How to one liner access numpy array in a list?

Question

Given a array in list

import numpy as np
n_pair = 5
np.random.seed ( 0 )
nsteps = 4
nmethod = 2
nbands = 3
t_band=0
t_method=0
t_step=0
t_sbj=0
t_gtmethod=1
all_sub = [[np.random.rand ( nmethod, nbands, 2 ) for _ in range ( nsteps  )] for _ in range ( 3)]

Then extract the array data point from each of the list as below

this_gtmethod=[x[t_step][t_method][t_band][t_gtmethod] for x in all_sub]

However, I would like to avoid the loop and instead would like to access directly all the three elements as below

this_gtmethod=all_sub[:][t_step][t_method][t_band][t_gtmethod]

But, it does not return the expected result when indexing the element as above

May I know where did I do wrong?

For a list [:] just returns a copy; it does not iterate. That's basic python behavior. List comprehension the standard tool for iterating through a list and returning a new list. Avoiding loops is a numpy idea, not a list one. — hpaulj
– hpaulj, Commented Dec 5, 2020 at 1:26

dschurman · Accepted Answer · 2020-12-05 00:23:34Z

2

This sort of slicing and indexing is best accomplished with Numpy arrays rather than lists.

If you make all_sub into a Numpy array, you can achieve your desired result with simple slicing.

all_sub = np.array(all_sub)
this_gtmethod = all_sub[:, t_step, t_method, t_band, t_gtmethod]

The result is the same as with your looping example.

answered Dec 5, 2020 at 0:23

dschurman

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hpaulj · Accepted Answer · 2020-12-05 02:49:19Z

You made a list of lists of arrays:

In [279]: type(all_sub), len(all_sub)
Out[279]: (list, 3)
In [280]: type(all_sub[0]), len(all_sub[0])
Out[280]: (list, 4)

In [282]: type(all_sub[0][0]), all_sub[0][0].shape
Out[282]: (numpy.ndarray, (2, 3, 2))

Lists can only be indexed with a scalar value or slice. List comprehension is the normal way of iterating through a list.

But an array can be indexed several dimensions at a time:

In [283]: all_sub[0][1][1,2,:]
Out[283]: array([0.46147936, 0.78052918])

Since the nested lists are all the same size, and arrays the same, it can be turned into a multidimensional array:

In [284]: M = np.array(all_sub)
In [285]: M.shape
Out[285]: (3, 4, 2, 3, 2)

2 ways of accessing the same subarrays:

In [286]: M[:,0,0,0,:]
Out[286]: 
array([[0.5488135 , 0.71518937],
       [0.31542835, 0.36371077],
       [0.58651293, 0.02010755]])
In [287]: [a[0][0,0,:] for a in all_sub]
Out[287]: 
[array([0.5488135 , 0.71518937]),
 array([0.31542835, 0.36371077]),
 array([0.58651293, 0.02010755])]

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