from collections import namedtuple
 
# 基本例子
Point = namedtuple('Point',['x','y']) # 类名为Point,属性有'x'和'y'
 
p = Point(11, y=22) # 用位置或关键字参数实例化，因为'x'在'y'前，所以x=11,和函数参数赋值一样的
print(p[0]+p[1]) # 我们也可以使用下标来访问
# 33
 
x, y = p # 也可以像一个元组那样解析
print(x,y)
# (11, 22)
 
print(p.x+p.y) # 也可以通过属性名来访问
# 33
 
print(p) # 通过内置的__repr__函数，显示该对象的信息
# Point(x=11, y=22)

t = [11, 22] # 列表 list
p = Point._make(t) # 从列表中直接赋值，返回对象
print(Point(x=11, y=22))
# Point(x=11, y=22)

p = Point(x=11, y=22) # 新建一个对象
d = p._asdict() # 解析并返回一个字典对象
print(d)
# {'x': 11, 'y': 22}

p = Point(x=11, y=22) # x=11,y=22
print(p)
# Point(x=11, y=22)
 
d = p._replace(x=33) # x=33,y=22 新的对象
print(p)
# Point(x=11, y=22)
print(d)
# Point(x=33, y=22)

print(p._fields) # 查看属性名
# ('x', 'y')
 
Color = namedtuple('Color', 'red green blue')
Pixel = namedtuple('Pixel', Point._fields + Color._fields) # 新建一个子类，使用多个属性名
q = Pixel(11, 22, 128, 255, 0)
print(q)

Account = namedtuple('Account', ['type', 'balance'], defaults=[0])
print(Account._field_defaults)
#{'balance': 0}
print(Account('premium'))
#Account(type='premium', balance=0)

print(getattr(p, 'x'))
# 11

d = {'x': 11, 'y': 22} # 字典
p = Point(**d) # 双星号是重点
print(p)
# Point(x=11, y=22)

EmployeeRecord = namedtuple('EmployeeRecord', 'name, age, title, department, paygrade')
 
import csv
for emp in map(EmployeeRecord._make, csv.reader(open("employees.csv", "r"))):
 # 这里每行返回一个对象 注意！
 print(emp.name, emp.title)
 
import sqlite3
conn = sqlite3.connect('/companydata') # 连接数据库
cursor = conn.cursor()
cursor.execute('SELECT name, age, title, department, paygrade FROM employees')
for emp in map(EmployeeRecord._make, cursor.fetchall()):
 # 每行返回一个对象 注意！
 print(emp.name, emp.title)

NODES = "nodes"
EDGES = "edges"
RECEIVERS = "receivers"
SENDERS = "senders"
GLOBALS = "globals"
N_NODE = "n_node"
N_EDGE = "n_edge"
 
GRAPH_DATA_FIELDS = (NODES, EDGES, RECEIVERS, SENDERS, GLOBALS)
GRAPH_NUMBER_FIELDS = (N_NODE, N_EDGE)
 
class GraphsTuple(
	# 定义元组子类名 以及字典形式的键名（属性名）
 collections.namedtuple("GraphsTuple",
    GRAPH_DATA_FIELDS + GRAPH_NUMBER_FIELDS)): 
 
	# 这个函数用来判断依赖是否满足，和我们的namedtuple关系不大
	def _validate_none_fields(self):
		"""Asserts that the set of `None` fields in the instance is valid."""
		if self.n_node is None:
		 raise ValueError("Field `n_node` cannot be None")
		if self.n_edge is None:
		 raise ValueError("Field `n_edge` cannot be None")
		if self.receivers is None and self.senders is not None:
		 raise ValueError(
			 "Field `senders` must be None as field `receivers` is None")
		if self.senders is None and self.receivers is not None:
		 raise ValueError(
			 "Field `receivers` must be None as field `senders` is None")
		if self.receivers is None and self.edges is not None:
		 raise ValueError(
			 "Field `edges` must be None as field `receivers` and `senders` are "
			 "None")
 
	# 用来初始化一些参数 不是重点
	def __init__(self, *args, **kwargs):
		del args, kwargs
		# The fields of a `namedtuple` are filled in the `__new__` method.
		# `__init__` does not accept parameters.
		super(GraphsTuple, self).__init__()
		self._validate_none_fields()
 
	# 这就用到了_replace()函数,注意只要修改了属性值
	# 那么就返回一个新的对象
	def replace(self, **kwargs):
		output = self._replace(**kwargs) # 返回一个新的实例 
		output._validate_none_fields() # pylint: disable=protected-access 验证返回的新实例是否满足要求
		return output
 
	# 这是为了针对tensorflow1版本的函数
	# 返回一个拥有相同属性的对象，但是它的属性值是输入的大小和类型
	def map(self, field_fn, fields=GRAPH_FEATURE_FIELDS): # 对每个键应用函数
	"""Applies `field_fn` to the fields `fields` of the instance.
	`field_fn` is applied exactly once per field in `fields`. The result must
	satisfy the `GraphsTuple` requirement w.r.t. `None` fields, i.e. the
	`SENDERS` cannot be `None` if the `EDGES` or `RECEIVERS` are not `None`,
	etc.
	Args:
	 field_fn: A callable that take a single argument.
	 fields: (iterable of `str`). An iterable of the fields to apply
		`field_fn` to.
	Returns:
	 A copy of the instance, with the fields in `fields` replaced by the result
	 of applying `field_fn` to them.
	"""
	return self.replace(**{k: field_fn(getattr(self, k)) for k in fields}) # getattr(self, k) 获取的是键值对中的值, k表示键