(ph S SK r S SKrS SKrS SKrS SKrS SKrS SKrS SKrSSKJ r S SK
J r J r J
r
S SKJrJrJr S SKr/ SQrS rS r " S S
5 rS rS rS
rS rS S jr S r Sq Sq S S jr S S jr!\ " S5 S S j5 r"\ " S5 \RF 4S j5 r$0 r%\RL " S\RN 5 r(\ " S5 S!S j5 r)S r*S r+S r,S r-S r.S r/g)" N )
set_module) issubclass_ issubsctype
issubdtype)ndarrayufuncasarray)
r r r deprecatedeprecate_with_doc get_includeinfosourcewholookfor byte_bounds safe_eval show_runtimec SSK Jn JnJn SSKJn [
R [ R [ R " 5 S./n/ / peU H0 nX ( a UR U5 M UR U5 M2 UR SUUUS.05 SSK J
n UR U" 5 5 U" U5 g ! [ a [! S5 N f = f)
a
Print information about various resources in the system
including available intrinsic support and BLAS/LAPACK library
in use
.. versionadded:: 1.24.0
See Also
--------
show_config : Show libraries in the system on which NumPy was built.
Notes
-----
1. Information is derived with the help of `threadpoolctl `_
library if available.
2. SIMD related information is derived from ``__cpu_features__``,
``__cpu_baseline__`` and ``__cpu_dispatch__``
r __cpu_features____cpu_baseline____cpu_dispatch__)pprint)
numpy_versionpythonunamesimd_extensions)baselinefound not_found)threadpool_infozWARNING: `threadpoolctl` not found in system! Install it by `pip install threadpoolctl`. Once installed, try `np.show_runtime` again for more detailed build informationN) numpy.core._multiarray_umathr r r r np __version__sysversionplatformr append
threadpoolctlr" extend ImportErrorprint) r r r r config_foundfeatures_foundfeatures_not_foundfeaturer" s B/var/www/html/venv/lib/python3.13/site-packages/numpy/lib/utils.pyr r s ( ++ !
L
*,R&# $ ! !' * % %g . $
(#+
61O -. < 6
5 66s B= =CCc V SSK n U R cJ [ R R [ R R U R 5 SS5 nU$ SSKJn [ R R [ R R UR 5 S5 nU$ )a|
Return the directory that contains the NumPy \*.h header files.
Extension modules that need to compile against NumPy should use this
function to locate the appropriate include directory.
Notes
-----
When using ``distutils``, for example in ``setup.py``::
import numpy as np
...
Extension('extension_name', ...
include_dirs=[np.get_include()])
...
r Ncoreinclude) numpy show_configospathjoindirname__file__
numpy.corer4 )r6 dr4 s r2 r
r
K sp $ GGLL 8&) L
H " GGLL 7 C H c ( \ rS rSrSrSS jrS rSrg)
_Deprecateh zn
Decorator class to deprecate old functions.
Refer to `deprecate` for details.
See Also
--------
deprecate
Nc ( Xl X l X0l g N)old_namenew_namemessage)selfrE rF rG s r2 __init___Deprecate.__init__t s
r? c ^^
U R nU R nU R nUc TR nUc SU- m
O SU< SU< S3m
Ub T
SU- -
m
[ R
" T5 U
U4S j5 nXGl TR nUc T
nOUR 5 R S5 n [ U SS 5 n
U S R 5 ( a U
S
- U- nOC[ U S 5 S- n U SS H% n [ U 5 U
: a OU [ U 5 S- -
n M' XS n[ R " T
S
U
- 5 m
S R T
U/5 nXl U$ ) z*
Decorator call. Refer to ``decorate``.
Nz`%s` is deprecated!`z` is deprecated, use `z
` instead!
c H > [ R " T[ SS9 T" U 0 UD6$ )Nr )
stacklevel)warningswarnDeprecationWarning)argskwdsdepdocfuncs r2 newfunc$_Deprecate.__call__..newfunc s$ MM&"4 C&& &r? r z
)rE rF rG __name__ functoolswraps__doc__
expandtabssplit _get_indentlstriplentextwrapindentr: )rH rV rS kwargsrE rF rG rW doclinesre skiplinerU s ` @r2 __call___Deprecate.__call__y sf
====,, }}H *X5F *F dWn $F '
' $ll ;CNN$**40E qr+FQx slS( 58}q( !!"ID4y6) CIM)D & %j __VS6\:F++vsm,C r? )rG rF rE )NNN)r[
__module__ __qualname____firstlineno__r^ rI rk __static_attributes__ r? r2 rA rA h s
0r? rA c [ R nU H= n[ UR 5 5 nU( d M% [ U[ U5 U-
5 nM? U[ R :X a SnU$ )zM
Determines the leading whitespace that could be removed from all the lines.
