class N_As_Connectivity:
    """
    N represents the number of edges connected to a node
    """
    def the_structure(self):
        return {
            'n_is': {
                'not_just': 'NAND gates (though related)',
                'not_just': 'Numbers',
                'but': 'Connectivity degree',
                'definition': 'Number of edges connected to node',
                'graph_theory': 'Degree of vertex',
                'fundamental': 'How many connections exist'
            },
            
            'for_a_node': {
                'node_x': 'Point in universal graph',
                'n_value': 'Count of edges connected to X',
                'n_equals_3': 'Node has 3 connections',
                'n_equals_7': 'Node has 7 connections',
                'n_equals_infinite': 'Universal connection point',
                'variable': 'N changes per node'
            },
            
            'the_edges': {
                'e_1': 'First edge connected to node',
                'e_2': 'Second edge connected to node',
                'e_n': 'Nth edge connected to node',
                'each_edge': 'Path for information flow',
                'total': 'N edges total',
                'structure': 'Defines node connectivity pattern'
            },
            
            'why_fundamental': {
                'connectivity': 'Defines information pathways',
                'structure': 'Shapes how signals can flow',
                'capacity': 'Limits/enables processing',
                'network_topology': 'Creates graph structure',
                'determines': 'What can connect to what',
                'base_layer': 'Foundation for S, T, P'
            },
            
            'universal_graph': {
                'all_nodes': 'Complete information substrate',
                'all_edges': 'All possible connections',
                'each_node': 'Has specific N value',
                'n_defines': 'Local connectivity',
                'network': 'Emerges from N values',
                'static': 'N unchanging (from Post 738)'
            }
        }

class S_As_Signals:
    """
    S represents symbolic signals flowing on N edges
    """
    def the_flow(self):
        return {
            's_is': {
                'not_continuous': 'Discrete symbols',
                'symbolic': 'Distinct signal types',
                'on_edges': 'Flows on the N edges',
                'examples': 'S₁, S₂, S₃... (different signal types)',
                'information': 'What is being transmitted',
                'data': 'Content flowing through structure'
            },
            
            'signal_types': {
                's_1': 'Signal type 1 (e.g., activation)',
                's_2': 'Signal type 2 (e.g., inhibition)',
                's_3': 'Signal type 3 (e.g., modulation)',
                'finite_types': 'Discrete symbol alphabet',
                'or_infinite': 'Could be infinite alphabet',
                'symbolic': 'Not continuous values'
            },
            
            'on_which_edge': {
                'edge_e_1': 'Can carry any signal S_x',
                'edge_e_2': 'Can carry any signal S_y',
                'mapping': 'Signal S on edge E',
                'combination': 'Multiple signals on different edges',
                'simultaneous': 'Parallel signal transmission',
                'pattern': 'S(N) = signals across all N edges'
            },
            
            's_of_n': {
                'notation': 'S(N)',
                'meaning': 'Signals on the N edges',
                'input': 'N (connectivity structure)',
                'output': 'Signal pattern across edges',
                'function': 'Maps structure to content',
                'layer_2': 'Built on N foundation'
            },
            
            'why_symbolic': {
                'discrete': 'Not continuous',
                'distinct': 'Clear signal types',
                'information': 'Carries meaning',
                'processable': 'Can be interpreted',
                'basis': 'For computation/experience',
                'content': 'What flows through structure'
            }
        }

class T_As_TimeHash:
    """
    T is multidimensional time hash: T(hop, signal, edge)
    """
    def the_hash(self):
        return {
            't_is_not': {
                'not': 'Linear time',
                'not': 'Sequential moments',
                'not': '1D timeline',
                'not': 'Physical time',
                'wrong_view': 't₁ → t₂ → t₃ (sequence)',
                'artifact': 'Of projection (Post 738)'
            },
            
            't_is': {
                'multidimensional': 'Hash space',
                'association': 'Linking hop + signal + edge',
                'tuple': 'T(hop, signal, edge)',
                'coordinate': 'Point in 3D+ space',
                'unique_state': 'Each combination distinct',
                'graph_traversal': 'State of information flow'
            },
            
