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use std::cmp::Ordering;
pub fn clamp(data: &mut [f64], lower: f64, upper: f64) {
if data.is_empty() {
return;
}
for x in data.iter_mut() {
*x = f64::min(upper, f64::max(lower, *x));
}
}
pub fn normalize(data: &mut [f64]) {
if data.is_empty() {
return;
}
let max = *data
.iter()
.max_by(|a, b| a.partial_cmp(b).unwrap_or(Ordering::Equal))
.unwrap();
let min = *data
.iter()
.min_by(|a, b| a.partial_cmp(b).unwrap_or(Ordering::Equal))
.unwrap();
for x in data.iter_mut() {
*x = (*x - min) / (max - min);
}
}
pub fn smooth(data: &mut [f64], sigma: f64) {
const WINDOW_SIZE: usize = 5;
if data.is_empty() {
return;
}
// Build Gaussian filter
let sigsig = 2.0 * sigma.powi(2);
let mut g: Vec<f64> = (0..WINDOW_SIZE)
.map(|i| {
let x2 = (i as f64).powi(2);
std::f64::consts::E.powf(-x2 / sigsig)
})
.collect();
// Normalize Gaussian filter
let filter_sum: f64 = g.iter().sum();
g.iter_mut().for_each(|x| *x /= filter_sum);
// Smooth data
let first = data.first().unwrap();
let mut buffer = [f64::NAN; WINDOW_SIZE];
let mut bi = 0;
for (_loc, x) in data.iter_mut().enumerate() {
buffer[bi] = *x;
*x = 0.0;
for (i, b) in g.iter().enumerate() {
*x += b * buffer[(bi + i) % WINDOW_SIZE];
}
bi = (bi + 1) % WINDOW_SIZE;
}
}
pub fn derive(data: &mut [f64]) {
const WINDOW_SIZE: usize = 5;
if data.is_empty() {
return;
}
// Calculate the derivative.
let first = data.first().unwrap();
let mut buffer = [f64::NAN; WINDOW_SIZE];
let mut bi = 0;
for (_loc, x) in data.iter_mut().enumerate() {
buffer[bi] = *x;
*x = (buffer[bi] - buffer[(bi + WINDOW_SIZE - 1) % (WINDOW_SIZE)])
/ (WINDOW_SIZE as f64);
bi = (bi + 1) % WINDOW_SIZE;
}
}
enum State {
Start,
NoClue(usize, f64),
SeekingMinimum(usize, f64),
SeekingMaximum(usize, f64),
}
#[derive(Debug)]
pub enum Extremum {
Maximum(usize),
Minimum(usize),
}
pub fn find_extrema(data: &[f64]) -> Vec<Extremum> {
let mut result = Vec::new();
let mut state = State::Start;
for (i, v) in data.iter().enumerate() {
let datum = *v;
state = match state {
State::Start => State::NoClue(i, datum),
State::NoClue(pi, prior) => {
if datum > prior {
result.push(Extremum::Minimum(pi));
State::SeekingMaximum(i, datum)
} else {
result.push(Extremum::Maximum(pi));
State::SeekingMinimum(i, datum)
}
}
State::SeekingMinimum(pi, prior) => {
if datum <= prior {
State::SeekingMinimum(i, datum)
} else {
result.push(Extremum::Minimum(pi));
State::SeekingMaximum(i, datum)
}
}
State::SeekingMaximum(pi, prior) => {
if datum >= prior {
State::SeekingMaximum(i, datum)
} else {
result.push(Extremum::Maximum(pi));
State::SeekingMinimum(i, datum)
}
}
};
}
match state {
State::Start => (),
State::NoClue(..) => (),
State::SeekingMinimum(i, _) => result.push(Extremum::Minimum(i)),
State::SeekingMaximum(i, _) => result.push(Extremum::Maximum(i)),
}
result
}
#[cfg(test)]
mod tests {
#[test]
fn normalize_normalizes() {
let mut x = vec![1.0, 2.0, 3.0, 4.0];
super::normalize(&mut x);
assert!((0.0 - x[0]).abs() < 0.001);
assert!((0.333 - x[1]).abs() < 0.001);
assert!((0.667 - x[2]).abs() < 0.001);
assert!((1.0 - x[3]).abs() < 0.001);
}
#[test]
fn first_run() {
// let mut values = [37200000.0, 13323636.0, 10937.16, 12403.11, 12771.62, 12771.62, 13511.11, 13140.96];
let mut values = [13323636.0, 10937.16, 12403.11, 12771.62, 12771.62, 13511.11, 13140.96];
for (i, x) in values.iter().enumerate() {
println!("original: {} => {:?}", i, x);
}
super::normalize(&mut values);
for (i, x) in values.iter().enumerate() {
println!("normalize: {} => {:?}", i, x);
}
// super::smooth(&mut values, 1.0);
// for (i, x) in values.iter().enumerate() {
// println!("smooth: {} => {:?}", i, x);
// }
super::derive(&mut values);
for (i, x) in values.iter().enumerate() {
println!("derive: {} => {:?}", i, x);
}
let extrema = super::find_extrema(&values);
for x in extrema {
println!("{:?}", x);
}
assert!(false);
}
}
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