wrestang - 11 days ago 7

Python Question

I have a list of 3D arrays that are all different shapes, but I need them to all be the same shape. Also, that shape needs to be the smallest shape in the list.

For example my_list with three arrays have the shapes (115,115,3), (111,111,3), and (113,113,3) then they all need to be (111,111,3). They are all square color images so they will be of shape (x,x,3).

So I have two main problems:

- How do I find the smallest shape array without looping or keeping a variable while creating the list?
- How do I efficiently set all arrays in a list to the smallest shape?

Currently I am keeping a variable for smallest shape while creating my_list so I can do this:

`for idx, img in enumerate(my_list):`

img = img[:smallest_shape,:smallest_shape]

my_list[idx] = img

I just feel like this is not the most efficient way, and I do realize I'm losing values by slicing, but I expect that.

Answer

I constructed a sample list with

```
In [513]: alist=[np.ones((512,512,3)) for _ in range(100)]
```

and did some timings.

Collecting shapes is fast:

```
In [515]: timeit [a.shape for a in alist]
10000 loops, best of 3: 31.2 µs per loop
```

Taking the min takes more time:

```
In [516]: np.min([a.shape for a in alist],axis=0)
Out[516]: array([512, 512, 3])
In [517]: timeit np.min([a.shape for a in alist],axis=0)
1000 loops, best of 3: 344 µs per loop
```

slicing is faster

```
In [518]: timeit [a[:500,:500,:] for a in alist]
10000 loops, best of 3: 133 µs per loop
```

now try to isolate the `min`

step.

```
In [519]: shapes=[a.shape for a in alist]
In [520]: timeit np.min(shapes, axis=0)
The slowest run took 5.75 times longer than the fastest. This could mean that an intermediate result is being cached.
10000 loops, best of 3: 136 µs per loop
```

When you have lists of objects, iteration is the only way to deal with all elements. Look at the code for `np.hstack`

and `np.vstack`

(and others). They do one or more list comprehensions to massage all the input arrays into the correct shape. Then they do `np.concatenate`

which iterates too, but in compiled code.