I create a network add nodes and edges. I view it (it creates a dot and pdf file automatically). Later, I want to create a second network with the same nodes but different edges. I want to place the nodes in the same coordinates, so that I can make a comparison of both graphs easily. I tried to get the coordinates of the first graph, and tried to set the coordinates of the nodes) but I couldn't find proper functions to do that. I also checked networkx package. I also tried to get a copy of the first network, and delete the edges with no success. Can someone please show me how to create a second network with the same node coordinates?
This is the simple network creation code
import graphviz as G
network1 = G.Digraph(
graph_attr={...},
node_attr={...},
edge_attr={...} )
network.node("xxx")
network.node("yyy")
network.node("zzz")
network.edge("xxx", "yyy")
network.edge("yyy", "zzz")
network1.view(file_name)
First, calculate the node positions for the first graph using the layout of your choice (say, the spring layout):
node_positions = nx.layout.spring_layout(G1)
Now, you can draw this graph and any other graph with the same nodes in the same positions:
nx.draw(G1, with_labels=True, pos=node_positions)
nx.draw(G2, with_labels=True, pos=node_positions)
Graphviz's layers feature might also be interesting:
https://graphviz.org/faq/#FaqOverlays
Here is a working example of using layers - ignore the last two lines that create a video.
https://forum.graphviz.org/t/stupid-dot-tricks-2-making-a-video/109
And here is some more background:
https://forum.graphviz.org/t/getting-layers-to-work-with-svg/107
Related
I am trying to draw a random network with a real sequence of degrees, using configuration model.
As I understood, the function returns a multigraph with self-loops and multiple edges.
How can I remove all self-loops and change all multiple edges for one simple edge?
I would appreciate any help!
I'm plotting networkx weighted graphs using the draw_networkx_edge_labels function. My problem is that, since edges sometimes cross each other, it is not always clear from the plot which weight belongs to which edge. For instance, in the following plot it is not immediately clear whether 2 is the weight of (1,2) or (3,7).
I'm currently using the neato layout, which does not take edge labels into account. In particular, this is how I'm drawing a weighted graph g:
layout = nx.nx_pydot.graphviz_layout(g, prog='neato')
nx.draw(g, pos=layout)
edge_labels = nx.get_edge_attributes(g, 'weight')
nx.draw_networkx_edge_labels(g, pos=layout, edge_labels=edge_labels)
I know I can manually control the position of the label along an edge using the label_pos parameter, but my question is whether there exists a way to automatically plot the graph such that edge labels do not usually collide (either using a layout that takes labels into account or a method that "neatly" selects label positions along edges).
I'm not expecting something that always works, but since my graphs are relatively sparsely connected, I hope there's a method that at least has a tendency to work well.
I have been meaning to implement this in netgraph, my fork of the networkx drawing utilities, for a while now. Unfortunately, I have a job interview on Thursday, so I won't have time to write this anytime soon. The basic idea, however, is pretty simple, and is also already implemented in some R packages such as ggrepel and also ggnetwork.
The basic idea is that you use a force directed layout to position your labels, given a predetermined and fixed layout for your nodes and edges. So:
Compute a node layout using the layout of your choice.
Partition each edge into a chain of many, many nodes, and compute the positions of the "edge nodes" using the already known positions of the source and target nodes of the edge. This partitioning is to give each edge a "mass" in the following force directed layout.
For each edge, add a "label" node and connect it to the most central "edge node".
Compute a force-directed layout keeping all nodes but the label nodes fixed (e.g. using spring_layout in networkx).
You should now have sensible edge label coordinates that do not overlap any of the edges. Use plt.annotate to plot a connection between the edge and the edge label.
I am using SUMO to simulate the LuST scenario from https://github.com/lcodeca/LuSTScenario. However, since the scenario is rather large, I would like to start with a simulation constrained to region of interest. Is there a straight forward way to select such a region and have vehicles only simulated in that part of the map?
You can crop the network either using netedit by selecting the region of interest (change to select mode and then draw a rectangle holding the shift key), then inverting the selection (invert button) and deleting the rest. Or if you already know the boundaries or the edges you want to keep you can use for instance with netconvert --keep-edges.in-boundary minX,minY,maxX,maxY -s large.net.xml -o small.net.xml. See here for more netconvert options.
