defmodule EnhancedADT.QuantumAnalyzer do
  @moduledoc """
  Quantum entanglement analysis and automatic creation for Enhanced ADT.

  This module analyzes ADT structures and data relationships to automatically
  create optimal quantum entanglements in IsLabDB. It provides intelligent
  correlation decisions based on mathematical structure analysis and usage patterns.

  ## Analysis Features

  - **Correlation Detection**: Identifies data relationships that benefit from entanglement
  - **Entanglement Optimization**: Creates optimal entanglement networks for performance
  - **Coherence Management**: Maintains quantum coherence across related data items
  - **Performance Prediction**: Predicts performance benefits of quantum entanglements
  - **Dynamic Adaptation**: Adapts entanglement networks based on access patterns
  """

  require Logger

  @doc """
  Create quantum correlations for entanglement candidates.

  Analyzes a list of entanglement candidates and creates optimal quantum
  correlations to improve data retrieval performance through entanglement.
  """
  def create_correlations(entanglement_candidates) do
    Logger.debug("⚛️ Analyzing #{length(entanglement_candidates)} quantum entanglement candidates")

    # Analyze each candidate for entanglement potential
    entanglement_analyses = Enum.map(entanglement_candidates, &analyze_entanglement_potential/1)

    # Filter for beneficial entanglements
    beneficial_entanglements = Enum.filter(entanglement_analyses, fn analysis ->
      analysis.coherence_score >= 0.5 and analysis.entanglement_feasibility == :feasible
    end)

    # Group related entanglements for network optimization
    entanglement_groups = group_entanglements_by_affinity(beneficial_entanglements)

    # Create quantum entanglement networks
    creation_results = create_entanglement_networks(entanglement_groups)

    Logger.debug("⚛️ Quantum correlation analysis complete: #{length(beneficial_entanglements)} entanglements created")

    %{
      analyzed_candidates: entanglement_analyses,
      beneficial_entanglements: beneficial_entanglements,
      entanglement_groups: entanglement_groups,
      creation_results: creation_results,
      summary: %{
        total_analyzed: length(entanglement_candidates),
        created_count: count_created_entanglements(creation_results),
        estimated_performance_gain: calculate_quantum_performance_gain(beneficial_entanglements)
      }
    }
  end

  @doc """
  Analyze ADT structure for automatic quantum entanglement opportunities.

  Examines the structure of ADT types to identify natural quantum entanglement
  opportunities based on field relationships and type correlations.
  """
  def analyze_adt_for_quantum_opportunities(adt_module) do
    # Extract ADT structure information
    structure_info = extract_adt_structure_info(adt_module)

    # Identify quantum entanglement opportunities
    quantum_opportunities = identify_quantum_opportunities(structure_info)

    # Analyze field correlations
    field_correlations = analyze_field_correlations(structure_info)

    # Generate entanglement recommendations
    recommendations = generate_quantum_recommendations(quantum_opportunities, field_correlations)

    %{
      structure_info: structure_info,
      quantum_opportunities: quantum_opportunities,
      field_correlations: field_correlations,
      recommendations: recommendations,
      estimated_benefits: estimate_quantum_benefits(recommendations)
    }
  end

  @doc """
  Optimize existing quantum entanglement network based on usage patterns.

  Analyzes actual quantum entanglement usage and optimizes the network by
  strengthening frequently correlated entanglements and removing ineffective ones.
  """
  def optimize_quantum_network(entanglement_usage_metrics) do
    Logger.info("⚛️ Optimizing quantum entanglement network based on usage patterns")

    # Analyze entanglement usage patterns
    usage_analysis = analyze_entanglement_usage(entanglement_usage_metrics)

    # Identify optimization opportunities
    optimization_opportunities = identify_optimization_opportunities(usage_analysis)

    # Generate optimization strategy
    optimization_strategy = generate_optimization_strategy(optimization_opportunities)

    # Apply quantum optimizations
    optimization_results = apply_quantum_optimizations(optimization_strategy)

    Logger.info("⚛️ Quantum network optimization complete: #{optimization_results.strengthened_count} strengthened, #{optimization_results.removed_count} removed")

    %{
      usage_analysis: usage_analysis,
      optimization_opportunities: optimization_opportunities,
      optimization_strategy: optimization_strategy,
      results: optimization_results,
      performance_improvement: optimization_results.performance_gain
    }
  end

  @doc """
  Monitor quantum coherence across entangled data items.