r )r& maxsizerc rb min)rh re rj contents r2 ra ra sY [[Fdkkm$ 7TW!45F Mr? c \ U ( a U S nU SS n [ U 0 UD6" U5 $ [ U 0 UD6$ )a
Issues a DeprecationWarning, adds warning to `old_name`'s
docstring, rebinds ``old_name.__name__`` and returns the new
function object.
This function may also be used as a decorator.
Parameters
----------
func : function
The function to be deprecated.
old_name : str, optional
The name of the function to be deprecated. Default is None, in
which case the name of `func` is used.
new_name : str, optional
The new name for the function. Default is None, in which case the
deprecation message is that `old_name` is deprecated. If given, the
deprecation message is that `old_name` is deprecated and `new_name`
should be used instead.
message : str, optional
Additional explanation of the deprecation. Displayed in the
docstring after the warning.
Returns
-------
old_func : function
The deprecated function.
Examples
--------
Note that ``olduint`` returns a value after printing Deprecation
Warning:
>>> olduint = np.deprecate(np.uint)
DeprecationWarning: `uint64` is deprecated! # may vary
>>> olduint(6)
6
r rY NrA )rS rf fns r2 r r sA X
!WABx4*6*2..4*6**r? c [ U S9$ )aI
Deprecates a function and includes the deprecation in its docstring.
This function is used as a decorator. It returns an object that can be
used to issue a DeprecationWarning, by passing the to-be decorated
function as argument, this adds warning to the to-be decorated function's
docstring and returns the new function object.
See Also
--------
deprecate : Decorate a function such that it issues a `DeprecationWarning`
Parameters
----------
msg : str
Additional explanation of the deprecation. Displayed in the
docstring after the warning.
Returns
-------
obj : object
)rG rw )msgs r2 r r s 0 c ""r? c U R nUS S nUS nUS n[ U 5 R R nU=pgUc XpR U- -
nXg4$ [ XC5 H! u pU S: a XhS-
U - -
nM XxS-
U - -
nM# Xu-
nXg4$ )a
Returns pointers to the end-points of an array.
Parameters
----------
a : ndarray
Input array. It must conform to the Python-side of the array
interface.
Returns
-------
(low, high) : tuple of 2 integers
The first integer is the first byte of the array, the second
integer is just past the last byte of the array. If `a` is not
contiguous it will not use every byte between the (`low`, `high`)
values.
Examples
--------
>>> I = np.eye(2, dtype='f'); I.dtype
dtype('float32')
>>> low, high = np.byte_bounds(I)
>>> high - low == I.size*I.itemsize
True
>>> I = np.eye(2); I.dtype
dtype('float64')
>>> low, high = np.byte_bounds(I)
>>> high - low == I.size*I.itemsize
True
datar stridesshaperY )__array_interface__r
dtypeitemsizesizezip)
aaia_dataastridesashapebytes_aa_lowa_highr~ strides
r2 r r s @
B
Z]F)}H
[Faj ''GE&&7"" =
! 2MEz'6))7F** 3
=r? c
U c+ [ R " 5 R nUR n / n0 nU R 5 H n[ X [ 5 ( d M X n[ U5 nXcR 5 ; a
USX6 - - nSnOXCU' UnSnSR [ [ UR 5 5 n [ UR 5 n
UR XyXR R U/5 M Sn Sn Sn
SnU H nU [! US 5 : a [! US 5 n U [! US 5 : a [! US 5 n U
[! US 5 : a [! US 5 n
US ( d Mo U[# US 5 -
nM [! U5 S: a_ [% SU 5 n[% SU 5 n[% SU
5 nS US
- < S US
- < S US
- < S
3n[' US- S[! U5 S- - - S- 5 U Ht n[' US < S
S
W[! US 5 -
S- - < S
US < S
S
W[! US 5 -
S- - < S
US < S
S
W[! US 5 -
S- - < S
US < 3
5 Mv [' SU- 5 g)a
Print the NumPy arrays in the given dictionary.
