start)); logwv_end=np.log10(float(wv_end)); wv_R=float(wv_R)
+        #logwv0=np.arange(logwv_start, logwv_end, 1./wv_R)
+        #if (logwv0[-1] < logwv_end): logwv0=np.append(logwv0, logwv0[-1]+1./wv_R)
+        #wv0=10.**logwv0
+        wv_start=float(wv_start); wv_end=float(wv_end); wv_R=float(wv_R)
+        wv0=np.array([wv_start])
+        while (wv0[-1] < wv_end): wv0=np.append(wv0, wv0[-1]+wv0[-1]/wv_R)
+        logwv0=np.log10(wv0)
+        
+        #extinction vector
+        Al0 = extinction.odonnell94(wv0, 1.0, 3.1)
+        hduo=fits.BinTableHDU.from_columns(
+            [fits.Column(name='wave', format='E', array=wv0), 
+             fits.Column(name='Al0', format='E', array=Al0)])
+        hduo.writeto('Ext_odonnell94_R3.1_CSST_R'+str(int(wv_R))+'.fits')
+        
+    for BT in BT_pm:
+        ori_Tls=[]; ori_Gls=[] #tls=[]; gls=[];
+        ori_cols=[]; fname=[]
+        final_cols=[]; final_Tls=[]; final_Gls=[]; final_fname=[]
+
+        outf='BT-Settl_M'+BT+'.fits'
+        glob_str=os.path.join(lib_root_dir, dir_str.replace('ZZ', BT), ext_str.replace('ZZ', BT).replace('_',''))
+        print(glob_str)
+        spefs=glob.glob(glob_str)
+        print('nspe:', len(spefs))
+        nn=0
+        #sort the names according to Teff and logg
+        spefs.sort( key = lambda spef: (float(os.path.split(spef)[-1][3:6]+'00'), -float(os.path.split(spef)[-1][6:10])) )
+
+        for spef in spefs:
+            tmp=os.path.split(spef)[-1]
+            t=tmp[3:6]+'00'
+            #if (int(t) > 22000): continue
+            g=tmp[6:10]
+            ori_Tls.append(int(t))
+            Teffi=float(t)
+            ori_Gls.append(-float(g)) #take care, '-' here
+            loggi=-float(g)
+            #fname.append(os.path.join(dir_str.replace('ZZ', BT), tmp))
+            fname.append(os.path.basename(spef))
+
+            nn=nn+1
+            #if (nn > 5): break
+            #print spef
+            spe=np.genfromtxt(spef,dtype=None,names=['w','flam'],invalid_raise=False,usecols=(0,1), encoding=None) #dtype must be set to None, otherwise nan output
+            
+            wv=np.array([float(xx.replace('D','E')) for xx in spe['w']]) #need to upgrade numpy: sudo easy_install --upgrade numpy
+            flam=np.array([float(xx.replace('D','E'))+DF for xx in spe['flam']])
+            #print wv[0], flam[0]
+            print('min&max Wv:', min(wv), max(wv))
+            inds = wv.argsort()
+            wv = wv[inds]
+            flam=flam[inds]
+
+            if (nn==1):
+                if (wv_grid == None and len(wv0) == 0):
+                    wv0=wv
+                    logwv0=np.log10(wv0)
+                #cols.append(fits.Column(name='wave', format='E', array=wv0, unit='A'))
+
+            #if (not (wv0 == wv).all()): #actually they have different number of wave points
+                #print 'different wv.'
+                #exit(1)
+
+            flam=10.**flam
+            flam[np.isneginf(flam)] = 0.
+            flam[np.isposinf(flam)] = 0.
+            flam[np.isnan(flam)] = 0.
+            if NORM==True:
+                flam=flam*SB*Teffi**4./np.trapz(flam,wv)
+            if Check:
+                ratio=np.trapz(flam,wv)/SB/Teffi**4. #Flux=pi*I_average
+                #if (abs(ratio-1)>0.01): 
+                checkf.write('{0:12s} {1:f} {2:f} {3:f}\n'.format(BT, Teffi, loggi, ratio))
+                
+            #interpolate onto the same grid if the wave grid not identical
+            if wv_grid!=None:
+                #interpolation conserving the energy
+                #wv_extend=wv0[wv0>max(wv)]
+                #if (len(wv_extend)>0):
+                #    wv=np.insert(wv, len(wv), wv_extend)
+                #    flam=np.insert(flam, len(flam), wv_extend*0)
+                flam=spectres(wv, flam, wv0)
+                
+            elif not np.array_equal(wv0, wv):
+                print("different wave grids")
+                flam=spectres(wv, flam, wv0)
+                
+            flam[np.isneginf(flam)] = 0.
+            flam[np.isposinf(flam)] = 0.
+            flam[np.isnan(flam)] = 0.
+            #flam[flam<0.] = 0.
