#简单介绍==============================================================
YAML使用寄主语言的数据类型，这在多种语言中流传的时候可能会引起兼容性的问题。
YAML语法规则：
http://www.ibm.com/developerworks/cn/xml/x-cn-yamlintro/
事例：
name: Tom Smith
age: 37
spouse:
name: Jane Smith
age: 25
children:
- name: Jimmy Smith
age: 15
- name1: Jenny Smith
age1: 12
使用python的yaml库PyYAML。http://pyyaml.org/
#在python中的应用========================================================
安装到python lib下后就可以正常使用了。
主要介绍yaml在python中如何读写。
1、读取单个yaml文件
yaml.load()方法
使用举例：
#加载yaml
import yaml #读取文件
f = open('test.yaml') #导入
x = yaml.load(f) print x
会得到结果：
{'age': 37, 'spouse': {'age': 25, 'name': 'Jane Smith'}, 'name': 'Tom Smith', 'children': [{'age': 15, 'name': 'Jimmy Smith'}, {'age1': 12, 'name1': 'Jenny Smith'}]}
在读取yaml文件时，对文件格式的要求极其的严格。有时候没有报错，却读不出任何内容。
文件的格式错误也是常有的事，注意：
yaml.load accepts a byte string, a Unicode string, an open binary file object, or an open text file object. A byte string or a file must be encoded with utf-8, utf-16-be or utf-16-le encoding. yaml.load detects the encoding by checking the BOM (byte order mark) sequence at the beginning of the string/file. If no BOM is present, theutf-8 encoding is assumed.
yaml.load可接收一个byte字符串，unicode字符串，打开的二进制文件或文本文件对象。字节字符串和文件必须是utf-8,utf-16-be或utf-16-le编码的.yaml.load通过检查字符串/文件开始的BOM（字节序标记）来确认编码。如果没有BOM，就默认为utf-8。
2、读取多个yaml
yaml.load_all()
如果string或文件包含几块yaml文档，你可以使用yaml.load_all来解析全部的文档。
yaml.load(stream, Loader=<class 'yaml.loader.Loader'>)
Parse the first YAML document in a stream #只解析第一个
and produce the corresponding Python object. yaml.load_all(stream, Loader=<class 'yaml.loader.Loader'>)
Parse all YAML documents in a stream
and produce corresponding Python objects.
注意，yaml.load_all 会生成一个迭代器，你要做的就是for 读出来
documents = """
name: The Set of Gauntlets 'Pauraegen'
description: >
A set of handgear with sparks that crackle
across its knuckleguards.
---
name: The Set of Gauntlets 'Paurnen'
description: >
A set of gauntlets that gives off a foul,
acrid odour yet remains untarnished.
---
name: The Set of Gauntlets 'Paurnimmen'
description: >
A set of handgear, freezing with unnatural cold.
""" for data in yaml.load_all(documents):
print data #{'description': 'A set of handgear with sparks that crackle across its #knuckleguards.\n',
#'name': "The Set of Gauntlets 'Pauraegen'"}
#{'description': 'A set of gauntlets that gives off a foul, acrid odour #yet remains untarnished.\n',
#'name': "The Set of Gauntlets 'Paurnen'"}
#{'description': 'A set of handgear, freezing with unnatural cold.\n',
#'name': "The Set of Gauntlets 'Paurnimmen'"}
safe_load描述
PyYAML allows you to construct a Python object of any type.
Even instances of Python classes can be constructed using the !!python/object tag.
PyYaml允许你构建任何类型的python对象，甚至是python类实例，只需要借助一下yaml标签!!python/object。
这个以后再说，非常有用的东西。
Note that the ability to construct an arbitrary Python object may be dangerous if you receive a YAML document from an untrusted source such as Internet. The function yaml.safe_load limits this ability to simple Python objects like integers or lists.
需要注意的是随意在yaml里构建python对象是有一定危险的，尤其是接收到一个未知的yaml文档。yaml.safe_load可以限制这个能力，就使用些简单的对象吧。
3、写yaml文件
yaml.dump 将一个python对象生成为yaml文档，与yaml.load搭配使用。
dump(data, stream=None, Dumper=<class 'yaml.dumper.Dumper'>, **kwds)
Serialize a Python object into a YAML stream.
If stream is None, return the produced string instead.
#很好，如果缺省数据流为空的话，就会给你返回个字符串作为yaml文档
应用事例：
aproject = {'name': 'Silenthand Olleander',
'race': 'Human',
'traits': ['ONE_HAND', 'ONE_EYE']
}
print yaml.dump(aproject)
#返回
#name: Silenthand Olleander
#race: Human
#traits: [ONE_HAND, ONE_EYE]
yaml.dump accepts the second optional argument, which must be an open text or binary file. In this case,yaml.dump will write the produced YAML document into the file. Otherwise, yaml.dump returns the produced document.
解释上面那句话的：yaml.dump接收的第二个参数一定要是一个打开的文本文件或二进制文件，yaml.dump会把生成的yaml文档写到文件里。否则，yaml.dump会返回生成的文档。
4、写多个yaml内容，yaml.dump_all函数
If you need to dump several YAML documents to a single stream, use the function yaml.dump_all.yaml.dump_all accepts a list or a generator producing
Python objects to be serialized into a YAML document. The second optional argument is an open file.
