model_catalog_filename=pro_path + "data/laes/mock_cat/model_"+str(w)+".txt"
np.savetxt(model_catalog_filename,(x_laes,y_laes,x_rand,y_rand))
#RR,bins=correlation.RR_histogram(x_laes,y_laes,x_random,y_random,distance,theta_min,theta_max,theta_bins,cat_number=random_cat_number)
print "computing DD"
DD,bins=correlation.DD_histogram(x_laes,y_laes,distance,theta_min,theta_max,theta_bins)
print "computed DD"
print "computing DR"
DR,bins=correlation.DR_histogram(x_laes,y_laes,x_drand,y_drand,distance,theta_min,theta_max,theta_bins,cat_number=1)
print "DR computed"
corr[j,:]=correlation.peebles_correlation(DD,DR)
max_density_index=np.argmax(n_laes)
number_laes=n_laes[max_density_index]
lae_pos_ini=np.sum( n_laes[ 0 : max_density_index ] )
lae_pos_end=lae_pos_ini + number_laes
x_laes_max = x_laes[lae_pos_ini:lae_pos_end]
y_laes_max = y_laes[lae_pos_ini:lae_pos_end]
x_random_max = x_random[lae_pos_ini:lae_pos_end]
y_random_max = y_random[lae_pos_ini:lae_pos_end]
x_drand_max = x_drand[lae_pos_ini:lae_pos_end]
y_drand_max = y_drand[lae_pos_ini:lae_pos_end]
model_catalog_filename=pro_path + "data/laes/mock_cat/maxden_model_"+str(w)+".txt"
np.savetxt(model_catalog_filename,(x_laes,y_laes,x_rand,y_rand))
#random-survey histogram generation
Xmin=x_width*i_s[i]
Xmax=Xmin + x_width
Ymin=y_width*j_s[i]
Ymax=Ymin + y_width
x_random= Xmin + ( Xmax - Xmin )*np.random.random_sample(n_laes)
y_random=Ymin + ( Ymax - Ymin )*np.random.random_sample(n_laes)
DR,bins=correlation.DR_histogram(x_laes,y_laes,x_random,y_random,distance,theta_min,theta_max,theta_bins,cat_number=1)
#survey histogram generation
DD,bins=correlation.DD_histogram(x_laes,y_laes,distance,theta_min,theta_max,theta_bins)
corr[i,:]=correlation.landy_correlation(DD,RR,DR)
print "CORR_landy=",corr[i,:]
corr_laes=np.mean(corr,axis=0)
std_corr=np.std(corr,axis=0)
print "corr_landy=",corr_laes, "std_landy=",std_corr
best_correlation[w,:]=corr_laes
std_correlation[w,:]=std_corr
dtheta=(theta_max - theta_min)/theta_bins
correlation_data=np.empty(( np.size(corr_laes) , 3 ) )
model='(Mmin,Mmax,focc)=({0},{1},{2})'.format(m_min, m_max, f_occ)
model_name = 'model_{0}_{1}_{2}'.format(m_min, m_max, f_occ)
filename=pro_path + "data/mock_survey/" + "correlation_best_models/" + survey_type + "_correlation_" + model_name + ".dat"
