Atomizer/tests/test_modal_deformation_request.py

"""
Test Research Agent Response to Complex Modal Analysis Request

This test simulates what happens when a user requests a complex feature
that doesn't exist: extracting modal deformation from modes 4 & 5, surface
mapping the results, and calculating deviations from nominal geometry.

This demonstrates the Research Agent's ability to:
1. Detect multiple knowledge gaps
2. Create a comprehensive research plan
3. Generate appropriate prompts for the user

Author: Atomizer Development Team
Version: 0.1.0 (Phase 2 Test)
Last Updated: 2025-01-16
"""

import sys
from pathlib import Path

# Set UTF-8 encoding for Windows console
if sys.platform == 'win32':
    import codecs
    sys.stdout = codecs.getwriter('utf-8')(sys.stdout.buffer, errors='replace')
    sys.stderr = codecs.getwriter('utf-8')(sys.stderr.buffer, errors='replace')

# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))

from optimization_engine.future.research_agent import ResearchAgent


def test_complex_modal_request():
    """Test how Research Agent handles complex modal analysis request."""

    print("\n" + "="*80)
    print("RESEARCH AGENT TEST: Complex Modal Deformation Request")
    print("="*80)

    # Initialize agent
    agent = ResearchAgent()
    print("\n[1] Research Agent initialized")

    # User's complex request
    user_request = """Make an optimization that loads the deformation of mode 4,5
    of the modal analysis and surface map the result deformation,
    and return deviations from the geometry surface."""

    print(f"\n[2] User Request:")
    print(f"    \"{user_request.strip()}\"")

    # Step 1: Detect Knowledge Gap
    print("\n" + "-"*80)
    print("[3] Knowledge Gap Detection")
    print("-"*80)

    gap = agent.identify_knowledge_gap(user_request)

    print(f"\n  Missing features: {gap.missing_features}")
    print(f"  Missing knowledge domains: {gap.missing_knowledge}")
    print(f"  Confidence level: {gap.confidence:.2f}")
    print(f"  Research needed: {gap.research_needed}")

    # Analyze the detected gaps
    print("\n  Analysis:")
    if gap.research_needed:
        print("    ✓ Agent correctly identified this as an unknown capability")
        print(f"    ✓ Detected {len(gap.missing_knowledge)} missing knowledge domains")
        for domain in gap.missing_knowledge:
            print(f"      - {domain}")
    else:
        print("    ✗ Agent incorrectly thinks it can handle this request")

    # Step 2: Create Research Plan
    print("\n" + "-"*80)
    print("[4] Research Plan Creation")
    print("-"*80)

    plan = agent.create_research_plan(gap)

    print(f"\n  Research plan has {len(plan.steps)} steps:")
    for step in plan.steps:
        action = step['action']
        priority = step['priority']
        expected_conf = step.get('expected_confidence', 0)
        print(f"\n  Step {step['step']}: {action}")
        print(f"    Priority: {priority}")
        print(f"    Expected confidence: {expected_conf:.2f}")

        if action == 'ask_user_for_example':
            prompt = step['details']['prompt']
            file_types = step['details']['file_types']
            print(f"    Suggested file types: {', '.join(file_types)}")

    # Step 3: Show User Prompt
    print("\n" + "-"*80)
    print("[5] Generated User Prompt")
    print("-"*80)

    user_prompt = agent._generate_user_prompt(gap)
    print("\n  The agent would ask the user:\n")
    print("  " + "-"*76)
    for line in user_prompt.split('\n'):
        print(f"  {line}")
    print("  " + "-"*76)

    # Step 4: What Would Be Needed
    print("\n" + "-"*80)
    print("[6] What Would Be Required to Implement This")
    print("-"*80)

    print("\n  To fully implement this request, the agent would need to learn:")
    print("\n  1. Modal Analysis Execution")
    print("     - How to run NX modal analysis")
    print("     - How to extract specific mode shapes (modes 4 & 5)")
    print("     - OP2 file structure for modal results")

    print("\n  2. Deformation Extraction")
    print("     - How to read nodal displacements for specific modes")
    print("     - How to combine deformations from multiple modes")
    print("     - Data structure for modal displacements")

    print("\n  3. Surface Mapping")
    print("     - How to map nodal displacements to surface geometry")
    print("     - Interpolation techniques for surface points")
    print("     - NX geometry API for surface queries")

    print("\n  4. Deviation Calculation")
    print("     - How to compute deformed geometry from nominal")
    print("     - Distance calculation from surfaces")
    print("     - Deviation reporting (max, min, RMS, etc.)")

    print("\n  5. Integration with Optimization")
    print("     - How to use deviations as objective/constraint")
    print("     - Workflow integration with optimization loop")
    print("     - Result extraction for Optuna")

    # Step 5: What User Would Need to Provide
    print("\n" + "-"*80)
    print("[7] What User Would Need to Provide")
    print("-"*80)

    print("\n  Based on the research plan, user should provide:")
    print("\n  Option 1 (Best): Working Example")
    print("     - Example .sim file with modal analysis setup")
    print("     - Example Python script showing modal extraction")
    print("     - Example of surface deviation calculation")

    print("\n  Option 2: NX Files")
    print("     - .op2 file from modal analysis")
    print("     - Documentation of mode extraction process")
    print("     - Surface geometry definition")

    print("\n  Option 3: Code Snippets")
    print("     - Journal script for modal analysis")
    print("     - Code showing mode shape extraction")
    print("     - Deviation calculation example")

    # Summary
    print("\n" + "="*80)
    print("TEST SUMMARY")
    print("="*80)

    print("\n  Research Agent Performance:")
    print(f"    ✓ Detected knowledge gap: {gap.research_needed}")
    print(f"    ✓ Identified {len(gap.missing_knowledge)} missing domains")
    print(f"    ✓ Created {len(plan.steps)}-step research plan")
    print(f"    ✓ Generated user-friendly prompt")
    print(f"    ✓ Suggested appropriate file types")

    print("\n  Next Steps (if user provides examples):")
    print("    1. Agent analyzes examples and extracts patterns")
    print("    2. Agent designs feature specification")
    print("    3. Agent would generate Python code (Phase 2 Week 2)")
    print("    4. Agent documents knowledge for future reuse")
    print("    5. Agent updates feature registry")

    print("\n  Current Limitation:")
    print("    - Agent can detect gap and plan research ✓")
    print("    - Agent can learn from examples ✓")
    print("    - Agent cannot yet auto-generate complex code (Week 2)")
    print("    - Agent cannot yet perform web research (Week 2)")

    print("\n" + "="*80)
    print("This demonstrates Phase 2 Week 1 capability:")
    print("Agent successfully identified a complex, multi-domain knowledge gap")
    print("and created an intelligent research plan to address it!")
    print("="*80 + "\n")

    return True


if __name__ == '__main__':
    try:
        success = test_complex_modal_request()
        sys.exit(0 if success else 1)
    except Exception as e:
        print(f"\n[ERROR] {e}")
        import traceback
        traceback.print_exc()
        sys.exit(1)