diff --git a/benchmarks/nb_gm_002_bm.py b/benchmarks/nb_gm_002_bm.py
index 64dc45e..c8af543 100644
--- a/benchmarks/nb_gm_002_bm.py
+++ b/benchmarks/nb_gm_002_bm.py
@@ -71,9 +71,35 @@
     plt.savefig(f'results/{figure_title}_targets_ratio.png', bbox_inches='tight')
     plt.show()
 
+
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+
+def profile():
+    from generators.nb_gm_002 import SequenceGenerator
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, int(lures/2), int(lures/2))
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_002_profile.csv', sep=',', encoding='utf-8')
+
 if __name__ == '__main__':
     alg = 'nb_gm_002'
     fn = f'results/{alg}_{n}back.csv'
-    benchmark(fn)
-    # skewness_diagram(fn, alg)
-    targets_ratio_diagram(fn, alg)
+    profile()

diff --git a/benchmarks/nb_gm_002_bm.py b/benchmarks/nb_gm_002_bm.py
index 64dc45e..c8af543 100644
--- a/benchmarks/nb_gm_002_bm.py
+++ b/benchmarks/nb_gm_002_bm.py
@@ -71,9 +71,35 @@
     plt.savefig(f'results/{figure_title}_targets_ratio.png', bbox_inches='tight')
     plt.show()
 
+
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+
+def profile():
+    from generators.nb_gm_002 import SequenceGenerator
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, int(lures/2), int(lures/2))
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_002_profile.csv', sep=',', encoding='utf-8')
+
 if __name__ == '__main__':
     alg = 'nb_gm_002'
     fn = f'results/{alg}_{n}back.csv'
-    benchmark(fn)
-    # skewness_diagram(fn, alg)
-    targets_ratio_diagram(fn, alg)
+    profile()
diff --git a/benchmarks/results/2v4_time_trials.png b/benchmarks/results/2v4_time_trials.png
new file mode 100644
index 0000000..e28fdcb
--- /dev/null
+++ b/benchmarks/results/2v4_time_trials.png
Binary files differ

diff --git a/benchmarks/nb_gm_002_bm.py b/benchmarks/nb_gm_002_bm.py
index 64dc45e..c8af543 100644
--- a/benchmarks/nb_gm_002_bm.py
+++ b/benchmarks/nb_gm_002_bm.py
@@ -71,9 +71,35 @@
     plt.savefig(f'results/{figure_title}_targets_ratio.png', bbox_inches='tight')
     plt.show()
 
+
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+
+def profile():
+    from generators.nb_gm_002 import SequenceGenerator
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, int(lures/2), int(lures/2))
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_002_profile.csv', sep=',', encoding='utf-8')
+
 if __name__ == '__main__':
     alg = 'nb_gm_002'
     fn = f'results/{alg}_{n}back.csv'
-    benchmark(fn)
-    # skewness_diagram(fn, alg)
-    targets_ratio_diagram(fn, alg)
+    profile()
diff --git a/benchmarks/results/2v4_time_trials.png b/benchmarks/results/2v4_time_trials.png
new file mode 100644
index 0000000..e28fdcb
--- /dev/null
+++ b/benchmarks/results/2v4_time_trials.png
Binary files differ
diff --git a/benchmarks/results/2vs4_skewness.png b/benchmarks/results/2vs4_skewness.png
new file mode 100644
index 0000000..93743c5
--- /dev/null
+++ b/benchmarks/results/2vs4_skewness.png
Binary files differ

diff --git a/benchmarks/nb_gm_002_bm.py b/benchmarks/nb_gm_002_bm.py
index 64dc45e..c8af543 100644
--- a/benchmarks/nb_gm_002_bm.py
+++ b/benchmarks/nb_gm_002_bm.py
@@ -71,9 +71,35 @@
     plt.savefig(f'results/{figure_title}_targets_ratio.png', bbox_inches='tight')
     plt.show()
 
+
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+
+def profile():
+    from generators.nb_gm_002 import SequenceGenerator
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, int(lures/2), int(lures/2))
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_002_profile.csv', sep=',', encoding='utf-8')
+
 if __name__ == '__main__':
     alg = 'nb_gm_002'
     fn = f'results/{alg}_{n}back.csv'
-    benchmark(fn)
-    # skewness_diagram(fn, alg)
-    targets_ratio_diagram(fn, alg)
+    profile()
diff --git a/benchmarks/results/2v4_time_trials.png b/benchmarks/results/2v4_time_trials.png
new file mode 100644
index 0000000..e28fdcb
--- /dev/null
+++ b/benchmarks/results/2v4_time_trials.png
Binary files differ
diff --git a/benchmarks/results/2vs4_skewness.png b/benchmarks/results/2vs4_skewness.png
new file mode 100644
index 0000000..93743c5
--- /dev/null
+++ b/benchmarks/results/2vs4_skewness.png
Binary files differ
diff --git a/benchmarks/results/2vs4_time_n.png b/benchmarks/results/2vs4_time_n.png
new file mode 100644
index 0000000..93ffae9
--- /dev/null
+++ b/benchmarks/results/2vs4_time_n.png
Binary files differ

diff --git a/benchmarks/nb_gm_002_bm.py b/benchmarks/nb_gm_002_bm.py
index 64dc45e..c8af543 100644
--- a/benchmarks/nb_gm_002_bm.py
+++ b/benchmarks/nb_gm_002_bm.py
@@ -71,9 +71,35 @@
     plt.savefig(f'results/{figure_title}_targets_ratio.png', bbox_inches='tight')
     plt.show()
 
+
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+
+def profile():
+    from generators.nb_gm_002 import SequenceGenerator
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, int(lures/2), int(lures/2))
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_002_profile.csv', sep=',', encoding='utf-8')
+
 if __name__ == '__main__':
     alg = 'nb_gm_002'
     fn = f'results/{alg}_{n}back.csv'
-    benchmark(fn)
-    # skewness_diagram(fn, alg)
-    targets_ratio_diagram(fn, alg)
+    profile()
diff --git a/benchmarks/results/2v4_time_trials.png b/benchmarks/results/2v4_time_trials.png
new file mode 100644
index 0000000..e28fdcb
--- /dev/null
+++ b/benchmarks/results/2v4_time_trials.png
Binary files differ
diff --git a/benchmarks/results/2vs4_skewness.png b/benchmarks/results/2vs4_skewness.png
new file mode 100644
index 0000000..93743c5
--- /dev/null
+++ b/benchmarks/results/2vs4_skewness.png
Binary files differ
diff --git a/benchmarks/results/2vs4_time_n.png b/benchmarks/results/2vs4_time_n.png
new file mode 100644
index 0000000..93ffae9
--- /dev/null
+++ b/benchmarks/results/2vs4_time_n.png
Binary files differ
diff --git a/benchmarks/results/2vs4_tlratio.png b/benchmarks/results/2vs4_tlratio.png
new file mode 100644
index 0000000..274ede9
--- /dev/null
+++ b/benchmarks/results/2vs4_tlratio.png
Binary files differ

diff --git a/benchmarks/nb_gm_002_bm.py b/benchmarks/nb_gm_002_bm.py
index 64dc45e..c8af543 100644
--- a/benchmarks/nb_gm_002_bm.py
+++ b/benchmarks/nb_gm_002_bm.py
@@ -71,9 +71,35 @@
     plt.savefig(f'results/{figure_title}_targets_ratio.png', bbox_inches='tight')
     plt.show()
 
