SciJo

<div align="center"> <img src="./assets/scijo.png" alt="SciJo Logo" width="200" style="border-radius: 50%; margin-bottom: 20px;"/> <p style="font-size: 1.2em; color: #666; margin: 0; padding: 10px 20px; line-height: 1.5;"> <em>High-performance scientific computing library for Mojo, written in pure Mojo, inspired by SciPy</em> </p> </div>

Overview

SciJo is a high-performance scientific computing library for Mojo that brings the power and familiarity of SciPy to the Mojo ecosystem. Written in pure Mojo and built on top of NuMojo, SciJo combines the performance benefits of native compilation with the type safety guarantees of Mojo's advanced type system.

Features

• Pure Mojo: Native implementation for maximum performance
• Familiar APIs: SciPy-inspired interfaces for easy adoption
• Type Safe: Compile-time guarantees with Mojo's type system
• NuMojo Backend: Efficient array operations and complex number support

Current Modules

Numerical Differentiation (scijo.differentiate)

Accurate derivatives using finite difference methods:

• Methods: Central, forward, and backward differences
• Order control: Specify accuracy order (1-6 for forward/backward, 2-8 for central)
• Adaptive stepping: Automatic step size refinement with Richardson extrapolation
• Error estimation: Built-in convergence tracking

Integration (scijo.integrate)

Numerical integration with adaptive algorithms:

• quad: (QUADPACK QNG algorithm)
  • Succesively increasing precision levels (10, 21, 43, 87 point rules)
• trapezoid: Basic trapezoidal rule for uniform or non-uniform grids

Interpolation (scijo.interpolate)

1D data interpolation:

• interp1d: Linear interpolation
• Handles both extrapolation and boundary fill methods
• Compatible with NuMojo arrays

FFT (scijo.fft)

Fast Fourier Transform operations:

• fft: Forward FFT using Cooley-Tukey algorithm
• ifft: Inverse FFT with proper normalization
• Supports complex arrays (power-of-2 sizes)
• Compatible with NumPy's FFT conventions

Physical Constants (scijo.constants)

Access fundamental physical constants from CODATA 2022:

• Lots physical constants with values, units, and uncertainties
• Compatible with scipy.constants structure
• Helper functions: value(), unit(), uncertainty()

Installation

Method 1:

1. Add to pixi.toml

[workspace]
preview = ["pixi-build"]

[dependencies]
modular = ">=25.6.1,<26"
scijo = { git = "https://github.com/shivasankarka/SciJo.git", branch = "main"}

Note that SciJo and NuMojo require the modular package. We will move to mojo only package in future if possible.

2. Install in pixi

pixi install

Method 2: Build from Source

# Clone and build
git clone https://github.com/shivasankarka/SciJo.git
cd SciJo
mojo build scijo

# Move package to your project
mv build/scijo.mojopkg /path/to/your/project

Quick Start

Numerical Differentiation

import scijo as sj
from scijo.differentiate import derivative

fn simple_function[dtype: DType](x: Scalar[dtype], args: Optional[List[Scalar[dtype]]] = None) -> Scalar[dtype]:
    var a = args.value()[0]
    return a * x * x + 2.0 * x + 1.0

fn main() raises:
    var result = derivative[sj.f64, simple_function, step_direction=0](
        x0=1.0,
        args=List[Scalar[sj.f64]](2.0),
        tolerance={"atol": 1e-8, "rtol": 1e-8},
        order=6
    )
    print("Derivative result:", result)

Integration

from scijo.integrate.quad import quad

fn simple_function[
    dtype: DType
](x: Scalar[dtype], args: Optional[List[Scalar[dtype]]] = None) -> Scalar[
    dtype
]:
    """A simple function for testing."""
    var a = args.value()[0]
    return a * x * x + 2.0 * x + 1.0

fn main():
    var result = quad[sj.f64, simple_function](
        a=0.0,
        b=1.0,
        args=List[Scalar[sj.f64]](2.0),
        epsabs=1e-6,
        epsrel=1e-6,
    )
    print("Integral value:", result.integral)

Interpolation

from scijo.interpolate.interp1d import interp1d
import numojo as nm

fn main() raises:
    var x = nm.arange[nm.f64](0, 5, 1)
    var y = x * x
    var xi = nm.linspace[nm.f64](0.5, 3.5, 4)

    var yi = interp1d[nm.f64, type="linear", fill_method="interpolate"](xi, x, y)
    print("Interpolated values:", yi)

FFT

from scijo.fft import fft, ifft
import numojo as nm

fn main() raises:
    # Create complex array
    var arr = nm.arange[nm.cf64](nm.CScalar[nm.cf64](0), nm.CScalar[nm.cf64](8))

    # Forward FFT
    var y_fft = fft[nm.cf64](arr)
    print("FFT result:", y_fft)

    # Inverse FFT
    var y_ifft = ifft[nm.cf64](y_fft)
    print("IFFT result:", y_ifft)

Physical Constants

from scijo.constants import physical_constants, value, unit

fn main() raises:
    print("Speed of light:", value("speed_of_light_in_vacuum"), "m/s")
    print("Planck constant:", value("Planck_constant"), unit("Planck_constant"))

Roadmap

Near Term

• More integration algorithms (Simpson's, Romberg, QAGSE etc)
• Real FFT (rfft, irfft) and 2D FFT support
• Additional interpolation methods (cubic, spline)
• Expand differentiation module.

Future

• Optimization: Minimization, root finding, curve fitting
• Statistics: Distributions, hypothesis tests, descriptive statistics
• Signal Processing: Filtering, windowing, convolution
• Linear Algebra: Matrix decompositions (SVD, QR, Cholesky)
• Sparse Matrices: Efficient storage and operations

Contributing

Contributions are most welcome! Feel free to add a functionality and open a PR!

Priority areas:

• Algorithm implementations (see Roadmap)
• Performance benchmarks and optimization
• Tests and documentation
• Bug reports and feature requests

License

Distributed under the Apache 2.0 License with LLVM Exceptions. See LICENSE for more information.

Citation

Feel free to cite SciJo in your work, helps with visibility :)

@software{scijo,
  author = {Shivasankar K.A. and SciJo Contributors},
  title = {SciJo: High-Performance Scientific Computing in Mojo},
  url = {https://github.com/shivasankarka/SciJo},
  year = {2025}
}

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⚠️ Note: This library is in early development and may introduce breaking changes between versions.