            'the_three_dimensions': {
                'hop_h': 'Graph traversal step (H₁, H₂, H₃...)',
                'signal_s': 'Which symbolic signal (S₁, S₂, S₃...)',
                'edge_e': 'Which edge it passes through (E₁, E₂... E_N)',
                'combination': 'T(H₇, S₃, E₂) = specific state',
                'unique': 'Each (H, S, E) is unique "time"',
                'not_sequential': 'Not H₁ then H₂ necessarily'
            },
            
            'what_time_means': {
                'time_is_about': 'Associating specific hop...',
                'with': '...specific symbolic signal...',
                'passing_through': '...specific edge',
                'not_duration': 'Not about passage of time',
                'but_state': 'State of graph traversal',
                'coordinate': 'Position in hash space',
                'fundamental': 'Association not sequence'
            },
            
            'example': {
                'node_with_n_5': '5 edges (E₁ to E₅)',
                'signal_s_3': 'Signal type 3',
                'hop_h_7': 'Seventh traversal step',
                'edge_e_2': 'Second edge',
                'time_moment': 'T(H₇, S₃, E₂)',
                'means': 'Signal S₃ on edge E₂ at hop H₇',
                'unique_state': 'Distinct from T(H₆, S₃, E₂) or T(H₇, S₂, E₂)'
            },
            
            't_of_s_of_n': {
                'notation': 'T(S(N))',
                'meaning': 'Time hash of signals on N edges',
                'input': 'S(N) = signal pattern',
                'output': 'Hash associating hop+signal+edge',
                'function': 'Maps signal flow to traversal state',
                'layer_3': 'Built on S(N) which is built on N'
            }
        }

class P_As_Perspective:
    """
    P wraps everything as perspective/observation
    """
    def the_wrapper(self):
        return {
            'p_is': {
                'outermost_layer': 'Wraps T(S(N))',
                'perspective': 'How we observe/experience',
                'observer': 'Point of view',
                'interpretation': 'How we process the structure',
                'cognitive': 'Mental processing (Post 738)',
                'experience': 'What we actually perceive'
            },
            
            'p_of_t_of_s_of_n': {
                'notation': 'P(T(S(N)))',
                'meaning': 'Perspective experiencing time hash of signals on edges',
                'full_stack': 'All four layers combined',
                'n': 'Graph structure (base)',
                's_n': 'Signals on structure',
                't_s_n': 'Time hash of signals',
                'p_t_s_n': 'Perspective experiencing it all'
            },
            
            'what_p_does': {
                'selects': 'Which T(S(N)) states to experience',
                'interprets': 'Gives meaning to hash coordinates',
                'creates': 'Experience from structure',
                'filters': 'What aspects are perceived',
                'transforms': 'Hash space into experience',
                'cognitive_state': 'How we process (Post 738)'
            },
            
            'from_post_738': {
                'information_static': 'T(S(N)) unchanging',
                'only_p_changes': 'Perspective shifts',
                'different_p': 'Different experience of same T(S(N))',
                'cognitive_changes': 'Only P varies',
                'experience_varies': 'Through P variations',
                'confirms': 'Only cognitive changes happened'
            },
            
            'the_formula_complete': {
                'base': 'N = graph connectivity',
                'layer_1': 'S(N) = signals on edges',
                'layer_2': 'T(S(N)) = hash(hop, signal, edge)',
                'layer_3': 'P(T(S(N))) = perspective experiencing hash',
                'reality': 'Complete formula for experience',
                'all_of_it': 'Structure + content + state + observer'
            }
        }

class TimeHashSpace:
    """
    The complete hash space structure
    """
    def the_space(self):
        return {
            'not_1d_time': {
                'wrong': 't₁ → t₂ → t₃ (linear sequence)',
                'artifact': 'Projection creates this illusion',
                'actually': '3D+ hash space',
                'coordinates': 'T(hop, signal, edge)',
                'multidimensional': 'Not sequential',
                'all_exist': 'All coordinates exist simultaneously'
            },
            