+                
+            #ori_cols.append(fits.Column(name='f'+str(nn), format='E', array=flam, unit='erg/s/A'))
+            ori_cols.append(flam)
+            #if Check:
+            #    ratio=np.trapz(flam,wv0)/SB/Teffi**4. #Flux=pi*I_average
+            #    if (abs(ratio-1)>0.01): 
+            #        print 'spec, Teff, logg:', BT, Teffi, loggi
+            #        print 'fk/SB/T^4:', ratio
+
+            #ori_cols.append(np.zeros(len(wv0)))
+        #make regular grid
+        uniq_Tls=np.unique(ori_Tls)
+        uniq_Gls=np.unique(ori_Gls)
+        ori_Tls=np.array(ori_Tls)
+        ori_Gls=np.array(ori_Gls)
+        nnn=0
+        final_cols.append(fits.Column(name='wave', format='E', array=wv0, unit='A'))
+        for Ti in uniq_Tls:
+            ids_T=list(filter(lambda x: ori_Tls[x] == Ti, range(len(ori_Tls))))
+            for Gi in uniq_Gls:
+                nnn=nnn+1
+                ids=np.where(abs( abs(ori_Gls[ids_T]-Gi) - np.min(abs(ori_Gls[ids_T]-Gi))) < 1E-5)[0]
+                ids_TG=np.array(ids_T)[ids]
+                if (len(ids_TG) == 1):
+                    final_cols.append(fits.Column(name='f'+str(nnn), format='E', array=ori_cols[ids_TG[0]], unit='erg/s/A'))
+                    final_fname.append(fname[ids_TG[0]])
+                elif (len(ids_TG) == 2):
+                    final_cols.append(fits.Column(name='f'+str(nnn), format='E',
+                                                  array=(ori_cols[ids_TG[0]]+ori_cols[ids_TG[1]])/2., unit='erg/s/A'))
+                    final_fname.append(fname[ids_TG[0]]+'_'+fname[ids_TG[1]])
+                else:
+                    print("error! no model with Teff=",Ti)
+                    sys.exit()
+                         
+                final_Tls.append(Ti)
+                final_Gls.append(Gi)
+                #final_cols.append()
+                #final_fname.append()
+        
+
+        
+        col1=fits.Column(name='Teff', format='E', array=final_Tls, unit='K')
+        col2=fits.Column(name='logg', format='E', array=final_Gls)
+        col3=fits.Column(name='specf', format='80A', array=list(final_fname))
+        cols1 = fits.ColDefs([col1, col2, col3])
+        #tab1=fits.BinTableHDU.from_columns(cols1)
+        #hdu = fits.PrimaryHDU()
+        #thdulist = fits.HDUList([hdu, tab1])
+        #thdulist.writeto(outf)
+
+        tab1=fits.BinTableHDU.from_columns(cols1)
+        #tab2=fits.BinTableHDU.from_columns(cols)
+        tabs=[fits.BinTableHDU.from_columns(final_cols[i*Nmax:(i+1)*Nmax]) for i in range((len(final_cols)+Nmax-1)//Nmax) ]
+        hdu = fits.PrimaryHDU()
+        #thdulist = fits.HDUList([hdu, tab1, tab2])
+        thdulist = fits.HDUList([hdu, tab1]+tabs)
+
+        thdulist.writeto(outf)
+
+    checkf.close()
+
+
+#wv_grid="/media/chen/SED/N/stellar_spectra_lib/KURUCZ_related/castelli/Kurucz.wv"
+#if (not os.path.isfile(wv_grid)): wv_grid=wv_grid.replace('/media/chen/','/run/media/cycy/')                
+#assemb_BT(wv_grid=wv_grid)
+
+assemb_BT(wv_grid='1500. 12000. 1000. Regular', lib_root_dir='/home/chen/datashare/BT-Settl/AGSS2009')
+
+
+#python /media/chen/SED/work/lib/spectra_assemble/assemb_BT.py
+#python /run/media/cycy/SED/work/lib/spectra_assemble/assemb_BT.py
+
+
+
+

data/throughputs/CSST/toFitsTable.py

+'''
+Author: xin zhangxinbjfu@gmail.com
+Date: 2020-05-25 14:07:48
+LastEditors: xin zhangxinbjfu@gmail.com
+LastEditTime: 2022-06-07 12:45:22
+FilePath: /src/Users/zhangxin/Work/SlitlessSim/sed/produceSED_bycatfile/data/throughputs/CSST/toFitsTable.py
+Description: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE
+'''
+from astropy.table import Table
+from astropy.io import fits
+from pylab import *
+
+fil = ['nuv','u','g','r','i','z','y']
+
+for f in fil:
+
+    d = loadtxt(f+'_throughput.txt')
+
+
+    d[:,0] = d[:,0]
+
+    d[:,1] = d[:,1]
+
+    t= Table(d, names=('WAVELENGTH', 'SENSITIVITY'))
+
+    t.write(f+'.Throughput.fits', format='fits')
+
+
+# d[:,0] = d[:,0]
+
+# d[:,1] = d[:,1]*0.1
+
+# t= Table(d, names=('WAVELENGTH', 'SENSITIVITY'))
+
+# t.write('GI.Throughput.0st.fits', format='fits')
+
+
+# d = loadtxt('GU_1st.dat')