I'm creating a graph out of given sequence of Y values held by curveSeq
. (the X values are enumerated automatically: 0,1,2...)
i.e for curveSeq = [10,20,30]
, my graph will contain the points:
<0,10>, <1,20>, <2,30>.
I'm drawing a series of graphs on the same nx.Graph
in order to present everything in one picture.
My problem is:
<0,10>
presents its respective label and I don't know how to remove it.for example, for the sequence:
[0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1]
The received graph is:
The code is:
for point in curveSeq:
cur_point = point
#assert len(cur_point) == 2
if prev_point is not None:
# Calculate the distance between the nodes with the Pythagorean
# theorem
b = cur_point[1] - prev_point[1]
c = cur_point[0] - prev_point[0]
a = math.sqrt(b ** 2 + c ** 2)
G.add_edge(cur_point, prev_point, weight=a)
G.add_node(cur_point)
pos[cur_point] = cur_point
prev_point = cur_point
#key:
G.add_node((curve+1,-1))
pos[(curve+1,-1)] = (curve+1,-1)
nx.draw(G, pos=pos, node_color = colors[curve],node_size=80)
nx.draw_networkx_edges(G,pos=pos,alpha=0.5,width=8,edge_color=colors[curve])
plt.savefig(currIteration+'.png')
For NetworkX, a graph with more than 100K nodes may be too large. I'll demonstrate that it can handle a network with 187K nodes in this post, but the centrality calculations were prolonged. Luckily, there are some other packages available to help us with even larger graphs.
In NetworkX, nodes can be any hashable object e.g., a text string, an image, an XML object, another Graph, a customized node object, etc. Python's None object is not allowed to be used as a node.
A DiGraph stores nodes and edges with optional data, or attributes. DiGraphs hold directed edges. Self loops are allowed but multiple (parallel) edges are not. Nodes can be arbitrary (hashable) Python objects with optional key/value attributes.
You can add the with_labels=False
keyword to suppress drawing of the labels with networkx.draw()
, e.g.
networkx.draw(G, pos=pos, node_color=colors[curve],
node_size=80, with_labels=False)
Then draw specific labels with
networkx.draw_networkx_labels(G,pos, labels)
where labels is a dictionary mapping node ids to labels.
Take a look at this example: https://networkx.org/documentation/stable/auto_examples/drawing/plot_labels_and_colors.html
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