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Proposed new feature or change:
Often, I've wanted to concatenate arrays with different ndims along a particular axis, broadcasting the other axes as needed. Others have sought this functionality as well:
- numpy.concat does not appear to work across broadcast axes (Trac #1518) #2115
- https://stackoverflow.com/questions/56357047/concatenate-with-broadcast
- https://stackoverflow.com/questions/52733240/concatenate-1d-array-to-a-3d-array
- https://stackoverflow.com/questions/46700081/concat-two-arrays-of-different-dimensions-numpy
- https://stackoverflow.com/questions/55879664/how-to-concatenate-a-2d-array-into-every-3d-array
- https://stackoverflow.com/questions/63453495/combining-two-numpy-arrays
However, numpy.concatenate raises an error if the ndims don't match. For example:
import numpy as np
a = np.full([2], 0.1)
b = np.full([3, 2], 0.2)
c = np.full([5, 3, 2], 0.3)
arrays = [a, b, c]
axis = -1
try:
np.concatenate(arrays, axis)
except ValueError as e:
print(repr(e))ValueError('all the input arrays must have same number of dimensions, but the array at index 0 has 1 dimension(s) and the array at index 1 has 2 dimension(s)')
It would be convenient to add to numpy.concatenate an optional boolean argument called broadcast that broadcasts the input arrays along the axes that are not the concatenation axis, before concatenating them. Its default value can be False, which is the current behavior.
Below is an example implementation:
def tuple_replace(tupl, index, item):
return tupl[:index] + (item,) + tupl[index:][1:]
def broadcast_concat(arrays, axis):
shape = np.broadcast_shapes(*(tuple_replace(a.shape, axis, 0) for a in arrays))
bcast_arrays = [
np.broadcast_to(a, tuple_replace(shape, axis, a.shape[axis])) for a in arrays
]
return np.concatenate(bcast_arrays, axis)
output = broadcast_concat(arrays, axis)
assert output.shape[axis] == sum(a.shape[axis] for a in arrays)If desired, I can submit a PR for this.
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