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

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

Common Tasks

Adding New Models

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

Running Parameter Estimation

Check

polypesto/examples/

for example scripts showing typical workflows.

Development Setup

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

pip install -e .

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

pytest

Tests are located in the

tests/

CLAUDE.md

CLAUDE.md

Project Overview

Development Setup

Installation

Dependencies

Testing

Running Tests

Test Structure

Single Test Execution

Architecture

Core Components

Key Design Patterns

Data Flow

Common Tasks

Adding New Models

Running Parameter Estimation

Related Skills

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CLAUDE.md

Project Overview

Development Setup

Installation

Dependencies

Testing

Running Tests

Test Structure

Single Test Execution

Architecture

Core Components

Key Design Patterns

Data Flow

Common Tasks

Adding New Models

Running Parameter Estimation