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A library for working with Adblock Plus filter lists.
Project description
This repository contains a library for working with Adblock Plus filter lists,a script for rendering diffs between filter lists, and the script that is usedfor building Adblock Plus filter lists from the form in which they are authoredinto the format suitable for consumption by the adblocking software (akarendering).
Contents
- Library API
- Using the library with R
Installation
Prerequisites:
- Linux, Mac OS X or Windows (any modern Unix should work too),
- Python (2.7 or 3.5+),
- pip.
To install:
Rendering of filter lists
The filter lists are originally authored in relatively smaller parts focusedon particular types of filters, related to a specific topic or relevant for aparticular geographical area.We call these parts filter list fragments (or just fragments) todistinguish them from full filter lists that are consumed by the adblockingsoftware such as Adblock Plus.
Rendering is a process that combines filter list fragments into a filter list.It starts with one fragment that can include other ones and so forth.The produced filter list is marked with a version and a timestamp.
Python-abp contains a script that can do this called flrender:
This will take the top level fragment in fragment.txt, render it and save itinto filterlist.txt.
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The flrender script can also be used by only specifying fragment.txt:
in which case the rendering result will be sent to stdout. Moreover, whenit’s run with no positional arguments:
it will read from stdin and send the results to stdout.
Fragments might reference other fragments that should be included into them.The references come in two forms: http(s) includes and local includes:
The http include contains a URL that will be fetched and inserted at the pointof reference.The local include contains a path inside the easylist repository.flrender needs to be able to find a copy of the repository on the localfilesystem. We use -i option to point it to to the right directory:
Now the local include referenced above will be resolved to:/home/abc/easylist/easylist/easylist_general_block.txtand the fragment will be loaded from this file.
Directories that contain filter list fragments that are used during renderingare called sources.They are normally working copies of the repositories that contain filter listfragments.Each source is identified by a name: that’s the part that comes before “:” inthe include instruction and it should be the same as what comes before “=” inthe -i option.
Commonly used sources have generally accepted names. For example the mainEasyList repository is referred to as easylist.If you don’t know all the source names that are needed to render some list,just run flrender and it will report what it’s missing:
You can clone the necessary repositories to a local directory and add -ioptions accordingly.
Generating diffs
A diff allows a client running ad blocking software such as Adblock Plus toupdate the filter lists incrementally, instead of downloading a new copy of afull list during each update. This is meant to lessen the amount of resourcesused when updating filter lists (e.g. network data, memory usage, batteryconsumption, etc.), allowing clients to update their lists more frequentlyusing less resources.
python-abp contains a script called fldiff that will find the diff betweenthe latest filter list, and any number of previous filter lists:
where -o diffs/easylist/ is the (optional) output directory where the diffsshould be written, easylist.txt is the most recent version of the filterlist, and archive/* is the directory where all the archived filter lists are.When called like this, the shell should automatically expand the archive/*directory, giving the script each of the filenames separately.
In the above example, the output of each archived list[version].txt will bewritten to diffs/diff[version].txt. If the output argument is omitted, thediffs will be written to the current directory.
The script produces three types of lines, as specified in the technicalspecification:
- Special comments of the form ! <name>:[ <value>]
- Added filters of the form + <filter-text>
- Removed filters of the form - <filter-text>
Library API
python-abp can also be used as a library for parsing filter lists. For exampleto read a filter list (we use Python 3 syntax here but the API is the same):
If filterlist.txt contains this filter list:
the output will look something like:
The abp.filters module also exports a lower-level function for parsingindividual lines of a filter list: parse_line. It returns a parsed lineobject just like the items in the iterator returned by parse_filterlist.
For further information on the library API use help() on abp.filters andits contents in an interactive Python session, read the docstrings, or look atthe tests for some usage examples.
Blocks of filters
Further processing of blocks of filters separated by comments can be performedusing to_blocks function from abp.filters.blocks:
Use help() on abp.filters.blocks for more information.
Testing
Unit tests for python-abp are located in the /tests directory. Pytestis used for quickly running the tests during development. Tox is used fortesting in different environments (Python 2.7, Python 3.5+ and PyPy) and codequality reporting.
Use tox for a comprehensive report of unit tests and test coverage:
Development
When adding new functionality, add tests for it (preferably first). If somecode will never be reached on a certain version of Python, it may be exemptedfrom coverage tests by adding a comment, e.g. # pragma: no py2 cover.
All public functions, classes and methods should have docstrings compliant withNumPy/SciPy documentation guide.One exception is the constructors of classes that the user is not expected toinstantiate (such as exceptions).
Using the library with R
Installation
python-abp can be installed from PyPI or from the source code, eitherdirectly onto a system or in a virtual environment.
To install from PyPI:
To install from a local source, clone the repo and then:
To use the virtual environment, it must first be created. Python 2 and 3 usedifferent scripts to create a virtualenv.
In Python 2:
In Python 3:
Then, use the virtualenv’s version of pip to install python-abp, either fromPyPI or from source (as shown above):
For more information about virtualenv, please see the User Guide and thedocs.