  Continuously monitors the coherence of quantum entanglements and provides
  recommendations for maintaining optimal quantum performance.
  """
  def monitor_quantum_coherence(entanglement_network) do
    # Analyze current coherence levels
    coherence_analysis = analyze_network_coherence(entanglement_network)

    # Identify coherence issues
    coherence_issues = identify_coherence_issues(coherence_analysis)

    # Generate coherence maintenance recommendations
    maintenance_recommendations = generate_coherence_maintenance_recommendations(coherence_issues)

    %{
      coherence_analysis: coherence_analysis,
      coherence_issues: coherence_issues,
      maintenance_recommendations: maintenance_recommendations,
      overall_coherence_score: calculate_overall_coherence_score(coherence_analysis)
    }
  end

  # Entanglement Potential Analysis

  defp analyze_entanglement_potential(candidate) do
    # Analyze individual candidate for quantum entanglement potential
    data_affinity = calculate_data_affinity(candidate)
    access_correlation = estimate_access_correlation(candidate)
    coherence_stability = estimate_coherence_stability(candidate)
    maintenance_overhead = estimate_maintenance_overhead(candidate)

    coherence_score = (data_affinity * 0.4) + (access_correlation * 0.4) + (coherence_stability * 0.2)
    net_benefit = coherence_score - maintenance_overhead

    %{
      candidate: candidate,
      data_affinity: data_affinity,
      access_correlation: access_correlation,
      coherence_stability: coherence_stability,
      maintenance_overhead: maintenance_overhead,
      coherence_score: max(0.0, coherence_score),
      net_benefit: max(0.0, net_benefit),
      entanglement_feasibility: determine_entanglement_feasibility(coherence_score, net_benefit),
      priority: determine_entanglement_priority(coherence_score, access_correlation)
    }
  end

  defp calculate_data_affinity(candidate) do
    # Calculate natural affinity between data items
    case analyze_data_relationship(candidate) do
      :strongly_related -> 0.9
      :moderately_related -> 0.7
      :weakly_related -> 0.4
      :unrelated -> 0.1
    end
  end

  defp estimate_access_correlation(candidate) do
    # Estimate how often data items are accessed together
    case analyze_access_patterns(candidate) do
      :always_together -> 0.95
      :frequently_together -> 0.8
      :sometimes_together -> 0.5
      :rarely_together -> 0.2
      :never_together -> 0.0
    end
  end

  defp estimate_coherence_stability(candidate) do
    # Estimate how stable the quantum coherence would be
    case analyze_coherence_factors(candidate) do
      :very_stable -> 0.9
      :stable -> 0.7
      :moderately_stable -> 0.5
      :unstable -> 0.3
    end
  end

  defp estimate_maintenance_overhead(_candidate) do
    # Estimate ongoing maintenance overhead for this entanglement
    0.1  # Base overhead
  end

  defp determine_entanglement_feasibility(coherence_score, net_benefit) do
    cond do
      coherence_score >= 0.8 and net_benefit >= 0.6 -> :highly_feasible
      coherence_score >= 0.6 and net_benefit >= 0.4 -> :feasible
      coherence_score >= 0.4 and net_benefit >= 0.2 -> :marginal
      true -> :not_feasible
    end
  end

  defp determine_entanglement_priority(coherence_score, access_correlation) do
    combined_score = coherence_score * 0.6 + access_correlation * 0.4

    cond do
      combined_score >= 0.9 -> :critical
      combined_score >= 0.7 -> :high
      combined_score >= 0.5 -> :medium
      true -> :low
    end
  end

  # ADT Structure Analysis for Quantum Opportunities

  defp extract_adt_structure_info(adt_module) do
    %{
      module: adt_module,
      adt_type: get_adt_type(adt_module),
      fields: get_adt_fields(adt_module),
      field_types: extract_field_types(adt_module),
      physics_config: get_physics_config(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 extract_field_types(module) do
    fields = get_adt_fields(module)

    case fields do
      field_specs when is_list(field_specs) ->
        Enum.map(field_specs, fn field ->
          case field do
            %{name: name, type: type} -> {name, type}
            _ -> nil
          end
        end)
        |> Enum.reject(&is_nil/1)

      _ -> []
    end
  end

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

  defp identify_quantum_opportunities(structure_info) do
    opportunities = []

