# Space = continuous manifold
# Time = separate dimension
# Light = wave/particle through space

class UniverseGraph:
    """
    Universe = dynamic graph structure
    """
    def __init__(self):
        self.nodes = set()  # Events/particles
        self.edges = {}     # Spacetime connections
        self.topology = DynamicTopology()
    
    def add_node(self, event):
        """
        New event in spacetime
        """
        self.nodes.add(event)
        
        # Connect to nearby nodes
        for neighbor in self.find_neighbors(event):
            self.add_edge(event, neighbor)
    
    def add_edge(self, node1, node2):
        """
        Spacetime connection between events
        """
        distance = self.calculate_distance(node1, node2)
        self.edges[(node1, node2)] = {
            'length': distance,
            'traversal_time': distance / c  # Information propagation
        }

class LightPacket:
    """
    Information propagating through graph
    """
    def __init__(self, origin_node, direction, energy):
        self.current_node = origin_node
        self.direction = direction
        self.energy = energy
        self.path = [origin_node]
    
    def propagate(self):
        """
        Move to next node in graph
        """
        # Find next edge in direction
        next_edge = self.find_next_edge(self.current_node, self.direction)
        
        # Traverse edge at constant rate
        # c = information propagation speed through edge
        traversal_time = next_edge.length / c
        
        # Move to next node
        next_node = next_edge.destination
        self.current_node = next_node
        self.path.append(next_node)
        
        return traversal_time

# Wave-like behavior
# = Information interference patterns through graph

# Particle-like behavior  
# = Discrete hops between nodes

# Wave-particle duality
# = Information packets traversing discrete graph

def measure_light_speed(observer_node):
    """
    Observer measures c from their graph node
    """
    # Observer is embedded in graph
    # Can only measure local propagation rate
    
    # Emit light from observer node
    light = LightPacket(origin=observer_node)
    
    # Light traverses to next node
    next_node = observer_node.neighbors[0]
    edge = observer_node.edges[next_node]
    
    # Measure: How long does edge traversal take?
    traversal_time = edge.length / c  # Always c locally!
    
    # This is LOCAL measurement
    # Doesn't depend on graph's global motion
    # Graph itself might be moving/changing
    # But edge traversal rate is constant
    
    return c  # Always measures same local rate

# Network packets travel at fixed rate through cables
# Even if entire network is moving

cable_speed = 1 Gbps  # Fixed per cable

# Your laptop moves (on train)
# Server moves (rotating with earth)
# But packet traversal through cable = 1 Gbps

# Same with light:
# Earth moves
# Satellite moves  
# But information through graph edge = c

class DynamicGraph:
    """
    Graph topology changes constantly
    """
    def __init__(self):
        self.nodes = {}
        self.edges = {}
        self.time = 0
    
    def evolve(self, dt):
        """
        Graph structure changes over time
        """
        self.time += dt
        
        # Nodes move relative to each other
        for node in self.nodes.values():
            node.position += node.velocity * dt
        
        # Edge lengths change
        for (n1, n2), edge in self.edges.items():
            new_distance = distance(n1.position, n2.position)
            edge.length = new_distance
        
        # New nodes created
        for event in self.quantum_events():
            self.add_node(event)
        
        # Old edges broken, new edges formed
        self.reconfigure_topology()

class EmbeddedObserver:
    """
    Observer = node in graph
    Can only measure locally
    """
    def __init__(self, node):
        self.node = node
        self.graph = node.graph
    
    def measure_light_speed(self):
        """
        Measure c from this node
        """
        # Can only observe local topology
        visible_neighbors = self.node.neighbors
        
        # Send light to neighbor
        light = LightPacket(self.node)
        neighbor = visible_neighbors[0]
        
        # Measure edge traversal
        edge = self.node.edges[neighbor]
        measured_c = edge.length / edge.traversal_time
        
        # Always gets: c = constant
        # Because edge traversal rate is fixed
        
        # Can't see:
        # - Graph's global motion
        # - Other nodes' movement
        # - Topology changes elsewhere
        
        return measured_c  # Always c

# What observer CAN measure:
local_edge_traversal_rate = c  # Constant

# What observer CANNOT measure:
global_graph_velocity = ???  # Inaccessible
graph_topology_drift = ???   # Not observable
absolute_node_positions = ??? # No reference frame

class GraphAddress:
    """
    From Post 843: Relative paths through topology
    """
    def __init__(self, from_node, path):
        self.origin = from_node
        self.path = path  # Sequence of edges
    
    def send_light(self, destination_path):
        """
        Send light packet through graph
        """
        light = LightPacket(self.origin)
        
