Projects

Trac

Trac is an enhanced wiki and issue tracking system for software development projects. Trac uses a minimalistic approach to web-based software project management. Our mission; to help developers write great software while staying out of the way. Trac should impose as little as possible on a ... [More] team's established development process and policies. It provides an interface to Subversion, an integrated Wiki and convenient report facilities. Trac allows wiki markup in issue descriptions and commit messages, creating links and seamless references between bugs, tasks, changesets, files and wiki pages. A timeline shows all project events in order, making getting an overview of the project and tracking progress very easy. [Less]

Deluge

Deluge is a full-featured BitTorrent client for Linux, OS X, Unix and Windows. It uses libtorrent in its backend and features multiple user-interfaces including: GTK+, web and console. It has been designed using the client server model with a daemon process that handles all the bittorrent ... [More] activity. The Deluge daemon is able to run on headless machines with the user-interfaces being able to connect remotely from any platform. Deluge features a rich plugin collection; in fact, most of Deluge's functionality is available in the form of plugins. Deluge was created with the intention of being lightweight and unobtrusive. It is our belief that downloading shouldn't be the primary task on your computer and therefore shouldn't monopolize system resources. [Less]

ViewVC

ViewVC is a browser interface for CVS and Subversion version control repositories. It generates templatized HTML to present navigable directory, revision, and change log listings. It can display specific versions of files as well as diffs between those versions. Basically, ViewVC provides the bulk ... [More] of the report-like functionality you expect out of your version control tool, but much more prettily than the average textual command-line program output. [Less]

Whoosh Python Search Library

Whoosh is a fast, featureful full-text indexing and searching library implemented in pure Python. Some of Whoosh's features include: * Pythonic API. * Pure-Python. No compilation or binary packages needed, no mysterious crashes. * Fielded indexing and search. * Fast indexing and ... [More] retrieval -- much faster than any other pure-Python solution. * Pluggable scoring algorithm (including BM25F), text analysis, storage, posting format, etc. * Powerful query language. * Pure Python spell-checker. [Less]

Hatta Wiki

Hatta is a small wiki engine for use inside a Mercurial repository. It can run locally and doesn't require any configuration; it's just a single Python file. It can be also configured to run on a Web server. Since the wiki can be cloned and merged along with the repository, it's perfect ... [More] for working on project documentation in small teams. [Less]

BlackRay Data Engine

BlackRay is a relational database system designed to offer performance features commonly associated with search engines. It offers SQL support and sophisticated operational and management features. Load-balancing and operational stability by means of N+1 redundance are included. BlackRay is ... [More] called a "Data Engine" since it combines traditional, relational database features and SQL with the power and flexibility of search engines. It is a true hybrid, offering transaction support, data-versioned snapshots, and sophisticated function-based indices. Wildcards, phonetic, and fuzzy logic searches are supported, as well. Commercial support is available, and the project is released under a the GPLv2 license. [Less]

Xappy

The "xappy" python module is an easy-to-use interface to the Xapian search engine. Xapian provides a low level interface, dealing with terms and documents, but not really worrying about where terms come from, or how to build searches to match the way in which data has been indexed. In ... [More] contrast, "xappy" allows you to design a field structure, specifying what kind of information is held in particular fields, and then uses this field structure to index data appropriately, and to build and perform searches. [Less]

GeoBases

Data services and visualization. This project provides tools to play with geographical data. It also works with non-geographical data, except for map visualizations :). There are embedded data sources in the project, but you can easily play with your own data in addition to the available ones. ... [More] After data loading, you can: - perform various types of queries (find this key, or find keys with this property) - make fuzzy searches based on string distance (find things roughly named like this) - make phonetic searches (find things sounding like this) - make geographical searches (find things next to this place) - get results on a map, or export it as csv data, or as a Python object [Less]

isk-daemon

isk-daemon is an open source standalone server or library capable of adding content-based (visual) image searching to any image related website or software.

djapian

If you are using Djapian please tell us about your project in reply to this post Use this package to allow full-text search in your Django project. Versions compatibility matrix: Djapian Django Xapian and python bindings = 2.31.11.0.7 Notice: there is an old issue with Xapian (< ... [More] 1.0.13) in mod_python environment. So be careful. Notice: with 2.2.2 release has been introduced database schema backward-incompatible bug fix - Change model has switched its object_id field type from integer to string. FeaturesMost of this features provided by Xapian itself and Djapian in this case plays role only as Django-compatible adaptation. High-level DSL for indexer declaration Result filtering with Django ORM like API Result set compatible with standard Django Paginator Indexing of field, method results and related model attributes Entry filtering before indexing (by trigger function) Results filtering with boolean lookups support Term tagging Spelling corrections Stemming Result ordering by fields Indexers auto discovery Index shell Model changes auto tracking Support for different index spaces Usage exampleAssume that we have this models in our imaginary application: class Person(models.Model): name = models.CharField(max_length=150) def __unicode__(self): return self.name class Entry(models.Model): author = models.ForeignKey(Person, related_name="entries") title = models.CharField(max_length=250) created_on = models.DateTimeField(default=datetime.now) is_active = models.BooleanField(default=True) text = models.TextField() editors = models.ManyToManyField(Person, related_name="edited_entries") def headline(self): return "%s - %s" % (self.author, self.title) def __unicode__(self): return self.titleAnd we want to apply indexing functionality for model Entry. The next step is to create Indexer instance with proper settings. Indexer may look like this: import djapian class EntryIndexer(djapian.Indexer): fields=["text"] tags=[ ("author", "author.name" ), ("title", "title", 3), ("date", "created_on" ), ("active", "is_active" ), ("editors", "editors" ) ] trigger=lambda indexer, obj: obj.is_active djapian.space.add_index(Entry, EntryIndexer, attach_as="indexer")In the django shell create some instances of models: >>> p = Person.objects.create(name="Alex") >>> Entry.objects.create(author=p, title="Test entry", text="Not large text field") >>> Entry.objects.create(author=p, title="Another test entry", is_active=False) >>> Entry.objects.create(author=p, title="Third small entry", text="Some another text") >>> Entry.indexer.update()Thats all! Each Entry instance has been indexed and now ready for search. Let's try: >>> result = Entry.indexer.search('title:entry') >>> len(result), result.count() 2, 2 >>> for row in result: ... row.percent, row.instance.headline() ... 99 Alex - Test entry 98 Alex - Third small entryYou can follow complete Tutorial for study Djapian basics. [Less]