#VDF Parser

Based on https://github.com/ValvePython/vdf

Using this as a tool to process theater files for Insurgency and Day of Infamy

#Requirements

##Must Have

[X] Needs to support deep merging of complex dictionary type object [X] Some nodes will have multiple values with the same key [X] Support #base and #include notation to source other files [X] Maintain ordering of all nodes. [ ] Arbitrary nodes need to be merged/overwritten when combining bases [ ] Support "?conditionals" at any level [ ] Link values to other types or lists, so "allowed_items" would ensure that each item exists.

##Nice to have

[ ] Basic syntax checker to report problems in files

##Long term goals

[ ] Basic conditional processing to allow things like "for each item in theater/weapons, set class_restricted to 0" or "for each item in player_classes with name matching ".officer." Add all grenades and bolt action rifle a to allowed_weapons.

#Data types [ ] Item: has key and value, default string type, like "ammo_type": "ammo_556x45" [ ] Section: a group of items or sections

#Notes * Need to have good way to merge sections, so we can merge lists without duplicates and maintain the ordering of items. * Create custom value data types on the fly or with module, like "vector" or "ammo" * Requesting ("theater","weapons","weapon-m16a2","ammo") will look up over structure.

VDF is Valve's KeyValue text file format

https://developer.valvesoftware.com/wiki/KeyValues

The module works just like json for (de)serialization to and from 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

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

d = vdf.binary_loads(vdf_bytes)
b = vdf.binary_dumps(d)

Using an alternative mapper

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.

>>> d = vdf.VDFDict()
>>> d['key'] = 111
>>> d['key'] = 222
>>> d
VDFDict([('key', 111), ('key', 222)])
>>> d.items()
[('key', 111), ('key2', 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