python - 优化嵌套循环操作

Question

我有一个包含图像每个像素数据的数据立方体(非常类似于高光谱成像)。我试图以一种有效的方式在图像的每个像素上画一条线。现在，我是这样做的:

我的数据立方体是一个 6X1024x1024 的 numpy 数组，我还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做法，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:要解压的值太多”

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 productIn [5]: %timeit multiline()10 loops, best of 3: 138 ms per loopIn [6]: %timeit single_line()10 loops, best of 3: 75.6 ms per loop