warp_engine

defmodule EnhancedADT.Physics do
  @moduledoc """
  Physics configuration and optimization for Enhanced ADT operations.

  This module provides physics-based configuration and optimization utilities
  for Enhanced ADT operations with IsLabDB integration. It translates mathematical
  ADT annotations into optimal physics parameters for database operations.

  ## Physics Annotations

  - `:gravitational_mass` - Controls shard placement and data settling patterns
  - `:quantum_entanglement_potential` - Influences automatic entanglement creation
  - `:temporal_weight` - Affects data lifecycle and temporal shard placement
  - `:access_pattern` - Hints for optimal spacetime shard selection
  - `:spacetime_shard_hint` - Direct shard placement guidance
  - `:entropy_optimization` - Enables entropy-based optimization

  ## Usage

  ```elixir
  # Physics annotations in ADT definitions
  defproduct User do
    id :: String.t()
    loyalty_score :: float(), physics: :gravitational_mass
    activity_level :: float(), physics: :quantum_entanglement_potential
    created_at :: DateTime.t(), physics: :temporal_weight
  end

  # Manual physics configuration
  physics_config = EnhancedADT.Physics.optimize_for_workload(:high_read_throughput)
  ```
  """

  @doc """
  Generate optimal physics configuration for a given workload pattern.

  Analyzes workload characteristics and generates physics parameters optimized
  for specific usage patterns and performance requirements.
  """
  def optimize_for_workload(workload_type) do
    base_config = get_base_physics_config()

    case workload_type do
      :high_read_throughput ->
        optimize_for_read_throughput(base_config)

      :high_write_throughput ->
        optimize_for_write_throughput(base_config)

      :balanced_workload ->
        optimize_for_balanced_workload(base_config)

      :analytical_workload ->
        optimize_for_analytical_workload(base_config)

      :real_time_streaming ->
        optimize_for_streaming(base_config)

      :archival_storage ->
        optimize_for_archival(base_config)

      _ ->
        base_config
    end
  end

  @doc """
  Analyze ADT structure and generate optimal physics configuration.

  Examines the structure of an ADT type and generates physics parameters
  optimized for the specific data access patterns implied by the structure.
  """
  def analyze_adt_physics(adt_module) do
    # Extract ADT structure information
    structure_info = extract_adt_structure_info(adt_module)

    # Analyze physics requirements
    physics_requirements = analyze_physics_requirements(structure_info)

    # Generate optimized configuration
    optimized_config = generate_optimized_config(physics_requirements)

    # Add structure-specific optimizations
    final_config = apply_structure_optimizations(optimized_config, structure_info)

    %{
      structure_info: structure_info,
      physics_requirements: physics_requirements,
      optimized_config: optimized_config,
      final_config: final_config,
      recommendations: generate_physics_recommendations(final_config)
    }
  end

  @doc """
  Configure physics parameters for optimal quantum entanglement performance.

  Generates physics configuration specifically optimized for quantum entanglement
  operations, considering coherence stability and correlation efficiency.
  """
  def configure_for_quantum_optimization(opts \\ []) do
    base_quantum_config = %{
      quantum_entanglement_potential: Keyword.get(opts, :base_potential, 0.8),
      coherence_stability: Keyword.get(opts, :coherence_stability, 0.9),
      entanglement_strength: Keyword.get(opts, :entanglement_strength, 0.7),
      correlation_threshold: Keyword.get(opts, :correlation_threshold, 0.5)
    }

    # Enhance with physics optimizations
    quantum_optimized_config = enhance_quantum_config(base_quantum_config, opts)

    # Add supporting physics parameters
    supporting_physics = generate_supporting_quantum_physics(quantum_optimized_config)

    Map.merge(quantum_optimized_config, supporting_physics)
  end

  @doc """
  Configure physics parameters for optimal wormhole network performance.

