有思想的Python中的线程FFT

本文介绍了有思想的Python中的线程FFT的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

Numpy/SciPy中的快速傅立叶变换(FFT)没有线程化. Enthought Python随Intel MKL数值库一起提供，该库具有线程化FFT的功能.一个人如何访问这些例程?

Fast Fourier Transforms (FFTs) in Numpy/SciPy are not threaded. Enthought Python is shipped with the Intel MKL numerical library, which is capable of threaded FFTs. How does one get access to these routines?

推荐答案

新版本和改进版本可以处理输入和输出数组中的任意跨度. 默认情况下，此位置现在不就位，并创建一个新的阵列. 它模仿Numpy FFT例程，但具有不同的归一化.

New and improved version which handles arbitrary strides in the input and output arrays. By default this one is now not-in-place and creates a new array. It mimics the Numpy FFT routines except that it has a different normalisation.

''' Wrapper to MKL FFT routines ''' import numpy as _np import ctypes as _ctypes mkl = _ctypes.cdll.LoadLibrary("mk2_rt.dll") _DFTI_COMPLEX = _ctypes.c_int(32) _DFTI_DOUBLE = _ctypes.c_int(36) _DFTI_PLACEMENT = _ctypes.c_int(11) _DFTI_NOT_INPLACE = _ctypes.c_int(44) _DFTI_INPUT_STRIDES = _ctypes.c_int(12) _DFTI_OUTPUT_STRIDES = _ctypes.c_int(13) def fft2(a, out=None): ''' Forward two-dimensional double-precision complex-complex FFT. Uses the Intel MKL libraries distributed with Enthought Python. Normalisation is different from Numpy! By default, allocates new memory like 'a' for output data. Returns the array containing output data. ''' assert a.dtype == _npplex128 assert len(a.shape) == 2 inplace = False if out is a: inplace = True elif out is not None: assert out.dtype == _npplex128 assert a.shape == out.shape assert not _np.may_share_memory(a, out) else: out = _np.empty_like(a) Desc_Handle = _ctypes.c_void_p(0) dims = (_ctypes.c_int*2)(*a.shape) mkl.DftiCreateDescriptor(_ctypes.byref(Desc_Handle), _DFTI_DOUBLE, _DFTI_COMPLEX, _ctypes.c_int(2), dims ) #Set input strides if necessary if not a.flags['C_CONTIGUOUS']: in_strides = (_ctypes.c_int*3)(0, a.strides[0]/16, a.strides[1]/16) mkl.DftiSetValue(Desc_Handle, _DFTI_INPUT_STRIDES, _ctypes.byref(in_strides)) if inplace: #Inplace FFT mkl.DftiCommitDescriptor(Desc_Handle) mkl.DftiComputeForward(Desc_Handle, a.ctypes.data_as(_ctypes.c_void_p) ) else: #Not-inplace FFT mkl.DftiSetValue(Desc_Handle, _DFTI_PLACEMENT, _DFTI_NOT_INPLACE) #Set output strides if necessary if not out.flags['C_CONTIGUOUS']: out_strides = (_ctypes.c_int*3)(0, out.strides[0]/16, out.strides[1]/16) mkl.DftiSetValue(Desc_Handle, _DFTI_OUTPUT_STRIDES, _ctypes.byref(out_strides)) mkl.DftiCommitDescriptor(Desc_Handle) mkl.DftiComputeForward(Desc_Handle, a.ctypes.data_as(_ctypes.c_void_p), out.ctypes.data_as(_ctypes.c_void_p) ) mkl.DftiFreeDescriptor(_ctypes.byref(Desc_Handle)) return out def ifft2(a, out=None): ''' Backward two-dimensional double-precision complex-complex FFT. Uses the Intel MKL libraries distributed with Enthought Python. Normalisation is different from Numpy! By default, allocates new memory like 'a' for output data. Returns the array containing output data. ''' assert a.dtype == _npplex128 assert len(a.shape) == 2 inplace = False if out is a: inplace = True elif out is not None: assert out.dtype == _npplex128 assert a.shape == out.shape assert not _np.may_share_memory(a, out) else: out = _np.empty_like(a) Desc_Handle = _ctypes.c_void_p(0) dims = (_ctypes.c_int*2)(*a.shape) mkl.DftiCreateDescriptor(_ctypes.byref(Desc_Handle), _DFTI_DOUBLE, _DFTI_COMPLEX, _ctypes.c_int(2), dims ) #Set input strides if necessary if not a.flags['C_CONTIGUOUS']: in_strides = (_ctypes.c_int*3)(0, a.strides[0]/16, a.strides[1]/16) mkl.DftiSetValue(Desc_Handle, _DFTI_INPUT_STRIDES, _ctypes.byref(in_strides)) if inplace: #Inplace FFT mkl.DftiCommitDescriptor(Desc_Handle) mkl.DftiComputeBackward(Desc_Handle, a.ctypes.data_as(_ctypes.c_void_p) ) else: #Not-inplace FFT mkl.DftiSetValue(Desc_Handle, _DFTI_PLACEMENT, _DFTI_NOT_INPLACE) #Set output strides if necessary if not out.flags['C_CONTIGUOUS']: out_strides = (_ctypes.c_int*3)(0, out.strides[0]/16, out.strides[1]/16) mkl.DftiSetValue(Desc_Handle, _DFTI_OUTPUT_STRIDES, _ctypes.byref(out_strides)) mkl.DftiCommitDescriptor(Desc_Handle) mkl.DftiComputeBackward(Desc_Handle, a.ctypes.data_as(_ctypes.c_void_p), out.ctypes.data_as(_ctypes.c_void_p) ) mkl.DftiFreeDescriptor(_ctypes.byref(Desc_Handle)) return out