"""噪声生成模块。""" import copy import random import numpy as np import pandas as pd from .leak_simulator import simple_add_leak, simple_recover_wn, simple_simulation_pf def add_noise_pd(data, noise_type, noise_para): output_data = copy.deepcopy(data) if type(output_data) == pd.core.frame.Series: if noise_type == "uni": for x in output_data.index: noise = (np.random.random() - 0.5) * 2 output_data[x] = output_data[x] + noise * noise_para elif noise_type == "gauss": noise = np.random.normal(loc=0, scale=noise_para, size=output_data.shape) output_data = output_data + noise elif type(output_data) == pd.core.frame.DataFrame: if noise_type == "uni": noise = (np.random.random(size=output_data.shape) - 0.5) * 2 output_data = output_data + noise * noise_para elif noise_type == "gauss": noise = np.random.normal(loc=0, scale=noise_para, size=output_data.shape) output_data = output_data + noise return output_data def add_noise_number(data, noise_type, noise_para): output_data = copy.deepcopy(data) if noise_type == "uni": noise = (np.random.random() - 0.5) * 2 output_data = output_data + noise * noise_para elif noise_type == "gauss": noise = random.gauss(0, noise_para) output_data = output_data + noise return output_data def add_noise_number_flow(data, noise_para_mean, noise_para_std1, noise_para_std2): output_data = copy.deepcopy(data) noise_flag1 = np.random.random() - 0.5 if noise_flag1 < 0: noise = noise_para_mean - abs(np.random.normal(loc=0, scale=noise_para_std1)) else: noise = noise_para_mean + abs(np.random.normal(loc=0, scale=noise_para_std2)) noise_flag2 = np.random.random() - 0.5 if noise_flag2 < 0: noise_f = noise * (-1) else: noise_f = noise output_data = output_data + noise_f return output_data def produce_noise_number(noise_type, noise_para): if noise_type == "uni": noise = (np.random.random() - 0.5) * 2 noise = noise * noise_para elif noise_type == "gauss": noise = random.gauss(0, noise_para) else: noise = 0 return noise def add_noise_percentage_pd(data, noise_type, noise_para): output_data = copy.deepcopy(data) if type(output_data) == pd.core.frame.Series: if noise_type == "uni": for x in output_data.index: noise = (np.random.random() - 0.5) * 2 output_data[x] = output_data[x] * (1 + noise * noise_para / 100) elif noise_type == "gauss": for x in output_data.index: noise = np.random.gauss(0, noise_para) output_data[x] = output_data[x] * (1 + noise / 100) # std_noise = noise.std() elif type(output_data) == pd.core.frame.DataFrame: if noise_type == "uni": noise = (np.random.random(size=output_data.shape) - 0.5) * 2 output_data = output_data * (1 + noise * noise_para / 100) elif noise_type == "gauss": noise = np.random.normal(loc=0, scale=noise_para, size=output_data.shape) output_data = output_data * (1 + noise / 100) # std_noise = noise.std().mean() return output_data def add_noise_in_wn_pf( wn, pipe_c_noise, timestep_list, pipe_coefficient, sensor_name, sensor_f_name, all_node, basic_demand_pd, noise_type, noise_para, leak_pipe, leak_flow, ): wn.options.time.duration = 0 pipe_roughness_change = add_noise_pd(pipe_coefficient, noise_type, pipe_c_noise) wn = change_para_of_wn(wn, pipe_roughness_change) record_pressure = pd.DataFrame(index=timestep_list, columns=sensor_name) record_flow = pd.DataFrame(index=timestep_list, columns=sensor_f_name) record_noise_all = pd.DataFrame( index=pd.MultiIndex.from_product([timestep_list, all_node]), columns=basic_demand_pd.columns, ) record_noise_all = record_noise_all.sort_index() # normal 获取添加噪声后的监测点数据 for i in range(len(timestep_list)): wn, record_noise = change_node_demand( wn, basic_demand_pd, all_node, noise_type, noise_para ) record_noise_all.loc[timestep_list[i]].loc[:, :] = record_noise pressure_temp, flow_temp = simple_simulation_pf( wn, sensor_name, sensor_f_name, [], [] ) record_pressure.iloc[i, :] = pressure_temp record_flow.iloc[i, :] = flow_temp # leak_simulation 获取添加漏损后的监测点数据 record_pressure_leak = pd.DataFrame(index=timestep_list, columns=sensor_name) record_flow_leak = pd.DataFrame(index=timestep_list, columns=sensor_f_name) # 改_wz_________________________________________ # add leak wn, whole_inf, add_pipe1 = simple_add_leak(wn, leak_flow, leak_pipe) # simulation for i in range(len(timestep_list)): record_noise = record_noise_all.loc[timestep_list[i]] wn = change_node_demand_leak(wn, record_noise, all_node) pressure_temp, flow_temp = simple_simulation_pf( wn, sensor_name, sensor_f_name, leak_pipe, add_pipe1 ) record_pressure_leak.iloc[i, :] = pressure_temp record_flow_leak.iloc[i, :] = flow_temp # delete leak wn = simple_recover_wn(wn, whole_inf) return wn, record_pressure, record_flow, record_pressure_leak, record_flow_leak def change_node_demand(wn, basic_demand_pd, all_node, noise_type, noise_para): # 改_wz_____________________________________ record_noise = pd.DataFrame(index=all_node, columns=basic_demand_pd.columns) for each_node in all_node: node = wn.get_node(each_node) num_columns = len(basic_demand_pd.columns) # 处理前N-1列（如果有） for i in range(num_columns - 1): # 获取原始值并添加噪声 record_noise.loc[each_node].iloc[i] = ( 1 + produce_noise_number(noise_type, noise_para) ) * basic_demand_pd.loc[each_node].iloc[i] node.demand_timeseries_list[i].base_value = record_noise.loc[ each_node ].iloc[i] # 处理最后一列（当列数>=1时） if num_columns >= 1: last_col = basic_demand_pd.columns[-1] original_last = basic_demand_pd.loc[each_node, last_col] record_noise.loc[each_node, last_col] = original_last node.demand_timeseries_list[-1].base_value = original_last return wn, record_noise def change_node_demand_leak(wn, record_noise, all_node): sample_node = wn.get_node(all_node[0]) # num_categories = len(sample_node.demand_timeseries_list) num_categories = 1 for each in all_node: node = wn.get_node(each) for i in range(num_categories): node.demand_timeseries_list[i].base_value = record_noise.loc[each].iloc[i] return wn def change_para_of_wn(wn, pipe_roughness_change): for pipe_name, pipe in wn.pipes(): pipe.roughness = pipe_roughness_change[pipe_name] return wn