1 files changed, 9 insertions, 4 deletions

diff --git a/smt_analysis/computeSMTslowdown.py b/smt_analysis/computeSMTslowdown.py
index 805def1..ba9fa84 100755
--- a/smt_analysis/computeSMTslowdown.py
+++ b/smt_analysis/computeSMTslowdown.py
@@ -34,8 +34,8 @@ OK_PAIRS_ONLY = len(sys.argv) > 4 and "--cij-ok" in sys.argv[4]
 if not LEVEL_C_ANALYSIS:
     baseline_times, baseline_sample_cnt, baseline_max_times = load_baseline(sys.argv[3])
     paired_times, paired_offsets, name_to_idx, idx_to_name = load_paired(sys.argv[1], sys.argv[2], len(list(baseline_times.keys())))
-    for key in baseline_times:
-        print(key,max(baseline_times[key]))
+#    for key in baseline_times:
+#        print(key,max(baseline_times[key]))
 else:
     # Paired times use an abuse of the baseline file format
     baseline_times, baseline_sample_cnt, baseline_max_times = load_baseline(sys.argv[2])
@@ -94,18 +94,23 @@ for b1 in reliableNames:
             print("{:>10.3}".format(M), end=" ")
     print("")
 # Print some statistics about the distribution
-print("Average: {:>5.3} with standard deviation {:>5.3} using `{}`".format(np.mean(M_vals), np.std(M_vals), sample_f.__name__))
+print("Overall average is {:>5.3} with standard deviation {:>5.3} using `{}`".format(np.mean(M_vals), np.std(M_vals), sample_f.__name__))
 Ms = np.asarray(M_vals, dtype=np.float32)
 if not LEVEL_C_ANALYSIS:
     print(np.sum(Ms <= 0), "of", len(M_vals), "M_i:j values are at most zero -", 100*np.sum(Ms <= 0)/len(M_vals), "percent")
     print(np.sum(Ms > 1), "of", len(M_vals), "M_i:j values are greater than one -", 100*np.sum(Ms > 1)/len(M_vals), "percent")
     M_vals_to_plot = Ms[np.logical_and(Ms > 0, Ms <= 1)]
+    # Sims' analysis
+    mean = np.mean(list(M_vals_to_plot))
+    std = np.std(list(M_vals_to_plot))
+    print("For {} of {} M_i:j values in (0, 1], average: {:>5.3} with std. dev. {:>4.3} (coeff. var. {:>4.3}) using `{}`".format(len(M_vals_to_plot), len(M_vals), mean, std, std/mean, sample_f.__name__))
 else:
     print(np.sum(Ms <= 1), "of", len(M_vals), "M_i:j values are at most one -", 100*np.sum(Ms <= 1)/len(M_vals), "percent")
     print(np.sum(Ms > 2), "of", len(M_vals), "M_i:j values are greater than two -", 100*np.sum(Ms > 2)/len(M_vals), "percent")
     M_vals_to_plot = Ms
+    # Sims' analysis
+    print("For {} of {} M_i:j values in (1, 2], average: {:>5.3} with std. dev. {:>4.3} (coeff. var. {:>4.3}) using `{}`".format(len(M_vals_to_plot), len(M_vals), np.mean(list(M_vals_to_plot)), np.std(list(M_vals_to_plot)), np.mean(list(M_vals_to_plot))/np.std(list(M_vals_to_plot)), sample_f.__name__))
 
-print("Using Sim's analysis, average: {:>5.3} with standard deviation {:>5.3} using `{}`".format(np.mean(list(M_vals_to_plot)), np.std(list(M_vals_to_plot)), sample_f.__name__))
 print(plt.hist(M_vals_to_plot, bins=10))
 
 ##### BELOW TEXT IS OLD OFFSET CODE (patched) #####