python - NumPy 数组中不需要的额外维度

Question

我打开了一张 .fits 图片:

scaled_flat1 = pyfits.open('scaled_flat1.fit')   
scaled_flat1a = scaled_flat1[0].data

当我打印它的形状时:

print scaled_flat1a.shape

我得到以下信息:

(1, 1, 510, 765)

我希望它显示为:

(510, 765)

我如何摆脱它之前的两个？

Answer 1

有一个方法叫做squeeze这正是你想要的:

Remove single-dimensional entries from the shape of an array.

Parameters

a : array_like
    Input data.
axis : None or int or tuple of ints, optional
    .. versionadded:: 1.7.0

    Selects a subset of the single-dimensional entries in the
    shape. If an axis is selected with shape entry greater than
    one, an error is raised.

Returns

squeezed : ndarray
    The input array, but with with all or a subset of the
    dimensions of length 1 removed. This is always `a` itself
    or a view into `a`.

例如:

import numpy as np

extra_dims = np.random.randint(0, 10, (1, 1, 5, 7))
minimal_dims = extra_dims.squeeze()

print minimal_dims.shape
# (5, 7)