# filters.iqr¶

The **Interquartile Range Filter** automatically crops the input point
cloud based on the distribution of points in the specified dimension.
The Interquartile Range (IQR) is defined as the range between
the first and third quartile (25th and 75th percentile). Upper and lower bounds
are determined by adding 1.5 times the IQR to the third quartile or subtracting
1.5 times the IQR from the first quartile. The multiplier, which defaults to
1.5, can be adjusted by the user.

Note

This method can remove real data, especially ridges and valleys in rugged terrain, or tall features such as towers and rooftops in flat terrain. While the number of deviations can be adjusted to account for such content-specific considerations, it must be used with care.

## Example¶

The sample pipeline below uses the filter to automatically crop the Z dimension and remove possible outliers. The multiplier to determine high/low thresholds has been adjusted to be less aggressive and to only crop those outliers that are greater than the third quartile plus 3 times the IQR or are less than the first quartile minus 3 times the IQR.

```
[
"input.las",
{
"type":"filters.iqr",
"dimension":"Z",
"k":3.0
},
"output.laz"
]
```

## Options¶

- k
The IQR multiplier used to determine upper/lower bounds. [Default: 1.5]

- dimension
The name of the dimension to filter.

- where
An expression that limits points passed to a filter. Points that don’t pass the expression skip the stage but are available to subsequent stages in a pipeline. [Default: no filtering]

- where_merge
A strategy for merging points skipped by a ‘where’ option when running in standard mode. If

`true`

, the skipped points are added to the first point view returned by the skipped filter. If`false`

, skipped points are placed in their own point view. If`auto`

, skipped points are merged into the returned point view provided that only one point view is returned and it has the same point count as it did when the filter was run. [Default:`auto`

]