GraphQL Joi Scalars
custom apollo link to allow to parse custom scalars
Scalars component library
GraphQL scalars for Date, DateTime and Time
Stringify an object sorting scalars before objects, and defaulting to 2-space indent
Graphql response transformer for custom scalars
Shared GraphQL scalars used by ComposeDB packages.
This repository contains custom GraphQL scalars used by Powerhouse.
graphql-custom-scalars
A collection of scalar types not included in base GraphQL.
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Client for scalars APIs
A collection of GraphQL custom scalars supporting cleanup, validation, and defaults via fluent api
Scalars SDK
Configurable custom GraphQL Scalars (string, number, date, etc) with sanitization / validation / transformation in TypeScript.
GraphQL Scalars for formatting Currency
Integration with [GraphQL Scalars](https://www.graphql-scalars.dev/)
A better way to import external GraphQL scalars into your Nest.js projects.
Custom Apollo Link for parsing and serializing custom scalars with Apollo Client v4
Shared GraphQL core: Pothos builder factory, scalars, tracing & complexity
Shared GraphQL core: Pothos builder factory, scalars, tracing & complexity
Provides GraphQL scalars for JavaScript's new Temporal specification.
Mars Framework, scalars
Client for scalars application
Minimal numeric traits: Zero, One, Inv, Sqrt, Exp, Logarithm, Trigonometry, Real, Integer
scalars modulo the group order of the secp256k1 curve
Core Scalar, DType, NullKind primitives for the frankenpandas workspace — the universal type system behind fp-frame.
Columnar storage primitives (Column, ValidityMask) for pandas-parity dataframes — the storage layer behind fp-frame.
Differential conformance harness for frankenpandas against a live pandas oracle — packet fixtures + fuzz seed tests + live-oracle parity gates.
Expression evaluation / query parser for fp-frame DataFrames — pandas df.eval / df.query parity.
DataFrame and Series with pandas-API parity, AACE index alignment, and GroupBy / Rolling / Resample integration.
FrankenTUI terminal interface for interactive fp-frame / frankenpandas workflows.
GroupBy engine for fp-frame with dense-key / arena-backed / hash-map execution paths.
Flat and multi-level row index types (Index, MultiIndex) for pandas-parity dataframes.
IO layer for frankenpandas: CSV, JSON, JSONL, Parquet, ORC, HDF5, Excel, Feather, Arrow IPC, Pickle, Stata, SQL (SqlConnection with SQLite default backend).
Merge and join engine (AG-05 leapfrog triejoin) for fp-frame DataFrames — inner / left / right / outer joins with index alignment.
A gem to automate using Scalar with Ruby apps
Adds a 'Map' scalar type for use with graphql-ruby
A simple type coercion library
Gem version of SVG:::Graph. SVG:::Graph is a pure Ruby library for generating charts, which are a type of graph where the values of one axis are not scalar. SVG::Graph has a verry similar API to the Perl library SVG::TT::Graph, and the resulting charts also look the same. This isn't surprising, because SVG::Graph started as a loose port of SVG::TT::Graph, although the internal code no longer resembles the Perl original at all.
Adds Object#mutate (similar to Enumerable#map), Object#keep (Enumerable#select), Object#toss (Enumerable#reject), Object#impart (Enumerable#inject). Object#tap already exists for Enumerable#each (for side effects).
TinyGID provides a compact syntax for building Global ID strings for things like GraphQL APIs and Rails apps
Minimal model support for redis-rb. Directly maps ruby properties to model_name:id:field_name keys in redis. Scalar, list and set properties are supported.
UOM implements Units of Measurement based on the International System of Units (SI). The base SI units, metric scalar factors and all possible combinations of these units are supported out of the box.
A standalone Ruby gem providing secp256k1 elliptic curve primitives via a native C extension. Implements field arithmetic, scalar operations, Jacobian point arithmetic, and constant-time Montgomery ladder scalar multiplication — all without any dependency on libsecp256k1. Suitable for any Ruby project requiring secp256k1 operations.
A Ruby gem for vector and matrix operations. Provides methods to calculate: - Matrix determinant: Determinant(matrix) input: matrix - Array of arrays size of nxn output: res[Int] - simple Integer - Scalar product of vectors scalar_prod(a, b) input: a[Array], b[Array]- vectors a and b output: res[Int] - simple Integer as a result of scalar prod - Cross product for 3D vectors cross_prod(a, b) input: a[Array], b[Array] - vectors a and b with dimension n = 3; output: res[Array] - vector with the size = 3 (its dimension) as a result of cross prod - Help function help() output: String with info about gem funcs Includes comprehensive error handling and input validation. Designed for educational use and basic linear algebra computations. Ruby-гем для операций с векторами и матрицами. Предоставляет методы для вычисления: - Определителя матрицы Determinant(matrix) input: matrix - матрица (массив массивов) размера nxn output: res[Int] - целое число - Скалярного произведения векторов scalar_prod(a, b) input: a[Array], b[Array] - векторы (массивы) a и b output: res[Int] - целое число как результат скалярного произведения - Векторного произведения для 3D векторов cross_prod(a, b) input: a[Array], b[Array] - векторы (массивы) a и b размером n = 3; output: res[Array] - вектор (массив) с размером = 3 (его размерность) как результат векторного произведения векторов - Функция "помощь" help() output: Строка с информацией про математические методы гема Включает обработку ошибок и валидацию входных данных. Разработан для образовательных целей и базовых вычислений линейной алгебры.
Qwack is an extensible, lightweight DLS to dynamically verify and mock scalar types. It is meant primilarily to handle parsed JSON objects eg: from an API or a database field
ActiveModel::Caching is a versatile gem for managing structured, temporary data using a caching backend, typically Rails cache for Rails applications. This gem provides an easy-to-use API for storing, retrieving, and manipulating data structures like scalars, lists, and JSON, making it simple to handle transient data without adding extra dependencies.
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