,VhD|dZgdZddlZddlmZmZmZmZm Z ddl m Z m Z  ddl Z e dZeZeZeZddlmZeZn*ddlmcmZej6Z[e j8ZdZd Zgd Zd ZGd d eZ Gdde Z!iZ"ddZ#e $dZ$dZ%e%Z&dZ'dZ(dZ)dZ*dZ+ddZdZyy#e$rdZ YwxYw)a7 ============================ ``ctypes`` Utility Functions ============================ See Also -------- load_library : Load a C library. ndpointer : Array restype/argtype with verification. as_ctypes : Create a ctypes array from an ndarray. as_array : Create an ndarray from a ctypes array. References ---------- .. [1] "SciPy Cookbook: ctypes", https://scipy-cookbook.readthedocs.io/items/Ctypes.html Examples -------- Load the C library: >>> _lib = np.ctypeslib.load_library('libmystuff', '.') #doctest: +SKIP Our result type, an ndarray that must be of type double, be 1-dimensional and is C-contiguous in memory: >>> array_1d_double = np.ctypeslib.ndpointer( ... dtype=np.double, ... ndim=1, flags='CONTIGUOUS') #doctest: +SKIP Our C-function typically takes an array and updates its values in-place. For example:: void foo_func(double* x, int length) { int i; for (i = 0; i < length; i++) { x[i] = i*i; } } We wrap it using: >>> _lib.foo_func.restype = None #doctest: +SKIP >>> _lib.foo_func.argtypes = [array_1d_double, c_int] #doctest: +SKIP Then, we're ready to call ``foo_func``: >>> out = np.empty(15, dtype=np.double) >>> _lib.foo_func(out, len(out)) #doctest: +SKIP ) load_library ndpointerc_intp as_ctypesas_arrayas_ctypes_typeN)integerndarraydtypeasarray frombuffer) _flagdictflagsobjctd)z Dummy object that raises an ImportError if ctypes is not available. Raises ------ ImportError If ctypes is not available. zctypes is not available.) ImportError)argskwdss ?/home/dcms/DCMS/lib/python3.12/site-packages/numpy/ctypeslib.py_dummyrCs455)intpc<tj|}tj|}tjj|d}|swddl}ddl}d}|j jdrd}n|j jdrd}||zg}|jd }||k(s|jd||zn|g}tjj|}tjj|s tjj|}n|}|D]W} tjj|| } tjj| sC tj | cSt#d #t"$rwxYw) a  It is possible to load a library using >>> lib = ctypes.cdll[] # doctest: +SKIP But there are cross-platform considerations, such as library file extensions, plus the fact Windows will just load the first library it finds with that name. NumPy supplies the load_library function as a convenience. .. versionchanged:: 1.20.0 Allow libname and loader_path to take any :term:`python:path-like object`. Parameters ---------- libname : path-like Name of the library, which can have 'lib' as a prefix, but without an extension. loader_path : path-like Where the library can be found. Returns ------- ctypes.cdll[libpath] : library object A ctypes library object Raises ------ OSError If there is no library with the expected extension, or the library is defective and cannot be loaded. rNz.sodarwinz.dylibwinz.dll EXT_SUFFIXzno file with expected extension)osfsdecodepathsplitextsys sysconfigplatform startswithget_config_varinsertabspathisdirdirnamejoinexistsctypescdllOSError) libname loader_pathextr!r"base_ext libname_extso_extlibdirlnlibpaths rrrZs[D++g&kk+. ggw'*  H||&&x0#((/!"X-.K--l;FX%""1g&67")Kggook2 ww}}[)WW__[1F F Bggll62.Gww~~g&!;;w//  788 s /F Fc0d}|D]}|t|z }|SNr)r)flaglistnumvals r_num_fromflagsr=s) C y~ Jr) C_CONTIGUOUS F_CONTIGUOUSALIGNED WRITEABLEOWNDATAWRITEBACKIFCOPYc`g}tD]"}t|}||zs|j|$|SN) _flagnamesrappend)r;reskeyvalues r_flags_fromnumrKs8 C# %K JJsO JrceZdZedZy)_ndptrct|ts td|j1|j|jk7rtd|jz|j 1|j |j k7rtd|j z|j:|j|jk7r!tdt|jz|jQ|jj|jz|jk7r!tdt|jz|jS)Nzargument must be an