Struct light_curve_feature::features::Bins

pub struct Bins<T, F>
where
    T: Float,
    F: FeatureEvaluator<T>,
{ /* private fields */ }

Sampled time series meta-feature

Binning time series to bins with width $\mathrm{window}$ with respect to some $\mathrm{offset}$. $j-th$ bin interval is $[j \cdot \mathrm{window} + \mathrm{offset}; (j + 1) \cdot \mathrm{window} + \mathrm{offset})$. Binned time series is defined by $$ t_j^* = (j + \frac12) \cdot \mathrm{window} + \mathrm{offset}, $$ $$ m_j^* = \frac{\sum{m_i / \delta_i^2}}{\sum{\delta_i^{-2}}}, $$ $$ \delta_j^* = \frac{N_j}{\sum{\delta_i^{-2}}}, $$ where $N_j$ is a number of sampling observations and all sums are over observations inside considering bin. Bins takes any other feature evaluators to extract features from sample time series

• Depends on: time, magnitude, magnitude error
• Minimum number of observations: as required by sub-features, but at least 1
• Number of features: as provided by sub-features

Implementations

impl<T, F> Bins<T, F>

where T: Float, F: FeatureEvaluator<T>,

pub fn new(window: T, offset: T) -> Self

pub fn set_window(&mut self, window: T) -> &mut Self

pub fn set_offset(&mut self, offset: T) -> &mut Self

pub fn add_feature(&mut self, feature: F) -> &mut Self

Extend a feature to extract from binned time series

pub fn default_window() -> T

pub fn default_offset() -> T

impl<T, F> Bins<T, F>

where T: Float, F: FeatureEvaluator<T>,

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

Trait Implementations

impl<T, F> Clone for Bins<T, F>

where T: Float + Clone, F: FeatureEvaluator<T> + Clone,

fn clone(&self) -> Bins<T, F>

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<T, F> Debug for Bins<T, F>

where T: Float + Debug, F: FeatureEvaluator<T> + Debug,

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

Formats the value using the given formatter.

impl<T, F> Default for Bins<T, F>

where T: Float, F: FeatureEvaluator<T>,

fn default() -> Self

Returns the “default value” for a type.

impl<'de, T, F> Deserialize<'de> for Bins<T, F>

where T: Float, F: FeatureEvaluator<T>,

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<T, F> EvaluatorInfoTrait for Bins<T, F>

where T: Float, F: FeatureEvaluator<T>,

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, F> FeatureEvaluator<T> for Bins<T, F>

where T: Float, F: FeatureEvaluator<T>,

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<T, F> FeatureNamesDescriptionsTrait for Bins<T, F>

where T: Float, F: FeatureEvaluator<T>,

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> From<Bins<T, Feature<T>>> for Feature<T>

where T: Float,

fn from(v: Bins<T, Self>) -> Feature<T>

Converts to this type from the input type.

impl<T, F> JsonSchema for Bins<T, F>

where T: Float, F: FeatureEvaluator<T>,

fn is_referenceable() -> bool

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

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

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

impl<T, F> Serialize for Bins<T, F>

where T: Float, F: FeatureEvaluator<T>,

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<Bins<T, Feature<T>>> 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<Bins<T, Self>, <Self as TryInto<Bins<T, Self>>>::Error>

Performs the conversion.