Current File : //opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/lib/type_check.py
"""Automatically adapted for numpy Sep 19, 2005 by convertcode.py
"""
import functools
__all__ = ['iscomplexobj', 'isrealobj', 'imag', 'iscomplex',
'isreal', 'nan_to_num', 'real', 'real_if_close',
'typename', 'asfarray', 'mintypecode',
'common_type']
from .._utils import set_module
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core import overrides, getlimits
from .ufunclike import isneginf, isposinf
array_function_dispatch = functools.partial(
overrides.array_function_dispatch, module='numpy')
_typecodes_by_elsize = 'GDFgdfQqLlIiHhBb?'
@set_module('numpy')
def mintypecode(typechars, typeset='GDFgdf', default='d'):
"""
Return the character for the minimum-size type to which given types can
be safely cast.
The returned type character must represent the smallest size dtype such
that an array of the returned type can handle the data from an array of
all types in `typechars` (or if `typechars` is an array, then its
dtype.char).
Parameters
----------
typechars : list of str or array_like
If a list of strings, each string should represent a dtype.
If array_like, the character representation of the array dtype is used.
typeset : str or list of str, optional
The set of characters that the returned character is chosen from.
The default set is 'GDFgdf'.
default : str, optional
The default character, this is returned if none of the characters in
`typechars` matches a character in `typeset`.
Returns
-------
typechar : str
The character representing the minimum-size type that was found.
See Also
--------
dtype, sctype2char, maximum_sctype
Examples
--------
>>> np.mintypecode(['d', 'f', 'S'])
'd'
>>> x = np.array([1.1, 2-3.j])
>>> np.mintypecode(x)
'D'
>>> np.mintypecode('abceh', default='G')
'G'
"""
typecodes = ((isinstance(t, str) and t) or asarray(t).dtype.char
for t in typechars)
intersection = set(t for t in typecodes if t in typeset)
if not intersection:
return default
if 'F' in intersection and 'd' in intersection:
return 'D'
return min(intersection, key=_typecodes_by_elsize.index)
def _asfarray_dispatcher(a, dtype=None):
return (a,)
@array_function_dispatch(_asfarray_dispatcher)
def asfarray(a, dtype=_nx.float_):
"""
Return an array converted to a float type.
Parameters
----------
a : array_like
The input array.
dtype : str or dtype object, optional
Float type code to coerce input array `a`. If `dtype` is one of the
'int' dtypes, it is replaced with float64.
Returns
-------
out : ndarray
The input `a` as a float ndarray.
Examples
--------
>>> np.asfarray([2, 3])
array([2., 3.])
>>> np.asfarray([2, 3], dtype='float')
array([2., 3.])
>>> np.asfarray([2, 3], dtype='int8')
array([2., 3.])
"""
if not _nx.issubdtype(dtype, _nx.inexact):
dtype = _nx.float_
return asarray(a, dtype=dtype)
def _real_dispatcher(val):
return (val,)
@array_function_dispatch(_real_dispatcher)
def real(val):
"""
Return the real part of the complex argument.
Parameters
----------
val : array_like
Input array.
Returns
-------
out : ndarray or scalar
The real component of the complex argument. If `val` is real, the type
of `val` is used for the output. If `val` has complex elements, the
returned type is float.
See Also
--------
real_if_close, imag, angle
Examples
--------
>>> a = np.array([1+2j, 3+4j, 5+6j])
>>> a.real
array([1., 3., 5.])
>>> a.real = 9
>>> a
array([9.+2.j, 9.+4.j, 9.+6.j])
>>> a.real = np.array([9, 8, 7])
>>> a
array([9.+2.j, 8.+4.j, 7.+6.j])
>>> np.real(1 + 1j)
1.0
"""
try:
return val.real
except AttributeError:
return asanyarray(val).real
def _imag_dispatcher(val):
return (val,)
@array_function_dispatch(_imag_dispatcher)
def imag(val):
"""
Return the imaginary part of the complex argument.
Parameters
----------
val : array_like
Input array.
Returns
-------
out : ndarray or scalar
The imaginary component of the complex argument. If `val` is real,
the type of `val` is used for the output. If `val` has complex
elements, the returned type is float.
See Also
--------
real, angle, real_if_close
Examples
--------
>>> a = np.array([1+2j, 3+4j, 5+6j])
>>> a.imag
array([2., 4., 6.])
