Struct light_curve_feature::features::VillarFit

pub struct VillarFit { /* fields omitted */ }

Villar fit

Five fit parameters and goodness of fit (reduced $\Chi^2$) of the Villar function developed for supernovae classification: $$ f(t) = c + \frac{ A + \beta (t - t_0) }{ 1 + \exp{\frac{-(t - t_0)}{\tau_\mathrm{rise}}}} \left{ \begin{split} &1, t < t_0 + \gamma \ &\exp{\frac{-(t-t_0-\gamma)}{\tau_\mathrm{fall}}}, t \geq t_0 + \gamma \end{split} \right. $$

Note, that the Villar function is developed to use with fluxes, not magnitudes.

Optimization is done using specified algorithm which is an instance of the CurveFitAlgorithm, currently supported algorithms are MCMC and LMSDER (a Levenberg–Marquard algorithm modification, requires gsl Cargo feature).

• Depends on: time, magnitude, magnitude error
• Minimum number of observations: 8
• Number of features: 8

Villar et al. 2019 DOI:10.3847/1538-4357/ab418c

Implementations

impl VillarFit

pub fn new(algorithm: CurveFitAlgorithm) -> Self

pub fn default_algorithm() -> CurveFitAlgorithm

VillarFit with the default McmcCurveFit

pub fn doc() -> &'static str

impl VillarFit

pub fn f<T>(t: T, values: &[T]) -> T where
    T: Float, 

Trait Implementations

impl Clone for VillarFit

fn clone(&self) -> VillarFit

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for VillarFit

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

Formats the value using the given formatter.

impl Default for VillarFit

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for VillarFit

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
    __D: Deserializer<'de>, 

Deserialize this value from the given Serde deserializer.

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

fn eval(&self, ts: &mut TimeSeries<'_, T>) -> Result<Vec<T>, EvaluatorError>

Should return the vector of feature values or EvaluatorError

fn get_info(&self) -> &EvaluatorInfo

Get feature evaluator meta-information

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

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

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

Should return the vector of feature descriptions. The length and feature order should correspond to eval() output

fn eval_or_fill(&self, ts: &mut TimeSeries<'_, T>, fill_value: T) -> Vec<T>

Should return the vector of feature values and fill invalid components with given value

fn size_hint(&self) -> usize

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

fn min_ts_length(&self) -> usize

Should return minimum time series length to successfully find feature value

fn is_t_required(&self) -> bool

fn is_m_required(&self) -> bool

fn is_w_required(&self) -> bool

fn is_sorting_required(&self) -> bool

fn check_ts_length(
    &self,
    ts: &TimeSeries<'_, T>
) -> Result<usize, EvaluatorError>

impl<T> From<VillarFit> for Feature<T> where
    T: Float, 

fn from(v: VillarFit) -> Feature<T>

Performs the conversion.

impl JsonSchema for VillarFit

fn schema_name() -> String

The name of the generated JSON Schema.

fn json_schema(gen: &mut SchemaGenerator) -> Schema

Generates a JSON Schema for this type.

fn is_referenceable() -> bool

Whether JSON Schemas generated for this type should be re-used where possible using the $ref keyword.

impl Serialize for VillarFit

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error> where
    __S: Serializer, 

Serialize this value into the given Serde serializer.

impl<T> TryInto<VillarFit> for Feature<T> where
    T: Float, 

type Error = &'static str

The type returned in the event of a conversion error.

fn try_into(self) -> Result<VillarFit, <Self as TryInto<VillarFit>>::Error>

Performs the conversion.