我有一个具有以下结构的字典:
D = {
'rows': 11,
'cols': 13,
(i, j): {
'meta': 'random string',
'walls': {
'E': True,
'O': False,
'N': True,
'S': True
}
}
}
# i ranging in {0 .. D['rows']-1}
# j ranging in {0 .. D['cols']-1}
我被要求编写一个函数,该函数将任意对象作为参数并检查它是否具有该结构。这就是我写的:
def well_formed(L):
if type(L) != dict:
return False
if 'rows' not in L:
return False
if 'cols' not in L:
return False
nr, nc = L['rows'], L['cols']
# I should also check the int-ness of nr and nc ...
if len(L) != nr*nc + 2:
return False
for i in range(nr):
for j in range(nc):
if not ((i, j) in L
and 'meta' in L[i, j]
and 'walls' in L[i, j]
and type(L[i, j]['meta']) == str
and type(L[i, j]['walls']) == dict
and 'E' in L[i, j]['walls']
and 'N' in L[i, j]['walls']
and 'O' in L[i, j]['walls']
and 'S' in L[i, j]['walls']
and type(L[i, j]['walls']['E']) == bool
and type(L[i, j]['walls']['N']) == bool
and type(L[i, j]['walls']['O']) == bool
and type(L[i, j]['walls']['S']) == bool):
return False
return True
虽然它有效,但我根本不喜欢它。是否有Pythonic方法可以做到这一点?
我只允许使用标准库。
答案 0 :(得分:16)
首先,我认为更多'Pythonic'可能是请求宽恕而不是许可 - 检查您何时需要一个属性,无论数据结构是否具有该属性。
但另一方面,如果你被要求创造一些东西来检查它是否格式正确,那就无济于事。 :)
因此,如果需要检查,可以使用the schema library之类的内容来定义数据结构的外观,然后根据该模式检查其他数据结构。
答案 1 :(得分:7)
在Python中,所涉及类型的确切身份不如值的行为重要。对于这样一个对象的已定义的使用,该对象是否足够?这意味着L
不一定是dict
,只是支持__getitem__
; L[(i,j)]['meta']
不一定是str
,只需通过str(L[(i,j)]['meta'])
支持转换到字符串;等
考虑到放松,我只会尝试捕获尝试此类操作引起的任何错误,如果发生任何错误,则返回False
。例如,
def well_formed(L):
try:
nr = L['rows']
nc = L['cols']
except KeyError:
return False
try:
for i in range(nr):
for j in range(nc):
str(L[(i,j)]['meta'])
walls = L[(i,j)]['walls']
for direction in walls:
# Necessary?
if direction not in "ENOS":
return False
if walls[direction] not in (True, False):
return False
except KeyError:
return False
return True
鉴于任何对象都有一个布尔值,尝试bool(walls[direction])
似乎没有意义;相反,如果确切地将True
或False
作为一个值并不是一个硬性要求,那么我只会对该值进行任何测试。同样地,额外的墙可能是也可能不是问题,并且不必明确地进行测试。
答案 2 :(得分:4)
您可以像这样编写验证(来自Scala提取器的想法)。优点是验证器结构类似于要测试的结构。
缺点是许多函数调用可能会使它慢得多。
class Mapping:
def __init__(self, **kwargs):
self.key_values = [KeyValue(k, v) for k, v in kwargs.items()]
def validate(self, to_validate):
if not isinstance(to_validate, dict):
return False
for validator in self.key_values:
if not validator.validate(to_validate):
return False
return True
class KeyValue:
def __init__(self, key, value):
self.key = key
self.value = value
def validate(self, to_validate):
return self.key in to_validate and self.value.validate(to_validate[self.key])
class Boolean:
def validate(self, to_validate):
return isinstance(to_validate, bool)
class Integer:
def validate(self, to_validate):
return isinstance(to_validate, int)
class String:
def validate(self, to_validate):
return isinstance(to_validate, str)
class CustomValidator:
def validate(self, to_validate):
if not Mapping(rows=Integer(), cols=Integer()).validate(to_validate):
return False
element_validator = Mapping(meta=String(), walls=Mapping(**{k: Boolean() for k in "EONS"}))
for i in range(to_validate['rows']):
for j in range(to_validate['cols']):
if not KeyValue((i, j), element_validator).validate(to_validate):
