本文整理汇总了Python中numpy.core.numeric.dtype方法的典型用法代码示例。如果您正苦于以下问题:Python numeric.dtype方法的具体用法?Python numeric.dtype怎么用?Python numeric.dtype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numpy.core.numeric
的用法示例。
在下文中一共展示了numeric.dtype方法的15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: has_nested_fields
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def has_nested_fields(ndtype):
"""
Returns whether one or several fields of a dtype are nested.
Parameters
----------
ndtype : dtype
Data-type of a structured array.
Raises
------
AttributeError
If `ndtype` does not have a `names` attribute.
Examples
--------
>>> dt = np.dtype([('name', 'S4'), ('x', float), ('y', float)])
>>> np.lib._iotools.has_nested_fields(dt)
False
"""
for name in ndtype.names or ():
if ndtype[name].names:
return True
return False
示例2: _strict_call
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [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3: test_flatten_dtype
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def test_flatten_dtype(self):
"Testing flatten_dtype"
# Standard dtype
dt = np.dtype([("a", "f8"), ("b", "f8")])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [float, float])
# Recursive dtype
dt = np.dtype([("a", [("aa", '|S1'), ("ab", '|S2')]), ("b", int)])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [np.dtype('|S1'), np.dtype('|S2'), int])
# dtype with shaped fields
dt = np.dtype([("a", (float, 2)), ("b", (int, 3))])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [float, int])
dt_flat = flatten_dtype(dt, True)
assert_equal(dt_flat, [float] * 2 + [int] * 3)
# dtype w/ titles
dt = np.dtype([(("a", "A"), "f8"), (("b", "B"), "f8")])
dt_flat = flatten_dtype(dt)
assert_equal(dt_flat, [float, float])
示例4: _vectorize_call
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def _vectorize_call(self, func, args):
"""Vectorized call to `func` over positional `args`."""
if self.signature is not None:
res = self._vectorize_call_with_signature(func, args)
elif not args:
res = func()
else:
ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)
# Convert args to object arrays first
inputs = [array(a, copy=False, subok=True, dtype=object)
for a in args]
outputs = ufunc(*inputs)
if ufunc.nout == 1:
res = array(outputs, copy=False, subok=True, dtype=otypes[0])
else:
res = tuple([array(x, copy=False, subok=True, dtype=t)
for x, t in zip(outputs, otypes)])
return res
示例5: asmatrix
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def asmatrix(data, dtype=None):
"""
Interpret the input as a matrix.
Unlike `matrix`, `asmatrix` does not make a copy if the input is already
a matrix or an ndarray. Equivalent to ``matrix(data, copy=False)``.
Parameters
----------
data : array_like
Input data.
dtype : data-type
Data-type of the output matrix.
Returns
-------
mat : matrix
`data` interpreted as a matrix.
Examples
--------
>>> x = np.array([[1, 2], [3, 4]])
>>> m = np.asmatrix(x)
>>> x[0,0] = 5
>>> m
matrix([[5, 2],
[3, 4]])
"""
return matrix(data, dtype=dtype, copy=False)
示例6: sum
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def sum(self, axis=None, dtype=None, out=None):
"""
Returns the sum of the matrix elements, along the given axis.
Refer to `numpy.sum` for full documentation.
See Also
--------
numpy.sum
Notes
-----
This is the same as `ndarray.sum`, except that where an `ndarray` would
be returned, a `matrix` object is returned instead.
Examples
--------
>>> x = np.matrix([[1, 2], [4, 3]])
>>> x.sum()
10
>>> x.sum(axis=1)
matrix([[3],
[7]])
>>> x.sum(axis=1, dtype='float')
matrix([[ 3.],
[ 7.]])
>>> out = np.zeros((1, 2), dtype='float')
>>> x.sum(axis=1, dtype='float', out=out)
matrix([[ 3.],
[ 7.]])
"""
return N.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis)
# To update docstring from array to matrix...
示例7: mean
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def mean(self, axis=None, dtype=None, out=None):
"""
Returns the average of the matrix elements along the given axis.
Refer to `numpy.mean` for full documentation.
See Also
--------
numpy.mean
Notes
-----
Same as `ndarray.mean` except that, where that returns an `ndarray`,
this returns a `matrix` object.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.mean()
5.5
>>> x.mean(0)
matrix([[ 4., 5., 6., 7.]])
