# Median Filter

A statistically robust alternative to the moving-average filter is the *median filter*. Where a moving average filter takes the arithmetic *mean* of the input over a moving sample window, a median filter (per the name) takes a median instead.

The median filter is most-useful for removing occasional outliers from an input stream. This makes it particularly well-suited to filtering inputs from distance sensors, which are prone to occasional interference. Unlike a moving average, the median filter will remain completely unaffected by small numbers of outliers, no matter how extreme.

The median filter is supported in WPILib through the `MedianFilter`

class (Java, C++, , `Python`

).

## Creating a MedianFilter

Note

The C++ `MedianFilter`

class is templated on the data type used for the input.

Note

Because filters have “memory”, each input stream requires its own filter object. Do *not* attempt to use the same filter object for multiple input streams.

Creating a `MedianFilter`

is simple:

```
// Creates a MedianFilter with a window size of 5 samples
MedianFilter filter = new MedianFilter(5);
```

```
// Creates a MedianFilter with a window size of 5 samples
frc::MedianFilter<double> filter(5);
```

```
from wpimath.filter import MedianFilter
# Creates a MedianFilter with a window size of 5 samples
filter = MedianFilter(5)
```

## Using a MedianFilter

Once your filter has been created, using it is easy - simply call the `calculate()`

method with the most recent input to obtain the filtered output:

```
// Calculates the next value of the output
filter.calculate(input);
```

```
// Calculates the next value of the output
filter.Calculate(input);
```

```
# Calculates the next value of the output
filter.calculate(input)
```