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hyanwong / giglib

MIT License
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Numpy edge table data #116

Closed hyanwong closed 7 months ago

hyanwong commented 7 months ago

This stores (most) table data in large memory-contiguous numpy arrays.

Ragged data such as the individuals.parents column and metadata are hacked into a separate array (and not really tested)

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Commits Files that changed from the base of the PR and between fd0b0c536bd73e7c92d84cbcde2218dc8a19b9f4 and ef6a13817e64d961e2c77ead64d46a70e4f0e59b.

Walkthrough

The GeneticInheritanceGraphLibrary has undergone a transformation, focusing on optimization and efficiency. Key changes include the shift from dataclasses to namedtuple for better memory usage and performance, refinement of data handling and validation mechanisms across tables, and enhancements in test utilities for streamlined code testing. This update signifies a move towards more efficient data structures and streamlined codebase, reflecting a thoughtful reconsideration of the library's foundational elements.

Changes

Files Change Summary
.../graph.py Shifted from dataclasses to namedtuple; updated iedges property; modified Items and IEdge classes; redefined Node and Individual classes.
.../tables.py Major restructuring; switched to namedtuple; optimized data handling and validation.
tests/conftest.py Streamlined code by replacing direct calls with add_iedge function.
tests/gigutil.py, tests/test_gigutil.py Renamed and updated add_iedge; adjusted make_diploids logic; fixed ie._asdict() usage.
tests/test_graph.py, tests/test_tables.py Added new methods and tests; improved error handling and validation; optimized test assertions.

🐰✨

In the library's heart, where data weaves and bends,
A rabbit hopped through, making amends.
From dataclasses to namedtuple, it leapt,
Optimizing the code, so carefully kept.

🌟 In tests and tables, changes abound,
Efficiency and clarity, in leaps and bounds.
Celebrate the changes, for they're quite profound!
🐾💫

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hyanwong commented 7 months ago

Probably worth merging, as it actually cuts the number of lines down a little (even though it does make the simulations at the scale I am using about 1% slower. Given that using numpy arrays opens up the possibility of using number, I think we should just merge this.