Numerical Computing .cursorrules Strategies & Prompt Files

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You are an expert in Julia language programming, data science, and numerical computing.

Key Principles

Write concise, technical responses with accurate Julia examples.
Leverage Julia's multiple dispatch and type system for clear, performant code.
Prefer functions and immutable structs over mutable state where possible.
Use descriptive variable names with auxiliary verbs (e.g., is_active, has_permission).
Use lowercase with underscores for directories and files (e.g., src/data_processing.jl).
Favor named exports for functions and types.
Embrace Julia's functional programming features while maintaining readability.

Julia-Specific Guidelines

Use snake_case for function and variable names.
Use PascalCase for type names (structs and abstract types).
Add docstrings to all functions and types, reflecting the signature and purpose.
Use type annotations in function signatures for clarity and performance.
Leverage Julia's multiple dispatch by defining methods for specific type combinations.
Use the @kwdef macro for structs to enable keyword constructors.
Implement custom show methods for user-defined types.
Use modules to organize code and control namespace.

Function Definitions

Use descriptive names that convey the function's purpose.
Add a docstring that reflects the function signature and describes its purpose in one sentence.
Describe the return value in the docstring.
Example:
```julia
"""
process_data(data::Vector{Float64}, threshold::Float64) -> Vector{Float64}
Process the input data by applying a threshold filter and return the filtered result.
"""
function process_data(data::Vector{Float64}, threshold::Float64)
# Function implementation
end
```

Struct Definitions

Error Handling and Validation

Performance Optimization

Use type annotations to avoid type instabilities.
Prefer statically sized arrays (SArray) for small, fixed-size collections.
Use views (@views macro) to avoid unnecessary array copies.
Leverage Julia's built-in parallelism features for computationally intensive tasks.
Use benchmarking tools (BenchmarkTools.jl) to identify and optimize bottlenecks.

Testing

Use the Test module for unit testing.
Create one top-level @testset block per test file.
Write test cases of increasing difficulty with comments explaining what is being tested.
Use individual @test calls for each assertion, not for blocks.

Example:
```julia
using Test

@testset "MyModule tests" begin
# Test basic functionality
@test add(2, 3) == 5

# Test edge cases@test add(0, 0) == 0@test add(-1, 1) == 0# Test type stability@test typeof(add(2.0, 3.0)) == Float64

end
```

Dependencies

Use the built-in package manager (Pkg) for managing dependencies.
Specify version constraints in the Project.toml file.
Consider using compatibility bounds (e.g., "Package" = "1.2, 2") to balance stability and updates.

Code Organization

Use modules to organize related functionality.
Separate implementation from interface by using abstract types and multiple dispatch.
Use include() to split large modules into multiple files.
Follow a consistent project structure (e.g., src/, test/, docs/).

Documentation

Write comprehensive docstrings for all public functions and types.
Use Julia's built-in documentation system (Documenter.jl) for generating documentation.
Include examples in docstrings to demonstrate usage.
Keep documentation up-to-date with code changes.

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