Пример #1
0
def clus_avg(data_loc,halo_file,chris_data_root,newfilename,write_data=True,clobber=True):

	C4 = CFOUR({'H0':70,'chris_data_root':chris_data_root})
	C = Caustic()

	# Load Halos
        halos = fits.open(data_loc+'/'+halo_file)[1].data
        HaloID = halos['orig_order']
        RA = halos['ra_bcg']
        DEC = halos['dec_bcg']
        Z = halos['z_biwt']
        RVIR = halos['RVIR']
        SINGLE = halos['single']
        SUB = halos['sub']
        NC4 = halos['nc4']

	RA_AVG,DEC_AVG,Z_AVG = [],[],[]

	# Loop Over Halos
	print ''
	print '-'*40
	print '...running average cluster center code'
	for i in range(len(halos)):
		if i % 100 == 0: print '...working on cluster '+str(i)+' out of '+str(len(halos))
		try:
			# Assign Halo Properties
			clus_ra = RA[i]
			clus_dec = DEC[i]
			clus_z = Z[i]

			# Load Galaxies
			galdata = C4.load_chris_gals(HaloID[i])
			gal_ra,gal_dec,gal_z,gal_gmags,gal_rmags,gal_imags = galdata

			# Take Iterative Average, four times
			# vlim = 1500, rlim = 1.5
			clus_ra,clus_dec,clus_z = proj_avg(clus_ra,clus_dec,clus_z,gal_ra,gal_dec,gal_z,1500,1.5,C)
			# vlim = 1000, rlim = 1.5
			clus_ra,clus_dec,clus_z = proj_avg(clus_ra,clus_dec,clus_z,gal_ra,gal_dec,gal_z,1000,1.5,C)
			# vlim = 1500, rlim = 1.5
			clus_ra,clus_dec,clus_z = proj_avg(clus_ra,clus_dec,clus_z,gal_ra,gal_dec,gal_z,1000,1.5,C)
			# vlim = 2000, rlim = 1.5
			clus_ra,clus_dec,clus_z = proj_avg(clus_ra,clus_dec,clus_z,gal_ra,gal_dec,gal_z,2000,1.5,C)

		except:
			print i
			clus_ra,clus_dec,clus_z = 0, 0, 0

		RA_AVG.append(clus_ra)
		DEC_AVG.append(clus_dec)
		Z_AVG.append(clus_z)

	RA_AVG,DEC_AVG,Z_AVG = np.array(RA_AVG),np.array(DEC_AVG),np.array(Z_AVG)

	print '...finished average cluster-center calculations'

	## Write Data Out
	if write_data == True:
		print '...writing out cluster catalgoue with average centers included'
		# Dictionary of new columns
		new_keys = ['RA_AVG','DEC_AVG','Z_AVG']
		new_dic = ez.create(new_keys,locals())

		# Original fits record file
		orig_table = halos

		# Write own fits file
		keys = ['HaloID','RA','DEC','Z','RVIR','RA_AVG','DEC_AVG','Z_AVG']
		dic = ez.create(keys,locals())
		fits_table(dic,keys,data_loc+'/avg_centers.fits',clobber=True)

		# Append new columns
		fits_append(orig_table,new_dic,new_keys,filename=data_loc+'/'+newfilename,clobber=clobber)
		print '-'*40
		print ''
Пример #2
0
        SINGLE = halos['single']
        SUB = halos['sub']
        NC4 = halos['nc4']

	RA_AVG,DEC_AVG,Z_AVG = [],[],[]
	# Loop Over Halos
	for i in range(len(halos)):
		if i % 100 == 0: print i

		# Assign Halo Properties
		clus_ra = RA[i]
		clus_dec = DEC[i]
		clus_z = Z[i]

		# Load Galaxies
		galdata = C4.load_chris_gals(HaloID[i])	
		gal_ra,gal_dec,gal_z,gal_gmags,gal_rmags,gal_imags = galdata

		# Project Galaxies
		ang_d,lum_d = C.zdistance(clus_z,H0)
		angles = C.findangle(gal_ra,gal_dec,clus_ra,clus_dec)
		rdata = angles * ang_d
		vdata = c * (gal_z - clus_z) / (1 + clus_z)

