Core Functionality
- ndarray:
ndarray
provides the core functionality for creating and manipulating n-dimensional arrays. It supports various array shapes, data types, and basic operations like indexing, slicing, and reshaping. - ndarray-pack:
ndarray-pack
focuses on packing and unpacking data in n-dimensional arrays. It provides utilities for converting arrays to and from binary formats, making it easier to handle data serialization and deserialization. - ndarray-ops:
ndarray-ops
extends the functionality ofndarray
by adding a comprehensive set of mathematical operations, including element-wise operations, reductions, and linear algebra functions. It requiresndarray
as a dependency. - ndarray-scratch:
ndarray-scratch
provides a system for allocating temporary arrays that can be reused during computations. This helps reduce memory allocation overhead and improve performance in scenarios where temporary arrays are frequently created and discarded.
Performance Optimization
- ndarray:
ndarray
is designed to be lightweight and efficient, with minimal overhead for array manipulation. However, it does not include specialized optimizations for performance-critical applications. - ndarray-pack:
ndarray-pack
is focused on efficient data packing and unpacking, which can improve performance in applications that require frequent serialization and deserialization of array data. However, it does not provide general performance optimizations for array manipulation. - ndarray-ops:
ndarray-ops
is optimized for performance when performing mathematical operations on n-dimensional arrays. It is designed to work efficiently with thendarray
library, but the performance may vary depending on the complexity of the operations being performed. - ndarray-scratch:
ndarray-scratch
is specifically designed to optimize memory usage and reduce the overhead of frequent array allocations. By reusing temporary arrays, it helps improve performance in scenarios where memory allocation is a bottleneck.
Use Case Scenarios
- ndarray:
ndarray
is suitable for a wide range of applications that require basic n-dimensional array manipulation, such as data processing, visualization, and simple scientific computing. - ndarray-pack:
ndarray-pack
is useful in applications that involve data serialization, such as sending array data over the network, saving to binary files, or interfacing with WebAssembly. - ndarray-ops:
ndarray-ops
is ideal for applications in scientific computing, machine learning, and data analysis that require advanced mathematical operations on n-dimensional arrays. - ndarray-scratch:
ndarray-scratch
is beneficial in performance-critical applications where minimizing memory allocation and garbage collection is important, such as real-time data processing or large-scale simulations.
Ease of Integration
- ndarray:
ndarray
can be easily integrated into any JavaScript project, as it has no external dependencies and a simple API for array manipulation. - ndarray-pack:
ndarray-pack
can be integrated into projects that require data packing and unpacking functionality, especially those that work with binary data or need efficient serialization methods. - ndarray-ops:
ndarray-ops
integrates seamlessly withndarray
, making it easy to add mathematical capabilities to projects that already use thendarray
library. - ndarray-scratch:
ndarray-scratch
can be used alongside other ndarray libraries to optimize memory usage during computations, making it a valuable addition to performance-sensitive applications.
Example Code
- ndarray:
Creating and manipulating n-dimensional arrays with
ndarray
const ndarray = require('ndarray'); const arr = ndarray(new Float32Array(12), [3, 4]); // Create a 3x4 array arr.set(1, 2, 42); // Set value at (1, 2) console.log(arr.get(1, 2)); // Get value at (1, 2) console.log(arr.shape); // Get array shape
- ndarray-pack:
Packing and unpacking data with
ndarray-pack
const ndarray = require('ndarray'); const pack = require('ndarray-pack'); const arr = ndarray(new Float32Array([1, 2, 3, 4]), [2, 2]); const packed = pack(arr); // Pack array data console.log(packed); // Packed binary data const unpacked = pack.unpack(packed, [2, 2]); // Unpack data console.log(unpacked.data); // Output: [1, 2, 3, 4]
- ndarray-ops:
Performing mathematical operations with
ndarray-ops
const ndarray = require('ndarray'); const ops = require('ndarray-ops'); const arr1 = ndarray(new Float32Array([1, 2, 3, 4]), [2, 2]); const arr2 = ndarray(new Float32Array([5, 6, 7, 8]), [2, 2]); ops.add(arr1, arr2, arr1); // Add arr2 to arr1 console.log(arr1.data); // Output: [6, 8, 10, 12]
- ndarray-scratch:
Using temporary arrays with
ndarray-scratch
const ndarray = require('ndarray'); const scratch = require('ndarray-scratch'); const arr = ndarray(new Float32Array(10), [10]); const temp = scratch.alloc1d(10); // Allocate temporary array for (let i = 0; i < arr.shape[0]; i++) { temp.set(i, arr.get(i) * 2); // Use temporary array } scratch.free(temp); // Free temporary array