LinearDigitalFilter

class wpilib.LinearDigitalFilter(source, ffGains, fbGains)[source]

Bases: wpilib.Filter

This class implements a linear, digital filter. All types of FIR and IIR filters are supported. Static factory methods are provided to create commonly used types of filters.

Filters are of the form:

y[n] = (b0*x[n] + b1*x[n-1] + ... + bP*x[n-P]) - (a0*y[n-1] + a2*y[n-2] + ... + aQ*y[n-Q])

Where:

  • y[n] is the output at time “n”
  • x[n] is the input at time “n”
  • y[n-1] is the output from the LAST time step (“n-1”)
  • x[n-1] is the input from the LAST time step (“n-1”)
  • b0...bP are the “feedforward” (FIR) gains
  • a0...aQ are the “feedback” (IIR) gains

Note

IMPORTANT! Note the “-” sign in front of the feedback term! This is a common convention in signal processing.

What can linear filters do? Basically, they can filter, or diminish, the effects of undesirable input frequencies. High frequencies, or rapid changes, can be indicative of sensor noise or be otherwise undesirable. A “low pass” filter smoothes out the signal, reducing the impact of these high frequency components. Likewise, a “high pass” filter gets rid of slow-moving signal components, letting you detect large changes more easily.

Example FRC applications of filters:

  • Getting rid of noise from an analog sensor input (note: the roboRIO’s FPGA can do this faster in hardware)
  • Smoothing out joystick input to prevent the wheels from slipping or the robot from tipping
  • Smoothing motor commands so that unnecessary strain isn’t put on electrical or mechanical components
  • If you use clever gains, you can make a PID controller out of this class!

For more on filters, I highly recommend the following articles:

Note

pidGet() should be called by the user on a known, regular period. You can set up a Notifier to do this (look at the PIDController class), or do it “inline” with code in a periodic function.

Note

For ALL filters, gains are necessarily a function of frequency. If you make a filter that works well for you at, say, 100Hz, you will most definitely need to adjust the gains if you then want to run it at 200Hz! Combining this with Note 1 - the impetus is on YOU as a developer to make sure pidGet() gets called at the desired, constant frequency!

There are static methods you can use to build common filters:

Constructor. Create a linear FIR or IIR filter

Parameters:
  • source (PIDSource, callable) – The PIDSource object that is used to get values
  • ffGains (list, tuple) – The “feed forward” or FIR gains
  • fbGains (list, tuple) – The “feed back” or IIR gains
get()[source]

Returns the current filter estimate without also inserting new data as pidGet() would do.

Returns:The current filter estimate
static highPass(source, timeConstant, period)[source]

Creates a first-order high-pass filter of the form:

y[n] = gain*x[n] + (-gain)*x[n-1] + gain*y[n-1]

where gain = e^(-dt / T), T is the time constant in seconds

This filter is stable for time constants greater than zero

Parameters:
  • source (PIDSource, callable) – The PIDSource object that is used to get values
  • timeConstant (float) – The discrete-time time constant in seconds
  • period (float) – The period in seconds between samples taken by the user
Returns:

LinearDigitalFilter

static movingAverage(source, taps)[source]

Creates a K-tap FIR moving average filter of the form:

y[n] = 1/k * (x[k] + x[k-1] + ... + x[0])

This filter is always stable.

Parameters:
  • source (PIDSource, callable) – The PIDSource object that is used to get values
  • taps – The number of samples to average over. Higher = smoother but slower
Raises:

ValueError if number of taps is less than 1

Returns:

LinearDigitalFilter

pidGet()[source]

Calculates the next value of the filter

Returns:The filtered value at this step
reset()[source]

Reset the filter state

static singlePoleIIR(source, timeConstant, period)[source]

Creates a one-pole IIR low-pass filter of the form:

y[n] = (1-gain)*x[n] + gain*y[n-1]

Where gain = e^(-dt / T), T is the time constant in seconds

This filter is stable for time constants greater than zero

Parameters:
  • source (PIDSource, callable) – The PIDSource object that is used to get values
  • timeConstant (float) – The discrete-time time constant in seconds
  • period (float) – The period in seconds between samples taken by the user
Returns:

LinearDigitalFilter