Make Python Fortran again!

Disclaimer: This is an experimental work-in-progress programme that is only partially implemented and where interfaces at all levels could undergo substantial changes.

lazy fortran solves the two-language problem in Fortran by providing the experience and tooling of modern languages while maintaining, performance and long-term stability of Fortran. It is inspired by the best features of Python, Rust, Julia and Go, builds on thefpm ecosystem, and integrates with standard Fortran compilers and tools.

Thelazy fortran toolchain splits work across several packages: fortfront, fluff, ffc, fad, fortrun, and fnb — each focused on a clear purpose. This keeps code organized, dependencies minimal, and interfaces straightforward.

Purpose: Code runner and package manager.

Features:

• Executeslazy fortran or Standard Fortran applications
• Global source and object cache for rapid incremental builds and runs
• Manages dependencies, project layout, and environments (inspired by Go, Rust, Julia, Python)
• Provides caching API for other packages

Interfaces: Provides high-level run and build commands for development workflows, plus API for cache management.

Inspired by: go, julia, lfortran

Dependencies:

• fortfront for parsing and analysis
• FPM for package resolution and build metadata
• Any supported Fortran compiler

Purpose: Source-to-source transformations and static checks.

Features:

• Source code formatter forlazy fortran and Standard Fortran
• Advanced linting with diagnostics (inspired by Rust analyzer)
• Static analysis checks and code quality enforcement
• Code transformations and refactoring suggestions

Interfaces: Provides CLI subcommands for formatting, linting, and static analysis.

Inspired by: ruff (code formatting and static analysis)

Dependencies:

• fortfront only

Purpose: Notebook interface for Standard Fortran andlazy fortran.

Features:

• Implements .f and .md files as notebooks similar to jupytext
• Supports both Standard Fortran andlazy fortran syntax
• Provides export to Markdown and PDF

Interfaces: Offers API and CLI to convert notebooks to Markdown or PDF

Inspired by: jupyter, jupytext, weave, blocks (caching runner), lfortran

Dependencies:

• fortrun for running notebook cells and managing cache
• fluff CLI for formatting and standardization (optional)

Thelazy fortran toolchain splits work across several packages: fortfront, fluff, ffc, fad, fortrun, and fnb — each focused on a clear purpose. This keeps code organized, dependencies minimal, and interfaces straightforward.

Purpose: Full compilation backend.

Features:

• Lower typed AST to HLFIR (an MLIR dialect)
• Continue lowering to LLVM IR and produce object code
• Includes the MLIR backend implementation

Interfaces: Offers a compiler CLI to produce object code.

Inspired by: flang (HLFIR -> FIR -> LLVM IR or SPIR-V for GPU -> OMP target), lfortran (typed AST -> LLVM IR)

Dependencies:

• fortfront for typed AST
• LLVM/MLIR libraries

Purpose: Automatic differentiation.

Features:

• Supports!$ad annotations to mark functions/subroutines for differentiation
• Integrates with Enzyme at the IR level during compilation
• Works with any Enzyme-enabled compiler (ffc, Flang, etc.)
• Details of IR integration to be defined

Interfaces: Exposes compiler flags or annotations to enable AD on marked routines.

Inspired by: tapenade (decorators, e.g. !$ad mode=reverse)

Dependencies:

• fortfront for AST and semantic info
• Any Enzyme-enabled Fortran compiler (ffc, Flang, LFortran, etc.)

Purpose: Core analysis.

Includes:

• AST types for untyped and typed trees
• Lexer and parser to build the untyped AST
• Semantic analyzer to resolve names, infer and check types, and produce a typed AST

Outputs:

• Untyped AST
• Typed AST
• Standard Fortran (F90) code emission
• JSON export of tokens, ASTs

API & CLI:

• Programmatic API for lexing, parsing, and semantic analysis
• API and minimal CLI (fortfront) with subcommands to dump tokens, AST, typed AST in JSON, or emit Standard Fortran

Inspired by: go, julia, rust, python, lfortran

Dependencies: None

Purpose: Multi-dimensional array operations and data analysis.

Features:

• xarray/ndarray-inspired interface: load, aggregate, plot data with ds%plot(), ds%var%mean()
• Labeled arrays with coordinate indexing and alignment
• Broadcasting, reduction, and transformation operations
• Integration with fortio for data loading

Interfaces: Exposes array API with method chaining for data manipulation and analysis.

Inspired by: xarray/ndarray (load, aggregate, plot data with ds%plot(), ds%var%mean())

Dependencies:

• fortio for data I/O

Purpose: Table-oriented data manipulation and analysis.

Features:

• pandas/excel-inspired interface using fortarray as backend
• SQL integration through fortsql for database operations
• Table joins, grouping, and aggregation operations
• Import/export from various tabular formats

Interfaces: Provides DataFrame-like API with SQL query capabilities and statistical methods.

Inspired by: pandas (as fortarray backend but with SQL and table oriented)

Dependencies:

• fortarray for underlying array operations
• fortsql for database connectivity

Purpose: Finite element method computations.

Features:

• FreeFEM-inspired finite element framework
• Mesh generation and adaptive refinement
• Variational formulation and solver integration
• Post-processing and visualization of FEM results

Interfaces: Offers high-level FEM API with mesh management and solver integration.

Inspired by: FreeFEM, FEniCS, scikit-fem

Dependencies: BLAS/LAPACK, sparse matrix libraries, mesh generation tools

Purpose: Unified I/O interface for scientific data formats.

Features:

• Low-level I/O routines for NetCDF/HDF5, parquet, csv formats under one API
• Memory-efficient streaming and chunked data access
• Format-agnostic data type mapping and conversion
• Error handling and data validation across formats

Interfaces: Provides standardized read/write API with format detection and uniform error handling.

Inspired by: Low-level I/O routines for NetCDF/HDF5, parquet, csv under one API

Dependencies: NetCDF, HDF5, and parquet libraries

Purpose: Database connectivity and SQL operations.

Features:

• Low-level SQL API for various databases (SQLite, PostgreSQL, MySQL)
• Connection pooling and transaction management
• Prepared statements and parameter binding
• Result set iteration and type-safe data extraction

Interfaces: Offers database-agnostic SQL execution API with connection management.

Inspired by: Low-level SQL API for various databases

Dependencies: Database-specific client libraries

Purpose: Scientific plotting and visualization.

Features:

• 2D/3D plotting with customizable styles and layouts
• Statistical plots, histograms, and scientific visualizations
• Export to various formats (PNG, PDF, SVG, EPS)

Interfaces: Provides high-level plotting API with figure and axis management.

Inspired by: matplotlib, pyplot-fortran, gnuplot, plotly

Dependencies: Graphics libraries (Cairo, FreeType), optional LaTeX for typesetting

Purpose: Bootstrap Fortran and an operating system from machine language.

Features:

• RISC-V architecture support with QEMU integration
• Kernel development framework in Fortran
• Hardware abstraction layer and device drivers
• System call interface and memory management

Interfaces: Provides OS development API with hardware abstraction and system service layers.

Inspired by: stage0 (bootstrap compiler from binary and hex code on bare metal)

Dependencies:

• RISC-V toolchain
• QEMU for emulation and testing

Repositories