scirs2_interpolate/

tensor.rs

//! Tensor product interpolation
//!
//! This module provides interpolation methods for structured high-dimensional data
//! using tensor product approaches.

use crate::error::InterpolateResult;
use crate::interp1d::InterpolationMethod;
use scirs2_core::ndarray::{Array1, ArrayView2};
use scirs2_core::numeric::{Float, FromPrimitive};
use std::fmt::Debug;

/// Tensor product interpolator for multi-dimensional data on structured grids
///
/// This interpolator uses a tensor product approach for efficient interpolation on
/// structured grids, where the data points form a regular grid along each dimension.
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct TensorProductInterpolator<F: Float + FromPrimitive + Debug> {
    /// Coordinates along each dimension
    coords: Vec<Array1<F>>,
    /// Interpolation method
    method: InterpolationMethod,
}

impl<F: Float + FromPrimitive + Debug> TensorProductInterpolator<F> {
    /// Create a new tensor product interpolator
    ///
    /// # Arguments
    ///
    /// * `coords` - Coordinates along each dimension
    /// * `method` - Interpolation method to use for each dimension
    ///
    /// # Returns
    ///
    /// A new `TensorProductInterpolator` object
    ///
    /// # Examples
    ///
    /// ```rust
    /// use scirs2_core::ndarray::Array1;
    /// use scirs2_interpolate::tensor::TensorProductInterpolator;
    /// use scirs2_interpolate::interp1d::InterpolationMethod;
    ///
    /// // Create coordinates for a 2D grid
    /// let x_coords = Array1::from_vec(vec![0.0, 1.0, 2.0]);
    /// let y_coords = Array1::from_vec(vec![0.0, 0.5, 1.0]);
    /// let coords = vec![x_coords, y_coords];
    ///
    /// // Create tensor product interpolator with linear interpolation
    /// let interpolator = TensorProductInterpolator::new(coords, InterpolationMethod::Linear);
    ///
    /// println!("Tensor product interpolator created for 2D grid");
    /// ```
    pub fn new(coords: Vec<Array1<F>>, method: InterpolationMethod) -> Self {
        Self { coords, method }
    }

    /// Evaluate the interpolator at multiple points
    ///
    /// # Arguments
    ///
    /// * `points` - Points at which to evaluate (one point per row)
    /// * `values` - Values on the grid
    ///
    /// # Returns
    ///
    /// Interpolated values at the points
    pub fn evaluate(
        &self,
        points: &ArrayView2<F>,
        _values: &scirs2_core::ndarray::ArrayD<F>,
    ) -> InterpolateResult<Array1<F>> {
        // This is a placeholder implementation
        // In a full implementation, we would:
        // 1. Check dimensions and validate input data
        // 2. Perform tensor product interpolation at each point

        // For now, just return zeros
        let n_points = points.shape()[0];
        let result = Array1::zeros(n_points);

        // In the future, implement proper tensor product interpolation here

        Ok(result)
    }
}

/// Interpolate N-dimensional data on a regular grid using tensor product methods
///
/// # Arguments
///
/// * `coords` - Coordinates along each dimension
/// * `values` - Values on the grid
/// * `points` - Points at which to evaluate (one point per row)
/// * `method` - Interpolation method to use
///
/// # Returns
///
/// Interpolated values at the points
#[allow(dead_code)]
pub fn tensor_product_interpolate<F>(
    _coords: &[Array1<F>],
    _values: &scirs2_core::ndarray::ArrayD<F>,
    points: &ArrayView2<F>,
    _method: InterpolationMethod,
) -> InterpolateResult<Array1<F>>
where
    F: Float + FromPrimitive + Debug,
{
    // This is a placeholder implementation
    // In a full implementation, we would:
    // 1. Check dimensions and validate input data
    // 2. Create a TensorProductInterpolator
    // 3. Evaluate at all points

    // For now, just return zeros
    let n_points = points.shape()[0];
    let result = Array1::zeros(n_points);

    // In the future, implement proper tensor product interpolation here

    Ok(result)
}

/// High-order tensor product interpolation using Lagrange polynomials
///
/// This interpolator uses tensor products of Lagrange polynomials for high-order
/// interpolation on structured grids.
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct LagrangeTensorInterpolator<F: Float + FromPrimitive + Debug> {
    /// Coordinates along each dimension
    coords: Vec<Array1<F>>,
}

impl<F: Float + FromPrimitive + Debug> LagrangeTensorInterpolator<F> {
    /// Create a new high-order tensor product interpolator using Lagrange polynomials
    ///
    /// # Arguments
    ///
    /// * `coords` - Coordinates along each dimension
    ///
    /// # Returns
    ///
    /// A new `LagrangeTensorInterpolator` object
    pub fn new(coords: Vec<Array1<F>>) -> Self {
        Self { coords }
    }

    /// Evaluate the interpolator at multiple points
    ///
    /// # Arguments
    ///
    /// * `points` - Points at which to evaluate (one point per row)
    /// * `values` - Values on the grid
    ///
    /// # Returns
    ///
    /// Interpolated values at the points
    pub fn evaluate(
        &self,
        points: &ArrayView2<F>,
        _values: &scirs2_core::ndarray::ArrayD<F>,
    ) -> InterpolateResult<Array1<F>> {
        // This is a placeholder implementation
        // In a full implementation, we would:
        // 1. Check dimensions and validate input data
        // 2. Perform Lagrange tensor product interpolation at each point

        // For now, just return zeros
        let n_points = points.shape()[0];
        let result = Array1::zeros(n_points);

        // In the future, implement proper high-order interpolation here

        Ok(result)
    }
}

/// Higher-order tensor product interpolation using Lagrange polynomials
///
/// # Arguments
///
/// * `coords` - Coordinates along each dimension
/// * `values` - Values on the grid
/// * `points` - Points at which to evaluate (one point per row)
///
/// # Returns
///
/// Interpolated values at the points
#[allow(dead_code)]
pub fn lagrange_tensor_interpolate<F>(
    _coords: &[Array1<F>],
    _values: &scirs2_core::ndarray::ArrayD<F>,
    points: &ArrayView2<F>,
) -> InterpolateResult<Array1<F>>
where
    F: Float + FromPrimitive + Debug,
{
    // This is a placeholder implementation
    // In a full implementation, we would:
    // 1. Check dimensions and validate input data
    // 2. Create a LagrangeTensorInterpolator
    // 3. Evaluate at all points

    // For now, just return zeros
    let n_points = points.shape()[0];
    let result = Array1::zeros(n_points);

    // In the future, implement proper high-order interpolation here

    Ok(result)
}

#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::array;

    #[test]
    fn test_tensor_product_interpolator() {
        // This is a placeholder test
        // In a full implementation, we would:
        // 1. Create a test grid and values
        // 2. Create a TensorProductInterpolator
        // 3. Evaluate at test points and verify results

        // For now, just check that the API works
        let x = array![0.0, 1.0, 2.0];
        let y = array![0.0, 1.0, 2.0];
        let coords = vec![x, y];

        let interp = TensorProductInterpolator::new(coords, InterpolationMethod::Linear);

        // Just a basic smoke test
        assert_eq!(interp.coords.len(), 2);
        assert_eq!(interp.method, InterpolationMethod::Linear);
    }
}