Metadata-Version: 1.1
Name: vdf
Version: 3.3
Summary: Library for working with Valve's VDF text format
Home-page: https://github.com/ValvePython/vdf
Author: Rossen Georgiev
Author-email: rossen@rgp.io
License: MIT
Description: |pypi| |license| |coverage| |scru| |master_build|
        
        Pure python module for (de)serialization to and from VDF that works just like ``json``.
        
        Tested and works on ``python2.7``, ``python3.3+``, ``pypy`` and ``pypy3``.
        
        VDF is Valve's KeyValue text file format
        
        https://developer.valvesoftware.com/wiki/KeyValues
        
        | Supported versions: ``kv1``
        | Unsupported: ``kv2`` and ``kv3``
        
        Install
        -------
        
        You can grab the latest release from https://pypi.org/project/vdf/ or via ``pip``
        
        .. code:: bash
        
            pip install vdf
        
        Install the current dev version from ``github``
        
        .. code:: bash
        
            pip install git+https://github.com/ValvePython/vdf
        
        
        Problems & solutions
        --------------------
        
        - There are known files that contain duplicate keys. This is supported the format and
          makes mapping to ``dict`` impossible. For this case the module provides ``vdf.VDFDict``
          that can be used as mapper instead of ``dict``. See the example section for details.
        
        - By default de-serialization will return a ``dict``, which doesn't preserve nor guarantee
          key order due to `hash randomization`_. If key order is important then
          I suggest using ``collections.OrderedDict``, or ``vdf.VDFDict``.
        
        Example usage
        -------------
        
        For text representation
        
        .. code:: python
        
            import vdf
        
            # parsing vdf from file or string
            d = vdf.load(open('file.txt'))
            d = vdf.loads(vdf_text)
            d = vdf.parse(open('file.txt'))
            d = vdf.parse(vdf_text)
        
            # dumping dict as vdf to string
            vdf_text = vdf.dumps(d)
            indented_vdf = vdf.dumps(d, pretty=True)
        
            # dumping dict as vdf to file
            vdf.dump(d, open('file2.txt','w'), pretty=True)
        
        
        For binary representation
        
        .. code:: python
        
            d = vdf.binary_loads(vdf_bytes)
            b = vdf.binary_dumps(d)
        
            # alternative format - VBKV
        
            d = vdf.binary_loads(vdf_bytes, alt_format=True)
            b = vdf.binary_dumps(d, alt_format=True)
        
            # VBKV with header and CRC checking
        
            d = vdf.vbkv_loads(vbkv_bytes)
            b = vdf.vbkv_dumps(d)
        
        Using an alternative mapper
        
        .. code:: python
        
          d = vdf.loads(vdf_string, mapper=collections.OrderedDict)
          d = vdf.loads(vdf_string, mapper=vdf.VDFDict)
        
        ``VDFDict`` works much like the regular ``dict``, except it handles duplicates and remembers
        insert order. Additionally, keys can only be of type ``str``. The most important difference
        is that when trying to assigning a key that already exist it will create a duplicate instead
        of reassign the value to the existing key.
        
        .. code:: python
        
          >>> d = vdf.VDFDict()
          >>> d['key'] = 111
          >>> d['key'] = 222
          >>> d
          VDFDict([('key', 111), ('key', 222)])
          >>> d.items()
          [('key', 111), ('key', 222)]
          >>> d['key']
          111
          >>> d[(0, 'key')]  # get the first duplicate
          111
          >>> d[(1, 'key')]  # get the second duplicate
          222
          >>> d.get_all_for('key')
          [111, 222]
        
          >>> d[(1, 'key')] = 123  # reassign specific duplicate
          >>> d.get_all_for('key')
          [111, 123]
        
          >>> d['key'] = 333
          >>> d.get_all_for('key')
          [111, 123, 333]
          >>> del d[(1, 'key')]
          >>> d.get_all_for('key')
          [111, 333]
          >>> d[(1, 'key')]
          333
        
          >>> print vdf.dumps(d)
          "key" "111"
          "key" "333"
        
          >>> d.has_duplicates()
          True
          >>> d.remove_all_for('key')
          >>> len(d)
          0
          >>> d.has_duplicates()
          False
        
        
        .. |pypi| image:: https://img.shields.io/pypi/v/vdf.svg?style=flat&label=latest%20version
            :target: https://pypi.org/project/vdf/
            :alt: Latest version released on PyPi
        
        .. |license| image:: https://img.shields.io/pypi/l/vdf.svg?style=flat&label=license
            :target: https://pypi.org/project/vdf/
            :alt: MIT License
        
        .. |coverage| image:: https://img.shields.io/coveralls/ValvePython/vdf/master.svg?style=flat
            :target: https://coveralls.io/r/ValvePython/vdf?branch=master
            :alt: Test coverage
        
        .. |scru| image:: https://scrutinizer-ci.com/g/ValvePython/vdf/badges/quality-score.png?b=master
            :target: https://scrutinizer-ci.com/g/ValvePython/vdf/?branch=master
            :alt: Scrutinizer score
        
        .. |master_build| image:: https://img.shields.io/travis/ValvePython/vdf/master.svg?style=flat&label=master%20build
            :target: http://travis-ci.org/ValvePython/vdf
            :alt: Build status of master branch
        
        .. _DuplicateOrderedDict: https://github.com/rossengeorgiev/dota2_notebooks/blob/master/DuplicateOrderedDict_for_VDF.ipynb
        
        .. _hash randomization: https://docs.python.org/2/using/cmdline.html#envvar-PYTHONHASHSEED
        
Keywords: valve keyvalue vdf tf2 dota2 csgo
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: Implementation :: PyPy
