Peterbe.com

Tonight I need a little function that let me define a list of whitelisted email address and a list of blacklisted email address. This is then "merged" in a function called acceptOriginatorEmail(emailaddress) which is used to see if a particular email address is acceptable.

I've never written something like this before so I had to reinvent the wheel and guess my way towards a solution. My assumptions are that you start with whitelist and return True on a match on the blacklist, then you check against the blacklist and return False on a match and default to True if no match is made.

This makes it possible to define which email addresses should be accepted and which ones should be rejected like this:

whitelist = ('*@peterbe.com', 'bill.gates@microsoft.com')
blacklist = ('*@microsoft.com')

To some Python users this is old-school old-news stuff but since I've never used it before I found it worth mentioning.

I have a script that scans a rather large tree of folders filled with files. None of the folders have the same name but they can mistakably contain the same files eg:

folder XYZ-2005-11-27/
   email1.bin
   email2.bin
folder CBA-2005-07-10/
   email1.bin
   email2.bin

Sometimes two different folders contain the same file names exactly. Sometimes, the file sizes as equal too. But in some of those cases, even though the file sizes and names are the same they are different files. But! If they are the same files just in different locations I want to find them. How to do that?

I've just uploaded my second Python Cookbook recipe. It's unfortunately not rocket science but it's application is potentially very useful. With this little function you can generate the next number in a string that contains at least one number.

The mini unittest is quite interesting perhaps:

$ python increment_strings.py
from 10dsc_0010.jpg to 10dsc_0011.jpg
from dsc_9.jpg to dsc_10.jpg
from 0000001.exe to 0000002.exe
from ref-04851 to ref-04852

I've been playing around with Reverend a bit on getting it to correctly guess appropriate "Sections" for issues on the Real issuetracker. What I did was that I downloaded all 140 issuetexts and their "Sections" attribute which is a list (that is often of length 1). From list dataset I did a loop over each text and the sections within it (skipped the default section General) so something like this:

data = ({'sections':['General','Installation'], 
         'text':"bla bla bla..."}
        {'sections':['Filter functions'], 
         'text':"Lorem ipsum foo bar..."}
        ...)
for item in data:
    secs = [each for each item['sections'] if each != 'General']
    for section in secs:
        guesser.train(section, item['text'])

At the moment I'm not running Squid for this site but if experimentation time permits I'll have it running again soon. One thing I feel uneasy about is how to "manually" purge cached pages that needs to be updated. For example, if you read this page (and it's cached for one hour) and post a comment, then I'd like to re-cache this page with a purge. Setting a HTTP header could be something but that I would only be able to do on the page where you have this in the URL:

?msg=Comment+added

which, because of the presence of a querystring, is not necessarily cached anyway. The effect is that as soon as the "?msg=Comment+added" is removed from the URL, the viewer will see the page as it was before she posted her comment. squidclient might be the solution. ...sort of.

Some Ruby (the programming language) blogger has tried to implement the Edit-Distance algorithm in three different ways in both Python and in Ruby. His conclusion is more steered towards the difference between the algorithms and not so much the language. I guess that's fair because the implementation might be done better in Python than it was in Ruby. But, please notice that this is a Ruby coder and yet his Python implementations are always faster.

A quick glance tells me that the Python programs run about 3 times as fast as the Ruby ones. See the benchmarks

If you thought the Web Service example on the about page of Smurl.name was complicated, here's a much simpler version. I use this code on my very own site for the email notifications which will contain long URLs.

Feel free to steal this code into your own projects:

from urllib import urlopen, quote

# variable 'url' is defined elsewhere
if len(url) > 80:
    url = urlopen('http://smurl.name/createSmurl?url=%s' % quote(url)).read()

Was that hard?

I've just discovered retest by Christof Hoeke which is a developers tool for testing and experimenting with regular expressions. It doesn't have a GUI so it uses SimpleHTTPServer to serve a web interface on http://localhost:8087 that uses AJAX to make the interface snappier. You use this if you feel uncertain how to write your regular expression syntax and need a helpful sandbox for playing in.

This is cool because as an application it's very modern. The source code is only 100 lines python code, some javascript code for the AJAX and a relatively simple HTML page. A genuine GUI app would be considerably much more code but would admittedly run faster. However, considering how "basic" this application is, speed is not an issue.

I was working on a little application that is similar to tinyurl.com (where any URL gets a unique random id that can be used to redirect with long clumsy URL strings) and came up with this little algorithm that I wanted to share with the world for some feedback.

There are two loops, one nested inside one big loop that goes on for infinity. The inner loop creates a list of possible combinations from a sample of letters and numbers eg (abc, acb, bac, bca, cab, cba). For each such found combinations it does a check to see if this has been saved before and if not it exists the loop like this:

if not hasattr(self, combination):
    return combination

Today I learnt about how to use the \B gadget in Python regular expressions. I've previously talked about the usefulness of \b but there's a big benefit to using \B sometimes too.

What \b does is that it is a word-boundary for alphanumerics. It allows you to find "peter" in "peter bengtsson" but not "peter" in "nickname: peterbe". In other words, all the letters have to be grouped prefixed or suffixed by a wordboundry such as newline, start-of-line, end-of-line or a non alpha character like (.