/
numerictypes.py
670 lines (540 loc) · 16.9 KB
/
numerictypes.py
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"""
numerictypes: Define the numeric type objects
This module is designed so "from numerictypes import \\*" is safe.
Exported symbols include:
Dictionary with all registered number types (including aliases):
sctypeDict
Type objects (not all will be available, depends on platform):
see variable sctypes for which ones you have
Bit-width names
int8 int16 int32 int64 int128
uint8 uint16 uint32 uint64 uint128
float16 float32 float64 float96 float128 float256
complex32 complex64 complex128 complex192 complex256 complex512
datetime64 timedelta64
c-based names
bool_
object_
void, str_, unicode_
byte, ubyte,
short, ushort
intc, uintc,
intp, uintp,
int_, uint,
longlong, ulonglong,
single, csingle,
float_, complex_,
longfloat, clongfloat,
As part of the type-hierarchy: xx -- is bit-width
generic
+-> bool_ (kind=b)
+-> number
| +-> integer
| | +-> signedinteger (intxx) (kind=i)
| | | byte
| | | short
| | | intc
| | | intp int0
| | | int_
| | | longlong
| | \\-> unsignedinteger (uintxx) (kind=u)
| | ubyte
| | ushort
| | uintc
| | uintp uint0
| | uint_
| | ulonglong
| +-> inexact
| +-> floating (floatxx) (kind=f)
| | half
| | single
| | float_ (double)
| | longfloat
| \\-> complexfloating (complexxx) (kind=c)
| csingle (singlecomplex)
| complex_ (cfloat, cdouble)
| clongfloat (longcomplex)
+-> flexible
| +-> character
| | str_ (string_, bytes_) (kind=S) [Python 2]
| | unicode_ (kind=U) [Python 2]
| |
| | bytes_ (string_) (kind=S) [Python 3]
| | str_ (unicode_) (kind=U) [Python 3]
| |
| \\-> void (kind=V)
\\-> object_ (not used much) (kind=O)
"""
import numbers
from numpy.core.multiarray import (
ndarray, array, dtype, datetime_data, datetime_as_string,
busday_offset, busday_count, is_busday, busdaycalendar
)
from numpy.core.overrides import set_module
# we add more at the bottom
__all__ = ['sctypeDict', 'sctypes',
'ScalarType', 'obj2sctype', 'cast', 'nbytes', 'sctype2char',
'maximum_sctype', 'issctype', 'typecodes', 'find_common_type',
'issubdtype', 'datetime_data', 'datetime_as_string',
'busday_offset', 'busday_count', 'is_busday', 'busdaycalendar',
]
# we don't need all these imports, but we need to keep them for compatibility
# for users using np.core.numerictypes.UPPER_TABLE
from ._string_helpers import (
english_lower, english_upper, english_capitalize, LOWER_TABLE, UPPER_TABLE
)
from ._type_aliases import (
sctypeDict,
allTypes,
bitname,
sctypes,
_concrete_types,
_concrete_typeinfo,
_bits_of,
)
from ._dtype import _kind_name
# we don't export these for import *, but we do want them accessible
# as numerictypes.bool, etc.
from builtins import bool, int, float, complex, object, str, bytes
from numpy.compat import long, unicode
# We use this later
generic = allTypes['generic']
genericTypeRank = ['bool', 'int8', 'uint8', 'int16', 'uint16',
'int32', 'uint32', 'int64', 'uint64', 'int128',
'uint128', 'float16',
'float32', 'float64', 'float80', 'float96', 'float128',
'float256',
'complex32', 'complex64', 'complex128', 'complex160',
'complex192', 'complex256', 'complex512', 'object']
@set_module('numpy')
def maximum_sctype(t):
"""
Return the scalar type of highest precision of the same kind as the input.
Parameters
----------
t : dtype or dtype specifier
The input data type. This can be a `dtype` object or an object that
is convertible to a `dtype`.
Returns
-------
out : dtype
The highest precision data type of the same kind (`dtype.kind`) as `t`.
See Also
--------
obj2sctype, mintypecode, sctype2char
dtype
Examples
--------
>>> np.maximum_sctype(int)
<class 'numpy.int64'>
>>> np.maximum_sctype(np.uint8)
<class 'numpy.uint64'>
>>> np.maximum_sctype(complex)
<class 'numpy.complex256'> # may vary
>>> np.maximum_sctype(str)
<class 'numpy.str_'>
>>> np.maximum_sctype('i2')
<class 'numpy.int64'>
>>> np.maximum_sctype('f4')
<class 'numpy.float128'> # may vary
"""
g = obj2sctype(t)
if g is None:
return t
t = g
base = _kind_name(dtype(t))
if base in sctypes:
return sctypes[base][-1]
else:
return t
@set_module('numpy')
def issctype(rep):
"""
Determines whether the given object represents a scalar data-type.
Parameters
----------
rep : any
If `rep` is an instance of a scalar dtype, True is returned. If not,
False is returned.
Returns
-------
out : bool
Boolean result of check whether `rep` is a scalar dtype.
