use crate::evaluator::*;
use itertools::Itertools;
macro_const! {
const DOC: &'static str = r#"
$\eta^e$ — modification of [Eta](crate::Eta) for unevenly time series
$$
\eta^e \equiv \frac{(t_{N-1} - t_0)^2}{(N - 1)^3} \frac{\sum_{i=0}^{N-2} \left(\frac{m_{i+1} - m_i}{t_{i+1} - t_i}\right)^2}{\sigma_m^2}
$$
where $N$ is the number of observations,
$\sigma_m = \sqrt{\sum_i (m_i - \langle m \rangle)^2 / (N-1)}$ is the magnitude standard deviation.
Note that this definition is a bit different from both Kim et al. 2014 and
[feets](https://feets.readthedocs.io/en/latest/)
Note that this feature can have very high values and be highly cadence-dependent in the case of large range of time
lags. In this case consider to use this feature with [Bins](crate::Bins).
- Depends on: **time**, **magnitude**
- Minimum number of observations: **2**
- Number of features: **1**
Kim et al. 2014, [DOI:10.1051/0004-6361/201323252](https://doi.org/10.1051/0004-6361/201323252)
"#;
}
#[doc = DOC!()]
#[derive(Clone, Default, Debug, Serialize, Deserialize, JsonSchema)]
pub struct EtaE {}
impl EtaE {
pub fn new() -> Self {
Self {}
}
pub fn doc() -> &'static str {
DOC
}
}
lazy_info!(
ETA_E_INFO,
EtaE,
size: 1,
min_ts_length: 2,
t_required: true,
m_required: true,
w_required: false,
sorting_required: true,
);
impl FeatureNamesDescriptionsTrait for EtaE {
fn get_names(&self) -> Vec<&str> {
vec!["eta_e"]
}
fn get_descriptions(&self) -> Vec<&str> {
vec!["generalised Von Neummann eta for irregular time-series"]
}
}
impl<T> FeatureEvaluator<T> for EtaE
where
T: Float,
{
fn eval(&self, ts: &mut TimeSeries<T>) -> Result<Vec<T>, EvaluatorError> {
self.check_ts_length(ts)?;
let m_std2 = get_nonzero_m_std2(ts)?;
let sq_slope_sum =
ts.t.as_slice()
.iter()
.zip(ts.m.as_slice().iter())
.tuple_windows()
.map(|((&t1, &m1), (&t2, &m2))| ((m2 - m1) / (t2 - t1)).powi(2))
.filter(|&x| x.is_finite())
.sum::<T>();
let value = (ts.t.sample[ts.lenu() - 1] - ts.t.sample[0]).powi(2) * sq_slope_sum
/ m_std2
/ (ts.lenf() - T::one()).powi(3);
Ok(vec![value])
}
}
#[cfg(test)]
#[allow(clippy::unreadable_literal)]
#[allow(clippy::excessive_precision)]
mod tests {
use super::*;
use crate::extractor::FeatureExtractor;
use crate::features::Eta;
use crate::tests::*;
check_feature!(EtaE);
feature_test!(
eta_e,
[EtaE::new()],
[0.6957894],
[1.0_f32, 2.0, 5.0, 10.0],
[1.0_f32, 1.0, 6.0, 8.0],
);
#[test]
fn eta_is_eta_e_for_even_grid() {
let fe = FeatureExtractor::<_, Feature<_>>::new(vec![
Eta::default().into(),
EtaE::default().into(),
]);
let x = linspace(0.0_f64, 1.0, 11);
let y: Vec<_> = x.iter().map(|&t| 3.0 + t.powi(2)).collect();
let mut ts = TimeSeries::new_without_weight(&x, &y);
let values = fe.eval(&mut ts).unwrap();
all_close(&values[0..1], &values[1..2], 1e-10);
}
#[test]
fn eta_e_finite() {
let eval = EtaE::default();
let x = [
58197.50390625,
58218.48828125,
58218.5078125,
58222.46875,
58222.4921875,
58230.48046875,
58244.48046875,
58246.43359375,
58247.4609375,
58247.48046875,
58247.48046875,
58249.44140625,
58249.4765625,
58255.4609375,
58256.41796875,
58256.45703125,
58257.44140625,
58257.4609375,
58258.44140625,
58259.4453125,
58262.3828125,
58263.421875,
58266.359375,
58268.42578125,
58269.41796875,
58270.40625,
58271.4609375,
58273.421875,
58274.33984375,
58275.40234375,