ndarrayzarray must have data type %szarray must have %d dimension(s)zarray must have shape %szarray must have flags %s) isinstancer TypeError_dtype_r _ndim_ndim_shape_shapestr_flags_flagsr;rKr,)clsobjs r from_paramz_ndptr.from_params#w'9: : ;; "99 +:S[[HI I :: !88szz)= JK K ;; "99 +6S[[9IIJ J ;; "YY]]S[[0S[[@6"3;;/01 1zzrN)__name__ __module__ __qualname__ classmethodr[rrrMrMsrrMc&eZdZdZdZedZy)_concrete_ndptrz Like _ndptr, but with `_shape_` and `_dtype_` specified. Notably, this means the pointer has enough information to reconstruct the array, which is not generally true. c|jS)z This method is called when this class is used as the .restype attribute for a shared-library function, to automatically wrap the pointer into an array. )contents)selfs r_check_retval_z_concrete_ndptr._check_retval_s }}rct|j|jf}tj|j z}tj |tj|j}t||jdS)z Get an ndarray viewing the data pointed to by this pointer. This mirrors the `contents` attribute of a normal ctypes pointer r r)axis) _dtyperQrTr,c_charitemsizecastPOINTERrdr squeeze)re full_dtype full_ctypebuffers rrdz_concrete_ndptr.contentssiT\\4<<89 ]]Z%8%88 T6>>*#=>GG& 3;;;CCrN)r\r]r^__doc__rfpropertyrdr`rrrbrbs"  D Drrbc | t|}d}|t|tr|jd}nKt|tt fr|}t |}n't|tr|j}t |}|7 |Dcgc] }|jj"}}t|}| t|}||||f} t|Scc}w#t$r}td|d}~wwxYw#t$r|f}Y@wxYw#t $rYnwxYw|d}n-|j"tt%|}n |j}||d|zz }||ddj'd|Dzz }||ddj'|zz }| |t(} nt*} t-d |z| f||||d } | t|<| S) a: Array-checking restype/argtypes. An ndpointer instance is used to describe an ndarray in restypes and argtypes specifications. This approach is more flexible than using, for example, ``POINTER(c_double)``, since several restrictions can be specified, which are verified upon calling the ctypes function. These include data type, number of dimensions, shape and flags. If a given array does not satisfy the specified restrictions, a ``TypeError`` is raised. Parameters ---------- dtype : data-type, optional Array data-type. ndim : int, optional Number of array dimensions. shape : tuple of ints, optional Array shape. flags : str or tuple of str Array flags; may be one or more of: - C_CONTIGUOUS / C / CONTIGUOUS - F_CONTIGUOUS / F / FORTRAN - OWNDATA / O - WRITEABLE / W - ALIGNED / A - WRITEBACKIFCOPY / X Returns ------- klass : ndpointer type object A type object, which is an ``_ndtpr`` instance containing dtype, ndim, shape and flags information. Raises ------ TypeError If a given array does not satisfy the specified restrictions. Examples -------- >>> clib.somefunc.argtypes = [np.ctypeslib.ndpointer(dtype=np.float64, ... ndim=1, ... flags='C_CONTIGUOUS')] ... #doctest: +SKIP >>> clib.somefunc(np.array([1, 2, 3], dtype=np.float64)) ... #doctest: +SKIP N,zinvalid flags specificationanyz_%dd_xc32K|]}t|ywrE)rV).0rys r zndpointer..Ls3SV3sz ndpointer_%s)rQrTrRrW)rjrOrVsplitintr rKrr;stripupper ExceptionrPr=tuple_pointer_type_cacheKeyErrornamesidr*rbrMtype) r rSrUrXr;rye cache_keynamebaseklasss rrrsj u  C  eS !KK$E W~ .C"3'E x ())C"3'E ; F49:q*::!'C  %LE eS)I "9--!; F =>AE F HE      }  2e9~yy     CHH3U3333  CHHUO## U. $tg"#!!