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 np
extra_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 np
a = 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)