doi%SrSSKrSSKrSSKrSSKrSSKJr SSKJr SSK J r SSK J r SSK JrJrJr SSKJr SSKrSSKJ r SS KJrJrJrJrJrJrJrJrJr J!r"J#r$J%r&J'r(J)r*J+r,J-r. SS K/J0r0 SS K1J2r2 SS K3J4r4J5r5J6r6 SS K7J8r8 SSK9J:r; /SQr<\r=\\>S'\=\R~-r?\\>S'S\=S\ SS4Sjr@S%SSSS.S\?S\S\SS-S\AS-S\ S-S\AS\AS\=4SjjjrBSS.S \=S\ S-S\=4S!jjrCS \=S\R4S"jrE\ "S#5rF\ \ \>S$'\S\ S\S%4S&j5rGSS'.S(jrHS)S)S)S)S*.S+jrIS,rJS)S)S)S)S-SS..S/jrKSSS)S)S)S)S-SS0.S1jrLS2S)S)S)S)S-SS3.S4jrMS)S)S)S)S-S)SS5.S6jrNSbS7jrOScS8jrPS9\S\Q4S:jrRS \=S\ S\A4S;jrSS\ S\Q4S<jrTS\ S\ S-4S=jrUSSS>SS?.S \=S@\V\W\V-S-SA\ASB\V\X-S\ S-S\=4 SCjjrYSS.S \=S\ S-S\=4SDjjrZSdSEjr[SSF.SGjr\SSSH.SIjr]SdSJ\=S\ S-S\=4SKjjr^SLr_SMr`S\ S-SN\\?S\=4SOjraSPrbSeSQjrcSRrd\R"SSS9"STSU55rfSfSWjrgSdSXjrhSSVSVSSSSSVSVSS)SSY. SZjriSS[.S\jrjSS[.S]jrkSS[.S^jrl0rmS_\=S\S`4Sajrng)ga6Utility functions to use Python Array API compatible libraries. For the context about the Array API see: https://data-apis.org/array-api/latest/purpose_and_scope.html The SciPy use case of the Array API is described on the following page: https://data-apis.org/array-api/latest/use_cases.html#use-case-scipy N) Generator)contextmanager) ContextVar) ModuleType)AnyLiteral TypeAlias)Iterable)is_array_api_obj is_lazy_arrayis_numpy_array is_cupy_arrayis_torch_array is_jax_array is_dask_arraysizenumpydeviceis_numpy_namespaceis_cupy_namespaceis_torch_namespaceis_jax_namespaceis_dask_namespaceis_array_api_strict_namespace)_compat_module_name)lazy_xp_function)array_namespaceSCIPY_ARRAY_API SCIPY_DEVICE) FunctionDoc)array_api_extra)"_asarrayrassert_almost_equalassert_array_almost_equal default_xp eager_warnsr is_marrayis_array_api_strict is_complexis_cupyis_jaxis_numpyis_torch np_compatget_native_namespace_namerrscipy_namespace_forxp_assert_closexp_assert_equalxp_assert_lessxp_copy xp_devicexp_ravelxp_sizexp_unsupported_param_msgxp_vector_normxp_capabilitiesxp_result_type xp_promotemake_xp_test_casemake_xp_pytest_marksmake_xp_pytest_paramArray ArrayLikearrayxpreturnchURURU55(d Sn[U5eg)zCheck for NaNs or Infs.z#array must not contain infs or NaNsN)allisfinite ValueError)rBrCmsgs Q/var/www/html/land-ocr/venv/lib/python3.13/site-packages/scipy/_lib/_array_api.py _check_finiterKEs. 66"++e$ % %3o &F)rC check_finitesubokdtypeorder)KACFcopyrMrNcUc [U5n[U5(aNUSLa[R"XXS9nODU(a[R"XUS9nO'[R "XUS9nOUR XUS9nU(a [X5 U$![ a- [UR S55nUR XUS9nNJf=f)a8SciPy-specific replacement for `np.asarray` with `order`, `check_finite`, and `subok`. Memory layout parameter `order` is not exposed in the Array API standard. `order` is only enforced if the input array implementation is NumPy based, otherwise `order` is just silently ignored. `check_finite` is also not a