Change the values of a NumPy array that are NOT in a list of indices
I don't know of a clean way to do something like this:
mask = np.ones(a.shape,dtype=bool) #np.ones_like(a,dtype=bool)mask[indices] = Falsea[~mask] = 999a[mask] = 888Of course, if you prefer to use the numpy data-type, you could use dtype=np.bool_ -- There won't be any difference in the output. it's just a matter of preference really.
Only works for 1d arrays:
a = np.arange(30)indices = [2, 3, 4]ia = np.indices(a.shape)not_indices = np.setxor1d(ia, indices)a[not_indices] = 888
Obviously there is no general not operator for sets. Your choices are:
- Subtracting your
indicesset from a universal set of indices (depends on the shape ofa), but that will be a bit difficult to implement and read. - Some kind of iteration (probably the
for-loop is your best bet since you definitely want to use the fact that your indices are sorted). Creating a new array filled with new value, and selectively copying indices from the old one.
b = np.repeat(888, a.shape)b[indices] = a[indices]