		# Take Average Three times
		cut1 = np.where((np.abs(vdata)<1000)&(rdata<1.5))[0]
		clus_ra = astats.biweight_location(gal_ra[cut1])
		clus_dec = astats.biweight_location(gal_dec[cut1])
		clus_z = astats.biweight_location(gal_z[cut1])

		ang_d,lum_d = C.zdistance(clus_z,H0)
Пример #3
0
def clus_avg(data_loc,
             halo_file,
             chris_data_root,
             newfilename,
             write_data=True,
             clobber=True):

    C4 = CFOUR({'H0': 70, 'chris_data_root': chris_data_root})
    C = Caustic()

    # Load Halos
    halos = fits.open(data_loc + '/' + halo_file)[1].data
    HaloID = halos['orig_order']
    RA = halos['ra_bcg']
    DEC = halos['dec_bcg']
    Z = halos['z_biwt']
    RVIR = halos['RVIR']
    SINGLE = halos['single']
    SUB = halos['sub']
    NC4 = halos['nc4']

    RA_AVG, DEC_AVG, Z_AVG = [], [], []

    # Loop Over Halos
    print ''
    print '-' * 40
    print '...running average cluster center code'
    for i in range(len(halos)):
        if i % 100 == 0:
            print '...working on cluster ' + str(i) + ' out of ' + str(
                len(halos))
        try:
            # Assign Halo Properties
            clus_ra = RA[i]
            clus_dec = DEC[i]
            clus_z = Z[i]

            # Load Galaxies
            galdata = C4.load_chris_gals(HaloID[i])
            gal_ra, gal_dec, gal_z, gal_gmags, gal_rmags, gal_imags = galdata

            # Take Iterative Average, four times
            # vlim = 1500, rlim = 1.5
            clus_ra, clus_dec, clus_z = proj_avg(clus_ra, clus_dec, clus_z,
                                                 gal_ra, gal_dec, gal_z, 1500,
                                                 1.5, C)
            # vlim = 1000, rlim = 1.5
            clus_ra, clus_dec, clus_z = proj_avg(clus_ra, clus_dec, clus_z,
                                                 gal_ra, gal_dec, gal_z, 1000,
                                                 1.5, C)
            # vlim = 1500, rlim = 1.5
            clus_ra, clus_dec, clus_z = proj_avg(clus_ra, clus_dec, clus_z,
                                                 gal_ra, gal_dec, gal_z, 1000,
                                                 1.5, C)
            # vlim = 2000, rlim = 1.5
            clus_ra, clus_dec, clus_z = proj_avg(clus_ra, clus_dec, clus_z,
                                                 gal_ra, gal_dec, gal_z, 2000,
                                                 1.5, C)

        except:
            print i
            clus_ra, clus_dec, clus_z = 0, 0, 0

        RA_AVG.append(clus_ra)
        DEC_AVG.append(clus_dec)
        Z_AVG.append(clus_z)

    RA_AVG, DEC_AVG, Z_AVG = np.array(RA_AVG), np.array(DEC_AVG), np.array(
        Z_AVG)

    print '...finished average cluster-center calculations'

    ## Write Data Out
    if write_data == True:
        print '...writing out cluster catalgoue with average centers included'
        # Dictionary of new columns
        new_keys = ['RA_AVG', 'DEC_AVG', 'Z_AVG']
        new_dic = ez.create(new_keys, locals())

        # Original fits record file
        orig_table = halos

        # Write own fits file
        keys = [
            'HaloID', 'RA', 'DEC', 'Z', 'RVIR', 'RA_AVG', 'DEC_AVG', 'Z_AVG'
        ]
        dic = ez.create(keys, locals())
        fits_table(dic, keys, data_loc + '/avg_centers.fits', clobber=True)

        # Append new columns
        fits_append(orig_table,
                    new_dic,
                    new_keys,
                    filename=data_loc + '/' + newfilename,
                    clobber=clobber)
        print '-' * 40
        print ''