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Contextual string manipulation

Project description


This package provides contex.rules, an interface which enables a very declarative form of string manipulation, where you can manipulate a string “in one go” in sophisticated ways.

This library also provides two related abstractions, StringContext and MatchContext, which can be used for a more stateful manipulation of strings. I recommend using contex.rules as I think that makes for more readable code. Nevertheless, those abstractions are well documented and might usefully serve as building blocks. Indeed, contex.rules is implemented on top of them.

The problem with our interfaces for string manipulation

My motivation for creating this package was that I was assigned a task in which it was necessary to change strings such as '1_Photo032-2008.jpg' into '1_Photo031-2008.jpg'. All the numbers could vary between filenames, and it seemed like I always had to do something inelegant to accomplish this task. Maybe it was to match the various parts and stich them back together:

>>> match = re.fullmatch('(\d+)_Photo(\d+)-(\d+)\.jpg', '1_Photo032-2008.jpg')
>>> '{}_Photo{}-{}.jpg'.format(, '{:0>3}'.format(int(,

Or using re.sub with non-consuming regex groups to match the correct area of the string:

>>> re.sub('(\d+)(?=-\d+\.jpg)', lambda m: '{:0>3}'.format(int(, '1_Photo032-2008.jpg')

Shouldn’t this be simpler? Describing that string with a regular expression is simple enough, and I’m only changing one little part of the string, so why do I have to fiddle around with indices, and why do I have to sacrifice readability? Most importantly, why do I have to experience this aesthetic pain deep in my heart?

First attempt: stateful manipulation

My first idea was that our abstractions aren’t fit for this sort of problem. Strings are flat, they have no sense of context, and if you pull out a substring then it requires special effort to stich it back together. The solution? Just keep track of the before and the after:

>>> view = contex.match('1_Photo032-2008.jpg', '\d+_Photo(?P<number>\d+)-\d+\.jpg')
>>> view
<MatchContext object; tup=('', '1_Photo032-2008.jpg', '')>
<MatchContext object; tup=('1_Photo', '032', '-2008.jpg')>
>>> result ='number').replace(lambda n: '{:0>3}'.format(int(n)-1))
>>> result
<MatchContext object; tup=('1_Photo', '031', '-2008.jpg')>
>>> str(result)

This way I can move around the “focus point” of the string with methods such as .group, manipulate that space, and when I’m done convert it back to a str. I can even manipulate more than one area of the string:

>>> view = contex.match('1_Photo032-2008.jpg', '\d+_Photo(?P<number>\d+)-(?P<year>\d+)\.jpg')
>>>'number').replace('').group('year').replace(lambda y: y[-2:])
<MatchContext object; tup=('1_Photo-', '08', '.jpg')>

MatchContext keeps track of where the matched regular expression groups are: Even though I removed the content of the “number” group, MatchContext knows where to find and replace the “year” group. It can also deal with nested regex groups, 0-length matches etc.


Previously (v2.0.1 and earlier) I allowed arbitrary slicing on MatchContext objects to select the focus point in addition to the .group method. This was a mistake. When you’re dealing with 0-length slices and adjacent regex groups that matched 0-length strings, there arises serious problems of semantics. I found out that the expected semantics is inextricably linked to which regex group you previously selected with .group, and therefore had to disallow slicing for MatchContext objects.

Removing the state: Vive la Revolution

The MatchContext abstraction certainly is an improvement for these particular types of problems, but there is one downside to it, and that is that it adds an additional layer of state to ordinary strings: The programmer must remember which part of the string is in “focus”, or, in other words, which state the string is in.

So my next challenge was to eliminate the state. What I found out was that only in rare cases is the state needed or useful, and this lead me to believe that the fundamental problem isn’t really the abstractions we use for representing strings, but rather the interfaces we have for manipulating them. Thus, pardon the pun, enter contex.rules:

>>> contex.rules('\d+_Photo(?P<number>\d+)-(?P<year>\d+)\.jpg', {
...     'number': lambda n: '{:0>3}'.format(int(n) - 1),
...     'year':   lambda y: y[-2:]
... }).apply('1_Photo032-2008.jpg')

Or maybe I want to change the layout of the filename completely:

>>> contex.rules('(\d+)_Photo(?P<number>\d+)-(?P<year>\d+)\.jpg', {
...     'number': lambda n: int(n) - 1,
...     'year':   lambda y: y[-2:]
... }).expand('1_Photo032-2008.jpg', 'Photo_{1}_{number:0>3}-{year}.jpeg')

The string manipulation is done in one go. The programmer doesn’t need to remember where the focus point is right now, or specify which order to do the replacements in. This is a much more declarative interface: you tell it what the string looks like, what changes you want made, and it figures out the rest. You don’t need to stich the pieces back together, and can create more readable regular expressions as well because of that.

Nested regex groups are also allowed: the nested one will be replaced first (which will make a difference if the replacement for the outer group is a callable).

More advanced example

Here’s an example using (as opposed to re.fullmatch, which is the default):

>>> contex.rules('(?P<millennium>\d)\d{3}', {
...      'millennium': lambda s: int(s)+1,
...      0:            lambda y: '<span class="year">{}</span>'.format(y)
... },'Current year: 2015')
'Current year: <span class="year">3015</span>'

Notice that the 'millennium' group is replaced before the 0 group.

contex.rules is explained in more detail in its very long docstring.

Doubtful stability

In order to retrieve certain information about the regular expressions to resolve ambiguities related to 0-length matches and so on, I’ve seen it necessary to use sre_parse.parse to parse the regular expressions. This is an “internal support module” or something like that, and the stability of this library becomes doubtful as a result. My judgement was that it would take a lot of time and effort to create my own parser for python regular expressions, and I could easily create some bugs in that parser too.


I hope that the examples of contex.rules I have given are sufficiently intuitive so that any programmer can look at them and infer pretty accurately what they do, because the whole point of this endeavor is to increase readability.

Furthermore, I’d be interested to see if other people can take this idea ^\w{7}

Using Contex

The contex package contains 5 functions:

  • rules(regex, rule_dict, method=re.fullmatch, flags=0) for declarative string manipulation.
  • T(string) for converting a string into a StringContext object.
  • search(string, pattern, flags=0) and
  • match(string, pattern, flags=0) for regex searches (with the same semantic difference as in the re module). They both return a MatchContext object.
  • find(string, substring, right_side=False) for finding a substring, returns a StringContext object.

contex also contains the StringContext and MatchContext classes.


contex should work in both Python 2.7 and 3.

Install with $ pip install contex. If you want to install for Python 3 you might want to replace pip with pip3, depending on how your system is configured.


Contex is documented and tested. Run $ nosetests or $ python3 test to run the tests. The code is hosted at


The library is licensed under the GNU General Public License 3 or later. This README file is public domain.

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