+
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+
+def profile():
+    from generators.nb_gm_002 import SequenceGenerator
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, int(lures/2), int(lures/2))
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_002_profile.csv', sep=',', encoding='utf-8')
+
 if __name__ == '__main__':
     alg = 'nb_gm_002'
     fn = f'results/{alg}_{n}back.csv'
-    benchmark(fn)
-    # skewness_diagram(fn, alg)
-    targets_ratio_diagram(fn, alg)
+    profile()
diff --git a/benchmarks/results/2v4_time_trials.png b/benchmarks/results/2v4_time_trials.png
new file mode 100644
index 0000000..e28fdcb
--- /dev/null
+++ b/benchmarks/results/2v4_time_trials.png
Binary files differ
diff --git a/benchmarks/results/2vs4_skewness.png b/benchmarks/results/2vs4_skewness.png
new file mode 100644
index 0000000..93743c5
--- /dev/null
+++ b/benchmarks/results/2vs4_skewness.png
Binary files differ
diff --git a/benchmarks/results/2vs4_time_n.png b/benchmarks/results/2vs4_time_n.png
new file mode 100644
index 0000000..93ffae9
--- /dev/null
+++ b/benchmarks/results/2vs4_time_n.png
Binary files differ
diff --git a/benchmarks/results/2vs4_tlratio.png b/benchmarks/results/2vs4_tlratio.png
new file mode 100644
index 0000000..274ede9
--- /dev/null
+++ b/benchmarks/results/2vs4_tlratio.png
Binary files differ
diff --git a/benchmarks/results/nb_gm_002_profile.csv b/benchmarks/results/nb_gm_002_profile.csv
new file mode 100644
index 0000000..da0f34f
--- /dev/null
+++ b/benchmarks/results/nb_gm_002_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_002,6,11,0.00013589859008789062,3,0,False,DADEBCDAFFB
+1,nb_gm_002,3,32,0.0003619194030761719,10,0,False,EAEBDEBDBBECDDDEBDABAAFEAECABFAD
+2,nb_gm_002,4,17,0.0002689361572265625,5,0,False,DCEAEFEBCFABCBAFA
+3,nb_gm_002,5,11,0.00011086463928222656,3,0,True,EEBFAEECACE
+4,nb_gm_002,5,34,0.0003609657287597656,11,0,False,EFFABFCDAEFCFBDACFBDEFDDDBCCEDEECE
+5,nb_gm_002,5,44,0.00046896934509277344,14,1,False,DFADDECDEBCCBEDCCDACCCBADABFEFABFAADFBACDFDA
+6,nb_gm_002,4,39,0.00039887428283691406,13,1,False,DBCDDFAFCFACCAAEFECFFBCBFBAAEEDDAEDDCEF
+7,nb_gm_002,3,52,0.000720977783203125,17,1,True,DBAFBFDAFDFCBACCADCADFDCCCFCCDDCBDBDDEDCCACEECAFBFFE
+8,nb_gm_002,6,53,0.0013470649719238281,17,2,False,EFACADDAAEAFDAEFCDDEDFBDDAEBAFEECFAEBEBFAEEFAFEEECADC
+9,nb_gm_002,6,16,0.00037479400634765625,5,0,True,EEAAFEEFACCEBFAB
+10,nb_gm_002,7,61,0.0010979175567626953,20,1,False,BADBEDEBFDFDFCBFDFCFABAEFCFAFAFDCFCBAFDEBAFEBDFBCBCAEEAFCFDDF
+11,nb_gm_002,2,31,0.00043487548828125,10,0,True,FDFAECDCDCACABFFAFEFBFAFDBAEFCA
+12,nb_gm_002,2,42,0.0005271434783935547,14,1,True,DBFBFECBBBABEBDFCCBCADDDDAFCFFFBFDADEDFBEB
+13,nb_gm_002,2,33,0.00045013427734375,11,0,True,CCCCBDADAECDCDCBAFACDEDFDBBBFCDBB
+14,nb_gm_002,2,49,0.0006201267242431641,16,2,True,CFEFADEEEEEECEAADDFECDBDDCFCEAEFAFEFEACAFFDFEFEDB
+15,nb_gm_002,2,31,0.000415802001953125,10,0,False,BFBFEEBAFCFAFACEFAADACACDCACEDB
+16,nb_gm_002,3,47,0.0006060600280761719,15,1,False,AEABECFFDBDDAAEFFEDBEDECBACEECEEAEFBEFBBCBBFFFE
+17,nb_gm_002,3,26,0.0007929801940917969,8,0,True,ACBACECCEBCECFDDBDCCFABEBB
+18,nb_gm_002,5,63,0.001611948013305664,21,2,True,BCABDCCCFFEFCAFADCFBAFAFAFFAACDAAABBAEBDFEFCDFEFCAFECCAEABFAFCC
+19,nb_gm_002,7,50,0.0005712509155273438,16,1,False,DCCAABEDACEAEEAFEEAEAFBEFABDCCBDBDECCBEFAEDCEEFEBA
+20,nb_gm_002,7,53,0.0013239383697509766,17,1,False,BDADEFAEEFCEAEDBFCFAEDFCCADCCFECAECBFAEFEBDACFFBBFACD
+21,nb_gm_002,3,43,0.0005009174346923828,14,2,False,CEDABDEBFCAECDFFFAFBAFBFDBDFDDFAEFBCCAECFEC
+22,nb_gm_002,5,18,0.0004649162292480469,6,0,False,ABFCEDCECDDFACDDFB
+23,nb_gm_002,3,10,0.0002720355987548828,3,0,True,DEEDBCDCFD
+24,nb_gm_002,7,50,0.0005559921264648438,18,1,True,BECFCBABABBCBFFABBDCEBBBFDABBBEADCCBCFFDEFFDBFDEFF
+25,nb_gm_002,4,16,0.00018310546875,5,0,True,BFADFDDEFDDFFADA
+26,nb_gm_002,7,63,0.0006709098815917969,22,1,False,FDFFFCDFCDEFADBEFEFECCCACAEFCCABBFACDADBCCFDAFECAEAAECDAEABECDC
+27,nb_gm_002,2,28,0.0003261566162109375,9,0,True,ADCBCACAEFDCDCBBABAFDCBCBFDC
+28,nb_gm_002,7,35,0.0003800392150878906,11,0,False,BDCABDDBDEFDCFAEECDEFECAADEFEDDBDAF
+29,nb_gm_002,5,15,0.00014209747314453125,5,0,True,FCFFEEAFFECACDE
+30,nb_gm_002,6,60,0.0006458759307861328,20,1,False,DBFBECCBCFFCABCBEDBBCDEDCDFDCACBFACBEACAFFEDDEBEEDDEAAECAEFD
+31,nb_gm_002,5,34,0.00037026405334472656,11,0,False,BCEFDCFEFEEFABEEDDBAEDAFFBBECABFEC
+32,nb_gm_002,5,63,0.0006792545318603516,21,2,False,FECACDDCBCBFCBFDFEAAEEEAFCFAAECBBAEBABAADACCACABFDAABFEFFDFACFC
+33,nb_gm_002,3,58,0.0006279945373535156,19,1,False,EDBDDADCBDABCAFCFDDFCDFCAFBDEBAABDEDDCCECEEFEFEACBACDFEAEA
+34,nb_gm_002,2,10,0.0001220703125,3,0,True,EAEBECCACF
+35,nb_gm_002,7,17,0.00016617774963378906,5,0,False,ADFDEACACFEEAFDCB
+36,nb_gm_002,6,61,0.0006701946258544922,20,1,False,DEEACEFEBCEFBECEBFBEDAAAACBAFBADAAEEFDBDBFDDBDBDDDBFFCADABFCA
+37,nb_gm_002,4,56,0.0005986690521240234,18,1,False,DAADDEDDAEDAACBAAEAEECADEDAFFDFBBDBDBFBECBECFFCCDFACEBAE
+38,nb_gm_002,6,11,0.00010180473327636719,3,0,False,CDBABBCDFAF
+39,nb_gm_002,3,46,0.0004971027374267578,16,1,False,CEDCAFCDFDEECAACAAFAAFAABBEFBECDADDEBBAAAACBAA
+40,nb_gm_002,5,19,0.00019097328186035156,7,0,True,DFCAEDEEAEDFEFCDEEB
+41,nb_gm_002,5,60,0.0006420612335205078,20,1,False,DEACCBFBDFEFCDFCAADFEFAFFEFBBBFADABFADFBCEDABCDFEBDECDCFCCBC
+42,nb_gm_002,4,20,0.00021409988403320312,6,0,False,DDBFADCEDDBECEBEFAAE
+43,nb_gm_002,2,42,0.00045800209045410156,15,1,False,AFADCEFFBAACAAEAEACFCFCDDEDADFBCECDCFBFDFD
+44,nb_gm_002,2,30,0.00035190582275390625,10,0,False,ADACFFDFCFDADFDFAEBCBAACFCEDED
+45,nb_gm_002,4,23,0.0002701282501220703,7,0,True,DDADDDFEFDAADDADFAACFED
+46,nb_gm_002,5,22,0.00023698806762695312,7,0,False,EBBBFADBDFABACCAEACEAD
+47,nb_gm_002,7,30,0.0003120899200439453,10,0,False,EFABBEAEFAFDCBDFDFAFBECCCACBEC
+48,nb_gm_002,4,50,0.0005471706390380859,16,1,True,BBDDADDBEDDEFFDAFFFAEBADEFDFEADEAADFAFCFEDCEECFFDA
+49,nb_gm_002,6,64,0.0007898807525634766,21,1,False,EDBDEDAFEDBACBABCAAAFBDFBAFBDADAFDDAFAFDDCBEFCFEBADCEDBAFFEBADDD
+50,nb_gm_002,6,45,0.0005559921264648438,15,1,True,FADFAFFDECDFFAAAFBFAAAFFCDDFFACEDBFDDECAFCDFB
+51,nb_gm_002,4,10,9.989738464355469e-05,3,0,True,EFFAADFAAB
+52,nb_gm_002,2,51,0.0005540847778320312,17,1,False,BDBFEEEFECDCBAEFFCCACABEDABEEFDFDBBFBCBFBEBEBCBCCDC
+53,nb_gm_002,3,20,0.00038504600524902344,6,0,True,DDAFCEFCAADEEFEEAECA
+54,nb_gm_002,4,43,0.0004611015319824219,14,1,False,FBDABAFABFBDBFDFBABEABCDFCCEFBCAFDCECDCDDDC
+55,nb_gm_002,3,52,0.0006678104400634766,17,1,True,ADEAEFBEFDAFEABECEEBACBDEFDEDDABEEFAEBAAACABEAEEFEDB
+56,nb_gm_002,5,38,0.00038504600524902344,12,1,True,CDBAEBBECECBAFBEBCBBCCCADCCCACEACFDEAB
+57,nb_gm_002,2,12,0.00014090538024902344,4,0,True,DEDADFBFAEDE
+58,nb_gm_002,4,12,0.00011324882507324219,4,0,False,ACBBEFBDEFBA
+59,nb_gm_002,5,43,0.0005619525909423828,14,1,False,BDCABACAEFFCDECFEDBADDDFADDDBAFDCBDFCFDDEDC
+60,nb_gm_002,5,29,0.0003631114959716797,9,0,True,BBCAABDADFBDADCEFABCDCADDAAFD
+61,nb_gm_002,3,53,0.0006711483001708984,17,1,False,CEACACFDFEDEEDAEDEEAACDDDCEDCDFCEFEEFAEBDFBFCDABDEBAA
+62,nb_gm_002,4,54,0.0006229877471923828,18,1,True,ADAFFDACFDFDBDCCBFACBDACFDEFFDADEDCBDFBFCBAABFDFCFCFCF
+63,nb_gm_002,5,60,0.0007681846618652344,20,1,False,BFCCBEACCEEABCAEDBDEEBBAEBFCABBEBFCBAACEBBECECCADFBAAFFEABCF
+64,nb_gm_002,4,14,0.00017595291137695312,4,0,True,AFEDDEBAEABAEA
+65,nb_gm_002,6,48,0.0005950927734375,16,1,True,FAFEDBABCBBBFBCBBBDCCECBEECDBDDAEDBDDCCAADDBDACF
+66,nb_gm_002,5,25,0.0003132820129394531,8,0,True,EFFADEFBABFFEFEDFDDDCFDAD
+67,nb_gm_002,6,47,0.0006089210510253906,15,1,False,AFEEDFAFFEAAADFDCABCAEBDFCDCBAFCDAECDCAEBCBCABC
+68,nb_gm_002,3,17,0.00021886825561523438,5,0,False,CFFBFFBAEACEADBCF
+69,nb_gm_002,7,37,0.00044989585876464844,12,2,True,EFDAFEEEDEABFDDEEBBFBAABBAFEAABBEBEBC
+70,nb_gm_002,6,62,0.0008101463317871094,22,0,False,FECDAFFACDCDFADDCFCEEDBFBEDFCFBFAFBBEEACBBACBCBCABBCDFAFEDECBE
+71,nb_gm_002,4,44,0.00054931640625,14,1,False,FBCCFBCCFBDAEBDBBDACBAFDAFECBFEDCFAFAFCDACAA
+72,nb_gm_002,2,57,0.0007417201995849609,19,2,False,CCFEBBDDDFFFAFCEAECEBEEBEBCBEDFACADABEBCCFCBCBDBAEDCBCBCF
+73,nb_gm_002,4,48,0.0005822181701660156,16,2,False,FABADABBAAEAFCFDDCDCCBDFABDEEEDFFDDAFDCCFACCFECA
+74,nb_gm_002,5,35,0.0004601478576660156,11,0,True,FDFDBFBFCADDCDECAADEBBAFFDBFFDDBFCD
+75,nb_gm_002,2,13,0.0002009868621826172,4,0,True,EBDDAEBFBFBAB
+76,nb_gm_002,6,42,0.0005300045013427734,14,2,True,ABCFDCADEEDAABEDDDEDCDCDBDBDDDCDCADACBCEFE
+77,nb_gm_002,3,34,0.00043511390686035156,11,0,False,FAFDBDDEDDFADCAEADECDCAECFBBDEBDFB
+78,nb_gm_002,2,28,0.0004279613494873047,9,0,False,BABADDEDEDBACACBEEEADCDFBEDC
+79,nb_gm_002,4,23,0.00030493736267089844,7,0,False,DDBABEFAEDBADDFCEBFCEAF
+80,nb_gm_002,6,53,0.0006649494171142578,17,1,False,EFEECFEFBFCFBBEABBCEEABFAEEBBDDEEDDAEEEFBCFDFFDBEDAAD
+81,nb_gm_002,3,61,0.0007090568542480469,22,1,False,EEEEBEAEEAFFACFEFEEDEFBCFAEFBDFBDACFEDBECBEDBDBFDEABEBBDBBCCA
+82,nb_gm_002,5,58,0.0006530284881591797,20,2,False,DFFBADFFFCBEBDCBCBCFBAAAECECFDADCDDEECECCEAEFBEAEFBEEECCED
+83,nb_gm_002,7,11,9.107589721679688e-05,3,0,False,FCEBFCDFAEB
+84,nb_gm_002,4,41,0.0004858970642089844,13,1,False,EBBAFFBADCCDBEEBAEEADEEDCEECCEEFDECEBEDAF
+85,nb_gm_002,6,22,0.00023603439331054688,7,0,True,AFDDAAADADEBAFEDEADFDD
+86,nb_gm_002,7,49,0.0005178451538085938,16,2,False,DBEFDEDDEEECCDEFECCFEFCECDDCBCFFBAFBDBFBDFEBBFBDE
+87,nb_gm_002,3,64,0.0008380413055419922,21,1,True,FEAEDEEDEDAABBDDDDAAEFFEDFEDAEAEEAFEAFBFBBEBDECEEBAEADFFBACAACEC
+88,nb_gm_002,5,11,0.0002498626708984375,3,0,True,CBBBCCABACA
+89,nb_gm_002,7,56,0.0007159709930419922,19,1,False,EFBFBEDEFDDBCBEADFBAEBBEBCFEBDACEDCFDCCECCFDEFFCAFDECDAA
+90,nb_gm_002,6,54,0.0006489753723144531,18,2,False,BCACBEBDFCDABDEDBABCFADACCFDFBEBABFAEBBEDAFCFFDACCFAEA
+91,nb_gm_002,3,26,0.0003719329833984375,9,0,True,BBEBFAFFAECDEBAFBAEDEEFFEF
+92,nb_gm_002,7,53,0.001650094985961914,18,0,False,BDAFBFECDFBEFEFABBFFDDCFEFDDDBFBFBAFFFEFADFDAEFACCBAE
+93,nb_gm_002,6,26,0.0002880096435546875,8,0,False,AEFFAEABDDABBFDDFDDEBDCDDE
+94,nb_gm_002,7,36,0.00036406517028808594,12,2,False,EBEECEFDCEEDFDBCBCAFDBCBEBBFBCEBBDFD
+95,nb_gm_002,4,20,0.0002219676971435547,6,0,True,DCAADEDEEBAEABEEDDEE
+96,nb_gm_002,3,12,0.0003161430358886719,4,0,True,CFEFFEFFCACE
+97,nb_gm_002,3,17,0.00019812583923339844,5,0,False,BDABCEBDEFDFBBFCA
+98,nb_gm_002,5,35,0.0004279613494873047,11,0,False,FCEBFECEBCDAEFDDBFAADFFEDCBCEFCBEFF
+99,nb_gm_002,2,21,0.0002372264862060547,7,0,True,BFFCFAAABABAEEBFBEACA

