README: Second-Layer Python-Based Research Language This project provides a second-layer programming language built on top of Python. It is designed to assist researchers in exploring computational complexity problems, particularly those related to P vs NP. By offering a simplified, more expressive syntax and a robust backend library, it streamlines the process of modeling, analyzing, and testing computational hypotheses.

Table of Contents Introduction Key Features Getting Started Core Components core_language.py builtin_functions.py custom_syntax_handlers.py integration_with_library.py code_generation.py Example Usage Testing Contributing License Introduction This second-layer programming language is a research-oriented tool for quickly prototyping and testing computational theories. It abstracts away much of Python’s boilerplate syntax, enabling researchers to focus on high-level logic and structure. Built-in integration with a sophisticated Python library further enhances its capabilities, making it easier to perform reductions, analyze proofs, and run experiments.

Key Features Simplified Syntax: Custom shorthand commands and handlers that reduce boilerplate and increase clarity. Built-in Support for Complexity Analysis: Direct interfaces to functions for verifying NP certificates, exploring circuit lower bounds, and more. Integrated Experimentation Framework: Easily run computational experiments, analyze SAT instances, and visualize complexity results. Code Generation: Translate second-layer language commands into native Python for execution or debugging. Customizable and Extensible: Users can define their own syntax handlers, add new built-ins, and extend the language’s functionality without deep knowledge of the core codebase. Getting Started Prerequisites Python 3.8 or higher A working Python environment with pip installed Installation bash Copy git clone https://github.com/yourusername/second-layer-lang.git cd second-layer-lang pip install -r requirements.txt Running the Language Interpreter To run your second-layer code, first create a .slp file containing your commands. For example, example.slp might contain:

plaintext Copy SET x 42 PRINT x Then, run the interpreter:

bash Copy python -m core_language example.slp Core Components core_language.py This module handles:

Parsing the second-layer language’s syntax Dispatching commands to appropriate handlers Managing the execution environment You can run commands like:

plaintext Copy SET counter 0 LOOP 10 INCREMENT counter PRINT counter And the interpreter will parse, dispatch, and execute each command in sequence.

builtin_functions.py This module defines built-in language functions, including:

loop(times, function): Runs a function a specified number of times. condition(test, true_block, false_block): A conditional structure. standard_print(*args): Outputs values to the console. custom_syntax_handlers.py This module allows you to register new keywords and their behaviors. For example:

python Copy handler = SyntaxHandler() handler.register_handler("INCREMENT", lambda var: environment[var] += 1) handler.handle("INCREMENT", "counter") integration_with_library.py Directly interfaces the second-layer language with the research library. For example:

Call complex SAT solvers with a single command. Analyze circuit properties and retrieve results for further processing. code_generation.py This module converts second-layer code into standard Python code. For example, the command:

plaintext Copy LOOP 5 PRINT "Hello" Might translate to:

python Copy for _ in range(5): print("Hello") Example Usage Simple Script

Create test.slp:

plaintext Copy SET x 10 PRINT x LOOP 3 PRINT "Repeating" Run it:

bash Copy python -m core_language test.slp Integration with Library

Use commands like RUN_SAT_SOLVER formula.cnf or ANALYZE_CIRCUIT circuit.json directly in your second-layer code. This enables seamless testing and analysis within the language’s simplified syntax.

Testing We include a comprehensive test suite in language_tests.py to ensure that:

All commands are parsed correctly Built-in functions behave as intended The integration with the research library is seamless Run tests using:

bash Copy python -m unittest discover Contributing We welcome contributions! If you have ideas for new syntax constructs, built-ins, or integrations:

Fork the repository. Create a new branch for your feature. Write tests for your changes. Submit a pull request with a clear description of the enhancement. License This project is licensed under the MIT License. See the LICENSE file for details.