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e47825e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | import json
import random
import networkx as nx
import os
from visualizer import get_sorted_nodes
def prepare_edges_for_json(G):
nodes_list = get_sorted_nodes(G)
nodes_list_dict = {str(i+1): node for i, node in enumerate(nodes_list)}
coord_to_id = {v: k for k, v in nodes_list_dict.items()}
edges_formatted = []
for u, v in G.edges():
if u in coord_to_id and v in coord_to_id:
edges_formatted.append({"room1": coord_to_id[u], "room2": coord_to_id[v]})
return edges_formatted, list(nodes_list_dict.keys()), nodes_list_dict
def prepare_parameter_for_json(G, I, nodes_list_dict):
n_count = len(G.nodes())
if n_count == 0:
return [], [], [], [], [], [], [], [], [], []
weights = [n_count / (n_count * (1 + (((i + 1) * 2) / 30))) for i in range(n_count)]
m_weights = random.choices(I, weights=weights, k=5)
t_weights_probs = [n_count / (n_count * (1 + (((i + 1) * 2) / 5))) for i in range(10)]
t_weights = random.choices(range(1, 11), weights=t_weights_probs, k=5)
dismantled, conditioningDuration, assignment, help_list = [], [], [], []
for m in range(5):
dismantled.append({"m": str(m + 1), "i": str(m_weights[m]), "t": t_weights[m], "value": 1})
conditioningDuration.append({"m": str(m + 1), "value": 1})
x = random.randint(1, 3)
if m > 2:
if 1 not in help_list: x = 1
if 2 not in help_list: x = 2
if 3 not in help_list: x = 3
help_list.append(x)
assignment.append({"m": str(m + 1), "r": str(x), "value": 1})
t_weights_del = random.choices(range(1, 11), weights=t_weights_probs[:10], k=3)
delivered = [{"r": str(r+1), "i": "1", "t": t_weights_del[r], "value": 1} for r in range(3)]
conditioningCapacity = [{"r": str(r+1), "value": 1} for r in range(3)]
CostMT, CostMB, CostRT, CostRB, Coord = [], [], [], [], []
for i in range(n_count):
s_id = str(i + 1)
CostMT.append({"i": s_id, "value": random.choice([2, 5])})
CostMB.append({"i": s_id, "value": random.choice([5, 10, 30])})
CostRT.append({"i": s_id, "value": random.choice([4, 10])})
CostRB.append({"i": s_id, "value": 1000 if i==0 else random.choice([20, 30, 100])})
if s_id in nodes_list_dict:
Coord.append({"i": s_id, "Coordinates": nodes_list_dict[s_id]})
return dismantled, assignment, delivered, conditioningCapacity, conditioningDuration, CostMT, CostMB, CostRT, CostRB, Coord
def generate_full_json_dict(G, loop=0):
edges, I, nodes_list_dict = prepare_edges_for_json(G)
dismantled, assignment, delivered, condCap, condDur, CostMT, CostMB, CostRT, CostRB, Coord = prepare_parameter_for_json(G, I, nodes_list_dict)
sets = {
"I": I, "E": {"bidirectional": True, "seed": 1, "edges": edges},
"M": ["1", "2", "3", "4", "5"], "R": ["1", "2", "3"]
}
params = {
"defaults": { "V": 1000, "CostMB": 100, "CostMT": 20, "CostRB": 300, "CostRT": 50 },
"t_max": 100, "V": [{"m": "1", "i": "1", "value": 42}],
"dismantled": dismantled, "delivered": delivered,
"conditioningCapacity": condCap, "conditioningDuration": condDur,
"assignment": assignment, "Coord": Coord,
"CostMT": CostMT, "CostMB": CostMB, "CostRT": CostRT, "CostRB": CostRB, "CostZR": 9, "CostZH": 5
}
return {"description": "Generated by Gradio", "sets": sets, "params": params}
def load_graph_from_data(data, name):
"""Core function to parse loaded JSON data into a NetworkX graph."""
G = nx.Graph()
id_to_coord = {}
if "params" in data and "Coord" in data["params"]:
for item in data["params"]["Coord"]:
coord = tuple(item["Coordinates"])
id_to_coord[item["i"]] = coord
G.add_node(coord)
if "sets" in data and "E" in data["sets"] and "edges" in data["sets"]["E"]:
for edge in data["sets"]["E"]["edges"]:
r1 = edge["room1"]
r2 = edge["room2"]
if r1 in id_to_coord and r2 in id_to_coord:
G.add_edge(id_to_coord[r1], id_to_coord[r2])
width = max([n[0] for n in G.nodes()]) if len(G.nodes()) > 0 else 10
height = max([n[1] for n in G.nodes()]) if len(G.nodes()) > 0 else 10
width = int(width + max(2, width * 0.1))
height = int(height + max(2, height * 0.1))
return {"name": name, "graph": G, "width": width, "height": height}
def load_graph_from_json(filepath):
with open(filepath, 'r') as f:
data = json.load(f)
return load_graph_from_data(data, os.path.basename(filepath)) |