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Can Python-Markdown support imageboard-style links?

I would like to add an additional syntax to Python-Markdown: if n is a positive integer, >>n should expand into n. (Double angled brackets (>>) is a conventional syntax for creating links in imageboard forums.)
By default, Python-Markdown expands >>n into nested blockquotes: <blockquote><blockquote>n</blockquote></blockquote>. Is there a way create links out of >>n, while preserving the rest of blockquote's default behavior? In other words, if x is a positive integer, >>x should expand into a link, but if x is not a positive integer, >>x should still expand into nested blockquotes.
I have read the relevant wiki article: Tutorial 1 Writing Extensions for Python Markdown. Based on what I learned in the wiki, I wrote a custom extension:
import markdown
import xml.etree.ElementTree as ET
from markdown.extensions import Extension
from markdown.inlinepatterns import Pattern
class ImageboardLinkPattern(Pattern):
def handleMatch(self, match):
number = match.group('number')
# Create link.
element = ET.Element('a', attrib={'href': f'#post-{number}'})
element.text = f'>>{number}'
return element
class ImageboardLinkExtension(Extension):
def extendMarkdown(self, md):
IMAGEBOARD_LINK_RE = '>>(?P<number>[1-9][0-9]*)'
imageboard_link = ImageboardLinkPattern(IMAGEBOARD_LINK_RE)
md.inlinePatterns['imageboard_link'] = imageboard_link
html = markdown.markdown('>>123',
extensions=[ImageboardLinkExtension()])
print(html)
However, >>123 still produces <blockquote><blockquote>123</blockquote></blockquote>. What is wrong with the implementation above?

The problem is that your new syntax conflicts with the preexisting blockquote syntax. Your extension would presumably work if it was ever called. However, due to the conflict, that never happens. Note that their are five types of processors. As documented:
Preprocessors alter the source before it is passed to the parser.
Block Processors work with blocks of text separated by blank lines.
Tree Processors modify the constructed ElementTree
Inline Processors are common tree processors for inline elements, such as *strong*.
Postprocessors munge of the output of the parser just before it is returned.
Of importance here is that the processors are run in that order. In other words, all block processors are run before any inline processors are run. Therefore, the blockquote block processor runs first on your input and removes the double angle bracket, wrapping the rest of the line in double blockquote tags. By the time your inline processor sees the document, your regex will no longer match and will therefore never be called.
That being said, an inline processor is the correct way to implement a link syntax. However, you would need to do one of two things to make it work.
Alter the syntax so that it does not clash with any preexisting syntax; or
Alter the blockquote behavior to avoid the conflict.
Personally, I would recommend option 1, but I understand you are trying to implement a preexisting syntax from another environment. So, if you want to explore option 2, then I would suggest perhaps making the blockquote syntax a little more strict. For example, while it is not required, the recommended syntax is to always insert a space after the angle bracket in a blockquote. It should be relatively simple to alter the BlockquoteProcessor to require the space, which would cause your syntax to no longer clash.
This is actually pretty simple. As you may note, the entire syntax is defined via a rather simple regex:
RE = re.compile(r'(^|\n)[ ]{0,3}>[ ]?(.*)')
You simply need to rewrite that so that 0 whitespace is no longer accepted (> rather than >[ ]?). First import and subclass the existing processor and then override the regex:
from markdown.blockprocessors import BlockquoteProcessor
class CustomBlockquoteProcessor(BlockquoteProcessor):
RE = re.compile(r'(^|\n)[ ]{0,3}> (.*)')
Finally, you just need to tell Markdown to use your custom class rather than the default. Add the following to the extendMarkdown method of your ImageboardLinkExtension class:
md.parser.blockprocessors.register(CustomBlockQuoteProcessor(md.parser), 'quote', 20)
Now the blockquote syntax will no longer clash with your link syntax and you will get an opportunity to have your code run on the text. Just be careful to remember to always include the now required space for any actual blockquotes.

Python pygments lexer state preservation

Running pygments default lexer on the following c++ text: class foo{};, results in this:
(Token.Keyword, 'class')
(Token.Text, ' ')
(Token.Name.Class, 'foo')
(Token.Punctuation, '{')
(Token.Punctuation, '}')
(Token.Punctuation, ';')
Note that the toke foo has the type Token.Name.Class.
If i change the class name to foobar i want to be able to run the default lexer only on the touched tokens, in this case original tokens foo and {.
Q: How can i save the lexer state so that tokenizing foobar{ will give tokens with type Token.Name.Class?
Having this feature would optimize syntax highlighting for large source files that suffered a change (user is typing text) right in the middle of the file for example. There seems no documented way of doing this and no information on how to do this using the default pygments lexers.
Are there any other syntax highlighting systems that have support for this behavior ?
EDIT:
Regarding performance here is an example: http://tpcg.io/ESYjiF