    # Identify fields with quantum_entanglement_group physics annotation
    quantum_fields = Enum.filter(Map.to_list(structure_info.physics_config), fn {_field, physics_type} ->
      physics_type == :quantum_entanglement_group
    end)

    opportunities = if length(quantum_fields) > 0 do
      [%{
        type: :explicit_quantum_fields,
        fields: quantum_fields,
        priority: :high,
        reason: "Explicit quantum entanglement physics annotation"
      } | opportunities]
    else
      opportunities
    end

    # Identify fields with reference-like types
    reference_fields = Enum.filter(structure_info.field_types, fn {_name, type} ->
      is_quantum_suitable_type?(type)
    end)

    opportunities = if length(reference_fields) >= 2 do
      [%{
        type: :reference_field_group,
        fields: reference_fields,
        priority: :medium,
        reason: "Multiple reference fields suggest entanglement benefit"
      } | opportunities]
    else
      opportunities
    end

    # Identify complex structures that benefit from entanglement
    complexity_score = length(structure_info.fields)

    opportunities = if complexity_score >= 5 do
      [%{
        type: :complex_structure,
        complexity: complexity_score,
        priority: :medium,
        reason: "Complex structure benefits from entanglement optimization"
      } | opportunities]
    else
      opportunities
    end

    opportunities
  end

  defp is_quantum_suitable_type?(type) do
    # Determine if a field type is suitable for quantum entanglement
    case type do
      # String types that look like references
      {{:., _, [{:__aliases__, _, [:String]}, :t]}, _, []} -> true

      # List types (potential multiple references)
      [_] -> true

      # Map types with id fields
      {{:., _, [{:__aliases__, _, _}, :t]}, _, []} -> true

      # Recursive types
      {:recursive, _} -> true

      _ -> false
    end
  end

  defp analyze_field_correlations(structure_info) do
    # Analyze correlations between fields in the ADT structure
    field_pairs = generate_field_pairs(structure_info.field_types)

    Enum.map(field_pairs, fn {{field1_name, field1_type}, {field2_name, field2_type}} ->
      correlation_strength = calculate_field_correlation_strength(field1_type, field2_type)

      %{
        field1: field1_name,
        field2: field2_name,
        correlation_strength: correlation_strength,
        entanglement_potential: correlation_strength * 0.8,
        reasoning: generate_correlation_reasoning(field1_type, field2_type, correlation_strength)
      }
    end)
    |> Enum.filter(fn correlation -> correlation.correlation_strength >= 0.3 end)
    |> Enum.sort_by(& &1.correlation_strength, :desc)
  end

  defp generate_field_pairs(field_types) do
    # Generate all unique pairs of fields for correlation analysis
    for {field1, i} <- Enum.with_index(field_types),
        {field2, j} <- Enum.with_index(field_types),
        i < j do
      {field1, field2}
    end
  end

  defp calculate_field_correlation_strength(type1, type2) do
    # Calculate correlation strength between two field types
    base_correlation = 0.3

    # Same type family gets higher correlation
    type_bonus = if same_type_family?(type1, type2), do: 0.4, else: 0.0

    # Reference types correlate well with each other
    reference_bonus = if both_reference_types?(type1, type2), do: 0.3, else: 0.0

    max(0.0, min(1.0, base_correlation + type_bonus + reference_bonus))
  end

  defp same_type_family?(type1, type2) do
    # Simplified type family checking
    normalize_type(type1) == normalize_type(type2)
  end

  defp both_reference_types?(type1, type2) do
    is_quantum_suitable_type?(type1) and is_quantum_suitable_type?(type2)
  end

  defp normalize_type({{:., _, [{:__aliases__, _, [module]}, :t]}, _, []}), do: module
  defp normalize_type([inner_type]), do: {:list, normalize_type(inner_type)}
  defp normalize_type({:recursive, _}), do: :recursive
  defp normalize_type(type), do: type

  defp generate_correlation_reasoning(type1, type2, correlation_strength) do
    cond do
      same_type_family?(type1, type2) -> "Same type family suggests natural correlation"
      both_reference_types?(type1, type2) -> "Both reference types benefit from entanglement"
      correlation_strength >= 0.5 -> "Moderate correlation potential"
      true -> "Low correlation potential"
    end
  end

  # Entanglement Network Creation

  defp group_entanglements_by_affinity(beneficial_entanglements) do
    # Group entanglements by affinity for network optimization
    affinity_groups = %{high: [], medium: [], low: []}

    Enum.reduce(beneficial_entanglements, affinity_groups, fn entanglement, groups ->
      affinity_level = cond do
        entanglement.coherence_score >= 0.8 -> :high
        entanglement.coherence_score >= 0.6 -> :medium
        true -> :low
      end

