def lucy_replicator(iters,phi):
#Initialize arrays that will hold goodness-of-fit information
#ks_zdist_psi = np.zeros(iters+1)
#cr_val_zdist_psi = np.zeros(iters+1)
#ks_phitilde_c = np.zeros(iters+1)
#cr_val_phi_tilde_c = np.zeros(iters+1)
chisq_psi_zdist = np.zeros(iters+1)
chisq_c_phitilde = np.zeros(iters+1)
#Use the same initial guess as in Lucy (1974)
initial_guess = (np.sqrt(2)/np.pi)/(1+zm**4)
#initial_guess=np.ones(200)
#Turn our observed data into a histogram
phi_tilde = np.histogram(phi,bins=bins,density=True)[0]
#Run the first iteration of the RLD algorithm
z_dist = df.integral_calc(initial_guess,lucy_conditional_dists,phi_tilde)
z_dist = z_dist/sum(z_dist)/.025
#Calculate the phi^r term, which we call c
c = np.dot(z_dist,lucy_conditional_dists)
c=c/sum(c)/.025
#Perform the KS tests, saving p values and test statistics
#ks_zdist_psi[0] = ks_test(z_dist,norm_vals,num_obs)[0]
#cr_val_zdist_psi[0] = ks_test(z_dist,norm_vals,num_obs)[1]
#ks_phitilde_c[0] = ks_test(phi_tilde,c,num_obs)[0]
#cr_val_phi_tilde_c[0] = ks_test(phi_tilde,c,num_obs)[1]
#Perform the chi-squared test
expected_psi = np.round(num_obs*norm_vals*.025)
observed_zdist = np.round(num_obs*z_dist*.025)
expected_c = np.round(num_obs*c*.025)
obs_phi = np.round(num_obs*phi_tilde*.025)
chisq_psi_zdist[0] = chisquare(observed_zdist,expected_psi)[0]
chisq_c_phitilde[0] = chisquare(obs_phi,expected_c)[0]
for i in range(0,iters):
#Run the RLD algorithm
z_dist = df.integral_calc(z_dist,lucy_conditional_dists,phi_tilde)
z_dist = z_dist/sum(z_dist)/.025
#Calculate the phi^r term, which we call c
c = np.dot(z_dist,lucy_conditional_dists)
c=c/sum(c)/.025
#Perform the KS tests, saving p values and test statistics
#ks_zdist_psi[i+1] = ks_test(z_dist,norm_vals,num_obs)[0]
#cr_val_zdist_psi[i+1] = ks_test(z_dist,norm_vals,num_obs)[1]
#ks_phitilde_c[i+1] = ks_test(phi_tilde,c,num_obs)[0]
#cr_val_phi_tilde_c[i+1] = ks_test(phi_tilde,c,num_obs)[1]
#Perform the chi-squared test
#expected_psi = (num_obs*norm_vals*.025)
observed_zdist = (num_obs*z_dist*.025)
expected_c = (num_obs*c*.025)
obs_phi = np.round(num_obs*phi_tilde*.025)
chisq_psi_zdist[i+1] = chisquare(observed_zdist,expected_psi)[0]
chisq_c_phitilde[i+1] = chisquare(obs_phi,expected_c)[0]
#Find the best iteration number to stop at, according the Lucy (1974)
ideal_chisq_stop = np.max(np.where(chisq_c_phitilde>233)[0])
#ideal_ks_stop = np.max(np.where(ks_phitilde_c > cr_val_phi_tilde_c[0]))
#Find best iteration number to stop at, in terms of "distance" between estimate and truth
min_chisq = np.argmin(chisq_psi_zdist)
#min_ks = np.argmin(ks_zdist_psi)
return z_dist, ideal_chisq_stop, min_chisq
del best_cells
###################
#for chi2
expected = sample_size*phi_tilde_pdf*.01
################
z_dist = df.max_finder(psf_base_single,phi_tilde_point)
base_point_dist.append(z_dist)
################
z_dist, c_val = df.integral_calc(initial_guess,psf_base_t,phi_tilde_pdf)
z_temp = z_dist
for j in xrange(max_iters):
#expected = sample_size*phi_tilde_pdf*.01
observed = sample_size*c_val*.01
chisq_stat = chisquare(observed,expected)[0]
if chisq_stat < 233:
base_pdf_dist.append(z_temp)
num_cut[0]+= 1
break
else:
z_temp = z_dist
z_dist, c_val = df.integral_calc(z_dist,psf_base_t,phi_tilde_pdf)
