swmmio 0.3.6

#write to a csvnodes.to_csv('/path/mynodes.csv')#calculate average and weighted average imperviousavg_imperviousness=subs.PercImperv.mean()weighted_avg_imp=(subs.Area*subs.PercImperv).sum()/len(subs)

生成图形

创建模型的图像（.png）可视化。默认情况下，如果模型包含输出数据（.inp应附带一个.rpt文件），则会显示管道应力和节点洪水持续时间。

fromswmmio.graphicsimportswmm_graphicsassgsg.draw_model(mymodel)

使用pandas计算一些有趣的统计数据，并生成一个突出显示的图像 对您的项目有兴趣或重要的内容：

#isolate nodes that have flooded for more than 30 minutesflooded_series=nodes.loc[nodes.HoursFlooded>0.5,'TotalFloodVol']flood_vol=sum(flooded_series)#total flood volume (million gallons)flooded_count=len(flooded_series)#count of flooded nodes#highlight these nodes in a graphicnodes['draw_color']='#787882'#grey, default node colornodes.loc[nodes.HoursFlooded>0.5,'draw_color']='#751167'#purple, flooded nodes#set the radius of flooded nodes as a function of HoursFloodednodes.loc[nodes.HoursFlooded>1,'draw_size']=nodes.loc[nodes.HoursFlooded>1,'HoursFlooded']*12#make the conduits grey, sized as function of their geometryconds['draw_color']='#787882'conds['draw_size']=conds.Geom1#add an informative annotation, and draw:annotation='Flooded Volume: {}MG\nFlooded Nodes:{}'.format(round(flood_vol),flooded_count)sg.draw_model(mymodel,annotation=annotation,file_path='flooded_anno_example.png')

建立模型的变体

从一个基本的swmm模型开始，可以通过将修改后的数据插入一个新的inp文件来创建其他模型。对于灵敏度分析或改变边界条件非常有用，可以使用一个相当简单的循环来创建模型，利用modify\u model包。

例如，可以通过创建一组具有不同排水口固定水位高程的模型来研究气候变化的影响：

importos,shutilimportswmmiofromswmmio.utils.modify_modelimportreplace_inp_sectionfromswmmioimportcreate_dataframeINP#initialize a baseline model objectbaseline=swmmio.Model(r'path\to\baseline.inp')rise=0.0#set the starting sea level rise condition#create models up to 5ft of sea level rise.whilerise<=5:#create a dataframe of the model's outfallsoutfalls=create_dataframeINP(baseline.inp.path,'[OUTFALLS]')#create the Pandas logic to access the StageOrTimeseries column of  FIXED outfallsslice_condition=outfalls.OutfallType=='FIXED','StageOrTimeseries'#add the current rise to the outfalls' stage elevationoutfalls.loc[slice_condition]=pd.to_numeric(outfalls.loc[slice_condition])+rise#copy the base model into a new directory    newdir=os.path.join(baseline.inp.dir,str(rise))os.mkdir(newdir)newfilepath=os.path.join(newdir,baseline.inp.name+"_"+str(rise)+'_SLR.inp')shutil.copyfile(baseline.inp.path,newfilepath)#Overwrite the OUTFALLS section of the new model with the adjusted datareplace_inp_section(newfilepath,'[OUTFALLS]',outfalls)#increase sea level rise for the next looprise+=0.25

接入模型网络

swmmio.model类通过network返回模型的networkx多向图表示

#access the model as a Networkx MutliDiGraphG=model.network#iterate through linksforu,v,key,datainmodel.network.edges(data=True,keys=True):print(key,data['Geom1'])# do stuff with the network

运行模式

使用命令行工具，可以通过调用shell中的swmmio模块来运行单独的swmm5模型：

$ python -m swmmio --run path/to/mymodel.inp

如果有许多模型要运行，并且希望利用计算机的核心，可以使用--start_pool（或-sp）命令启动一个模拟池。将-sp指向一个或多个目录后，swmmio将搜索swmm.inp文件并将所有文件添加到多处理池中。默认情况下，-sp会让您的机器的4个核心未使用。这可以通过-cores_left参数更改。

$ #run all models in models in directories Model_Dir1 Model_Dir2
$ python -m swmmio -sp Model_Dir1 Model_Dir2  

$ #leave 1 core unused
$ python -m swmmio -sp Model_Dir1 Model_Dir2  -cores_left=1