Free matching software for linux

JavaMatch provides a match engine for Java data structures.

JavaMatch is an engine that can search inside runtime Java data structures and look for objects that best match the criteria that you specify.

JavaMatch is a generic match engine, not targeted at a specific domain. It can be applied to any runtime data structure, and the extensive query mechanism allows for highly customizable tuning of your searches.

JavaMatch can be integrated with object- relational mapping tools. This allows you to match inside databases.

JavaMatchs functionality compared to "normal" database searching can be described by using an analogy:

If you search for something in Google, you dont just see the pages that exactly match your search query, youll also get the results that best match the search query (unless you use the "+"-syntax).

JavaMatch provides this Google-like functionality, but then applied to a Java data structure: It looks for the objects that best match the query: Out of the entire data set, it returns the top-10 (or more, if you specify so) of the best matching objects.

hachoir-regex application is regex manipulation Python library. Its used by hachoir-subfile for fast pattern matching (find file header).
Examples
Regex creation
>>> from hachoir_core.regex import parse, createString
>>> createString("bike") | createString("motor")
< RegexOr (bike|motor) >
>>> createString("big ") + createString("bike")
< RegexString big bike >
>>> r=parse((cat|horse))
>>> r.minLength(), r.maxLength()
(3, 5)
Optimizations
>>> from hachoir_core.regex import parse, createString
>>> parse("(ma|mb|mc)")
< RegexAnd m[a-c] >
>>> createString("moto") | parse("mot.")
< RegexAnd mot. >
Pattern matching
from hachoir_core.regex import PatternMatching
p = PatternMatching()
p.addString("un", 1)
p.addString("deux", 2)
p.addRegex("(trois|three)", 3)
for start, end, item in p.search("un deux trois"):
print "%r at %s: user=%r" % (item, start, item.user)
find
< StringPattern un > at 0: user=1
< StringPattern deux > at 3: user=2
< RegexPattern t(rois|hree) > at 8: user=3
Enhancements:
- This version supports most regex structures and a lot of regex optimization, but keeps regex order.

Batchput transfers files from a local directory, matching the regex you specify, to an FTP server. Written in Python, the program is focused on doing this safely and efficiently, using per-directory locking and a single ftp session.
Batchput was originally written as a program to transfer text files containing business transactions from one system to another. With this in mind, it is not a useful tool for doing things like mirroring a directory etc. If thats what you want to do, there are many good programs
out there that will perform nicely.
To use batchput, simply copy the batchput Python script to somewhere
useful and call it from, for example, crontab.
Enhancements:
- Added --ftp-ascii-mode option to support ASCII mode transfer.
- Added --auth-netrc-* options for dealing with netrc files. Batchput now allows username/password either through the URL, through netrc files, or through forced keyboard / terminal input.

Markup::MatchTree is a Perl module for building trees to be compared to Markup::Trees.

SYNOPSIS

use Markup::MatchTree;

my $match_tree = Markup::MatchTree->new( no_squash_whitespace => @same_as_I_used_for_Markup__Tree);
$match_tree->parse_file (http://localhost/site_template.xml);

Most likely you wont need to use this module explicitly unless you are doing some custom matching/parsing/extracting. Mainly this will be used by the upcoming Markup::Content module.

See Markup::Tree for a description of the methods. The only difference is MatchTree dosent accpet no_indent as an argument. no_squash_whitespace should be the same value that was used for Markup::Tree.

Fontconfig is a library designed to provide system-wide font configuration, customization and application access.

The configuration module consists of the FcConfig datatype, libexpat and FcConfigParse which walks over an XML tree and ammends a configuration with data found within.

From an external perspective, configuration of the library consists of generating a valid XML tree and feeding that to FcConfigParse.

The only other mechanism provided to applications for changing the running configuration is to add fonts and directories to the list of application-provided font files.

The intent is to make font configurations relatively static, and shared by as many applications as possible. It is hoped that this will lead to more stable font selection when passing names from one application to another.

XML was chosen as a configuration file format because it provides a format which is easy for external agents to edit while retaining the correct structure and syntax.

