Package: matsimtools-ifpen
matsim-toolsOfficial tools for working with MATSim output filesMATSim is an open-source, multi-agent transportation simulation framework. Find out more about MATSim athttps://matsim.orgAbout this libraryWe are at the very early stages of building this library. The API will change, things will break, and there are certainly bugs. You probably shouldn't use this for anything.Our primary goal is to make MATSim play nice withpandasandgeopandas, for data analysis workflows.We have only tested this using Anaconda Python. Only Python 3.x is supported.Currently MATSim network, event, and plans files are supported. Hopefully more will be coming soon.For Geopandas network support, you also need to installgeopandasandshapely.Supports JSON and Protobuf event file formatsCan write MATSim files too!QuickstartInstall usingpip install matsim-toolsIn lieu of real documentation, here is some sample code to get you started. Good luck!Read a networkimportmatsimimportpandasaspdfromcollectionsimportdefaultdict%matplotlibinline# -------------------------------------------------------------------# 1. NETWORK: Read a MATSim network:net=matsim.read_network('output_network.xml.gz')net.nodes# Dataframe output:#           x        y node_id# 0  -20000.0      0.0       1# 1  -15000.0      0.0       2# 2    -865.0   5925.0       3# ...net.links# Dataframe output:#      length  capacity  freespeed  ...  link_id from_node to_node# 0   10000.0   36000.0      27.78  ...        1         1       2# 1   10000.0    3600.0      27.78  ...        2         2       3# 2   10000.0    3600.0      27.78  ...        3         2       4# ...geo=net.as_geo()# combines links+nodes into a Geopandas dataframe with LINESTRINGsgeo.plot()# try this in a notebook to see your network!Stream through events# -------------------------------------------------------------------# 2. EVENTS: Stream through a MATSim event file.# The event_reader returns a python generator function, which you can then# loop over without loading the entire events file in memory.# In this example let's sum up all 'entered link' events to get link volumes.## NEW! Now supports .xml.gz and protobuf .pb.gz event file formats!events=matsim.event_reader('output_events.xml.gz',types='entered link,left link')link_counts=defaultdict(int)# defaultdict creates a blank dict entry on first referenceforeventinevents:ifevent['type']=='entered link':link_counts[event['link']]+=1# convert our link_counts dict to a pandas dataframe,# with 'link_id' column as the index and 'count' column with value:link_counts=pd.DataFrame.from_dict(link_counts,orient='index',columns=['count']).rename_axis('link_id')# attach counts to our Geopandas network from abovevolumes=geo.merge(link_counts,on='link_id')volumes.plot(column='count',figsize=(10,10),cmap='Wistia')#cmap is colormapRead plans# -------------------------------------------------------------------# 3. PLANS: Stream through a MATSim plans file.plans=matsim.plan_reader('output_plans.xml.gz',selectedPlansOnly=True)# Each plan is returned as a tuple with its owning person (for now, is this ok?)# - The name of the element is in its .tag (e.g. 'plan', 'leg', 'route', 'attributes')# - An element's attributes are accessed using .attrib['attrib-name']# - Use the element's .text field to get data outside of attributes (e.g. a route's list of links)# - Every element can be iterated on to get its children (e.g. the plan's activities and legs)# - Emits person even if that person has no plansforperson,planinplans:# do stuff with this plan, e.g.work_activities=filter(lambdae:e.tag=='activity'ande.attrib['type']=='w',plan)print('person',person.attrib['id'],'selected plan w/',len(list(work_activities)),'work-act')activities.append(num_activities)# person 1 selected plan w/ 2 work-act# person 10 selected plan w/ 1 work-act# person 100 selected plan w/ 1 work-act# ...Write MATSim input XML files# -------------------------------------------------------------------# 4. WRITERS: Write MATSim input files.# MATSim input files may be iteratively constructed as in the example below.# For a more detailed example, see tests/test_MatsimPlansWriter.pywithopen("plans.xml",'wb+')asf_write:writer=matsim.writers.PopulationWriter(f_write)writer.start_population()writer.start_person("person_id_123")writer.start_plan(selected=True)writer.add_activity(type='home',x=0.0,y=0.0,end_time=8*3600)writer.add_leg(mode='walk')writer.add_activity(type='work',x=10.0,y=0.0,end_time=18*3600)writer.add_leg(mode='pt')writer.add_activity(type='home',x=0.0,y=0.0)writer.end_plan()writer.end_person()writer.end_population()CalibrationScenarios created with thematsim-applicationcontrib provide an interface that can be used with the automatic calibration module:# -------------------------------------------------------------------# 5. CALIBRATION: Automatic calibration for MATSim scenario.modes=["walk","car","pt","bike"]fixed_mode="walk"target={"walk":0.277,"bike":0.175,"pt":0.19,"car":0.359}study,obj=calibration.create_mode_share_study("calib","./matsim-scenario-1.0-SNAPSHOT.jar","./scenarios/input/scenario-v1.0-10pct.config.xml",modes,fixed_mode,target)study.optimize(obj,10)