U b@sjdZddlmZmZmZddlmZmZdgZd ddddZ eed d ddd dZ ee e Z dS) z Functions in the ``as*array`` family that promote array-likes into arrays. `require` fits this category despite its name not matching this pattern. )array_function_dispatchset_array_function_like_doc set_module)array asanyarrayrequireN)likecCs|fS)N)adtype requirementsrr r S/home/fireinfo/NEWAFireInfo/venv/lib/python3.8/site-packages/numpy/core/_asarray.py_require_dispatchersrZnumpycs|dk rt||||dSddddddddddddddd |sJt||d Sfd d |D}d|krt|dd }nd}d}|ddhkrtdn.d|krd}|dnd|krd}|dt|||d |d}|D]}|j|s||}qq|S)a Return an ndarray of the provided type that satisfies requirements. This function is useful to be sure that an array with the correct flags is returned for passing to compiled code (perhaps through ctypes). Parameters ---------- a : array_like The object to be converted to a type-and-requirement-satisfying array. dtype : data-type The required data-type. If None preserve the current dtype. If your application requires the data to be in native byteorder, include a byteorder specification as a part of the dtype specification. requirements : str or list of str The requirements list can be any of the following * 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array * 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array * 'ALIGNED' ('A') - ensure a data-type aligned array * 'WRITEABLE' ('W') - ensure a writable array * 'OWNDATA' ('O') - ensure an array that owns its own data * 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass ${ARRAY_FUNCTION_LIKE} .. versionadded:: 1.20.0 Returns ------- out : ndarray Array with specified requirements and type if given. See Also -------- asarray : Convert input to an ndarray. asanyarray : Convert to an ndarray, but pass through ndarray subclasses. ascontiguousarray : Convert input to a contiguous array. asfortranarray : Convert input to an ndarray with column-major memory order. ndarray.flags : Information about the memory layout of the array. Notes ----- The returned array will be guaranteed to have the listed requirements by making a copy if needed. Examples -------- >>> x = np.arange(6).reshape(2,3) >>> x.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : False WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False >>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F']) >>> y.flags C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False N)r r rCFAWOE)rZ C_CONTIGUOUSZ CONTIGUOUSrZ F_CONTIGUOUSZFORTRANrZALIGNEDrZ WRITEABLErZOWNDATArZ ENSUREARRAY)r csh|]}|qSr )upper).0xZpossible_flagsr r mszrequire..FTz%Cannot specify both "C" and "F" order)r ordercopysubok)_require_with_likerremove ValueErrorrflagsr)r r r rrrZarrpropr rr rsTF        )NN)NN) __doc__Z overridesrrrZ multiarrayrr__all__rrrr r r r sp