keyword in the array API standard; included here for convenience rather than that having to be a separate function call inside SciPy functions. `subok` is included to allow this function to preserve the behaviour of `np.asanyarray` for NumPy based inputs. T)rPrOrN)rPrO)rOrU)rr,nprB asanyarrayasarray TypeErrorrK)rBrOrPrUrCrMrN coerced_xps rJr"r"Ks0 z U #|| 4<HHUuJE MM%EBEJJu?E FJJuJ=E e L F(A7J&&u&EE Fs.B4C C rCxc4Uc [U5n[USUS9$)z Copies an array. Parameters ---------- x : array xp : array_namespace Returns ------- copy : array Copied array Notes ----- This copy function does not offer all the semantics of `np.copy`, i.e. the `subok` and `order` keywords are not used. T)rUrC)rr"r^rCs rJr4r4zs", z Q  ADR ((rLc[U5n[U5(aUR5$[U5(aUR 5$[ U5(aUR 5R5$[U5(a1[R"URXRS5S9SS9$[R"USS9$)aICopies a possibly on device array to a NumPy array. This function is intended only for converting alternative backend arrays to numpy arrays within test code, to make it easier for use of the alternative backend to be isolated only to the function being tested. `_xp_copy_to_numpy` should NEVER be used except in test code for the specific purpose mentioned above. In production code, attempts to copy device arrays to NumPy arrays should fail, or else functions may appear to be working on the GPU when they actually aren't. Parameters ---------- x : array Returns ------- ndarray CPU_DEVICE)rT)rU) rr,rUr*getr-cpurr(rXrZDevicer`s rJ_xp_copy_to_numpyrfs&  B||vvxr{{uuw||uuw}}2zz JJq)rrrrqFr) risdtyperOfinfoepsr*rassert_allcloser-rrX) rrrrrwrxryrzrrCrfloatings rJr1r1.s''FR zz&,,(MNH |xx %))3.2 r{{zz))&/3*F F "!R-$Wzz&&vT155g'W W :: % %fD+/ & BBrL)nulprwrxryrzrrCc Sn [XUX4XVS9upn[[X45up[RR XUS9$)NTrv)r)rmaprfrXrassert_array_almost_equal_nulp) rrrrwrxryrzrrCrs rJxp_assert_close_nulprNsR''FR +f->?OF :: 4 4V4 4 PPrL)rwrxryrzrverboserCc |Sn [XXX4US9upn[U5(aURRXXgS9$[ U5(aTUR R S:waUR5nUR R S:waUR5n[RRXXgS9$)NTrv)rrrd) rr*rassert_array_lessr-rrrdrX) rrrwrxryrzrrrCrs rJr3r3]s'FR r{{zz++F4;,N N " ==   &ZZ\F >>  % 'kkmG :: ' '07 ( JJrLc<SSSU*--pe[X/UQ7XeSSS.UD6$zPBackwards compatible replacement. In new code, use xp_assert_close instead. rg? F)rrrxryr1rrdecimalargskwdsrrs rJr$r$tBCgX&$ 6 *  * $UPU *$( **rLc<SSSU*--pe[X/UQ7XeSSS.UD6$rrrs rJr#r#}rrLparamcSU<S3$)Nz Providing z$ is only supported for numpy arrays.)rs rJr8r8s y D EErLc:URURS5$)Nr)rrOr`s rJr)r)s ::agg1 22rLcRURnUR[5S35$)zCReturn name for native namespace (without array_api_compat prefix)..)r removeprefixr)rCnames rJr/r/s( ;;D    3 56a8 99rLc[U5(aSSKnUR$[U5(aSSKnUR$[ U5(aU$g)aReturn the `scipy`-like namespace of a non-NumPy backend That is, return the namespace corresponding with