Low pass filtering: Measuring barometric Pressure with a BMP280
In the pulse rate estimation example, we used a filter to remove the low frequency (slowly changing) part of a signal and preserve the high frequency part. This is known as a high pass filter.
To measure barometric pressure, we instead want to preserve the slowly changing part of the signal and exclude the quickly changing part, AKA noise. This is known as a low-pass filter.
This example is designed for CLUE or Feather Sense, but can be adapted to other supported boards with the BMP280 breakout.
The image below shows a typical plot in Mu. The green trace is unfiltered, the blue is filtered. Notice how the blue trace is smoother than the green, but is also moved further to the right. This represents the phase shift of the filter, which is about 10 seconds.
You'll need to manually install the necessary libraries from the bundle:
- adafruit_bmp280.mpy
- adafruit_bus_device
Before continuing make sure your board's lib folder or root filesystem has the adafruit_bmp280.mpy, and adafruit_bus_device files and folders copied over. Put the following Python code into code.py and then open the Mu plotter window. In the plot, you will see a noisy, unfiltered trace and a smooth filtered trace.
Note that the barometric pressure value is offset by 980, which happened to be the local barometric pressure when this example was written. This is to move the values closer to the center of the Mu plot, which accentuates the variation in the value. You can remove the subtraction so that you are dealing with absolute, not relative, values.
# SPDX-FileCopyrightText: 2020 Jeff Epler for Adafruit Industries
#
# SPDX-License-Identifier: MIT
import time
import adafruit_bmp280
import board
import displayio
from ulab import numpy as np
# Blank the screen. Scrolling text causes unwanted delays.
d = displayio.Group()
board.DISPLAY.root_group = d
# Sampling rate: 16Hz
# Cutoff frequency: 0.16Hz
# Transition bandwidth: 0.16Hz
# Window type: Hamming
# Filter has 311 coefficients
taps = np.array([
-0.000050679794726066, -0.000041099278318167, -0.000031279920668665,
-0.000021183486597150, -0.000010770285292045, +0.000000000000000000,
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+0.000438798585855176, +0.000415285995764155, +0.000387525565446236] +
[+0.000355376044004699, +0.000318715033091691, +0.000277440901501588,
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+0.017166546887869318, +0.017535153501103896, +0.017881182383493146] +
[+0.018203584559716979, +0.018501378539810983, +0.018773654273367416,
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-0.000587657355512251, -0.000852850856865070, -0.001098869965763655] +
[-0.001325738543930948, -0.001533548017297868, -0.001722455325210333,
-0.001892680620886389, -0.002044504738458278, -0.002178266442894981,
-0.002294359479963247, -0.002393229444145818, -0.002475370483091317,
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+0.000000000000000000, +0.000065010261293198, +0.000125273788160487,
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+0.000060551498686721, +0.000047431049079466, +0.000034847558259864,
+0.000022770999889941, +0.000011167446754809, +0.000000000000000000,
-0.000010770285292045, -0.000021183486597150, -0.000031279920668665,
-0.000041099278318167, -0.000050679794726066,
])
# How often we are going to poll the sensor (If you change this, you need
# to change the filter above and the integration time below)
dt = 62500000 # 16Hz, 62.5ms
# Wait until after deadline_ns has passed
def sleep_deadline(deadline_ns):
while time.monotonic_ns() < deadline_ns:
pass
# Initialize our sensor
i2c = board.I2C() # uses board.SCL and board.SDA
# i2c = board.STEMMA_I2C() # For using the built-in STEMMA QT connector on a microcontroller
sensor = adafruit_bmp280.Adafruit_BMP280_I2C(i2c)
sensor.standby_period = adafruit_bmp280.STANDBY_TC_1000
# Disable in-sensor filtering, because we want to show how it's done in
# CircuitPython
sensor.iir_filter = adafruit_bmp280.IIR_FILTER_DISABLE
sensor.overscan_pressure = adafruit_bmp280.OVERSCAN_X1
# And our data structures
# The most recent data samples, equal in number to the filter taps
data = np.zeros(len(taps))
t0 = deadline = time.monotonic_ns()
n = 0
# Take an initial reading to subtract off later, so that the graph in mu
# accentuates the small short term changes in pressure rather than the large
# DC offset of around 980
offset = sensor.pressure
while True:
deadline += dt
sleep_deadline(deadline)
# Move the trace near the origin so small differences can be seen in the mu
# plot window .. you wouldn't do this subtraction step if you are really
# interested in absolute barometric pressure.
value = sensor.pressure - offset
if n == 0:
# The first time, fill the filter with the initial value
data = data + value
else:
# Otherwise, add it as the next sample
data = np.roll(data, 1)
data[-1] = value
filtered = np.sum(data * taps)
# Actually print every 10th value. This prints about 1.6 values per
# second. You can print values more quickly by removing the 'if' and
# making the print unconditional, or change the frequency of prints up
# or down by changing the number '10'.
if n % 10 == 0:
print((filtered, value))
n += 1
Page last edited January 21, 2025
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