light-curve-feature 0.5.2

Feature extractor from noisy time series
Documentation
use crate::evaluator::*;

macro_const! {
    const DOC: &'static str = r#"
Maximum deviation of magnitude from its median

$$
\mathrm{percent~amplitude} \equiv \max_i\left|m_i - \mathrm{Median}(m)\right|
    = \max(\max(m) - \mathrm{Median}(m), \mathrm{Median}(m) - \min(m)).
$$

- Depends on: **magnitude**
- Minimum number of observations: **1**
- Number of features: **1**

D’Isanto et al. 2016 [DOI:10.1093/mnras/stw157](https://doi.org/10.1093/mnras/stw157)
"#;
}

#[doc = DOC!()]
#[derive(Clone, Default, Debug, Serialize, Deserialize, JsonSchema)]
pub struct PercentAmplitude {}

lazy_info!(
    PERCENT_AMPLITUDE_INFO,
    PercentAmplitude,
    size: 1,
    min_ts_length: 1,
    t_required: false,
    m_required: true,
    w_required: false,
    sorting_required: false,
);

impl PercentAmplitude {
    pub fn new() -> Self {
        Self {}
    }

    pub fn doc() -> &'static str {
        DOC
    }
}

impl FeatureNamesDescriptionsTrait for PercentAmplitude {
    fn get_names(&self) -> Vec<&str> {
        vec!["percent_amplitude"]
    }

    fn get_descriptions(&self) -> Vec<&str> {
        vec!["maximum absolute deviation of magnitude from its median"]
    }
}

impl<T> FeatureEvaluator<T> for PercentAmplitude
where
    T: Float,
{
    fn eval(&self, ts: &mut TimeSeries<T>) -> Result<Vec<T>, EvaluatorError> {
        self.check_ts_length(ts)?;
        let m_min = ts.m.get_min();
        let m_max = ts.m.get_max();
        let m_median = ts.m.get_median();
        Ok(vec![T::max(m_max - m_median, m_median - m_min)])
    }
}

#[cfg(test)]
#[allow(clippy::unreadable_literal)]
#[allow(clippy::excessive_precision)]
mod tests {
    use super::*;
    use crate::tests::*;

    check_feature!(PercentAmplitude);

    feature_test!(
        percent_amplitude,
        [PercentAmplitude::new()],
        [96.0],
        [1.0_f32, 1.0, 1.0, 2.0, 4.0, 5.0, 5.0, 98.0, 100.0],
    );
}