From my understanding of the source code what you want is not possible.
I won't dig and try to explain every single relevant lines of code, but basically, here is what happend:
Your Lexer class is pygments.lexers.c_cpp.CLexer, which inherits from pygments.lexer.RegexLexer.
pygments.lex(lexer, code) function do nothing more than calling get_tokens method on lexer and handle errors.
lexer.get_tokens basically parse source code in unicode string and call self.get_tokens_unprocessed
get_tokens_unprocessed is defined by each Lexer in your case the relevant method is pygments.lexers.c_cpp.CFamilyLexer.get_tokens_unprocessed.
CFamilyLexer.get_tokens_unprocessed basically get tokens from RegexLexer.get_tokens_unprocessed and reprocess some of them.
Finally, RegexLexer.get_tokens_unprocessed loop on defined token types (something like (("function", ('pattern-to-find-c-function',)), ("class", ('function-to-find-c-class',)))) and for each type (function, class, comment...) find all matches within the source text, then process the next type.
This behavior make what you want impossible because it loops on token types, not on text.
To make more obvious my point, I added 2 lines of code in the lib, file: pygments/lexer.py, line: 628
for rexmatch, action, new_state in statetokens:
print('looking for {}'.format(action))
m = rexmatch(text, pos)
print('found: {}'.format(m))
And ran it with this code:
import pygments
import pygments.lexers
lexer = pygments.lexers.get_lexer_for_filename("foo.h")
sample="""
class foo{};
"""
print(list(lexer.get_tokens(sample)))
Output:
[...]
looking for Token.Keyword.Reserved
found: None
looking for Token.Name.Builtin
found: None
looking for <function bygroups.<locals>.callback at 0x7fb1f29b52f0>
found: None
looking for Token.Name
found: <_sre.SRE_Match object; span=(6, 9), match='foo'>
[...]
As you can see, the token types are what the code iterate on.
Taking that and (as Tarun Lalwani said in comments) the fact that a single new character can break the whole source-code structure, you cannot do better than re-lexing the whole text at each update.

Parser that preserves comments and recover from error

I'm working on a GUI editor for a propriety config format. Basically the editor will parse the config file, display the object properties so that users can edit from GUI and then write the objects back to the file.
I've got the parse - edit - write part done, except for:
The parsed data structure only include object properties information, so comments and whitespaces are lost on write
If there is any syntax error, the rest of the file is skipped
How would you address these issues? What is the usual approach to this problem? I'm using Python and Parsec module https://pythonhosted.org/parsec/documentation.html, however and help and general direction is appreciated.
I've also tried Pylens (https://pythonhosted.org/pylens/), which is really close to what I need except it can not skip syntax errors.

You asked about typical approaches to this problem. Here are two projects which tackle similar challenges to the one you describe:
sketch-n-sketch: "Direct manipulation" interface for vector images, where you can either edit the image-describing source language, or edit the image it represents directly and see those changes reflected in the source code. Check out the video presentation, it's super cool.
Boomerang: Using lenses to "focus" on the abstract meaning of some concrete syntax, alter that abstract model, and then reflect those changes in the original source.
Both projects have yielded several papers describing the approaches their authors took. As far as I can tell, the Lens approach is popular, where parsing and printing become the get and put functions of a Lens which takes a some source code and focuses on the abstract concept which that code describes.

Eventually I ran out of research time and have to settle with a rather manual skipping. Basically each time the parser fail we try to advance the cursor one character and repeat. Any parts skipped by the process, regardless of whitespace/comment/syntax error is dump into a Text structure. The code is quite reusable, except for the part you have to incorporate it to all the places with repeated results and the original parser may fail.
Here's the code, in case it helps anyone. It is written for Parsy.
class Text(object):
'''Structure to contain all the parts that the parser does not understand.
A better name would be Whitespace
'''
def __init__(self, text=''):
self.text = text
def __repr__(self):
return "Text(text='{}')".format(self.text)
def __eq__(self, other):
return self.text.strip() == getattr(other, 'text', '').strip()
def many_skip_error(parser, skip=lambda t, i: i + 1, until=None):
'''Repeat the original `parser`, aggregate result into `values`
and error in `Text`.
'''
#Parser
def _parser(stream, index):
values, result = [], None
while index < len(stream):
result = parser(stream, index)
# Original parser success
if result.status:
values.append(result.value)
index = result.index
# Check for end condition, effectively `manyTill` in Parsec
elif until is not None and until(stream, index).status:
break
# Aggregate skipped text into last `Text` value, or create a new one
else:
if len(values) > 0 and isinstance(values[-1], Text):
values[-1].text += stream[index]
else:
values.append(Text(stream[index]))
index = skip(stream, index)
return Result.success(index, values).aggregate(result)
return _parser
# Example usage
skip_error_parser = many_skip_error(original_parser)
On other note, I guess the real issue here is I'm using a parser combinator library instead of a proper two stages parsing process. In traditional parsing, the tokenizer will handle retaining/skipping any whitespace/comment/syntax error, making them all effectively whitespace and are invisible to the parser.