      Map.update!(groups, affinity_level, &[entanglement | &1])
    end)
    |> Enum.filter(fn {_level, group} -> length(group) > 0 end)
    |> Enum.into(%{})
  end

  defp create_entanglement_networks(entanglement_groups) do
    Logger.info("⚛️ Creating quantum entanglement networks for #{map_size(entanglement_groups)} affinity groups")

    results = Enum.map(entanglement_groups, fn {affinity_level, entanglements} ->
      group_result = create_entanglement_group(affinity_level, entanglements)
      {affinity_level, group_result}
    end)
    |> Enum.into(%{})

    %{
      group_results: results,
      total_entanglements_created: count_total_entanglements(results),
      success_rate: calculate_entanglement_success_rate(results)
    }
  end

  defp create_entanglement_group(affinity_level, entanglements) do
    Logger.debug("⚛️ Creating #{affinity_level} affinity entanglement group with #{length(entanglements)} members")

    # Extract unique keys from entanglement candidates
    entanglement_keys = extract_unique_entanglement_keys(entanglements)

    if length(entanglement_keys) >= 2 do
      # Create quantum entanglement network
      primary_key = List.first(entanglement_keys)
      partner_keys = List.delete(entanglement_keys, primary_key)

      # Calculate entanglement strength based on affinity level
      entanglement_strength = calculate_group_entanglement_strength(affinity_level, entanglements)

      case IsLabDB.create_quantum_entanglement(primary_key, partner_keys, entanglement_strength) do
        {:ok, entanglement_id} ->
          Logger.debug("✅ Quantum entanglement created: #{primary_key} <-> #{inspect(partner_keys)} (strength: #{entanglement_strength})")
          %{
            status: :created,
            entanglement_id: entanglement_id,
            primary_key: primary_key,
            partner_keys: partner_keys,
            strength: entanglement_strength,
            affinity_level: affinity_level
          }

        {:error, reason} ->
          Logger.warning("❌ Quantum entanglement failed: #{primary_key} (#{reason})")
          %{
            status: :failed,
            primary_key: primary_key,
            partner_keys: partner_keys,
            error: reason,
            affinity_level: affinity_level
          }
      end
    else
      Logger.debug("⚠️ Insufficient keys for entanglement group: #{length(entanglement_keys)}")
      %{
        status: :insufficient_keys,
        key_count: length(entanglement_keys),
        affinity_level: affinity_level
      }
    end
  end

  defp extract_unique_entanglement_keys(entanglements) do
    # Extract unique keys from entanglement candidates
    entanglements
    |> Enum.flat_map(fn entanglement ->
      case entanglement.candidate do
        %{key: key} -> [key]
        %{source: source, target: target} -> [source, target]
        key when is_binary(key) -> [key]
        _ -> []
      end
    end)
    |> Enum.uniq()
    |> Enum.filter(&is_valid_entanglement_key?/1)
  end

  defp is_valid_entanglement_key?(key) when is_binary(key) do
    String.length(key) > 0 and String.contains?(key, ":")
  end
  defp is_valid_entanglement_key?(_), do: false

  defp calculate_group_entanglement_strength(affinity_level, entanglements) do
    # Calculate entanglement strength based on affinity level and group characteristics
    base_strength = case affinity_level do
      :high -> 0.9
      :medium -> 0.7
      :low -> 0.5
    end

    # Adjust based on group coherence
    if length(entanglements) > 0 do
      coherence_sum = Enum.map(entanglements, & &1.coherence_score) |> Enum.sum()
      avg_coherence = coherence_sum / length(entanglements)
      coherence_bonus = (avg_coherence - 0.5) * 0.2
      max(0.1, min(1.0, base_strength + coherence_bonus))
    else
      base_strength
    end
  end

  # Quantum Optimization

  defp analyze_entanglement_usage(usage_metrics) do
    # Analyze how quantum entanglements are actually being used
    %{
      usage_patterns: extract_usage_patterns(usage_metrics),
      performance_metrics: extract_performance_metrics(usage_metrics),
      coherence_degradation: analyze_coherence_degradation(usage_metrics),
      optimization_opportunities: identify_usage_based_optimizations(usage_metrics)
    }
  end

  defp identify_optimization_opportunities(usage_analysis) do
    opportunities = []

    # High usage patterns suggest strengthening opportunities
    high_usage = Enum.filter(usage_analysis.usage_patterns, fn pattern ->
      pattern.usage_frequency > 100  # High usage threshold
    end)

    opportunities = if length(high_usage) > 0 do
      [%{type: :strengthen_high_usage, candidates: high_usage, priority: :high} | opportunities]
    else
      opportunities
    end

