def n_merge(z1, z2, r_list, m_sat, m_cen):
t_between = WMAP9.lookback_time(z1).value - WMAP9.lookback_time(z2).value
# t_merge_list = merge_est_k(r_list, m_cen, z1)
t_merge_list = merge_est_j(r_list, m_sat, m_cen)
# merge
m_sat = m_sat[t_merge_list < t_between]
m_merge = sum(10**(m_sat - 8))
n_merge = sum(np.ones(len(r_list)) * t_merge_list / t_between)
# alternatively
# n_merge = r_list[t_merge_list<t_between]
return n_merge, m_merge
def cosmoLookBack(redshift,
WMAP9=False,
H0=70.0,
Om0=0.30,
Planck15=False,
Myr=False):
"""
Get the Look-back Time at redshift=z.
This is simply a wrapper of astropy.cosmology
The input redsfhit can be an array
"""
if WMAP9:
from astropy.cosmology import WMAP9 as cosmo
elif Planck15:
from astropy.cosmology import Planck15 as cosmo
else:
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=H0, Om0=Om0)
lbt = cosmo.lookback_time(redshift)
if not Myr:
return lbt.value
else:
return lbt.to(u.Myr).value
def cosmoLookBack(redshift,
WMAP9=False,
H0=70.0,
Om0=0.30,
Planck15=False,
Myr=False):
"""
Get the Look-back Time at redshift=z.
This is simply a wrapper of astropy.cosmology
The input redsfhit can be an array
"""
if WMAP9:
from astropy.cosmology import WMAP9 as cosmo
elif Planck15:
from astropy.cosmology import Planck15 as cosmo
else:
from astropy.cosmology import FlatLambdaCDM
cosmo = FlatLambdaCDM(H0=H0, Om0=Om0)
lbt = cosmo.lookback_time(redshift)
if not Myr:
return lbt.value
else:
return lbt.to(u.Myr).value
def make_scattered_galaxy(filename, **kwargs):
print "attempting to read in ", filename
input_galaxy = ascii.read(filename)
galaxy = initialize_galaxy(len(input_galaxy))
dt = 1.e7
print "assuming timestep is 1.e7 years!!!!!"
galaxy['dt'] += 1.e7*u.yr
galaxy['sfr'] = np.power(10.0,input_galaxy['SFR']) * u.Msun/u.yr
galaxy['dMrdt'] = np.power(10.0,input_galaxy['Mrec'])* u.Msun / galaxy['dt']
# galaxy['dMrdt'].unit = u.Msun / u.yr
## Need to get gas masses from Gergo's code
fake_radius = 3000. + np.zeros(len(galaxy['sfr']))
gas = ig.gas_masses(galaxy['sfr'], input_galaxy['Ms'], fake_radius)
Mgas, Mhi, Mh2 = gas[0], gas[1], gas[2]
galaxy['Mg'] = Mgas * HELIUM_CORR * u.Msun
galaxy['Mhi'] = Mhi * u.Msun
galaxy['Mh2'] = Mh2 * u.Msun
galaxy['Ms'] = np.power(10.0,input_galaxy['Ms']) * u.Msun
col_dummy = Column(name='z',data=input_galaxy['z'])
galaxy.add_column(col_dummy)
print "ASSUMING PROBABLY A WRONG COSMOLOGY FYI"
age = cosmo.age(galaxy['z']) ### THIS COSMOLOGY IS WRONG
galaxy['age'] = age
tlb = cosmo.lookback_time(galaxy['z'])
galaxy['lookback'] = tlb
galaxy.remove_column('Mh')
galaxy.remove_column('Mcgm')
galaxy.remove_column('tlogoh')
## check to make sure sorted so earlier is earlier
galaxy.sort('age')
print galaxy
return galaxy
def read_in_galaxy(filename, index):
print "attempting to read in",filename
if ".pkl" in filename:
print "assuming that",filename,"is a pickle file with only a dict of galaxies in it"
galaxies = pickle.load(open(filename,"r"))
print "for now we're only dealing with one galaxy at a time; taking galaxy #",index
assert(index in galaxies.keys()), "index %i not in file :-(" % index
galaxy = galaxies[index]