diff --git a/benchmarks/nb_gm_002_bm.py b/benchmarks/nb_gm_002_bm.py
index 64dc45e..c8af543 100644
--- a/benchmarks/nb_gm_002_bm.py
+++ b/benchmarks/nb_gm_002_bm.py
@@ -71,9 +71,35 @@
     plt.savefig(f'results/{figure_title}_targets_ratio.png', bbox_inches='tight')
     plt.show()
 
+
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+
+def profile():
+    from generators.nb_gm_002 import SequenceGenerator
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, int(lures/2), int(lures/2))
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_002_profile.csv', sep=',', encoding='utf-8')
+
 if __name__ == '__main__':
     alg = 'nb_gm_002'
     fn = f'results/{alg}_{n}back.csv'
-    benchmark(fn)
-    # skewness_diagram(fn, alg)
-    targets_ratio_diagram(fn, alg)
+    profile()
diff --git a/benchmarks/results/2v4_time_trials.png b/benchmarks/results/2v4_time_trials.png
new file mode 100644
index 0000000..e28fdcb
--- /dev/null
+++ b/benchmarks/results/2v4_time_trials.png
Binary files differ
diff --git a/benchmarks/results/2vs4_skewness.png b/benchmarks/results/2vs4_skewness.png
new file mode 100644
index 0000000..93743c5
--- /dev/null
+++ b/benchmarks/results/2vs4_skewness.png
Binary files differ
diff --git a/benchmarks/results/2vs4_time_n.png b/benchmarks/results/2vs4_time_n.png
new file mode 100644
index 0000000..93ffae9
--- /dev/null
+++ b/benchmarks/results/2vs4_time_n.png
Binary files differ
diff --git a/benchmarks/results/2vs4_tlratio.png b/benchmarks/results/2vs4_tlratio.png
new file mode 100644
index 0000000..274ede9
--- /dev/null
+++ b/benchmarks/results/2vs4_tlratio.png
Binary files differ
diff --git a/benchmarks/results/nb_gm_002_profile.csv b/benchmarks/results/nb_gm_002_profile.csv
new file mode 100644
index 0000000..da0f34f
--- /dev/null
+++ b/benchmarks/results/nb_gm_002_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_002,6,11,0.00013589859008789062,3,0,False,DADEBCDAFFB
+1,nb_gm_002,3,32,0.0003619194030761719,10,0,False,EAEBDEBDBBECDDDEBDABAAFEAECABFAD
+2,nb_gm_002,4,17,0.0002689361572265625,5,0,False,DCEAEFEBCFABCBAFA
+3,nb_gm_002,5,11,0.00011086463928222656,3,0,True,EEBFAEECACE
+4,nb_gm_002,5,34,0.0003609657287597656,11,0,False,EFFABFCDAEFCFBDACFBDEFDDDBCCEDEECE
+5,nb_gm_002,5,44,0.00046896934509277344,14,1,False,DFADDECDEBCCBEDCCDACCCBADABFEFABFAADFBACDFDA
+6,nb_gm_002,4,39,0.00039887428283691406,13,1,False,DBCDDFAFCFACCAAEFECFFBCBFBAAEEDDAEDDCEF
+7,nb_gm_002,3,52,0.000720977783203125,17,1,True,DBAFBFDAFDFCBACCADCADFDCCCFCCDDCBDBDDEDCCACEECAFBFFE
+8,nb_gm_002,6,53,0.0013470649719238281,17,2,False,EFACADDAAEAFDAEFCDDEDFBDDAEBAFEECFAEBEBFAEEFAFEEECADC
+9,nb_gm_002,6,16,0.00037479400634765625,5,0,True,EEAAFEEFACCEBFAB
+10,nb_gm_002,7,61,0.0010979175567626953,20,1,False,BADBEDEBFDFDFCBFDFCFABAEFCFAFAFDCFCBAFDEBAFEBDFBCBCAEEAFCFDDF
+11,nb_gm_002,2,31,0.00043487548828125,10,0,True,FDFAECDCDCACABFFAFEFBFAFDBAEFCA
+12,nb_gm_002,2,42,0.0005271434783935547,14,1,True,DBFBFECBBBABEBDFCCBCADDDDAFCFFFBFDADEDFBEB
+13,nb_gm_002,2,33,0.00045013427734375,11,0,True,CCCCBDADAECDCDCBAFACDEDFDBBBFCDBB
+14,nb_gm_002,2,49,0.0006201267242431641,16,2,True,CFEFADEEEEEECEAADDFECDBDDCFCEAEFAFEFEACAFFDFEFEDB
+15,nb_gm_002,2,31,0.000415802001953125,10,0,False,BFBFEEBAFCFAFACEFAADACACDCACEDB
+16,nb_gm_002,3,47,0.0006060600280761719,15,1,False,AEABECFFDBDDAAEFFEDBEDECBACEECEEAEFBEFBBCBBFFFE
+17,nb_gm_002,3,26,0.0007929801940917969,8,0,True,ACBACECCEBCECFDDBDCCFABEBB
+18,nb_gm_002,5,63,0.001611948013305664,21,2,True,BCABDCCCFFEFCAFADCFBAFAFAFFAACDAAABBAEBDFEFCDFEFCAFECCAEABFAFCC
+19,nb_gm_002,7,50,0.0005712509155273438,16,1,False,DCCAABEDACEAEEAFEEAEAFBEFABDCCBDBDECCBEFAEDCEEFEBA
+20,nb_gm_002,7,53,0.0013239383697509766,17,1,False,BDADEFAEEFCEAEDBFCFAEDFCCADCCFECAECBFAEFEBDACFFBBFACD
+21,nb_gm_002,3,43,0.0005009174346923828,14,2,False,CEDABDEBFCAECDFFFAFBAFBFDBDFDDFAEFBCCAECFEC
+22,nb_gm_002,5,18,0.0004649162292480469,6,0,False,ABFCEDCECDDFACDDFB
+23,nb_gm_002,3,10,0.0002720355987548828,3,0,True,DEEDBCDCFD
+24,nb_gm_002,7,50,0.0005559921264648438,18,1,True,BECFCBABABBCBFFABBDCEBBBFDABBBEADCCBCFFDEFFDBFDEFF
+25,nb_gm_002,4,16,0.00018310546875,5,0,True,BFADFDDEFDDFFADA
+26,nb_gm_002,7,63,0.0006709098815917969,22,1,False,FDFFFCDFCDEFADBEFEFECCCACAEFCCABBFACDADBCCFDAFECAEAAECDAEABECDC
+27,nb_gm_002,2,28,0.0003261566162109375,9,0,True,ADCBCACAEFDCDCBBABAFDCBCBFDC
+28,nb_gm_002,7,35,0.0003800392150878906,11,0,False,BDCABDDBDEFDCFAEECDEFECAADEFEDDBDAF
+29,nb_gm_002,5,15,0.00014209747314453125,5,0,True,FCFFEEAFFECACDE
+30,nb_gm_002,6,60,0.0006458759307861328,20,1,False,DBFBECCBCFFCABCBEDBBCDEDCDFDCACBFACBEACAFFEDDEBEEDDEAAECAEFD
+31,nb_gm_002,5,34,0.00037026405334472656,11,0,False,BCEFDCFEFEEFABEEDDBAEDAFFBBECABFEC
+32,nb_gm_002,5,63,0.0006792545318603516,21,2,False,FECACDDCBCBFCBFDFEAAEEEAFCFAAECBBAEBABAADACCACABFDAABFEFFDFACFC
+33,nb_gm_002,3,58,0.0006279945373535156,19,1,False,EDBDDADCBDABCAFCFDDFCDFCAFBDEBAABDEDDCCECEEFEFEACBACDFEAEA
+34,nb_gm_002,2,10,0.0001220703125,3,0,True,EAEBECCACF
+35,nb_gm_002,7,17,0.00016617774963378906,5,0,False,ADFDEACACFEEAFDCB
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+40,nb_gm_002,5,19,0.00019097328186035156,7,0,True,DFCAEDEEAEDFEFCDEEB
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+42,nb_gm_002,4,20,0.00021409988403320312,6,0,False,DDBFADCEDDBECEBEFAAE
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+84,nb_gm_002,4,41,0.0004858970642089844,13,1,False,EBBAFFBADCCDBEEBAEEADEEDCEECCEEFDECEBEDAF
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+88,nb_gm_002,5,11,0.0002498626708984375,3,0,True,CBBBCCABACA
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+90,nb_gm_002,6,54,0.0006489753723144531,18,2,False,BCACBEBDFCDABDEDBABCFADACCFDFBEBABFAEBBEDAFCFFDACCFAEA
+91,nb_gm_002,3,26,0.0003719329833984375,9,0,True,BBEBFAFFAECDEBAFBAEDEEFFEF
+92,nb_gm_002,7,53,0.001650094985961914,18,0,False,BDAFBFECDFBEFEFABBFFDDCFEFDDDBFBFBAFFFEFADFDAEFACCBAE
+93,nb_gm_002,6,26,0.0002880096435546875,8,0,False,AEFFAEABDDABBFDDFDDEBDCDDE
+94,nb_gm_002,7,36,0.00036406517028808594,12,2,False,EBEECEFDCEEDFDBCBCAFDBCBEBBFBCEBBDFD
+95,nb_gm_002,4,20,0.0002219676971435547,6,0,True,DCAADEDEEBAEABEEDDEE
+96,nb_gm_002,3,12,0.0003161430358886719,4,0,True,CFEFFEFFCACE
+97,nb_gm_002,3,17,0.00019812583923339844,5,0,False,BDABCEBDEFDFBBFCA
+98,nb_gm_002,5,35,0.0004279613494873047,11,0,False,FCEBFECEBCDAEFDDBFAADFFEDCBCEFCBEFF
+99,nb_gm_002,2,21,0.0002372264862060547,7,0,True,BFFCFAAABABAEEBFBEACA
diff --git a/benchmarks/results/skewness_002_vs_004.png b/benchmarks/results/skewness_002_vs_004.png
new file mode 100644
index 0000000..93743c5
--- /dev/null
+++ b/benchmarks/results/skewness_002_vs_004.png
Binary files differ