  Generates physics configuration specifically optimized for wormhole routing
  operations, considering network topology and traversal efficiency.
  """
  def configure_for_wormhole_optimization(opts \\ []) do
    base_wormhole_config = %{
      wormhole_creation_threshold: Keyword.get(opts, :creation_threshold, 0.4),
      route_strength_multiplier: Keyword.get(opts, :strength_multiplier, 1.2),
      network_density_target: Keyword.get(opts, :density_target, 0.6),
      traversal_efficiency_weight: Keyword.get(opts, :efficiency_weight, 0.8)
    }

    # Enhance with physics optimizations
    wormhole_optimized_config = enhance_wormhole_config(base_wormhole_config, opts)

    # Add supporting physics parameters
    supporting_physics = generate_supporting_wormhole_physics(wormhole_optimized_config)

    Map.merge(wormhole_optimized_config, supporting_physics)
  end

  @doc """
  Configure physics parameters for temporal data optimization.

  Generates physics configuration optimized for temporal data operations,
  considering data lifecycle, aging patterns, and temporal query efficiency.
  """
  def configure_for_temporal_optimization(opts \\ []) do
    base_temporal_config = %{
      temporal_weight_decay_rate: Keyword.get(opts, :decay_rate, 0.98),
      lifecycle_transition_threshold: Keyword.get(opts, :transition_threshold, 0.3),
      temporal_shard_affinity: Keyword.get(opts, :shard_affinity, :adaptive),
      aging_acceleration_factor: Keyword.get(opts, :aging_factor, 1.0)
    }

    # Enhance with physics optimizations
    temporal_optimized_config = enhance_temporal_config(base_temporal_config, opts)

    # Add supporting physics parameters
    supporting_physics = generate_supporting_temporal_physics(temporal_optimized_config)

    Map.merge(temporal_optimized_config, supporting_physics)
  end

  @doc """
  Validate physics configuration for consistency and optimal performance.

  Checks physics configuration for internal consistency and identifies
  potential optimization opportunities or configuration conflicts.
  """
  def validate_physics_config(physics_config) do
    # Check for consistency issues
    consistency_issues = check_physics_consistency(physics_config)

    # Identify optimization opportunities
    optimization_opportunities = identify_optimization_opportunities(physics_config)

    # Generate performance predictions
    performance_predictions = predict_performance_impact(physics_config)

    # Generate validation report
    validation_score = calculate_validation_score(consistency_issues, optimization_opportunities)

    %{
      validation_score: validation_score,
      consistency_issues: consistency_issues,
      optimization_opportunities: optimization_opportunities,
      performance_predictions: performance_predictions,
      recommendations: generate_validation_recommendations(consistency_issues, optimization_opportunities),
      overall_assessment: determine_overall_assessment(validation_score)
    }
  end

  # Physics Configuration Generation

  defp get_base_physics_config do
    %{
      gravitational_mass: 1.0,
      quantum_entanglement_potential: 0.5,
      temporal_weight: 1.0,
      access_pattern: :warm,
      spacetime_shard_hint: :auto,
      entropy_optimization: true,
      coherence_stability: 0.7,
      wormhole_creation_threshold: 0.4,
      temporal_decay_rate: 0.95
    }
  end

  defp optimize_for_read_throughput(base_config) do
    # Optimize for maximum read performance
    Map.merge(base_config, %{
      access_pattern: :hot,
      quantum_entanglement_potential: 0.9,  # High entanglement for read acceleration
      gravitational_mass: 2.0,  # Settle in hot shard
      coherence_stability: 0.9,  # High stability for consistent reads
      wormhole_creation_threshold: 0.3,  # Lower threshold for more routes
      entropy_optimization: true
    })
  end

  defp optimize_for_write_throughput(base_config) do
    # Optimize for maximum write performance
    Map.merge(base_config, %{
      access_pattern: :sequential,
      quantum_entanglement_potential: 0.6,  # Moderate entanglement to avoid write contention
      gravitational_mass: 1.5,  # Balanced placement
      temporal_weight: 0.8,  # Reduced temporal tracking overhead
      entropy_optimization: false,  # Disable for write performance
      wormhole_creation_threshold: 0.6  # Higher threshold to avoid write-time overhead
    })
  end

