#!/usr/bin/env python
# coding: utf-8

# # Analyzing Data from Multiple Files
# 
# <br/>
# 
# <section class="objectives panel panel-warning">
# <div class="panel-heading">
# <h2><span class="fa fa-certificate"></span> Learning Objectives </h2>
# </div>
# <ul>
# <li> Use a library function to get a list of filenames that match a simple wildcard pattern. </li>
# <li> Use a for loop to process multiple files. </li>
# </ul>
# </section>
# 
# 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:

# In[ ]:





# The `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:

# In[ ]:





# 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.
# 
# ```python
# 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.