#obs_num = sample_points[j] #Find the datapoints that fall in the SOMz bin #indices = finished_SOMz.ivals[finished_SOMz.get_best(X[obs_num])] #Pull the relevant galaxies from the full data-set new_data = test_data[obs_num:obs_num+1] #new_data.reset_index(inplace=True) #Calculate the psf psf_base = df.point_spread_function(new_data,smoothed=False) psf_matias = df.conditional_matrix_calc_single(new_data,pdfs=test_BP[obs_num]) ################ phi_tilde_point = histogram(Mean_test[obs_num],bins=bins,normed=True)[0] z_dist = df.integral_calc(initial_guess,psf_base,phi_tilde_point) for i in range(0,20): z_dist = df.integral_calc(z_dist,psf_base,phi_tilde_point) base_point_dist.append(z_dist) ################ phi_tilde_pdf = test_BP[obs_num] z_dist = df.integral_calc(initial_guess,psf_base,phi_tilde_pdf) for i in range(0,20): z_dist = df.integral_calc(z_dist,psf_base,phi_tilde_pdf) base_pdf_dist.append(z_dist) ############ z_dist = df.integral_calc(initial_guess,psf_matias,phi_tilde_point)
final_z_dist = [] for jj in xrange(L0,L1): obs_num = sample_points[jj] indices = finished_SOMz.ivals[finished_SOMz.get_best(X[obs_num])] #Pull the relevant galaxies from the full data-set new_data = train_data_nocol.iloc[indices] new_data.reset_index(inplace=True) #Calculate the point spread function psf = df.point_spread_function(new_data,smoothed=False) phi_tilde = histogram(Mean_test[obs_num],bins=bins,normed=True)[0] z_dist = df.integral_calc(initial_guess,psf,phi_tilde) for i in range(0,25): z_dist = df.integral_calc(z_dist,psf,phi_tilde) final_z_dist.append(z_dist) save('Base_Method_Point_900_Sample_'+str(rank)+'.npy',final_z_dist) #Run second method #Phi_tilde = MLZ estimated PDF #psf = psf from all data points in the SOMz cell #Initial guess = uniform