# Where raw data transforms into human narrative
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

class MeaningEngine:
    def __init__(self, esrd_data_path):
        self.patients = pd.read_csv(esrd_data_path)
        self.stories = {}  # Patient narratives
        
    def spark_understanding(self, patient_id):
        # Transform clinical data into lived experience
        patient = self.patients[self.patients.id == patient_id]
        waiting_days = (datetime.now() - patient.list_date).days
        
        story = {
            'urgency': self.calculate_urgency(waiting_days),
            'hope_index': self.generate_hope_metric(patient),
            'meaning_vector': self.extract_narrative(patient)
        }
        
        return story
        
    def neurosymbolic_bridge(self, clinical_data):
        # Bridge between synaptic firing and symbolic meaning
        return {
            'dopamine_trigger': clinical_data.improvement_likelihood,
            'prior_experience': self.weight_historical_outcomes(clinical_data),
            'molecular_signature': self.encode_patient_uniqueness(clinical_data)
        }
                    

# Every function call is a moral choice
from flask import Flask, request, jsonify
from datetime import datetime
import hashlib

class EthicalMatchingEngine:
    def __init__(self):
        self.hipaa_compliance = True
        self.fairness_algorithm = 'equity_weighted'
        self.audit_trail = []
        
    def match_with_justice(self, donor, recipient):
        # Each match decision carries moral weight
        compatibility = self.check_medical_compatibility(donor, recipient)
        equity_score = self.calculate_equity_impact(recipient)
        urgency = self.assess_medical_urgency(recipient)
        
        # Ethical constraints as code
        if self.violates_fairness_principle(recipient):
            self.log_ethical_decision("REJECTED: Fairness violation", recipient.id)
            return None
            
        match_score = self.weighted_ethical_score(
            compatibility * 0.4,  # Medical necessity
            equity_score * 0.3,   # Justice principle
            urgency * 0.3         # Time sensitivity
        )
        
        self.audit_trail.append({
            'timestamp': datetime.now(),
            'decision': 'MATCH_PROPOSED',
            'ethical_framework': 'kantian_utilitarian_hybrid',
            'score_breakdown': { 'medical': compatibility, 'equity': equity_score, 'urgency': urgency }
        })
        
        return match_score
                    

# Modeling the recursive game of social trust and exchange
import networkx as nx

class SocialGameEngine:
    def __init__(self):
        self.trust_graph = nx.DiGraph() # Directed graph of trust
        
    def model_kidney_chain(self, pairs):
        # Build a graph of potential donations
        G = nx.DiGraph()
        for donor, recipient in pairs:
            G.add_node(donor.id, type='donor', family=donor.family_id)
            G.add_node(recipient.id, type='recipient', family=recipient.family_id)
            # Add edges for potential matches
            for other_recipient in all_recipients:
                if is_compatible(donor, other_recipient):
                    G.add_edge(donor.id, other_recipient.id, weight=calculate_match_score(donor, other_recipient))
        
        return self.find_optimal_cycles(G)
        
    def find_optimal_cycles(self, graph):
        # Find kidney exchange cycles that maximize utility
        cycles = list(nx.simple_cycles(graph))
        # This is an NP-hard problem, use approximation
        return max(cycles, key=len) # Simplistic: longest chain
        
    def temporal_recursion(self, system_state, depth=0):
        # How the game evolves over time
        new_state = self.apply_rules(system_state)
        self.log_state(new_state, depth)
        if depth > MAX_RECURSION:
            return new_state
        return self.temporal_recursion(new_state, depth + 1)
                    

// Manages the state of the user interface

class InterfaceManager {
    constructor(mainViewId, glyphViewClass) {
        this.mainView = document.getElementById(mainViewId);
        this.glyphViews = document.querySelectorAll(glyphViewClass);
        this.activeGlyph = null;
    }

    showGlyph(glyphId) {
        this.mainView.classList.add('hidden');
        
        const viewToShow = document.getElementById(glyphId + '-view');
        if (viewToShow) {
            viewToShow.classList.add('active');
            this.activeGlyph = viewToShow;
        }
    }

    closeGlyph() {
        if (this.activeGlyph) {
            this.activeGlyph.classList.remove('active');
        }
        this.mainView.classList.remove('hidden');
        this.activeGlyph = null;
    }
}
                    

# This file defines the fundamental reality of the UKUSOMA stack.
# Changing these values alters the entire cosmological model.

version: 1.0
ontology_name: "CosmologicalCare_v1"

ethical_framework:
  base: "kantian_utilitarian_hybrid"
  weights:
    medical_necessity: 0.4
    justice_principle: 0.3
    time_sensitivity: 0.3
  allow_overrides: false # Critical systemic choice

meaning_engine:
  narrative_depth: 5 # How deep to go in patient stories
  hope_metric_source: "bayesian_inference"

social_game:
  max_recursion_depth: 10
  trust_model: "pagerank_weighted"
  chain_optimization_goal: "maximize_lives_saved"

simulation_parameters:
  time_dilation_factor: 1.0 # 1.0 = real-time
  reality_drift_monitoring: true