Retain unmodeled species

[JR-1991]

This pull request is based on the discussion in #82 and introduces functionality to automatically remove unmodeled species from EnzymeML documents in the thin layer base class, improves model copying in the Psyces thin layer, and adds comprehensive unit tests for the new behavior. The most important changes are grouped below by theme.

Core functionality: Automatic removal of unmodeled species

• Added a remove_unmodeled_species parameter to the BaseThinLayer constructor in pyenzyme/thinlayers/base.py. When enabled, species not referenced in any reaction or ODE equation, as well as measurements containing only such species, are removed from the EnzymeML document. The new static method _remove_unmodeled_species implements this logic.

Testing and validation

• Added a new unit test suite in tests/unit/test_thinlayer.py that verifies correct removal of unmodeled species and measurements, and ensures the option to preserve unmodeled species works as intended. Includes a mock thin layer for testing.

Serialization and model handling

• Switched from using deepcopy to dill for copying PySCeS models in to_pysces_model to improve compatibility and reliability. Also added dill as a dependency in pyproject.toml. [1] [2] [3]

Dependency management

• Added dill as a required dependency to pyproject.toml to support serialization of complex Python objects.

These changes improve the robustness of thin layer initialization, ensure data consistency, and provide thorough test coverage for the new species filtering logic.

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This change is 

[Copilot]

Pull Request Overview

This pull request introduces functionality to automatically remove unmodeled species from EnzymeML documents in the thin layer base class, improving data consistency and model handling. The changes enable filtering out species that aren't referenced in reactions or ODE equations, along with their associated measurements.

• Adds a remove_unmodeled_species parameter to BaseThinLayer with automatic species filtering
• Replaces deepcopy with dill for better PySCeS model serialization
• Adds comprehensive unit tests for the new species removal functionality

Reviewed Changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 2 comments.

File	Description
pyenzyme/thinlayers/base.py	Implements core species filtering logic and adds constructor parameter
pyenzyme/thinlayers/psyces.py	Switches from deepcopy to dill for model copying
pyproject.toml	Adds dill dependency for serialization
tests/unit/test_thinlayer.py	Adds comprehensive test suite for species removal functionality

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[JR-1991]

@jmrohwer As discussed today, I’ve moved the dataset pruning of unmodeled species outside of the __init__-method and into the property-method df which holds the experimental data for fitting. This means the original EnzymeML document remains unchanged, and the pruned data is used for fitting. I’ve added this method to the base thin layer class, so other layers can also utilize it.

Can you test this PR with your data?