python - NumPy indexing with varying position -
i have array input_data of shape (a, b, c), , array ind of shape (b,). want loop through b axis , take sum of elements c[b[i]] , c[b[i]+1]. desired output of shape (a, b). have following code works, feel inefficient due index-based looping through b axis. there more efficient method?
import numpy np input_data = np.random.rand(2, 6, 10) ind = [ 2, 3, 5, 6, 5, 4 ] out = np.zeros( ( input_data.shape[0], input_data.shape[1] ) ) in range( len(ind) ): d = input_data[:, i, ind[i]:ind[i]+2] out[:, i] = np.sum(d, axis = 1)
edited based on divakar's answer:
import timeit import numpy np n = 1000 input_data = np.random.rand(10, n, 5000) ind = ( 4999 * np.random.rand(n) ).astype(np.int) def test_1(): # old loop-based method out = np.zeros( ( input_data.shape[0], input_data.shape[1] ) ) in range( len(ind) ): d = input_data[:, i, ind[i]:ind[i]+2] out[:, i] = np.sum(d, axis = 1) return out def test_2(): extent = 2 # comes 2 in "ind[i]:ind[i]+2" m,n,r = input_data.shape idx = (np.arange(n)*r + ind)[:,none] + np.arange(extent) out1 = input_data.reshape(m,-1)[:,idx].reshape(m,n,-1).sum(2) return out1 print timeit.timeit(stmt = test_1, number = 1000) print timeit.timeit(stmt = test_2, number = 1000) print np.all( test_1() == test_2(), keepdims = true ) >> 7.70429363482 >> 0.392034666757 >> [[ true]]
here's vectorized approach using linear indexing
broadcasting
. merge last 2 axes of input array, calculate linear indices corresponding last 2 axes, perform slicing , reshape 3d shape. finally, summation along last axis desired output. implementation -
extent = 2 # comes 2 in "ind[i]:ind[i]+2" m,n,r = input_data.shape idx = (np.arange(n)*r + ind)[:,none] + np.arange(extent) out1 = input_data.reshape(m,-1)[:,idx].reshape(m,n,-1).sum(2)
if extent
going 2
stated in question - "... sum of elements c[b[i]] , c[b[i]+1]"
, -
m,n,r = input_data.shape ind_arr = np.array(ind) axis1_r = np.arange(n) out2 = input_data[:,axis1_r,ind_arr] + input_data[:,axis1_r,ind_arr+1]
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