sumolib is a set of python modules for working with sumo networks, simulation output and other simulation artifacts. For a detailed list of available functions see the pydoc generated documentation. You can browse the code here.
importing sumolib in a script#
To use the library, the <SUMO_HOME>/tools directory must be on the python load path. This is typically done with a stanza like this:
import os, sys if 'SUMO_HOME' in os.environ: tools = os.path.join(os.environ['SUMO_HOME'], 'tools') sys.path.append(tools) else: sys.exit("please declare environment variable 'SUMO_HOME'")
loading a network file#
# import the library import sumolib # parse the net net = sumolib.net.readNet('myNet.net.xml')
The following named arguments may be given to the readNet
function (i.e. readNet('myNet.net.xml', withInternal=True)
):
- withPrograms (bool): import all traffic light programs (default False)
- withLatestPrograms (bool) : import only the last program for each traffic light. This is the program that would be active in sumo by default. (default False)
- withConnections (bool) : import all connections (default True)
- withFoes (bool) : import right-of-way information (default True)
- withInternal (bool) : import internal edges and lanes (default False)
- withPedestrianConnections (bool) : import connections between sidewalks, crossings (default False)
usage examples#
import a network and retrieve nodes and edges#
# import the library import sumolib # parse the net net = sumolib.net.readNet('myNet.net.xml') # retrieve the coordinate of a node based on its ID print net.getNode('myNodeID').getCoord() # retrieve the successor node ID of an edge nextNodeID = net.getEdge('myEdgeID').getToNode().getID()
compute the average edge speed in a plain xml edge file#
speedSum = 0.0 edgeCount = 0 for edge in sumolib.xml.parse('myNet.edg.xml', ['edge']): speedSum += float(edge.speed) edgeCount += 1 avgSpeed = speedSum / edgeCount
Note
This is just a processing example. To compute average travel speeds in a network, process edgeData, tripinfos or summary-output instead.
compute the median speed using the Statistics module#
edgeStats = sumolib.miscutils.Statistics("edge speeds") for edge in sumolib.xml.parse('myNet.edg.xml', ['edge']): edgeStats.add(float(edge.speed)) avgSpeed = edgeStats.median()
Note
Attribute speed is optional in user-generated .edg.xml files but will always be included if that file was written by netconvert or netedit.
locate nearby edges based on the geo-coordinate#
This requires the module pyproj to be installed. For larger networks rtree is also strongly recommended.
net = sumolib.net.readNet('myNet.net.xml') radius = 0.1 x, y = net.convertLonLat2XY(lon, lat) edges = net.getNeighboringEdges(x, y, radius) # pick the closest edge if len(edges) > 0: distancesAndEdges = sorted([(dist, edge) for edge, dist in edges]) dist, closestEdge = distancesAndEdges[0]
parse all edges in a route file#
for route in sumolib.xml.parse_fast("myRoutes.rou.xml", 'route', ['edges']): edge_ids = route.edges.split() # do something with the vector of edge ids
parse vehicles and their route edges in a route file#
for vehicle in sumolib.xml.parse("myRoutes.rou.xml", "vehicle"): route = vehicle.route[0] # access the first (and only) child element with name 'route' edges = route.edges.split()
with automatic data conversions (including depart time as "HH:MM:SS"):
from sumolib.miscutils import parseTime for vehicle in sumolib.xml.parse("myRoutes.rou.xml", "vehicle", attr_conversions={ 'depart' : parseTime, 'edges' : (lambda x : x.split())}): edges = vehicle.route[0].edges if vehicle.depart > 42: ...
parse all edges in a edge data (meanData) file#
for interval in sumolib.xml.parse("edgedata.xml", "interval"): for edge in interval.edge: # do something with the edge attributes i.e. edge.entered
coordinate transformations#
net = sumolib.net.readNet('myNet.net.xml') # network coordinates (lower left network corner is at x=0, y=0) x, y = net.convertLonLat2XY(lon, lat) lon, lat = net.convertXY2LonLat(x, y) # raw UTM coordinates x, y = net.convertLonLat2XY(lon, lat, True) lon, lat = net.convertXY2LonLat(x, y, True) # lane/offset coordinates # from lane position to network coordinates x,y = sumolib.geomhelper.positionAtShapeOffset(net.getLane(laneID).getShape(), lanePos) # from network coordinates to lane position lane, d = net.getNeighboringLanes(x, y, radius)[0] (see "locate nearby edges based on the geo-coordinate" above) lanePos, dist = sumolib.geomhelper.polygonOffsetAndDistanceToPoint((x,y), lane.getShape())
see also TraCI/Interfacing_TraCI_from_Python#coordinate_transformations
Manipulating and writing xml#
with open("patched.nod.xml", 'w') as outf: # setting attrs is optional, it results in a cleaner patch file attrs = {'node': ['id', 'x', 'y']} # other attrs are not needed for patching # parse always returns a generator but there is only one root element nodes = list(sumolib.xml.parse('plain.nod.xml', 'nodes', attrs))[0] for node in nodes.node: node.addChild("param", { "key": "origPos", "value" : "%s %s" % (node.x, node.y) } ) node.x = float(node.x) + random.randint(-20, 20) node.y = float(node.y) + random.randint(-20, 20) outf.write(nodes.toXML())
Further Examples#
The runner.py files in the test subfolders of <SUMO_HOME>/tests/tools/sumolib provide additional examples for sumolib use.