本文整理汇总了Python中__builtin__.int方法的典型用法代码示例。如果您正苦于以下问题:Python __builtin__.int方法的具体用法?Python __builtin__.int怎么用?Python __builtin__.int使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类__builtin__
的用法示例。
在下文中一共展示了__builtin__.int方法的11个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _strict_call
# 需要导入模块: import __builtin__ [as 别名]
# 或者: from __builtin__ import int [as 别名]
def _strict_call(self, value):
try:
# We check if we can convert the value using the current function
new_value = self.func(value)
# In addition to having to check whether func can convert the
# value, we also have to make sure that we don't get overflow
# errors for integers.
if self.func is int:
try:
np.array(value, dtype=self.type)
except OverflowError:
raise ValueError
# We're still here so we can now return the new value
return new_value
except ValueError:
if value.strip() in self.missing_values:
if not self._status:
self._checked = False
return self.default
raise ValueError("Cannot convert string '%s'" % value)
#
示例2: _set_array_types
# 需要导入模块: import __builtin__ [as 别名]
# 或者: from __builtin__ import int [as 别名]
def _set_array_types():
ibytes = [1, 2, 4, 8, 16, 32, 64]
fbytes = [2, 4, 8, 10, 12, 16, 32, 64]
for bytes in ibytes:
bits = 8*bytes
_add_array_type('int', bits)
_add_array_type('uint', bits)
for bytes in fbytes:
bits = 8*bytes
_add_array_type('float', bits)
_add_array_type('complex', 2*bits)
_gi = dtype('p')
if _gi.type not in sctypes['int']:
indx = 0
sz = _gi.itemsize
_lst = sctypes['int']
while (indx < len(_lst) and sz >= _lst[indx](0).itemsize):
indx += 1
sctypes['int'].insert(indx, _gi.type)
sctypes['uint'].insert(indx, dtype('P').type)
示例3: __init__
# 需要导入模块: import __builtin__ [as 别名]
# 或者: from __builtin__ import int [as 别名]
def __init__(self, delimiter=None, comments=asbytes('#'), autostrip=True):
self.comments = comments
# Delimiter is a character
if isinstance(delimiter, unicode):
delimiter = delimiter.encode('ascii')
if (delimiter is None) or _is_bytes_like(delimiter):
delimiter = delimiter or None
_handyman = self._delimited_splitter
# Delimiter is a list of field widths
elif hasattr(delimiter, '__iter__'):
_handyman = self._variablewidth_splitter
idx = np.cumsum([0] + list(delimiter))
delimiter = [slice(i, j) for (i, j) in zip(idx[:-1], idx[1:])]
# Delimiter is a single integer
elif int(delimiter):
(_handyman, delimiter) = (
self._fixedwidth_splitter, int(delimiter))
else:
(_handyman, delimiter) = (self._delimited_splitter, None)
self.delimiter = delimiter
if autostrip:
self._handyman = self.autostrip(_handyman)
else:
self._handyman = _handyman
#
示例4: __init__
# 需要导入模块: import __builtin__ [as 别名]
# 或者: from __builtin__ import int [as 别名]
def __init__(self, delimiter=None, comments=asbytes('#'), autostrip=True):
self.comments = comments
# Delimiter is a character
if isinstance(delimiter, unicode):
delimiter = delimiter.encode('ascii')
if (delimiter is None) or _is_bytes_like(delimiter):
delimiter = delimiter or None
_handyman = self._delimited_splitter
# Delimiter is a list of field widths
elif hasattr(delimiter, '__iter__'):
_handyman = self._variablewidth_splitter
idx = np.cumsum([0] + list(delimiter))
delimiter = [slice(i, j) for (i, j) in zip(idx[:-1], idx[1:])]
# Delimiter is a single integer
elif int(delimiter):
(_handyman, delimiter) = (self._fixedwidth_splitter, int(delimiter))
else:
(_handyman, delimiter) = (self._delimited_splitter, None)
self.delimiter = delimiter
if autostrip:
self._handyman = self.autostrip(_handyman)
else:
self._handyman = _handyman
#
示例5: flatten_dtype
# 需要导入模块: import __builtin__ [as 别名]
# 或者: from __builtin__ import int [as 别名]
def flatten_dtype(ndtype, flatten_base=False):
"""
Unpack a structured data-type by collapsing nested fields and/or fields
with a shape.
Note that the field names are lost.
Parameters
----------
ndtype : dtype
The datatype to collapse
flatten_base : bool, optional
If True, transform a field with a shape into several fields. Default is
False.
