Equivalence between accumarray and aggregate?

I need to translate in Python the following Matlab instruction coming from here:

binned_data = accumarray(bins(all(bins>0,2),:),1/nrows,M(ones(1,ncols)));

I tried to replace it with

binned_data = aggregate( bins[ all(bins > 0, 1), : ], 1 / nrows, size = M * ones( (1, ncols) ) )

using all and ones from numpy, and aggregate from numpy_groupies.

However, I get the following error message:

binned_data = numpy_groupies.aggregate( bins[ numpy.all(bins > 0, 1), : ], 1 / nrows, size = M * numpy.ones( (1, ncols) ) ) File “/usr/local/lib/python3.5/dist-packages/numpy_groupies/aggregate_numpy.py”, line 288, in aggregate _impl_dict=_impl_dict, _nansqueeze=True, **kwargs) File “/usr/local/lib/python3.5/dist-packages/numpy_groupies/aggregate_numpy.py”, line 257, in _aggregate_base size=size, order=order, axis=axis) File “/usr/local/lib/python3.5/dist-packages/numpy_groupies/utils_numpy.py”, line 200, in input_validation raise TypeError(“group_idx must be of integer type”) TypeError: group_idx must be of integer type

Do you understand why?

[Joeb34]

Glad to help: glad you it working.

[cdoucet]

Thank you so much for your help!

Thanks to your help, I think I managed to get the same behavior as Matlab’s accumarray by modifying a little bit your code. Actually, I made a mistake in handling the first argument as it represents indices (as you suggested). The correct way of translating this argument in Python seems to be the following:

first = bins[all(bins > 0, 1), :].astype(int) first[:] = [x-1 for x in first]

As you mentioned, array indices start at 0 in python and at 1 in matlab. Therefore, it is necessary to substract 1 to indices coming from matlab. The first line is also necessary as 0’s are not expected in matlab and they do not have the same meaning as in python (see description of subs argument here).

With this modification, the size can maintained to [128, 128]. Thus, the final code is the following:

from numpy import all
from numpy import amax
from numpy import array
from numpy import loadtxt
from numpy import max
from numpy import savetxt

from numpy_groupies import aggregate

if __name__ == "__main__":
    
    # input data
    bins = loadtxt('bins.txt', delimiter='\t')
    nrows, ncols = bins.shape

    # computation of arguments
    first = bins[all(bins > 0, 1), :].astype(int) # beware 0's from matlab have not the same meaning as in python
    first[:] = [x-1 for x in first] # arrays start at 0 in python and at 1 in matlab
    second = 1 / nrows
    third = max(bins, axis =0).astype(int)

    # aggregation
    binned_data = aggregate(first.T, second, size = third)

    # functional test
    binned_data_ref = loadtxt('binned_data.txt', delimiter='\t')
    assert amax( abs( binned_data - binned_data_ref ) ) <= 1e-6