How to split a numpy array and perform certain actions on split arrays [Python]

Question

First of all, a question was asked about this question ( [1] [2] ), which explained how to split numpy arrays. I am new to Python. I have an array containing 262144 elements, and you want to split it into small arrays with a length of 512, sort them individually and summarize their first five values, but I'm not sure what is behind this line:

np.array_split(vector, 512)

How can I call and parse each array? Would it be nice to keep using the numpy array, or should I go back and use the dictionary instead?

Answer 1

, , 2D. , . argsort , , .

, -

N = 512 # Number of elements in each split array
M = 5   # Number of elements in each subarray for sorting and summing

b = a.reshape(-1,N)
out = b[np.arange(b.shape[0])[:,None], b.argsort(1)[:,:M]].sum(1)

-

In [217]: a   # Input array
Out[217]: array([45, 19, 71, 53, 20, 33, 31, 20, 41, 19, 38, 31, 86, 34])

In [218]: N = 7 # 512 for original case, 7 for sample

In [219]: M = 5

# Reshape into M rows 2D array
In [220]: b = a.reshape(-1,N)

In [224]: b
Out[224]: 
array([[45, 19, 71, 53, 20, 33, 31],
       [20, 41, 19, 38, 31, 86, 34]])

# Get argsort indices per row
In [225]: b.argsort(1)
Out[225]: 
array([[1, 4, 6, 5, 0, 3, 2],
       [2, 0, 4, 6, 3, 1, 5]])

# Select first M ones
In [226]: b.argsort(1)[:,:M]
Out[226]: 
array([[1, 4, 6, 5, 0],
       [2, 0, 4, 6, 3]])

# Use fancy-indexing to select those M ones per row
In [227]: b[np.arange(b.shape[0])[:,None], b.argsort(1)[:,:M]]
Out[227]: 
array([[19, 20, 31, 33, 45],
       [19, 20, 31, 34, 38]])

# Finally sum along each row
In [228]: b[np.arange(b.shape[0])[:,None], b.argsort(1)[:,:M]].sum(1)
Out[228]: array([148, 142])

np.argpartition -

out = b[np.arange(b.shape[0])[:,None], np.argpartition(b,M,axis=1)[:,:M]].sum(1)

-

In [236]: a = np.random.randint(11,99,(512*512))

In [237]: N = 512

In [238]: M = 5

In [239]: b = a.reshape(-1,N)

In [240]: %timeit b[np.arange(b.shape[0])[:,None], b.argsort(1)[:,:M]].sum(1)
100 loops, best of 3: 14.2 ms per loop

In [241]: %timeit b[np.arange(b.shape[0])[:,None], \
                np.argpartition(b,M,axis=1)[:,:M]].sum(1)
100 loops, best of 3: 3.57 ms per loop

Answer 2

,

import numpy as np
from numpy.testing.utils import assert_array_equal

vector = np.random.rand(262144)

splits = np.array_split(vector, 512)

sums = []
for split in splits:
   # sort it
   split.sort()
   # sublist
   subSplit = split[:5]
   #build sum
   splitSum = sum(subSplit)
   # add to new list
   sums.append(splitSum)

print np.array(sums).shape

, @Divakar