numpy.ravel giving 2D array - can anyone explain?-CodePudding

I've come across some code where the use of numpy.ravel() is resulting in a 2D array - I've had a look at the documentation, which says that ravel() returns a 1D array (see https://numpy.org/doc/stable/reference/generated/numpy.ravel.html).

Here's a code snippet that shows this:

def jumbo():
    import numpy as np
    my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9]
    matrix = np.zeros((3,3))
    matrix.ravel()[:] = my_list
    return matrix

new_matrix = jumbo()
print(f"new matrix is:\n{new_matrix}")

I suppose part of what I'm asking is what is the function of the range specifier [:] here?

CodePudding user response：

What you did is assigned values at "raveled" matrix, wihtout actually saving ravel operation.

matrix = np.empty((3,3))  # created empty matrix
matrix.ravel()  # created 1d view of matrix
matrix.ravel()[:]  # indexing every element of matrix to make assignment possible (matrix is still in (3,3) shape)
matrix.ravel()[:] = my_list  #  assigned values.

if you want return to be 1D then return raveled array like this

def jumbo():
    import numpy as np
    my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9]
    matrix = np.empty((3,3))
    matrix.ravel()[:] = my_list
    return matrix.ravel()

new_matrix = jumbo()
print(f"new matrix is:\n{new_matrix}")

CodePudding user response：

ravel returns a view of the numpy array, thus when you do:

my_list = [1, 2, 3, 4, 5, 6, 7, 8, 9]
matrix = np.zeros((3,3))
matrix.ravel()[:] = my_list

You are using the view as a way to index the matrix as 1D, temporarily.

This enables here to set the values from a 1D list, but the underlying array remains 2D.

The matrix.ravel()[:] is used to enable setting the data. You could also use 2 steps:

view = matrix.ravel()
view[:] = my_list

output:

array([[1., 2., 3.],
       [4., 5., 6.],
       [7., 8., 9.]])

important note on the views

As @Stef nicely pointed out in the comments, this "trick" will only work for C-contiguous arrays, meaning you couldn't use ravel('F'):

demonstration:

view1 = matrix.ravel()
view2 = matrix.ravel('F') # actually not a view!

id(matrix)
# 140406991308816
id(view1.base)
# 140406991308816
id(view2.base)
# 9497104          # different id, we have a copy!