Struct light_curve_feature::features::MeanVariance

pub struct MeanVariance {}

Standard deviation to mean ratio

$$ \frac{\sigma_m}{\langle m \rangle} $$

• Depends on: magnitude
• Minimum number of observations: 2
• Number of features: 1

Implementations

impl MeanVariance

pub fn new() -> Self

pub fn doc() -> &'static str

Trait Implementations

impl Clone for MeanVariance

fn clone(&self) -> MeanVariance

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for MeanVariance

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

Formats the value using the given formatter.

impl Default for MeanVariance

fn default() -> MeanVariance

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for MeanVariance

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

Deserialize this value from the given Serde deserializer.

impl EvaluatorInfoTrait for MeanVariance

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 MeanVariance 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 MeanVariance

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<MeanVariance> for Feature<T> where
    T: Float, 

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

Performs the conversion.

impl JsonSchema for MeanVariance

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 MeanVariance

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

Performs the conversion.