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ctypeslib.cpython-39.opt-1.pyc
a z[yc4D � @ s` d Z g d�ZddlZddlmZmZmZmZm Z ddl m Z m Z z ddl Z W n eyb dZ Y n0 e du r�dd� ZeZeZeZddlmZ eZn*ddlm mZ e�� Z[e j Zd d � Zd d � Z g d �Z dd� Z G dd� de�Z G dd� de �Z!i Z"d(dd�Z#e du�r\dd� Z$dd� Z%e%� Z&dd� Z'd d � Z(d d � Z)d d!� Z*d"d#� Z+d)d$d%�Zd&d'� ZdS )*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� ndpointer�c_intp� as_ctypes�as_array�as_ctypes_type� N)�integer�ndarray�dtype�asarray� frombuffer)� _flagdict�flagsobjc O s t d��dS )z� Dummy object that raises an ImportError if ctypes is not available. Raises ------ ImportError If ctypes is not available. zctypes is not available.N)� ImportError)�args�kwds� r �5/usr/lib64/python3.9/site-packages/numpy/ctypeslib.py�_dummyC s r )�intpc C s t jdk r ddl}|jddd� t�| �} t�|�}tj�| �d }|s�ddlm } |� }| | g}|d d �}||ks�|� d| | � n| g}tj� |�}tj� |�s�tj� |�}n|}|D ]F} tj�|| �} tj�| �r�zt j| W S t�y � Y q�0 q�td ��dS ) a It is possible to load a library using >>> lib = ctypes.cdll[<full_path_name>] # 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. z1.0.1r NzAAll features of ctypes interface may not work with ctypes < 1.0.1� )� stacklevel� )�get_shared_lib_extensionT)Z is_python_extz no file with expected extension)�ctypes� __version__�warnings�warn�os�fsdecode�path�splitextZnumpy.distutils.misc_utilr �insert�abspath�isdir�dirname�join�existsZcdll�OSError) ZlibnameZ loader_pathr �extr Zso_extZ libname_extZso_ext2�libdirZlnZlibpathr r r r Z s8 ! � r c C s d}| D ]}|t | 7 }q|S �Nr )r )Zflaglist�num�valr r r �_num_fromflags� s r. )Z C_CONTIGUOUSZ F_CONTIGUOUSZALIGNEDZ WRITEABLEZOWNDATAZWRITEBACKIFCOPYc C s, g }t D ] }t| }| |@ r|�|� q|S �N)� _flagnamesr �append)r, �res�key�valuer r r �_flags_fromnum� s r5 c @ s e Zd Zedd� �ZdS )�_ndptrc C s� t |t�std��| jd ur6|j| jkr6td| j ��| jd urZ|j| jkrZtd| j ��| jd ur�|j| jkr�tdt | j� ��| j d ur�|j j | j @ | j kr�tdt | j � ��|jS )Nzargument must be an ndarrayz array must have data type %sz array must have %d dimension(s)zarray must have shape %szarray must have flags %s)� isinstancer � TypeError�_dtype_r �_ndim_�ndim�_shape_�shape�str�_flags_�flagsr, r5 r )�cls�objr r r � from_param� s* � � � ��z_ndptr.from_paramN)�__name__� __module__� __qualname__� classmethodrC r r r r r6 � s r6 c @ s$ e Zd ZdZdd� Zedd� �ZdS )�_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 C s | j S )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)�selfr r r �_check_retval_� s z _concrete_ndptr._check_retval_c C sD t | j| jf�}tj|j }t�| t�|��j}t ||d�j dd�S )z� Get an ndarray viewing the data pointed to by this pointer. This mirrors the `contents` attribute of a normal ctypes pointer �r r )Zaxis) �_dtyper9 r< r �c_char�itemsize�cast�POINTERrI r Zsqueeze)rJ Z full_dtypeZ full_ctype�bufferr r r rI � s z_concrete_ndptr.contentsN)rD rE rF �__doc__rK �propertyrI r r r r rH � s rH c C s� | durt | �} d}|dur�t|t�r2|�d�}n4t|ttf�rN|}t|�}nt|t�rf|j}t|�}|du r�zdd� |D �}W n. t y� } zt d�|�W Y d}~n d}~0 0 t |�}|dur�z t |�}W n t y� |f}Y n0 | |||f}z t | W S t�y Y n0 | du �r"d}n | jdu�r<tt| ��}n| j}|du�rX|d| 7 }|du�r~|dd �d d � |D �� 7 }|du�r�|dd�|� 7 }| du�r�|du�r�t}nt}td | |f| |||d ��} | t |< | S )aF 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�,c S s g | ]}|� � �� �qS r )�strip�upper��.0�xr r r � <listcomp>0 � z ndpointer.