diff --git a/benchmarks/nb_gm_002_bm.py b/benchmarks/nb_gm_002_bm.py
index 64dc45e..c8af543 100644
--- a/benchmarks/nb_gm_002_bm.py
+++ b/benchmarks/nb_gm_002_bm.py
@@ -71,9 +71,35 @@
     plt.savefig(f'results/{figure_title}_targets_ratio.png', bbox_inches='tight')
     plt.show()
 
+
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+
+def profile():
+    from generators.nb_gm_002 import SequenceGenerator
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, int(lures/2), int(lures/2))
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_002_profile.csv', sep=',', encoding='utf-8')
+
 if __name__ == '__main__':
     alg = 'nb_gm_002'
     fn = f'results/{alg}_{n}back.csv'
-    benchmark(fn)
-    # skewness_diagram(fn, alg)
-    targets_ratio_diagram(fn, alg)
+    profile()
diff --git a/benchmarks/results/2v4_time_trials.png b/benchmarks/results/2v4_time_trials.png
new file mode 100644
index 0000000..e28fdcb
--- /dev/null
+++ b/benchmarks/results/2v4_time_trials.png
Binary files differ
diff --git a/benchmarks/results/2vs4_skewness.png b/benchmarks/results/2vs4_skewness.png
new file mode 100644
index 0000000..93743c5
--- /dev/null
+++ b/benchmarks/results/2vs4_skewness.png
Binary files differ
diff --git a/benchmarks/results/2vs4_time_n.png b/benchmarks/results/2vs4_time_n.png
new file mode 100644
index 0000000..93ffae9
--- /dev/null
+++ b/benchmarks/results/2vs4_time_n.png
Binary files differ
diff --git a/benchmarks/results/2vs4_tlratio.png b/benchmarks/results/2vs4_tlratio.png
new file mode 100644
index 0000000..274ede9
--- /dev/null
+++ b/benchmarks/results/2vs4_tlratio.png
Binary files differ
diff --git a/benchmarks/results/nb_gm_002_profile.csv b/benchmarks/results/nb_gm_002_profile.csv
new file mode 100644
index 0000000..da0f34f
--- /dev/null
+++ b/benchmarks/results/nb_gm_002_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_002,6,11,0.00013589859008789062,3,0,False,DADEBCDAFFB
+1,nb_gm_002,3,32,0.0003619194030761719,10,0,False,EAEBDEBDBBECDDDEBDABAAFEAECABFAD
+2,nb_gm_002,4,17,0.0002689361572265625,5,0,False,DCEAEFEBCFABCBAFA
+3,nb_gm_002,5,11,0.00011086463928222656,3,0,True,EEBFAEECACE
+4,nb_gm_002,5,34,0.0003609657287597656,11,0,False,EFFABFCDAEFCFBDACFBDEFDDDBCCEDEECE
+5,nb_gm_002,5,44,0.00046896934509277344,14,1,False,DFADDECDEBCCBEDCCDACCCBADABFEFABFAADFBACDFDA
+6,nb_gm_002,4,39,0.00039887428283691406,13,1,False,DBCDDFAFCFACCAAEFECFFBCBFBAAEEDDAEDDCEF
+7,nb_gm_002,3,52,0.000720977783203125,17,1,True,DBAFBFDAFDFCBACCADCADFDCCCFCCDDCBDBDDEDCCACEECAFBFFE
+8,nb_gm_002,6,53,0.0013470649719238281,17,2,False,EFACADDAAEAFDAEFCDDEDFBDDAEBAFEECFAEBEBFAEEFAFEEECADC
+9,nb_gm_002,6,16,0.00037479400634765625,5,0,True,EEAAFEEFACCEBFAB
+10,nb_gm_002,7,61,0.0010979175567626953,20,1,False,BADBEDEBFDFDFCBFDFCFABAEFCFAFAFDCFCBAFDEBAFEBDFBCBCAEEAFCFDDF
+11,nb_gm_002,2,31,0.00043487548828125,10,0,True,FDFAECDCDCACABFFAFEFBFAFDBAEFCA
+12,nb_gm_002,2,42,0.0005271434783935547,14,1,True,DBFBFECBBBABEBDFCCBCADDDDAFCFFFBFDADEDFBEB
+13,nb_gm_002,2,33,0.00045013427734375,11,0,True,CCCCBDADAECDCDCBAFACDEDFDBBBFCDBB
+14,nb_gm_002,2,49,0.0006201267242431641,16,2,True,CFEFADEEEEEECEAADDFECDBDDCFCEAEFAFEFEACAFFDFEFEDB
+15,nb_gm_002,2,31,0.000415802001953125,10,0,False,BFBFEEBAFCFAFACEFAADACACDCACEDB
+16,nb_gm_002,3,47,0.0006060600280761719,15,1,False,AEABECFFDBDDAAEFFEDBEDECBACEECEEAEFBEFBBCBBFFFE
+17,nb_gm_002,3,26,0.0007929801940917969,8,0,True,ACBACECCEBCECFDDBDCCFABEBB
+18,nb_gm_002,5,63,0.001611948013305664,21,2,True,BCABDCCCFFEFCAFADCFBAFAFAFFAACDAAABBAEBDFEFCDFEFCAFECCAEABFAFCC
+19,nb_gm_002,7,50,0.0005712509155273438,16,1,False,DCCAABEDACEAEEAFEEAEAFBEFABDCCBDBDECCBEFAEDCEEFEBA
+20,nb_gm_002,7,53,0.0013239383697509766,17,1,False,BDADEFAEEFCEAEDBFCFAEDFCCADCCFECAECBFAEFEBDACFFBBFACD
+21,nb_gm_002,3,43,0.0005009174346923828,14,2,False,CEDABDEBFCAECDFFFAFBAFBFDBDFDDFAEFBCCAECFEC
+22,nb_gm_002,5,18,0.0004649162292480469,6,0,False,ABFCEDCECDDFACDDFB
+23,nb_gm_002,3,10,0.0002720355987548828,3,0,True,DEEDBCDCFD
+24,nb_gm_002,7,50,0.0005559921264648438,18,1,True,BECFCBABABBCBFFABBDCEBBBFDABBBEADCCBCFFDEFFDBFDEFF
+25,nb_gm_002,4,16,0.00018310546875,5,0,True,BFADFDDEFDDFFADA
+26,nb_gm_002,7,63,0.0006709098815917969,22,1,False,FDFFFCDFCDEFADBEFEFECCCACAEFCCABBFACDADBCCFDAFECAEAAECDAEABECDC
+27,nb_gm_002,2,28,0.0003261566162109375,9,0,True,ADCBCACAEFDCDCBBABAFDCBCBFDC
+28,nb_gm_002,7,35,0.0003800392150878906,11,0,False,BDCABDDBDEFDCFAEECDEFECAADEFEDDBDAF
+29,nb_gm_002,5,15,0.00014209747314453125,5,0,True,FCFFEEAFFECACDE
+30,nb_gm_002,6,60,0.0006458759307861328,20,1,False,DBFBECCBCFFCABCBEDBBCDEDCDFDCACBFACBEACAFFEDDEBEEDDEAAECAEFD
+31,nb_gm_002,5,34,0.00037026405334472656,11,0,False,BCEFDCFEFEEFABEEDDBAEDAFFBBECABFEC
+32,nb_gm_002,5,63,0.0006792545318603516,21,2,False,FECACDDCBCBFCBFDFEAAEEEAFCFAAECBBAEBABAADACCACABFDAABFEFFDFACFC
+33,nb_gm_002,3,58,0.0006279945373535156,19,1,False,EDBDDADCBDABCAFCFDDFCDFCAFBDEBAABDEDDCCECEEFEFEACBACDFEAEA
+34,nb_gm_002,2,10,0.0001220703125,3,0,True,EAEBECCACF
+35,nb_gm_002,7,17,0.00016617774963378906,5,0,False,ADFDEACACFEEAFDCB
+36,nb_gm_002,6,61,0.0006701946258544922,20,1,False,DEEACEFEBCEFBECEBFBEDAAAACBAFBADAAEEFDBDBFDDBDBDDDBFFCADABFCA
+37,nb_gm_002,4,56,0.0005986690521240234,18,1,False,DAADDEDDAEDAACBAAEAEECADEDAFFDFBBDBDBFBECBECFFCCDFACEBAE
+38,nb_gm_002,6,11,0.00010180473327636719,3,0,False,CDBABBCDFAF
+39,nb_gm_002,3,46,0.0004971027374267578,16,1,False,CEDCAFCDFDEECAACAAFAAFAABBEFBECDADDEBBAAAACBAA
+40,nb_gm_002,5,19,0.00019097328186035156,7,0,True,DFCAEDEEAEDFEFCDEEB
+41,nb_gm_002,5,60,0.0006420612335205078,20,1,False,DEACCBFBDFEFCDFCAADFEFAFFEFBBBFADABFADFBCEDABCDFEBDECDCFCCBC
+42,nb_gm_002,4,20,0.00021409988403320312,6,0,False,DDBFADCEDDBECEBEFAAE
+43,nb_gm_002,2,42,0.00045800209045410156,15,1,False,AFADCEFFBAACAAEAEACFCFCDDEDADFBCECDCFBFDFD
