Struct light_curve_feature::features::VillarFit

pub struct VillarFit { /* private fields */ }

Villar function fit

Seven fit parameters and goodness of fit (reduced $\chi^2$) of the Villar function developed for supernovae classification:

$$ f(t) = c + \frac{A}{ 1 + \exp{\frac{-(t - t_0)}{\tau_\mathrm{rise}}}} \left\{ \begin{array}{ll} 1 - \frac{\nu (t - t_0)}{\gamma}, &t < t_0 + \gamma \\ (1 - \nu) \exp{\frac{-(t-t_0-\gamma)}{\tau_\mathrm{fall}}}, &t \geq t_0 + \gamma \end{array} \right. $$ where $A, \gamma, \tau_\mathrm{rise}, \tau_\mathrm{fall} > 0$, $\nu \in [0; 1)$.

Here we introduce a new dimensionless parameter $\nu$ instead of the plateau slope $\beta$ from the orioginal paper: $\nu \equiv -\beta \gamma / A$.

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

• 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<VLP>( algorithm: CurveFitAlgorithm, ln_prior: VLP, inits_bounds: VillarInitsBounds ) -> Self

where VLP: Into<VillarLnPrior>,

New VillarFit instance

algorithm specifies which optimization method is used, it is an instance of the CurveFitAlgorithm, currently supported algorithms are MCMC and LMSDER (a Levenberg–Marquard algorithm modification, requires gsl Cargo feature).

ln_prior is an instance of LnPrior and specifies the natural logarithm of the prior to use. Some curve-fit algorithms doesn’t support this and ignores the prior

pub fn default_algorithm() -> CurveFitAlgorithm

Default McmcCurveFit for VillarFit

pub fn default_ln_prior() -> VillarLnPrior

Default VillarLnPrior for VillarFit

pub fn default_inits_bounds() -> VillarInitsBounds

pub const fn doc() -> &'static str

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 EvaluatorInfoTrait for VillarFit

fn get_info(&self) -> &EvaluatorInfo

Get feature evaluator meta-information

fn size_hint(&self) -> usize

Size of vectors returned by eval(), get_names() and get_descriptions()

fn min_ts_length(&self) -> usize

Minimum time series length required to successfully evaluate feature

fn is_t_required(&self) -> bool

If time array used by the feature

fn is_m_required(&self) -> bool

If magnitude array is used by the feature

fn is_w_required(&self) -> bool

If weight array is used by the feature

fn is_sorting_required(&self) -> bool

If feature requires time-sorting on the input TimeSeries

impl<T> FeatureEvaluator<T> for VillarFit

where T: Float,

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

Vector of feature values or EvaluatorError

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

Returns vector of feature values and fill invalid components with given value

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

Checks if TimeSeries has enough points to evaluate the feature

impl FeatureNamesDescriptionsTrait for VillarFit

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

Vector of feature names. The length and feature order corresponds to eval() output

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

Vector of feature descriptions. The length and feature order corresponds to eval() output

impl<T> FitDerivalivesTrait<T, NPARAMS> for VillarFit

where T: Float,

fn derivatives(t: T, param: &[T; 7], jac: &mut [T; 7])

impl FitFeatureEvaluatorGettersTrait<NPARAMS> for VillarFit

fn get_algorithm(&self) -> &CurveFitAlgorithm

fn ln_prior_from_ts<T: Float>( &self, ts: &mut TimeSeries<'_, T> ) -> LnPrior<NPARAMS>

impl<T> FitFunctionTrait<T, NPARAMS> for VillarFit

where T: Float,

fn f(t: T, values: &[T]) -> T

impl<T> FitInitsBoundsTrait<T, NPARAMS> for VillarFit

where T: Float,

fn init_and_bounds_from_ts( &self, ts: &mut TimeSeries<'_, T> ) -> FitInitsBoundsArrays<NPARAMS>

impl<T, U> FitModelTrait<T, U, NPARAMS> for VillarFit

where T: Float + Into<U>, U: LikeFloat,

fn model(t: T, param: &[U; 7]) -> U

impl<U> FitParametersInternalDimlessTrait<U, NPARAMS> for VillarFit

where U: LikeFloat,

fn dimensionless_to_internal(params: &[U; 7]) -> [U; 7]

fn internal_to_dimensionless(params: &[U; 7]) -> [U; 7]

impl FitParametersInternalExternalTrait<NPARAMS> for VillarFit

fn convert_to_internal( norm_data: &NormalizedData<f64>, orig: &[f64; NPARAMS] ) -> [f64; NPARAMS]

fn convert_to_external( norm_data: &NormalizedData<f64>, params: &[f64; NPARAMS] ) -> [f64; NPARAMS]

impl FitParametersOriginalDimLessTrait<NPARAMS> for VillarFit

fn orig_to_dimensionless( norm_data: &NormalizedData<f64>, orig: &[f64; 7] ) -> [f64; 7]

fn dimensionless_to_orig( norm_data: &NormalizedData<f64>, norm: &[f64; 7] ) -> [f64; 7]

impl<T> From<VillarFit> for Feature<T>

where T: Float,

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

Converts to this type from the input type.

impl JsonSchema for VillarFit

fn schema_name() -> String

The name of the generated JSON Schema.

fn schema_id() -> Cow<'static, str>

Returns a string that uniquely identifies the schema produced by this type.

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.