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%matplotlib osx
import pyfits
import numpy as np
import matplotlib.pyplot as plt
import scipy
from astropy.cosmology import Planck15 as cosmo


    

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def comov_volume(omega_sample, zmin, zmax):
    "Compute the comoving volume between two redshifts in a solid angle."
    V = omega_sample/41253*(cosmo.comoving_volume(zmax)-cosmo.comoving_volume(
        zmin))
    return V


    

In [3]:

    

"""Load Jean's Catalog"""
hdulist = pyfits.open('../Data/COSMOS2015_clustering_v2.0_clean.fits')
tdata = hdulist[1].data
hdulist.close()

tdata = tdata[tdata['photo_z']<99]
tdata = tdata[tdata['clean']>0]
tdata = tdata[tdata['mstar_cosmo']>7.2]


    

In [6]:

    

#Redshift selection
zmin = 0.3
zmax=0.7
zbin = tdata[tdata['photo_z']>zmin]
zbin = zbin[zbin['photo_z']<zmax]

n = 100 #number of mass bins for our graph
mmin = zbin['mstar_cosmo'].min()
mmax = zbin['mstar_cosmo'].max()
step = (mmax-mmin)/n #resolution

omega_sample = 1.2
V = comov_volume(omega_sample, zmin, zmax)

zmoy = np.average(zbin['photo_z']) #We take the average z to compute h
h = cosmo.H(zmoy)/(100*cosmo.H0.unit)
V_corr = V*h*h*h
V_corr = V_corr.value


    

    




0.50943
0.8970835390062818
1343920.5112229285

In [7]:

    

N = np.empty(n)
for i in range(n):
    "Compute the number of galaxies more massive than m for each mass bin"
    N[i] = np.sum(zbin['mstar_cosmo']>(mmin+step*i)) 
N = N / (V*h*h*h)


    

In [9]:

    

fig, ax = plt.subplots()
ax.plot(np.linspace(mmin, mmax, num=n)*h,N)
ax.set_title('Abundance for Jean\'s catalog')
ax.set_ylabel('N(>M*), $h^{3}.Mpc^{-3}$', size=14)
ax.set_xlabel('Mass, $log(M_{\odot}/h)$', size=14)


    

    
Out[9]:





<matplotlib.text.Text at 0x184dcce80>

In [51]:

    

plt.close()


    

In [62]:

    

a = 1/(1+zmoy)
print('Le redshift moyen est '+str(zmoy)+', le facteur d\'échelle est donc de '+str(a))


    

    




Le redshift moyen est 0.50943, le facteur d'échelle est donc de 0.662501873715

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