+44,nb_gm_002,2,30,0.00035190582275390625,10,0,False,ADACFFDFCFDADFDFAEBCBAACFCEDED
+45,nb_gm_002,4,23,0.0002701282501220703,7,0,True,DDADDDFEFDAADDADFAACFED
+46,nb_gm_002,5,22,0.00023698806762695312,7,0,False,EBBBFADBDFABACCAEACEAD
+47,nb_gm_002,7,30,0.0003120899200439453,10,0,False,EFABBEAEFAFDCBDFDFAFBECCCACBEC
+48,nb_gm_002,4,50,0.0005471706390380859,16,1,True,BBDDADDBEDDEFFDAFFFAEBADEFDFEADEAADFAFCFEDCEECFFDA
+49,nb_gm_002,6,64,0.0007898807525634766,21,1,False,EDBDEDAFEDBACBABCAAAFBDFBAFBDADAFDDAFAFDDCBEFCFEBADCEDBAFFEBADDD
+50,nb_gm_002,6,45,0.0005559921264648438,15,1,True,FADFAFFDECDFFAAAFBFAAAFFCDDFFACEDBFDDECAFCDFB
+51,nb_gm_002,4,10,9.989738464355469e-05,3,0,True,EFFAADFAAB
+52,nb_gm_002,2,51,0.0005540847778320312,17,1,False,BDBFEEEFECDCBAEFFCCACABEDABEEFDFDBBFBCBFBEBEBCBCCDC
+53,nb_gm_002,3,20,0.00038504600524902344,6,0,True,DDAFCEFCAADEEFEEAECA
+54,nb_gm_002,4,43,0.0004611015319824219,14,1,False,FBDABAFABFBDBFDFBABEABCDFCCEFBCAFDCECDCDDDC
+55,nb_gm_002,3,52,0.0006678104400634766,17,1,True,ADEAEFBEFDAFEABECEEBACBDEFDEDDABEEFAEBAAACABEAEEFEDB
+56,nb_gm_002,5,38,0.00038504600524902344,12,1,True,CDBAEBBECECBAFBEBCBBCCCADCCCACEACFDEAB
+57,nb_gm_002,2,12,0.00014090538024902344,4,0,True,DEDADFBFAEDE
+58,nb_gm_002,4,12,0.00011324882507324219,4,0,False,ACBBEFBDEFBA
+59,nb_gm_002,5,43,0.0005619525909423828,14,1,False,BDCABACAEFFCDECFEDBADDDFADDDBAFDCBDFCFDDEDC
+60,nb_gm_002,5,29,0.0003631114959716797,9,0,True,BBCAABDADFBDADCEFABCDCADDAAFD
+61,nb_gm_002,3,53,0.0006711483001708984,17,1,False,CEACACFDFEDEEDAEDEEAACDDDCEDCDFCEFEEFAEBDFBFCDABDEBAA
+62,nb_gm_002,4,54,0.0006229877471923828,18,1,True,ADAFFDACFDFDBDCCBFACBDACFDEFFDADEDCBDFBFCBAABFDFCFCFCF
+63,nb_gm_002,5,60,0.0007681846618652344,20,1,False,BFCCBEACCEEABCAEDBDEEBBAEBFCABBEBFCBAACEBBECECCADFBAAFFEABCF
+64,nb_gm_002,4,14,0.00017595291137695312,4,0,True,AFEDDEBAEABAEA
+65,nb_gm_002,6,48,0.0005950927734375,16,1,True,FAFEDBABCBBBFBCBBBDCCECBEECDBDDAEDBDDCCAADDBDACF
+66,nb_gm_002,5,25,0.0003132820129394531,8,0,True,EFFADEFBABFFEFEDFDDDCFDAD
+67,nb_gm_002,6,47,0.0006089210510253906,15,1,False,AFEEDFAFFEAAADFDCABCAEBDFCDCBAFCDAECDCAEBCBCABC
+68,nb_gm_002,3,17,0.00021886825561523438,5,0,False,CFFBFFBAEACEADBCF
+69,nb_gm_002,7,37,0.00044989585876464844,12,2,True,EFDAFEEEDEABFDDEEBBFBAABBAFEAABBEBEBC
+70,nb_gm_002,6,62,0.0008101463317871094,22,0,False,FECDAFFACDCDFADDCFCEEDBFBEDFCFBFAFBBEEACBBACBCBCABBCDFAFEDECBE
+71,nb_gm_002,4,44,0.00054931640625,14,1,False,FBCCFBCCFBDAEBDBBDACBAFDAFECBFEDCFAFAFCDACAA
+72,nb_gm_002,2,57,0.0007417201995849609,19,2,False,CCFEBBDDDFFFAFCEAECEBEEBEBCBEDFACADABEBCCFCBCBDBAEDCBCBCF
+73,nb_gm_002,4,48,0.0005822181701660156,16,2,False,FABADABBAAEAFCFDDCDCCBDFABDEEEDFFDDAFDCCFACCFECA
+74,nb_gm_002,5,35,0.0004601478576660156,11,0,True,FDFDBFBFCADDCDECAADEBBAFFDBFFDDBFCD
+75,nb_gm_002,2,13,0.0002009868621826172,4,0,True,EBDDAEBFBFBAB
+76,nb_gm_002,6,42,0.0005300045013427734,14,2,True,ABCFDCADEEDAABEDDDEDCDCDBDBDDDCDCADACBCEFE
+77,nb_gm_002,3,34,0.00043511390686035156,11,0,False,FAFDBDDEDDFADCAEADECDCAECFBBDEBDFB
+78,nb_gm_002,2,28,0.0004279613494873047,9,0,False,BABADDEDEDBACACBEEEADCDFBEDC
+79,nb_gm_002,4,23,0.00030493736267089844,7,0,False,DDBABEFAEDBADDFCEBFCEAF
+80,nb_gm_002,6,53,0.0006649494171142578,17,1,False,EFEECFEFBFCFBBEABBCEEABFAEEBBDDEEDDAEEEFBCFDFFDBEDAAD
+81,nb_gm_002,3,61,0.0007090568542480469,22,1,False,EEEEBEAEEAFFACFEFEEDEFBCFAEFBDFBDACFEDBECBEDBDBFDEABEBBDBBCCA
+82,nb_gm_002,5,58,0.0006530284881591797,20,2,False,DFFBADFFFCBEBDCBCBCFBAAAECECFDADCDDEECECCEAEFBEAEFBEEECCED
+83,nb_gm_002,7,11,9.107589721679688e-05,3,0,False,FCEBFCDFAEB
+84,nb_gm_002,4,41,0.0004858970642089844,13,1,False,EBBAFFBADCCDBEEBAEEADEEDCEECCEEFDECEBEDAF
+85,nb_gm_002,6,22,0.00023603439331054688,7,0,True,AFDDAAADADEBAFEDEADFDD
+86,nb_gm_002,7,49,0.0005178451538085938,16,2,False,DBEFDEDDEEECCDEFECCFEFCECDDCBCFFBAFBDBFBDFEBBFBDE
+87,nb_gm_002,3,64,0.0008380413055419922,21,1,True,FEAEDEEDEDAABBDDDDAAEFFEDFEDAEAEEAFEAFBFBBEBDECEEBAEADFFBACAACEC
+88,nb_gm_002,5,11,0.0002498626708984375,3,0,True,CBBBCCABACA
+89,nb_gm_002,7,56,0.0007159709930419922,19,1,False,EFBFBEDEFDDBCBEADFBAEBBEBCFEBDACEDCFDCCECCFDEFFCAFDECDAA
+90,nb_gm_002,6,54,0.0006489753723144531,18,2,False,BCACBEBDFCDABDEDBABCFADACCFDFBEBABFAEBBEDAFCFFDACCFAEA
+91,nb_gm_002,3,26,0.0003719329833984375,9,0,True,BBEBFAFFAECDEBAFBAEDEEFFEF
+92,nb_gm_002,7,53,0.001650094985961914,18,0,False,BDAFBFECDFBEFEFABBFFDDCFEFDDDBFBFBAFFFEFADFDAEFACCBAE
+93,nb_gm_002,6,26,0.0002880096435546875,8,0,False,AEFFAEABDDABBFDDFDDEBDCDDE
+94,nb_gm_002,7,36,0.00036406517028808594,12,2,False,EBEECEFDCEEDFDBCBCAFDBCBEBBFBCEBBDFD
+95,nb_gm_002,4,20,0.0002219676971435547,6,0,True,DCAADEDEEBAEABEEDDEE
+96,nb_gm_002,3,12,0.0003161430358886719,4,0,True,CFEFFEFFCACE
+97,nb_gm_002,3,17,0.00019812583923339844,5,0,False,BDABCEBDEFDFBBFCA
+98,nb_gm_002,5,35,0.0004279613494873047,11,0,False,FCEBFECEBCDAEFDDBFAADFFEDCBCEFCBEFF
+99,nb_gm_002,2,21,0.0002372264862060547,7,0,True,BFFCFAAABABAEEBFBEACA
diff --git a/benchmarks/results/skewness_002_vs_004.png b/benchmarks/results/skewness_002_vs_004.png
new file mode 100644
index 0000000..93743c5
--- /dev/null
+++ b/benchmarks/results/skewness_002_vs_004.png
Binary files differ
diff --git a/benchmarks/results/time_002_vs_004.png b/benchmarks/results/time_002_vs_004.png
new file mode 100644
index 0000000..93ffae9
--- /dev/null
+++ b/benchmarks/results/time_002_vs_004.png
Binary files differ

diff --git a/benchmarks/nb_gm_002_bm.py b/benchmarks/nb_gm_002_bm.py
index 64dc45e..c8af543 100644
--- a/benchmarks/nb_gm_002_bm.py
+++ b/benchmarks/nb_gm_002_bm.py
@@ -71,9 +71,35 @@
     plt.savefig(f'results/{figure_title}_targets_ratio.png', bbox_inches='tight')
     plt.show()
 
