Cythonize两个小numpy函数,帮助需要

Question

问题

我正在尝试Cythonize两个小函数,主要处理numpy ndarray用于某些科学目的.这两个小函数在遗传算法中被称为数百万次,占算法占用的大部分时间.

我自己取得了一些进展并且都很好地工作,但我只获得了很小的速度提升(10%).更重要的是,cython --annotate表明大多数代码仍然通过Python.

代码

第一个功能:

此函数的目的是获取数据切片,并在内部嵌套循环中调用数百万次.根据数据[1] [1]中的bool,我们要么以正序或反向顺序得到切片.

#Ipython notebook magic for cython
%%cython --annotate
import numpy as np
from scipy import signal as scisignal

cimport cython
cimport numpy as np
def get_signal(data):
    #data[0] contains the data structure containing the numpy arrays
    #data[1][0] contains the position to slice
    #data[1][1] contains the orientation to slice, forward = 0, reverse = 1

    cdef int halfwinwidth = 100
    cdef int midpoint = data[1][0]
    cdef int strand = data[1][1]
    cdef int start = midpoint - halfwinwidth
    cdef int end = midpoint + halfwinwidth
    #the arrays we want to slice
    cdef np.ndarray r0 = data[0]['normals_forward']
    cdef np.ndarray r1 = data[0]['normals_reverse']
    cdef np.ndarray r2 = data[0]['normals_combined']
    if strand == 0:
        normals_forward = r0[start:end]
        normals_reverse = r1[start:end]
        normals_combined = r2[start:end]
    else:
        normals_forward = r1[end - 1:start - 1: -1]
        normals_reverse = r0[end - 1:start - 1: -1]
        normals_combined = r2[end - 1:start - 1: -1]
    #return the result as a tuple
    row = (normals_forward,
           normals_reverse,
           normals_combined)
    return row

第二功能

这个得到numpy数组的元组列表,我们想要明智地添加数组元素,然后规范化它们并获得交集的集成.

def calculate_signal(list signal):
    cdef int halfwinwidth = 100
    cdef np.ndarray profile_normals_forward = np.zeros(halfwinwidth * 2, dtype='f')
    cdef np.ndarray profile_normals_reverse = np.zeros(halfwinwidth * 2, dtype='f')
    cdef np.ndarray profile_normals_combined = np.zeros(halfwinwidth * 2, dtype='f')
    #b is a tuple of 3 np.ndarrays containing 200 floats
    #here we add them up elementwise
    for b in signal:
        profile_normals_forward += b[0]
        profile_normals_reverse += b[1]
        profile_normals_combined += b[2]
    #normalize the arrays
    cdef int count = len(signal)

    #print "Normalizing to number of elements"
    profile_normals_forward /= count
    profile_normals_reverse /= count
    profile_normals_combined /= count
    intersection_signal = scisignal.detrend(np.fmin(profile_normals_forward, profile_normals_reverse))
    intersection_signal[intersection_signal < 0] = 0
    intersection = np.sum(intersection_signal)

    results = {"intersection": intersection,
               "profile_normals_forward": profile_normals_forward,
               "profile_normals_reverse": profile_normals_reverse,
               "profile_normals_combined": profile_normals_combined,
               }
    return results

感谢任何帮助 - 我尝试使用内存视图,但由于某种原因,代码变得更快,更慢.

Answer 1

修复数组 cdef 后（如前所述，指定了 dtype），您可能应该将例程放入 cdef 函数中（只能由同一脚本中的 def 函数调用）。

在函数的声明中，您需要提供类型（如果是数组 numpy，则需要提供维度）：

cdef get_signal(numpy.ndarray[DTYPE_t, ndim=3] data):

但我不确定使用字典是个好主意。您可以使用 numpy 的列或行切片，例如 data[:, 0]。