  defp optimize_for_balanced_workload(base_config) do
    # Optimize for balanced read/write performance
    Map.merge(base_config, %{
      access_pattern: :balanced,
      quantum_entanglement_potential: 0.7,
      gravitational_mass: 1.2,
      temporal_weight: 1.0,
      coherence_stability: 0.8,
      wormhole_creation_threshold: 0.4,
      entropy_optimization: true
    })
  end

  defp optimize_for_analytical_workload(base_config) do
    # Optimize for complex analytical queries
    Map.merge(base_config, %{
      access_pattern: :analytical,
      quantum_entanglement_potential: 0.95,  # Maximum entanglement for complex correlations
      gravitational_mass: 0.8,  # Allow flexible placement
      temporal_weight: 1.5,  # Enhanced temporal analysis
      coherence_stability: 0.95,  # Very high stability for consistent analysis
      wormhole_creation_threshold: 0.2,  # Very low threshold for maximum connectivity
      entropy_optimization: true
    })
  end

  defp optimize_for_streaming(base_config) do
    # Optimize for real-time streaming workloads
    Map.merge(base_config, %{
      access_pattern: :streaming,
      quantum_entanglement_potential: 0.4,  # Lower entanglement for stream performance
      gravitational_mass: 2.5,  # Strong hot placement
      temporal_weight: 0.5,  # Reduced temporal overhead
      temporal_decay_rate: 0.9,  # Faster decay for streaming data
      entropy_optimization: false,  # Disable for stream performance
      wormhole_creation_threshold: 0.8  # High threshold for streaming efficiency
    })
  end

  defp optimize_for_archival(base_config) do
    # Optimize for long-term archival storage
    Map.merge(base_config, %{
      access_pattern: :cold,
      quantum_entanglement_potential: 0.2,  # Minimal entanglement for archived data
      gravitational_mass: 0.3,  # Settle in cold storage
      temporal_weight: 2.0,  # High temporal tracking for archival
      temporal_decay_rate: 0.99,  # Very slow decay
      entropy_optimization: true,
      wormhole_creation_threshold: 0.9,  # Very high threshold
      compression_enabled: true
    })
  end

  # ADT Structure Analysis

  defp extract_adt_structure_info(adt_module) do
    %{
      module: adt_module,
      adt_type: get_adt_type(adt_module),
      fields: get_adt_fields(adt_module),
      physics_annotations: get_physics_annotations(adt_module),
      complexity_metrics: calculate_complexity_metrics(adt_module)
    }
  end

  defp get_adt_type(module) do
    if function_exported?(module, :__adt_type__, 0) do
      module.__adt_type__()
    else
      :unknown
    end
  end

  defp get_adt_fields(module) do
    cond do
      function_exported?(module, :__adt_field_specs__, 0) -> module.__adt_field_specs__()
      function_exported?(module, :__adt_variants__, 0) -> module.__adt_variants__()
      true -> []
    end
  end

  defp get_physics_annotations(module) do
    if function_exported?(module, :__adt_physics_config__, 0) do
      module.__adt_physics_config__()
    else
      %{}
    end
  end

  defp calculate_complexity_metrics(module) do
    fields = get_adt_fields(module)
    physics_annotations = get_physics_annotations(module)

    %{
      field_count: length(fields),
      physics_annotation_count: map_size(physics_annotations),
      estimated_data_size: estimate_data_size(fields),
      reference_complexity: calculate_reference_complexity(fields),
      overall_complexity: calculate_overall_complexity(fields, physics_annotations)
    }
  end

  defp estimate_data_size(fields) do
    # Estimate typical data size based on field types
    base_size = length(fields) * 50  # 50 bytes per field average

