Create graphGen7.py

graphGen7.py

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+import streamlit as st
+import numpy as np
+import networkx as nx
+import matplotlib.pyplot as plt
+import random
+import time
+
+
+class NetworkGenerator:
+    def __init__(self, 
+                 width=10,
+                 height=10,
+                 variant="F", 
+                 topology="highly_connected",
+                 node_drop_fraction=0.1,
+                 bottleneck_cluster_count=None,
+                 bottleneck_edges_per_link=1):
+        
+        self.variant = variant.upper() # "F" = Fixed Density, "R" = Random/Custom Density
+        self.topology = topology.lower()
+        self.width = int(width)
+        self.height = int(height)
+        self.node_drop_fraction = float(node_drop_fraction)
+
+        # Standard config
+        self.node_factor = 0.4 
+
+        # Bottleneck settings
+        if bottleneck_cluster_count is None:
+            area = self.width * self.height
+            self.bottleneck_cluster_count = max(2, int(area / 18))
+        else:
+            self.bottleneck_cluster_count = int(bottleneck_cluster_count)
+            
+        self.bottleneck_edges_per_link = int(bottleneck_edges_per_link)
+
+        self.graph = None
+        self.active_positions = None 
+
+    def generate(self):
+        """Generate a connected network representing rooms in a building."""
+        max_attempts = 20 
+
+        for attempt in range(max_attempts):
+            self._build_node_mask()
+            self._initialize_graph()
+            self._add_nodes()
+
+            nodes = list(self.graph.nodes())
+            if len(nodes) < 2:
+                continue
+
+            # Topology-specific edge construction
+            if self.topology == "bottlenecks":
+                self._build_bottleneck_clusters(nodes)
+            else:
+                self._connect_all_nodes_by_nearby_growth(nodes)
+                self._add_edges()
+
+            # Cleanup - STRICT MODE
+            self._remove_intersections()
+            self._enforce_edge_budget()
+            
+            if not nx.is_connected(self.graph):
+                self._force_connect_components()
+            
+            # Final Safety Check
+            self._remove_intersections() 
+            
+            if nx.is_connected(self.graph):
+                return self.graph
+
+        raise RuntimeError("Failed to generate a valid network. Try reducing Void Fraction.")
+
+    # --- INTERNAL HELPERS ---
+    def _effective_node_drop_fraction(self):
+        base = self.node_drop_fraction
+        if self.topology == "highly_connected": return max(0.0, base * 0.8)
+        if self.topology == "linear": return min(0.95, base * 1.2)
+        return base
+
+    def _build_node_mask(self):
+        all_positions = [(x, y) for x in range(self.width + 1) for y in range(self.height + 1)]
+        drop_frac = self._effective_node_drop_fraction()
+        drop = int(drop_frac * len(all_positions))
+        
+        deactivated = set(random.sample(all_positions, drop)) if drop > 0 else set()
+        self.active_positions = set(all_positions) - deactivated
+
+    def _initialize_graph(self):
+        self.graph = nx.Graph()
+        # Seed near center
+        margin_x = max(1, self.width // 4)
+        margin_y = max(1, self.height // 4)
+        
+        low_x, high_x = margin_x, self.width - margin_x
+        low_y, high_y = margin_y, self.height - margin_y
+        
+        middle_active = [
+            (x, y) for (x, y) in self.active_positions 
+            if low_x <= x <= high_x and low_y <= y <= high_y
+        ]
+        
+        if middle_active:
+            seed = random.choice(middle_active)
+        elif self.active_positions:
+            seed = random.choice(list(self.active_positions))
+        else:
+            return 
+
+        self.graph.add_node(tuple(seed))
+
+    def _compute_nodes(self):
+        total_possible = (self.width + 1) * (self.height + 1)
+        # Variant "F" (Fixed) uses stable logic. Variant "R" (Custom) adds randomization.
