## find_peaks

Find peaks that match criteria in 1D data

### 6 releases

 0.1.5 May 18, 2022 May 2, 2022 Mar 6, 2022 Oct 8, 2020 Sep 24, 2020

#223 in Math

Used in mathbox

53KB
547 lines

## Description

Find a filtered subset of local maxima in 1D slice of data.

The functionality implemented here is might be familiar to anyone using MATLAB's `findpeaks`, or Python's `scipy.signal.find_peaks`.

Arguably, the most useful feature in this package is filtering peaks through prominence. This parameter allows you to get the subset of local maxima that optically look like peaks even in noisy data.

Filtering conditions that can be set are:

• prominence,
• height,
• the absolute value of the difference between neighboring data points,
• number of plateau points a peak can have,
• distance between peaks.

All parameters can be specified by minimum and maximum bound.

Elements of the data slice need not be of a specific type, as long as they implement a few traits (for cloning, subtraction, comparison).

## Example

Copied from examples/spectrum.rs.

``````use find_peaks::PeakFinder;

use std::fs::File;
use std::io::prelude::*;

fn read_file(path: &str) -> std::io::Result<String> {
let mut file = File::open(path)?;
let mut contents = String::new();
Ok(contents)
}

fn main() -> () {
.split_whitespace()
.map(|x| x.parse::<f64>().unwrap())
.collect();

let mut fp = PeakFinder::new(&data);
fp.with_min_prominence(200.);
fp.with_min_height(0.);

let peaks = fp.find_peaks();
for p in peaks {
println!("{} {}", p.middle_position(), p.height.unwrap());
}
}
``````

The result visualized: