Guides

These guides show how to use PyCauset in practice, from first setup to large-scale runs.

Start here

• Installation: Install PyCauset (pip or from source).
• User Guide: First workflow: create, visualize, save/load.
• Causal Sets: Core object model and common operations.

Release 1 (what shipped)

• Release 1: What Shipped: A guided map of the Release 1 foundations (NxM shapes, storage container, semantic properties, dtype/overflow rules, and core linalg endpoints).

Common workflows

• Visualization: Creating interactive plots of causal sets.
• Spacetime: Choosing a spacetime region and sprinkling points.
• Field Theory: Simulating quantum fields on causal sets.
• NumPy Integration: Interfacing with NumPy arrays.

Performance and scaling

• Storage and Memory: How PyCauset handles large datasets on disk.
• Performance Guide: Tips for optimizing your simulations.

Linear algebra

• Matrix Guide: Matrix storage, dtypes, and operations.
• Vector Guide: Vector storage and operations.
• Linear Algebra Operations: End-to-end linalg workflows (matmul, solves, factorizations, spectral/SVD, routing, warnings).

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When you need exact details

• API Reference (function/class signatures and behaviors)
• Internals (how things work under the hood)