Struct light_curve_feature::BeyondNStd

pub struct BeyondNStd<T> { /* fields omitted */ }

Fraction of observations beyond $n\,\sigma_m$ from the mean magnitude $\langle m \rangle$

$$ \mathrm{beyond}~n\,\sigma_m \equiv \frac{\sum_i I_{|m - \langle m \rangle| > n\,\sigma_m}(m_i)}{N}, $$ where $I$ is the indicator function, $N$ is the number of observations, $\langle m \rangle$ is the mean magnitude and $\sigma_m = \sqrt{\sum_i (m_i - \langle m \rangle)^2 / (N-1)}$ is the magnitude standard deviation.

• Depends on: magnitude
• Minimum number of observations: 2
• Number of features: 1

D’Isanto et al. 2016 DOI:10.1093/mnras/stw157

use light_curve_feature::*;
use light_curve_common::all_close;
use std::f64::consts::SQRT_2;

let fe = feat_extr!(BeyondNStd::default(), BeyondNStd::new(2.0));
let time = [0.0; 21];  // Doesn't depend on time
let mut magn = vec![0.0; 17];
magn.extend_from_slice(&[SQRT_2, -SQRT_2, 2.0 * SQRT_2, -2.0 * SQRT_2]);
let mut ts = TimeSeries::new(&time[..], &magn[..], None);
assert_eq!(0.0, ts.m.get_mean());
assert!((1.0 - ts.m.get_std()).abs() < 1e-15);
assert_eq!(vec![4.0 / 21.0, 2.0 / 21.0], fe.eval(ts));

Implementations

impl<T> BeyondNStd<T> where
    T: Float, 

pub fn new(nstd: T) -> Self

pub fn set_name(&mut self, name: String)

Trait Implementations

impl<T> Default for BeyondNStd<T> where
    T: Float, 

fn default() -> Self

Returns the "default value" for a type.

impl<T> FeatureEvaluator<T> for BeyondNStd<T> where
    T: Float, 

fn eval(&self, ts: &mut TimeSeries<T>) -> Vec<T>

Should return the non-empty vector of feature values. The length and feature order should correspond to get_names() output

fn get_names(&self) -> Vec<&str>

Should return the non-empty vector of feature names. The length and feature order should correspond to eval() output

fn size_hint(&self) -> usize

Should return the size of vectors returned by eval() and get_names()