    # Adjust for complex field types
    complex_field_bonus = Enum.count(fields, &is_complex_field_type?/1) * 200

    base_size + complex_field_bonus
  end

  defp is_complex_field_type?(field) do
    case field do
      %{type: type} ->
        case type do
          [_] -> true  # List types
          {:recursive, _} -> true  # Recursive types
          {{:., _, _}, _, _} -> true  # Module types
          _ -> false
        end
      _ -> false
    end
  end

  defp calculate_reference_complexity(fields) do
    reference_fields = Enum.count(fields, &is_reference_field?/1)

    cond do
      reference_fields >= 5 -> :high
      reference_fields >= 3 -> :medium
      reference_fields >= 1 -> :low
      true -> :none
    end
  end

  defp is_reference_field?(field) do
    case field do
      %{name: name} ->
        name_str = Atom.to_string(name)
        String.ends_with?(name_str, "_id") or
        String.ends_with?(name_str, "_ref") or
        String.contains?(name_str, "reference")
      _ -> false
    end
  end

  defp calculate_overall_complexity(fields, physics_annotations) do
    field_complexity = length(fields)
    physics_complexity = map_size(physics_annotations) * 2

    total_complexity = field_complexity + physics_complexity

    cond do
      total_complexity >= 20 -> :very_high
      total_complexity >= 15 -> :high
      total_complexity >= 10 -> :medium
      total_complexity >= 5 -> :low
      true -> :minimal
    end
  end

  # Physics Requirements Analysis

  defp analyze_physics_requirements(structure_info) do
    # Analyze what physics optimizations would benefit this ADT structure
    requirements = %{
      gravitational_optimization: analyze_gravitational_needs(structure_info),
      quantum_optimization: analyze_quantum_needs(structure_info),
      temporal_optimization: analyze_temporal_needs(structure_info),
      wormhole_optimization: analyze_wormhole_needs(structure_info),
      entropy_optimization: analyze_entropy_needs(structure_info)
    }

    # Add priority scoring
    Map.put(requirements, :optimization_priorities, calculate_optimization_priorities(requirements))
  end

  defp analyze_gravitational_needs(structure_info) do
    # Analyze if gravitational optimization would benefit this structure
    field_count = length(structure_info.fields)
    data_size = structure_info.complexity_metrics.estimated_data_size

    priority = cond do
      field_count >= 10 and data_size >= 1000 -> :high
      field_count >= 5 and data_size >= 500 -> :medium
      field_count >= 3 -> :low
      true -> :none
    end

    %{
      priority: priority,
      reasoning: generate_gravitational_reasoning(field_count, data_size),
      recommended_mass: calculate_recommended_mass(field_count, data_size)
    }
  end

  defp analyze_quantum_needs(structure_info) do
    # Analyze if quantum optimization would benefit this structure
    reference_complexity = structure_info.complexity_metrics.reference_complexity
    has_quantum_annotations = Map.has_key?(structure_info.physics_annotations, :quantum_entanglement_group)

    priority = cond do
      has_quantum_annotations -> :high
      reference_complexity in [:high, :medium] -> :medium
      reference_complexity == :low -> :low
      true -> :none
    end

    %{
      priority: priority,
      reasoning: generate_quantum_reasoning(reference_complexity, has_quantum_annotations),
      recommended_potential: calculate_recommended_potential(reference_complexity)
    }
  end

  defp analyze_temporal_needs(structure_info) do
    # Analyze if temporal optimization would benefit this structure
    has_datetime_fields = has_datetime_fields?(structure_info.fields)
    has_temporal_annotations = has_temporal_physics_annotations?(structure_info.physics_annotations)

    priority = cond do
      has_temporal_annotations -> :high
      has_datetime_fields -> :medium
      true -> :low
    end

    %{
      priority: priority,
      reasoning: generate_temporal_reasoning(has_datetime_fields, has_temporal_annotations),
      recommended_weight: calculate_recommended_temporal_weight(has_datetime_fields)
    }
  end

  defp analyze_wormhole_needs(structure_info) do
    # Analyze if wormhole optimization would benefit this structure
    reference_complexity = structure_info.complexity_metrics.reference_complexity
    field_count = length(structure_info.fields)

    priority = cond do
      reference_complexity == :high and field_count >= 8 -> :high
      reference_complexity in [:high, :medium] -> :medium
      reference_complexity == :low -> :low
      true -> :none
    end

