Playing with filtering and smooting in CircuitPython

README.md

Sources

• https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
• https://www.geeksforgeeks.org/python/how-to-calculate-moving-averages-in-python/
• https://www.geeksforgeeks.org/python/kalman-filter-in-python/
• https://www.hackster.io/geehysemiconductor/visualizing-and-filtering-hc-sr04-sensor-data-on-the-apm32f4-9e3f9b

continous_exponential_smoothing.py

from random import randint
from time import sleep

DELAY = 0.05

# simulate data coming from a noisy analog sensor, where the ADC range is 0-65536
def source():
    return randint(0, 2 ** 16)

# exponential smoothing (AKA exponential moving average)
# ALPHA should be between 0 and 1, and may be changed to adjust the smoothing
# the lower the ALPHA, the greater the smoothing
ALPHA = 0.15 
def exp_smooth(alpha, new_sample, filtered):
    return alpha * new_sample + (1 - alpha) * filtered

smoothed = 0

while True:
    sample = source()
    smoothed = exp_smooth(ALPHA, sample, smoothed)
    # print out the actual and smoothed/filtered results, good for use with a plotter
    print((sample, smoothed))
    sleep(DELAY)

moving_average.py

from random import randint
from ulab import numpy as np

# simple moving average
# see also:
# https://www.geeksforgeeks.org/python/how-to-calculate-moving-averages-in-python/

# simulate data coming from a noisy analog sensor, where the ADC range is 0-65536
def source():
    return randint(0, 2 ** 16)

def print_buffer(buff):
    for v in buff:
        print((v,))
    

# store a bunch of "readings"
ma_buffer = [source() for x in range(0,100)]

window_size = 10
i = 0
moving_averages = []

while i < len(ma_buffer) - window_size + 1:

    # Calculate the average of current window
    window_average = round(np.sum(ma_buffer[
      i:i+window_size]) / window_size, 2)
    
    # Store the average of current
    # window in moving average list
    moving_averages.append(window_average)
    
    # Shift window to right by one position
    i += 1

while len(moving_averages) < len(ma_buffer):
    moving_averages.insert(0,0)
    
for v in range(0, 100):
    print((ma_buffer[v], moving_averages[v]))