Struct light_curve_feature::features::LinexpFit

pub struct LinexpFit { /* private fields */ }

Linexp function fit

Four fit parameters and goodness of fit (reduced $\chi^2$) of the Linexp function developed for core-collapsed supernovae:

$$ f(t) = A \frac{(t-t_0)}{\tau} \times \exp{\left(\frac{(t-t_0)}{\tau}\right)} + B. $$

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

• Depends on: time, flux, flux error
• Minimum number of observations: 5
• Number of features: 5

Implementations

impl LinexpFit

pub fn new<BLP>( algorithm: CurveFitAlgorithm, ln_prior: BLP, inits_bounds: LinexpInitsBounds ) -> Self

where BLP: Into<LinexpLnPrior>,

New LinexpFit instance

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

ln_prior is an instance of LinexpLnPrior 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 LinexpFit

pub fn default_ln_prior() -> LinexpLnPrior

Default LnPrior for LinexpFit

pub fn default_inits_bounds() -> LinexpInitsBounds

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

Trait Implementations

impl Clone for LinexpFit

fn clone(&self) -> LinexpFit

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for LinexpFit

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

Formats the value using the given formatter.

impl Default for LinexpFit

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for LinexpFit

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl EvaluatorInfoTrait for LinexpFit

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 LinexpFit

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 LinexpFit

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 LinexpFit

where T: Float,

fn derivatives(t: T, param: &[T; 4], jac: &mut [T; 4])

impl FitFeatureEvaluatorGettersTrait<NPARAMS> for LinexpFit

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 LinexpFit

where T: Float,

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

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

where T: Float,

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

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

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

fn model(t: T, param: &[U; 4]) -> U

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

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

where U: LikeFloat,

fn dimensionless_to_internal(params: &[U; 4]) -> [U; 4]

fn internal_to_dimensionless(params: &[U; 4]) -> [U; 4]

impl FitParametersInternalExternalTrait<NPARAMS> for LinexpFit

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 LinexpFit

fn orig_to_dimensionless( norm_data: &NormalizedData<f64>, orig: &[f64; 4] ) -> [f64; 4]

fn dimensionless_to_orig( norm_data: &NormalizedData<f64>, norm: &[f64; 4] ) -> [f64; 4]

impl<T> From<LinexpFit> for Feature<T>

where T: Float,

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

Converts to this type from the input type.

impl JsonSchema for LinexpFit

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 LinexpFit

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<LinexpFit> 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<LinexpFit, <Self as TryInto<LinexpFit>>::Error>

Performs the conversion.