""" plt.figure(figsize=(15,7)) plt.scatter(z_d, mr, marker="o", s=1, color="b") plt.scatter(z_mng, mr_mng, marker="o", s=1, color="r") plt.title("entire set") plt.show() exit() """ # Do the correlation function for the bright subsample nr_bt = len(bt_rand_cart) nd_bt = len(bright_cart) DD = md.auto_sort_pair_counter(bright_cart[:,0:3], bin_select, 0) RR = md.auto_sort_pair_counter(bt_rand_cart, bin_select, 0) DR = md.improved_cross_correlate(bright_cart[:,0:3], bt_rand_cart, bin_select, 0) bright_est1 = md.davis_peebles_simple(DD, RR, nd_bt, nr_bt) bright_est2 = md.davis_peebles(DD, DR, nd_bt, nr_bt) bright_est3 = md.landay_szalay(DD, RR, DR, nd_bt, nr_bt) print("Bright:") print("DD: " + str(DD)) print("RR: " + str(RR)) print("DR: " + str(DR)) print(bright_est1) print(bright_est2) # Do the correlation function for the dim sample nr_dm = len(dm_rand_cart) nd_dm = len(dim_cart) DD = md.auto_sort_pair_counter(dim_cart[:,0:3], bin_select, 0) RR = md.auto_sort_pair_counter(dm_rand_cart, bin_select, 0)
#plt.gca().set_yscale("log") plt.title("ACF With Jackknife Error Bars") plt.show() exit() ################################## ### Jackknife Tests ### ################################## # on the whole data set. Nd = len(data_cartesian) Nr = len(rand_cartesian) DD = md.auto_sort_pair_counter(data_cartesian, bin_select, 0) RR = md.auto_sort_pair_counter(rand_cartesian, bin_select, 0) DR = md.improved_cross_correlate(data_cartesian, rand_cartesian, bin_select, 0) (xi_1_m, xi_2_m, xi_3_m) = md.estimator_calculator(Nd, Nr, DD, RR, DR, 3) #print(xi_2_m) RA_sub = 2 DEC_sub = 2 RA_subs = np.linspace(RA_low, RA_high, RA_sub+1, endpoint=True) DEC_subs = np.linspace(DEC_low, DEC_high, DEC_sub+1, endpoint=True) #print(RA_subs) #print(DEC_subs) plot_subs_d = [] plot_subs_r = [] data_subs = [] rand_subs = [] # Loop over all subsections and select the data for ii in range(RA_sub):
RA_r = [] DEC_r = [] for jj in rand_3vec_dwn: RA_r.append(jj[0]) DEC_r.append(jj[1]) RA_array = [RA_1, RA_r] DEC_array = [DEC_1, DEC_r] pot.sky_positions_2(RA_array, DEC_array) plt.show() """ # size of data sets N_d = len(data_cartesian) N_r = len(rand_cart_dwn) # Calculate the pair counts RR = md.auto_sort_pair_counter(rand_cart_dwn, bin_select, 0) DR = md.improved_cross_correlate(data_cartesian, rand_cart_dwn, bin_select, 0) # calculate the estimators (xi_1, xi_2, xi_3) = md.estimator_calculator(N_d, N_r, DD, RR, DR, 3) end = time.time() # Display data print("Factor Multiple:", ii) print("time taken: " + str(end - start)) print("data len = " + str(N_d)) print("rand len = " + str(N_r)) print("DD count = " + str(DD)) print("RR count = " + str(RR)) print("DR count = " + str(DR)) print("Simple Davis & Peebles estimator per bin:" + "\n", xi_1) print("Davis & Peebles estimator per bin:" + "\n", xi_2) print("Landay & Szalay estimator per bin:" + "\n", xi_3) print("### \n")
test_data = manga_c rand_data = mrand_c #kdtree = build_kdtree(rand_data, tree_depth) kd_counts = kd_tree_pair_counter(test_data, rand_data, bin_select, tree_depth) end = time.time() - start print("Bin Counts:") print(kd_counts) print("Total time taken for k-d tree search: " + str(end)) # Run a comparison test with existing ACF and CCFs # ACF start = time.time() #ACF = md.auto_sort_pair_counter(data_c, bin_select, 1) CCF = md.improved_cross_correlate(manga_c, mrand_c, bin_select, 0) end = time.time() - start print("ACF module pair count:") print(CCF) print("Total time taken for ACF: " + str(end)) """ # Most basic crude cross correlation start = time.time() bin_counts = [] for ii in range(0, len(bin_select)-1): bin = [bin_select[ii], bin_select[ii+1]] CCF = md.crude_cross_correlate(data_c, rand_c, bin[0], bin[1]) bin_counts.append(CCF) end = time.time() - start print("ACF module pair count:") print(bin_counts)