Python 3.3 library abpy, file undefined

I'm using a library ABPY (library here) for python but it is in older version i think. I'm using Python 3.3.
I did fix some PRINT errors, but that's how much i know, I'm really new on programing.
I want to fetch some webpage and filter it from advertising and then print it again.
EDITED after Sg'te'gmuj told me how to convert from python 2.x to 3.x this is my new code:
#!/usr/local/bin/python3.1
import cgitb;cgitb.enable()
import urllib.request
response = urllib.request.build_opener()
response.addheaders = [('User-agent', 'Mozilla/5.0')]
response = urllib.request.urlopen("http://www.youtube.com")
html = response.read()
from abpy import Filter
with open("easylist.txt") as f:
ABPFilter = Filter(file('easylist.txt'))
ABPFilter.match(html)
print("Content-type: text/html")
print()
print (html)
Now it is displaying a blank page

Just took a peek at the library, it seems that the file "easylist.txt" does not exist; you need to create the file, and populate it with the appropriate filters (in whatever format ABP specifies).
Additionally, it appears it takes a file object; try something like this instead:
with open("easylist.txt") as f:
ABPFilter = Filter(f)
I can't say this is wholly accurate though since I have no experience with the library, but looking at it's code I'd suspect either of the two are the problem, if not both.
Addendum #1
Looking at the code more in-depth, I have to agree that even if that fix I supplied does work, you're going to have more problems (it's in 2.x as you suggested, when you're using 3.x). I'd suggest utilizing Python's 2to3 function, to convert from typical Python 2 to Python 3 code (it's not foolproof though). The command line would be as so:
2to3 -w abpy.py
That will convert it from Python 2.x to 3.x code, and re-write the source file.
Addendum #2
The code to pass the file object should be the "f" variable, as shown above (modified to represent that; I wasn't paying attention and just left the old file function call in the argument).
You need to pass a URI to the function as well:
ABPFilter.match(URI)
You'll need to modify the code to pass those items into an array (I'm assuming at least); I'm playing with it now to see. At present I'm getting a rule error (not a Python error; but merely error handling used by abpy.py, which is good because it suggests that it's the right train of thought).
The code for the Filter.match function is as following (after using the 2to3 Python script):
def match(self, url, elementtype=None):
tokens = RE_TOK.split(url)
print(tokens)
for tok in tokens:
if len(tok) > 2:
if tok in self.index:
for rule in self.index[tok]:
if rule.match(url, elementtype=elementtype):
print(str(rule))
What this means is you're, at present, at a point where you need to program the functionality; it appears this module only indicates the rule. However, that is still useful.
What this means is that you're going to have to modify this function to take the HTML, in place of the the "url" parameter. You're going to regex the HTML (this may be rather intensive) for a list of URIs and then run each item through the match loop Where you go from there to actually filter the nodes, I'm not sure; but there is a list of filter types, so I'm assuming there is a typical procedural ABP does to remove the nodes (possibly, in some cases merely by removing the given URI from the HTML?)
References
http://docs.python.org/3.3/library/2to3.html

Writing a compiler for a DSL in python

I am writing a game in python and have decided to create a DSL for the map data files. I know I could write my own parser with regex, but I am wondering if there are existing python tools which can do this more easily, like re2c which is used in the PHP engine.
Some extra info:
Yes, I do need a DSL, and even if I didn't I still want the experience of building and using one in a project.
The DSL contains only data (declarative?), it doesn't get "executed". Most lines look like:
SOMETHING: !abc #123 #xyz/123
I just need to read the tree of data.

I've always been impressed by pyparsing. The author, Paul McGuire, is active on the python list/comp.lang.python and has always been very helpful with any queries concerning it.

Here's an approach that works really well.
abc= ONETHING( ... )
xyz= ANOTHERTHING( ... )
pqr= SOMETHING( this=abc, that=123, more=(xyz,123) )
Declarative. Easy-to-parse.
And...
It's actually Python. A few class declarations and the work is done. The DSL is actually class declarations.
What's important is that a DSL merely creates objects. When you define a DSL, first you have to start with an object model. Later, you put some syntax around that object model. You don't start with syntax, you start with the model.