>>> a.imag = np.array([8, 10, 12])
>>> a
array([1. +8.j, 3.+10.j, 5.+12.j])
>>> np.imag(1 + 1j)
1.0
"""
try:
return val.imag
except AttributeError:
return asanyarray(val).imag
def _is_type_dispatcher(x):
return (x,)
@array_function_dispatch(_is_type_dispatcher)
def iscomplex(x):
"""
Returns a bool array, where True if input element is complex.
What is tested is whether the input has a non-zero imaginary part, not if
the input type is complex.
Parameters
----------
x : array_like
Input array.
Returns
-------
out : ndarray of bools
Output array.
See Also
--------
isreal
iscomplexobj : Return True if x is a complex type or an array of complex
numbers.
Examples
--------
>>> np.iscomplex([1+1j, 1+0j, 4.5, 3, 2, 2j])
array([ True, False, False, False, False, True])
"""
ax = asanyarray(x)
if issubclass(ax.dtype.type, _nx.complexfloating):
return ax.imag != 0
res = zeros(ax.shape, bool)
return res[()] # convert to scalar if needed
@array_function_dispatch(_is_type_dispatcher)
def isreal(x):
"""
Returns a bool array, where True if input element is real.
If element has complex type with zero complex part, the return value
for that element is True.
Parameters
----------
x : array_like
Input array.
Returns
-------
out : ndarray, bool
Boolean array of same shape as `x`.
Notes
-----
`isreal` may behave unexpectedly for string or object arrays (see examples)
See Also
--------
iscomplex
isrealobj : Return True if x is not a complex type.
Examples
--------
>>> a = np.array([1+1j, 1+0j, 4.5, 3, 2, 2j], dtype=complex)
>>> np.isreal(a)
array([False, True, True, True, True, False])
The function does not work on string arrays.
>>> a = np.array([2j, "a"], dtype="U")
>>> np.isreal(a) # Warns about non-elementwise comparison
False
Returns True for all elements in input array of ``dtype=object`` even if
any of the elements is complex.
>>> a = np.array([1, "2", 3+4j], dtype=object)
>>> np.isreal(a)
array([ True, True, True])
isreal should not be used with object arrays
>>> a = np.array([1+2j, 2+1j], dtype=object)
>>> np.isreal(a)
array([ True, True])
"""
return imag(x) == 0
@array_function_dispatch(_is_type_dispatcher)
def iscomplexobj(x):
"""
Check for a complex type or an array of complex numbers.
The type of the input is checked, not the value. Even if the input
has an imaginary part equal to zero, `iscomplexobj` evaluates to True.
Parameters
----------
x : any
The input can be of any type and shape.
Returns
-------
iscomplexobj : bool
The return value, True if `x` is of a complex type or has at least
one complex element.
See Also
--------
isrealobj, iscomplex
Examples
--------
>>> np.iscomplexobj(1)
False
>>> np.iscomplexobj(1+0j)
True
>>> np.iscomplexobj([3, 1+0j, True])
True
"""
try:
dtype = x.dtype
type_ = dtype.type
except AttributeError:
type_ = asarray(x).dtype.type
return issubclass(type_, _nx.complexfloating)
@array_function_dispatch(_is_type_dispatcher)
def isrealobj(x):
"""
Return True if x is a not complex type or an array of complex numbers.
The type of the input is checked, not the value. So even if the input
has an imaginary part equal to zero, `isrealobj` evaluates to False
if the data type is complex.
Parameters
----------
x : any
The input can be of any type and shape.
Returns
-------
y : bool
The return value, False if `x` is of a complex type.
See Also
--------
iscomplexobj, isreal
Notes
-----
The function is only meant for arrays with numerical values but it
accepts all other objects. Since it assumes array input, the return
value of other objects may be True.
>>> np.isrealobj('A string')
True
>>> np.isrealobj(False)
True
>>> np.isrealobj(None)
True
Examples
--------
>>> np.isrealobj(1)
True
>>> np.isrealobj(1+0j)
False
>>> np.isrealobj([3, 1+0j, True])
False
"""
return not iscomplexobj(x)
#-----------------------------------------------------------------------------
def _getmaxmin(t):
from numpy.core import getlimits
f = getlimits.finfo(t)
return f.max, f.min
def _nan_to_num_dispatcher(x, copy=None, nan=None, posinf=None, neginf=None):
return (x,)
@array_function_dispatch(_nan_to_num_dispatcher)
def nan_to_num(x, copy=True, nan=0.0, posinf=None, neginf=None):
"""
Replace NaN with zero and infinity with large finite numbers (default
behaviour) or with the numbers defined by the user using the `nan`,
`posinf` and/or `neginf` keywords.
