问题
我正在尝试 Cythonize 两个主要处理 numpy ndarrays 的小函数，用于某些科学目的。这两个小函数在遗传算法中被调用了数百万次，占算法花费的大部分时间。
我自己取得了一些进展，并且都运行良好，但我只获得了微小的速度提升（10%）。更重要的是，cython --annotate 表明大部分代码仍在通过 Python。
编码
第一个功能：
这个函数的目的是取回数据片段，它在一个内部嵌套循环中被调用了数百万次。根据 data[1][1] 中的布尔值，我们可以正向或反向获取切片。
#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
第二个功能
这个得到一个 numpy 数组的元组列表，我们希望将数组元素明智地相加，然后对它们进行规范化并获得交集的积分。
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
任何帮助表示赞赏 - 我尝试使用内存视图，但由于某种原因，代码变得非常非常慢。