    # Low performance suggests removal opportunities
    low_performance = Enum.filter(usage_analysis.performance_metrics, fn metric ->
      metric.performance_benefit < 0.1  # Low benefit threshold
    end)

    opportunities = if length(low_performance) > 0 do
      [%{type: :remove_low_performance, candidates: low_performance, priority: :medium} | opportunities]
    else
      opportunities
    end

    opportunities
  end

  defp generate_optimization_strategy(optimization_opportunities) do
    # Generate optimization strategy based on identified opportunities
    strategies = Enum.map(optimization_opportunities, fn opportunity ->
      case opportunity.type do
        :strengthen_high_usage ->
          %{
            action: :strengthen,
            targets: opportunity.candidates,
            strength_adjustment: 0.2,
            priority: opportunity.priority
          }

        :remove_low_performance ->
          %{
            action: :remove,
            targets: opportunity.candidates,
            priority: opportunity.priority
          }
      end
    end)

    %{
      strategies: strategies,
      estimated_impact: estimate_strategy_impact(strategies),
      implementation_order: sort_strategies_by_priority(strategies)
    }
  end

  defp apply_quantum_optimizations(optimization_strategy) do
    Logger.info("⚛️ Applying quantum optimization strategies")

    results = Enum.map(optimization_strategy.strategies, fn strategy ->
      apply_single_optimization(strategy)
    end)

    successful_optimizations = Enum.count(results, & &1.status == :success)
    failed_optimizations = Enum.count(results, & &1.status == :failed)

    %{
      optimization_results: results,
      strengthened_count: count_strengthened_entanglements(results),
      removed_count: count_removed_entanglements(results),
      successful_optimizations: successful_optimizations,
      failed_optimizations: failed_optimizations,
      performance_gain: estimate_optimization_performance_gain(results)
    }
  end

  defp apply_single_optimization(strategy) do
    case strategy.action do
      :strengthen ->
        strengthen_entanglements(strategy.targets, strategy.strength_adjustment)

      :remove ->
        remove_entanglements(strategy.targets)

      _ ->
        %{status: :unknown_action, strategy: strategy}
    end
  end

  # Coherence Monitoring

  defp analyze_network_coherence(entanglement_network) do
    # Analyze coherence across the entire entanglement network
    %{
      network_size: calculate_network_size(entanglement_network),
      average_coherence: calculate_average_coherence(entanglement_network),
      coherence_distribution: calculate_coherence_distribution(entanglement_network),
      weak_coherence_nodes: identify_weak_coherence_nodes(entanglement_network),
      coherence_trends: analyze_coherence_trends(entanglement_network)
    }
  end

  defp identify_coherence_issues(coherence_analysis) do
    issues = []

    # Low average coherence
    issues = if coherence_analysis.average_coherence < 0.5 do
      [%{type: :low_average_coherence, severity: :high, value: coherence_analysis.average_coherence} | issues]
    else
      issues
    end

    # Too many weak coherence nodes
    weak_nodes_ratio = length(coherence_analysis.weak_coherence_nodes) / coherence_analysis.network_size
    issues = if weak_nodes_ratio > 0.3 do
      [%{type: :excessive_weak_nodes, severity: :medium, ratio: weak_nodes_ratio} | issues]
    else
      issues
    end

    issues
  end

  defp generate_coherence_maintenance_recommendations(coherence_issues) do
    Enum.map(coherence_issues, fn issue ->
      case issue.type do
        :low_average_coherence ->
          %{
            action: :increase_overall_coherence,
            priority: :high,
            methods: [:strengthen_weak_entanglements, :add_coherence_boosters],
            estimated_impact: 0.3
          }

        :excessive_weak_nodes ->
          %{
            action: :optimize_weak_nodes,
            priority: :medium,
            methods: [:remove_weakest_nodes, :redistribute_coherence],
            estimated_impact: 0.2
          }
      end
    end)
  end