return False
return True
d = {
'rows': 11,
'cols': 13,
}
d.update({(i, j): {
'meta': 'random string',
'walls': {
'E': True,
'O': False,
'N': True,
'S': True
}
} for i in range(11) for j in range(13)})
assert CustomValidator().validate(d)
覆盖isinstance(使用Python 3.5测试)
同样如此class IsInstanceCustomMeta(type):
def __instancecheck__(self, instance):
return self.validate(instance)
def create_custom_isinstance_class(f):
class IsInstanceCustomClass(metaclass=IsInstanceCustomMeta):
validate = f
return IsInstanceCustomClass
def Mapping(**kwargs):
key_values = [KeyValue(k, v) for k, v in kwargs.items()]
def validate(to_validate):
if not isinstance(to_validate, dict):
return False
for validator in key_values:
if not isinstance(to_validate, validator):
return False
return True
return create_custom_isinstance_class(validate)
def KeyValue(key, value):
return create_custom_isinstance_class(lambda to_validate: key in to_validate and isinstance(to_validate[key], value))
def my_format_validation(to_validate):
if not isinstance(to_validate, Mapping(rows=int, cols=int)):
return False
element_validator = Mapping(meta=str, walls=Mapping(**{k: bool for k in "EONS"}))
for i in range(to_validate['rows']):
for j in range(to_validate['cols']):
if not isinstance(to_validate, KeyValue((i, j), element_validator)):
return False
return True
MyFormat = create_custom_isinstance_class(my_format_validation)
d = {
'rows': 11,
'cols': 13,
}
d.update({(i, j): {
'meta': 'random string',
'walls': {
'E': True,
'O': False,
'N': True,
'S': True
}
} for i in range(11) for j in range(13)})
assert isinstance(d, MyFormat)
答案 3 :(得分:3)
如果您的格式更简单,我同意其他答案/评论,以使用现有的架构验证库,例如schema和voluptuous。但是,考虑到你必须检查带有元组键的字典的特定情况,以及那些元组'值取决于你的dict的其他成员的值,我认为你最好编写自己的验证器,而不是试图哄骗架构以适应你的格式。
答案 4 :(得分:1)
from itertools import product
def isvalid(d):
try:
for key in product(range(d['rows']), range(d['cols'])):
sub = d[key]
assert (isinstance(sub['meta'], str) and
all(isinstance(sub['walls'][c], bool)
for c in 'EONS'))
except (KeyError, TypeError, AssertionError):
return False
return True
如果Python 2兼容性很重要或者必须断言没有其他密钥,请告诉我。
答案 5 :(得分:0)
如果你使用这样的东西:
def get_deep_keys(d, depth=2):
"""Gets a representation of all dictionary keys to a set depth.
If a (sub)dictionary contains all non-dictionary values, a list of keys
will be returned.
If a dictionary contains a mix of types, a dictionary of dicts/lists/types
will be returned.
"""
if isinstance(d, dict):
if depth > 0 and any(isinstance(v, dict) for v in d.values()):
return {k: get_deep_keys(v, depth=depth - 1) for k, v in d.items()}
else:
return set(d.keys())
else:
return type(d)
然后你可以这样做:
assert get_deep_keys(D[i, j]) == {
'meta': str, 'walls': {'E', 'N', 'O', 'S'}}
在 i, j
上的循环内。这很容易修改以返回底层元素的类型:
def get_deep_keys(d, depth=2):
"""Gets a representation of all dictionary keys to a set depth, with types.
"""
if isinstance(d, dict) and depth > 0:
return {k: get_deep_keys(v, depth=depth - 1) for k, v in d.items()}
return type(d)
get_deep_keys(D)
# {'meta': str, 'walls': {'E': bool, 'O': bool, 'N': bool, 'S': bool}}