If there is no dictionary passed in or `vardict` is None then returns
NumPy arrays in the globals() dictionary (all NumPy arrays in the
namespace).
Parameters
----------
vardict : dict, optional
A dictionary possibly containing ndarrays. Default is globals().
Returns
-------
out : None
Returns 'None'.
Notes
-----
Prints out the name, shape, bytes and type of all of the ndarrays
present in `vardict`.
Examples
--------
>>> a = np.arange(10)
>>> b = np.ones(20)
>>> np.who()
Name Shape Bytes Type
===========================================================
a 10 80 int64
b 20 160 float64
Upper bound on total bytes = 240
>>> d = {'x': np.arange(2.0), 'y': np.arange(3.0), 'txt': 'Some str',
... 'idx':5}
>>> np.who(d)
Name Shape Bytes Type
===========================================================
x 2 16 float64
y 3 24 float64
Upper bound on total bytes = 40
Nz (%s)r rY z x r
zName rZ z Shape z Bytes z TyperM = z'
Upper bound on total bytes = %d)r& _getframef_back f_globalskeys
isinstancer idr: mapstrr~ nbytesr) r namerc intmaxr- )vardictframestacacher varidvnamestroriginalshapestrbytestrmaxnamemaxshapemaxbyte
totalbytesvalsp1sp2sp3prvals r2 r r G sa X
&&//
C E gmW - --CS'Cjjl" 5:!55 !c
zz#c399"56H#**oG JJ7IINN "
# GHGJ SQ[ #a&kG c#a&k !3q6{H SQ[ #a&kG q66 #c!f+ %J 3x!|"g "h "g 47GSWc#gN
edlS#e*Q, //$67
ASSV_Q5F0G(+ASSV_Q5F0G(+ASSV_Q5F0G(+A0 1
4z
AB
r? c [ U 5 nUnU nSnUR U5 nU HW nXC:X a Sn OUn U[ U5 - [ U 5 - nXB: a$ US- [ U5 - nUS- SUS- - - U- nMP XY- U- nMY U$ )Nz, rY z,
rZ r )rc r` )
r argumentswidth
firstwidthknewstrsepstrarglistargumentaddstrs
r2 _split_liner s TJA
F
Foof%G ?FF
H
F + 9QX .Ae^c:a<&888CF_x/F Mr? r6 c [ U [ 5 [ 5 / 5 n U R U R 0nU R /nU R /n [ U5 S:X a X4$ UR
S5 nUR 5 Ho n[ XE [ R 5 ( d M& XE R nXb; d M; XE R nUR U5 UR U5 XqU' Mq M Nr )
__import__globalslocalsr[ __dict__rc popr r types
ModuleTyper) )modulethedictdictlist
totraversethisdictxmodnamemoddicts r2
_makenamedictr s
68R
8Fv/G H//"J
z?a >>!$ A(+u'7'788"+..* &k 2 2G OOG, %%g.'.G$ ! r? c SnSnS n[ U S[ U 5 5 n[ USU5 nU R nU R R nUc [
R n[ SXaS9 [ SU R US9 [ S XqS9 [ S
U R US9 [ S U" U R R 5 US9 [ S U" U R R 5 US9 [ S
U R R US9 [ S[ U R R R" 5 < U< 3US9 [ SSUS9 US; a$ [ U< [
R < U< 3US9 Sn OOUS:X a% [ U< SU< 3US9 [
R S:g n O$[ U< SU< 3US9 [
R S:g n [ SU" U 5 US9 [ SU R - US9 g)a Provide information about ndarray obj.
Parameters
----------
obj : ndarray
Must be ndarray, not checked.
output
Where printed output goes.
Notes
-----
Copied over from the numarray module prior to its removal.
Adapted somewhat as only numpy is an option now.
Called by info.
r c U $ rD rq )r s r2 _info.. s 1r? __class__r[ Nzclass: filezshape: z strides: z
itemsize: z aligned: z contiguous: z fortran: zdata pointer: z byteorder: rZ )endr )|r F>biglittlez
byteswap: ztype: %s)getattrtyper} r byteorderr& stdoutr- r~ r flagsaligned
contiguousfortranhexctypes_as_parameter_value)
objoutputextraticbpclsnmr} endianbyteswaps
r2 _infor s $
E
C B
#{DI
.C j# &BkkG
YY
F
~ )R% )SYYV, +w, , 62 +r#))++,6: ."SYY 1 12@ +syy((v6 #CJJ$=$=$C$C DeL
-Sv.
