API Reference#
This section provides detailed API documentation for all PyEEPAS modules.
Module Organization#
PyEEPAS is organized into several functional groups:
- Core Modules - Main workflow scripts
The five main scripts that implement the PyEEPAS workflow:
Core Modules - PPE Learning, EEPAS Learning, Aftershock Fitting, Forecast Generation
- Utility Modules - Support functions
Helper modules for data loading, processing, and numerical operations:
Utility Modules - Data Loader, Catalog Processor, Region Manager, Numerical Integration
- Analysis Modules - \(\Psi\) phenomenon detection and scaling relations
Tools for precursory scale increase analysis and parameter estimation:
Analysis Modules - \(\Psi\) Detection, Deduplication, Scaling Relations, Dataset Tools
- Optimization Modules - Parameter learning engines
Internal optimization logic (called by core modules):
PPE Optimization
EEPAS Likelihood Calculation
Negative Log-Likelihood Functions
Quick Reference#
Task |
Function |
Module |
|---|---|---|
Learn PPE parameters |
|
|
Fit aftershock parameters |
|
|
Learn EEPAS parameters |
|
|
Generate PPE forecast |
|
|
Generate EEPAS forecast |
|
|
Load configuration |
|
|
Load catalog |
|
|
Filter catalog |
|
Common Usage Patterns#
Usage Examples#
from utils.data_loader import DataLoader
from utils.catalog_processor import CatalogProcessor
# Load configuration and data
cfg = DataLoader.load_config('config_italy_reproduce.json')
catalog = DataLoader.load_catalogs('config_italy_reproduce.json')
# Filter catalog
filtered = CatalogProcessor.filter_catalog(
catalog, min_mag=2.45, start_year=1990, end_year=2012
)