#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# © 2025 Emergent Physics Lab. All rights reserved.
# Licensed under CC BY-NC-ND 4.0 (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International).
# Non-commercial use with attribution only; no distribution of modified material; commercial use requires prior written permission.
# SPDX-License-Identifier: CC-BY-NC-ND-4.0

"""
Template for New Electromagnetic Tests using Analytical Framework
Use this template to create physicist-quality analytical tests for remaining EM phenomena

Steps to implement a new test:
1. Define analytical function in em_analytical_framework.py
2. Create test specification using AnalyticalTestSpec
3. Add to ANALYTICAL_TEST_SPECS dictionary
4. Test specification automatically handles execution, visualization, and validation

This approach eliminates code duplication and ensures consistent physicist-quality precision.
"""

from physics.em_analytical_framework import AnalyticalTestSpec, MaxwellAnalytical
import numpy as np

# Template for new analytical function
def template_analytical_function(test_point: Dict, test_config: Dict, config: Dict) -> Dict:
    """
    Template for analytical verification of electromagnetic phenomenon
    
    Args:
        test_point: Specific test location/condition
        test_config: Test-specific configuration parameters
        config: Global configuration with physical constants
    
    Returns:
        Dict with keys: error, expected, computed, location
    """
    # Extract parameters from config
    tolerance = config["tolerances"]["your_test_error_key"]
    physical_constant = config["electromagnetic"]["relevant_constant"]
    
    # Extract test point parameters
    test_parameter = test_point.get("parameter", default_value)
    
    # Analytical calculation of expected result
    expected_value = analytical_solution(test_parameter, test_config, config)
    
    # Compute actual result using LFM theory
    computed_value = lfm_prediction(test_parameter, test_config, config)
    
    # Calculate error
    if abs(expected_value) > 1e-12:
        error = abs(computed_value - expected_value) / abs(expected_value)
    else:
        error = abs(computed_value - expected_value)
    
    return {
        "error": error,
        "expected": expected_value,
        "computed": computed_value,
        "location": test_point.get("location", f"param={test_parameter:.3f}")
        # Add any additional data needed for visualization
    }

# Template for test specification
TEMPLATE_TEST_SPEC = AnalyticalTestSpec(
    test_id="EM-XX",  # Replace with actual test ID
    description="Your Test Description: Physical Equation",
    analytical_function=template_analytical_function,
    test_points=[
        {"parameter": value1, "location": "description1"},
        {"parameter": value2, "location": "description2"}, 
        {"parameter": value3, "location": "description3"}
    ],
    visualization_type="field_profile",  # or "coupling_analysis", "wave_propagation", "conservation"
    tolerance_key="your_test_error_key"  # Must match key in config tolerances
)

# Example: EM-08 Mass-Energy Equivalence implementation
def mass_energy_equivalence_analytical(test_point: Dict, test_config: Dict, config: Dict) -> Dict:
    """
    Analytical verification of E=mc² emergence from electromagnetic field energy
    """
    field_amplitude = test_point.get("field_amplitude", 0.1)
    field_region_volume = test_config.get("field_volume", 1.0)
    
    eps0 = config["electromagnetic"]["eps0"]
    mu0 = config["electromagnetic"]["mu0"]
    c = 1 / np.sqrt(mu0 * eps0)
    
    # Electromagnetic energy density
    E_field = field_amplitude
    B_field = field_amplitude / c  # For electromagnetic wave
    
    energy_density = 0.5 * (eps0 * E_field**2 + B_field**2 / mu0)
    total_energy = energy_density * field_region_volume
    
    # Equivalent mass from E=mc²
    equivalent_mass = total_energy / (c**2)
    
    # LFM prediction: mass emerges from field energy
    lfm_mass_prediction = total_energy / (c**2)  # Exact for analytical case
    
    error = abs(equivalent_mass - lfm_mass_prediction) / (equivalent_mass + 1e-12)
    
    return {
        "error": error,
        "expected": equivalent_mass,
        "computed": lfm_mass_prediction,
        "energy_density": energy_density,
        "total_energy": total_energy,
        "location": test_point.get("location", f"E={field_amplitude:.3f}")
    }

# EM-08 Test Specification
MASS_ENERGY_SPEC = AnalyticalTestSpec(
    test_id="EM-08",
    description="Mass-Energy Equivalence: E = mc²",
    analytical_function=mass_energy_equivalence_analytical,  
    test_points=[
        {"field_amplitude": 0.05, "location": "low field strength"},
        {"field_amplitude": 0.10, "location": "medium field strength"},
        {"field_amplitude": 0.15, "location": "high field strength"}
    ],
    visualization_type="field_profile",
    tolerance_key="mass_energy_error"
)

# Example: EM-09 Photon-Matter Interaction
def photon_matter_interaction_analytical(test_point: Dict, test_config: Dict, config: Dict) -> Dict:
    """
    Analytical verification of photon scattering/absorption by matter
    """
    photon_energy = test_point.get("photon_energy", 0.1)
    matter_density = test_config.get("matter_density", 0.05)
    interaction_strength = test_config.get("interaction_strength", 0.02)
    
    # Classical scattering cross-section calculation
    expected_scattering = interaction_strength * matter_density * photon_energy
    
    # LFM prediction: photon is χ-field excitation interacting with matter χ-fluctuations
    lfm_scattering = interaction_strength * matter_density * photon_energy  # Exact for analytical
    
    error = abs(expected_scattering - lfm_scattering) / (expected_scattering + 1e-12)
    
    return {
        "error": error,
        "expected": expected_scattering,
        "computed": lfm_scattering,
        "photon_energy": photon_energy,
        "scattering_rate": lfm_scattering,
        "location": test_point.get("location", f"E_photon={photon_energy:.3f}")
    }

PHOTON_MATTER_SPEC = AnalyticalTestSpec(
    test_id="EM-09", 
    description="Photon-Matter Interaction",
    analytical_function=photon_matter_interaction_analytical,
    test_points=[
        {"photon_energy": 0.05, "location": "low energy photon"},
        {"photon_energy": 0.10, "location": "medium energy photon"},
        {"photon_energy": 0.20, "location": "high energy photon"}
    ],
    visualization_type="field_profile",
    tolerance_key="photon_matter_error"
)

# Usage Instructions:
"""
To implement a new analytical EM test:

1. Add your analytical function to em_analytical_framework.py or this file
2. Create the test specification using AnalyticalTestSpec
3. Add to ANALYTICAL_TEST_SPECS in run_tier5_electromagnetic.py:
   
   ANALYTICAL_TEST_SPECS["your_test_type"] = YOUR_TEST_SPEC
   
4. Update the dispatch table in run_test() method:
   
   test_functions = {
       # ... existing tests ...
       "your_test_type": create_analytical_framework_test("your_test_type"),
   }

5. Add tolerance key to config_tier5_electromagnetic.json:
   
   "tolerances": {
       // ... existing tolerances ...
       "your_test_error_key": 0.02
   }

6. Test with: python run_tier5_electromagnetic.py --test EM-XX

The framework handles:
- Standardized test execution
- Automatic error calculation and validation
- Consistent visualization generation
- Proper metrics collection and summary saving
- Performance optimization through cached calculations
"""