Font configuration is separate from font matching; applications needing to do their own matching can access the available fonts from the library and perform private matching.

The intent is to permit applications to pick and choose appropriate functionality from the library instead of forcing them to choose between this library and a private configuration mechanism.

The hope is that this will ensure that configuration of fonts for all applications can be centralized in one place. Centralizing font configuration will simplify and regularize font installation and customization.

Pathological is an enriched clone of the game "Logical" by Rainbow Arts. To solve a level, fill each wheel with four marbles of matching color.
Various board elements such as teleporters, switches, filters, etc., make the game interesting and challenging. New levels can be created using your favorite text editor.
Main features:
- Sharp 800x600 graphics
- 50 diverse and challenging levels (more to come...)
- A cool 6-minute ambient soundtrack by an award-winning musician

Array::PatternMatcher is a pattern matching for arrays.

SYNOPSIS

This section inlines the entire test suite. Please excuse the ok()s.

use Array::PatternMatcher;

Matching logical variables to input stream

# 1 - simple match of logical variable to input
my $pattern = AGE ;
my $input = 969 ;
my $result = pat_match ($pattern, $input, {} ) ;
ok($result->{AGE}, 969) ;

# 2 - if binding exists, it must equal the input
$input = 12;
my $new_result = pat_match ($pattern, $input, $result) ;
ok(!defined($new_result)) ;

# 3 - bind the pattern logical variables to the input list

$pattern = [qw(X Y)] ;
$input = [ 77, 45 ] ;
my $result = pat_match ($pattern, $input, {} ) ;
ok($result->{X}, 77) ;
Matching segments (quantifying) portions of the input stream
# 1
{
my $pattern = [a, [qw(X *)], d] ;
my $input = [a, b, c, d] ;

my $result = pat_match ($pattern, $input, {} ) ;
ok ("@{$result->{X}}","b c") ;
}

# 2
{

my $pattern = [a, [qw(X *)], [qw(Y *)], d] ;
my $input = [a, b, c, d] ;
my $result = pat_match ($pattern, $input, {} ) ;
ok ("@{$result->{Y}}","b c") ;

}
# 3
{
my $pattern = [a, [qw(X +)], d] ;
my $input = [a, b, c, d] ;
ok ("@{$result->{X}}","b c") ;
}
# 4
{
my $pattern = [ a, [qw(X ?)], c ] ;
my $input = [ a, b, c ] ;
my $result = pat_match ($pattern, $input, {} ) ;
ok ("$result->{X}","b") ;
}
# 5
{
my $pattern = [ qw(X OP Y is Z),
[
sub { "($_->{X} $_->{OP} $_->{Y}) == $_->{Z}" },
IF?
]
] ;
my $input = [qw(3 + 4 is 7) ] ;
my $result = pat_match ($pattern, $input, {} ) ;
ok ($result) ;
}
Single-matching:
Take a single input and a series of patterns and decide which pattern
matches the input:

# 1 - Here all input patterns must match the input

{
my @pattern ;
push @pattern, [ qw(X Y) ] ;
push @pattern, [ qw(22 Z ) ] ;
push @pattern, [ qw(M 33) ] ;

my $input = [ qw(22 33) ] ;

my $meta_pattern = [ AND?, @pattern ] ;

# if no bindings, add a binding between pattern and input
my $result = pat_match ($meta_pattern, $input, {} ) ;
ok ($result->{Z},33) ;
}

# 2 - Here, any one of the patterns must match the input

{
my @pattern ;
push @pattern, [ qw(99 22) ] ;
push @pattern, [ qw(33 22) ] ;
push @pattern, [ qw(44 3) ] ;
push @pattern, [ qw(22 Z) ] ;

my $input = [ qw(22 33) ] ;

my $meta_pattern = [ OR?, @pattern ] ;

# if no bindings, add a binding between pattern and input
my $result = pat_match ($meta_pattern, $input, {} ) ;
ok ($result->{Z},33) ;
}

