Numpy: chained boolean indexing not properly updating boolean array without using np.where-CodePudding

I'm writing a simple root finder in numpy that is designed to operate on entire numpy arrays simultaneously. The basic idea of the solver is that the array not_converged is the size of all the data points while newly_converged is a different array that is the size of the subset of not_coverged that is True` in that iteration.

I'd like to update the values in the overall not_converged array to be False when entries in the newly_converged array become True, but doing so using boolean indexing doesn't seem to work as I'd expect:

not_converged = np.array([True, False, True])
# A previously not converged point has now converged
newly_converged = np.array([True, False])
# Now update the converged state
not_converged[not_converged][newly_converged] = False
print(not_converged)  # should be [False False True] but the array hasn't been updated

I can get this to work if I use np.where though:

not_converged = np.array([True, False, True])
# A previously not converged point has now converged
newly_converged = np.array([True, False])
# Now update the converged state
not_converged[not_converged] = np.where(newly_converged, False, True)
print(not_converged)  # Correctly updates the state

But I would have thought that the two methods above would yield identical results. Any idea why only the second method works?

CodePudding user response：

The np.where() is equivalent to the following indexing.

Note that not_converged[not_converged][newly_converged] is not a view into not_converged, it's a copy, so nothing should change.

not_converged = np.array([True, False, True])
# A previously not converged point has now converged
newly_converged = np.array([True, False])
# Now update the converged state
not_converged[not_converged] = np.logical_not(newly_converged)
print(not_converged)  # should be [False False True] but the array hasn't been updated

CodePudding user response：

This question is similar to one question asked before, as hpaulj comments (1, 2) well explained the reason, you can do this as:

sample = not_converged[not_converged]
sample[newly_converged] = False
not_converged[not_converged] = sample

or in simple way use ~ to invert newly_converged instead np.logical_not:

not_converged[not_converged] = ~newly_converged