Chain Id allow you to generate unique identifiers from an object's properties chain.
Maps ENS names to their resolved addresses by chain id
Maps ENS names to their resolved addresses by chain id
Cross Chain ID
<img src="talisman.svg" alt="Talisman" width="15%" align="right" />
This tool provides functionality to interact with blockchain data, specifically to retrieve the total supply of a token on a given chain, retrieve the chain ID for a given chain name, and retrieve a filtered list of RPC endpoints for a given chain ID.
A package to get blockchain info by chain ID
This plugin covers the [V2.1 LB Router contract](https://docs.traderjoexyz.com/contracts/LBRouter) on arbitrum. Other networks can be added easily by adding the chain id to the `CHAIN_ID_ARRAY` in `chain-ids.ts` and adding supported token addresses in `co
Blockchain and DLT utils: connect wallet, check chain id, work with contrats and strings.
Blockchain explorer URLs keyed by chain id
Chain functions, generators, Node streams, and Web streams into a pipeline with backpressure support.
HANDLE CONFIGURATION ONCE AND FOR ALL
MCP server for IronClaw Bank - autonomous lending protocol on HSK Testnet (Chain ID: 133).
API for combining call site modifiers
A utility for managing a prototype chain
TypeScript definitions for stream-chain
A simple asynchronous tool
The Aikido Safe Chain wraps around the [npm cli](https://github.com/npm/cli), [npx](https://github.com/npm/cli/blob/latest/docs/content/commands/npx.md), [yarn](https://yarnpkg.com/), [pnpm](https://pnpm.io/), [pnpx](https://pnpm.io/cli/dlx), [rush](https
Light client that connects to Polkadot and Substrate-based blockchains
Old abstractions from LangChain.js
[![NPM version][npm-image]][npm-url] [![NPM downloads][npm-downloads]][npm-url] [![CI Status][ci-image]][ci-url]
Promise and RxJS wrappers around the Polkadot JS RPC
Node.js implementation of a proxy server (think Squid) with support for SSL, authentication, upstream proxy chaining, and protocol tunneling.
Chain Registry types
Fogo Sessions chain-id program
EVM transactions monitoring and querying CLI/TUI powered by Revm
Comprehensive Ethereum CLI for logs, transactions, accounts, and contracts
newton protocol cli
Bindings for CosmWasm contracts to call into custom modules of Cudos Node
Etherscan API CLI wrapper
A sale contract for CW-20 tokens
CLI for ERC-5564 compliant stealth address management on evm chains
newton prover rpc
CLI tool for fetching verified smart contract source code from blockchain explorers
A cosmwasm contract that filters desmos' network posts based on the number of reports a post has
A headless CLI tool for locally testing EIP-5792 batch transactions against a simulated Base environment
Geoptima is a suite of applications for measuring and locating mobile/cellular subscriber experience on GPS enabled smartphones. It is produced by AmanziTel AB in Helsingborg, Sweden, and supports many phone manufacturers, with free downloads from the various app stores, markets or marketplaces. This Ruby library is capable of reading the JSON format files produced by these phones and reformating them as CSV, GPX and PNG for further analysis in Excel. This is a simple and independent way of analysing the data, when compared to the full-featured analysis applications and servers available from AmanziTel. If you want to analyse a limited amount of data in excel, or with Ruby, then this GEM might be for you. If you want to analyse large amounts of data, from many subscribers, or over long periods of time then rather consider the NetView and Customer IQ applications from AmanziTel at www.amanzitel.com. Current features available in the library and the show_geoptima command: * Import one or many JSON files * Organize data by device id (IMEI) into datasets * Split by event type * Time ordering and time correlation (associate data from one event to another): ** Add GPS locations to other events (time window and interpolation algorithms) ** Add signal strenth, battery level, etc. to other events * Export event tables to CSV format for further processing in excel * Make and export GPS traces in GPX and PNG format for simple map reports The amount of data possible to process is limited by memory, since all data is imported in ruby data structures for procssing. If you need to process larger amounts of data, you will need a database-driven approach, like that provided by AmanziTel's NetView and Customer IQ solutions. This Ruby gem is actually used by parts of the data pre-processing chain of 'Customer IQ', but it not used by the main database and statistics engine that generates the reports.
In computer science, a disjoint-set data structure, also called a union–find data structure or merge–find set, is a data structure that keeps track of a set of elements partitioned into a number of disjoint (non-overlapping) subsets. It provides near-constant-time operations (bounded by the inverse Ackermann function) to add new sets, to merge existing sets, and to determine whether elements are in the same set. In addition to many other uses (see the Applications section), disjoint-sets play a key role in Kruskal's algorithm for finding the minimum spanning tree of a graph. A disjoint-set forest consists of a number of elements each of which stores an id, a parent pointer, and, in efficient algorithms, a value called the "rank". The parent pointers of elements are arranged to form one or more trees, each representing a set. If an element's parent pointer points to no other element, then the element is the root of a tree and is the representative member of its set. A set may consist of only a single element. However, if the element has a parent, the element is part of whatever set is identified by following the chain of parents upwards until a representative element (one without a parent) is reached at the root of the tree. Forests can be represented compactly in memory as arrays in which parents are indicated by their array index. Disjoint-set data structures model the partitioning of a set, for example to keep track of the connected components of an undirected graph. This model can then be used to determine whether two vertices belong to the same component, or whether adding an edge between them would result in a cycle. The Union–Find algorithm is used in high-performance implementations of unification. This data structure is used by the Boost Graph Library to implement its Incremental Connected Components functionality. It is also a key component in implementing Kruskal's algorithm to find the minimum spanning tree of a graph. Note that the implementation as disjoint-set forests doesn't allow the deletion of edges, even without path compression or the rank heuristic. Sharir and Agarwal report connections between the worst-case behavior of disjoint-sets and the length of Davenport–Schinzel sequences, a combinatorial structure from computational geometry.
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