    %{
      priority: priority,
      reasoning: generate_wormhole_reasoning(reference_complexity, field_count),
      recommended_threshold: calculate_recommended_threshold(reference_complexity)
    }
  end

  defp analyze_entropy_needs(structure_info) do
    # Analyze if entropy optimization would benefit this structure
    complexity = structure_info.complexity_metrics.overall_complexity

    priority = case complexity do
      :very_high -> :high
      :high -> :medium
      :medium -> :low
      _ -> :none
    end

    %{
      priority: priority,
      reasoning: "Entropy optimization benefit based on overall complexity: #{complexity}",
      recommended_enabled: priority in [:high, :medium]
    }
  end

  # Configuration Generation and Optimization

  defp generate_optimized_config(physics_requirements) do
    # Generate physics configuration based on requirements analysis
    config = %{}

    # Apply gravitational optimization
    config = if physics_requirements.gravitational_optimization.priority != :none do
      Map.put(config, :gravitational_mass, physics_requirements.gravitational_optimization.recommended_mass)
    else
      config
    end

    # Apply quantum optimization
    config = if physics_requirements.quantum_optimization.priority != :none do
      Map.put(config, :quantum_entanglement_potential, physics_requirements.quantum_optimization.recommended_potential)
    else
      config
    end

    # Apply temporal optimization
    config = if physics_requirements.temporal_optimization.priority != :none do
      Map.put(config, :temporal_weight, physics_requirements.temporal_optimization.recommended_weight)
    else
      config
    end

    # Apply wormhole optimization
    config = if physics_requirements.wormhole_optimization.priority != :none do
      Map.put(config, :wormhole_creation_threshold, physics_requirements.wormhole_optimization.recommended_threshold)
    else
      config
    end

    # Apply entropy optimization
    config = if physics_requirements.entropy_optimization.priority != :none do
      Map.put(config, :entropy_optimization, physics_requirements.entropy_optimization.recommended_enabled)
    else
      config
    end

    config
  end

  defp apply_structure_optimizations(config, structure_info) do
    # Apply ADT structure-specific optimizations
    optimized_config = config

    # Optimize based on ADT type
    optimized_config = case structure_info.adt_type do
      :product -> optimize_for_product_type(optimized_config, structure_info)
      :sum -> optimize_for_sum_type(optimized_config, structure_info)
      _ -> optimized_config
    end

    # Apply field-specific optimizations
    optimized_config = apply_field_optimizations(optimized_config, structure_info.fields)

    # Apply physics annotation optimizations
    apply_annotation_optimizations(optimized_config, structure_info.physics_annotations)
  end

  defp optimize_for_product_type(config, structure_info) do
    # Product types benefit from certain optimizations
    field_count = length(structure_info.fields)

    # Product types with many fields benefit from quantum entanglement
    config = if field_count >= 6 do
      Map.update(config, :quantum_entanglement_potential, 0.7, &max(&1, 0.7))
    else
      config
    end

    # Product types are typically accessed as units, good for gravitational settling
    Map.update(config, :gravitational_mass, 1.2, &max(&1, 1.0))
  end

  defp optimize_for_sum_type(config, structure_info) do
    # Sum types benefit from wormhole networks between variants
    variants = structure_info.fields

    # Sum types with many variants benefit from wormhole networks
    config = if length(variants) >= 3 do
      Map.update(config, :wormhole_creation_threshold, 0.3, &min(&1, 0.4))
    else
      config
    end

    # Sum types have variable access patterns
    Map.put(config, :access_pattern, :adaptive)
  end

  defp apply_field_optimizations(config, fields) do
    # Apply optimizations based on field characteristics
    reference_fields = Enum.filter(fields, &is_reference_field?/1)