如果你需要把几段yaml文档同时写进一个数据流中，请使用yaml.dump_all函数。yaml.dump_all可以接收一个列表或者生成python对象的可序列化生成器（好别扭啊），第二个参数是打开的文件。这完全是对应yaml.load_all的。
yaml.dump supports a number of keyword arguments that specify formatting details for the emitter. For instance, you may set the preferred intendation and width, use the canonical YAML format or force preferred style for scalars and collections.
yaml.dump支持很多种确定格式化发射器的关键字参数（请先无视这句- -#）。比如你可以设置缩进和宽度（指的yaml文档），使用标准yaml格式或者强制优先样式对于标量和收集（请继续无视- -#）。
dump_all(documents, stream=None, Dumper=<class 'yaml.dumper.Dumper'>, default_style=None, default_flow_style=None, canonical=None, indent=None, width=None, allow_unicode=None, line_break=None, encoding='utf-8', explicit_start=None, explicit_end=None, version=None, tags=None) #不过对应具体的函数参数可以看出所叙述的几个参数
#cannonical
#indent
#width
#等等
例：
>>> print yaml.dump(range(50))
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42,
43, 44, 45, 46, 47, 48, 49] >>> print yaml.dump(range(50), width=50, indent=4)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27,
28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39,
40, 41, 42, 43, 44, 45, 46, 47, 48, 49] >>> print yaml.dump(range(5), canonical=True)
---
!!seq [
!!int "",
!!int "",
!!int "",
!!int "",
!!int "",
] >>> print yaml.dump(range(5), default_flow_style=False)
- 0
- 1
- 2
- 3
- 4 >>> print yaml.dump(range(5), default_flow_style=True, default_style='"')
[!!int "", !!int "", !!int "", !!int "", !!int ""]
参数仍需要研究。
#下面没看懂，先记下来，慢慢研究===================================================================
Constructors, representers, resolvers
构造器，描绘器（？），解析器
You may define your own application-specific tags. The easiest way to do it is to define a subclass ofyaml.YAMLObject
你可以自定义一个程序专属标签（tag），定义一个yaml.YAMLObject的子类的最简单方法可以这么干：
class Monster(yaml.YAMLObject):
yaml_tag = u'!Monster'
def __init__(self, name, hp, ac, attacks):
self.name = name
self.hp = hp
self.ac = ac
self.attacks = attacks
def __repr__(self):
return "%s(name=%r, hp=%r, ac=%r, attacks=%r)" % (
self.__class__.__name__, self.name, self.hp, self.ac,self.attacks)
The above definition is enough to automatically load and dump Monster objects:
上面这个定义的Monster类已经足够用来load和dump了:
>>> yaml.load("""
... --- !Monster
... name: Cave spider
... hp: [2,6] # 2d6
... ac: 16
... attacks: [BITE, HURT]
... """)
Monster(name='Cave spider', hp=[2, 6], ac=16, attacks=['BITE', 'HURT'])
>>> print yaml.dump(Monster(
... name='Cave lizard', hp=[3,6], ac=16, attacks=['BITE','HURT']))
!Monster
ac: 16
attacks: [BITE, HURT]
hp: [3, 6]
name: Cave lizard
yaml.YAMLObject uses metaclass magic to register a constructor, which transforms a YAML node to a class instance, and a representer, which serializes a class instance to a YAML node.
yaml.YAMLObject 使用魔法元类注册一个把yaml编码转成类实例的构造器，还有一个把类实例序列化成yaml编码的描述器。
If you don't want to use metaclasses, you may register your constructors and representers using the functionsyaml.add_constructor and yaml.add_representer. For instance, you may want to add a constructor and a representer for the following Dice class:
如果不想使用元类，也可以使用函数yaml.add_constructor和yaml.add_representer来注册构造器和描述器。例如，你可以把一个构造器和描述器加到下面这个Dice类里：
>>> class Dice(tuple):
... def __new__(cls, a, b):
... return tuple.__new__(cls, [a, b])
... def __repr__(self):
... return "Dice(%s,%s)" % self >>> print Dice(3,6)
Dice(3,6)
The default representation for Dice objects is not nice:
这个Dice对象默认的yaml描述可不怎么好看：
>>> print yaml.dump(Dice(3,6)) !!python/object/new:__main__.Dice
- !!python/tuple [3, 6]
Suppose you want a Dice object to represented as AdB in YAML:
好，现在假设你想把Dice对象描述成在yaml里为"AdB"的形式（A,B为变量）。
First we define a representer that convert a dice object to scalar node with the tag !dice and register it.
首先我们定义一个可以把Dice对象转换成带有'!dice'标签节点的描述器，然后注册。
>>> def dice_representer(dumper, data):
... return dumper.represent_scalar(u'!dice', u'%sd%s' % data) >>> yaml.add_representer(Dice, dice_representer)
Now you may dump an instance of the Dice object:
现在你就可以dump一个Dice实例了：
>>> print yaml.dump({'gold': Dice(10,6)})
{gold: !dice '10d6'}
Let us add the code to construct a Dice object:
让我们把节点加到Dice对象的构造器中。
>>> def dice_constructor(loader, node):
... value = loader.construct_scalar(node)
... a, b = map(int, value.split('d'))
... return Dice(a, b) >>> yaml.add_constructor(u'!dice', dice_constructor)
Then you may load a Dice object as well:
然后就可以使用了
>>> print yaml.load("""
... initial hit points: !dice 8d4
... """)
{'initial hit points': Dice(8,4)}
从这里可以看出了，constructor和representer是相对的，一个为load，一个为dump。