Update example_runcounting.py

example_runcounting.py

@@ -26,19 +26,15 @@
     # run the main counting code
     results = countoscope.calculate_nmsd(data=data, window_size_x=Lx, window_size_y=Ly, box_sizes=box_sizes, sep_sizes=sep_sizes)
 
+    # load the results and save the data
     N2_mean = results.nmsd
     N2_std = results.nmsd_std
-    # now do what you want with the data!
-    # I would save it into a numpy archive with np.savez:
-    # np.savez(f'{folder}{filename}_counted.npz', N2_mean=N2_mean, N2_std=N2_std, box_sizes=box_sizes, sep_sizes=sep_sizes)
-
-    # but for backward compatibility or for further processing outside of Python, you could also do this:
-    # for i, L in enumerate(N_stats[:, 0]):
-    #     np.savetxt(f'{folder}{filename}_{L}_mean.txt', N2_mean[i, :], delimiter=' ', fmt='%.10f')
-    #     np.savetxt(f'{folder}{filename}{L}_std.txt',  N2_std [i, :], delimiter=' ', fmt='%.10f')
-    # np.savetxt(f'{folder}{filename}N_stats.txt',  N_stats, delimiter=' ', fmt='%.10f')                                                     
-
+    N_mean = results.N_mean   
+    N_var_mod = results.N_var_mod
+    np.savez(f'{folder}{filename}_counted.npz', N2_mean=N2_mean, N2_std=N2_std, N_mean= N_mean, N_var_mod = N_var_mod, box_sizes=box_sizes, sep_sizes=sep_sizes)
 
+
+
     # a few plots for sanity checks 
 
     cmap = cm.viridis #pick a great colormap