    # Many reference fields suggest wormhole benefits
    if length(reference_fields) >= 3 do
      Map.update(config, :wormhole_creation_threshold, 0.4, &min(&1, 0.5))
    else
      config
    end
  end

  defp apply_annotation_optimizations(config, physics_annotations) do
    # Apply optimizations based on explicit physics annotations
    Enum.reduce(physics_annotations, config, fn {_field, annotation}, acc ->
      case annotation do
        :gravitational_mass ->
          Map.update(acc, :gravitational_mass, 1.5, &max(&1, 1.2))

        :quantum_entanglement_group ->
          Map.update(acc, :quantum_entanglement_potential, 0.8, &max(&1, 0.7))

        :temporal_weight ->
          Map.update(acc, :temporal_weight, 1.2, &max(&1, 1.0))

        _ -> acc
      end
    end)
  end

  # Enhanced Configuration Functions

  defp enhance_quantum_config(base_config, opts) do
    # Enhance quantum configuration with advanced optimizations
    coherence_boost = Keyword.get(opts, :coherence_boost, 0.1)
    entanglement_multiplier = Keyword.get(opts, :entanglement_multiplier, 1.0)

    Map.merge(base_config, %{
      coherence_stability: min(1.0, base_config.coherence_stability + coherence_boost),
      entanglement_strength: min(1.0, base_config.entanglement_strength * entanglement_multiplier)
    })
  end

  defp generate_supporting_quantum_physics(quantum_config) do
    %{
      gravitational_mass: 1.0 + (quantum_config.quantum_entanglement_potential * 0.5),
      access_pattern: :quantum_optimized,
      entropy_optimization: true
    }
  end

  defp enhance_wormhole_config(base_config, opts) do
    # Enhance wormhole configuration with advanced optimizations
    network_optimization = Keyword.get(opts, :network_optimization, true)
    route_caching = Keyword.get(opts, :route_caching, true)

    enhanced_config = base_config

    enhanced_config = if network_optimization do
      Map.put(enhanced_config, :network_auto_optimization, true)
    else
      enhanced_config
    end

    if route_caching do
      Map.put(enhanced_config, :route_caching_enabled, true)
    else
      enhanced_config
    end
  end

  defp generate_supporting_wormhole_physics(_wormhole_config) do
    %{
      access_pattern: :locality_sensitive,
      gravitational_mass: 1.2,
      quantum_entanglement_potential: 0.6
    }
  end

  defp enhance_temporal_config(base_config, opts) do
    # Enhance temporal configuration with advanced optimizations
    lifecycle_awareness = Keyword.get(opts, :lifecycle_awareness, true)
    predictive_aging = Keyword.get(opts, :predictive_aging, false)

    enhanced_config = base_config

    enhanced_config = if lifecycle_awareness do
      Map.put(enhanced_config, :lifecycle_awareness_enabled, true)
    else
      enhanced_config
    end

    if predictive_aging do
      Map.put(enhanced_config, :predictive_aging_enabled, true)
    else
      enhanced_config
    end
  end

  defp generate_supporting_temporal_physics(temporal_config) do
    %{
      access_pattern: :temporal,
      entropy_optimization: true,
      gravitational_mass: 0.8 + (temporal_config.temporal_weight_decay_rate * 0.3)
    }
  end

  # Validation and Analysis Functions

  defp check_physics_consistency(physics_config) do
    issues = []

    # Check for conflicting configurations
    issues = if Map.get(physics_config, :gravitational_mass, 1.0) > 3.0 and
                Map.get(physics_config, :access_pattern) == :cold do
      ["High gravitational mass with cold access pattern may cause conflicts" | issues]
    else
      issues
    end

    # Check quantum configuration consistency
    issues = if Map.get(physics_config, :quantum_entanglement_potential, 0.5) > 0.9 and
                Map.get(physics_config, :entropy_optimization, true) == false do
      ["High quantum potential with disabled entropy optimization may reduce efficiency" | issues]
    else
      issues
    end

    issues
  end

  defp identify_optimization_opportunities(physics_config) do
    opportunities = []

