用python解析SWMM的.OUT输出文件

一、.OUT文件概述

SWMM中.OUT输出文件为二进制文件，用记事本打开会呈现如下图所示的乱码状态。但是.OUT文件中有很多重要结果信息（例如：节点全模拟时段水深、侧向来水、出口总流量等数据），用官方提供的pyswmm库对.OUT文件进行解析。将二进制数据输出为Excel。

文件主要包括三种类型数据，节点类型数据、管道类型数据、汇水区类型数据。节点类型包括检查井、排口、调蓄池、分流器；管道类型包括管道、泵、孔口、堰、出口；汇水区包括汇水分区。节点输出数据包括：水深、水头、拦截容积、侧入流量、节点总入流量、节点溢流量及污染物，7个输出数据；管道输出数据包括：流量、水深、流速、Froude数、过流能力及污染物，6个输出数据；子汇水区输出数据包括：降雨量、积雪深度、蒸发量、入渗量、地表径流量、地下水径流量、地下水位、土壤含水量及污染物浓度，9个输出数据。

二、.OUT文件解析

01.OUT头文件解析，头文件包含以下几个数据：

版本号、流量单位、污染物单位、汇水分区数量、节点数量、管段数量、污染物数量、报告开始时间及报告结束时间。代码如下：

 # 文件初始化内容def push_s(self):file = str(self.lineEdit.text())start = time.time()output_Subcatch_data(file)end = time.time()all_time = "Subcatch数据导出时长为：" + str("%.3f" % (end - start)) + 's'with Output(file) as out:version = '版本号为:' + str(out.version)flow_units = "流量单位:" + out.units['flow']poll_units = "污染物单位:" + out.units['pollutant']sub_num = "汇水分区数量:" + str(len(out.subcatchments))nodes_num = "节点数量:" + str(len(out.nodes))links_num = "管段数量:" + str(len(out.links))poll_num = "污染物数量:" + str(len(out.pollutants))rep_start = "报告开始时间:" + str(out.start)rep_end = "报告结束时间:" + str(out.end)head_file_list = [version, flow_units, poll_units, sub_num, nodes_num, links_num, poll_num, rep_start,rep_end]

02管段输出数据解析

将管段模拟数据（流量、水深、流速等）输出到Excel中，方便后期处理。

输出结果：

代码如下：

def output_link_data(file):'''路径不能有中文字符，否则会报错。头文件内容读取可以做为一个装饰器F:\test_file_SWMM\Example1.out''''''所有管段模拟时间内数据输出'''with Output(file) as out:# data_dict = {}year_s = int(str(out.start)[0:4])month_s = int(str(out.start)[5:7])day_s = int(str(out.start)[8:10])hour_s = int(str(out.start)[11:13])if hour_s == 0:hour_s = hour_s + 1year_e = int(str(out.end)[0:4])month_e = int(str(out.end)[5:7])day_e = int(str(out.end)[8:10])hour_e = int(str(out.end)[11:13])data_list = []link_id = []for i in out.links:'''--------------------全模拟时段下单一管段流量数据--------------------'''ts = out.link_series(i, LinkAttribute.FLOW_RATE, datetime(year_s, month_s, day_s, hour_s), datetime(year_e, month_e, day_e, hour_e))time_index = []flow_index = []for index in ts:time_index.append(index)flow_index.append(ts[index])# dict_FLOW_RATE[index] = ts[index]# print(index, ts[index])dict_FLOW_RATE = {"time":time_index,'流量(flow_rate)':flow_index}data_FLOW_RATE = pd.DataFrame(dict_FLOW_RATE)# print(data_FLOW_RATE)'''--------------------全模拟时段下单一管段水深数据--------------------'''ts1 = out.link_series(i, LinkAttribute.FLOW_DEPTH, datetime(year_s, month_s, day_s, hour_s), datetime(year_e, month_e, day_e, hour_e))time_index1 = []flow_index1 = []for index in ts1:time_index1.append(index)flow_index1.append(ts1[index])# dict_FLOW_DEPTH[index] = ts1[index]dict_FLOW_DEPTH = {"time":time_index1,"水深(flow_depth)":flow_index1}data_FLOW_DEPTH = pd.DataFrame(dict_FLOW_DEPTH)# print(data_FLOW_DEPTH.index)# u = input()'''--------------------全模拟时段下单一管段流速数据--------------------'''ts2 = out.link_series(i, LinkAttribute.FLOW_VELOCITY, datetime(year_s, month_s, day_s, hour_s), datetime(year_e, month_e, day_e, hour_e))time_index2 = []flow_index2 = []for index in ts2:time_index2.append(index)flow_index2.append(ts2[index])# dict_FLOW_DEPTH[index] = ts1[index]dict_FLOW_VELOCITY = {"time": time_index2, "流速(flow_velocity)": flow_index2}data_FLOW_VELOCITY = pd.DataFrame(dict_FLOW_VELOCITY)# print(data_FLOW_DEPTH.index)'''--------------------全模拟时段下单一管段体积数据--------------------'''ts3 = out.link_series(i, LinkAttribute.FLOW_VOLUME, datetime(year_s, month_s, day_s, hour_s), datetime(year_e, month_e, day_e, hour_e))time_index3 = []flow_index3 = []for index in ts3:time_index3.append(index)flow_index3.append(ts3[index])dict_FLOW_VOLUME = {"time": time_index3, "体积(flow_volume)": flow_index3}data_FLOW_VOLUME = pd.DataFrame(dict_FLOW_VOLUME)'''--------------------全模拟时段下单一管段过流能力数据--------------------'''ts4 = out.link_series('16', LinkAttribute.CAPACITY, datetime(year_s, month_s, day_s, hour_s), datetime(year_e, month_e, day_e, hour_e))time_index4 = []flow_index4 = []for index in ts4:time_index4.append(index)flow_index4.append(ts4[index])dict_CAPACITY = {"time": time_index4, "过流能力(capacity)": flow_index4}data_CAPACITY = pd.DataFrame(dict_CAPACITY)'''--------------------全模拟时段下单一管段污染物数据--------------------'''ts5 = out.link_series(i, LinkAttribute.POLLUT_CONC_0, datetime(year_s, month_s, day_s, hour_s), datetime(year_e, month_e, day_e, hour_e))time_index5 = []flow_index5 = []for index in ts5:time_index5.append(index)flow_index5.append(ts5[index])dict_POLLUT_CONC_0 = {"time": time_index5, "水质(pollut_conc)": flow_index5}data_POLLUT_CONC_0 = pd.DataFrame(dict_POLLUT_CONC_0)'''--------------------数据合并--------------------'''data_all1 = pd.merge(data_FLOW_RATE,data_FLOW_DEPTH,on='time',how='outer')data_all2 = pd.merge(data_all1,data_FLOW_VELOCITY,on='time',how='outer')data_all3 = pd.merge(data_all2,data_FLOW_VOLUME,on='time',how='outer')data_all4 = pd.merge(data_all3, data_CAPACITY, on='time', how='outer')data_all = pd.merge(data_all4, data_POLLUT_CONC_0,on='time', how='outer')link_id.append(i)data_list.append(data_all)data_list1 = pd.concat(data_list,axis=1,keys=link_id)data_list1.to_excel(file[:-4] + '_links.xlsx')

节点及汇水分区输出数据解析类似上述操作，不在赘述。

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