We now have almost everything we need to process all our data files. The only thing that’s missing is a library with a rather unpleasant name:
glob library contains a single function, also called
glob, that finds files whose names match a pattern. We provide those patterns as strings: the character
* matches zero or more characters, while
? matches any one character. We can use this to get the names of all the HTML files in the current directory:
As these examples show, glob.glob’s result is a list of strings, which means we can loop over it to do something with each filename in turn. In our case, the “something” we want to do is generate a set of plots for each file in our inflammation dataset.
import numpy import matplotlib.pyplot filenames = glob.glob('data/inflammation*.csv') filenames = filenames[0:3] for f in filenames: print(f) data = numpy.loadtxt(fname=f, delimiter=',') fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0)) axes1 = fig.add_subplot(1, 3, 1) axes2 = fig.add_subplot(1, 3, 2) axes3 = fig.add_subplot(1, 3, 3) axes1.set_ylabel('average') axes1.plot(data.mean(axis=0)) axes2.set_ylabel('max') axes2.plot(data.max(axis=0)) axes3.set_ylabel('min') axes3.plot(data.min(axis=0)) fig.tight_layout() matplotlib.pyplot.show(fig)
Sure enough, the maxima of the first two data sets show exactly the same ramp as the first, and their minima show the same staircase structure; a different situation has been revealed in the third dataset, where the maxima are a bit less regular, but the minima are consistently zero.