    # Identify potential improvements
    opportunities = if Map.get(physics_config, :wormhole_creation_threshold, 0.4) > 0.7 do
      [%{
        type: :wormhole_threshold,
        suggestion: "Consider lowering wormhole creation threshold for better connectivity",
        potential_benefit: :medium
      } | opportunities]
    else
      opportunities
    end

    opportunities = if not Map.get(physics_config, :entropy_optimization, true) do
      [%{
        type: :entropy_optimization,
        suggestion: "Enable entropy optimization for better system balance",
        potential_benefit: :high
      } | opportunities]
    else
      opportunities
    end

    opportunities
  end

  defp predict_performance_impact(physics_config) do
    # Predict performance impact of physics configuration
    base_performance = 1.0

    # Quantum enhancement impact
    quantum_boost = Map.get(physics_config, :quantum_entanglement_potential, 0.5) * 0.3

    # Wormhole network impact
    wormhole_boost = if Map.get(physics_config, :wormhole_creation_threshold, 0.4) < 0.5, do: 0.2, else: 0.1

    # Entropy optimization impact
    entropy_boost = if Map.get(physics_config, :entropy_optimization, true), do: 0.15, else: 0.0

    predicted_performance = base_performance + quantum_boost + wormhole_boost + entropy_boost

    %{
      predicted_performance_multiplier: predicted_performance,
      quantum_contribution: quantum_boost,
      wormhole_contribution: wormhole_boost,
      entropy_contribution: entropy_boost,
      confidence_level: calculate_prediction_confidence(physics_config)
    }
  end

  # Helper Functions

  defp calculate_optimization_priorities(requirements) do
    priorities = Enum.map(requirements, fn {optimization_type, requirement} ->
      case requirement do
        %{priority: priority} -> {optimization_type, priority}
        _ -> {optimization_type, :none}
      end
    end)

    Enum.sort_by(priorities, fn {_type, priority} ->
      case priority do
        :high -> 3
        :medium -> 2
        :low -> 1
        :none -> 0
      end
    end, :desc)
  end

  defp generate_gravitational_reasoning(field_count, data_size) do
    "Field count: #{field_count}, estimated size: #{data_size}B - " <>
    case {field_count, data_size} do
      {fc, ds} when fc >= 10 and ds >= 1000 -> "High complexity suggests strong gravitational settling"
      {fc, ds} when fc >= 5 and ds >= 500 -> "Medium complexity benefits from moderate gravitational effects"
      {fc, _} when fc >= 3 -> "Basic structure benefits from light gravitational optimization"
      _ -> "Simple structure requires minimal gravitational effects"
    end
  end

  defp generate_quantum_reasoning(reference_complexity, has_quantum_annotations) do
    cond do
      has_quantum_annotations -> "Explicit quantum annotations indicate high quantum optimization potential"
      reference_complexity == :high -> "High reference complexity suggests strong quantum entanglement benefits"
      reference_complexity == :medium -> "Medium reference complexity indicates moderate quantum benefits"
      reference_complexity == :low -> "Low reference complexity suggests limited quantum benefits"
      true -> "No reference complexity detected, minimal quantum optimization needed"
    end
  end

  defp generate_temporal_reasoning(has_datetime_fields, has_temporal_annotations) do
    cond do
      has_temporal_annotations -> "Explicit temporal annotations indicate high temporal optimization potential"
      has_datetime_fields -> "DateTime fields suggest temporal optimization benefits"
      true -> "No temporal characteristics detected, basic temporal configuration sufficient"
    end
  end

  defp generate_wormhole_reasoning(reference_complexity, field_count) do
    "Reference complexity: #{reference_complexity}, field count: #{field_count} - " <>
    case {reference_complexity, field_count} do
      {:high, fc} when fc >= 8 -> "High complexity with many fields strongly benefits from wormhole networks"
      {:high, _} -> "High reference complexity suggests wormhole network benefits"
      {:medium, fc} when fc >= 6 -> "Medium complexity with multiple fields benefits from selective wormholes"
      {:medium, _} -> "Medium reference complexity indicates moderate wormhole benefits"
      _ -> "Low complexity suggests minimal wormhole optimization needed"
    end
  end

