python_freak - 3 months ago 24

Python Question

I have a 2D array

`v`

`v.shape=(M_1,M_2)`

`v.shape=(M_2,N_1,N_2)`

`M_1=N_1*N_2`

I came up with the following lines which produce the same result:

`np.reshape(v.T, reshape_tuple)`

and

`np.reshape(v.ravel(order='F'), reshape_tuple)`

for

`reshape_tuple=(M_2,N_1,N_2)`

Which one is computationally better and in what sense (comp time, memory, etc.) if the original

`v`

My guess would be that using the transpose is better, but if

`reshape`

`ravel`

`reshape`

`ravel`

Answer

The order in which they do things - reshape, change strides, and make a copy - differs, but they end up doing the same thing.

I like to use `__array_interface__`

to see where the data buffer is located, and other changes. I suppose I should add the `flags`

to see the `order`

. But we/you know that `transpose`

changes the order to to `F`

already, right?

```
In [549]: x=np.arange(6).reshape(2,3)
In [550]: x.__array_interface__
Out[550]:
{'data': (187732024, False),
'descr': [('', '<i4')],
'shape': (2, 3),
'strides': None,
'typestr': '<i4',
'version': 3}
```

transpose is a view, with different shape, strides and order:

```
In [551]: x.T.__array_interface__
Out[551]:
{'data': (187732024, False),
'descr': [('', '<i4')],
'shape': (3, 2),
'strides': (4, 12),
'typestr': '<i4',
'version': 3}
```

ravel with different order is a copy (different data buffer pointer)

```
In [552]: x.ravel(order='F').__array_interface__
Out[552]:
{'data': (182286992, False),
'descr': [('', '<i4')],
'shape': (6,),
'strides': None,
'typestr': '<i4',
'version': 3}
```

transpose ravel is also a copy. I think the same data pointer is just a case of memory reuse (since I'm not assigning to a variable) - but that can be checked.

```
In [553]: x.T.ravel().__array_interface__
Out[553]:
{'data': (182286992, False),
'descr': [('', '<i4')],
'shape': (6,),
'strides': None,
'typestr': '<i4',
'version': 3}
```

add the reshape:

```
In [554]: x.T.ravel().reshape(2,3).__array_interface__
Out[554]:
{'data': (182286992, False),
'descr': [('', '<i4')],
'shape': (2, 3),
'strides': None,
'typestr': '<i4',
'version': 3}
In [555]: x.ravel(order='F').reshape(2,3).__array_interface__
Out[555]:
{'data': (182286992, False),
'descr': [('', '<i4')],
'shape': (2, 3),
'strides': None,
'typestr': '<i4',
'version': 3}
```

I think there's an implicit 'ravel' in reshape:

```
In [558]: x.T.reshape(2,3).__array_interface__
Out[558]:
{'data': (182286992, False),
'descr': [('', '<i4')],
'shape': (2, 3),
'strides': None,
'typestr': '<i4',
'version': 3}
```

(I should rework these examples to get rid of that memory reuse ambiguity.) In any case, reshape after transpose requires the same memory copy that a ravel with order change does. And as far as I can tell only one copy is required for either case. The other operations just involve changes to attributes like shape.

May be it's clearer if we just look at the arrays

```
In [565]: x.T
Out[565]:
array([[0, 3],
[1, 4],
[2, 5]])
```

In the `T`

we can still step through the array in numeric order. But after reshape, the `1`

isn't anywhere close to the `0`

. Clearly there's been a copy.

```
In [566]: x.T.reshape(2,3)
Out[566]:
array([[0, 3, 1],
[4, 2, 5]])
```

the order of values after the ravel looks similar, and more obviously so after reshape.

```
In [567]: x.ravel(order='F')
Out[567]: array([0, 3, 1, 4, 2, 5])
In [568]: x.ravel(order='F').reshape(2,3)
Out[568]:
array([[0, 3, 1],
[4, 2, 5]])
```