Getting Started with DH_Array2 — Basics & Examples

Getting Started with DH_Array2 — Basics & ExamplesDH_Array2 is a two-dimensional array utility designed to simplify handling matrix-like data structures in applications that require efficient access, manipulation, and iteration. This article walks through the basics of DH_Array2, common use cases, API patterns, and practical examples to help you get up to speed quickly.

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What is DH_Array2?

DH_Array2 is a data structure that represents a rectangular grid (rows × columns) of elements. It provides convenient methods for indexing, row/column operations, slicing, and transformations while aiming for clear semantics and predictable performance. Think of it as a lightweight matrix class tailored for use in general applications rather than specialized numerical computing.

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Core concepts

• Elements are stored in row-major order (rows laid out contiguously).
• Dimensions are fixed at creation time but some implementations may allow resizing.
• Indexing uses zero-based coordinates: (row, column).
• Common operations include: get/set, row/column iteration, slice extraction, mapping, and transposition.

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Typical API (example)

Below is a representative API for DH_Array2. Implementations may vary; adjust according to your specific library.

• constructor(rows, cols, initialValue)
• get(row, col)
• set(row, col, value)
• rows()
• cols()
• map(fn)
• sliceRow®
• sliceCol©
• transpose()
• toArray() — returns nested native arrays

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Creating and initializing

You can create a DH_Array2 with a default value or from an existing nested array.

// JavaScript example const A = new DH_Array2(3, 4, 0); // 3 rows, 4 cols, initialized with 0 A.set(1, 2, 5); console.log(A.get(1,2)); // 5 const B = DH_Array2.fromArray([   [1,2,3],   [4,5,6],   [7,8,9] ]); 

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Accessing elements

Indexing is straightforward. Use bounds-checked accessors to avoid errors.

# Python-like pseudo-code val = A.get(0, 1) A.set(2, 3, val * 2) 

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Iteration patterns

Row-major iteration is the most cache-friendly and typically the default.

// C++-like pseudo-code for (int r = 0; r < A.rows(); ++r) {   for (int c = 0; c < A.cols(); ++c) {     process(A.get(r,c));   } } 

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Slicing rows and columns

Extracting a view or copy of a row/column is useful for many algorithms.

const row1 = A.sliceRow(1); // [ ... ] const col2 = A.sliceCol(2); // [ ... ] 

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Mapping and transformations

Apply a function to every element to produce a new DH_Array2.

const B = A.map((v, r, c) => v + r + c); 

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Transpose

Transposition swaps rows and columns.

B = A.transpose() 

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Practical examples

1. Image-like data: store pixel values, apply filters by mapping over neighborhoods.
2. Game boards: represent grid-based games (tic-tac-toe, chess simplified).
3. Spreadsheet-like data: store cell values and perform row/column computations.
4. Pathfinding grids: store weights/obstacles for algorithms like A*.

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Performance considerations

• Prefer row-major iteration for locality.
• Use in-place operations when possible to reduce allocations.
• For very large matrices consider specialized numerical libraries.

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Example project: Conway’s Game of Life (JavaScript)

// assuming DH_Array2 API from above function step(grid) {   const R = grid.rows(), C = grid.cols();   const out = new DH_Array2(R, C, 0);   for (let r = 0; r < R; r++) {     for (let c = 0; c < C; c++) {       let live = grid.get(r,c);       let neighbors = 0;       for (let dr = -1; dr <= 1; dr++) {         for (let dc = -1; dc <= 1; dc++) {           if (dr === 0 && dc === 0) continue;           const rr = r + dr, cc = c + dc;           if (rr >= 0 && rr < R && cc >= 0 && cc < C) {             neighbors += grid.get(rr, cc);           }         }       }       if (live === 1 && (neighbors === 2 || neighbors === 3)) out.set(r,c,1);       else if (live === 0 && neighbors === 3) out.set(r,c,1);       else out.set(r,c,0);     }   }   return out; } 

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Tips and best practices

• Validate indices when exposing low-level access.
• Provide both views and copies for slices to balance safety and performance.
• Document whether methods mutate in place or return new instances.
• Offer bulk operations (fill, copyFrom) to optimize common patterns.

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Conclusion

DH_Array2 is a practical abstraction for 2D data that balances simplicity and utility. Start by mastering creation, indexing, iteration, and mapping; then apply these operations to real problems like image processing or grid-based simulations. With careful attention to iteration order and memory use, DH_Array2 can be both easy to use and performant.