  defp calculate_recommended_mass(field_count, data_size) do
    base_mass = 1.0
    field_bonus = min(1.0, field_count * 0.1)
    size_bonus = min(0.5, data_size / 2000.0)

    base_mass + field_bonus + size_bonus
  end

  defp calculate_recommended_potential(reference_complexity) do
    case reference_complexity do
      :high -> 0.9
      :medium -> 0.7
      :low -> 0.4
      :none -> 0.2
    end
  end

  defp calculate_recommended_temporal_weight(has_datetime_fields) do
    if has_datetime_fields, do: 1.3, else: 1.0
  end

  defp calculate_recommended_threshold(reference_complexity) do
    case reference_complexity do
      :high -> 0.2
      :medium -> 0.4
      :low -> 0.6
      :none -> 0.8
    end
  end

  defp has_datetime_fields?(fields) do
    Enum.any?(fields, fn field ->
      case field do
        %{type: {{:., _, [{:__aliases__, _, [:DateTime]}, :t]}, _, []}} -> true
        _ -> false
      end
    end)
  end

  defp has_temporal_physics_annotations?(physics_annotations) do
    Map.values(physics_annotations) |> Enum.member?(:temporal_weight)
  end

  defp calculate_validation_score(consistency_issues, optimization_opportunities) do
    base_score = 1.0

    # Penalize consistency issues
    consistency_penalty = length(consistency_issues) * 0.1

    # Bonus for optimization opportunities (indicates room for improvement)
    optimization_bonus = length(optimization_opportunities) * 0.05

    max(0.0, min(1.0, base_score - consistency_penalty + optimization_bonus))
  end

  defp generate_validation_recommendations(consistency_issues, optimization_opportunities) do
    recommendations = []

    # Add recommendations for consistency issues
    recommendations = Enum.reduce(consistency_issues, recommendations, fn issue, acc ->
      [%{type: :fix_consistency, issue: issue, priority: :high} | acc]
    end)

    # Add recommendations for optimization opportunities
    Enum.reduce(optimization_opportunities, recommendations, fn opportunity, acc ->
      [%{type: :apply_optimization, opportunity: opportunity, priority: opportunity.potential_benefit} | acc]
    end)
  end

  defp determine_overall_assessment(validation_score) do
    cond do
      validation_score >= 0.9 -> :excellent
      validation_score >= 0.8 -> :good
      validation_score >= 0.6 -> :acceptable
      validation_score >= 0.4 -> :needs_improvement
      true -> :poor
    end
  end

  defp calculate_prediction_confidence(physics_config) do
    # Calculate confidence based on configuration completeness
    total_params = 10  # Total number of physics parameters
    configured_params = map_size(physics_config)

    base_confidence = configured_params / total_params

    # Adjust based on configuration quality
    quality_adjustment = if Map.get(physics_config, :entropy_optimization, false), do: 0.1, else: 0.0

    max(0.0, min(1.0, base_confidence + quality_adjustment))
  end

  defp generate_physics_recommendations(final_config) do
    recommendations = []

    # Recommend quantum optimization if high potential
    recommendations = if Map.get(final_config, :quantum_entanglement_potential, 0.5) > 0.8 do
      ["Consider enabling quantum correlation monitoring for optimal performance" | recommendations]
    else
      recommendations
    end

    # Recommend wormhole optimization if low threshold
    recommendations = if Map.get(final_config, :wormhole_creation_threshold, 0.4) < 0.3 do
      ["Monitor wormhole network density to prevent over-connection" | recommendations]
    else
      recommendations
    end

    # Recommend entropy monitoring if enabled
    recommendations = if Map.get(final_config, :entropy_optimization, true) do
      ["Enable entropy monitoring dashboard for system health insights" | recommendations]
    else
      recommendations
    end

    recommendations
  end
end