            'the_dimensions': {
                'dimension_1': 'Hops (H₁, H₂, H₃, ...)',
                'dimension_2': 'Signals (S₁, S₂, S₃, ...)',
                'dimension_3': 'Edges (E₁, E₂, ... E_N)',
                'minimum': '3 dimensions',
                'possibly_more': 'Could have additional dimensions',
                'space': 'H × S × E = complete hash space'
            },
            
            'each_coordinate': {
                'unique_state': 'T(H_i, S_j, E_k) is unique',
                'meaning': 'Signal S_j on edge E_k at hop H_i',
                'not_moment': 'Not time moment in sequence',
                'but_state': 'State of graph traversal',
                'all_coexist': 'All coordinates exist',
                'static': 'Hash space unchanging'
            },
            
            'traversal_path': {
                'sequence_exists': 'Can have path through space',
                'h_1_h_2_h_3': 'Hop sequence',
                'but': 'Path is perspective (P)',
                'not_inherent': 'Not in T(S(N)) itself',
                'p_creates': 'Perspective creates sequence',
                'artifact': 'Of how P navigates hash space'
            },
            
            'the_grid': {
                'rows': 'Hops',
                'columns': 'Edges',
                'depth': 'Signals',
                'each_cell': 'T(H, S, E) coordinate',
                '3d_grid': 'Or higher dimensional',
                'all_populated': 'All states exist',
                'static_structure': 'Unchanging information'
            },
            
            'navigation': {
                'p_selects': 'Which coordinates to experience',
                'creates_path': 'Sequence through hash space',
                'seems_temporal': 'Feels like time passing',
                'actually': 'Just P navigating static space',
                'different_p': 'Different path through same space',
                'experience_varies': 'But space unchanging'
            }
        }

class RealityAsGraphTraversal:
    """
    Complete understanding of P(T(S(N)))
    """
    def complete_picture(self):
        return {
            'reality_is': {
                'not': 'Physical universe evolving',
                'not': 'Events in time',
                'but': 'Graph traversal observed',
                'structure': 'Universal information graph',
                'process': 'Signals flowing on edges',
                'state': 'Hash of hop+signal+edge',
                'experience': 'Perspective observing traversal'
            },
            
            'the_layers': {
                'layer_0_n': 'Graph structure (connectivity)',
                'layer_1_s': 'Signals flowing on edges',
                'layer_2_t': 'Hash associating hop+signal+edge',
                'layer_3_p': 'Perspective experiencing hash',
                'complete': 'P(T(S(N))) = full reality formula',
                'each_builds': 'On previous layer'
            },
            
            'what_exists': {
                'universal_graph': 'All nodes and edges (N for each)',
                'signal_flows': 'All possible signals (S)',
                'hash_space': 'All T(H,S,E) coordinates',
                'all_static': 'Unchanging structure',
                'perspectives': 'Different P navigating',
                'only_p_changes': 'Cognitive states vary'
            },
            
            'what_time_really_is': {
                'not_dimension': 'Not physical time dimension',
                'but_association': 'Linking hop with signal with edge',
                'hash_coordinate': 'Point in multidimensional space',
                'traversal_state': 'Where in graph process we are',
                'experienced_as': 'Seems like time passing',
                'actually': 'Just navigation through hash space'
            },
            
            'examples': {
                'jellyfish_movement': {
                    'n': 'Neural network connectivity',
                    's': 'Neural signals',
                    't': 'Hash of neural firing patterns',
                    'p': 'Jellyfish perspective',
                    'result': 'Experience of swimming'
                },
                'helicopter_disruption': {
                    'n': 'Air molecule connections',
                    's': 'Pressure wave signals',
                    't': 'Hash of wave propagation',
                    'p': 'Pilot perspective',
                    'result': 'Experience of instability'
                },
                'universe_observation': {
                    'n': 'Cosmic connection structure',
                    's': 'Information signals',
                    't': 'Hash of states we call "history"',
                    'p': 'Human perspective',
                    'result': 'Experience of time/causality'
                }
            },
            
            'implications': {
                'no_physical_time': 'Time is hash coordinate',
                'no_causality': 'Just graph traversal paths',
                'no_events': 'Just hash space coordinates',
                'only_structure': 'N + S + T + P',
                'experience_emerges': 'From P navigating T(S(N))',
                'reality': 'Is this formula'
            }
        }