+
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+
+def profile():
+    from generators.nb_gm_002 import SequenceGenerator
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, int(lures/2), int(lures/2))
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_002_profile.csv', sep=',', encoding='utf-8')
+
 if __name__ == '__main__':
     alg = 'nb_gm_002'
     fn = f'results/{alg}_{n}back.csv'
-    benchmark(fn)
-    # skewness_diagram(fn, alg)
-    targets_ratio_diagram(fn, alg)
+    profile()
diff --git a/benchmarks/results/2v4_time_trials.png b/benchmarks/results/2v4_time_trials.png
new file mode 100644
index 0000000..e28fdcb
--- /dev/null
+++ b/benchmarks/results/2v4_time_trials.png
Binary files differ
diff --git a/benchmarks/results/2vs4_skewness.png b/benchmarks/results/2vs4_skewness.png
new file mode 100644
index 0000000..93743c5
--- /dev/null
+++ b/benchmarks/results/2vs4_skewness.png
Binary files differ
diff --git a/benchmarks/results/2vs4_time_n.png b/benchmarks/results/2vs4_time_n.png
new file mode 100644
index 0000000..93ffae9
--- /dev/null
+++ b/benchmarks/results/2vs4_time_n.png
Binary files differ
diff --git a/benchmarks/results/2vs4_tlratio.png b/benchmarks/results/2vs4_tlratio.png
new file mode 100644
index 0000000..274ede9
--- /dev/null
+++ b/benchmarks/results/2vs4_tlratio.png
Binary files differ
diff --git a/benchmarks/results/nb_gm_002_profile.csv b/benchmarks/results/nb_gm_002_profile.csv
new file mode 100644
index 0000000..da0f34f
--- /dev/null
+++ b/benchmarks/results/nb_gm_002_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_002,6,11,0.00013589859008789062,3,0,False,DADEBCDAFFB
+1,nb_gm_002,3,32,0.0003619194030761719,10,0,False,EAEBDEBDBBECDDDEBDABAAFEAECABFAD
+2,nb_gm_002,4,17,0.0002689361572265625,5,0,False,DCEAEFEBCFABCBAFA
+3,nb_gm_002,5,11,0.00011086463928222656,3,0,True,EEBFAEECACE
+4,nb_gm_002,5,34,0.0003609657287597656,11,0,False,EFFABFCDAEFCFBDACFBDEFDDDBCCEDEECE
+5,nb_gm_002,5,44,0.00046896934509277344,14,1,False,DFADDECDEBCCBEDCCDACCCBADABFEFABFAADFBACDFDA
+6,nb_gm_002,4,39,0.00039887428283691406,13,1,False,DBCDDFAFCFACCAAEFECFFBCBFBAAEEDDAEDDCEF
+7,nb_gm_002,3,52,0.000720977783203125,17,1,True,DBAFBFDAFDFCBACCADCADFDCCCFCCDDCBDBDDEDCCACEECAFBFFE
+8,nb_gm_002,6,53,0.0013470649719238281,17,2,False,EFACADDAAEAFDAEFCDDEDFBDDAEBAFEECFAEBEBFAEEFAFEEECADC
+9,nb_gm_002,6,16,0.00037479400634765625,5,0,True,EEAAFEEFACCEBFAB
+10,nb_gm_002,7,61,0.0010979175567626953,20,1,False,BADBEDEBFDFDFCBFDFCFABAEFCFAFAFDCFCBAFDEBAFEBDFBCBCAEEAFCFDDF
+11,nb_gm_002,2,31,0.00043487548828125,10,0,True,FDFAECDCDCACABFFAFEFBFAFDBAEFCA
+12,nb_gm_002,2,42,0.0005271434783935547,14,1,True,DBFBFECBBBABEBDFCCBCADDDDAFCFFFBFDADEDFBEB
+13,nb_gm_002,2,33,0.00045013427734375,11,0,True,CCCCBDADAECDCDCBAFACDEDFDBBBFCDBB
+14,nb_gm_002,2,49,0.0006201267242431641,16,2,True,CFEFADEEEEEECEAADDFECDBDDCFCEAEFAFEFEACAFFDFEFEDB
+15,nb_gm_002,2,31,0.000415802001953125,10,0,False,BFBFEEBAFCFAFACEFAADACACDCACEDB
+16,nb_gm_002,3,47,0.0006060600280761719,15,1,False,AEABECFFDBDDAAEFFEDBEDECBACEECEEAEFBEFBBCBBFFFE
+17,nb_gm_002,3,26,0.0007929801940917969,8,0,True,ACBACECCEBCECFDDBDCCFABEBB
+18,nb_gm_002,5,63,0.001611948013305664,21,2,True,BCABDCCCFFEFCAFADCFBAFAFAFFAACDAAABBAEBDFEFCDFEFCAFECCAEABFAFCC
+19,nb_gm_002,7,50,0.0005712509155273438,16,1,False,DCCAABEDACEAEEAFEEAEAFBEFABDCCBDBDECCBEFAEDCEEFEBA
+20,nb_gm_002,7,53,0.0013239383697509766,17,1,False,BDADEFAEEFCEAEDBFCFAEDFCCADCCFECAECBFAEFEBDACFFBBFACD
+21,nb_gm_002,3,43,0.0005009174346923828,14,2,False,CEDABDEBFCAECDFFFAFBAFBFDBDFDDFAEFBCCAECFEC
+22,nb_gm_002,5,18,0.0004649162292480469,6,0,False,ABFCEDCECDDFACDDFB
+23,nb_gm_002,3,10,0.0002720355987548828,3,0,True,DEEDBCDCFD
+24,nb_gm_002,7,50,0.0005559921264648438,18,1,True,BECFCBABABBCBFFABBDCEBBBFDABBBEADCCBCFFDEFFDBFDEFF
+25,nb_gm_002,4,16,0.00018310546875,5,0,True,BFADFDDEFDDFFADA
+26,nb_gm_002,7,63,0.0006709098815917969,22,1,False,FDFFFCDFCDEFADBEFEFECCCACAEFCCABBFACDADBCCFDAFECAEAAECDAEABECDC
+27,nb_gm_002,2,28,0.0003261566162109375,9,0,True,ADCBCACAEFDCDCBBABAFDCBCBFDC
+28,nb_gm_002,7,35,0.0003800392150878906,11,0,False,BDCABDDBDEFDCFAEECDEFECAADEFEDDBDAF
+29,nb_gm_002,5,15,0.00014209747314453125,5,0,True,FCFFEEAFFECACDE
+30,nb_gm_002,6,60,0.0006458759307861328,20,1,False,DBFBECCBCFFCABCBEDBBCDEDCDFDCACBFACBEACAFFEDDEBEEDDEAAECAEFD
+31,nb_gm_002,5,34,0.00037026405334472656,11,0,False,BCEFDCFEFEEFABEEDDBAEDAFFBBECABFEC
+32,nb_gm_002,5,63,0.0006792545318603516,21,2,False,FECACDDCBCBFCBFDFEAAEEEAFCFAAECBBAEBABAADACCACABFDAABFEFFDFACFC
+33,nb_gm_002,3,58,0.0006279945373535156,19,1,False,EDBDDADCBDABCAFCFDDFCDFCAFBDEBAABDEDDCCECEEFEFEACBACDFEAEA
+34,nb_gm_002,2,10,0.0001220703125,3,0,True,EAEBECCACF
+35,nb_gm_002,7,17,0.00016617774963378906,5,0,False,ADFDEACACFEEAFDCB
+36,nb_gm_002,6,61,0.0006701946258544922,20,1,False,DEEACEFEBCEFBECEBFBEDAAAACBAFBADAAEEFDBDBFDDBDBDDDBFFCADABFCA
+37,nb_gm_002,4,56,0.0005986690521240234,18,1,False,DAADDEDDAEDAACBAAEAEECADEDAFFDFBBDBDBFBECBECFFCCDFACEBAE
+38,nb_gm_002,6,11,0.00010180473327636719,3,0,False,CDBABBCDFAF
+39,nb_gm_002,3,46,0.0004971027374267578,16,1,False,CEDCAFCDFDEECAACAAFAAFAABBEFBECDADDEBBAAAACBAA
+40,nb_gm_002,5,19,0.00019097328186035156,7,0,True,DFCAEDEEAEDFEFCDEEB
+41,nb_gm_002,5,60,0.0006420612335205078,20,1,False,DEACCBFBDFEFCDFCAADFEFAFFEFBBBFADABFADFBCEDABCDFEBDECDCFCCBC
+42,nb_gm_002,4,20,0.00021409988403320312,6,0,False,DDBFADCEDDBECEBEFAAE
+43,nb_gm_002,2,42,0.00045800209045410156,15,1,False,AFADCEFFBAACAAEAEACFCFCDDEDADFBCECDCFBFDFD
+44,nb_gm_002,2,30,0.00035190582275390625,10,0,False,ADACFFDFCFDADFDFAEBCBAACFCEDED