backend `xp` that contains `scipy` sub-namespaces like `linalg` and `special`. If no such namespace exists, return ``None``. Useful for dispatching. rN)r*cupyxscipyr+jaxr-)rCrrs rJr0r0sAr{{{{ bzzyy|| rL)axiskeepdimsordrCrrrc6Uc [U5OUn[(aa[US5(aURR XX#S9$US:wa [ S5eUR URU5U-XS9S-$[RRXXS9$)Nlinalg)rrrrzonly the Euclidean norm (`ord=2`) is currently supported in `xp_vector_norm` for backends not implementing the `linalg` extension.rrr)rrr) rrhasattrr vector_normrHsumconjrXnorm)r^rrrrCs rJr9r9s "z rB 2x 99(((R Rax !66"''!*q.t6GL Lyy~~at~GGrLcFUc [U5OUnURUS5$)N))rreshaper`s rJr6r6s% "z rB ::a rLcUc [U5OUn[[UR55nXBXAsXA'XB'UR X5nU$N)rlistrangendim permute_dims)aaxis1axis2rCaxess rJ xp_swapaxesrsK!z rB aff D#{DKDK  A HrL)force_floatingc 2UVs/sH;n[U5(d[R"U5(a [USUS9OUPM= nnUVs/sH o3cMUPM nnU(aUR S5 [ U5(aNUR S:a>UVs/sH"n[USS5S:Xa UROUPM$ nnUR"U6$UR"U6$s snfs snfs snf![a U(de/nUHhn[R"U5(aURU5OUn[USU5nURUS 5(dMWUR U5 Mj UR"/UQ[U5P76s$f=f) a Returns the dtype that results from applying type promotion rules (see Array API Standard Type Promotion Rules) to the arguments. Augments standard `result_type` in a few ways: - There is a `force_floating` argument that ensures that the result type is floating point, even when all args are integer. - When a TypeError is raised (e.g. due to an unsupported promotion) and `force_floating=True`, we define a custom rule: use the result type of the default float and any other floats passed. See https://github.com/scipy/scipy/pull/22695/files#r1997905891 for rationale. - This function accepts array-like iterables, which are immediately converted to the namespace's arrays before result type calculation. Consequently, the result dtype may be different when an argument is `1.` vs `[1.]`. Typically, this function will be called shortly after `array_namespace` on a subset of the arguments passed to `array_namespace`. TrNrCg?z2.0rrrrOr)rrXiterabler"appendr, __version__getattrrO result_typer[rrZrxp_default_dtype)rrCrarg args_not_none float_args arg_arrayrOs rJr;r;s0"& '!%#2@1D1D TWHXHXXc" -!%  '$( !  rLarraysc UVs/sH(n[R"URU5SUS9PM* nnURU5$s snf)zWA replacement for `np.r_` as `xp.concat` does not accept python scalars or 0-D arrays. r)rrC)xpx atleast_ndrZconcat)rCrraryss rJ concat_1drysCCI I&QCNN2::a=qR 8&D I 99T? Js/Ac SUR;$)z=Returns True if `xp` is an MArray namespace; False otherwise.marray)rr]s rJr'r's r{{ ""rLcUc [U5OUn[U5(aR[R"U5(a Sn[ U5eUR UR XUS9UR5$Uc [U5$[[R"[R"UR5[R"U555$)Nz8`axis` must be an integer or None for use with `MArray`.r) rr'rXrNotImplementedErrorrcountrOr7intprodrZr)r^rrrCrs rJ_length_nonmaskedr s!z rB}} ;;t  PG%g. .yy!BAGGLL,GAJA  177+BJJt,<=> ?ArLc[U5(aU[R"[RSU55nUVs/sHo0R UR US9PM nn[U5S:XaUS$U$s snf)Nc38# UHoRv M g7frmask).0rs rJ _share_masks..s.H4Cxx4srrr)r' functoolsreduceoperatoror_rZdatar)rCrrrs rJ _share_masksrsi}} .H4.HI;?