def main():
filepath_mock = cu.get_output_path() + 'processed_data/campbell_mocks/'
catalogues = ['sm_9.5_s0.0_sfr_c-1.0_250','sm_9.5_s0.1_sfr_c-1.0_250',\
'sm_9.5_s0.2_sfr_c-1.0_250','sm_9.5_s0.3_sfr_c-1.0_250']
sigmas = [0.0,0.1,0.2,0.3]
fig = plt.figure(figsize=(3.3,3.3))
fig.subplots_adjust(left=0.2, right=0.85, bottom=0.2, top=0.9)
plt.xlabel(r'$r~(h^{-1}\rm Mpc)$')
plt.ylabel(r'$f_{\rm quenched}$')
plt.title(r'$10.0<\log(M_{*, \rm host}/M_{\odot}h^{-2})<10.5$')
plt.ylim([0,1])
plt.xlim([0.1,10])
plt.xscale('log')
a = [1.0,0.8,0.6,0.4,0.2]
#red_colors=['#FF0000','#FF2900','#FF5C00','#FF8F00','#FFCC00']
#blue_colors=['#0000FF','#0040FF','#0080FF','#00BFFF','#00FFFF']
p1 = [None]*5
p2 = [None]*5
for i, catalogue in enumerate(catalogues):
#open catalogue
print(i,catalogue)
f = h5py.File(filepath_mock+catalogue+'.hdf5', 'r') #open catalogue file
mock = f.get(catalogue)
Lbox = np.array([250.0,250.0,250.0])
#define centrals and satellites
host = (mock['upid']==-1)
sub = (mock['upid']!=-1)
#define quenched and star-forming
LHS = -11.0
blue = (mock['SSFR']>LHS) #indices of blue galaxies
red = (mock['SSFR']<LHS) #indicies of red galaxies
#define samples
red_host = host & red
blue_host = host & blue
#host mass selection
bin = (mock['Mstar']>10.0) & (mock['Mstar']<10.5)
red_host = red_host & bin
blue_host = blue_host & bin
#count pairs
rbins = np.linspace(-1,1,20)
rbin_centers = (rbins[1:]+rbins[:-1])/2.0
rbins = 10**rbins
data1 = np.vstack((mock['x'], mock['y'], mock['z'])).T[red_host]
data2 = np.vstack((mock['x'], mock['y'], mock['z'])).T[red]
data3 = np.vstack((mock['x'], mock['y'], mock['z'])).T[blue]
print(len(data1), len(data2), len(data3))
red_pairs = rect_cuboid_pairs.npairs(data1, data2, rbins, period=Lbox)
red_pairs = np.diff(red_pairs)
blue_pairs = rect_cuboid_pairs.npairs(data1, data3, rbins, period=Lbox)
blue_pairs = np.diff(blue_pairs)
all_pairs = red_pairs+blue_pairs
red_quenched_frac = red_pairs/all_pairs
data1 = np.vstack((mock['x'], mock['y'], mock['z'])).T[blue_host]
data2 = np.vstack((mock['x'], mock['y'], mock['z'])).T[red]
data3 = np.vstack((mock['x'], mock['y'], mock['z'])).T[blue]
print(len(data1), len(data2), len(data3))
red_pairs = rect_cuboid_pairs.npairs(data1, data2, rbins, period=Lbox)
red_pairs = np.diff(red_pairs)
blue_pairs = rect_cuboid_pairs.npairs(data1, data3, rbins, period=Lbox)
blue_pairs = np.diff(blue_pairs)
all_pairs = red_pairs+blue_pairs
blue_quenched_frac = red_pairs/all_pairs
p1[i], = plt.plot(10**rbin_centers,red_quenched_frac, color='red', alpha=a[i], lw=1)
p2[i], =plt.plot(10**rbin_centers,blue_quenched_frac, color='blue', alpha=a[i], lw=1)
p1 = tuple(p1)
p2 = tuple(p2)
plt.legend(p1, (r"$\sigma=0.0$",r"$\sigma=0.1$",r"$\sigma=0.2$",r"$\sigma=0.3$"),\
loc=4, fontsize=10, frameon=False, labelspacing=0.01, title='quenched centrals')
fig.savefig('/Users/duncan/Desktop/conformity.pdf')
plt.show()
savepath = cu.get_plot_path()+'/analysis/central_quenching/'
filename = 'conformity_rho_effect'
fig.savefig(savepath+filename+'.pdf')