col_z = Column(name='z',data=galaxies['z'])
galaxy.add_column(col_z,0)
elif "peter" in filename:
print "assuming that",filename,"is an ascii file from peter behroozi and gergo popping with Mh,Ms,dMhdt,sfr,Mg,Mhi,Mh2"
print "also assuming that",filename,"only has one iteration"
z,Mh,Ms,dMhdt,sfr,Mg,Mhi,Mh2 = np.loadtxt(filename,usecols=(0,7*index+1,7*index+2,7*index+3,7*index+4,7*index+5,7*index+6,7*index+7),unpack=True)
galaxy = QTable([z,Mh,Ms,dMhdt,sfr,Mg,Mhi,Mh2],names=('z','Mh','Ms','dMhdt','sfr','Mg','Mhi','Mh2'))
## need to add units
galaxy['Mh'].unit = u.Msun
galaxy['Ms'].unit = u.Msun
galaxy['Mg'].unit = u.Msun
galaxy['Mhi'].unit = u.Msun
galaxy['Mh2'].unit = u.Msun
galaxy['dMhdt'].unit = u.Msun / u.yr
if 'dMrdt' in galaxy.colnames:
galaxy['dMrdt'].unit = u.Msun / u.yr
galaxy['sfr'].unit = u.Msun / u.yr
### Helium is not included in the gas masses!!
galaxy['Mg'] = galaxy['Mg'] * HELIUM_CORR
### Let's make a CGM! ###
galaxy['Mcgm'] = galaxy['Mh']*cosmo.Ob0/cosmo.Om0 - galaxy['Ms'] - galaxy['Mg']
age = cosmo.age(galaxy['z'])
col_age = Column(name='age', data=age)
galaxy.add_column(col_age,1)
tlb = cosmo.lookback_time(galaxy['z'])
col_tlb = Column(name='lookback', data=tlb)
galaxy.add_column(col_tlb,2)
## check to make sure sorted so earlier is earlier
galaxy.sort('age')
return galaxy
chival = np.sum((obj_fluxes/obj_fluxerrs - const*th_fluxes/obj_fluxerrs)**2)
print chival
raw_input()
if chival < output[m,7]:
output[m,:] = np.array([m+1, all_obj_specz[m], z, ages[j], float(tauvals[l]), EBV, const, chival])
print "Age: " + str(ages[j]) + ", tau: " + tauvals[l] + ", redshift: " + str(z)
np.savetxt("photoz_grid84876.txt", output, header="obj_no spec_z phot_z age tau EBV norm chi")
"""
#the chi squared array is now 2D over EBV and the different objects
for j in range(128):
for l in range(3):
th_mag_array = np.loadtxt("synmags_T" + tauvals[l] + "/synmags/synmags_age_" + str(ages[j]) + ".txt", usecols=(1,2,3,4,5,6,7,8,9,10,11,12))
for i in range(1, 501):
z = 0.01*i
th_mags = th_mag_array[i-1,:]
if ages[j]*(10**-9) < 14.00 - WMAP9.lookback_time(z).value:
for k in range(41):
EBV = 0.05*k
th_fluxes = 10**((23.9 - EBV*coef - th_mags)/2.5) #microjanskys
const = np.sum(fulldet_obj_fluxes*th_fluxes/fulldet_obj_fluxerrs**2, axis=1)/np.sum(th_fluxes**2/fulldet_obj_fluxerrs**2, axis=1)
chival = np.sum((fulldet_obj_fluxes/fulldet_obj_fluxerrs - (const*(th_fluxes/fulldet_obj_fluxerrs).T).T)**2, axis=1)
for m in range(len(fulldet_obj_fluxes)):
if chival[m] < output[m,7]:
output[m,:] = np.array([m+1, all_obj_specz[m], z, ages[j], float(tauvals[l]), EBV, const[m], chival[m]])
print "Age: " + str(ages[j]) + ", tau: " + tauvals[l] + ", redshift: " + str(z)
np.savetxt("photoz_grid86548.txt", output, header="obj_no spec_z phot_z age tau EBV norm chi")
offset_errs[0, i, 0] = 0.
all_obj_fluxes = all_obj_fluxes + offsets
all_obj_fluxerrs = np.sqrt(all_obj_fluxerrs**2 + offset_errs**2)
### Build array for photometric redshift and parameter outputs
output = np.zeros(len(all_obj_fluxes)*8, dtype="float")
output.shape = (len(all_obj_fluxes), 8)
output[:,7] = 9999999999.