+        base = self.node_factor if self.variant == "F" else random.uniform(0.3, 0.6)
+        scale = {"highly_connected": 1.2, "bottlenecks": 0.85, "linear": 0.75}.get(self.topology, 1.0)
+        
+        target = int(base * scale * total_possible)
+        return min(target, len(self.active_positions))
+
+    def _add_nodes(self):
+        total_nodes = self._compute_nodes()
+        attempts = 0
+        while len(self.graph.nodes()) < total_nodes and attempts < (total_nodes * 20):
+            attempts += 1
+            x = random.randint(0, self.width)
+            y = random.randint(0, self.height)
+            
+            if (x, y) in self.active_positions and (x, y) not in self.graph:
+                self.graph.add_node((x, y))
+
+    def _connect_all_nodes_by_nearby_growth(self, nodes):
+        connected = set()
+        remaining = set(nodes)
+        if not remaining: return
+        
+        current = random.choice(nodes)
+        connected.add(current)
+        remaining.remove(current)
+
+        while remaining:
+            candidates = []
+            for n in remaining:
+                for c in connected:
+                    if abs(n[0]-c[0]) <= 2 and abs(n[1]-c[1]) <= 2:
+                        candidates.append(n)
+                        break
+            
+            if not candidates:
+                n = min(remaining, key=lambda r: min(abs(r[0]-c[0]) + abs(r[1]-c[1]) for c in connected))
+                candidates.append(n)
+
+            candidate = random.choice(candidates)
+            
+            neighbors = [c for c in connected if abs(c[0]-candidate[0])<=3 and abs(c[1]-candidate[1])<=3]
+            neighbors.sort(key=lambda c: abs(c[0]-candidate[0]) + abs(c[1]-candidate[1]))
+            
+            n = neighbors[0] if neighbors else random.choice(list(connected))
+            
+            self.graph.add_edge(n, candidate)
+            connected.add(candidate)
+            remaining.remove(candidate)
+
+    def _compute_edge_count(self):
+        n = len(self.graph.nodes())
+        if self.topology == "highly_connected": return int(3.5 * n)
+        if self.topology == "bottlenecks": return int(1.8 * n)
+        return int(random.uniform(1.2, 2.0) * n)
+
+    def _add_edges(self):
+        nodes = list(self.graph.nodes())
+        if self.topology == "highly_connected":
+            self._add_cluster_dense(nodes, self._compute_edge_count())
+        elif self.topology == "linear":
+            self._make_linear(nodes)
+
+    def _make_linear(self, nodes):
+        nodes_sorted = sorted(nodes, key=lambda x: (x[0], x[1]))
+        if not nodes_sorted: return
+        prev = nodes_sorted[0]
+        for nxt in nodes_sorted[1:]:
+            if not self._would_create_intersection(prev, nxt):
+                self.graph.add_edge(prev, nxt)
+            prev = nxt
+
+    def _add_cluster_dense(self, nodes, max_edges):
+        edges_added = 0
+        nodes = list(nodes)
+        random.shuffle(nodes)
+        
+        for i in range(len(nodes)):
+            for j in range(i + 1, len(nodes)):
+                if edges_added >= max_edges: return
+
+                n1, n2 = nodes[i], nodes[j]
+                dist = max(abs(n1[0]-n2[0]), abs(n1[1]-n2[1]))
+                
+                if dist <= 4: 
+                    if not self._would_create_intersection(n1, n2):
+                        self.graph.add_edge(n1, n2)
+                        edges_added += 1
+
+    # --- BOTTLENECK LOGIC ---
+    def _build_bottleneck_clusters(self, nodes):
+        self.graph.remove_edges_from(list(self.graph.edges()))
+        clusters, centers = self._spatial_cluster_nodes(nodes, k=self.bottleneck_cluster_count)
+        
+        for cluster in clusters:
+            if len(cluster) < 2: continue
+            self._connect_cluster_by_nearby_growth(cluster)
+            self._add_cluster_dense(list(cluster), max_edges=max(1, int(3.5 * len(cluster))))
+
+        order = sorted(range(len(clusters)), key=lambda i: (centers[i][0], centers[i][1]))
+        for a_idx, b_idx in zip(order[:-1], order[1:]):
