It looks like your code is wrong, and numpy 1.12 happened to let you get away with it
This line: evals = evals[evals > tolerance] Reduces the eigenvalues to only those which are greater than the tolerance When you do U[:, evals > tolerance], evals > tolerance is just going to be [True, True, ...]. You need to swap the last two lines, to U = U[:, evals > tolerance] evals = evals[evals > tolerance] Or better yet, introduce an intermediate variable: keep = evals > tolerance evals = evals[keep] U = U[:, keep] Eric On Tue, 12 Feb 2019 at 15:16 Mauro Cavalcanti <mauro...@gmail.com> wrote: > Dear ALL, > > I am trying to port an eigenalysis function that runs smoothly on Numpy > 1.12 but fail miserably on Numpy 1.13 or higher with the dreadful error > "boolean index did not match indexed array along dimension 1". > > Here is a fragment of the code, where the error occurrs: > > evals, evecs = np.linalg.eig(Syy) > idx = evals.argsort()[::-1] > evals = np.real(evals[idx]) > U = np.real(evecs[:, idx]) > evals = evals[evals > tolerance] > U = U[:, evals > tolerance] # Here is where the error occurs > > So, I ask: is there a way out of this? > > Thanks in advance for any assistance you can provide. > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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