Struct light_curve_feature::features::BazinFit

pub struct BazinFit { /* private fields */ }

Bazin function fit

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

$$ f(t) = A \frac{ \mathrm{e}^{ -(t-t_0)/\tau_\mathrm{fall} } }{ 1 + \mathrm{e}^{ -(t - t_0) / \tau_\mathrm{rise} } } + B. $$

Note, that the Bazin function is developed to be used with fluxes, not magnitudes. Also note a typo in the Eq. (1) of the original paper, the minus sign is missed in the “rise” exponent.

• Depends on: time, magnitude, magnitude error
• Minimum number of observations: 6
• Number of features: 6

Bazin et al. 2009 DOI:10.1051/0004-6361/200911847

Implementations

impl BazinFit

pub fn new<BLP>( algorithm: CurveFitAlgorithm, ln_prior: BLP, inits_bounds: BazinInitsBounds ) -> Selfwhere BLP: Into<BazinLnPrior>,

New BazinFit 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 BazinLnPrior 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 BazinFit

pub fn default_ln_prior() -> BazinLnPrior

Default LnPrior for BazinFit

pub fn default_inits_bounds() -> BazinInitsBounds

pub fn doc() -> &'static str

Trait Implementations

impl Clone for BazinFit

fn clone(&self) -> BazinFit

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for BazinFit

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

Formats the value using the given formatter.

impl Default for BazinFit

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for BazinFit

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

Deserialize this value from the given Serde deserializer.

impl EvaluatorInfoTrait for BazinFit

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 BazinFitwhere 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 BazinFit

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 BazinFitwhere T: Float,

fn derivatives(t: T, param: &[T; 5], jac: &mut [T; 5])

impl FitFeatureEvaluatorGettersTrait<NPARAMS> for BazinFit

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 BazinFitwhere T: Float,

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

impl<T> FitInitsBoundsTrait<T, NPARAMS> for BazinFitwhere T: Float,

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

impl<T, U> FitModelTrait<T, U, NPARAMS> for BazinFitwhere T: Float + Into<U>, U: LikeFloat,

fn model(t: T, param: &[U; 5]) -> Uwhere T: Float + Into<U>, U: LikeFloat,

impl<U> FitParametersInternalDimlessTrait<U, NPARAMS> for BazinFitwhere U: LikeFloat,

fn dimensionless_to_internal(params: &[U; 5]) -> [U; 5]

fn internal_to_dimensionless(params: &[U; 5]) -> [U; 5]

impl FitParametersInternalExternalTrait<NPARAMS> for BazinFit

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 BazinFit

fn orig_to_dimensionless( norm_data: &NormalizedData<f64>, orig: &[f64; 5] ) -> [f64; 5]

fn dimensionless_to_orig( norm_data: &NormalizedData<f64>, norm: &[f64; 5] ) -> [f64; 5]

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

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

Converts to this type from the input type.

impl JsonSchema for BazinFit

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 BazinFit

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

Performs the conversion.