If `x` is inexact, NaN is replaced by zero or by the user defined value in
`nan` keyword, infinity is replaced by the largest finite floating point
values representable by ``x.dtype`` or by the user defined value in
`posinf` keyword and -infinity is replaced by the most negative finite
floating point values representable by ``x.dtype`` or by the user defined
value in `neginf` keyword.
For complex dtypes, the above is applied to each of the real and
imaginary components of `x` separately.
If `x` is not inexact, then no replacements are made.
Parameters
----------
x : scalar or array_like
Input data.
copy : bool, optional
Whether to create a copy of `x` (True) or to replace values
in-place (False). The in-place operation only occurs if
casting to an array does not require a copy.
Default is True.
.. versionadded:: 1.13
nan : int, float, optional
Value to be used to fill NaN values. If no value is passed
then NaN values will be replaced with 0.0.
.. versionadded:: 1.17
posinf : int, float, optional
Value to be used to fill positive infinity values. If no value is
passed then positive infinity values will be replaced with a very
large number.
.. versionadded:: 1.17
neginf : int, float, optional
Value to be used to fill negative infinity values. If no value is
passed then negative infinity values will be replaced with a very
small (or negative) number.
.. versionadded:: 1.17
Returns
-------
out : ndarray
`x`, with the non-finite values replaced. If `copy` is False, this may
be `x` itself.
See Also
--------
isinf : Shows which elements are positive or negative infinity.
isneginf : Shows which elements are negative infinity.
isposinf : Shows which elements are positive infinity.
isnan : Shows which elements are Not a Number (NaN).
isfinite : Shows which elements are finite (not NaN, not infinity)
Notes
-----
NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Examples
--------
>>> np.nan_to_num(np.inf)
1.7976931348623157e+308
>>> np.nan_to_num(-np.inf)
-1.7976931348623157e+308
>>> np.nan_to_num(np.nan)
0.0
>>> x = np.array([np.inf, -np.inf, np.nan, -128, 128])
>>> np.nan_to_num(x)
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary
-1.28000000e+002, 1.28000000e+002])
>>> np.nan_to_num(x, nan=-9999, posinf=33333333, neginf=33333333)
array([ 3.3333333e+07, 3.3333333e+07, -9.9990000e+03,
-1.2800000e+02, 1.2800000e+02])
>>> y = np.array([complex(np.inf, np.nan), np.nan, complex(np.nan, np.inf)])
array([ 1.79769313e+308, -1.79769313e+308, 0.00000000e+000, # may vary
-1.28000000e+002, 1.28000000e+002])
>>> np.nan_to_num(y)
array([ 1.79769313e+308 +0.00000000e+000j, # may vary
0.00000000e+000 +0.00000000e+000j,
0.00000000e+000 +1.79769313e+308j])
>>> np.nan_to_num(y, nan=111111, posinf=222222)
array([222222.+111111.j, 111111. +0.j, 111111.+222222.j])
"""
x = _nx.array(x, subok=True, copy=copy)
xtype = x.dtype.type
isscalar = (x.ndim == 0)
if not issubclass(xtype, _nx.inexact):
return x[()] if isscalar else x
iscomplex = issubclass(xtype, _nx.complexfloating)
dest = (x.real, x.imag) if iscomplex else (x,)
maxf, minf = _getmaxmin(x.real.dtype)
if posinf is not None:
maxf = posinf
if neginf is not None:
minf = neginf
for d in dest:
idx_nan = isnan(d)
idx_posinf = isposinf(d)
idx_neginf = isneginf(d)
_nx.copyto(d, nan, where=idx_nan)
_nx.copyto(d, maxf, where=idx_posinf)
_nx.copyto(d, minf, where=idx_neginf)
return x[()] if isscalar else x
#-----------------------------------------------------------------------------
def _real_if_close_dispatcher(a, tol=None):
return (a,)
@array_function_dispatch(_real_if_close_dispatcher)
def real_if_close(a, tol=100):
"""
If input is complex with all imaginary parts close to zero, return
real parts.
"Close to zero" is defined as `tol` * (machine epsilon of the type for
`a`).
Parameters
----------
a : array_like
Input array.
tol : float
Tolerance in machine epsilons for the complex part of the elements
in the array. If the tolerance is <=1, then the absolute tolerance
is used.
Returns
-------
out : ndarray
If `a` is real, the type of `a` is used for the output. If `a`
has complex elements, the returned type is float.
