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use crate::evaluator::{FeatureEvaluator, VecFE};
use crate::float_trait::Float;
use crate::time_series::TimeSeries;

/// Constructs a `FeatureExtractor` object from a list of objects that implement `FeatureEvaluator`
/// ```
/// use light_curve_feature::*;
///
/// let fe = feat_extr!(BeyondNStd::new(1.0), Cusum::default());
/// ```
#[macro_export]
macro_rules! feat_extr{
    ( $( $x: expr ),* $(,)? ) => {
        FeatureExtractor::new(
            vec![$(
                Box::new($x),
            )*]
        )
    }
}

/// The engine that extracts features one by one
#[derive(Clone)]
pub struct FeatureExtractor<T: Float> {
    features: VecFE<T>,
}

impl<T> FeatureExtractor<T>
where
    T: Float,
{
    pub fn new(features: VecFE<T>) -> Self {
        Self { features }
    }

    /// Get a vector of computed features.
    /// The length of the returned vector is guaranteed to be the same as returned by `get_names()`
    pub fn eval(&self, mut ts: TimeSeries<T>) -> Vec<T> {
        self.features.iter().flat_map(|x| x.eval(&mut ts)).collect()
    }

    /// Get a vector of feature names.
    /// The length of the returned vector is guaranteed to be the same as returned by `eval()`
    pub fn get_names(&self) -> Vec<&str> {
        self.features.iter().flat_map(|x| x.get_names()).collect()
    }

    /// Total number of features
    pub fn size_hint(&self) -> usize {
        self.features.iter().map(|x| x.size_hint()).sum()
    }

    /// Copy of the feature vector
    pub fn clone_features(&self) -> VecFE<T> {
        self.features.clone()
    }

    pub fn add_feature(&mut self, feature: Box<dyn FeatureEvaluator<T>>) {
        self.features.push(feature);
    }
}