+45,nb_gm_002,4,23,0.0002701282501220703,7,0,True,DDADDDFEFDAADDADFAACFED
+46,nb_gm_002,5,22,0.00023698806762695312,7,0,False,EBBBFADBDFABACCAEACEAD
+47,nb_gm_002,7,30,0.0003120899200439453,10,0,False,EFABBEAEFAFDCBDFDFAFBECCCACBEC
+48,nb_gm_002,4,50,0.0005471706390380859,16,1,True,BBDDADDBEDDEFFDAFFFAEBADEFDFEADEAADFAFCFEDCEECFFDA
+49,nb_gm_002,6,64,0.0007898807525634766,21,1,False,EDBDEDAFEDBACBABCAAAFBDFBAFBDADAFDDAFAFDDCBEFCFEBADCEDBAFFEBADDD
+50,nb_gm_002,6,45,0.0005559921264648438,15,1,True,FADFAFFDECDFFAAAFBFAAAFFCDDFFACEDBFDDECAFCDFB
+51,nb_gm_002,4,10,9.989738464355469e-05,3,0,True,EFFAADFAAB
+52,nb_gm_002,2,51,0.0005540847778320312,17,1,False,BDBFEEEFECDCBAEFFCCACABEDABEEFDFDBBFBCBFBEBEBCBCCDC
+53,nb_gm_002,3,20,0.00038504600524902344,6,0,True,DDAFCEFCAADEEFEEAECA
+54,nb_gm_002,4,43,0.0004611015319824219,14,1,False,FBDABAFABFBDBFDFBABEABCDFCCEFBCAFDCECDCDDDC
+55,nb_gm_002,3,52,0.0006678104400634766,17,1,True,ADEAEFBEFDAFEABECEEBACBDEFDEDDABEEFAEBAAACABEAEEFEDB
+56,nb_gm_002,5,38,0.00038504600524902344,12,1,True,CDBAEBBECECBAFBEBCBBCCCADCCCACEACFDEAB
+57,nb_gm_002,2,12,0.00014090538024902344,4,0,True,DEDADFBFAEDE
+58,nb_gm_002,4,12,0.00011324882507324219,4,0,False,ACBBEFBDEFBA
+59,nb_gm_002,5,43,0.0005619525909423828,14,1,False,BDCABACAEFFCDECFEDBADDDFADDDBAFDCBDFCFDDEDC
+60,nb_gm_002,5,29,0.0003631114959716797,9,0,True,BBCAABDADFBDADCEFABCDCADDAAFD
+61,nb_gm_002,3,53,0.0006711483001708984,17,1,False,CEACACFDFEDEEDAEDEEAACDDDCEDCDFCEFEEFAEBDFBFCDABDEBAA
+62,nb_gm_002,4,54,0.0006229877471923828,18,1,True,ADAFFDACFDFDBDCCBFACBDACFDEFFDADEDCBDFBFCBAABFDFCFCFCF
+63,nb_gm_002,5,60,0.0007681846618652344,20,1,False,BFCCBEACCEEABCAEDBDEEBBAEBFCABBEBFCBAACEBBECECCADFBAAFFEABCF
+64,nb_gm_002,4,14,0.00017595291137695312,4,0,True,AFEDDEBAEABAEA
+65,nb_gm_002,6,48,0.0005950927734375,16,1,True,FAFEDBABCBBBFBCBBBDCCECBEECDBDDAEDBDDCCAADDBDACF
+66,nb_gm_002,5,25,0.0003132820129394531,8,0,True,EFFADEFBABFFEFEDFDDDCFDAD
+67,nb_gm_002,6,47,0.0006089210510253906,15,1,False,AFEEDFAFFEAAADFDCABCAEBDFCDCBAFCDAECDCAEBCBCABC
+68,nb_gm_002,3,17,0.00021886825561523438,5,0,False,CFFBFFBAEACEADBCF
+69,nb_gm_002,7,37,0.00044989585876464844,12,2,True,EFDAFEEEDEABFDDEEBBFBAABBAFEAABBEBEBC
+70,nb_gm_002,6,62,0.0008101463317871094,22,0,False,FECDAFFACDCDFADDCFCEEDBFBEDFCFBFAFBBEEACBBACBCBCABBCDFAFEDECBE
+71,nb_gm_002,4,44,0.00054931640625,14,1,False,FBCCFBCCFBDAEBDBBDACBAFDAFECBFEDCFAFAFCDACAA
+72,nb_gm_002,2,57,0.0007417201995849609,19,2,False,CCFEBBDDDFFFAFCEAECEBEEBEBCBEDFACADABEBCCFCBCBDBAEDCBCBCF
+73,nb_gm_002,4,48,0.0005822181701660156,16,2,False,FABADABBAAEAFCFDDCDCCBDFABDEEEDFFDDAFDCCFACCFECA
+74,nb_gm_002,5,35,0.0004601478576660156,11,0,True,FDFDBFBFCADDCDECAADEBBAFFDBFFDDBFCD
+75,nb_gm_002,2,13,0.0002009868621826172,4,0,True,EBDDAEBFBFBAB
+76,nb_gm_002,6,42,0.0005300045013427734,14,2,True,ABCFDCADEEDAABEDDDEDCDCDBDBDDDCDCADACBCEFE
+77,nb_gm_002,3,34,0.00043511390686035156,11,0,False,FAFDBDDEDDFADCAEADECDCAECFBBDEBDFB
+78,nb_gm_002,2,28,0.0004279613494873047,9,0,False,BABADDEDEDBACACBEEEADCDFBEDC
+79,nb_gm_002,4,23,0.00030493736267089844,7,0,False,DDBABEFAEDBADDFCEBFCEAF
+80,nb_gm_002,6,53,0.0006649494171142578,17,1,False,EFEECFEFBFCFBBEABBCEEABFAEEBBDDEEDDAEEEFBCFDFFDBEDAAD
+81,nb_gm_002,3,61,0.0007090568542480469,22,1,False,EEEEBEAEEAFFACFEFEEDEFBCFAEFBDFBDACFEDBECBEDBDBFDEABEBBDBBCCA
+82,nb_gm_002,5,58,0.0006530284881591797,20,2,False,DFFBADFFFCBEBDCBCBCFBAAAECECFDADCDDEECECCEAEFBEAEFBEEECCED
+83,nb_gm_002,7,11,9.107589721679688e-05,3,0,False,FCEBFCDFAEB
+84,nb_gm_002,4,41,0.0004858970642089844,13,1,False,EBBAFFBADCCDBEEBAEEADEEDCEECCEEFDECEBEDAF
+85,nb_gm_002,6,22,0.00023603439331054688,7,0,True,AFDDAAADADEBAFEDEADFDD
+86,nb_gm_002,7,49,0.0005178451538085938,16,2,False,DBEFDEDDEEECCDEFECCFEFCECDDCBCFFBAFBDBFBDFEBBFBDE
+87,nb_gm_002,3,64,0.0008380413055419922,21,1,True,FEAEDEEDEDAABBDDDDAAEFFEDFEDAEAEEAFEAFBFBBEBDECEEBAEADFFBACAACEC
+88,nb_gm_002,5,11,0.0002498626708984375,3,0,True,CBBBCCABACA
+89,nb_gm_002,7,56,0.0007159709930419922,19,1,False,EFBFBEDEFDDBCBEADFBAEBBEBCFEBDACEDCFDCCECCFDEFFCAFDECDAA
+90,nb_gm_002,6,54,0.0006489753723144531,18,2,False,BCACBEBDFCDABDEDBABCFADACCFDFBEBABFAEBBEDAFCFFDACCFAEA
+91,nb_gm_002,3,26,0.0003719329833984375,9,0,True,BBEBFAFFAECDEBAFBAEDEEFFEF
+92,nb_gm_002,7,53,0.001650094985961914,18,0,False,BDAFBFECDFBEFEFABBFFDDCFEFDDDBFBFBAFFFEFADFDAEFACCBAE
+93,nb_gm_002,6,26,0.0002880096435546875,8,0,False,AEFFAEABDDABBFDDFDDEBDCDDE
+94,nb_gm_002,7,36,0.00036406517028808594,12,2,False,EBEECEFDCEEDFDBCBCAFDBCBEBBFBCEBBDFD
+95,nb_gm_002,4,20,0.0002219676971435547,6,0,True,DCAADEDEEBAEABEEDDEE
+96,nb_gm_002,3,12,0.0003161430358886719,4,0,True,CFEFFEFFCACE
+97,nb_gm_002,3,17,0.00019812583923339844,5,0,False,BDABCEBDEFDFBBFCA
+98,nb_gm_002,5,35,0.0004279613494873047,11,0,False,FCEBFECEBCDAEFDDBFAADFFEDCBCEFCBEFF
+99,nb_gm_002,2,21,0.0002372264862060547,7,0,True,BFFCFAAABABAEEBFBEACA
diff --git a/benchmarks/results/skewness_002_vs_004.png b/benchmarks/results/skewness_002_vs_004.png
new file mode 100644
index 0000000..93743c5
--- /dev/null
+++ b/benchmarks/results/skewness_002_vs_004.png
Binary files differ
diff --git a/benchmarks/results/time_002_vs_004.png b/benchmarks/results/time_002_vs_004.png
new file mode 100644
index 0000000..93ffae9
--- /dev/null
+++ b/benchmarks/results/time_002_vs_004.png
Binary files differ
diff --git a/benchmarks/results/time_trials_002_vs_004.png b/benchmarks/results/time_trials_002_vs_004.png
new file mode 100644
index 0000000..274ede9
--- /dev/null
+++ b/benchmarks/results/time_trials_002_vs_004.png
Binary files differ