See Also
--------
issubsctype, issubdtype, obj2sctype, sctype2char
Examples
--------
>>> np.issctype(np.int32)
True
>>> np.issctype(list)
False
>>> np.issctype(1.1)
False
Strings are also a scalar type:
>>> np.issctype(np.dtype('str'))
True
"""
if not isinstance(rep, (type, dtype)):
return False
try:
res = obj2sctype(rep)
if res and res != object_:
return True
return False
except Exception:
return False
@set_module('numpy')
def obj2sctype(rep, default=None):
"""
Return the scalar dtype or NumPy equivalent of Python type of an object.
Parameters
----------
rep : any
The object of which the type is returned.
default : any, optional
If given, this is returned for objects whose types can not be
determined. If not given, None is returned for those objects.
Returns
-------
dtype : dtype or Python type
The data type of `rep`.
See Also
--------
sctype2char, issctype, issubsctype, issubdtype, maximum_sctype
Examples
--------
>>> np.obj2sctype(np.int32)
<class 'numpy.int32'>
>>> np.obj2sctype(np.array([1., 2.]))
<class 'numpy.float64'>
>>> np.obj2sctype(np.array([1.j]))
<class 'numpy.complex128'>
>>> np.obj2sctype(dict)
<class 'numpy.object_'>
>>> np.obj2sctype('string')
>>> np.obj2sctype(1, default=list)
<class 'list'>
"""
# prevent abstract classes being upcast
if isinstance(rep, type) and issubclass(rep, generic):
return rep
# extract dtype from arrays
if isinstance(rep, ndarray):
return rep.dtype.type
# fall back on dtype to convert
try:
res = dtype(rep)
except Exception:
return default
else:
return res.type
@set_module('numpy')
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)
False
>>> np.issubclass_(np.int32, float)
False
>>> np.issubclass_(np.float64, float)
True
"""
try:
return issubclass(arg1, arg2)
except TypeError:
return False
@set_module('numpy')
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)
False
>>> np.issubsctype(np.array([1]), int)
True
>>> np.issubsctype(np.array([1]), float)
False
"""
return issubclass(obj2sctype(arg1), obj2sctype(arg2))
@set_module('numpy')
def issubdtype(arg1, arg2):
r"""
Returns True if first argument is a typecode lower/equal in type hierarchy.
This is like the builtin :func:`issubclass`, but for `dtype`\ s.
Parameters
----------
arg1, arg2 : dtype_like
`dtype` or object coercible to one
Returns
-------
out : bool
See Also
--------
:ref:`arrays.scalars` : Overview of the numpy type hierarchy.
issubsctype, issubclass_
Examples
--------
`issubdtype` can be used to check the type of arrays:
>>> ints = np.array([1, 2, 3], dtype=np.int32)
>>> np.issubdtype(ints.dtype, np.integer)
True
>>> np.issubdtype(ints.dtype, np.floating)
False
>>> floats = np.array([1, 2, 3], dtype=np.float32)
>>> np.issubdtype(floats.dtype, np.integer)
False
>>> np.issubdtype(floats.dtype, np.floating)
True
Similar types of different sizes are not subdtypes of each other:
>>> np.issubdtype(np.float64, np.float32)
False
>>> np.issubdtype(np.float32, np.float64)
False
but both are subtypes of `floating`:
>>> np.issubdtype(np.float64, np.floating)
True
>>> np.issubdtype(np.float32, np.floating)
True
For convenience, dtype-like objects are allowed too:
>>> np.issubdtype('S1', np.string_)
True
>>> np.issubdtype('i4', np.signedinteger)
True
"""
if not issubclass_(arg1, generic):
arg1 = dtype(arg1).type
if not issubclass_(arg2, generic):
arg2 = dtype(arg2).type
return issubclass(arg1, arg2)
# This dictionary allows look up based on any alias for an array data-type
class _typedict(dict):
"""
Base object for a dictionary for look-up with any alias for an array dtype.
Instances of `_typedict` can not be used as dictionaries directly,
first they have to be populated.
"""
def __getitem__(self, obj):
return dict.__getitem__(self, obj2sctype(obj))
nbytes = _typedict()
_alignment = _typedict()
_maxvals = _typedict()
_minvals = _typedict()
def _construct_lookups():
for name, info in _concrete_typeinfo.items():
obj = info.type
nbytes[obj] = info.bits // 8
_alignment[obj] = info.alignment
if len(info) > 5:
_maxvals[obj] = info.max
_minvals[obj] = info.min
else:
_maxvals[obj] = None
_minvals[obj] = None
_construct_lookups()
@set_module('numpy')
def sctype2char(sctype):
"""
Return the string representation of a scalar dtype.
Parameters
----------
sctype : scalar dtype or object
If a scalar dtype, the corresponding string character is
returned. If an object, `sctype2char` tries to infer its scalar type
and then return the corresponding string character.
Returns
-------
typechar : str
The string character corresponding to the scalar type.
Raises
------
ValueError
If `sctype` is an object for which the type can not be inferred.
