removed micropython-specific code

The update to this PR removes `micropython`-specific code. Tested on an adafruit feather board with the following results:

```
Computing the RMS value of 100 numbers
traditional          :    5.472ms [result=3535.843611]
ulab, with ndarray, some implementation in python :    0.500ms [result=3535.853624]
ulab only, with list :    0.655ms [result=3535.854340]
ulab only, with ndarray :    0.139ms [result=3535.854340]
```
The result of the second run might be misleading (seems to be faster than the third), because it does not contain the costs of the list-to-ndarray conversion (since that happens earlier, outside of time measurement).

Author: v923z

ulab_Crunch_Numbers_Fast/benchmark.py

@@ -1,21 +1,11 @@
-import utime
+import time
 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(
@@ -25,37 +15,31 @@ def normalized_rms(values):
 
     return math.sqrt(samples_sum / len(values))
 
-@timeit
 def normalized_rms_ulab(values):
+	# this function works with ndarrays only
     minbuf = ulab.numerical.mean(values)
     values = values - minbuf
     samples_sum = ulab.numerical.sum(values * 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)
 
-print("Computing the RMS value of 100 numbers")
-
-print('in python')
-normalized_rms(nums_list)
-
-print('\nin ulab, with some implementation in python')
-normalized_rms_ulab(nums_array)
+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('\nin ulab only, with ndarray')
-normalized_std_ulab(nums_array)
-
-print('\nin ulab only, with list')
-normalized_std_ulab_iterable(nums_list)
+print("Computing the RMS value of 100 numbers")
+timeit("traditional", lambda: normalized_rms(nums_list))
+timeit("ulab, with ndarray, some implementation in python", lambda: normalized_rms_ulab(nums_array))
+timeit("ulab only, with list", lambda: normalized_std_ulab(nums_list))
+timeit("ulab only, with ndarray", lambda: normalized_std_ulab(nums_array))