>>> x.mean(1)
matrix([[ 1.5],
[ 5.5],
[ 9.5]])
"""
return N.ndarray.mean(self, axis, dtype, out, keepdims=True)._collapse(axis)
示例8: std
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def std(self, axis=None, dtype=None, out=None, ddof=0):
"""
Return the standard deviation of the array elements along the given axis.
Refer to `numpy.std` for full documentation.
See Also
--------
numpy.std
Notes
-----
This is the same as `ndarray.std`, except that where an `ndarray` would
be returned, a `matrix` object is returned instead.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.std()
3.4520525295346629
>>> x.std(0)
matrix([[ 3.26598632, 3.26598632, 3.26598632, 3.26598632]])
>>> x.std(1)
matrix([[ 1.11803399],
[ 1.11803399],
[ 1.11803399]])
"""
return N.ndarray.std(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis)
示例9: prod
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def prod(self, axis=None, dtype=None, out=None):
"""
Return the product of the array elements over the given axis.
Refer to `prod` for full documentation.
See Also
--------
prod, ndarray.prod
Notes
-----
Same as `ndarray.prod`, except, where that returns an `ndarray`, this
returns a `matrix` object instead.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.prod()
0
>>> x.prod(0)
matrix([[ 0, 45, 120, 231]])
>>> x.prod(1)
matrix([[ 0],
[ 840],
[7920]])
"""
return N.ndarray.prod(self, axis, dtype, out, keepdims=True)._collapse(axis)
示例10: getH
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
示例11: flatten_dtype
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [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12: _getdtype
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def _getdtype(cls, val):
"""Returns the dtype of the input variable."""
return np.array(val).dtype
#
示例13: _dtypeortype
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def _dtypeortype(cls, dtype):
"""Returns dtype for datetime64 and type of dtype otherwise."""
if dtype.type == np.datetime64:
return dtype
return dtype.type
#
示例14: upgrade_mapper
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def upgrade_mapper(cls, func, default=None):
"""
Upgrade the mapper of a StringConverter by adding a new function and
its corresponding default.
The input function (or sequence of functions) and its associated
default value (if any) is inserted in penultimate position of the
mapper. The corresponding type is estimated from the dtype of the
default value.
Parameters
----------
func : var
Function, or sequence of functions
Examples
--------
>>> import dateutil.parser
>>> import datetime
>>> dateparser = datetustil.parser.parse
>>> defaultdate = datetime.date(2000, 1, 1)
>>> StringConverter.upgrade_mapper(dateparser, default=defaultdate)
"""
# Func is a single functions
if hasattr(func, '__call__'):
cls._mapper.insert(-1, (cls._getsubdtype(default), func, default))
return
elif hasattr(func, '__iter__'):
if isinstance(func[0], (tuple, list)):
for _ in func:
cls._mapper.insert(-1, _)
return
if default is None:
default = [None] * len(func)
else:
default = list(default)
default.append([None] * (len(func) - len(default)))
for (fct, dft) in zip(func, default):
cls._mapper.insert(-1, (cls._getsubdtype(dft), fct, dft))
#
示例15: test_upgrade
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import dtype [as 别名]
def test_upgrade(self):
"Tests the upgrade method."
converter = StringConverter()
assert_equal(converter._status, 0)
# test int
assert_equal(converter.upgrade('0'), 0)
assert_equal(converter._status, 1)
# On systems where long defaults to 32-bit, the statuses will be
# offset by one, so we check for this here.
import numpy.core.numeric as nx
status_offset = int(nx.dtype(nx.int_).itemsize < nx.dtype(nx.int64).itemsize)
# test int > 2**32
assert_equal(converter.upgrade('17179869184'), 17179869184)
assert_equal(converter._status, 1 + status_offset)
# test float
assert_allclose(converter.upgrade('0.'), 0.0)
assert_equal(converter._status, 2 + status_offset)
# test complex
assert_equal(converter.upgrade('0j'), complex('0j'))
assert_equal(converter._status, 3 + status_offset)
# test str
# note that the longdouble type has been skipped, so the
# _status increases by 2. Everything should succeed with
# unicode conversion (5).
for s in ['a', u'a', b'a']:
res = converter.upgrade(s)
assert_(type(res) is unicode)
assert_equal(res, u'a')
assert_equal(converter._status, 5 + status_offset)