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CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

Project Overview

PolyPESTO is a parameter estimation package for polymerization data using PyPESTO. It provides a framework for modeling chemical kinetics, particularly polymerization reactions, and estimating parameters from experimental data.

Development Setup

Installation

The project uses setuptools with pyproject.toml configuration. Install in development mode:

pip install -e .

Dependencies

  • Core dependencies include pypesto[amici,petab,fides], python-libsbml, h5py
  • Development dependencies include pytest, pytest-cov for testing
  • Visualization tools: plotly, matplotlib, seaborn
  • Jupyter support included for notebook development

Testing

Running Tests

pytest

Test Structure

  • Tests are located in the tests/ directory
  • Main test fixtures are defined in tests/conftest.py
  • Test models include "BinaryIrreversible" and "LotkaVolterra"
  • Tests use parameterized fixtures for different model types

Single Test Execution

pytest tests/path/to/specific_test.py
pytest -k "test_name_pattern"

Architecture

Core Components

polypesto.core: Central framework components

  • Study: Main class for managing parameter estimation studies across multiple problems and parameter sets
  • Problem: Individual optimization problems with model, data, and parameter definitions
  • experiment.py: Experimental data handling
  • conditions.py: Simulation condition management
  • params.py: Parameter group and set definitions
  • petab.py: Integration with PEtab standard
  • pypesto/: PyPESTO integration utilities

polypesto.models: Model definitions

  • ModelBase: Abstract base class for all models
  • binary/: Binary polymerization models
  • example/: Example models like Lotka-Volterra
  • sbml.py: SBML model utilities
  • Models automatically generate AMICI models in amici_models/ directory

polypesto.visualization: Plotting and visualization utilities

  • Modular visualization components for different analysis types
  • Integration with plotly, matplotlib, and seaborn

polypesto.utils: Utility functions

  • file.py: JSON I/O utilities
  • logging.py: Logging configuration
  • patches.py: Compatibility patches

Key Design Patterns

  • Models inherit from ModelBase and define SBML representations
  • Study objects manage collections of problems with different parameter sets
  • Type aliases used extensively for complex nested dictionaries (e.g., ProbParamDict)
  • AMICI integration for efficient simulation and sensitivity analysis

Data Flow

  1. Models define chemical kinetics via SBML
  2. Study objects coordinate parameter estimation across multiple problems
  3. Problems contain model + data + parameter bounds
  4. PyPESTO performs optimization
  5. Results stored and visualized through dedicated modules

Common Tasks

Adding New Models

  1. Create new model class inheriting from ModelBase in polypesto/models/
  2. Implement required abstract methods for SBML definition
  3. Add observables and parameter definitions
  4. Test with existing test framework fixtures

Running Parameter Estimation

Check polypesto/examples/ for example scripts showing typical workflows.