Examples
--------
>>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),
... ('block', int, (2, 3))])
>>> np.lib._iotools.flatten_dtype(dt)
[dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32')]
>>> np.lib._iotools.flatten_dtype(dt, flatten_base=True)
[dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32'),
dtype('int32'), dtype('int32'), dtype('int32'), dtype('int32'),
dtype('int32')]
"""
names = ndtype.names
if names is None:
if flatten_base:
return [ndtype.base] * int(np.prod(ndtype.shape))
return [ndtype.base]
else:
types = []
for field in names:
info = ndtype.fields[field]
flat_dt = flatten_dtype(info[0], flatten_base)
types.extend(flat_dt)
return types
示例6: __init__
# 需要导入模块: import __builtin__ [as 别名]
# 或者: from __builtin__ import int [as 别名]
def __init__(self, delimiter=None, comments='#', autostrip=True, encoding=None):
delimiter = _decode_line(delimiter)
comments = _decode_line(comments)
self.comments = comments
# Delimiter is a character
if (delimiter is None) or isinstance(delimiter, basestring):
delimiter = delimiter or None
_handyman = self._delimited_splitter
# Delimiter is a list of field widths
elif hasattr(delimiter, '__iter__'):
_handyman = self._variablewidth_splitter
idx = np.cumsum([0] + list(delimiter))
delimiter = [slice(i, j) for (i, j) in zip(idx[:-1], idx[1:])]
# Delimiter is a single integer
elif int(delimiter):
(_handyman, delimiter) = (
self._fixedwidth_splitter, int(delimiter))
else:
(_handyman, delimiter) = (self._delimited_splitter, None)
self.delimiter = delimiter
if autostrip:
self._handyman = self.autostrip(_handyman)
else:
self._handyman = _handyman
self.encoding = encoding
#
示例7: issubclass_
# 需要导入模块: import __builtin__ [as 别名]
# 或者: from __builtin__ import int [as 别名]
def issubclass_(arg1, arg2):
"""
Determine if a class is a subclass of a second class.
`issubclass_` is equivalent to the Python built-in ``issubclass``,
except that it returns False instead of raising a TypeError if one
of the arguments is not a class.
Parameters
----------
arg1 : class
Input class. True is returned if `arg1` is a subclass of `arg2`.
arg2 : class or tuple of classes.
Input class. If a tuple of classes, True is returned if `arg1` is a
subclass of any of the tuple elements.
Returns
-------
out : bool
Whether `arg1` is a subclass of `arg2` or not.
See Also
--------
issubsctype, issubdtype, issctype
Examples
--------
>>> np.issubclass_(np.int32, int)
True
>>> np.issubclass_(np.int32, float)
False
"""
try:
return issubclass(arg1, arg2)
except TypeError:
return False
示例8: _evalname
# 需要导入模块: import __builtin__ [as 别名]
# 或者: from __builtin__ import int [as 别名]
def _evalname(name):
k = 0
for ch in name:
if ch in '0123456789':
break
k += 1
try:
bits = int(name[k:])
except ValueError:
bits = 0
base = name[:k]
return base, bits
示例9: _add_aliases
# 需要导入模块: import __builtin__ [as 别名]
# 或者: from __builtin__ import int [as 别名]
def _add_aliases():
for type_name, info in typeinfo.items():
if isinstance(info, type):
continue
name = english_lower(type_name)
# insert bit-width version for this class (if relevant)
base, bit, char = bitname(info.type)
if base[-3:] == 'int' or char[0] in 'ui':
continue
if base != '':
myname = "%s%d" % (base, bit)
if (name not in ('longdouble', 'clongdouble') or
myname not in allTypes):
base_capitalize = english_capitalize(base)
if base == 'complex':
na_name = '%s%d' % (base_capitalize, bit//2)
elif base == 'bool':
na_name = base_capitalize
else:
na_name = "%s%d" % (base_capitalize, bit)
allTypes[myname] = info.type
# add mapping for both the bit name and the numarray name
sctypeDict[myname] = info.type
sctypeDict[na_name] = info.type
# add forward, reverse, and string mapping to numarray
sctypeNA[na_name] = info.type
sctypeNA[info.type] = na_name
sctypeNA[info.char] = na_name
if char != '':
sctypeDict[char] = info.type
sctypeNA[char] = na_name
示例10: issubsctype
# 需要导入模块: import __builtin__ [as 别名]
# 或者: from __builtin__ import int [as 别名]
def issubsctype(arg1, arg2):
"""
Determine if the first argument is a subclass of the second argument.
Parameters
----------
arg1, arg2 : dtype or dtype specifier
Data-types.
Returns
-------
out : bool
The result.
See Also
--------
issctype, issubdtype,obj2sctype
Examples
--------
>>> np.issubsctype('S8', str)
True
>>> np.issubsctype(np.array([1]), int)
True
>>> np.issubsctype(np.array([1]), float)
False
"""
return issubclass(obj2sctype(arg1), obj2sctype(arg2))
示例11: flatten_dtype
# 需要导入模块: import __builtin__ [as 别名]
# 或者: from __builtin__ import int [as 别名]
def flatten_dtype(ndtype, flatten_base=False):
"""
Unpack a structured data-type by collapsing nested fields and/or fields
with a shape.
Note that the field names are lost.
Parameters
----------
ndtype : dtype
The datatype to collapse
flatten_base : {False, True}, optional
Whether to transform a field with a shape into several fields or not.
Examples
--------
>>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float),
... ('block', int, (2, 3))])
>>> np.lib._iotools.flatten_dtype(dt)
[dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32')]
>>> np.lib._iotools.flatten_dtype(dt, flatten_base=True)
[dtype('|S4'), dtype('float64'), dtype('float64'), dtype('int32'),
dtype('int32'), dtype('int32'), dtype('int32'), dtype('int32'),
dtype('int32')]
"""
names = ndtype.names
if names is None:
if flatten_base:
return [ndtype.base] * int(np.prod(ndtype.shape))
return [ndtype.base]
else:
types = []
for field in names:
info = ndtype.fields[field]
flat_dt = flatten_dtype(info[0], flatten_base)
types.extend(flat_dt)
return types