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developer_tools Module Overview

The **`developer_tools`** module is designed specifically for contributors and developers working on enhancing or maintaining the library. It provides a collection of utilities, experimental features, and testing tools that assist in the development process, but are not essential for regular usage of the library. This module contains advanced functionality, internal tools, and experimental methods that help streamline development, testing, and debugging of the library.

Key Purposes of this Module:

1. **Development Support**:
   - The module contains tools and utilities that assist developers in building, modifying, and extending the core functionality of the library.

2. **Experimental Features**:
   - Includes experimental or in-development features that may not yet be fully integrated into the main library but are available for testing and evaluation by developers.

3. **Testing and Validation**:
   - Provides methods and utilities for testing the accuracy, performance, and reliability of different components within the library. This includes custom samplers, debugging tools, and numerical methods that are used during development.

4. **Not Required for General Usage**:
   - For most users of the library, this module is **not required**. It is designed for internal development purposes and should be used primarily by those who are contributing to or experimenting with the underlying codebase.

Structure of the Module:

1. **`custom_samplers`**:
   - Contains utilities for creating custom random samplers from cumulative distribution functions (CDFs) or characteristic functions (CFs). These tools are useful for testing custom distribution behaviors and validating numerical methods, particularly for experimental distributions or stochastic processes.

2. **`experimental`**:
   - Includes experimental features and methods that are in development or under evaluation. This section provides cutting-edge tools for developers to test new ideas, methods, or integrations that may later become core parts of the library.

3. **`testing_tools`** (notional):
   - Provides various utilities for testing and benchmarking the functionality of different library components. Developers can use these tools to ensure correctness, stability, and performance of new features before they are released.

Use Cases:

- **For Contributors**: The `developer_tools` module provides an essential set of utilities for contributors who want to experiment with new features or test the library's internal components.

- **Experimental Methods**: Developers looking to test the boundaries of current implementations or introduce new experimental features can use this module as a sandbox environment.

- **Library Maintenance**: This module also aids in testing and debugging the overall library to maintain high standards of reliability and performance.

Important Notes:

- **Experimental Nature**: Many features in this module are experimental and may not be fully tested or integrated into the main library. Developers should be cautious when using these features, as they may change or be deprecated in future versions.

- **Developer Warnings**: Some of the functions in this module include custom warnings to notify users of potential issues or instability, especially when working with unvalidated or experimental components.

- **Not for End Users**: This module is primarily for **development purposes** and is not intended to be used by the general user base of the library. Regular users should focus on the core functionalities provided by the main modules, while this module remains a behind-the-scenes tool for developers.

Dependencies:

- This module relies on the same core dependencies as the rest of the library, including NumPy, SciPy, Matplotlib, and SymPy. Additionally, it uses some development-specific libraries like `inspect` and `warnings` to manage experimental features and assist in debugging.

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