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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,110 @@ | ||
| use ndarray::{Array, ArrayBase, Axis, Data, Dimension}; | ||
| use num_traits::{Float, FromPrimitive}; | ||
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| use crate::{array_like, BorderMode}; | ||
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| use super::{con_corr::inner_correlate1d, symmetry::SymmetryStateCheck}; | ||
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| /// Uniform filter for n-dimensional arrays. | ||
| /// | ||
| /// Currently hardcoded with the `PadMode::Reflect` padding mode. | ||
| /// | ||
| /// * `data` - The input N-D data. | ||
| /// * `size` - The len | ||
| /// * `mode` - Method that will be used to select the padded values. See the | ||
| /// [`BorderMode`](crate::BorderMode) enum for more information. | ||
| /// | ||
| /// **Panics** if `size` is zero, or one of the axis' lengths is lower than `size`. | ||
| pub fn uniform_filter<S, A, D>( | ||
| data: &ArrayBase<S, D>, | ||
| size: usize, | ||
| mode: BorderMode<A>, | ||
| ) -> Array<A, D> | ||
| where | ||
| S: Data<Elem = A>, | ||
| A: Float + FromPrimitive + 'static, | ||
| for<'a> &'a [A]: SymmetryStateCheck, | ||
| D: Dimension, | ||
| { | ||
| let weights = weights(size); | ||
| let half = weights.len() / 2; | ||
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| // We need 2 buffers because | ||
| // * We're reading neighbours so we can't read and write on the same location. | ||
| // * The process is applied for each axis on the result of the previous process. | ||
| // * It's uglier (using &mut) but much faster than allocating for each axis. | ||
| let mut data = data.to_owned(); | ||
| let mut output = array_like(&data, data.dim(), A::zero()); | ||
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| for d in 0..data.ndim() { | ||
| // TODO This can be made to work if the padding modes (`reflect`, `symmetric`, `wrap`) are | ||
| // more robust. One just needs to reflect the input data several times if the `weights` | ||
| // length is greater than the input array. It works in SciPy because they are looping on a | ||
| // size variable instead of running the algo only once like we do. | ||
| let n = data.len_of(Axis(d)); | ||
| if half > n { | ||
| panic!("Data size is too small for the inputs (sigma and truncate)"); | ||
| } | ||
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| inner_correlate1d(&data, &weights, Axis(d), mode, 0, &mut output); | ||
| if d != data.ndim() - 1 { | ||
| std::mem::swap(&mut output, &mut data); | ||
| } | ||
| } | ||
| output | ||
| } | ||
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| /// Uniform filter for 1-dimensional arrays. | ||
| /// | ||
| /// * `data` - The input N-D data. | ||
| /// * `size` - Length of the uniform filter. | ||
| /// * `axis` - The axis of input along which to calculate. | ||
| /// * `mode` - Method that will be used to select the padded values. See the | ||
| /// [`BorderMode`](crate::BorderMode) enum for more information. | ||
| /// | ||
| /// **Panics** if `size` is zero, or the axis length is lower than `size`. | ||
| pub fn uniform_filter1d<S, A, D>( | ||
| data: &ArrayBase<S, D>, | ||
| size: usize, | ||
| axis: Axis, | ||
| mode: BorderMode<A>, | ||
| ) -> Array<A, D> | ||
| where | ||
| S: Data<Elem = A>, | ||
| A: Float + FromPrimitive + 'static, | ||
| for<'a> &'a [A]: SymmetryStateCheck, | ||
| D: Dimension, | ||
| { | ||
| let weights = weights(size); | ||
| let mut output = array_like(&data, data.dim(), A::zero()); | ||
| inner_correlate1d(data, &weights, axis, mode, 0, &mut output); | ||
| output | ||
| } | ||
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| fn weights<A>(size: usize) -> Vec<A> | ||
| where | ||
| A: Float + FromPrimitive + 'static, | ||
| { | ||
| if size == 0 { | ||
| panic!("Size is zero"); | ||
| } | ||
| [A::one() / A::from_usize(size).unwrap()].repeat(size) | ||
| } | ||
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| #[cfg(test)] | ||
| mod tests { | ||
| use super::*; | ||
| use approx::assert_relative_eq; | ||
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| #[test] | ||
| fn test_weights() { | ||
| assert_relative_eq!(weights(5).as_slice(), &[0.2, 0.2, 0.2, 0.2, 0.2][..], epsilon = 1e-7); | ||
| assert_relative_eq!(weights(1).as_slice(), &[1.0][..], epsilon = 1e-7); | ||
| } | ||
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| #[should_panic] | ||
| #[test] | ||
| fn test_weights_zero() { | ||
| weights::<f64>(0); | ||
| } | ||
| } |
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