# Covering array scraper and sorter

A covering array is an $N \times k$ array in which each element is a from a set of $v$ symbols, and for every $t$ columns, every possible set of $v^t$ choices of the symbols appears at least once. The covering array number $\mathrm{CA}(t,k,v)$ is the smallest $N$ for which a covering array exists, given $t$, $k$, and $v$. A list of known covering array numbers (CANs) is available here.

I want to parse all of the known CANs from these pages and to find how "efficient" they are—what I mean by this is the ratio of $\mathrm{CA}(t,k,v)$ compared to $v^t$.

I developed Python code that access every covering array page, and parses the tables. I then sort the list of covering arrays by this ratio, and plot it using matplotlib.pyplot (using a log-scale for the y axis).

from urllib.request import urlopen
from bs4 import BeautifulSoup
import matplotlib.pyplot as plt

# covering array object
class CAElement:
def __init__(self, N, t, k, v):
self.N = N
self.t = t
self.k = k
self.v = v
def set_ratio(self, ratio):
self.ratio = ratio
def __str__(self):
return "CA(N=%d; t=%d, k=%d, v=%d) -> %f" % (self.N, self.t, self.k, self.v, self.ratio)

CAArray = []

# iterate over v, t in the known table ranges
for v in range(2, 26):
for t in range(2, 7):
url = "http://www.public.asu.edu/~ccolbou/src/tabby/%d-%d-ca.html" % (t, v)
response = urlopen(url)
soup = BeautifulSoup(response)
tables = soup.findChildren('table')
table = tables[0]
rows = table.findChildren('tr')

# iterate over all rows in the one table
for row in rows:
cells = row.findChildren('td') # has all of the table's elements
elements = []
for cell in cells:
value = cell.string
if value is not None and value != "k" and value != "N" and value != "Source":
elements.append(value)
if len(elements) >= 2:
kParsed = int(elements[0])
NParsed = int(elements[1])
element = CAElement(NParsed, t, kParsed, v)
ratio = element.N / pow(element.v, element.t)
element.set_ratio(ratio)
CAArray.append(element)

# sort by N/(v^t)
CAArray.sort(key=lambda x: (x.ratio, x.N, x.v, x.t, x.k), reverse=True)

# print each element (in sorted order)
for element in CAArray:
print(element)

# plotting - using log scale for y axis
# each point is colored according to t (i.e., the "strength" of the CA)
xs = range(0, len(CAArray))
ys = [y.ratio for y in CAArray]
colors = {2:"red", 3:"blue", 4:"green", 5:"yellow", 6:"orange"}
plt.scatter(xs, ys, c=[colors[x.t] for x in CAArray])
plt.axis([min(xs), max(xs), min(ys), max(ys)])
plt.yscale('log')
plt.show()


This code does exactly what I want it to do. However, there are some problems:

• Accessing the webpages is somewhat slow, and could be faster.
• The code does not seem very Pythonic (and by making it so could make iterating over the array much faster).

Any suggestions are welcome!

Here are some of the quick performance wins you may get:

• switch to using requests and reuse a Session instance which should provide a performance boost:

if you're making several requests to the same host, the underlying TCP connection will be reused, which can result in a significant performance increase (see HTTP persistent connection).

import requests

session = requests.Session()

# iterate over v, t in the known table ranges
for v in range(2, 26):
for t in range(2, 7):
url = "http://www.public.asu.edu/~ccolbou/src/tabby/%d-%d-ca.html" % (t, v)
response = session.get(url)

• use the fastest available underlying parser - lxml (requires to install lxml):

soup = BeautifulSoup(response, 'lxml')

• parse only table elements from a page source via SoupStrainer:

parse_only = SoupStrainer('table')
soup = BeautifulSoup(response, 'lxml', parse_only=parse_only)


Requires importing SoupStrainer from bs4.

And, applying some bs4 related shortcuts, the HTML parsing part would transform into:

table = soup.table
for row in table('tr'):
elements = []
for cell in row('td'):
value = cell.string
if value is not None and value not in ("k", "N", "Source"):
elements.append(value)

1. When you have two or more independent loops:

# iterate over v, t in the known table ranges
for v in range(2, 26):
for t in range(2, 7):


you can use itertools.product to combine these into one loop:

for v, t in product(range(2, 26), range(2, 7)):


This saves a level of indentation.

2. The CAElement class doesn't have any behaviour, it's just a repository for data. So I would recommend making it a collections.namedtuple:

CAElement = namedtuple('CAElement', 'ratio N v t k')


Putting the attributes in this order means that you no longer need a key function in the call to CAArray.sort.

This requires a small change to the initialization, which becomes:

ratio = NParsed / pow(v, t)
element = CAElement(ratio, NParsed, t, kParsed, v)


Note that pow(v, t) is the same for every row so you could cache this in a local variable, but I doubt this makes a noticeable difference to the runtime.