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#define NPY_BEHAVED BEHAVED_FLAGS
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\struct fff_func_iterator
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\brief Structure to iterate a vector-based function over arbitrary
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This structure, which is close in spirit to numpy's universal
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functions, makes it easy to implement a C function that iterates
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over several numpy arrays simultaneously and calls a lower-level
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function whose arguments (in and out) are lists of fff_vectors. Both
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input and output numpy arrays must have the same dimension, except
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in the direction defining the orientation of the fff_vectors.
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npy_intp nargout; /*!< number of output numpy arrays */
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npy_intp nargin; /*!< number of input numpy arrays */
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npy_intp axis; /*!< fff_vector direction */
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npy_intp nitems; /*!< number of iterations required */
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PyArrayIterObject** PyIter_argout; /*!< list of output PyArrays */
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PyArrayIterObject** PyIter_argin; /*!< list of input PyArrays */
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fff_vector** fff_argout; /*!< list of output fff_vectors */
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fff_vector** fff_argin; /*!< list of input fff_vectors */
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\brief Import numpy C API
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Any Python module written in C, and using the fffPy interface, must
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Any Python module written in C, and using the fffpy interface, must
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call this function to work, because \c PyArray_API is defined
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static, in order not to share that symbol within the
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dso. (import_array() asks the pointer value to the python process)
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void fffPy_import_array( void );
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extern void fffpy_import_array(void);
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\brief Convert \c PyArrayObject to \c fff_vector
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\param x input numpy array
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This function may be seen as a \c fff_vector constructor compatible
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with \c fff_vector_delete. If the input has type \c PyArray_DOUBLE,
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whether or not it is contiguous, the new \c fff_vector is not
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self-owned and borrows a reference to the PyArrayObject's
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data. Otherwise, data are copied and the \c fff_vector is
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self-owned (hence contiguous) just like when created from
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scratch. Notice, the function returns \c NULL if the input array
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has more than one dimension.
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\brief Convert \c PyArrayObject to \c fff_vector
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\param x input numpy array
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This function may be seen as a \c fff_vector constructor compatible
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with \c fff_vector_delete. If the input has type \c PyArray_DOUBLE,
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whether or not it is contiguous, the new \c fff_vector is not
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self-owned and borrows a reference to the PyArrayObject's
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data. Otherwise, data are copied and the \c fff_vector is
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self-owned (hence contiguous) just like when created from
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scratch. Notice, the function returns \c NULL if the input array
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has more than one dimension.
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fff_vector* fff_vector_fromPyArray( const PyArrayObject* x );
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\param y input matrix
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Conversely to \c fff_matrix_fromPyArray, this function acts as a \c
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fff_matrix destructor compatible with \c fff_matrix_alloc, returning
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fff_matrix destructor compatible with \c fff_matrix_new, returning
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a new PyArrayObject reference. If the input matrix is contiguous and
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self-owned, array ownership is simply transferred to Python;
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otherwise, the data array is copied.
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PyArrayObject* fff_matrix_const_toPyArray( const fff_matrix* y );
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\brief Constructor for the \c fff_func_iterator structure
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\param nargout number of output numpy arrays
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\param argout list of output numpy arrays
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\param nargin number of input numpy arrays
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\param argin list of input numpy arrays
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\param axis vector orientation
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This constructor assumes that all PyArrays involved are already
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allocated, including output arrays. Moreover, they must have the
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same dimensions except in the direction given by \c axis.
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fff_func_iterator* fff_func_iterator_new( npy_intp nargout, PyArrayObject** argout,
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npy_intp nargin, PyArrayObject** argin,
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\brief Iterate a C function over several PyArrays
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\param thisone existing iterator structure
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\param func pointer to the vector-based function to iterate
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\param par auxiliary function parameters
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\c func is intended to be a simple gateway function to an existing
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\a fff function. It should conform with the syntax:
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void func( fff_vector** argout, fff_vector** argin, void* par )
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where \c argout is the list of output fff_vectors, \c argin is the
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list of input fff_vectors, and \c par is typically a reference to a
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pre-allocated structure in order to avoid memory allocations on
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each iteration. It is left to the developer to determine whether \c
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func assumes the correct number of input and output
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arguments. Hence, it is strongly recommended that such a gateway
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function be defined static in a Python module C file.
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void fff_func_iterator_eval( fff_func_iterator* thisone,
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void (*func)( fff_vector**, fff_vector**, void* ),
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\brief Reset a \c fff_func_iterator before new loop
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\param thisone existing iterator structure
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This is useful when several loops over arrays are to be performed
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in the same C function.
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void fff_func_iterator_reset( fff_func_iterator* thisone );
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\brief Destructor for the \c fff_func_iterator structure
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\param thisone iterator to delete
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Use after \c fff_func_iterator_new.
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void fff_func_iterator_delete( fff_func_iterator* thisone );
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fff_array* fff_array_fromPyArray( const PyArrayObject* x );
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PyArrayObject* fff_array_toPyArray( fff_array* y );
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\param n number of arrays
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\param axis non-iterated axis for each array
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PyArrayObject *X, *Y;
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PyArrayMultiIterObject* multi = (PyArrayMultiIterObject*)PyArray_MultiIterAllButAxis(2, axis, X, Y);
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while(multi->index < multi->size) {
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x = fff_vector_view(multi->iters[0]->dataptr, dim_axisX, multi->iters[0]->strides[axis]);
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y = fff_vector_view(multi->iters[1]->dataptr, dim_axisY, multi->iters[1]->strides[axis]);
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PyArray_MultiIter_NEXT(multi);
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extern PyObject* PyArray_MultiIterAllButAxis( int n, int axis, ... );
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extern fff_vector* fff_vector_fromPyArrayIter(const PyArrayIterObject* it, npy_intp axis);
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extern void fff_vector_get_fromPyArrayIter(fff_vector* y, const PyArrayIterObject* it, npy_intp axis);
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extern void fff_vector_const_toPyArrayIter(const fff_vector* y, PyArrayIterObject* it, npy_intp axis);
added
removed
python/wrapper/fffpy.h
