physics_in_parallel 3.0.3

High-performance infrastructure for numerical simulations in physics
Documentation 
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use ndarray::{Array2, array};

use physics_in_parallel::math::scalar::{Complex, ScalarCastError};
use physics_in_parallel::math::tensor::{Dense, Sparse, Tensor, TensorError, rank_n::ops};

fn assert_tensor_eq_array2(tensor: &Tensor<f64, Dense>, expected: &Array2<f64>) {
    assert_eq!(tensor.shape(), expected.shape());
    for ((i, j), value) in expected.indexed_iter() {
        assert_eq!(tensor.get(&[i as isize, j as isize]), *value);
    }
}

fn dense_2x3() -> Tensor<f64, Dense> {
    let mut t = Tensor::<f64, Dense>::empty(&[2, 3]);
    for i in 0..2 {
        for j in 0..3 {
            t.set(&[i, j], (1 + i * 3 + j) as f64);
        }
    }
    t
}

#[test]
fn dense_tensor_basic_metadata_access_and_wrapping() {
    let t = dense_2x3();
    let expected = array![[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]];

    assert_eq!(t.shape(), &[2, 3]);
    assert_eq!(t.rank(), 2);
    assert_eq!(t.size(), 6);
    assert_eq!(t.sum(), expected.sum());
    assert_tensor_eq_array2(&t, &expected);

    assert_eq!(t.get(&[-1, -1]), expected[[1, 2]]);
    assert_eq!(t.get(&[2, 3]), expected[[0, 0]]);
}

#[test]
fn tensor_shape_size_validation_rejects_invalid_shapes() {
    assert_eq!(ops::size(&[2, 3, 4]), 24);
    assert!(std::panic::catch_unwind(|| ops::size(&[])).is_err());
    assert!(std::panic::catch_unwind(|| ops::size(&[2, 0])).is_err());
    assert!(std::panic::catch_unwind(|| ops::size(&[usize::MAX, 2])).is_err());
}

#[test]
fn tensor_error_helpers_report_shape_contract_violations() {
    let invalid = physics_in_parallel::math::tensor::rank_n::errors::checked_num_elements(&[2, 0])
        .unwrap_err();
    assert!(matches!(invalid, TensorError::InvalidShape { .. }));

    let overflow =
        physics_in_parallel::math::tensor::rank_n::errors::checked_num_elements(&[usize::MAX, 2])
            .unwrap_err();
    assert!(matches!(overflow, TensorError::ShapeProductOverflow { .. }));

    let mismatch =
        physics_in_parallel::math::tensor::rank_n::errors::ensure_same_shape(&[2, 3], &[3, 2])
            .unwrap_err();
    assert!(matches!(mismatch, TensorError::ShapeMismatch { .. }));
}

#[test]
fn dense_tensor_type_conversion_uses_scalar_cast_contract() {
    let mut t = Tensor::<f64, Dense>::empty(&[2]);
    t.set(&[0], 1.25);
    t.set(&[1], 2.5);

    let casted = t.try_cast_to::<f32>().unwrap();
    assert_eq!(casted.get(&[0]), 1.25f32);
    assert_eq!(casted.get(&[1]), 2.5f32);

    t.set(&[1], f64::MAX);
    let err = t.try_cast_to::<f32>().unwrap_err();
    assert!(matches!(err, ScalarCastError::RealPartOutOfRange { .. }));

    let mut z = Tensor::<Complex<f64>, Dense>::empty(&[1]);
    z.set(&[0], Complex::new(3.0, 4.0));
    let real = z.cast_to::<f64>();
    assert_eq!(real.get(&[0]), 3.0);
}

#[test]
fn dense_tensor_elementwise_ops_match_ndarray() {
    let a = dense_2x3();
    let expected_a = array![[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]];
    let expected_b = array![[2.0, 4.0, 6.0], [8.0, 10.0, 12.0]];

    let scaled = a.scalar_mul(2.0);
    assert_tensor_eq_array2(&scaled, &expected_b);

    let shifted = a.map(|x| x + 1.0);
    assert_tensor_eq_array2(&shifted, &(expected_a.clone() + 1.0));

    let elem_mul = a.elem_mul(&scaled);
    assert_tensor_eq_array2(&elem_mul, &(expected_a.clone() * expected_b.clone()));

    let elem_div = scaled.elem_div(&a);
    assert_tensor_eq_array2(&elem_div, &(expected_b / expected_a));
}

#[test]
fn sparse_tensor_basic_elementwise_semantics_match_stored_entries() {
    let mut a = Tensor::<i64, Sparse>::empty(&[3, 3]);
    a.set(&[0, 0], 2);
    a.set(&[1, 1], -3);
    a.set(&[2, 2], 0);

    assert_eq!(a.rank(), 2);
    assert_eq!(a.size(), 9);
    assert_eq!(a.nnz(), 2);
    assert_eq!(a.sum(), -1);
    assert_eq!(a.get(&[2, 2]), 0);

    let abs = a.abs();
    assert_eq!(abs.get(&[0, 0]), 2);
    assert_eq!(abs.get(&[1, 1]), 3);
    assert_eq!(abs.nnz(), 2);

    let squared = a.norm_sqr();
    assert_eq!(squared.get(&[0, 0]), 4);
    assert_eq!(squared.get(&[1, 1]), 9);
    assert_eq!(squared.nnz(), 2);
}

#[test]
fn dense_and_sparse_facades_interoperate_for_common_math_ops() {
    let dense = Tensor::<i64, Dense>::from_vec(&[3], vec![2, 3, 4]);
    let sparse = Tensor::<i64, Sparse>::from_triplets(
        vec![3],
        vec![(vec![0], 10), (vec![2], -1), (vec![1], 0)],
    );

    assert_eq!(dense.dot(&sparse), 16);
    assert_eq!(sparse.dot(&dense), 16);

    let dense_product = dense.elem_mul(&sparse);
    assert_eq!(dense_product.get(&[0]), 20);
    assert_eq!(dense_product.get(&[1]), 0);
    assert_eq!(dense_product.get(&[2]), -4);

    let sparse_product = sparse.elem_mul(&dense);
    assert_eq!(sparse_product.get(&[0]), 20);
    assert_eq!(sparse_product.get(&[1]), 0);
    assert_eq!(sparse_product.get(&[2]), -4);
    assert_eq!(sparse_product.nnz(), 2);
}