多线程运行情况下的字典赋值——水资源优化配置

kerln888

陈勃

尹玄睿

123

hveagle

这是Python吗？

gametsbug

学习

一位小白

重新输入一下代码

1. # -*- coding: utf-8 -*-
   
2. import numpy as np
   
3. import os
   
4. import pandas as pd
   
5. from deap import base, creator, tools, algorithms
   
6. import subprocess
   
7. import random
   
8. import openpyxl
   
9. from netCDF4 import Dataset
   
10. import multiprocessing
    
11. import configparser
    
12. import shutil
    
13. 
    
14. 
    
15. global result_list
    
16. result_list = {}
    
17. excel_template = r'E:\cwat_new\diversion_optimization\diversion_template.xlsx'
    
18. ini_file = r'E:\cwat_new\diversion_optimization\optim.ini'
    
19. total_water = 15128
    
20. used_ids = set()
    
21. 
    
22. 
    
23. def generate_unique_id():
    
24.     unique_id = random.randint(1, 100000000)
    
25.     while unique_id in used_ids:
    
26.         unique_id = random.randint(1, 100000000)
    
27.     used_ids.add(unique_id)
    
28.     return unique_id
    
29. 
    
30. def read_initial_values(excel_file_path, column_name):
    
31.     workbook = openpyxl.load_workbook(excel_file_path)
    
32.     sheet = workbook.active
    
33.     # 查找指定列的索引
    
34.     column_index = None
    
35.     for cell in sheet[1]:
    
36.         if cell.value == column_name:
    
37.             column_index = cell.column_letter
    
38.             break
    
39.     if column_index is None:
    
40.         print(f"找不到名为 '{column_name}' 的列标题")
    
41.         return []
    
42.     # 获取指定列下的所有值
    
43.     column_values = []
    
44.     for cell in sheet[column_index][1:]:
    
45.         column_values.append(cell.value)
    
46.     return column_values
    
47. 
    
48. def evaluate_model_output(nc_file):
    
49.     with Dataset(nc_file, 'r') as nc:
    
50.         times = nc.variables['time'][:]
    
51.         data_var = nc.variables['unmetDemandM3_annualavg']  
    
52.         # 遍历每个时间段，计算总和
    
53.         for i, _ in enumerate(times):
    
54.             data_at_time = data_var[i, :, :]
    
55.             data_at_time = data_at_time * 365
    
56.             # 计算当前时间段的总和
    
57.             total = np.sum(data_at_time)
    
58.             #print(f"Time {i} total: {total}")
    
59.     total = tuple([total])
    
60.     return total
    
61. 
    
62. # 更新Excel文件中的月份分配数据
    
63. def update_excel_data(filename, allocations):
    
64.     df = pd.DataFrame(allocations, columns=['Allocation'])
    
65.     df['Month'] = range(1, 13)
    
66.     with pd.ExcelWriter(filename, engine='openpyxl', mode='w') as writer:
    
67.         df.to_excel(writer, index=False)
    
68. 
    
69. def mutate_individual(individual, mutation_probability):
    
70.     for i in range(len(individual)):
    
71.         if random.random() < mutation_probability:
    
72.             individual[i] += random.gauss(0, 100)  # 变异操作
    
73. 
    
74.             # 修正子代，确保在非负范围内
    
75.             individual[i] = max(individual[i], 0)
    
76. 
    
77.     return [individual]
    
78. 
    
79. def cxBlendBounded(ind1, ind2, alpha=0.5, low=0):
    
80.     """执行cxBlend交叉操作，并确保子代的值位于[low, up]范围内。"""
    
81.     for i in range(len(ind1)):
    
82.         gamma = (1. + 2. * alpha) * random.random() - alpha
    
83.         ind1[i] = (1. - gamma) * ind1[i] + gamma * ind2[i]
    
84.         ind2[i] = gamma * ind1[i] + (1. - gamma) * ind2[i]
    
85.         # 确保值不小于下限
    
86.         ind1[i] = max(low, ind1[i])
    
87.         ind2[i] = max(low, ind2[i])
    
88.     return ind1, ind2
    
89. 
    
