scientific-cal 0.2.4

scientific cal
Documentation 
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use ndarray::Array1;
use num_traits::{Float, FromPrimitive, One, Zero};
use crate::filters::{fir::{correlate1d}, iir::{sos_filt}, FilterApply, FilterError};
pub struct IIRFilter {
    pub(crate) sections: Vec<[f64; 6]>
}
impl<T> FilterApply<T> for IIRFilter
where
    T: Float + FromPrimitive + Zero + Clone + One,
{
    fn apply(&self, data: &Vec<T>) -> Result<Vec<T>, FilterError> {
        let mut input: Vec<T> = data.clone();
        let _ = sos_filt(&mut input, &self.sections).map_err(|_| FilterError::FilterApplyError);
        Ok(input)
    }

    fn apply_inplase(&self, data: &mut Vec<T>) -> Result<(), FilterError> {
        sos_filt(data, &self.sections).map_err(|_| FilterError::FilterApplyError)
    }
}
pub struct FIRFilter {
    pub(crate) sections: Array1<f64>
}

impl<T> FilterApply<T> for FIRFilter
where
    T: Float + FromPrimitive + Zero + Clone + One,
{
    fn apply(&self, data: &Vec<T>) -> Result<Vec<T>, FilterError> {
        let out = correlate1d(data, &self.sections, -1, "reflect", 0.0, 0).map_err(|_| FilterError::FilterApplyError);
        out
    }

    fn apply_inplase(&self, _data: &mut Vec<T>) -> Result<(), FilterError> {
        todo!()
    }
}