### Perform model fitting using a 2D chi squared array over object and redshift with max age of stellar pop physically determined
tauvals = np.array([0.05, 1, 10], dtype="str")
zarr = np.arange(0.01, 5.001, 0.01)
lbtarr = WMAP9.lookback_time(zarr).value
EBVarr = np.expand_dims(np.expand_dims(np.expand_dims(np.arange(0, 1.50001, 0.025), axis=0), axis=0), axis=0)
for j in range(minage, maxage+1):
arg = 0
while ages[j]*(10**-9) < 14.0 - lbtarr[arg] and arg < 499:
arg = arg+1
for l in range(3):
print "Age: " + str(ages[j]) + ", fitting to redshift " + str((arg+1)*0.01) + ", with tau: " + tauvals[l]
th_mag_array = np.expand_dims(np.expand_dims(np.loadtxt("models/" + tauvals[l] + "/synmags_age_" + str(ages[j]) + ".txt", usecols=(1,2,3,4,5,6,7,8,9,10,11,12))[0:arg, :].T, axis=0), axis=3)
print "th_mag_array computed"
print th_mag_array.nbytes
th_flux_array = (10**((23.9 - EBVarr*coef - th_mag_array)/2.5)) #microjanskys
print "th_flux_array computed"
print th_flux_array.nbytes
const = np.expand_dims(np.sum(all_obj_fluxes*th_flux_array/all_obj_fluxerrs**2, axis=1)/np.sum(th_flux_array**2/all_obj_fluxerrs**2, axis=1), axis=1)
PROTONMASS = 1.67262178e-27
#Thomson constant
THOMPSON = 6.65245873e-29
#1e8 Solar units
E8MSUN = 1.989e38
#Seconds per year
SECPERYEAR = 3.15569e7
#Solar mass
SOLARMASS = 1.989e30
#Standard units
unitsstd = {'M': 1.989e40, 'L': 3.08568e19, 'T': 3.15569e16, 'V': 1000.}
#Creating time for cosmological setups
atmp = np.arange(0.001, 1.02, 0.001)
t_tmp = WMAP9.lookback_time(1.0 / atmp[0] -
1) - WMAP9.lookback_time(1.0 / atmp - 1)
def physical_time(a):
time = np.interp(a, atmp, t_tmp)
return time
#========================================================================================
# FUNCTIONS
#========================================================================================
#RK4 integrator
def RK4_step(f, r, v, t, dt):
#Creating solutions
K0r, K0v = f(r, v, t)
K1r, K1v = f(r + 0.5 * dt * K0r, v + 0.5 * dt * K0v, t + 0.5 * dt)
cat_neighbors['MASS_BEST']
): # exclude central gals which has larger mass companion
continue
# cat_neighbors = cat_neighbors[cat_neighbors['MASS_BEST']>8]
coord_neighbors = SkyCoord(cat_neighbors['RA'] * u.deg,
cat_neighbors['DEC'] * u.deg)
radius_list = coord_neighbors.separation(
coord_gal).degree / 180. * np.pi * dis * 1000 # in kpc
mass_list = cat_neighbors['MASS_BEST']
# t_merge_list = merge_est_k(radius_list, mass_list, z)
t_merge_list = merge_est_j(radius_list, mass_list, gal['MASS_BEST'])
# select companions that merge with central in the next redshift bin
t_merge_h = WMAP9.lookback_time(
gal['ZPHOT']).value - WMAP9.lookback_time(z - 0.2).value
t_merge_l = WMAP9.lookback_time(
gal['ZPHOT']).value - WMAP9.lookback_time(z).value
mass_grow_list = mass_list[np.absolute(t_merge_list -
(t_merge_h + t_merge_l) / 2.) <
(t_merge_h - t_merge_l) / 2.]
