#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
