In this simple benchmark, we compare two ways of finding the amplitude of a signal. normalized_rms computes it in a traditional way, handling each number one by one in Python code. normalized_rms_ulab computes more quickly by working on groups of numbers in a ulab array at the same time. ulab.numpy.std computes most quickly by moving all operations from Python to ulab.
# SPDX-FileCopyrightText: 2020 Jeff Epler for Adafruit Industries
# SPDX-FileCopyrightText: 2020 Zoltán Vörös for Adafruit Industries
#
# SPDX-License-Identifier: MIT
import time
import math
from ulab import numpy as np
def mean(values):
return sum(values) / len(values)
def normalized_rms(values):
minbuf = int(mean(values))
samples_sum = sum(
float(sample - minbuf) * (sample - minbuf)
for sample in values
)
return math.sqrt(samples_sum / len(values))
def normalized_rms_ulab(values):
# this function works with ndarrays only
minbuf = np.mean(values)
values = values - minbuf
samples_sum = np.sum(values * values)
return math.sqrt(samples_sum / len(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 = np.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("%-30s : %8.3fms [result=%f]" % (s, r, x))
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: np.std(nums_list))
timeit("ulab only, with ndarray", lambda: np.std(nums_array))
On my Metro M4, the ulab code computes almost exactly the same value, but over 40 times faster. Take care if running this on a board with an LCD display such as the CLUE, printing the results on the display takes a lot longer than the computation itself, and completely distorts the results.
traditional : 2.951ms [result=3535.843611] ulab, algorithm in python : 0.251ms [result=3535.853624] ulab only, with list : 0.336ms [result=3535.854340] ulab only, with ndarray : 0.068ms [result=3535.854340]