mass_growth_bkg = bkg(gal['RA'], cat_all_z_slice, t_merge_h, t_merge_l,
z, gal['MASS_BEST'])
mass_growth_gal.append(
sum(10**(mass_grow_list - 10)) - mass_growth_bkg)
print(len(mass_grow_list), len(mass_list),
sum(10**(mass_grow_list - 10)), mass_growth_bkg)
mass_growth_z_median = np.median(mass_growth_gal)
filter_bounds, filter_widths = make_bins(filter[:,0], make_rhs="True")
top = np.zeros(len(spec_widths)*len(filter_widths))
bottom = np.copy(top)
top = (np.expand_dims(filter_bounds[1:], axis=1) - np.expand_dims(spec_bounds[:-1], axis=0))/np.expand_dims(filter_widths, axis=1)
bottom = (np.expand_dims(spec_bounds[1:], axis=0) - np.expand_dims(filter_bounds[:-1], axis=1))/np.expand_dims(filter_widths, axis=1)
part1 = np.logical_and(top>=1, bottom>=1).astype("float")
part2 = np.logical_and(np.logical_and(0<top, top<1), bottom>=1).astype("float")
part3 = np.logical_and(np.logical_and(0<bottom, bottom<1), top>=1).astype("float")
part4 = np.logical_and(np.logical_and(0<top, top<1), np.logical_and(0<bottom, bottom<1)).astype("float")
crossover = part1 + top*part2 + bottom*part3 + (top+bottom-1)*part4
flux_total = np.sum(np.sum(crossover*np.expand_dims(filter[:,1], axis=1)*np.expand_dims(spec[:,1], axis=0)*np.expand_dims(filter_widths, axis=1), axis=1), axis=0)
return flux_total
zarr = np.arange(0.01, 7.001, 0.01)
lbtarr = cosmo.lookback_time(zarr).value
D = np.loadtxt("../IGM_Da_Db.txt")
### Load up the data necessary to build the "filter" to rebin the model spectra onto
hdulist1 = fits.open("../../VANDELS_data/spectra/sc_206806_UDS_P1M1_MR_Q1_029_1.fits")
wavzpt = hdulist1[0].header["CRVAL1"]
dwav = hdulist1[0].header["CDELT1"]
fluxes1 = hdulist1[4].data
maxwav = wavzpt + dwav*(len(fluxes1))
objwavs_nobin = np.arange(wavzpt, maxwav, dwav)
objwavs = specbin(objwavs_nobin, 2)
filter_bounds, filter_widths = make_bins(filter[:,0], make_rhs="True")
top = np.zeros(len(spec_widths)*len(filter_widths))
bottom = np.copy(top)
top = (np.expand_dims(filter_bounds[1:], axis=1) - np.expand_dims(spec_bounds[:-1], axis=0))/np.expand_dims(filter_widths, axis=1)
bottom = (np.expand_dims(spec_bounds[1:], axis=0) - np.expand_dims(filter_bounds[:-1], axis=1))/np.expand_dims(filter_widths, axis=1)
part1 = np.logical_and(top>=1, bottom>=1).astype("float")
part2 = np.logical_and(np.logical_and(0<top, top<1), bottom>=1).astype("float")
part3 = np.logical_and(np.logical_and(0<bottom, bottom<1), top>=1).astype("float")
part4 = np.logical_and(np.logical_and(0<top, top<1), np.logical_and(0<bottom, bottom<1)).astype("float")
crossover = part1 + top*part2 + bottom*part3 + (top+bottom-1)*part4
flux_total = np.sum(np.sum(crossover*np.expand_dims(filter[:,1], axis=1)*np.expand_dims(spec[:,1], axis=0)*np.expand_dims(filter_widths, axis=1), axis=1), axis=0)
return flux_total
zarr = np.arange(0.01, 5.001, 0.01)
lbtarr = cosmo.lookback_time(zarr).value
### Load up the data necessary to build the "filter" to rebin the model spectra onto
hdulist1 = fits.open("../VANDELS_data/spectra/sc_206806_UDS_P1M1_MR_Q1_029_1.fits")
wavzpt = hdulist1[0].header["CRVAL1"]
dwav = hdulist1[0].header["CDELT1"]
fluxes1 = hdulist1[4].data#*10**19
print wavzpt
print dwav
raw_input()
maxwav = wavzpt + dwav*(len(fluxes1))
objwavs_nobin = np.arange(wavzpt, maxwav, dwav)
tauvals = np.array([0.05, 1, 10], dtype="str")
