LatexOfMe

A Python package for converting numbers to LaTeX-formatted scientific notation in Jupyter notebooks. This package provides utilities for displaying scientific numbers in a clean, readable LaTeX format with proper scientific notation.

Features

• Scientific Notation Formatting: Convert numbers to LaTeX scientific notation format
• Unit Support: Handle numbers with units (e.g., from astropy quantities)
• Precision Control: Configurable decimal precision for mantissa
• Jupyter Integration: Seamless integration with IPython/Jupyter display system
• Automatic Formatting: Intelligently chooses between regular and scientific notation

Installation

From PyPI (recommended)

pip install latexofme

From Source

git clone https://github.com/SOYONAOC/latexofme.git
cd latexofme
pip install -e .

Quick Start

from latexofme import scicount
from IPython.display import Math, display

# Basic usage with numbers
number = 1.23456e15
formatted = scicount(number, pci=3)
display(Math(formatted))
# Output: 1.235 \times 10^{15}

# With precision control
number = 0.00456
formatted = scicount(number, pci=2)
display(Math(formatted))
# Output: 4.56 \times 10^{-3}

# Numbers between 1 and 10 (no scientific notation)
number = 5.67
formatted = scicount(number, pci=2)
display(Math(formatted))
# Output: 5.67

Core Function

scicount(x, pci=2)

Converts a number to LaTeX-formatted scientific notation.

Parameters:

• x (float or quantity): The number to format. Can be a plain number or a quantity with units
• pci (int, optional): Number of decimal places for the mantissa (default: 2)

Returns:

• str: LaTeX-formatted string ready for display

Behavior:

• Numbers with absolute value between 1 and 10: Returns regular decimal format
• Numbers outside this range: Returns scientific notation format
• Numbers with units: Includes unit information in the output

Advanced Usage

Working with Units

If you're using libraries like astropy that provide quantities with units:

import astropy.units as u
from latexofme import scicount
from IPython.display import Math, display

# Number with units
mass = 1.989e30 * u.kg
formatted = scicount(mass, pci=3)
display(Math(formatted))
# Output: 1.989 \times 10^{30} \, kg

Precision Examples

# Different precision levels
number = 1.23456789e-12

# Low precision
low_prec = scicount(number, pci=1)
display(Math(low_prec))
# Output: 1.2 \times 10^{-12}

# High precision
high_prec = scicount(number, pci=5)
display(Math(high_prec))
# Output: 1.23457 \times 10^{-12}

In Jupyter Notebooks

# Create a helper function for easy display
def show_scientific(value, precision=2):
    """Display a number in scientific notation"""
    from IPython.display import Math, display
    from latexofme import scicount
    display(Math(scicount(value, pci=precision)))

# Usage
show_scientific(6.626e-34, 3)  # Planck constant
show_scientific(9.109e-31, 2)  # Electron mass
show_scientific(299792458, 1)  # Speed of light

Use Cases

• Scientific Publications: Format numbers for LaTeX documents
• Educational Materials: Display scientific constants and calculations
• Data Analysis: Present results in clean, professional format
• Research Notebooks: Maintain consistent number formatting across analyses

Requirements

• Python ≥ 3.7
• IPython ≥ 7.0.0 (for Jupyter notebook integration)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

If you encounter any problems or have suggestions, please open an issue on GitHub.

Examples Gallery

Physical Constants

# Display common physical constants
constants = {
    'Speed of light': 299792458,
    'Planck constant': 6.62607015e-34,
    'Electron mass': 9.1093837015e-31,
    'Proton mass': 1.67262192369e-27,
    'Avogadro number': 6.02214076e23
}

for name, value in constants.items():
    print(f"{name}: {scicount(value, pci=3)}")

Astronomical Values

# Astronomical distances and masses
astro_values = {
    'Earth mass': 5.972e24,
    'Solar mass': 1.989e30,
    'Earth-Sun distance': 1.496e11,
    'Hubble constant': 2.3e-18,
    'Age of universe': 4.35e17
}

for name, value in astro_values.items():
    formatted = scicount(value, pci=2)
    display(Math(f"\\text{{{name}}}: {formatted}"))

fesc_values = [0.1, 0.2, 0.3] barriers = [Barrier(fesc=f, z_v=12.0) for f in fesc_values] results = [b.Calcul_deltaVM(1e15) for b in barriers]

print("Escape fraction vs Barrier height:") for fesc, delta_v in zip(fesc_values, results): print(f"fesc = {fesc:.1f}: δ_v = {delta_v:.3f}")

## Physical Background

This package implements models for:

1. **Barrier Method**: Calculates the density threshold required for halo formation in ionized regions
2. **Ionization Balance**: Self-consistent treatment of ionization and recombination
3. **Mass Functions**: Modified halo mass functions accounting for ionization feedback
4. **X-ray Heating**: Effects of X-ray sources from minihalos on reionization

The models are particularly useful for:
- 21cm signal predictions
- Reionization simulations
- Studying the connection between galaxies and ionized bubbles
- Modeling the impact of X-ray sources on reionization

## Dependencies

- `numpy >= 1.18.0`
- `scipy >= 1.5.0`
- `astropy >= 4.0`
- `matplotlib >= 3.0.0`
- `pandas >= 1.0.0`
- `joblib >= 1.0.0`
- `massfunc` (custom cosmology package - required but not available on PyPI)

## Examples

See the `examples/` directory for Jupyter notebooks demonstrating:
- Basic barrier calculations
- Parameter sensitivity studies
- Parallel computation examples
- Minihalo effects analysis

## Citation

If you use this package in your research, please cite:

```bibtex
@misc{bubblebarrier2025,
  author = {Hajime Hinata},
  title = {BubbleBarrier: A Python package for reionization bubble modeling},
  year = {2025},
  url = {https://github.com/SOYONAOC/BubbleBarrier}
}

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

• Author: Hajime Hinata
• Email: onmyojiflow@gmail.com
• GitHub: SOYONAOC

Acknowledgments

• Based on the barrier method for reionization modeling
• Implements algorithms from modern reionization literature
• Thanks to the astrophysics community for theoretical foundations