block_reduce

astropy.nddata.block_reduce(data, block_size, func=<function sum>)[source]

Downsample a data array by applying a function to local blocks.

If data is not perfectly divisible by block_size along a given axis then the data will be trimmed (from the end) along that axis.

Parameters:
datanumpy:array_like

The data to be resampled.

block_sizepython:int or numpy:array_like (python:int)

The integer block size along each axis. If block_size is a scalar and data has more than one dimension, then block_size will be used for for every axis.

funcpython:callable(), optional

The method to use to downsample the data. Must be a callable that takes in a 4D ndarray (the 2D ndarray input into block_reduce gets reshaped as 4D) and has an axis keyword that accepts tuples. This function will be called with axis=(2, 3) and it should return a 2D array. The default is sum, which provides block summation (and conserves the data sum).

Returns:
outputnumpy:array_like

The resampled data. Note the depending on the input func, the dtype of the output array may not match the input array.

Examples

>>> import numpy as np
>>> from astropy.nddata import block_reduce
>>> data = np.arange(16).reshape(4, 4)
>>> block_reduce(data, 2)  
array([[10, 18],
       [42, 50]])

>>> block_reduce(data, 2, func=np.mean)  
array([[  2.5,   4.5],
       [ 10.5,  12.5]])