Slice Ops

Version: v0.2.0

Status: Complete

Header: include/stratax/core/ops/Slice.hpp

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Overview

ops/Slice.hpp implements slicing for vector, matrix, and tensor containers using stratax::core::Slice ranges.

All overloads return owning containers that copy selected elements from the source.

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Responsibilities

The slice ops module is responsible for:

• Normalizing signed slice ranges against container extents
• Applying strided selection for 1D, 2D, and ND containers
• Building result shapes and copying selected elements

The slice ops module is not responsible for:

• Returning non-owning views
• Defining Slice semantics itself
• Broadcasting or advanced gather indexing

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Relationships

slice(...) overloads
├── detail::normalize_slice(...)
├── core::Slice metadata
├── validation::require_rank(...) for tensor rank check
└── validation::checked_* for tensor offset arithmetic

Depends on:

• include/stratax/core/Slice.hpp
• include/stratax/core/containers/Vector.hpp
• include/stratax/core/containers/Matrix.hpp
• include/stratax/core/containers/Tensor.hpp
• include/stratax/core/validation/Validation.hpp

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Invariants

The following conditions are always true:

• Output containers are owning copies.
• Slice normalization is half-open and step-aware for both step directions.
• Out-of-range start/stop values are clamped, not rejected.
• Tensor slicing requires slice count equal to tensor rank.
• Empty output shapes return early without element copy loops.

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Public Interface

Vector slicing

template<typename T>
stratax::container::Vector<T>
slice(const stratax::container::Vector<T>& vec, const stratax::core::Slice& slice);

Behavior

• Normalizes and clamps range against vec.size()
• Copies selected elements into a new vector

Throws

• Exceptions::IndexError if vec.size() exceeds std::ptrdiff_t representable range

Complexity

• O(k), where k is output size

Matrix slicing

template<typename T>
stratax::container::Matrix<T>
slice(
    const stratax::container::Matrix<T>& mat,
    const stratax::core::Slice& rows,
    const stratax::core::Slice& cols);

Behavior

• Normalizes/clamps row and column slices independently
• Copies rectangular strided region into a new matrix

Throws

• Exceptions::IndexError if row/column extent exceeds std::ptrdiff_t range

Complexity

• O(r_out * c_out)

Tensor slicing

template<typename T, typename... Slices>
stratax::container::Tensor<T>
slice(const stratax::container::Tensor<T>& tensor, Slices... slices);

Behavior

• Requires all variadic arguments to be stratax::core::Slice
• Requires number of slices to match tensor rank
• Computes output shape from resolved slice sizes
• Copies result in flat order using source/result strides

Throws

• Exceptions::DimensionError on rank mismatch
• Exceptions::IndexError when an extent exceeds std::ptrdiff_t range
• Exceptions::DimensionError on offset overflow from checked arithmetic

Complexity

• O(n_out * rank)

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Complexity Summary

Operation	Complexity
normalize_slice	O(1)
Vector slice	O(k)
Matrix slice	O(r_out * c_out)
Tensor slice	O(n_out * rank)

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Examples

const auto v2 = slice(v, stratax::core::Slice(0, 10, 2));
const auto m2 = slice(m, stratax::core::Slice(1, -1, 1), stratax::core::Slice(0, 5, 2));
const auto t2 = slice(t, stratax::core::Slice(0, 2), stratax::core::Slice(0, 3), stratax::core::Slice(0, 4));

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Design Notes

Normalization is permissive by design: start/stop values are clamped to legal bounds, which keeps slicing behavior predictable and pythonic.

Tensor slicing currently copies into new storage rather than producing a view to preserve ownership simplicity.

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Future Improvements

• Add non-owning slice/view support
• Add richer slice composition helpers
• Evaluate vectorized copy kernels for dense slices

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See Also

• include/stratax/core/Slice.hpp
• include/stratax/core/containers/Tensor.hpp