Struct light_curve_feature::features::Periodogram

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

Peaks of Lomb–Scargle periodogram and periodogram as a meta-feature

Periodogram $P(\omega)$ is an estimate of spectral density of unevenly time series. peaks argument corresponds to a number of the most significant spectral density peaks to return. For each peak its period and “signal to noise” ratio is returned:

$$ \mathrm{signal~to~noise~of~peak} \equiv \frac{P(\omega_\mathrm{peak}) - \langle P(\omega) \rangle}{\sigma_{P(\omega)}}. $$

Periodogram can accept other features for feature extraction from periodogram as it was time series without observation errors (unity weights are used if required). You can even pass one Periodogram to another one if you are crazy enough

• Depends on: time, magnitude
• Minimum number of observations: as required by sub-features, but at least two
• Number of features: $2 \times \mathrm{peaks}$ plus sub-features

Implementations

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

pub fn default_peaks() -> usize

pub fn default_resolution() -> f32

pub fn default_max_freq_factor() -> f32

pub fn set_freq_resolution(&mut self, resolution: f32) -> &mut Self

Set frequency resolution

The larger frequency resolution allows to find peak period with better precision

pub fn set_max_freq_factor(&mut self, max_freq_factor: f32) -> &mut Self

Multiply maximum (Nyquist) frequency

Maximum frequency is Nyquist frequncy multiplied by this factor. The larger factor allows to find larger frequency and makes PeriodogramPowerFft more precise. However large frequencies can show false peaks

pub fn set_nyquist(&mut self, nyquist: NyquistFreq) -> &mut Self

Define Nyquist frequency

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

Extend a feature to extract from periodogram

pub fn set_periodogram_algorithm(
    &mut self,
    periodogram_power: PeriodogramPower<T>
) -> &mut Self

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

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

impl<T, F> Periodogram<T, F>where
    T: Float,
    F: FeatureEvaluator<T> + From<PeriodogramPeaks>,

pub fn new(peaks: usize) -> Self

New Periodogram that finds given number of peaks

impl<T, F> Periodogram<T, F>where
    T: Float,

pub fn doc() -> &'static str

Trait Implementations

impl<T: Clone, F: Clone> Clone for Periodogram<T, F>where
    T: Float,

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

Returns a copy of the value.

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

Performs copy-assignment from source.

impl<T: Debug, F: Debug> Debug for Periodogram<T, F>where
    T: Float,

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

Formats the value using the given formatter.

impl<T, F> Default for Periodogram<T, F>where
    T: Float,
    F: FeatureEvaluator<T> + From<PeriodogramPeaks>,

fn default() -> Self

Returns the “default value” for a type.

impl<'de, T, F> Deserialize<'de> for Periodogram<T, F>where
    T: Float,
    T: Float,
    F: FeatureEvaluator<T> + From<PeriodogramPeaks> + TryInto<PeriodogramPeaks>,
    <F as TryInto<PeriodogramPeaks>>::Error: Debug,

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 Periodogram<T, F>where
    T: Float,
    F: FeatureEvaluator<T> + From<PeriodogramPeaks> + TryInto<PeriodogramPeaks>,
    <F as TryInto<PeriodogramPeaks>>::Error: Debug,

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 Periodogram<T, F>where
    T: Float,
    F: FeatureEvaluator<T> + From<PeriodogramPeaks> + TryInto<PeriodogramPeaks>,
    <F as TryInto<PeriodogramPeaks>>::Error: Debug,

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 Periodogram<T, F>where
    T: Float,
    F: FeatureEvaluator<T> + From<PeriodogramPeaks> + TryInto<PeriodogramPeaks>,
    <F as TryInto<PeriodogramPeaks>>::Error: Debug,

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

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

Converts to this type from the input type.

impl<T, F> JsonSchema for Periodogram<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.

impl<T, F> Serialize for Periodogram<T, F>where
    T: Float,
    T: Float,
    F: FeatureEvaluator<T> + From<PeriodogramPeaks> + TryInto<PeriodogramPeaks>,
    <F as TryInto<PeriodogramPeaks>>::Error: Debug,

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<Periodogram<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<Periodogram<T, Self>, <Self as TryInto<Periodogram<T, Self>>>::Error>

Performs the conversion.