Unwanted extra dimensions in NumPy array
There is the method called squeeze which does just what you want:
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`.
for example:
import numpy as npextra_dims = np.random.randint(0, 10, (1, 1, 5, 7))minimal_dims = extra_dims.squeeze()print minimal_dims.shape# (5, 7)
I'm assuming scaled_flat1a is a numpy array? In that case, it should be as simple as a reshape command.
import numpy as npa = np.array([[[[1, 2, 3], [4, 6, 7]]]])print(a.shape)# (1, 1, 2, 3)a = a.reshape(a.shape[2:]) # You can also use np.reshape()print(a.shape)# (2, 3)