The next step is cutting the routes, which usually means a call like this:
$SUMO_HOME/tools/route/cutRoutes.py small.net.xml large.rou.xml --routes-output small.rou.xml --orig-net large.net.xml
This will not only remove the edges but also try to adapt departure times.
I've read the document but still confused of them, could any guy can give me a clearly explaining, e.g.any image comparison? Thanks.
The Wikipedia article on Pathfinding might help, as might the related topics on graphs and graph search algorithms linked from there. Beyond that, here's an attempt at a quick explainer.
Nodes are places that someone can be, and their connections to other nodes define someone can travel between places. Together, a collection of (connected) nodes form a graph.
GKGraphNode is the most general form of node — these nodes don't know anything about where they are in space, just about their connections to other nodes. (That's enough for basic pathfinding, though... if you have a graph where A is connected to B and B is connected to C, the path from A to C goes through B regardless of where those nodes are located, like below.)
GKGraph is a collection of nodes, and provides functions that work the graph as a whole, like the important one for finding paths.
GKGridGraphNode and GKGraphNode2D are specialized versions of GKGraphNode that add knowledge of the node's position in space — either integer grid space (like a chessboard) or open 2D space. Once you've added that kind of information, a GKGraph containing these kinds of nodes can take distance into account when pathfinding.
For example, look at this image:
If we're just using GKGraphNode, all we're talking about is which nodes are connected to which. So if we ask for the shortest path from A to D, we can get either ACD or ABD, because it's an qual number of connections either way. But if we use GKGridGraphNode or GKGraphNode2D, we're looking at the lengths of the lines between nodes, in which case ACD is the shortest path.
Once you start locating your nodes in (some sort of coordinate) space, it helps to be able to operate on the graph as a whole in that space. That's where GKGridGraph and GKObstacleGraph come in.
GKGridGraph works with GKGridGraphNodes and lets you do things like create a graph to fill a set of dimensions (say, a 10x10 grid, with diagonal movement allowed) instead of making you create and connect a bunch of nodes yourself.
GKObstacleGraph adds more to free-2D-space graphs by letting you mark areas as impassable obstacles and automatically managing the nodes and connections to route around obstacles.
Hopefully this helps a bit. For more, besides the reference docs and guide, Apple also has a WWDC video that shows how this stuff works.
I would like to add additional forces to networkx spring_layout.
I have a directected graph and I would like nodes to move to different sides according to the edges that they have. Nodes that have more outgoing edges should drift to nodes that have more ingoing edges should drift right. Another alternative would be. That these groups of nodes would drift towards each other, nodes with outgoing edges would get closer while nodes with ingoing edges would also get closer to each other.
I managed to look into to the source code of spring_layout of networkx http://networkx.lanl.gov/archive/networkx-0.37/networkx.drawing.layout-pysrc.html#spring_layout
but everything there is just beyond my comprehension
G.DiGraph()
G.add_edges_from([(1,5),(2,5),(3,5),(5,6),(5,7)])
The layout should show edges 1,2,3 closer to each other, the same regarding 6 and 7.
I imagine, that I could solve this by adding invisible edges via using MultiDiGraph. I could count ingoing and outgoing edges of each node and add invisible edges that connect the two groups. However, I am very sure that there are better ways of solving the problem.
Adding weights into the mix would be a good way to group things (with those invisible nodes). But the layouts have no way of knowing left from right. To get the exact layout you want you could specify each point's x,y coordinates.
import networkx as nx
G=nx.Graph()
G.add_node(1,pos=(1,1))
G.add_node(2,pos=(2,3))
G.add_node(3,pos=(3,4))
G.add_node(4,pos=(4,5))
G.add_node(5,pos=(5,6))
G.add_node(6,pos=(6,7))
G.add_node(7,pos=(7,9))
G.add_edges_from([(1,5),(2,5),(3,5),(5,6),(5,7)])
pos=nx.get_node_attributes(G,'pos')
nx.draw(G,pos)