@4C 388$ /4@$i1n47.$.As#A<)reprcr\rSrSr%\S-\S'\S-\S'\R"\S9r \\ \S'Sr Sr S r g) _XPSphinxCapabilityiNrdgpu)default_factorywarningscUcgU(dgUR(a6SSRUR5-n[U5S::dS5eU$g)Nzn/au⛔u⚠️ z; zWarnings too longu✅)r joinr)selfvalueress rJ_render_XPSphinxCapability._rendersL = ==dii 66Cs8r> 6#6 6>JrLcURUR5nURUR5nUSSUS3$)N20z )r'rdr)r$rdrs rJ__str___XPSphinxCapability.__str__s;ll488$ll488$bC8$$rLr)r __module__ __qualname____firstlineno__bool__annotations__ dataclassesfieldrr strr'r+__static_attributes__rrLrJrrs9  %++DAHd3iA %rLrrc U(aSS0$[U5n[SSS9[SSS9[SSS9[SSS9[SSU(a/OS/S9[SSU(aS/O/S9S.n [U5[U5-H3upXn U RbSU lU RcM,SU lM5 U R 5HgupU(a5XS 1-;a,U RbSU lU Rb SU lM?MAU(dMJX;dMQU RcM`SU lMi UH$upXn U R RU5 M& U $) N out_of_scopeT)rdrzno JIT)rdrr zcomputes graph)rarray_api_strictcupytorch jax.numpy dask.arrayFr)rjrrrdritemsr r) skip_backendsxfail_backendscpu_onlynp_onlyr7 exceptionsallow_dask_computejax_jitr reason capabilitiesmodule_backendwarnings rJ_make_sphinx_capabilitiesrKs\%%ZJ%48/DdC#$7$48(Tt"R 4*d+=&'2G L-(4+?? & ;; "GK ;; "GK @(--/ v7)%;;{{&# {{&# ' X&2w{{7NGK0$&($ rLc SU;aSUS3n[R"U5$SUSUSSUSS US S US S USS3 U=(d S-S-n[R"U5$)Nr7z2 **Array API Standard Support** `z` is not in-scope for support of Python Array API Standard compatible backends other than NumPy. See :ref:`dev-arrayapi` for more information. z* **Array API Standard Support** `aG` has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable ``SCIPY_ARRAY_API=1`` and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported. ==================== ==================== ==================== Library CPU GPU ==================== ==================== ==================== NumPy rz CuPy r9z PyTorch r:z JAX r;z Dask r<zK ==================== ==================== ==================== rqz1 See :ref:`dev-arrayapi` for more information.)textwrapdedent)fun_namerF extra_notenotes rJ_make_capabilities_noterRs%    t$$Z (0DE'/DE'0DE' 4DE' 5DE! &  ' & S' SD* ??4  rL) capabilities_tabler>r?r@rArEr7rBr rCrDrPc ^^ ^ ^Tc[OTmU(aSn[UUUUUUUU U US9 m [S0T D6mU UU U4Sjn U $)aEDecorator for a function that states its support among various Array API compatible backends. This decorator has two effects: 1. It allows tagging tests with ``@make_xp_test_case`` or ``make_xp_pytest_param`` (see below) to automatically generate SKIP/XFAIL markers and perform additional backend-specific testing, such as extra validation for Dask and JAX; 2. It automatically adds a note to the function's docstring, containing a table matching what has been tested. See Also -------- make_xp_test_case make_xp_pytest_param array_api_extra.testing.lazy_xp_function T) r>r?r@rAr7rErBrCrDr c>TTU'[URTT5n[U5nUSRU5 [ U5R SS5SR S5nX lU$![a U$f=f)NNotes rz ) rRrr rr4splitlstrip__doc__AttributeError)frQdocrFrSrPsphinx_capabilitiess rJ decorator"xp_capabilities..decoratorHs!