{"id":22964920,"url":"https://github.com/anishpras/rust_scicrate","last_synced_at":"2025-04-05T12:44:08.849Z","repository":{"id":252804997,"uuid":"841560812","full_name":"Anishpras/rust_scicrate","owner":"Anishpras","description":"Rust SciCrate is a scientific computing library for Rust, inspired by SciPy.","archived":false,"fork":false,"pushed_at":"2024-08-12T17:06:15.000Z","size":7,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-03T00:15:55.316Z","etag":null,"topics":["computing","mathematics","rust-science","scientific","scientific-computing"],"latest_commit_sha":null,"homepage":"https://crates.io/crates/rust_scicrate","language":"Rust","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Anishpras.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-08-12T16:44:01.000Z","updated_at":"2024-08-12T17:06:18.000Z","dependencies_parsed_at":"2024-08-12T17:22:52.938Z","dependency_job_id":"a446b23f-9763-43ae-93f7-5b0095b35bef","html_url":"https://github.com/Anishpras/rust_scicrate","commit_stats":null,"previous_names":["anishpras/rust_scicrate"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anishpras%2Frust_scicrate","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anishpras%2Frust_scicrate/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anishpras%2Frust_scicrate/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Anishpras%2Frust_scicrate/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Anishpras","download_url":"https://codeload.github.com/Anishpras/rust_scicrate/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247339124,"owners_count":20923009,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["computing","mathematics","rust-science","scientific","scientific-computing"],"created_at":"2024-12-14T20:13:03.162Z","updated_at":"2025-04-05T12:44:08.826Z","avatar_url":"https://github.com/Anishpras.png","language":"Rust","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Rust SciCrate\n\nRust SciCrate is a scientific computing library for Rust, inspired by SciPy. It aims to provide a comprehensive set of tools for numerical operations, statistical analysis, optimization, and more, all implemented in pure Rust for maximum performance and safety.\n\n## Features\n\n- **Linear Algebra**: Basic matrix operations, including multiplication and vector norms.\n- **Statistics**: Fundamental statistical functions like mean and standard deviation.\n- **Optimization**: Implementation of optimization algorithms, including Golden Section Search.\n- **Numerical Integration**: Trapezoidal rule for numerical integration.\n\n## Installation\n\nAdd this to your `Cargo.toml`:\n\n```toml\n[dependencies]\nrust_scicrate = \"0.1.0\"\n```\n\n## Usage\n\nHere are some examples of how to use Rust SciCrate:\n\n### Linear Algebra\n\n```rust\nuse rust_scicrate::linalg::matrix_multiply;\nuse ndarray::array;\n\nfn main() {\n    let a = array![[1., 2.], [3., 4.]];\n    let b = array![[5., 6.], [7., 8.]];\n    let c = matrix_multiply(\u0026a, \u0026b).unwrap();\n    println!(\"Result of matrix multiplication:\\n{}\", c);\n}\n```\n\n### Statistics\n\n```rust\nuse rust_scicrate::stats::{mean, standard_deviation};\nuse ndarray::array;\n\nfn main() {\n    let data = array![1., 2., 3., 4., 5.];\n    println!(\"Mean: {}\", mean(\u0026data).unwrap());\n    println!(\"Standard Deviation: {}\", standard_deviation(\u0026data).unwrap());\n}\n```\n\n### Optimization\n\n```rust\nuse rust_scicrate::optimize::golden_section_search;\n\nfn main() {\n    let f = |x: f64| (x - 2.).powi(2);\n    let min = golden_section_search(f, 0., 4., 1e-6, 100).unwrap();\n    println!(\"Minimum found at x = {}\", min);\n}\n```\n\n### Numerical Integration\n\n```rust\nuse rust_scicrate::integrate::trapezoidal;\nuse std::f64::consts::PI;\n\nfn main() {\n    let f = |x: f64| x.sin();\n    let integral = trapezoidal(f, 0., PI, 1000).unwrap();\n    println!(\"Integral of sin(x) from 0 to pi: {}\", integral);\n}\n```\n\n## Documentation\n\nFor more detailed documentation, please visit [docs.rs/rust_scicrate](https://docs.rs/rust_scicrate).\n\n## Contributing\n\nWe welcome contributions to Rust SciCrate! Here are ways you can contribute:\n\n1. Implement new features or improve existing ones\n2. Write tests to increase code coverage\n3. Improve documentation\n4. Report bugs and suggest enhancements\n\nTo contribute:\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/AmazingFeature`)\n3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)\n4. Push to the branch (`git push origin feature/AmazingFeature`)\n5. Open a Pull Request\n\nPlease make sure to update tests as appropriate and adhere to the existing coding style.\n\n## Roadmap\n\n- Implement more advanced linear algebra operations (e.g., eigenvalue computation, matrix decompositions)\n- Add more statistical functions and distributions\n- Implement additional optimization algorithms\n- Add support for solving ordinary differential equations\n- Implement Fourier transforms\n\n## License\n\nThis project is licensed under\n\n- MIT license ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT)\n\nat your option.\n\n## Acknowledgments\n\n- Inspired by the SciPy library for Python\n- Built with Rust and love for scientific computing\n\n## Contact\n\nIf you want to contact me you can reach me at \u003canishprashun118@gmail.com\u003e.\n\n## Disclaimer\n\nThis library is in early stages of development. While we strive for correctness and efficiency, it may contain bugs or inefficiencies. Use with caution in production environments.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanishpras%2Frust_scicrate","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanishpras%2Frust_scicrate","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanishpras%2Frust_scicrate/lists"}