+            self._add_bottleneck_links(clusters[a_idx], clusters[b_idx], self.bottleneck_edges_per_link)
+        
+        if not nx.is_connected(self.graph):
+            self._force_connect_components()
+
+    def _force_connect_components(self):
+        components = list(nx.connected_components(self.graph))
+        while len(components) > 1:
+            c1 = list(components[0])
+            c2 = list(components[1])
+            
+            best_pair = None
+            min_dist = float('inf')
+            
+            for u in c1:
+                for v in c2:
+                    d = (u[0]-v[0])**2 + (u[1]-v[1])**2
+                    if d < min_dist:
+                        if not self._would_create_intersection(u, v):
+                            min_dist = d
+                            best_pair = (u, v)
+            
+            if best_pair:
+                self.graph.add_edge(best_pair[0], best_pair[1])
+            else:
+                pass 
+            
+            prev_len = len(components)
+            components = list(nx.connected_components(self.graph))
+            if len(components) == prev_len: break
+
+    def _spatial_cluster_nodes(self, nodes, k):
+        nodes = list(nodes)
+        if k >= len(nodes): return [[n] for n in nodes], nodes[:]
+        centers = random.sample(nodes, k)
+        clusters = [[] for _ in range(k)]
+        for n in nodes:
+            best_i = min(range(k), key=lambda i: max(abs(n[0]-centers[i][0]), abs(n[1]-centers[i][1])))
+            clusters[best_i].append(n)
+        return clusters, centers
+
+    def _connect_cluster_by_nearby_growth(self, cluster_nodes):
+        self._connect_all_nodes_by_nearby_growth(cluster_nodes)
+
+    def _add_bottleneck_links(self, cluster_a, cluster_b, m):
+        pairs = []
+        for u in cluster_a:
+            for v in cluster_b:
+                dist = max(abs(u[0]-v[0]), abs(u[1]-v[1]))
+                pairs.append((dist, u, v))
+        pairs.sort(key=lambda t: t[0])
+        
+        added = 0
+        for _, u, v in pairs:
+            if added >= m: break
+            if not self.graph.has_edge(u, v) and not self._would_create_intersection(u, v): 
+                self.graph.add_edge(u, v)
+                added += 1
+
+    # --- GEOMETRY & CLEANUP ---
+    def _remove_intersections(self):
+        pass_no = 0
+        while pass_no < 8:
+            pass_no += 1
+            edges = list(self.graph.edges())
+            intersections = []
+            
+            for i in range(len(edges)):
+                for j in range(i+1, len(edges)):
+                    e1 = edges[i]
+                    e2 = edges[j]
+                    if self._segments_intersect(e1[0], e1[1], e2[0], e2[1]):
+                        intersections.append((e1, e2))
+            
+            if not intersections: break
+            
+            for e1, e2 in intersections:
+                if not self.graph.has_edge(*e1) or not self.graph.has_edge(*e2): continue
+                l1 = (e1[0][0]-e1[1][0])**2 + (e1[0][1]-e1[1][1])**2
+                l2 = (e2[0][0]-e2[1][0])**2 + (e2[0][1]-e2[1][1])**2
+                rem = e1 if l1 > l2 else e2
+                self.graph.remove_edge(*rem)
+
+    def _enforce_edge_budget(self):
+        budget = self._compute_edge_count()
+        while len(self.graph.edges()) > budget:
+            edges = list(self.graph.edges())
+            rem = random.choice(edges)
+            self.graph.remove_edge(*rem)
+            if not nx.is_connected(self.graph):
+                self.graph.add_edge(*rem)
+                break 
+
+    def _segments_intersect(self, a, b, c, d):
+        if a == c or a == d or b == c or b == d: return False
+        def ccw(A,B,C): return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])
+        return ccw(a,c,d) != ccw(b,c,d) and ccw(a,b,c) != ccw(a,b,d)
+
+    def _would_create_intersection(self, u, v):
+        for a, b in self.graph.edges():
+            if u == a or u == b or v == a or v == b: continue
+            if self._segments_intersect(u, v, a, b): return True
+        return False
+