#s}}c2@ 3
3$62==E)
c3'f5==H, ,8 62 *syy
v.r? c SSK nSSKn[ U S5 ( d [ U S5 ( a
U R n O [ U S5 ( a U R n Uc [
R nU c [ [ 5 g[ U [ 5 ( a
[ XS9 g[ U [ 5 ( a [ c [ U5 u q qSn/ n[ Hq n [ U U n [ U 5 U; a [! SU- US9 O?UR# [ U 5 5 [! S U- US9 [ U 5 [! S
U- US9 US -
nMs US:X a [! S U - US9 g[! S
U- US9 gUR' U 5 ( d UR) U 5 ( as U R* n
[ UR- U 5 5 n [1 X- 5 U: a
[3 XU5 n OX- n [! SU - S- US9 [! UR5 U 5 US9 gUR7 U 5 ( GaQ U R* n
[ UR- U 5 5 n [1 X- 5 U: a
[3 XU5 n OX- n [! SU - S- US9 UR5 U 5 n
U
c5 [ U S5 ( a# [! UR5 U R8 5 US9 O[! UR5 U 5 US9 UR; U 5 nU Vs/ s H oS S:w d M
UPM nnU( aa [! SUS9 U HP n[= XS5 nUb, UR? UR5 U5 =( d S5 u nn[! SU< SW< 3US9 MR gg[ U S5 ( a [! UR5 U 5 US9 gg! [$ a GM f = f! [. a Sn GNf = f! [. a Sn GNf = fs snf )a
Get help information for an array, function, class, or module.
Parameters
----------
object : object or str, optional
Input object or name to get information about. If `object` is
an `ndarray` instance, information about the array is printed.
If `object` is a numpy object, its docstring is given. If it is
a string, available modules are searched for matching objects.
If None, information about `info` itself is returned.
maxwidth : int, optional
Printing width.
output : file like object, optional
File like object that the output is written to, default is
``None``, in which case ``sys.stdout`` will be used.
The object has to be opened in 'w' or 'a' mode.
toplevel : str, optional
Start search at this level.
See Also
--------
source, lookfor
Notes
-----
When used interactively with an object, ``np.info(obj)`` is equivalent
to ``help(obj)`` on the Python prompt or ``obj?`` on the IPython
prompt.
Examples
--------
>>> np.info(np.polyval) # doctest: +SKIP
polyval(p, x)
Evaluate the polynomial p at x.
...
When using a string for `object` it is possible to get multiple results.
>>> np.info('fft') # doctest: +SKIP
*** Found in numpy ***
Core FFT routines
...
*** Found in numpy.fft ***
fft(a, n=None, axis=-1)
...
*** Repeat reference found in numpy.fft.fftpack ***
*** Total of 3 references found. ***
When the argument is an array, information about the array is printed.
>>> a = np.array([[1 + 2j, 3, -4], [-5j, 6, 0]], dtype=np.complex64)
>>> np.info(a)
class: ndarray
shape: (2, 3)
strides: (24, 8)
itemsize: 8
aligned: True
contiguous: True
fortran: False
data pointer: 0x562b6e0d2860 # may vary
byteorder: little
byteswap: False
type: complex64
r N_ppimport_importer_ppimport_module_ppimport_attr)r z+
*** Repeat reference found in %s *** r z *** Found in %s ***-rY zHelp for %s not found.z+
*** Total of %d references found. ***z()rZ rM rI _z
Methods:
Nonez z -- r^ ) pydocinspecthasattrr r r& r r r r r r _namedictr _dictlistr r- r) KeyError
isfunctionismethodr[ signature Exceptionrc r getdocisclassrI
allmethodsr splitdoc)objectmaxwidthr toplevelr r numfoundobjlistr r r r argstrdoc1methodsmethpublic_methodsthisobjmethstrothers r2 r r s L ,-- 6- . .(( ) * *&&
~
~ T
FG $ $
f$ FC #0#: Iy G
(0c7g% BDKL %
NN2c7+4w>VLI#h,V4A
! q= *V3& A :>> np.source(np.interp) #doctest: +SKIP
In file: /usr/lib/python2.6/dist-packages/numpy/lib/function_base.py
def interp(x, xp, fp, left=None, right=None):
""".... (full docstring printed)"""
if isinstance(x, (float, int, number)):
return compiled_interp([x], xp, fp, left, right).item()
else:
return compiled_interp(x, xp, fp, left, right)
The source code is only returned for objects written in Python.
