Results for “numeric-functions”

MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.

Provides GSAP utility functions for math, value mapping, randomization, snapping, and unit parsing in animations.

Performs arbitrary-precision arithmetic calculations including addition, subtraction, multiplication, division, and exponents. Use when the user asks to calculate, compute, or evaluate math expressions, or when precise decimal arithmetic is needed to avoid floating-point errors.

Refactors Swift code toward declarative functional programming with immutability, value types, pure functions, transformations, and closure-based APIs.

Segments abdominal organs from CT NIfTI volumes using the NV-Segment-CT VISTA3D model, producing label maps and structured evidence JSON.

Runs NV-Segment-CTMR segmentation on CT or MRI NIfTI volumes and records label-map evidence.

Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.

Unified math capabilities - computation, solving, and explanation. I route to the right tool.

Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.

Apply functional programming patterns with fp-ts in React, covering state management, form validation, and data fetching for React 18/19 and Next.js 14/15.

Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.

Implement the NIST Privacy Framework CONTROL function covering CT.DM data management, CT.DP data processing policies and procedures, and CT.PO disassociated processing. Provides technical control architectures, data management workflows, and de-identification implementation guidance.

Improves code readability and maintainability by applying SOLID principles, meaningful naming, small functions, proper error handling, and DRY practices.

Influence Functions in Deep Learning

Provides comprehensive guidance for performing symbolic algebra, calculus, linear algebra, equation solving, physics calculations, and code generation using the SymPy library.

Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working...

Use for writing, reviewing, debugging, testing, or optimizing Python SymPy symbolic mathematics. Trigger on Symbol, assumptions, Expr, Eq, solve/solveset, simplify, factor, expand, calculus, matrices, exact arithmetic, lambdify, code generation, or symbolic-to-numeric conversion. Do not use for NumPy-only arrays, mpmath-only arbitrary-precision numerics, CVXPY optimization models, or parsing untrusted mathematical text.

Numerical computing with MATLAB and GNU Octave for matrix operations, data analysis, visualization, and scientific computing, including script execution and syntax guidance.

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Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.

Provides comprehensive guidance for symbolic mathematics in Python using SymPy, covering algebra, calculus, equation solving, linear algebra, physics, and code generation.

Use esta competência ao trabalhar com matemática simbólica em Python. Esta competência deve ser usada para tarefas de computação simbólica incluindo resolução de equações algebraicamente, realização de operações de cálculo (derivadas, integrais, limites), manipulação de expressões algébricas, trabalho com matrizes simbolicamente, cálculos de física, problemas de teoria dos números, computações de geometria e geração de código executável a partir de expressões matemáticas. Aplique esta competência quando o usuário precisa de resultados simbólicos exatos em vez de aproximações numéricas, ou ao trabalhar com fórmulas matemáticas que contêm variáveis e parâmetros.

Normalize long-form CODEX cycle folders to short form before notebooks run. Trigger: cyc001_reg001_*, hard-coded cyc paths breaking, staged CODEX raw data failing in Notebooks 1/2.

MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.