Usage
In R, python-abp can be imported with reticulate:
Python Program
Now you can use the functions with abp$functionname, e.g.abp$line2dict('@@||g.doubleclick.net/pagead/$subdocument,domain=hon30.org')
For more information about the reticulate package, see their guide.
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As a popular open source development project, Python has an activesupporting community of contributors and users that also make their softwareavailable for other Python developers to use under open source license terms.
This allows Python users to share and collaborate effectively, benefitingfrom the solutions others have already created to common (and sometimeseven rare!) problems, as well as potentially contributing their ownsolutions to the common pool.
This guide covers the installation part of the process. For a guide tocreating and sharing your own Python projects, refer to thedistribution guide.
Note
For corporate and other institutional users, be aware that manyorganisations have their own policies around using and contributing toopen source software. Please take such policies into account when makinguse of the distribution and installation tools provided with Python.
Key terms¶
pipis the preferred installer program. Starting with Python 3.4, itis included by default with the Python binary installers.A virtual environment is a semi-isolated Python environment that allowspackages to be installed for use by a particular application, rather thanbeing installed system wide.
venvis the standard tool for creating virtual environments, and hasbeen part of Python since Python 3.3. Starting with Python 3.4, itdefaults to installingpipinto all created virtual environments.virtualenvis a third party alternative (and predecessor) tovenv. It allows virtual environments to be used on versions ofPython prior to 3.4, which either don’t providevenvat all, oraren’t able to automatically installpipinto created environments.The Python Packaging Index is a publicrepository of open source licensed packages made available for use byother Python users.
the Python Packaging Authority is the group ofdevelopers and documentation authors responsible for the maintenance andevolution of the standard packaging tools and the associated metadata andfile format standards. They maintain a variety of tools, documentation,and issue trackers on both GitHub andBitbucket.
distutilsis the original build and distribution system first added tothe Python standard library in 1998. While direct use ofdistutilsisbeing phased out, it still laid the foundation for the current packagingand distribution infrastructure, and it not only remains part of thestandard library, but its name lives on in other ways (such as the nameof the mailing list used to coordinate Python packaging standardsdevelopment).
Changed in version 3.5: The use of venv is now recommended for creating virtual environments.
See also
Basic usage¶
The standard packaging tools are all designed to be used from the commandline.
The following command will install the latest version of a module and itsdependencies from the Python Packaging Index:
Note
For POSIX users (including Mac OS X and Linux users), the examples inthis guide assume the use of a virtual environment.
For Windows users, the examples in this guide assume that the option toadjust the system PATH environment variable was selected when installingPython.
It’s also possible to specify an exact or minimum version directly on thecommand line. When using comparator operators such as >, < or some otherspecial character which get interpreted by shell, the package name and theversion should be enclosed within double quotes:
Normally, if a suitable module is already installed, attempting to installit again will have no effect. Upgrading existing modules must be requestedexplicitly:
More information and resources regarding pip and its capabilities can befound in the Python Packaging User Guide.
Creation of virtual environments is done through the venv module.Installing packages into an active virtual environment uses the commands shownabove.
See also
How do I …?¶
These are quick answers or links for some common tasks.
… install pip in versions of Python prior to Python 3.4?¶
Python only started bundling pip with Python 3.4. For earlier versions,pip needs to be “bootstrapped” as described in the Python PackagingUser Guide.
See also
… install packages just for the current user?¶
Passing the --user option to python-mpipinstall will install apackage just for the current user, rather than for all users of the system.
… install scientific Python packages?¶
A number of scientific Python packages have complex binary dependencies, andaren’t currently easy to install using pip directly. At this point intime, it will often be easier for users to install these packages byother meansrather than attempting to install them with pip.
See also
… work with multiple versions of Python installed in parallel?¶
On Linux, Mac OS X, and other POSIX systems, use the versioned Python commandsin combination with the -m switch to run the appropriate copy ofpip:
Appropriately versioned pip commands may also be available.
On Windows, use the py Python launcher in combination with the -mswitch:
Common installation issues¶
Installing into the system Python on Linux¶
On Linux systems, a Python installation will typically be included as partof the distribution. Installing into this Python installation requiresroot access to the system, and may interfere with the operation of thesystem package manager and other components of the system if a componentis unexpectedly upgraded using pip.
On such systems, it is often better to use a virtual environment or aper-user installation when installing packages with pip.
Pip not installed¶
It is possible that pip does not get installed by default. One potential fix is:
There are also additional resources for installing pip.
Installing binary extensions¶
Python has typically relied heavily on source based distribution, with endusers being expected to compile extension modules from source as part ofthe installation process.
With the introduction of support for the binary wheel format, and theability to publish wheels for at least Windows and Mac OS X through thePython Packaging Index, this problem is expected to diminish over time,as users are more regularly able to install pre-built extensions ratherthan needing to build them themselves.
Some of the solutions for installing scientific softwarethat are not yet available as pre-built wheel files may also help withobtaining other binary extensions without needing to build them locally.
See also