diff --git a/benchmarks/nb_gm_002_bm.py b/benchmarks/nb_gm_002_bm.py
index 64dc45e..c8af543 100644
--- a/benchmarks/nb_gm_002_bm.py
+++ b/benchmarks/nb_gm_002_bm.py
@@ -71,9 +71,35 @@
     plt.savefig(f'results/{figure_title}_targets_ratio.png', bbox_inches='tight')
     plt.show()
 
+
+import time
+import numpy as np
+import pandas as pd
+
+import benchmarks.common as common
+
+def profile():
+    from generators.nb_gm_002 import SequenceGenerator
+
+    res = []
+
+    for s in range(common.sample_size):
+        trials = np.random.randint(common.trials_range[0], common.trials_range[1])
+        targets = int(trials / 3)
+        lures = int(targets / 6)  # tl_ratio = 2.0
+        n = np.random.randint(2, 8)
+        gen = SequenceGenerator(common.choices, n)
+        st = time.time()
+        seq = gen.generate(trials, targets, int(lures/2), int(lures/2))
+        st = time.time() - st
+        t, lu = common.count_targets_and_lures(seq, n)
+        skewed = common.skewness(seq, common.choices)
+        res.append(['nb_gm_004', n, trials, st, t, lu, skewed, ''.join(seq)])
+        print(f"sequence #{s+1} generated")
+    res_df = pd.DataFrame(res, columns=['alg', 'n', 'trials', 'time', 'targets', 'lures', 'skewed', 'sequence'])
+    res_df.to_csv('benchmarks/results/nb_gm_002_profile.csv', sep=',', encoding='utf-8')
+
 if __name__ == '__main__':
     alg = 'nb_gm_002'
     fn = f'results/{alg}_{n}back.csv'
-    benchmark(fn)
-    # skewness_diagram(fn, alg)
-    targets_ratio_diagram(fn, alg)
+    profile()
diff --git a/benchmarks/results/2v4_time_trials.png b/benchmarks/results/2v4_time_trials.png
new file mode 100644
index 0000000..e28fdcb
--- /dev/null
+++ b/benchmarks/results/2v4_time_trials.png
Binary files differ
diff --git a/benchmarks/results/2vs4_skewness.png b/benchmarks/results/2vs4_skewness.png
new file mode 100644
index 0000000..93743c5
--- /dev/null
+++ b/benchmarks/results/2vs4_skewness.png
Binary files differ
diff --git a/benchmarks/results/2vs4_time_n.png b/benchmarks/results/2vs4_time_n.png
new file mode 100644
index 0000000..93ffae9
--- /dev/null
+++ b/benchmarks/results/2vs4_time_n.png
Binary files differ
diff --git a/benchmarks/results/2vs4_tlratio.png b/benchmarks/results/2vs4_tlratio.png
new file mode 100644
index 0000000..274ede9
--- /dev/null
+++ b/benchmarks/results/2vs4_tlratio.png
Binary files differ
diff --git a/benchmarks/results/nb_gm_002_profile.csv b/benchmarks/results/nb_gm_002_profile.csv
new file mode 100644
index 0000000..da0f34f
--- /dev/null
+++ b/benchmarks/results/nb_gm_002_profile.csv
@@ -0,0 +1,101 @@
+,alg,n,trials,time,targets,lures,skewed,sequence
+0,nb_gm_002,6,11,0.00013589859008789062,3,0,False,DADEBCDAFFB
+1,nb_gm_002,3,32,0.0003619194030761719,10,0,False,EAEBDEBDBBECDDDEBDABAAFEAECABFAD
+2,nb_gm_002,4,17,0.0002689361572265625,5,0,False,DCEAEFEBCFABCBAFA
+3,nb_gm_002,5,11,0.00011086463928222656,3,0,True,EEBFAEECACE
+4,nb_gm_002,5,34,0.0003609657287597656,11,0,False,EFFABFCDAEFCFBDACFBDEFDDDBCCEDEECE
+5,nb_gm_002,5,44,0.00046896934509277344,14,1,False,DFADDECDEBCCBEDCCDACCCBADABFEFABFAADFBACDFDA
+6,nb_gm_002,4,39,0.00039887428283691406,13,1,False,DBCDDFAFCFACCAAEFECFFBCBFBAAEEDDAEDDCEF
+7,nb_gm_002,3,52,0.000720977783203125,17,1,True,DBAFBFDAFDFCBACCADCADFDCCCFCCDDCBDBDDEDCCACEECAFBFFE
+8,nb_gm_002,6,53,0.0013470649719238281,17,2,False,EFACADDAAEAFDAEFCDDEDFBDDAEBAFEECFAEBEBFAEEFAFEEECADC
+9,nb_gm_002,6,16,0.00037479400634765625,5,0,True,EEAAFEEFACCEBFAB
+10,nb_gm_002,7,61,0.0010979175567626953,20,1,False,BADBEDEBFDFDFCBFDFCFABAEFCFAFAFDCFCBAFDEBAFEBDFBCBCAEEAFCFDDF
+11,nb_gm_002,2,31,0.00043487548828125,10,0,True,FDFAECDCDCACABFFAFEFBFAFDBAEFCA
+12,nb_gm_002,2,42,0.0005271434783935547,14,1,True,DBFBFECBBBABEBDFCCBCADDDDAFCFFFBFDADEDFBEB
+13,nb_gm_002,2,33,0.00045013427734375,11,0,True,CCCCBDADAECDCDCBAFACDEDFDBBBFCDBB
+14,nb_gm_002,2,49,0.0006201267242431641,16,2,True,CFEFADEEEEEECEAADDFECDBDDCFCEAEFAFEFEACAFFDFEFEDB
+15,nb_gm_002,2,31,0.000415802001953125,10,0,False,BFBFEEBAFCFAFACEFAADACACDCACEDB
+16,nb_gm_002,3,47,0.0006060600280761719,15,1,False,AEABECFFDBDDAAEFFEDBEDECBACEECEEAEFBEFBBCBBFFFE
+17,nb_gm_002,3,26,0.0007929801940917969,8,0,True,ACBACECCEBCECFDDBDCCFABEBB
+18,nb_gm_002,5,63,0.001611948013305664,21,2,True,BCABDCCCFFEFCAFADCFBAFAFAFFAACDAAABBAEBDFEFCDFEFCAFECCAEABFAFCC
+19,nb_gm_002,7,50,0.0005712509155273438,16,1,False,DCCAABEDACEAEEAFEEAEAFBEFABDCCBDBDECCBEFAEDCEEFEBA
+20,nb_gm_002,7,53,0.0013239383697509766,17,1,False,BDADEFAEEFCEAEDBFCFAEDFCCADCCFECAECBFAEFEBDACFFBBFACD
+21,nb_gm_002,3,43,0.0005009174346923828,14,2,False,CEDABDEBFCAECDFFFAFBAFBFDBDFDDFAEFBCCAECFEC
+22,nb_gm_002,5,18,0.0004649162292480469,6,0,False,ABFCEDCECDDFACDDFB
+23,nb_gm_002,3,10,0.0002720355987548828,3,0,True,DEEDBCDCFD
+24,nb_gm_002,7,50,0.0005559921264648438,18,1,True,BECFCBABABBCBFFABBDCEBBBFDABBBEADCCBCFFDEFFDBFDEFF
+25,nb_gm_002,4,16,0.00018310546875,5,0,True,BFADFDDEFDDFFADA
+26,nb_gm_002,7,63,0.0006709098815917969,22,1,False,FDFFFCDFCDEFADBEFEFECCCACAEFCCABBFACDADBCCFDAFECAEAAECDAEABECDC
+27,nb_gm_002,2,28,0.0003261566162109375,9,0,True,ADCBCACAEFDCDCBBABAFDCBCBFDC
+28,nb_gm_002,7,35,0.0003800392150878906,11,0,False,BDCABDDBDEFDCFAEECDEFECAADEFEDDBDAF
+29,nb_gm_002,5,15,0.00014209747314453125,5,0,True,FCFFEEAFFECACDE
+30,nb_gm_002,6,60,0.0006458759307861328,20,1,False,DBFBECCBCFFCABCBEDBBCDEDCDFDCACBFACBEACAFFEDDEBEEDDEAAECAEFD
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+57,nb_gm_002,2,12,0.00014090538024902344,4,0,True,DEDADFBFAEDE
+58,nb_gm_002,4,12,0.00011324882507324219,4,0,False,ACBBEFBDEFBA
+59,nb_gm_002,5,43,0.0005619525909423828,14,1,False,BDCABACAEFFCDECFEDBADDDFADDDBAFDCBDFCFDDEDC
+60,nb_gm_002,5,29,0.0003631114959716797,9,0,True,BBCAABDADFBDADCEFABCDCADDAAFD
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+73,nb_gm_002,4,48,0.0005822181701660156,16,2,False,FABADABBAAEAFCFDDCDCCBDFABDEEEDFFDDAFDCCFACCFECA
+74,nb_gm_002,5,35,0.0004601478576660156,11,0,True,FDFDBFBFCADDCDECAADEBBAFFDBFFDDBFCD
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+78,nb_gm_002,2,28,0.0004279613494873047,9,0,False,BABADDEDEDBACACBEEEADCDFBEDC
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+83,nb_gm_002,7,11,9.107589721679688e-05,3,0,False,FCEBFCDFAEB
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+88,nb_gm_002,5,11,0.0002498626708984375,3,0,True,CBBBCCABACA
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+92,nb_gm_002,7,53,0.001650094985961914,18,0,False,BDAFBFECDFBEFEFABBFFDDCFEFDDDBFBFBAFFFEFADFDAEFACCBAE
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+94,nb_gm_002,7,36,0.00036406517028808594,12,2,False,EBEECEFDCEEDFDBCBCAFDBCBEBBFBCEBBDFD
+95,nb_gm_002,4,20,0.0002219676971435547,6,0,True,DCAADEDEEBAEABEEDDEE
+96,nb_gm_002,3,12,0.0003161430358886719,4,0,True,CFEFFEFFCACE
+97,nb_gm_002,3,17,0.00019812583923339844,5,0,False,BDABCEBDEFDFBBFCA
+98,nb_gm_002,5,35,0.0004279613494873047,11,0,False,FCEBFECEBCDAEFDDBFAADFFEDCBCEFCBEFF
+99,nb_gm_002,2,21,0.0002372264862060547,7,0,True,BFFCFAAABABAEEBFBEACA
diff --git a/benchmarks/results/skewness_002_vs_004.png b/benchmarks/results/skewness_002_vs_004.png
new file mode 100644
index 0000000..93743c5
--- /dev/null
+++ b/benchmarks/results/skewness_002_vs_004.png
Binary files differ
diff --git a/benchmarks/results/time_002_vs_004.png b/benchmarks/results/time_002_vs_004.png
new file mode 100644
index 0000000..93ffae9
--- /dev/null
+++ b/benchmarks/results/time_002_vs_004.png
Binary files differ
diff --git a/benchmarks/results/time_trials_002_vs_004.png b/benchmarks/results/time_trials_002_vs_004.png
new file mode 100644
index 0000000..274ede9
--- /dev/null
+++ b/benchmarks/results/time_trials_002_vs_004.png
Binary files differ
diff --git a/demo/nb_gm_002_demo.py b/demo/nb_gm_002_demo.py
index 776b951..98b8358 100644
--- a/demo/nb_gm_002_demo.py
+++ b/demo/nb_gm_002_demo.py
@@ -17,27 +17,35 @@
 # left and right arrow keys for responses
 response_keys = [misc.constants.K_LEFT, misc.constants.K_RIGHT]
 
-expyriment.control.start(exp)
-
 # initialize three blocks
-for b in range(0, 2):
+for b in range(2):
     block = design.Block()
-    block.set_factor("block", b)
-    for item in nback_sequence:
+    block.set_factor("index", b)
+    for tindex, item in enumerate(nback_sequence):
         trial = design.Trial()
         trial.set_factor("stimulus", item)
-        stim = stimuli.TextLine(item, text_size=200)
-        stim.preload()
-        trial.add_stimulus(stim)
+        trial.set_factor("index", tindex)
+        for x in range(10):
+            stim = stimuli.TextLine(item, text_size=200, background_colour=[0,0,0], text_colour=[255,0,0])
+            c = stimuli.Canvas(stim.surface_size)
+            stim.plot(c)
+            c._get_surface().set_alpha(x*25)
+            c.preload()
+            trial.add_stimulus(c)
         block.add_trial(trial, random_position=False)
     exp.add_block(block)
 
+expyriment.control.start(exp)
+
+# present trials
 for block in exp.blocks:
     for trial in block.trials:
         exp.clock.wait(1000 - fixation.present())
-        trial.stimuli[0].present()
+        for stim in trial.stimuli:
+            stim.present()
+            exp.clock.wait(5)
         pressed_key, rt = exp.keyboard.wait(response_keys)
-        exp.data.add([block.get_factor("block"), trial.get_factor("stimulus"), pressed_key, rt])
+        exp.data.add([block.get_factor("index"), trial.get_factor("stimulus"), pressed_key, rt])
         exp.clock.wait(1000 - expyriment.stimuli.BlankScreen().present() - trial.stimuli[0].unload())
 
 expyriment.control.end("Goodbye!", goodbye_delay=1000)