>>> np.source(np.array) #doctest: +SKIP
Not available for this object.
r Nz In file: %s
r z Not available for this object.)r r-
getsourcefile getsourcer )r r r s r2 r r sV R =
o 5 5f ==FK
g 'f5 =
.V<=s 5< AAz[a-z0-9_]+\(.*[,=].*\)c ^^^^^ SSK n[ XU5 m/ n[ U 5 R 5 R 5 mT( d gTR 5 HN u nu pn
U S; a M UR 5 m[
U4S jT 5 5 ( d M= UR U5 MP SSSSS.mUU4S jmUU4S jn UR U S
9 S S R T5 - n U S
[ U 5 - /n
USSS2 H nTU u mpTR 5 R S5 Vs/ s H* nUR 5 ( d M UR 5 PM, nn US R 5 n[ R U5 ( a US R 5 nU
R U< SU< 35 M U( d U
R S5 Ub! UR SR U
5 5 g[ U
5 S: a( UR! 5 nU" SR U
5 5 g[# SR U
5 5 gs snf ! [ a Sn Nf = f)a&
Do a keyword search on docstrings.
A list of objects that matched the search is displayed,
sorted by relevance. All given keywords need to be found in the
docstring for it to be returned as a result, but the order does
not matter.
Parameters
----------
what : str
String containing words to look for.
module : str or list, optional
Name of module(s) whose docstrings to go through.
import_modules : bool, optional
Whether to import sub-modules in packages. Default is True.
regenerate : bool, optional
Whether to re-generate the docstring cache. Default is False.
output : file-like, optional
File-like object to write the output to. If omitted, use a pager.
See Also
--------
source, info
Notes
-----
Relevance is determined only roughly, by checking if the keywords occur
in the function name, at the start of a docstring, etc.
Examples
--------
>>> np.lookfor('binary representation') # doctest: +SKIP
Search results for 'binary representation'
------------------------------------------
numpy.binary_repr
Return the binary representation of the input number as a string.
numpy.core.setup_common.long_double_representation
Given a binary dump as given by GNU od -b, look for long double
numpy.base_repr
Return a string representation of a number in the given base system.
...
r N)r r c 3 , ># U H oT; v M g 7frD rq ).0wrg s r2 lookfor..2 s 'ACxs i )rV classr r c > SnSR UR 5 R 5 R S5 S S 5 nU[ T Vs/ s H ofU; d M
SPM sn5 -
nU[ T Vs/ s H ofU ; d M
SPM sn5 -
nU[ U 5 * S- -
nUTR
US5 -
nX@R S5 * S - -
nU[ U* S
- S 5 -
nU$ s snf s snf ) Nr rM r r r .r d i) r: lowerstripr` sumrc getcountr ) r docstrkindindexr first_docr kind_relevancewhatss r2 relevancelookfor..relevance< s
IIflln 2 2 4 : :4 @! DE Su7u!Y#u7
88 Se1eDy"e1
22 c$iZ!^ ^
e
,, jjo
"" S%# t
$$ 81s
C"
C"
3 C'
C'
c > T" U /TU Q76 $ rD rq )r r r0 s r2 relevance_value lookfor..relevance_valueL s &U1X&&r? )keyzSearch results for '%s'rZ r rM rY r z
zNothing found.r )r _lookfor_generate_cacher r$ r` itemsallr) sortr: rc r% _function_signature_research
IndexErrorwritegetpagerr- )whatr import_modules
regenerater r r r docstringr* r+ r3 s help_textixrj doclinesr- pagerr rg r. r0 r/ s @@@@@r2 r r s ^
