scipy.ndimage中的许多函数接受一个可选的mode=nearest|wrap|reflect|constant参数，该参数确定如何处理该函数需要来自图像区域之外的某些数据的情况（填充）。填充由本机代码中的NI_ExtendLine()内部处理。
我不想在填充数据上运行ndimage函数，而是想使用与ndimage相同的填充模式选择来获取填充数据。
这是一个示例（仅对于mode = nearest，假设为2d图像）：
"""
Get padded data. Returns numpy array with shape (y1-y0, x1-x0, ...)
Any of x0, x1, y0, y1 may be outside of the image
"""
def get(img, y0, y1, x0, x1, mode="nearest"):
out_img = numpy.zeros((y1-y0, x1-x0))
for y in range(y0, y1):
for x in range(x0, x1):
yc = numpy.clip(y, 0, img.shape[0])
xc = numpy.clip(x, 0, img.shape[1])
out_img[y-y0, x-x0] = img[yc, xc]
return out_img
这样做是正确的，但是很慢，因为每次迭代一个像素。
什么是最好的方法（最快，最清晰，最pythonic）？
最佳答案
def get(img, y0, y1, x0, x1, mode="nearest"):
xs, ys = numpy.mgrid[y0:y1, x0:x1]
height, width = img.shape
if mode == "nearest":
xs = numpy.clip(xs, 0, height-1)
ys = numpy.clip(ys, 0, width-1)
elif mode == "wrap":
xs = xs % height
ys = ys % width
elif mode == "reflect":
maxh = height-1
maxw = width-1
# An unobvious way of performing reflecting modulo
# You should comment this
xs = numpy.absolute((xs + maxh) % (2 * maxh) - maxh)
ys = numpy.absolute((ys + maxw) % (2 * maxw) - maxw)
elif mode == "constant":
output = numpy.empty((y1-y0, x1-x0))
output.fill(0) # WHAT THE CONSTANT IS
# LOADS of bounds checks and restrictions
# You should comment this
target_section = output[max(0, -y0):min(y1-y0, -y0+height), max(0, -x0):min(x1-x0, -x0+width)]
new_fill = img[max(0, y0):min(height, y1), max(0, x0):min(width, x1)]
# Crop both the sections, so that they're both the size of the smallest
# Use more lines; I'm too lazy right now
target_section[:new_fill.shape[0], :new_fill.shape[1]] = new_fill[:target_section.shape[0], :target_section.shape[1]]
return output
else:
raise NotImplementedError("Unknown mode")
return img[xs, ys]