Yes, there are many -- too many -- parsing tools, but none in the standard library.
From what what I saw PLY and SPARK are popular. PLY is like yacc, but you do everything in Python because you write your grammar in docstrings.
Personally, I like the concept of parser combinators (taken from functional programming), and I quite like pyparsing: you write your grammar and actions directly in python and it is easy to start with. I ended up producing my own tree node types with actions though, instead of using their default ParserElement type.
Otherwise, you can also use existing declarative language like YAML.

I have written something like this in work to read in SNMP notification definitions and automatically generate Java classes and SNMP MIB files from this. Using this little DSL, I could write 20 lines of my specification and it would generate roughly 80 lines of Java code and a 100 line MIB file.
To implement this, I actually just used straight Python string handling (split(), slicing etc) to parse the file. I find Pythons string capabilities to be adequate for most of my (simple) parsing needs.
Besides the libraries mentioned by others, if I were writing something more complex and needed proper parsing capabilities, I would probably use ANTLR, which supports Python (and other languages).

For "small languages" as the one you are describing, I use a simple split, shlex (mind that the # defines a comment) or regular expressions.
>>> line = 'SOMETHING: !abc #123 #xyz/123'
>>> line.split()
['SOMETHING:', '!abc', '#123', '#xyz/123']
>>> import shlex
>>> list(shlex.shlex(line))
['SOMETHING', ':', '!', 'abc', '#', '123']
The following is an example, as I do not know exactly what you are looking for.
>>> import re
>>> result = re.match(r'([A-Z]*): !([a-z]*) #([0-9]*) #([a-z0-9/]*)', line)
>>> result.groups()
('SOMETHING', 'abc', '123', 'xyz/123')

DSLs are a good thing, so you don't need to defend yourself :-)
However, have you considered an internal DSL ? These have so many pros versus external (parsed) DSLs that they're at least worth consideration. Mixing a DSL with the power of the native language really solves lots of the problems for you, and Python is not really bad at internal DSLs, with the with statement handy.

On the lines of declarative python, I wrote a helper module called 'bpyml' which lets you declare data in python in a more XML structured way without the verbose tags, it can be converted to/from XML too, but is valid python.
https://svn.blender.org/svnroot/bf-blender/trunk/blender/release/scripts/modules/bpyml.py
Example Use
http://wiki.blender.org/index.php/User:Ideasman42#Declarative_UI_In_Blender

Here is a simpler approach to solve it
What if I can extend python syntax with new operators to introduce new functionally to the language? For example, a new operator <=> for swapping the value of two variables.
How can I implement such behavior? Here comes AST module.
The last module is a handy tool for handling abstract syntax trees. What’s cool about this module is it allows me to write python code that generates a tree and then compiles it to python code.
Let’s say we want to compile a superset language (or python-like language) to python:
from :
a <=> b
to:
a , b = b , a
I need to convert my 'python like' source code into a list of tokens.
So I need a tokenizer, a lexical scanner for Python source code. Tokenize module
I may use the same meta-language to define both the grammar of new 'python-like' language and then build the structure of the abstract syntax tree AST
Why use AST?
AST is a much safer choice when evaluating untrusted code
manipulate the tree before executing the code Working on the Tree
from tokenize import untokenize, tokenize, NUMBER, STRING, NAME, OP, COMMA
import io
import ast
s = b"a <=> b\n" # i may read it from file
b = io.BytesIO(s)
g = tokenize(b.readline)
result = []
for token_num, token_val, _, _, _ in g:
# naive simple approach to compile a<=>b to a,b = b,a
if token_num == OP and token_val == '<=' and next(g).string == '>':
first = result.pop()
next_token = next(g)
second = (NAME, next_token.string)
result.extend([
first,
(COMMA, ','),
second,
(OP, '='),
second,
(COMMA, ','),
first,
])
else:
result.append((token_num, token_val))
src = untokenize(result).decode('utf-8')
exp = ast.parse(src)
code = compile(exp, filename='', mode='exec')
def my_swap(a, b):
global code
env = {
"a": a,
"b": b
}
exec(code, env)
return env['a'], env['b']
print(my_swap(1,10))
Other modules using AST, whose source code may be a useful reference:
textX-LS: A DSL used to describe a collection of shapes and draw it for us.
pony orm: You can write database queries using Python generators and lambdas with translate to SQL query sting—pony orm use AST under the hood
osso: Role Based Access Control a framework handle permissions.