58276.42578125,
58277.3984375,
58279.40625,
58280.36328125,
58282.44921875,
58283.3828125,
58285.3828125,
58286.34375,
58288.44921875,
58289.4453125,
58290.3828125,
58291.3203125,
58292.3359375,
58293.30078125,
58296.32421875,
58297.33984375,
58298.33984375,
58301.36328125,
58302.359375,
58303.33984375,
58304.36328125,
58305.36328125,
58307.3828125,
58308.37890625,
58310.38671875,
58311.3828125,
58313.38671875,
58314.421875,
58315.33984375,
58316.34375,
58317.40625,
58318.33984375,
58320.3515625,
58321.3515625,
58322.359375,
58323.27734375,
58324.23828125,
58326.3203125,
58327.31640625,
58329.3203125,
58330.37890625,
58332.296875,
58333.32421875,
58334.34765625,
58336.23046875,
58338.2109375,
58340.3046875,
58341.328125,
58342.328125,
58343.32421875,
58344.31640625,
58345.32421875,
58348.21875,
58351.234375,
58354.2578125,
58355.2734375,
58356.1953125,
58358.25390625,
58360.21875,
58361.234375,
58366.18359375,
58370.15234375,
58373.171875,
58374.171875,
58376.171875,
58425.0859375,
58427.0859375,
58428.1015625,
58430.0859375,
58431.12890625,
58432.0859375,
58433.08984375,
58436.0859375,
];
let y = [
17.357999801635742,
17.329999923706055,
17.332000732421875,
17.312999725341797,
17.30500030517578,
17.31599998474121,
17.27899932861328,
17.305999755859375,
17.333999633789062,
17.332000732421875,
17.332000732421875,
17.323999404907227,
17.256000518798828,
17.308000564575195,
17.290000915527344,
17.298999786376953,
17.270000457763672,
17.270000457763672,
17.297000885009766,
17.288000106811523,
17.358999252319336,
17.273000717163086,
17.354999542236328,
17.301000595092773,
17.2810001373291,
17.299999237060547,
17.341999053955078,
17.30500030517578,
17.29599952697754,
17.336000442504883,
17.31399917602539,
17.336999893188477,
17.304000854492188,
17.309999465942383,
17.304000854492188,
17.29199981689453,
17.31100082397461,
17.28499984741211,
17.327999114990234,
17.347999572753906,
17.32200050354004,
17.319000244140625,
17.2810001373291,
17.327999114990234,
17.291000366210938,
17.3439998626709,
17.336000442504883,
17.27899932861328,
17.38800048828125,
17.27899932861328,
17.297000885009766,
17.29599952697754,
17.312000274658203,
17.253999710083008,
17.312000274658203,
17.284000396728516,
17.319000244140625,
17.32200050354004,
17.290000915527344,
17.31599998474121,
17.28499984741211,
17.30299949645996,
17.284000396728516,
17.336000442504883,
17.31399917602539,
17.356000900268555,
17.308000564575195,
17.31999969482422,
17.301000595092773,
17.325000762939453,
17.30900001525879,
17.29800033569336,
17.29199981689453,
17.339000701904297,
17.32699966430664,
17.31800079345703,
17.320999145507812,
17.315000534057617,
17.304000854492188,
17.327999114990234,
17.308000564575195,
17.34000015258789,
17.325000762939453,
17.322999954223633,
17.30900001525879,
17.308000564575195,
17.275999069213867,
17.33799934387207,
17.343000411987305,
17.437999725341797,
17.280000686645508,
17.305999755859375,
17.320999145507812,
17.325000762939453,
17.32699966430664,
17.339000701904297,
17.298999786376953,
17.29199981689453,
17.336000442504883,
17.32699966430664,
17.28499984741211,
17.284000396728516,
17.257999420166016,
];
let mut ts = TimeSeries::new_without_weight(&x[..], &y[..]);
let actual: f32 = eval.eval(&mut ts).unwrap()[0];
assert!(actual.is_finite());
}
}