A comprehensive Python client for the EPISuite API with advanced DataFrame utilities for environmental chemistry and toxicology research.
- 🔗 Complete API Integration: Access EPA's EPISuite and EcoSAR models
- 📊 DataFrame Support: Convert results to pandas DataFrames for analysis
- 📈 Data Analysis Tools: Built-in statistical and visualization utilities
- 🧪 Experimental Data: Access curated experimental datasets for validation
- 📑 Excel Export: Multi-sheet export with customizable formatting
- 📚 Comprehensive Documentation: Detailed guides and examples
pip install pyepisuitegit clone https://github.com/USEtox/PyEPISuite.git
cd PyEPISuite
pip install -e .from pyepisuite import search_episuite_by_cas, submit_to_episuite
from pyepisuite.dataframe_utils import episuite_to_dataframe, ecosar_to_dataframe
# Search for chemicals
cas_list = ["50-00-0", "100-00-5", "100-02-7"]
ids = search_episuite_by_cas(cas_list)
# Get predictions
epi_results, ecosar_results = submit_to_episuite(ids)
# Convert to DataFrames for analysis
epi_df = episuite_to_dataframe(epi_results)
ecosar_df = ecosar_to_dataframe(ecosar_results)
print(f"Retrieved data for {len(epi_df)} chemicals")
print(f"Properties available: {list(epi_df.columns)}")PyEPISuite provides powerful DataFrame utilities for data analysis:
from pyepisuite.dataframe_utils import (
episuite_to_dataframe,
ecosar_to_dataframe,
combine_episuite_ecosar_dataframes,
export_to_excel,
create_summary_statistics
)
# Convert API results to structured DataFrames
epi_df = episuite_to_dataframe(epi_results) # 41 EPI Suite properties
ecosar_df = ecosar_to_dataframe(ecosar_results) # 12 EcoSAR columns
# Combine datasets
combined_df = combine_episuite_ecosar_dataframes(epi_df, ecosar_df)
# Generate summary statistics
stats = create_summary_statistics(epi_df)
# Export to Excel with multiple sheets
export_to_excel({
'EPI_Suite': epi_df,
'EcoSAR': ecosar_df,
'Statistics': stats
}, 'analysis_results.xlsx')Access curated experimental datasets for model validation:
from pyepisuite.expdata import HenryData, SolubilityData
# Load experimental data
henry_data = HenryData()
solubility_data = SolubilityData()
# Get experimental values
cas = "50-00-0"
experimental_hlc = henry_data.HLC(cas)
experimental_solubility = solubility_data.solubility(cas)
# Compare with predictions
predicted_hlc = epi_df[epi_df['cas'] == cas]['henrys_law_constant_estimated'].iloc[0]
print(f"Predicted: {predicted_hlc}, Experimental: {experimental_hlc}")PyEPISuite provides access to 40+ environmental and physical-chemical properties:
- Log Kow, Melting Point, Boiling Point
- Vapor Pressure, Water Solubility, Henry's Law Constant
- Log Koa, Log Koc
- Atmospheric Half-life, Biodegradation Rates
- Aerosol Adsorption, Bioconcentration Factor
- Water Volatilization, Fugacity Model Results
- Acute and chronic toxicity predictions
- Fish, Daphnid, and Algae endpoints
- Multiple QSAR model classes
- Installation Guide
- Quick Start Tutorial
- DataFrame Utilities Guide
- Data Analysis Examples
- API Reference
PyEPISuite is ideal for:
- Environmental Risk Assessment: Screening chemicals for persistence, bioaccumulation, and toxicity
- QSAR Model Development: Large-scale property prediction and validation
- Regulatory Compliance: Generating data for chemical registration
- Academic Research: High-throughput environmental fate modeling
- Chemical Prioritization: Ranking chemicals by environmental concern
We welcome contributions! Please see our Contributing Guide for details.
git clone https://github.com/your-username/PyEPISuite.git
cd PyEPISuite
pip install -e ".[dev]"
pytest # Run tests
flake8 src tests # Linting
mypy src # Type checkingpip install -e ".[docs]"
mkdocs serve # Local documentation serverIf you use PyEPISuite in your research, please cite:
Eftekhari, A. A. (2024). PyEPISuite: A Python client for EPA's EPISuite API
with DataFrame utilities. https://github.com/USEtox/PyEPISuite
This project is licensed under the MIT License - see the LICENSE file for details.
- EPA for providing the EPISuite and EcoSAR APIs
- The pandas development team for the excellent DataFrame library
- All contributors and users of the package
- ✨ Complete DataFrame utilities for data manipulation
- 📊 Excel export functionality with multi-sheet support
- 🧮 Statistical analysis tools and summary functions
- 📚 Comprehensive documentation with MkDocs
- 🧪 Enhanced experimental data access
- 🔧 Improved API client with better error handling
- 📈 Advanced examples for data analysis workflows
API Reference: See EPISuite API Documentation for the underlying web service.