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Kill Switch Hidden in npm Packages Typosquatting Chalk and Chokidar
Socket researchers found several malicious npm packages typosquatting Chalk and Chokidar, targeting Node.js developers with kill switches and data theft.
The 'heap' npm package provides an easy-to-use implementation of the heap data structure, allowing users to manage a collection of items with operations such as insertion, removal, and retrieval of the smallest (or largest) item efficiently. It supports both min-heaps and max-heaps, making it versatile for various applications such as priority queues, sorting algorithms, and more.
Creating and using a min heap
This code demonstrates how to create a min heap, insert items into it, and then remove the smallest item. The 'heap' package automatically organizes the items so that the smallest item can be efficiently removed.
const Heap = require('heap');
let minHeap = new Heap();
minHeap.push(2);
minHeap.push(3);
minHeap.push(1);
console.log(minHeap.pop()); // Outputs: 1
Creating and using a max heap
This code shows how to create a max heap by providing a custom comparison function. Items are inserted into the heap, and the largest item is removed efficiently.
const Heap = require('heap');
let maxHeap = new Heap((a, b) => b - a);
maxHeap.push(2);
maxHeap.push(3);
maxHeap.push(1);
console.log(maxHeap.pop()); // Outputs: 3
Heapify an existing array
This example demonstrates how to transform an existing array into a heap structure in-place using the 'heapify' method. This is useful for efficiently preparing data for heap operations.
const Heap = require('heap');
let numbers = [3, 1, 2];
Heap.heapify(numbers);
console.log(numbers); // Outputs a heapified array
The 'binary-heap' package also provides an implementation of the binary heap data structure. It is similar to 'heap' in functionality but might have differences in API design and performance characteristics.
This package offers a priority queue implementation using a binary heap. While it serves a similar purpose to 'heap', it is specifically tailored for priority queue use cases and might offer a more specialized API for those scenarios.
A binary heap implementation in CoffeeScript/JavaScript. Ported from Python's heapq module.
This module can be used in either the browser or node.js.
for browser use, you may download the script and include it in you web page.
<script type="text/javascript" src="./heap.js"></script>
for node.js, you may install it via npm:
npm install heap
then require it:
var Heap = require('heap');
push and pop
var heap = new Heap();
heap.push(3);
heap.push(1);
heap.push(2);
heap.pop(); // 1
custom comparison function
var heap = new Heap(function(a, b) {
return a.foo - b.foo;
});
heap.push({foo: 3});
heap.push({foo: 1});
heap.push({foo: 2});
heap.pop(); // {foo: 1}
find 3 largest/smallest items in an array
var array = [1, 3, 4, 2, 5];
Heap.nlargest(array, 3); // [5, 4, 3]
Heap.nsmallest(array, 3); // [1, 2, 3]
This module exposes only one object, namely the Heap class.
The constructor receives a comparison function as an optional parameter. If omitted, the heap is built as a min-heap, which means that the smallest element will be popped out first.
If the comparison function is supplied, the heap will be built according to the return value of the comparison function.
So, the comparison function has the following form:
function cmp(a, b) {
if (a is prior to b) {
return -1;
}
if (b is prior to a) {
return 1;
}
return 0;
}
To compare numbers, simply:
function cmp(a, b) {
return a - b;
}
push(item) (alias: insert)
Push item onto heap.
pop()
Pop the smallest item off the heap and return it.
peek() (alias: top / front)
Return the smallest item of the heap.
replace(item)
Pop and return the current smallest value, and add the new item.
This is more efficient than pop() followed by push(), and can be more appropriate when using a fixed size heap. Note that the value returned may be larger than item!
pushpop(item)
Fast version of a push followed by a pop.
heapify()
Rebuild the heap. This method may come handy when the priority of the internal data is being modified.
updateItem(item)
Update the position of the given item in the heap. This function should be called every time the item is being modified.
empty()
Determine whether the heap is empty.
size()
Get the number of elements stored in the heap.
toArray()
Return the array representation of the heap. (note: the array is a shallow copy of the heap's internal nodes)
clone() (alias: copy)
Return a clone of the heap. (note: the internal data is a shallow copy of the original one)
NOTE: All the static methods are designed to be applied on arrays.
push(array, item, [cmp])
Push item onto array, maintaining the heap invariant.
pop(array, [cmp])
Pop the smallest item off the array, maintaining the heap invariant.
replace(array, item, [cmp])
Pop and return the current smallest value, and add the new item.
This is more efficient than heappop() followed by heappush(), and can be more appropriate when using a fixed size heap. Note that the value returned may be larger than item!
pushpop(array, item, [cmp])
Fast version of a heappush followed by a heappop.
heapify(array, [cmp])
Build the heap.
updateItem(array, item, [cmp])
Update the position of the given item in the heap. This function should be called every time the item is being modified.
nlargest(array, n, [cmp])
Find the n largest elements in a dataset.
nsmallest(array, n, [cmp])
Find the n smallest elements in a dataset.
FAQs
binary heap (priority queue) algorithms (ported from Python's heapq module)
We found that heap demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
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