# $E &+ " LsNC%C+C; C8 D C C5$ C00C58 DD DDc8|dddD]}||z}d|_|S)z7 Create an ndarray of the given element type and shape N)r]) element_typerUdims r_ctype_ndarrayr_s32; +C-L&*L # +rc nt}|j|j|j|j|j |j |j|j|j|j|j|j|jg }|Dcic]}t||c}Scc}w)zX Return a dictionary mapping native endian scalar dtype to ctypes types )r,c_bytec_shortc_intc_long c_longlongc_ubytec_ushortc_uintc_ulong c_ulonglongc_floatc_doublec_boolrj)ct simple_typesctypes r_get_scalar_type_maprhs IIrzz288RYY JJ RYY BNN JJ II  3??u u$???sB2cD|jdjd}|jd} t|}|j dk(r|j }|S|j dk(r |j}|S#t$r }tdj |dd}~wwxYw)NS=z Converting {!r} to a ctypes type><) newbyteorder_scalar_type_maprNotImplementedErrorformat byteorder __ctype_be__ __ctype_le__)r dtype_with_endian dtype_nativerrs r_ctype_from_dtype_scalarrys!..s3@@E))#.  $\2E  & &# -&&E  ( (C /&&E  %299%@  s A66 B?BBcN|j\}}t|}t||SrE)subdtype_ctype_from_dtyper)r element_dtyperUrs r_ctype_from_dtype_subarrayrs'$~~ u!-0eU++rc g}|jD]4}|j|dd\}}|j||t|f6t |d}t |dkDrt d|Drd}g}|D]8\}}}|j||ft|tj|}:|j|k7r.|jdtj|jzftdtjft|dd Sd}g}|D]o\}}}||z } | dkr td | dkDr$|jdtj| zf|j||f|tj|z}q|j|z } | dkDr$|jdtj| zftd tj ft|dd S) Nc |dSr9r`)fs rz._ctype_from_dtype_structured..s adr)rIrc3.K|] \}}}|dk(yw)rNr`)r{offsetrrs rr|z/_ctype_from_dtype_structured..s&Y7JvtUv{&Ysrunion)_fields__pack_r]zOverlapping fieldsstruct)rfieldsrGrsortedlenallmaxr,sizeofrlrkrUniondictr Structure) r field_datar field_dtypersizerr last_offsetpaddings r_ctype_from_dtype_structuredrs KK ND"',,t"4Ra"8 K   vt->{-KL M N JN; z?Q 3&Yj&Y#YDH'1 7#eu .4u!56 7 ~~%V]]U^^%C DE&,,$!3  KH'1 <#e ;.Q;-.BCCQ;OOR)@$ABu .$v}}U';;  <nn{2G{V]]W%< =>6#3#3"5t!8 rct|j t|S|j t|St |SrE)rrrrrrhs rrrs7 << #/6 6 ^^ '-e4 4+E2 2rc*tt|S)a  Convert a dtype into a ctypes type. Parameters ---------- dtype : dtype The dtype to convert Returns ------- ctype A ctype scalar, union, array, or struct Raises ------ NotImplementedError If the conversion is not possible Notes ----- This function does not losslessly round-trip in either direction. ``np.dtype(as_ctypes_type(dt))`` will: - insert padding fields - reorder fields to be sorted by offset - discard field titles ``as_ctypes_type(np.dtype(ctype))`` will: - discard the class names of `ctypes.Structure`\ s and `ctypes.Union`\ s - convert single-element `ctypes.Union`\ s into single-element `ctypes.Structure`\ s - insert padding fields Examples -------- Converting a simple dtype: >>> dt = np.dtype('int8') >>> ctype = np.ctypeslib.as_ctypes_type(dt) >>> ctype Converting a structured dtype: >>> dt = np.dtype([('x', 'i4'), ('y', 'f4')]) >>> ctype = np.ctypeslib.as_ctypes_type(dt) >>> ctype )rrjrhs rrrsl!//rct|tjrV| tdtjt |j |}tj||j}t|S)a" Create a numpy array from a ctypes array or POINTER. The numpy array shares the memory with the ctypes object. The shape parameter must be given if converting from a ctypes POINTER. The shape parameter is ignored if converting from a ctypes array z=as_array() requires a shape argument when called on a pointer) rOr,_PointerrPrnr_type_rmrdr )rZrU p_arr_types rrr sd c6?? +} szz5(IJJ++c:.77Cs|rc|j}|dr td|ddk7r td|d\}}|r tdt|d}t||d }|j |}||_|S) zCreate and return a ctypes object from a numpy array. Actually anything that exposes the __array_interface__ is accepted.strideszstrided arrays not supportedversionz,only __array_interface__ version 3 supporteddatazreadonly arrays unsupportedtypestrrU)__array_interface__rPrr from_address__keep)rZaiaddrreadonly ctype_scalar result_typeresults rrrs $ $ i=:; ; i=A JK KFh 9: :&bm4 $\2g;? ))$/  r)NNNNrE),rs__all__rnumpyr r r rjr r numpy._core.multiarrayrrr,rrrrrrrobject _ndptr_basenumpy._core._internal_core _internalnic_getintp_ctypec_void_pr=rFrKrMrbrrrrrrrrrrr`rrrs2f  7 > 6LIH$K'' S   !F //KG9T , [*DfD<rj  @,-&, 3l360r*C FsB11B;:B;