"Scientific Clojure, a bird's eye view" by Thomas Clark

Scientific Clojure focuses on flexible, data-oriented approaches rather than rigid frameworks

Key libraries replacing Python scientific stack:

• DtypeNext (NumPy replacement)
• FastMath (mathematical operations)
• Tablecloth (Pandas-like data manipulation)

Wolfram integration available through:

• Wolframite 1.0 bridge
• EMI library for math operations
• Support for custom symbols and Unicode

Visual tools and plotting:

• Clay for lightweight documentation and visualization
• Hanami/Vega system for interactive plots
• Plots treated as data for flexibility
• Table Plot library in development

Documentation and testing:

• Documentation generated from running code
• Documentation serves as tests
• Focus on tutorials that are executable tests
• Integration with Quarto for multiple output formats

Scientific workflow considerations:

• Scientists prioritize novelty over stability
• Need for simple data transformations between domains
• Importance of equation handling and symbolic math
• Focus on adding new code rather than changing existing code

Community aspects:

• Growing scientific Clojure ecosystem
• Emphasis on voluntary cooperation
• Benefits of being a smaller, focused community
• Active development in scientific computing space

Interoperability:

• Strong Python interop capabilities
• Ability to gradually integrate existing workflows
• Data-oriented approach to library interfaces
• Support for larger-than-memory datasets