See Also
--------
obj2sctype, issctype, issubsctype, mintypecode
Examples
--------
>>> for sctype in [np.int32, np.double, np.complex_, np.string_, np.ndarray]:
... print(np.sctype2char(sctype))
l # may vary
d
D
S
O
>>> x = np.array([1., 2-1.j])
>>> np.sctype2char(x)
'D'
>>> np.sctype2char(list)
'O'
"""
sctype = obj2sctype(sctype)
if sctype is None:
raise ValueError("unrecognized type")
if sctype not in _concrete_types:
# for compatibility
raise KeyError(sctype)
return dtype(sctype).char
# Create dictionary of casting functions that wrap sequences
# indexed by type or type character
cast = _typedict()
for key in _concrete_types:
cast[key] = lambda x, k=key: array(x, copy=False).astype(k)
def _scalar_type_key(typ):
"""A ``key`` function for `sorted`."""
dt = dtype(typ)
return (dt.kind.lower(), dt.itemsize)
ScalarType = [int, float, complex, bool, bytes, str, memoryview]
ScalarType += sorted(_concrete_types, key=_scalar_type_key)
ScalarType = tuple(ScalarType)
# Now add the types we've determined to this module
for key in allTypes:
globals()[key] = allTypes[key]
__all__.append(key)
del key
typecodes = {'Character':'c',
'Integer':'bhilqp',
'UnsignedInteger':'BHILQP',
'Float':'efdg',
'Complex':'FDG',
'AllInteger':'bBhHiIlLqQpP',
'AllFloat':'efdgFDG',
'Datetime': 'Mm',
'All':'?bhilqpBHILQPefdgFDGSUVOMm'}
# backwards compatibility --- deprecated name
# Formal deprecation: Numpy 1.20.0, 2020-10-19 (see numpy/__init__.py)
typeDict = sctypeDict
# b -> boolean
# u -> unsigned integer
# i -> signed integer
# f -> floating point
# c -> complex
# M -> datetime
# m -> timedelta
# S -> string
# U -> Unicode string
# V -> record
# O -> Python object
_kind_list = ['b', 'u', 'i', 'f', 'c', 'S', 'U', 'V', 'O', 'M', 'm']
__test_types = '?'+typecodes['AllInteger'][:-2]+typecodes['AllFloat']+'O'
__len_test_types = len(__test_types)
# Keep incrementing until a common type both can be coerced to
# is found. Otherwise, return None
def _find_common_coerce(a, b):
if a > b:
return a
try:
thisind = __test_types.index(a.char)
except ValueError:
return None
return _can_coerce_all([a, b], start=thisind)
# Find a data-type that all data-types in a list can be coerced to
def _can_coerce_all(dtypelist, start=0):
N = len(dtypelist)
if N == 0:
return None
if N == 1:
return dtypelist[0]
thisind = start
while thisind < __len_test_types:
newdtype = dtype(__test_types[thisind])
numcoerce = len([x for x in dtypelist if newdtype >= x])
if numcoerce == N:
return newdtype
thisind += 1
return None
def _register_types():
numbers.Integral.register(integer)
numbers.Complex.register(inexact)
numbers.Real.register(floating)
numbers.Number.register(number)
_register_types()
@set_module('numpy')
def find_common_type(array_types, scalar_types):
"""
Determine common type following standard coercion rules.
Parameters
----------
array_types : sequence
A list of dtypes or dtype convertible objects representing arrays.
scalar_types : sequence
A list of dtypes or dtype convertible objects representing scalars.
Returns
-------
datatype : dtype
The common data type, which is the maximum of `array_types` ignoring
`scalar_types`, unless the maximum of `scalar_types` is of a
different kind (`dtype.kind`). If the kind is not understood, then
None is returned.
See Also
--------
dtype, common_type, can_cast, mintypecode
Examples
--------
>>> np.find_common_type([], [np.int64, np.float32, complex])
dtype('complex128')
>>> np.find_common_type([np.int64, np.float32], [])
dtype('float64')
The standard casting rules ensure that a scalar cannot up-cast an
array unless the scalar is of a fundamentally different kind of data
(i.e. under a different hierarchy in the data type hierarchy) then
the array:
>>> np.find_common_type([np.float32], [np.int64, np.float64])
dtype('float32')
Complex is of a different type, so it up-casts the float in the
`array_types` argument:
>>> np.find_common_type([np.float32], [complex])
dtype('complex128')
Type specifier strings are convertible to dtypes and can therefore
be used instead of dtypes:
>>> np.find_common_type(['f4', 'f4', 'i4'], ['c8'])
dtype('complex128')
"""
array_types = [dtype(x) for x in array_types]
scalar_types = [dtype(x) for x in scalar_types]
maxa = _can_coerce_all(array_types)
maxsc = _can_coerce_all(scalar_types)
if maxa is None:
return maxsc
if maxsc is None:
return maxa
try:
index_a = _kind_list.index(maxa.kind)
index_sc = _kind_list.index(maxsc.kind)
except ValueError:
return None
if index_sc > index_a:
return _find_common_coerce(maxsc, maxa)
else:
return maxa