本文整理汇总了Python中numpy.core.multiarray.dtype方法的典型用法代码示例。如果您正苦于以下问题:Python multiarray.dtype方法的具体用法?Python multiarray.dtype怎么用?Python multiarray.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.multiarray
的用法示例。
在下文中一共展示了multiarray.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: _set_array_types
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [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2: _mean
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
arr = asanyarray(a)
rcount = _count_reduce_items(arr, axis)
# Make this warning show up first
if rcount == 0:
warnings.warn("Mean of empty slice.", RuntimeWarning)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
ret = umr_sum(arr, axis, dtype, out, keepdims)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
示例3: _getintp_ctype
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def _getintp_ctype():
from .multiarray import dtype
val = _getintp_ctype.cache
if val is not None:
return val
char = dtype('p').char
import ctypes
if (char == 'i'):
val = ctypes.c_int
elif char == 'l':
val = ctypes.c_long
elif char == 'q':
val = ctypes.c_longlong
else:
val = ctypes.c_long
_getintp_ctype.cache = val
return val
示例4: test_column_subset_detect_empty
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def test_column_subset_detect_empty(self):
"""
Tests that when ordered=False, validation is possible by
passing a subset of the columns contained in the schema
Schema a b* (validation)
Data Frame b (error) a
column* is not being passed
There will be an error if other than zero errors are found.
"""
df = pd.read_csv(StringIO('''
b,a
1,1
2,3
3,3
'''), sep=',', header=0, dtype=str)
# should detect no errors
results_empty = self.schema.validate(df, columns=['a'])
self.assertEqual(len(results_empty), 0, 'There should be no errors')
示例5: test_column_subset_error
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def test_column_subset_error(self):
"""
Tests that when ordered=False, validation is possible by
passing a subset of the columns contained in the schema
Schema a b (validation)
Data Frame b (error) a
There will be an error if a column different than 'a' or 'b' is passed
"""
df = pd.read_csv(StringIO('''
b,a
1,1
2,3
3,3
'''), sep=',', header=0, dtype=str)
# should raise a PanSchArgumentError
self.assertRaises(PanSchArgumentError, self.schema.validate, df, columns=['c'])
示例6: test_mixed_columns
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def test_mixed_columns(self):
"""
Tests that when ordered=True, the schema columns are associated with data frame columns by position, not name.
In this case, the schema's column order is [a, b], while the data frame's order is [b, a]. There is an error in
column b in the data frame (leading whitespace), and a validation on column a in the schema.
Schema a (validation) b
Data Frame b (error) a
Thus there will only be an error if column b in the schema is linked to column a in the data frame,
as is correct behaviour when ordered=True.
"""
df = pd.read_csv(StringIO('''
b,a
1,1
2,3
3,3
'''), sep=',', header=0, dtype=str)
results = self.schema.validate(df)
self.assertEqual(len(results), 1, 'There should be 1 error')
self.assertEqual(results[0].row, 0)
self.assertEqual(results[0].column, 'b', 'The Schema object is not associating columns and column schemas by position')
示例7: _bits_of
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def _bits_of(obj):
try:
info = next(v for v in _concrete_typeinfo.values() if v.type is obj)
except StopIteration:
if obj in _abstract_types.values():
raise ValueError("Cannot count the bits of an abstract type")
# some third-party type - make a best-guess
return dtype(obj).itemsize * 8
else:
return info.bits
示例8: bitname
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def bitname(obj):
"""Return a bit-width name for a given type object"""
bits = _bits_of(obj)
dt = dtype(obj)
char = dt.kind
base = _kind_name(dt)
if base == 'object':
bits = 0
if bits != 0:
char = "%s%d" % (char, bits // 8)
return base, bits, char
示例9: _sum
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def _sum(a, axis=None, dtype=None, out=None, keepdims=False,
initial=_NoValue):
return umr_sum(a, axis, dtype, out, keepdims, initial)
示例10: _prod
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def _prod(a, axis=None, dtype=None, out=None, keepdims=False,
initial=_NoValue):
return umr_prod(a, axis, dtype, out, keepdims, initial)
示例11: _all
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def _all(a, axis=None, dtype=None, out=None, keepdims=False):
return umr_all(a, axis, dtype, out, keepdims)
示例12: _mean
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
arr = asanyarray(a)
is_float16_result = False
rcount = _count_reduce_items(arr, axis)
# Make this warning show up first
if rcount == 0:
warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None:
if issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
elif issubclass(arr.dtype.type, nt.float16):
dtype = mu.dtype('f4')
is_float16_result = True
ret = umr_sum(arr, axis, dtype, out, keepdims)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
if is_float16_result and out is None:
ret = arr.dtype.type(ret)
elif hasattr(ret, 'dtype'):
if is_float16_result:
ret = arr.dtype.type(ret / rcount)
else:
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
示例13: _std
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims)
if isinstance(ret, mu.ndarray):
ret = um.sqrt(ret, out=ret)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(um.sqrt(ret))
else:
ret = um.sqrt(ret)
return ret
示例14: _any
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def _any(a, axis=None, dtype=None, out=None, keepdims=False):
return umr_any(a, axis, dtype, out, keepdims)
示例15: ascontiguousarray
# 需要导入模块: from numpy.core import multiarray [as 别名]
# 或者: from numpy.core.multiarray import dtype [as 别名]
def ascontiguousarray(a, dtype=None):
"""
Return a contiguous array in memory (C order).
Parameters
----------
a : array_like
Input array.
dtype : str or dtype object, optional
Data-type of returned array.
Returns
-------
out : ndarray
Contiguous array of same shape and content as `a`, with type `dtype`
if specified.
See Also
--------
asfortranarray : Convert input to an ndarray with column-major
memory order.
require : Return an ndarray that satisfies requirements.
ndarray.flags : Information about the memory layout of the array.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> np.ascontiguousarray(x, dtype=np.float32)
array([[ 0., 1., 2.],
[ 3., 4., 5.]], dtype=float32)
>>> x.flags['C_CONTIGUOUS']
True
"""
return array(a, dtype, copy=False, order='C', ndmin=1)