-1&qzz3F S!n G D!#hnnT1%a(//6 I      s+A33 BBr)xp_capabilities_tabledictrK)rSr>r?r@rArEr7rBr rCrDrPr_rFr^s` ` @@rJr:r:slH4F3M/1#%!- L4ClC rLrSc>^Uc[OUn[USU06mU4Sj$)NrSc6>[R"STU5$)NcU"U5$rr)r\gs rJ5make_xp_test_case....sadrL)rr)funcmarkss rJrh#make_xp_test_case..s (():E4HrL)rar>)rSfuncsrks @rJr=r=[s23E3M/1*V !% Orr)rjrSrrrrks rJr?r?s/Z  ME << Ct C5]] CCrLc JUc[OUnSSKn/nUGHnXnUSnUSnUS(a)URURR SXgS95 US(a)URURR SXgS 95 US H-upURURR XS 95 M/ US H-upURURR XS 95 M/ S V s0sHn XU _M n n [ U40U D6 GM U$s sn f)aVariant of ``make_xp_test_case`` that returns a list of pytest marks, which can be used with the module-level `pytestmark = ...` variable:: pytestmark = make_xp_pytest_marks(f1, f2) def test(xp): ... In this example, the whole test module is dedicated to testing `f1` or `f2`, and the two functions have the same capabilities, so it's unnecessary to cherry-pick which test tests which function. The above is equivalent to:: pytestmark = [ pytest.mark.skip_xp_backends(...), pytest.mark.xfail_xp_backends(...), ...]), ] def test(xp): ... See Also -------- xp_capabilities make_xp_test_case make_xp_pytest_param array_api_extra.testing.lazy_xp_function NrrBrEr@T)r@rBrErA)rArBrEr>)rEr?)rCrD)rarrrmarkskip_xp_backendsxfail_xp_backendsr) rSrmrrrkrjrFrBrEmod_namek lazy_kwargss rJr>r>sO:4F3M/1 E)/ !,/ h'  # LL55*6E F  " LL556D E!-_ = H LL55h5N O!> ,-= > H LL66x6O P!?!BC A1q/) A C--'* L Cs<D r)rdcudaNc>[U5(ag[U5(ag[U5(aURR$[ U5(a URR =nS:XaS$U$[U5(a[UR5$g)Nrdrwr) r rrrrrplatformrxp_device_type_meta)rps rJrzrzs~aQaxx}}A xx000!U:vAAQagg&& rL))r)FN) rrFFFrFTrN)orZrr2rrMcollections.abcr contextlibr contextvarsrtypesrtypingrrr r rrX numpy.typingnptscipy._lib.array_api_compatr r r rrrrrr7r.rr5rr,rr*rr-rr+rrrr(+scipy._lib.array_api_compat.common._helpersr"scipy._lib.array_api_extra.testingrscipy._lib._array_api_overriderrrscipy._lib._docscraper scipy._libr!r__all__r@r1rArKr0r"r4ndarrayrfrhr%r&rr~r2r1rr3r$r#r4r8r)r/r0r rfloatr9r6rr;r<rrrrr'r r dataclassrrKrRr:r=r?r>rarzrrLrJrsC%%"**$$L?.- ys}}, 9,J448 , !%",,,)*T1,Tk ,   ,,, ,^26)u)Z$.)%)8&$&$2::&$R.8 -FJz*F (: (),<"= ( ( (, L#'Dd%P($9=$ $tRDK,.24 $$4B@34T%)td!# Q8<#dBQUJ.**FCFCF3%3Z3D3:*:: J:+<048$)&'+/ HeHuSz)D0H!He H"D( H5: H46:  d!2 e  */2Aj!&eA1H U  T(9 U  *t#hy.Ae# A/ E"%%#%0&(BD&* 3l%!XR E$2 d#HV26/Id:>0Df597ve(; <rL