CODEWITHSUNDEEP
arrayspython-2.7numpymultidimensional-array
Amir
Amir

Reputation: 1428

Creating a list of many ndarrays (different size) in python

I am new to python. Do we have any similar structure like Matlab's Multidimensional structure arrays in Python 2.7 that handles many ndarrays in a list. For instance, I have 15 of these layers (i.e. layer_X, X=[1,15]) with different size but all are 4D:

>>>type(layer_1)
<type 'numpy.ndarray'>
>>> np.shape(layer_1)
(1, 1, 32, 64)
>>> np.shape(layer_12)
(1, 1, 512, 1024)

How do I assign a structure that handles these ndarray with their position X?

Upvotes: 1

Views: 2930

Answers (2)

atru
atru

Reputation: 4744

You can use a dictionary:

layer_dict = {}
for X in range(1,16):
    layer_dict['layer_' + str(X)] = np.ndarray(shape=(1, 1, 32, 64))

This allows to store arrays of various sizes (and any other datatypes to be precise), add and remove components. It also allows you to access your arrays efficiently.

To add a layer type:

layer_dict['layer_16'] = np.ndarray(shape=(1, 1, 512, 1024))

To delete one:

del layer_dict['layer_3']

Note that the items are not stored in order, but that does not prevent you from efficient in-order processing with approaches similar to one in the initial construction loop. If you want to have an ordered dictionary, you can use OrderedDict from the collections module.

If there is any particular rule for choosing the size of each layer, update your question and I will edit my answer.

This is an example of sequential usage:

for X in range(1,16):
    temp = layer_dict['layer_' + str(X)]
    print type(temp)

The type of temp is an ndarray that you can use as any other ndarray.

A more detailed usage example:

for X in range(1,16):
    temp = layer_dict['layer_' + str(X)]
    temp[0, 0, 2, 0] = 1
    layer_dict['layer_' + str(X)] = temp

Here each layer is fetched into temp, modified, and then reassigned to layer_dict.

Upvotes: 4

John Zwinck
John Zwinck

Reputation: 249123

You can just use a list:

layers = [layer_1, layer_12]

Upvotes: 1

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