Struct light_curve_feature::TimeSeries

pub struct TimeSeries<'a, T>where
    T: Float,{
    pub t: DataSample<'a, T>,
    pub m: DataSample<'a, T>,
    pub w: DataSample<'a, T>,
    /* private fields */
}

Time series object to be put into Feature

This struct caches it’s properties, like mean magnitude value, etc., that’s why mutable reference is required fot feature evaluation

Fields

t: DataSample<'a, T>

m: DataSample<'a, T>

w: DataSample<'a, T>

Implementations

impl<'a, T> TimeSeries<'a, T>where T: Float,

pub fn new( t: impl Into<DataSample<'a, T>>, m: impl Into<DataSample<'a, T>>, w: impl Into<DataSample<'a, T>> ) -> Self

Construct TimeSeries from array-like objects

t is time, m is magnitude (or flux), w is weights.

All arrays must have the same length, t must increase monotonically. Input arrays could be ndarray::Array1, ndarray::ArrayView1, 1-D ndarray::CowArray, or &[T]. Several features assumes that w array corresponds to inverse square errors of m.

pub fn new_without_weight( t: impl Into<DataSample<'a, T>>, m: impl Into<DataSample<'a, T>> ) -> Self

Construct TimeSeries from time and magnitude (flux)

It is the same as TimeSeries::new, but sets unity weights. It doesn’t recommended to use it for features dependent on weights / observation errors like crate::StetsonK or crate::LinearFit.

pub fn lenu(&self) -> usize

Time series length

pub fn lenf(&self) -> T

Float approximating time series length

pub fn get_m_weighted_mean(&mut self) -> T

pub fn get_m_reduced_chi2(&mut self) -> T

pub fn is_plateau(&mut self) -> bool

pub fn get_t_min_m(&mut self) -> T

pub fn get_t_max_m(&mut self) -> T

Trait Implementations

impl<'a, T> Clone for TimeSeries<'a, T>where T: Float + Clone,

fn clone(&self) -> TimeSeries<'a, T>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<'a, T> Debug for TimeSeries<'a, T>where T: Float + Debug,

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.