API Reference

This section provides comprehensive API documentation for all modules, classes, and functions in ibleatools.

Package Overview

ibleatools is organized into several core modules, each providing specific functionality for electrophysiological data analysis:

• ephysatlas: Main package containing all modules

• features: Feature extraction and computation

• plots: Visualization and plotting utilities

• reveal: High-level analysis and figure generation

• utils: Utility functions and data management

• anatomy: Brain region classification

• regionclassifier: Machine learning models

• data: Data loading and management

Module Documentation

Core Modules

• ephysatlas Package
  • Package Overview
  • Key Components
• Features Module
  • Module Overview
  • Core Functions
• Plots Module
  • Module Overview
  • Core Functions
• Reveal Module
  • Module Overview
  • Core Classes
  • Core Visualization Methods
  • Utility Methods
  • Module Functions
• Utils Module
  • Module Overview
  • Core Functions
  • Internal Functions
  • Usage Examples

Quick Reference

Common Functions

• Feature Extraction: ephysatlas.feature_computation.compute_features_from_pid() / compute_features_from_file()

• OOP calculators: ephysatlas.feature_calculators.IBLPIDFeatureCalculator, SpikeGLXFileFeatureCalculator

• Visualization: ephysatlas.reveal.AtlasReveal

• Data Loading: ephysatlas.data.load_raw_data()

• Utilities: ephysatlas.utils.setup_output_directory()

Data Types

• Features: ephysatlas.features.ModelRawFeatures

• Parameters: ephysatlas.features.DartParameters

• Transformers: ephysatlas.features.EphysTransformer

Configuration

• Schemas: Pandera-based data validation schemas

• Parameters: Configurable parameters for all major functions

• Defaults: Sensible defaults for common use cases

Usage Patterns

Basic Workflow:

1. Load data using a PID

2. Extract features using appropriate functions

3. Visualize results with plotting functions

4. Save outputs for further analysis

Advanced Workflow:

1. Customize parameters for specific needs

2. Use transformers for data preprocessing

3. Generate comprehensive figures with AtlasReveal

4. Apply machine learning models for classification

Data Management:

1. Set up output directories with utilities

2. Manage metadata and file organization

3. Aggregate results across multiple experiments

4. Export data in various formats

Examples

For practical examples and step-by-step guides, see the How-to Guides section.

Getting Help

• Documentation: This reference guide

• Examples: Code examples in the How-to Guides section

• Source Code: Full source code with detailed docstrings

• Issues: Report bugs or request features on the project repository

Contributing

• Code Style: Follow PEP 8 and use Google Style docstrings

• Documentation: Keep docstrings up to date with code changes

• Testing: Add tests for new functionality

• Examples: Provide examples for new features