Even though this is a somewhat old question, I thought I’d give a practical answer for anyone stumbling across this. Let’s say you get your input data for your connections as a list of tuples like so: [(‘A’, ‘B’), (‘B’, ‘C’), (‘B’, ‘D’), (‘C’, ‘D’), (‘E’, ‘F’), (‘F’, ‘C’)] The data structure I’ve found to … Read more
It depends on the problem. Adjacency Matrix Uses O(n^2) memory It is fast to lookup and check for presence or absence of a specific edge between any two nodes O(1) It is slow to iterate over all edges It is slow to add/delete a node; a complex operation O(n^2) It is fast to add a … Read more
I think you are looking for this: require(ggplot2) df <- data.frame(x=seq(1, 1e9, length.out=100), y=sample(100)) # displays x-axis in scientific notation p <- ggplot(data = df, aes(x=x, y=y)) + geom_line() + geom_point() p # displays as you require require(scales) p + scale_x_continuous(labels = comma)
Try gnuplot. It has very powerful graphing possibilities. It can output to your terminal in the following way: gnuplot> set terminal dumb Terminal type set to ‘dumb’ Options are ‘feed 79 24’ gnuplot> plot sin(x) 1 ++—————-**—————+—-**———–+——–**—–++ + *+ * + * * + sin(x) ****** + 0.8 ++ * * * * * * … Read more
Using vmin and vmax forces the range for the colors. Here’s an example: import matplotlib as m import matplotlib.pyplot as plt import numpy as np cdict = { ‘red’ : ( (0.0, 0.25, .25), (0.02, .59, .59), (1., 1., 1.)), ‘green’: ( (0.0, 0.0, 0.0), (0.02, .45, .45), (1., .97, .97)), ‘blue’ : ( (0.0, … Read more
Update: This answer is outdated since matplotlib version 3.5. The plot function now handles datetime data directly. See https://matplotlib.org/3.5.1/api/_as_gen/matplotlib.pyplot.plot_date.html The use of plot_date is discouraged. This method exists for historic reasons and may be deprecated in the future. datetime-like data should directly be plotted using plot. If you need to plot plain numeric data as … Read more
If you were using JGraph, you should give a try to JGraphT which is designed for algorithms. One of its features is visualization using the JGraph library. It’s still developed, but pretty stable. I analyzed the complexity of JGraphT algorithms some time ago. Some of them aren’t the quickest, but if you’re going to implement … Read more
The main problem is that (on your system) matplotlib chooses an x-using backend by default. I just had the same problem on one of my servers. The solution for me was to add the following code in a place that gets read before any other pylab/matplotlib/pyplot import: import matplotlib # Force matplotlib to not use … Read more
For a small number of variables, you can build the plot manually yourself: ggplot(test_data, aes(date)) + geom_line(aes(y = var0, colour = “var0”)) + geom_line(aes(y = var1, colour = “var1”))
There are two excellent choices: NetworkX and igraph I like NetworkX, but I read good things about igraph as well. I routinely use NetworkX with graphs with 1 million nodes with no problem (it’s about double the overhead of a dict of size V + E) If you want a feature comparison, see this from … Read more