diff --git a/Python/benchmarking.py b/Python/benchmarking.py
index ec9e4db6ffbfaf7b23008649dd5d8f0e9e49ffc8..6796be74d3eca61d5d95a9420a689d765a9c9cae 100644
--- a/Python/benchmarking.py
+++ b/Python/benchmarking.py
@@ -25,7 +25,7 @@ def count(paths, path_table, off):
 
 
 data = np.load("table/table-half_31_120722.npz", allow_pickle=True)
-table = data['table'].item()
+_table = data['table'].item()
 twzr = data['wfm'].item()
 static_sig = data['static_sig']
 target = data['target']
@@ -33,19 +33,22 @@ t_idx = np.nonzero(target)[0]
 nt = twzr.omega.size
 
 _table_cp = {}
-
-for key in table:
-    _table_cp[key] = cp.array(table[key])
+# _sig = np.copy(static_sig)
+for key in _table:
+    _table_cp[key] = cp.array(_table[key])
 
 n_repeat = 500
-total_t = np.zeros((nt+1, 2))
-gpu_t = np.zeros((nt+1, 2))
-cpu_t = np.zeros((nt+1, 2))
+times = {
+    'total_t': np.zeros((nt + 1, 2)),
+    'gpu_t': np.zeros((nt + 1, 2)),
+    'cpu_t': np.zeros((nt + 1, 2))
+}
 filling_ratio = np.zeros((nt+1, 2))
 n_move = np.zeros((nt+1, 2))
 
 for i in range(nt+1):
-    _sig = cp.array(static_sig)
+    # _sig = cp.array(static_sig)
+    _sig = np.copy(static_sig)
     f_prob = i/nt
     t_t = np.zeros(n_repeat)
     g_t = np.zeros(n_repeat)
@@ -54,11 +57,12 @@ for i in range(nt+1):
     nm = np.zeros(n_repeat)
     print(i, f_prob)
     for j in range(n_repeat):
-        filled = np.random.rand(nt)
-        tr = filled < f_prob
-        fa = filled >= f_prob
-        filled[tr] = 1
-        filled[fa] = 0
+        # filled = np.random.rand(nt)
+        # tr = filled < f_prob
+        # fa = filled >= f_prob
+        # filled[tr] = 1
+        # filled[fa] = 0
+        filled = np.random.binomial(1, f_prob, nt)
         f_idx = np.nonzero(filled)[0]
         b = benchmark(
             test,
@@ -75,21 +79,18 @@ for i in range(nt+1):
 
     n_move[i,0] = np.mean(nm)
     n_move[i,1] = np.var(nm)
-    gpu_t[i,0] = np.mean(g_t)
-    gpu_t[i,1] = np.var(g_t)
-    cpu_t[i,0] = np.mean(c_t)
-    cpu_t[i,1] = np.var(c_t)
-    total_t[i,0] = np.mean(t_t)
-    total_t[i,1] = np.var(t_t)
+    times['gpu_t'][i,0] = np.mean(g_t)
+    times['gpu_t'][i,1] = np.var(g_t)
+    times['cpu_t'][i,0] = np.mean(c_t)
+    times['cpu_t'][i,1] = np.var(c_t)
+    times['total_t'][i,0] = np.mean(t_t)
+    times['total_t'][i,1] = np.var(t_t)
     filling_ratio[i,0] = np.mean(ratio)
     filling_ratio[i,1] = np.var(ratio)
 
-times = {}
-times['gpu_t'] = gpu_t
-times['cpu_t'] = cpu_t
-times['total_t'] = total_t
+
 np.savez(
-    f"data/120722_benchmark_{nt}-half_wfm-and-transfer.npz",
+    f"data/120822_{nt}-half_wfm-and-transfer_CPU.npz",
     wfm=twzr,
     target=target,
     filling_ratio=filling_ratio,