$FJ GE
E IOO # # %E */++-&&y ' ' oo '' ' ' LL
+8 #T %8N ' JJ?J# "SXXe_5ACAJIdd d
T-0YY[->->t-D %-DTzz| !DJJL-D % ))+I%,,Y77 $QK - - / y9: )* TYYy)* Y "
dii "#
dii "#-% I s %IIA I IIc SSK nSSKJn U c Sn [ U [ 5 ( a [ U 5 [ R U n OR[ U [ 5 ( d [ U [ 5 ( a( 0 nU H nUR [ XaU5 5 M U$ [ U 5 [ ; a U( d [ [ U 5 $ 0 nU[ [ U 5 ' 0 nSnU R U 4/n U ( Ga U R! S5 u p[ U 5 U; a M, SU[ U 5 ' US-
nSn UR# U 5 ( Ga1 Sn U R$ n
U( Ga` [) U S 5 ( GaN U R* GH= n[, R. " U5 GH n[, R0 R3 X5 n[, R0 R3 XS
5 n[, R0 R5 U5 ( a UR7 S 5 ( a USS nO)[, R0 R5 U5 ( a UnOM US
:X a M [ R8 n[ R: n U" 5 [ l U" 5 [ l [ U
< SU< 35 U[ l U[ l GM! GM@ [A U 5 H u nn [C USU
< SU< 35 n[C USS5 nSU; a U( a U< SU< 3nURG U
S- 5 ( d [ U[H 5 ( a O#Mo UR# U5 ( d U
b UU
; d M U RK U
< SU< 3U45 M O[URM U 5 ( a2 Sn [A U 5 H u nnU RK U
< SU< 3U45 M" O[) U S5 ( a Sn URO U 5 nUb UX4XZ' U ( a GM U$ ! [ a 0 s $ f = f! [& a Sn
GNf = f! U[ l U[ l f = f! [< a e [> a GM f = f! [D a U
< SU< 3nSn GN}f = f! [D a Sn Nf = f)a
Generate docstring cache for given module.
Parameters
----------
module : str, None, module
Module for which to generate docstring cache
import_modules : bool
Whether to import sub-modules in packages.
regenerate : bool
Re-generate the docstring cache
Returns
-------
cache : dict {obj_full_name: (docstring, kind, index), ...}
Docstring cache for the module, either cached one (regenerate=False)
or newly generated.
r N)StringIOr6 TrY r r __path__z __init__.pyz.pyrI r" r[ rm r rk rV )(r iorJ r r r r, r& moduleslisttupleupdater7 r _lookfor_cachesr[ r ismodule__all__AttributeErrorr rK r8 listdirr9 r: isfileendswithr stderrKeyboardInterrupt
BaseException _getmembersr NameError
startswithr r) r r )r rA rB r rJ r modseenr+ stackr itemr* _allpthmod_paththis_pyinit_py to_import
old_stdout
old_stderrnv item_namemod_namerg s r2 r7 r7 n s *
~&# v V$ FD ! !Z%>%>C LL01;=
> &z_$Z r&z**
E"'OBvJ
D
Eoov
& 'E
YYq\
d8t RX
D ! !D
||
'$
";"; ==C$&JJsO"$'',,s"="$'',,sm"L GGNN7 3 3 ( 1 1% 8 8(0"
I WW^^G 4 4(0I $$
2 $%),J),J 8-5Z
-5Z
*dI+F G-7
-7
- %4 )@ $D)1$ ':$7J KI &q, =H i'H+3Y ?I ++D3J77!!U++ !**1-- d q 11 56+ *, __T
"
"D#D)1 q 11 56 *
T:
&
&D ..&C ? ,EKk %n LU I @ "
6 .8
-7
0 " ! , % %% ! $ ,0 3I #H $6 C sq O5 P P5'2PP5 !QQ- 5 PP PPP22P55Q
QQ*)Q*- Q<;Q<c SS K n UR U 5 nU$ ! [ aB [ U 5 Vs/ s H" n[ X5 ( d M U[ X5 4PM$ Os snf nn U$ f = fr )r
getmembersr dirr r )rb r membersr s r2 r\ r\ sq ($$T* N (25d) ()Q d& )Awt'() ( ( N(s A%AAA%$A%c , SSK nUR U 5 $ )a
Protected string evaluation.
Evaluate a string containing a Python literal expression without
allowing the execution of arbitrary non-literal code.
.. warning::
This function is identical to :py:meth:`ast.literal_eval` and
has the same security implications. It may not always be safe
to evaluate large input strings.
