gafoo

Posted on Jun 21 • Edited on Jun 24

Calco: Lightweight, High-Speed Mathematical Library for Python

#python #c #programming

🚀 Calco: A Ready-to-Use Math Library for Python, Powered by C

If you’re looking for a math library that provides a wide range of ready-to-use functions — and operates at speeds comparable to Python’s built-in math module — then Calco may be a suitable choice.

📌 What is Calco?

Calco is a cross-platform math library written in C and exposed as a native Python extension. It covers over 60 mathematical functions across arithmetic, trigonometry, logarithms, special functions, and more — offering a more complete set of tools than Python's standard modules.

Calco is designed for developers who want compact, native-speed utilities without the need to write or interface with C manually.

⚙️ Key Features

⚡ C-level performance
🧮 60+ built-in math functions, including:
- Arithmetic: add, subtract, divide, power, etc.
- Trigonometric: sine, cosine, arctangent2, etc.
- Logarithmic and exponential functions
- Hyperbolic and inverse hyperbolic functions
- Special functions: gamma, erf, fma, etc.
- Rounding, flooring, truncation, etc.
🧩 Cross-platform support: Windows, Linux, macOS
📦 Lightweight .pyd / .so package for direct Python import

📦 Installation

You can install calco directly using pip:

pip install calco

Or visit the official website for full installation instructions and downloadable builds:

👉 https://calcolib.netlify.app/

🧪 Example Usage

import calco

# Arithmetic
print(calco.add(3.0, 4.5))             # 7.5
print(calco.divide(10.0, 3.0))         # ~3.333...

# Trigonometry
print(calco.sine(1.57))                # ≈ sin(90°)
print(calco.arctangent2(2.0, 3.0))     # atan2(y, x)

# Logarithmic
print(calco.log_base10(1000))          # 3.0

# Special functions
print(calco.gamma_function(0.5))       # ≈ sqrt(pi)
print(calco.fused_multiply_add(2, 3, 4))  # (2×3)+4 = 10

📚 Available Functions (by Category)

Category	Functions
Arithmetic	`add`, `subtract`, `multiply`, `divide`, `power`, `square_root`, `cube_root`, `absolute_value`, `float_modulo`, `positive_difference`, `copy_sign_double`, `fused_multiply_add`
Rounding	`floor_val`, `ceil_val`, `round_val`, `nearbyint_val`, `truncate_val`
Trigonometric	`sine`, `cosine`, `tangent`, `arcsine`, `arccosine`, `arctangent`, `arctangent2`
Hyperbolic	`hyperbolic_sine`, `hyperbolic_cosine`, `hyperbolic_tangent`, `inverse_hyperbolic_sine`, `inverse_hyperbolic_cosine`, `inverse_hyperbolic_tangent`
Logarithmic	`natural_log`, `log_base10`, `log_base2`, `log_custom_base`
Exponential	`exponential`, `exponential_base2`, `exponential_minus_1`
Special	`gamma_function`, `log_gamma_function`, `error_function`, `complementary_error_function`, `next_after_double`
Constants	`get_pi`, `get_e`
Converters	`degrees_to_radians`, `radians_to_degrees`
Checks	`is_nan`, `is_infinity`

💻 Platform Support

OS	Architectures
Windows	x86 / x64
Linux	x86_64, ARM
macOS	Intel, Apple Silicon (ARM64)

📄 License

🔗 Resources

🌐 Website: https://calcolib.netlify.app
🧑‍💻 GitHub: https://github.com/gafoo173/calco

🌎 Behind the Scenes

To use Calco, function calls in Python pass through an additional API layer before reaching the C core.

In contrast, Python's built-in math functions are executed directly at the C level with no API overhead.

Despite this extra layer, Calco delivers nearly identical performance to the math module, and in some cases, it performs slightly faster or marginally slower depending on the specific function and platform.

Diagram: Function call path for Calco

Python Code
   |
   v
[ Python Wrapper ]
   |
   v
[ Calco API Layer ]
   |
   v
[ Native C Function ]

Diagram: Function call path for math

Python Code
   |
   v
[ Built-in math (direct C call) ]

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