1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137

use crate::evaluator::*;

/// Stetson $K$ coefficient described light curve shape
///
/// $$
/// \mathrm{Stetson}~K \equiv \frac{\sum_i\left|\frac{m_i - \langle m \rangle}{\delta_i}\right|}{\sqrt{N\\,\chi^2}},
/// $$
/// where N is the number of observations,
/// $\langle m \rangle$ is the mean magnitude
/// and $\chi^2 = \sum_i\left(\frac{m_i - \langle m \rangle}{\delta\_i}\right)^2$.
///
/// - Depends on: **magnitude**, **magnitude error**
/// - Minimum number of observations: **2**
/// - Number of features: **1**
///
/// P. B. Statson, 1996. [DOI:10.1086/133808](https://doi.org/10.1086/133808)
#[derive(Clone, Default, Debug)]
pub struct StetsonK {}

lazy_info!(
    STETSON_K_INFO,
    size: 1,
    min_ts_length: 2,
    t_required: false,
    m_required: true,
    w_required: true,
    sorting_required: false,
);

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

impl<T> FeatureEvaluator<T> for StetsonK
where
    T: Float,
{
    fn eval(&self, ts: &mut TimeSeries<T>) -> Result<Vec<T>, EvaluatorError> {
        self.check_ts_length(ts)?;
        let chi2 = get_nonzero_reduced_chi2(ts)? * (ts.lenf() - T::one());
        let mean = ts.get_m_weighted_mean();
        let value = ts
            .mw_iter()
            .map(|(y, w)| T::abs(y - mean) * T::sqrt(w))
            .sum::<T>()
            / T::sqrt(ts.lenf() * chi2);
        Ok(vec![value])
    }

    fn get_info(&self) -> &EvaluatorInfo {
        &STETSON_K_INFO
    }

    fn get_names(&self) -> Vec<&str> {
        vec!["stetson_K"]
    }
}

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

    use std::f64::consts::*;

    eval_info_test!(stetson_k_info, StetsonK::default());

    feature_test!(
        stetson_k_square_wave,
        [Box::new(StetsonK::new())],
        [1.0],
        [1.0; 1000], // isn't used
        (0..1000)
            .map(|i| {
                if i < 500 {
                    1.0
                } else {
                    -1.0
                }
            })
            .collect::<Vec<_>>(),
        Some(&[1.0; 1000]),
    );

    // Slow convergence, use high tol
    feature_test!(
        stetson_k_sinus,
        [Box::new(StetsonK::new())],
        [8_f64.sqrt() / PI],
        [1.0; 1000], // isn't used
        linspace(0.0, 2.0 * PI, 1000)
            .iter()
            .map(|&x| f64::sin(x))
            .collect::<Vec<_>>(),
        None,
        1e-3,
    );

    feature_test!(
        stetson_k_sawtooth,
        [Box::new(StetsonK::new())],
        [12_f64.sqrt() / 4.0],
        [1.0; 1000], // isn't used
        linspace(0.0, 1.0, 1000),
        None,
    );

    // It seems that Stetson (1996) formula for this case is wrong by the factor of 2 * sqrt((N-1) / N)
    feature_test!(
        stetson_k_single_peak,
        [Box::new(StetsonK::new())],
        [2.0 * 99.0_f64.sqrt() / 100.0],
        [1.0; 100], // isn't used
        (0..100)
            .map(|i| {
                if i == 0 {
                    1.0
                } else {
                    -1.0
                }
            })
            .collect::<Vec<_>>(),
        None,
    );

    #[test]
    fn stetson_k_plateau() {
        let fe = feat_extr!(StetsonK::new());
        let x = [0.0; 10];
        let mut ts = TimeSeries::new(&x, &x, None);
        assert_eq!(fe.eval(&mut ts), Err(EvaluatorError::FlatTimeSeries));
    }
}