See Also
--------
real, imag, angle
Notes
-----
Machine epsilon varies from machine to machine and between data types
but Python floats on most platforms have a machine epsilon equal to
2.2204460492503131e-16. You can use 'np.finfo(float).eps' to print
out the machine epsilon for floats.
Examples
--------
>>> np.finfo(float).eps
2.2204460492503131e-16 # may vary
>>> np.real_if_close([2.1 + 4e-14j, 5.2 + 3e-15j], tol=1000)
array([2.1, 5.2])
>>> np.real_if_close([2.1 + 4e-13j, 5.2 + 3e-15j], tol=1000)
array([2.1+4.e-13j, 5.2 + 3e-15j])
"""
a = asanyarray(a)
type_ = a.dtype.type
if not issubclass(type_, _nx.complexfloating):
return a
if tol > 1:
f = getlimits.finfo(type_)
tol = f.eps * tol
if _nx.all(_nx.absolute(a.imag) < tol):
a = a.real
return a
#-----------------------------------------------------------------------------
_namefromtype = {'S1': 'character',
'?': 'bool',
'b': 'signed char',
'B': 'unsigned char',
'h': 'short',
'H': 'unsigned short',
'i': 'integer',
'I': 'unsigned integer',
'l': 'long integer',
'L': 'unsigned long integer',
'q': 'long long integer',
'Q': 'unsigned long long integer',
'f': 'single precision',
'd': 'double precision',
'g': 'long precision',
'F': 'complex single precision',
'D': 'complex double precision',
'G': 'complex long double precision',
'S': 'string',
'U': 'unicode',
'V': 'void',
'O': 'object'
}
@set_module('numpy')
def typename(char):
"""
Return a description for the given data type code.
Parameters
----------
char : str
Data type code.
Returns
-------
out : str
Description of the input data type code.
See Also
--------
dtype, typecodes
Examples
--------
>>> typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q',
... 'S', 'U', 'V', 'b', 'd', 'g', 'f', 'i', 'h', 'l', 'q']
>>> for typechar in typechars:
... print(typechar, ' : ', np.typename(typechar))
...
S1 : character
? : bool
B : unsigned char
D : complex double precision
G : complex long double precision
F : complex single precision
I : unsigned integer
H : unsigned short
L : unsigned long integer
O : object
Q : unsigned long long integer
S : string
U : unicode
V : void
b : signed char
d : double precision
g : long precision
f : single precision
i : integer
h : short
l : long integer
q : long long integer
"""
return _namefromtype[char]
#-----------------------------------------------------------------------------
#determine the "minimum common type" for a group of arrays.
array_type = [[_nx.half, _nx.single, _nx.double, _nx.longdouble],
[None, _nx.csingle, _nx.cdouble, _nx.clongdouble]]
array_precision = {_nx.half: 0,
_nx.single: 1,
_nx.double: 2,
_nx.longdouble: 3,
_nx.csingle: 1,
_nx.cdouble: 2,
_nx.clongdouble: 3}
def _common_type_dispatcher(*arrays):
return arrays
@array_function_dispatch(_common_type_dispatcher)
def common_type(*arrays):
"""
Return a scalar type which is common to the input arrays.
The return type will always be an inexact (i.e. floating point) scalar
type, even if all the arrays are integer arrays. If one of the inputs is
an integer array, the minimum precision type that is returned is a
64-bit floating point dtype.
All input arrays except int64 and uint64 can be safely cast to the
returned dtype without loss of information.
Parameters
----------
array1, array2, ... : ndarrays
Input arrays.
Returns
-------
out : data type code
Data type code.
See Also
--------
dtype, mintypecode
Examples
--------
>>> np.common_type(np.arange(2, dtype=np.float32))
<class 'numpy.float32'>
>>> np.common_type(np.arange(2, dtype=np.float32), np.arange(2))
<class 'numpy.float64'>
>>> np.common_type(np.arange(4), np.array([45, 6.j]), np.array([45.0]))
<class 'numpy.complex128'>
"""
is_complex = False
precision = 0
for a in arrays:
t = a.dtype.type
if iscomplexobj(a):
is_complex = True
if issubclass(t, _nx.integer):
p = 2 # array_precision[_nx.double]
else:
p = array_precision.get(t, None)
if p is None:
raise TypeError("can't get common type for non-numeric array")
precision = max(precision, p)
if is_complex:
return array_type[1][precision]
else:
return array_type[0][precision]
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