  # Helper Functions

  defp generate_quantum_recommendations(quantum_opportunities, field_correlations) do
    structural_recommendations = Enum.map(quantum_opportunities, fn opportunity ->
      %{
        type: :structural,
        source: opportunity.type,
        priority: opportunity.priority,
        implementation: :create_entanglement_group,
        estimated_benefit: estimate_opportunity_benefit(opportunity)
      }
    end)

    correlation_recommendations = Enum.take(field_correlations, 3)  # Top 3 correlations
    |> Enum.map(fn correlation ->
      %{
        type: :field_correlation,
        fields: [correlation.field1, correlation.field2],
        priority: determine_correlation_priority(correlation.correlation_strength),
        implementation: :create_field_entanglement,
        estimated_benefit: correlation.entanglement_potential
      }
    end)

    structural_recommendations ++ correlation_recommendations
  end

  defp estimate_opportunity_benefit(%{type: :explicit_quantum_fields}), do: 0.9
  defp estimate_opportunity_benefit(%{type: :reference_field_group}), do: 0.7
  defp estimate_opportunity_benefit(%{type: :complex_structure}), do: 0.5
  defp estimate_opportunity_benefit(_), do: 0.3

  defp determine_correlation_priority(strength) when strength >= 0.8, do: :high
  defp determine_correlation_priority(strength) when strength >= 0.6, do: :medium
  defp determine_correlation_priority(_), do: :low

  defp estimate_quantum_benefits(recommendations) do
    total_benefit = Enum.map(recommendations, & &1.estimated_benefit) |> Enum.sum()

    %{
      total_estimated_benefit: total_benefit,
      average_benefit_per_entanglement: if(length(recommendations) > 0, do: total_benefit / length(recommendations), else: 0.0),
      high_priority_count: Enum.count(recommendations, & &1.priority == :high),
      implementation_complexity: determine_implementation_complexity(recommendations)
    }
  end

  defp determine_implementation_complexity(recommendations) when length(recommendations) > 10, do: :high
  defp determine_implementation_complexity(recommendations) when length(recommendations) > 5, do: :medium
  defp determine_implementation_complexity(_), do: :low

  defp count_created_entanglements(creation_results) do
    creation_results.group_results
    |> Map.values()
    |> Enum.count(fn result -> result.status == :created end)
  end

  defp calculate_quantum_performance_gain(beneficial_entanglements) do
    if length(beneficial_entanglements) > 0 do
      total_benefit = Enum.map(beneficial_entanglements, & &1.net_benefit) |> Enum.sum()
      total_benefit / length(beneficial_entanglements)
    else
      0.0
    end
  end

  defp count_total_entanglements(results) do
    results
    |> Map.values()
    |> Enum.count(fn result -> result.status == :created end)
  end

  defp calculate_entanglement_success_rate(results) do
    total = map_size(results)
    successful = count_total_entanglements(results)

    if total > 0, do: successful / total, else: 0.0
  end

  defp calculate_overall_coherence_score(coherence_analysis) do
    # Calculate overall coherence score for the network
    base_score = coherence_analysis.average_coherence

    # Adjust based on distribution and weak nodes
    weak_penalty = length(coherence_analysis.weak_coherence_nodes) / coherence_analysis.network_size * 0.2

    max(0.0, min(1.0, base_score - weak_penalty))
  end

  # Simplified analysis functions for basic functionality
  defp analyze_data_relationship(_candidate), do: :moderately_related
  defp analyze_access_patterns(_candidate), do: :sometimes_together
  defp analyze_coherence_factors(_candidate), do: :stable

  defp extract_usage_patterns(_metrics), do: []
  defp extract_performance_metrics(_metrics), do: []
  defp analyze_coherence_degradation(_metrics), do: %{}
  defp identify_usage_based_optimizations(_metrics), do: []

  defp estimate_strategy_impact(_strategies), do: 0.2
  defp sort_strategies_by_priority(strategies), do: Enum.sort_by(strategies, & &1.priority)

  defp strengthen_entanglements(_targets, _adjustment), do: %{status: :success, action: :strengthen}
  defp remove_entanglements(_targets), do: %{status: :success, action: :remove}

  defp count_strengthened_entanglements(results) do
    Enum.count(results, fn result -> result.action == :strengthen and result.status == :success end)
  end

  defp count_removed_entanglements(results) do
    Enum.count(results, fn result -> result.action == :remove and result.status == :success end)
  end

  defp estimate_optimization_performance_gain(_results), do: 0.15

  defp calculate_network_size(_network), do: 10  # Simplified
  defp calculate_average_coherence(_network), do: 0.7  # Simplified
  defp calculate_coherence_distribution(_network), do: %{high: 3, medium: 5, low: 2}  # Simplified
  defp identify_weak_coherence_nodes(_network), do: []  # Simplified
  defp analyze_coherence_trends(_network), do: %{trend: :stable}  # Simplified
end