Parameters
----------
source : str
The string to evaluate.
Returns
-------
obj : object
The result of evaluating `source`.
Raises
------
SyntaxError
If the code has invalid Python syntax, or if it contains
non-literal code.
Examples
--------
>>> np.safe_eval('1')
1
>>> np.safe_eval('[1, 2, 3]')
[1, 2, 3]
>>> np.safe_eval('{"foo": ("bar", 10.0)}')
{'foo': ('bar', 10.0)}
>>> np.safe_eval('import os')
Traceback (most recent call last):
...
SyntaxError: invalid syntax
>>> np.safe_eval('open("/home/user/.ssh/id_dsa").read()')
Traceback (most recent call last):
...
ValueError: malformed node or string: <_ast.Call object at 0x...>
r N)ast literal_eval)r rt s r2 r r s d F ##r? c z U R S:X a U$ U R SUS9n[ R " U5 n[ R R U5 ( a UR
S5 nUR 5 ( d U$ [ U[ R 5 ( a U$ [ R " XUS9 U$ )a7
Utility function to check median result from data for NaN values at the end
and return NaN in that case. Input result can also be a MaskedArray.
Parameters
----------
data : array
Sorted input data to median function
result : Array or MaskedArray
Result of median function.
axis : int
Axis along which the median was computed.
Returns
-------
result : scalar or ndarray
Median or NaN in axes which contained NaN in the input. If the input
was an array, NaN will be inserted in-place. If a scalar, either the
input itself or a scalar NaN.
r r6 )axisF)where) r taker$ isnanma
isMaskedArrayfilledanyr genericcopyto)r| resultrw potential_nansrk s r2 _median_nancheckr 9 s * yyA~
YYrY-N
A
uu1
HHUO 5577
&"**%% IIfA. Mr? c SSK Jn JnJn [ U5 S:X a [ U5 S:X a gSR U5 nU H nX ( a USU S3-
nM USU S3-
nM" U$ )ai
Returns a string contains the supported CPU features by the current build.
The string format can be explained as follows:
- dispatched features that are supported by the running machine
end with `*`.
- dispatched features that are "not" supported by the running machine
end with `?`.
- remained features are representing the baseline.
r r r rZ *?)r# r r r rc r: )r r r enabled_featuresr1 s r2 _opt_infor c sz !c*:&;q&@xx 01# $ !G9A . !G9A . $ r? c U R b U R SLn/ n/ n/ n/ nU R R 5 Hw u pg[ US 5 nXS La SnUR U5 UR U5 UR US 5 UR [ U5 S: a SOUS 5 My U( d U $ [
X#XEU R S9n [ R " XR S9$ U R bC U R u p[ U
5 n U R c XL a U $ [ R " X45 $ U R c U $ [ R " U R 5 $ ) a
Returns the dtype unchanged if it contained no metadata or a copy of the
dtype if it (or any of its structure dtypes) contained metadata.
This utility is used by `np.save` and `np.savez` to drop metadata before
saving.
.. note::
Due to its limitation this function may move to a more appropriate
home or change in the future and is considered semi-public API only.
.. warning::
This function does not preserve more strange things like record dtypes
and user dtypes may simply return the wrong thing. If you need to be
sure about the latter, check the result with:
``np.can_cast(new_dtype, dtype, casting="no")``.
Nr TrY r r )namesformatsoffsetstitlesr )align)
fieldsmetadatar8
drop_metadatar) rc dictr r$ r isalignedstructsubdtyper )
r found_metadatar r r r r fieldfield_dt structurer r~ new_subdtypes
r2 r r sJ *
|| t3 <<--/KD$U1X.HQx'!% LL NN8 $ NN58 $ MM#e*q.$eAh ? 0 L'^^%
xx )>)>?? # ..$X. >> !l&>Lxx -.. >> !Lxx ""r? rD )r6 )NL Nr6 )NTFN)0r8 r& rd r rerP r\ r( _utilsr numpy.core.numerictypesr r r r= r r r
r6 r$ rT r r
rA ra r r r r r r r r r r r r rR compileIr; r r7 r\ r r r r rq r? r2 r s'
H H . . 2j
:A AH 2+j#>1r\ N&
(3/l Gc3 c3L G ** -= -=f $=rttD G?Dy$ y$vIV3$l(T 8<#r?