Struct light_curve_feature::Periodogram

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

A number of features based on Lomb–Scargle periodogram

Periodogram $P(\omega)$ is an estimate of spectral density of unevenly time series. Periodogram::new's peaks argument corresponds to a number of the most significant spectral density peaks to return. For each peak its period and "signal to noise" ratio is returned.

$$ \mathrm{signal~to~noise~of~peak} \equiv \frac{P(\omega_\mathrm{peak}) - \langle P(\omega) \rangle}{\sigma_{P(\omega)}}. $$

Periodogram can accept another dyn FeatureEvaluator for feature extraction from periodogram as it was time series without observation errors. You can even pass one Periodogram to another if you are crazy enough

• Depends on: time, magnitude
• Minimum number of observations: 2
• Number of features: $2 \times \mathrm{peaks}~+...$

Methods

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

pub fn new(peaks: usize) -> Self

pub fn set_freq_vec(&mut self, freq: Vec<T>) -> &mut Self

pub fn set_freq_factors(&mut self, resolution: T, nyquist: T) -> &mut Self

pub fn add_features(&mut self, features: VecFE<T>) -> &mut Self

Trait Implementations

impl<T> FeatureEvaluator<T> for Periodogram<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

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

fn default() -> Self

Returns the "default value" for a type.