90. # 运行水文模型的命令行函数
    
91. def run_hydrological_model(unique_id, monthly_allocation):
    
92.     folder_name = f'E:\\cwat_new\\diversion_optimization\\{unique_id}'
    
93.     os.makedirs(folder_name, exist_ok=True)
    
94.     output_folder = os.path.join(folder_name, 'output')
    
95.     os.makedirs(output_folder, exist_ok=True)   
    
96.     excel_file = os.path.join(folder_name, f'diversion_{unique_id}.xlsx')   
    
97.     # 使用模板文件创建新的Excel
    
98.     shutil.copy(excel_template, excel_file)   
    
99.     #创建新的ini文件
    
100.     config = configparser.ConfigParser()
     
101.     config.optionxform = lambda option: option
     
102.     config.read(ini_file)
     
103.     # 修改参数值
     
104.     config.set('WATERDIVERSION', 'monthly_water_quota', excel_file)
     
105.     config.set('FILE_PATHS', 'PathOut', output_folder)
     
106.     # 保存修改后的ini文件
     
107.     ini_fileC = os.path.join(folder_name, f'optim_{unique_id}.ini')
     
108.     with open(ini_fileC, 'w') as configfile:
     
109.         config.write(configfile)
     
110.     # 更新Excel文件以供模型使用
     
111.     update_excel_data(excel_file, monthly_allocation)
     
112.    
     
113.     # 运行水文模型，假设模型的可执行文件名为 "hydro_model"，并且它使用excel文件
     
114.     subprocess.run(['python', r'E:\cwat_new\CWatM-main\run_cwatm.py', ini_fileC], check=True)
     
115.     nc_file = os.path.join(output_folder, 'unmetDemandM3_annualavg.nc')
     
116.     result_list[int(evaluate_model_output(nc_file)[0])] = unique_id
     
117.     # 从生成的nc文件中读取评价指标
     
118.     return evaluate_model_output(nc_file), unique_id
     
119. 
     
120. 
     
121. # 评价函数，目标是最小化评价指标
     
122. def evaluate(individual):
     
123.    
     
124.     unique_id = generate_unique_id()
     
125.     # 确保分配水量不超过限制
     
126.     if not isinstance(individual, list):
     
127.         individual = list(individual)
     
128.     # 确保 individual 中的元素都是整数
     
129.     individual = [int(x) for x in individual]
     
130.    
     
131.     # 计算分配方案的总和
     
132.     allocation_sum = sum(individual)
     
133.     penalty = abs(allocation_sum - total_water) * 1000000
     
134.    
     
135.     if allocation_sum > total_water:
     
136.         return 1e10,  # 返回一个很大的值，表示不可行的解
     
137. 
     
138.     result, unique_id = run_hydrological_model(unique_id, individual)
     
139. 
     
140.     return result[0] + penalty,
     
141. 
     
142. # 设置遗传算法
     
143. creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
     
144. creator.create("Individual", list, fitness=creator.FitnessMin)
     
145. 
     
146. toolbox = base.Toolbox()
     
147. toolbox.register("attr_float", random.uniform, 0, total_water/12)  # 假设平均每月分配
     
148. toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=12)
     
149. toolbox.register("population", tools.initRepeat, list, toolbox.individual)
     
150. 
     
151. toolbox.register("evaluate", evaluate)
     
152. toolbox.register("mate", tools.cxBlend, alpha=0.5)
     
153. toolbox.register("mutate", tools.mutGaussian, mu=0, sigma=100, indpb=0.2)
     
154. toolbox.register("select", tools.selTournament, tournsize=3)
     
155. 
     
156. # 遗传算法参数
     
157. population_size = 1
     
158. crossover_probability = 0.7
     
159. mutation_probability = 0.2
     
160. number_of_generations = 0
     
161. toolbox.register("mutate", mutate_individual, mutation_probability=mutation_probability)
     
162. toolbox.register("mate", cxBlendBounded, alpha=0.5, low=0)
     
163. 
     
164. if __name__ == "__main__":
     
165.     manager = multiprocessing.Manager()
     
166.     result_list = manager.dict()
     
167.     pool_size = int(multiprocessing.cpu_count() * 0.8)
     
168.     pool = multiprocessing.Pool(processes=pool_size)
     
169.     toolbox.register("map", pool.map)
     
170.     population = toolbox.population(n=population_size)
     
171.     #设置初始值
     
172.     initial_values = read_initial_values(excel_template, 'Allocation')   
     
173.     population[0][:] = initial_values
     
174.    
     
175.     final_population, logbook = algorithms.eaSimple(population, toolbox, cxpb=crossover_probability, mutpb=mutation_probability, ngen=number_of_generations, verbose=True)
     
176.     # 找到最优解
     
177.     best_ind = tools.selBest(population, 1)[0]
     
178.     best_fitness = best_ind.fitness.values[0]
     
179.     best_unique_id = result_list[int(best_fitness)]
     
180.     print("Best Individual is: ", best_ind)
     
181.     print("Best Individual fitness:", best_fitness)
     
182.     print("Best Individual ID is:", best_unique_id)

复制代码

tanmengyu0811

Reganrts

6

很cool的阳

很cool的阳

很cool的阳 发表于 2024-8-17 16:58

某一个“天”

，

很cool的阳

森林格格屋

谢谢

森林格格屋

谢谢

乱世老高

一位小白 发表于 2024-4-25 21:07
重新输入一下代码

感谢