How to repeat elements of an array along two axes?

Question

I want to repeat elements of an array along axis 0 and axis 1 for M and N times respectively:

import numpy as np  a = np.arange(12).reshape(3, 4) b = a.repeat(2, 0).repeat(2, 1) print(b)  [[ 0  0  1  1  2  2  3  3]  [ 0  0  1  1  2  2  3  3]  [ 4  4  5  5  6  6  7  7]  [ 4  4  5  5  6  6  7  7]  [ 8  8  9  9 10 10 11 11]  [ 8  8  9  9 10 10 11 11]] 

This works, but I want to know are there better methods without create a temporary array.

Answer 1

You could use the Kronecker product, see numpy.kron:

>>> a = np.arange(12).reshape(3,4) >>> print(np.kron(a, np.ones((2,2), dtype=a.dtype))) [[ 0  0  1  1  2  2  3  3]  [ 0  0  1  1  2  2  3  3]  [ 4  4  5  5  6  6  7  7]  [ 4  4  5  5  6  6  7  7]  [ 8  8  9  9 10 10 11 11]  [ 8  8  9  9 10 10 11 11]] 

Your original method is OK too, though!

Answer 2

You can make use of np.broadcast_to here:

def broadcast_tile(a, h, w):     x, y = a.shape     m, n = x * h, y * w     return np.broadcast_to(         a.reshape(x, 1, y, 1), (x, h, y, w)     ).reshape(m, n)  broadcast_tile(a, 2, 2) 

array([[ 0,  0,  1,  1,  2,  2,  3,  3],        [ 0,  0,  1,  1,  2,  2,  3,  3],        [ 4,  4,  5,  5,  6,  6,  7,  7],        [ 4,  4,  5,  5,  6,  6,  7,  7],        [ 8,  8,  9,  9, 10, 10, 11, 11],        [ 8,  8,  9,  9, 10, 10, 11, 11]]) 

Performance

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Functions

def chris(a, h, w):     x, y = a.shape     m, n = x * h, y * w     return np.broadcast_to(         a.reshape(x, 1, y, 1), (x, h, y, w)     ).reshape(m, n)  def alex_riley(a, b0, b1):     r, c = a.shape     rs, cs = a.strides     x = np.lib.stride_tricks.as_strided(a, (r, b0, c, b1), (rs, 0, cs, 0))     return x.reshape(r*b0, c*b1)  def paul_panzer(a, b0, b1):     r, c = a.shape     out = np.empty((r, b0, c, b1), a.dtype)     out[...] = a[:, None, :, None]     return out.reshape(r*b0, c*b1)  def wim(a, h, w):     return np.kron(a, np.ones((h,w), dtype=a.dtype)) 

Setup

import numpy as np import pandas as pd from timeit import timeit  res = pd.DataFrame(        index=['chris', 'alex_riley', 'paul_panzer', 'wim'],        columns=[5, 10, 20, 50, 100, 500, 1000],        dtype=float )  a = np.arange(100).reshape((10,10))  for f in res.index:     for c in res.columns:         h = w = c         stmt = '{}(a, h, w)'.format(f)         setp = 'from __main__ import h, w, a, {}'.format(f)         res.at[f, c] = timeit(stmt, setp, number=50) 

Output