Release for JOSS submission

What's Changed

• Add script to generate test benchmark locally by @SCiarella in #57
• Update CITATION.cff by @v1kko in #58
• fix some broken links in README and CONTRIBUTING by @luisaforozco in #60
• fix build action by @SCiarella in #61
• Update tests to work on all systems by @v1kko in #62
• improve docs by @SCiarella in #63
• Update image single prediction by @luisaforozco in #64

Full Changelog: 1.0.0...1.0.1

1.0.0

Release Notes for SpeckleCn2Profiler v1.0.0

We are thrilled to announce the first release of SpeckleCn2Profiler, a cutting-edge Python package designed to estimate atmospheric turbulence strength using SCIDAR and machine learning techniques. This tool aims to improve satellite communication by providing a robust framework for turbulence profiling, even in challenging rural and urban environments.

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Highlights of v1.0.0

Core Features

• Turbulence Strength Estimation:
  • Predict instantaneous turbulence strength with uncertainty estimates using machine learning.
• Parameter Estimation:
  • Estimate key parameters like:
    • Fried parameter (r₀): Describes atmospheric coherence.
    • Isoplanatic angle (θ₀): Useful for adaptive optics systems.
    • Rytov Index (σ): Quantifies turbulence impact on optical links.
  • Visualize results with histograms and error distributions.

Data Processing Workflow

• Supports the full pipeline for SCIDAR image analysis using machine learning models.
• Easily integrates with configuration files for automated training and inference.

Comprehensive Documentation

• Extensive documentation available online for:
  • Setting up the package.
  • Example runs and use cases.
  • Parameter explanations and configuration guidelines.

Examples Repository

• Explore practical examples and configurations in the examples submodule.

Installation Options

• Install via PyPI:

  python -m pip install speck2cn

• Alternatively, clone the repository with submodules for full control over the development setup.

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Future Plans

We aim to expand the package capabilities with:

• Support for additional turbulence metrics.
• Enhanced pre-trained models for diverse geographic locations.
• Tools for real-time turbulence monitoring.

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We invite you to explore, experiment, and contribute to this project. Check out our Contributing Guidelines for details on how to get involved.

Thank you for using SpeckleCn2Profiler! Together, let's improve satellite communications with science and innovation.

0.1.4

What's Changed

• Update README with citation info and link to license by @luisaforozco in #36
• fix the dependencies by @v1kko in #35
• Mkdocs by @SCiarella in #40

New Contributors

• @luisaforozco made their first contribution in #36
• @v1kko made their first contribution in #35

Full Changelog: 0.1.3...0.1.4

0.1.3

Bump version to: 0.1.3