update benchmark.py

ulab_Crunch_Numbers_Fast/benchmark.py

@@ -1,11 +1,21 @@
-import time
+import utime
 import math
 import ulab
 import ulab.numerical
 
+def timeit(f, *args, **kwargs):
+    func_name = str(f).split(' ')[1]
+    def new_func(*args, **kwargs):
+        t = utime.ticks_us()
+        result = f(*args, **kwargs)
+        print('execution time: ', utime.ticks_diff(utime.ticks_us(), t), ' us')
+        return result
+    return new_func
+
 def mean(values):
     return sum(values) / len(values)
 
+@timeit
 def normalized_rms(values):
     minbuf = int(mean(values))
     samples_sum = sum(
@@ -15,6 +25,7 @@ def normalized_rms(values):
 
     return math.sqrt(samples_sum / len(values))
 
+@timeit
 def normalized_rms_ulab(values):
     minbuf = ulab.numerical.mean(values)
     values = values - minbuf
@@ -22,19 +33,29 @@ def normalized_rms_ulab(values):
     return math.sqrt(samples_sum / len(values))
 
 
+@timeit
+def normalized_std_ulab(values):
+    return ulab.numerical.std(values)
+
+@timeit
+def normalized_std_ulab_iterable(values):
+    return ulab.numerical.std(values)
+
 # Instead of using sensor data, we generate some data
 # The amplitude is 5000 so the rms should be around 5000/1.414 = 3536
 nums_list = [int(8000 + math.sin(i) * 5000) for i in range(100)]
 nums_array = ulab.array(nums_list)
 
-def timeit(s, f, n=100):
-    t0 = time.monotonic_ns()
-    for _ in range(n):
-        x = f()
-    t1 = time.monotonic_ns()
-    r = (t1 - t0) * 1e-6 / n
-    print("%-20s : %8.3fms [result=%f]" % (s, r, x))
-
 print("Computing the RMS value of 100 numbers")
-timeit("traditional", lambda: normalized_rms(nums_list))
-timeit("ulab", lambda: normalized_rms_ulab(nums_array))
+
+print('in python')
+normalized_rms(nums_list)
+
+print('\nin ulab, with some implementation in python')
+normalized_rms_ulab(nums_array)
+
+print('\nin ulab only, with ndarray')
+normalized_std_ulab(nums_array)
+
+print('\nin ulab only, with list')
+normalized_std_ulab_iterable(nums_list)