# 3 - Here, none of the patterns must match the input

{
my @pattern ;
push @pattern, [ qw(99 22) ] ;
push @pattern, [ qw(33 22) ] ;
push @pattern, [ qw(44 3) ] ;
push @pattern, [ qw(22 Z) ] ;

my $input = [ qw(22 33) ] ;

my $meta_pattern = [ NOT?, @pattern ] ;

# if no bindings, add a binding between pattern and input
my $result = pat_match ($meta_pattern, $input, {} ) ;
ok (scalar keys %$result == 0) ;
}

# 4 - here the input must satisfy the predicate
{
sub numberp { $_[0] =~ /d+/ }

my $pattern = [ qw(X age), [qw(IS? N), νmberp] ] ;
my $input = [ qw(Mary age), thirty-four ] ;

# if no bindings, add a binding between pattern and input
my $result = pat_match ($pattern, $input, {} ) ;
ok (!defined($result));
}

# 5 - same thing, but this time a failing result ---
# not undef because it is the return val of numberp
{
sub numberp { $_[0] =~ /d+/ }

my $pattern = [ qw(X age), [qw(IS? N), νmberp] ] ;
my $input = [ qw(Mary age), 34 ] ;
my $result = pat_match ($pattern, $input, {} ) ;

ok ($result->{N},34) ;
}
Segment-matching:
Match a chunk of the input stream using *, +, ?

# 1 - * is greedy in this case, but not with 2 consecutve * patterns
{
my $pattern = [a, [qw(X *)], d] ;
my $input = [a, b, c, d] ;

# if no bindings, add a binding between pattern and input
my $result = pat_match ($pattern, $input, {} ) ;
warn sprintf "X*RETVAL: %s", Data::Dumper::Dumper($result) ;
ok ("@{$result->{X}}","b c") ;
}
# 2 - X* gets nothing, Y* gets all it can:
{

my $pattern = [a, [qw(X *)], [qw(Y *)], d] ;
my $input = [a, b, c, d] ;

# if no bindings, add a binding between pattern and input
my $result = pat_match ($pattern, $input, {} ) ;
warn sprintf "X*Y*RETVAL: %s", Data::Dumper::Dumper($result) ;
ok ("@{$result->{Y}}","b c") ;

}
# 3 - samething , but require at least one match for X
{
my $pattern = [a, [qw(X +)], d] ;
my $input = [a, b, c, d] ;

my $result = pat_match ($pattern, $input, {} ) ;
warn sprintf "RETVAL: @{$result->{X}}" ;
ok ("@{$result->{X}}","b c") ;
}
# 4 - require 0 or 1 match for X
{
my $pattern = [ a, [qw(X ?)], c ] ;
my $input = [ a, b, c ] ;


my $result = pat_match ($pattern, $input, {} ) ;

ok ("$result->{X}","b") ;
}
# 5 - evaluate a sub on the fly after match
{
my $pattern = [ qw(X OP Y is Z),
[
sub { "($_->{X} $_->{OP} $_->{Y}) == $_->{Z}" },
IF?
]
] ;
my $input = [qw(3 + 4 is 7) ] ;

my $result = pat_match ($pattern, $input, {} ) ;

ok ($result) ;
}
# --- 6 same thing, but fail
{
my $pattern = [ qw(X OP Y is Z),
[
sub { "($_->{X} $_->{OP} $_->{Y}) == $_->{Z}" },
IF?
]
] ;
my $input = [qw(3 + 4 is 8) ] ;

my $result = pat_match ($pattern, $input, {} ) ;
warn sprintf "IF_RETVAL2: *%s*", Data::Dumper::Dumper($result);
ok ($result eq ) ;
}

Duplicate Music Matcher is a script to quickly find duplicate MP3 files based on letter matching.
Duplicate Music Matcher is helpful for weeding out duplicates that may not be of the same encoding, format, or even the same filename.
Enhancements:
- pymad pyvorbis python-flac deps were all removed in favor of mutagen
- GUI code was updated (no more SimpleGladeApp)
- Delete key have been bound in the GUI
- More accurate matching for ogg and flac files
- The filename column is now resizable
- The GUI play and stop buttons now use audacious.FLAC should now also give a length value
- Some oggs might have a huge bitrate value due to a bug in mutagen.