<locals>.<listcomp>zinvalid flags specification�anyz_%dd�_rZ c s s | ]}t |�V qd S r/ )r> rX r r r � <genexpr>N r\ z ndpointer.<locals>.<genexpr>z ndpointer_%s)r9 r< r: r? )rM r7 r> �split�intr r5 r r, � Exceptionr8 r. �tuple�_pointer_type_cache�KeyError�names�idr& rH r6 �type) r r; r= r@ r, �eZ cache_key�name�base�klassr r r r � sf 5 ��r c C s& |ddd� D ]}|| } d| _ q| S )z7 Create an ndarray of the given element type and shape N���)rE )Z element_typer= Zdimr r r �_ctype_ndarraya s rn c C sJ t } | j| j| j| j| j| j| j| j| j | j | j | j | j g }dd� |D �S )zX Return a dictionary mapping native endian scalar dtype to ctypes types c S s i | ]}t |�|�qS r )rM )rY �ctyper r r � <dictcomp>u r\ z(_get_scalar_type_map.<locals>.<dictcomp>)r Zc_byteZc_shortZc_intZc_longZ c_longlongZc_ubyteZc_ushortZc_uintZc_ulongZ c_ulonglongZc_floatZc_doubleZc_bool)ZctZ simple_typesr r r �_get_scalar_type_mapj s �rq c C s� | � d�� d�}| � d�}z t| }W n4 tyZ } z td�| ��d �W Y d }~n d }~0 0 |jdkrn|j}n|jdkr~|j}|S )N�S�=z Converting {!r} to a ctypes type�>�<)Z newbyteorder�_scalar_type_mapre �NotImplementedError�format� byteorderZ __ctype_be__Z __ctype_le__)r Zdtype_with_endianZ dtype_nativero ri r r r �_ctype_from_dtype_scalar{ s �� rz c C s | j \}}t|�}t||�S r/ )�subdtype�_ctype_from_dtypern )r Z element_dtyper= ro r r r �_ctype_from_dtype_subarray� s r} c C s� g }| j D ].}| j| d d� \}}|�||t|�f� q t|dd� d�}t|�dkr�tdd� |D ��r�d}g }|D ](\}}}|�||f� t|t� |��}qt| j |kr�|�d tj | j f� t d tj ft|dd d ��S d}g }|D ]^\}}}|| } | dk �rtd ��| dk�r&|�d tj | f� |�||f� |t� |� }q�| j | } | dk�rl|�d tj | f� t d tjft|dd d ��S d S )Nr c S s | d S r+ r )�fr r r �<lambda>� r\ z._ctype_from_dtype_structured.<locals>.<lambda>)r3 r c s s | ]\}}}|d kV qdS )r Nr )rY �offsetrj ro r r r r_ � r\ z/_ctype_from_dtype_structured.<locals>.<genexpr>r � �union)�_fields_Z_pack_rE zOverlapping fields�struct)rf �fieldsr1 r| �sorted�len�all�maxr ZsizeofrO rN rh �Union�dictrw Z Structure) r Z field_datarj Z field_dtyper� �sizer� ro Z last_offsetZpaddingr r r � _ctype_from_dtype_structured� sH � �r� c C s0 | j d urt| �S | jd ur$t| �S t| �S d S r/ )r� r� r{ r} rz rL r r r r| � s r| c C s t t| ��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 )r| rM rL r r r r � s &r c C sD t | tj�r<|du r td��t�t| j|��}t�| |�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 Nz=as_array() requires a shape argument when called on a pointer) r7 r Z_Pointerr8 rQ rn Z_type_rP rI r )rB r= Z p_arr_typer r r r � s �r c C sp | j }|d rtd��|d dkr*td��|d \}}|rBtd��t|d �}t||d �}|�|�}| |_|S ) z�Create and return a ctypes object from a numpy array. Actually anything that exposes the __array_interface__ is accepted.�stridesz strided arrays not supported�version� z,only __array_interface__ version 3 supported�datazreadonly arrays unsupportedZtypestrr= )Z__array_interface__r8 r rn Z from_addressZ__keep)rB Zai�addr�readonlyZ ctype_scalarZ result_type�resultr r r r s r )NNNN)N),rS �__all__r Znumpyr r r rM r r Znumpy.core.multiarrayr r r r r r r r r r �objectZ _ndptr_baseZnumpy.core._internalZcoreZ _internalZnicZ_getintp_ctypeZc_void_pr. r0 r5 r6 rH rd r rn rq rv rz r} r� r| r r r r r �<module> sL 3 L u 6 )