        # Light follows path through topology
        for edge in destination_path:
            # Traverse each edge at rate c
            light.propagate_through(edge)
        
        return light.current_node  # Arrived

# From Earth to Moon
# Not: absolute coordinates
# But: path through graph

light_path = [
    earth_node,
    edge_earth_to_space,
    space_node,
    edge_space_to_moon,
    moon_node
]

# Light hops through this topology
# Each edge traversed at rate c
# Total time = sum of edge traversals

def time_dilation(velocity):
    """
    Why moving clocks run slow
    """
    # Moving node traverses MORE edges per unit "external" time
    # Because graph topology stretches in direction of motion
    
    stationary_edges_per_tick = 1
    moving_edges_per_tick = 1 / sqrt(1 - v²/c²)
    
    # More edges to traverse = more "local time" per "external time"
    # Clock appears to run slow from outside
    
    return sqrt(1 - v²/c²)

def length_contraction(velocity):
    """
    Why moving rulers shrink
    """
    # Moving node's local edges compress in direction of motion
    # Graph topology contracts
    
    stationary_length = L
    moving_length = L * sqrt(1 - v²/c²)
    
    # Ruler measures fewer edges in motion direction
    # Appears contracted from outside
    
    return L * sqrt(1 - v²/c²)

class MassNode:
    """
    Mass warps local graph topology
    """
    def __init__(self, mass, position):
        self.mass = mass
        self.position = position
    
    def warp_topology(self, graph):
        """
        Curve graph structure around mass
        """
        for node in graph.nearby_nodes(self.position):
            # Edge lengths change near mass
            for edge in node.edges:
                # Edges curve toward mass
                curvature = self.gravitational_curvature(edge)
                edge.length *= (1 + curvature)
        
        # Light follows curved edges
        # Appears to "bend" near mass
        # Actually just following graph topology

# Light near massive object
# Follows shortest path through graph
# But graph is curved by mass
# So path curves

light_path = find_shortest_path(
    graph=curved_graph,  # Warped by mass
    start=distant_star,
    end=earth
)

# Path curves around sun
# Not because "gravity pulls light"
# But because graph topology is curved

# c = edge traversal rate (constant)
c = 299792458  # m/s per edge

# Graph velocity = topology drift (variable)
graph_velocity = [30, 230, 600]  # km/s at different scales

# These are DIFFERENT measurements:

# Observer measures: edge traversal rate
measured_c = edge.length / traversal_time  # Always c

# External view: graph topology motion
graph_drift = topology_change_per_time  # Variable

# No paradox:
# c = local (constant)
# drift = global (variable)

class InformationBit:
    """
    Light = information propagating through graph
    """
    def __init__(self, bit_value):
        self.value = bit_value  # 0 or 1
        self.energy = h * frequency  # Planck relation
    
    def propagate_through_edge(self, edge):
        """
        Information travels through graph connection
        """
        # Edge = possible information channel
        # Information traverses at rate c
        
        time_to_traverse = edge.length / c
        
        return time_to_traverse

# Frequency = information update rate
frequency = updates_per_second

# Wavelength = spatial period of information
wavelength = c / frequency

# Energy = information density
energy = h * frequency

# Momentum = information directionality
momentum = h / wavelength

# Meatspace projection
meatspace_view = graph.project_to_3d_space()

# Computational projection  
computational_view = graph.project_to_turing_machine()

# Information projection
information_view = graph.project_to_bit_flow()

# All same underlying graph!

# Wave projection
wave_view = light.as_wave()  # Continuous propagation view

# Particle projection
particle_view = light.as_particle()  # Discrete hop view

# Information projection
info_view = light.as_information()  # Bit propagation view

# All describe same: information through graph edges

# If universe = discrete graph
# Then space has minimum length scale

planck_length = sqrt(h * G / c³)  # ~10⁻³⁵ meters

# This IS observed
# Consistent with graph having minimum edge length

# Information through edge = limited by c
# Maximum information = c / edge_length

max_information_density = c / planck_length
# This matches holographic principle!
# Information on surface, not in volume
# Because surface = graph edges

# Graph changes in discrete steps
# Not continuous

quantum_of_action = h  # Planck constant
# Minimum change in graph topology
# This IS observed
# Quantum mechanics = discrete graph changes

# R³ = information routing through DHT graph
dht_graph = {
    'nodes': EigenDHT nodes,
    'edges': peer connections,
    'information': series data,
    'propagation': gossip protocol
}

# Same pattern as light!

# Network addressing = paths through graph
message_path = ['node1', 'node2', 'node3']

# Same as light path through spacetime graph
light_path = [event1, event2, event3]

# c measurement = observation gap
# Because can't measure from outside graph

# Same in R³:
# Can't verify global state
# Only local observations