优化嵌套循环操作

Question

我有这个数据立方体包含图像的每个像素的数据(非常像高光谱成像).我试图以有效的方式在图像的每个像素上插入一条线.现在,我这样做:

我的datacube是一个6X1024x1024 numpy数组,我有另一个变量包含我的数据的自变量.

map = np.zeros((1024,1024))
for i in np.mgrid[1:1024]:
    for j in np.mgrid[1:1024]:
        x = independent_variable # This is my independent variable
        y = spec_cube[:,i,j] # The Y data to be fitted is the power at each scale, for a pixel
        index = polyfit(x,y,1) # Outputs the slope and the offset
        map[i,j] = index[0] # The pixel value is the index

我知道嵌套for循环通常是最糟糕的事情,但我想不出更好的方法.

我尝试了以下但它给出了这个错误:"ValueError:解压缩的值太多了"

map = np.zeros((1024,1024))
for i,j in map:
    x = independent_variable # This is my independent variable
    y = spec_cube[:,i,j] # The Y data to be fitted is the power at each scale, for a pixel
    index = polyfit(x,y,1) # Outputs the slope and the offset
    map[i,j] = index[0] # The pixel value is the index

Answer 1

一种加快速度的方法:使用itertools.product:

for (i, j) in itertools.product(np.mgrid[1:1024], np.mgrid[1:1024]):
    ... stuff ...

改进(Python 2.7.1):

In [2]: def multiline():
   ...:     for i in np.mgrid[1:1024]:
   ...:         for j in np.mgrid[1:1024]:
   ...:             pass
   ...:        

In [3]: def single_line():
   ...:     for i, j in product(np.mgrid[1:1024], np.mgrid[1:1024]):
   ...:         pass
   ...:    

In [4]: from itertools import product

In [5]: %timeit multiline()
10 loops, best of 3: 138 ms per loop

In [6]: %timeit single_line()
10 loops, best of 3: 75.6 ms per loop