I think enumerating the cases along the way makes it a bit more tangible for the discussion
import numpy as np z = 1+1j z.conjugate() # 1-1j zz = np.array(z) zz # array(1+1j) zz.T # array(1+1j) # OK expected. zz.conj() # 1-1j ?? what happened; no arrays? zz.conjugate() # 1-1j ?? same zz1d = np.array([z]*3) zz1d.T # no change so this is not the regular 2D array zz1d.conj() # array([1.-1.j, 1.-1.j, 1.-1.j]) zz1d.conj().T # array([1.-1.j, 1.-1.j, 1.-1.j]) zz1d.T.conj() # array([1.-1.j, 1.-1.j, 1.-1.j]) zz1d[:, None].conj() # 2D column vector - no surprises if [:, None] is known zz2d = zz1d[:, None] # 2D column vector - no surprises if [:, None] is known zz2d.conj() # 2D col vec conjugated zz2d.conj().T # 2D col vec conjugated transposed zz3d = np.arange(24.).reshape(2,3,4).view(complex) zz3d.conj() # no surprises, conjugated zz3d.conj().T # ?? Why not the last two dims swapped like other stacked ops # For scalar arrays conjugation strips the number # For 1D arrays transpose is a no-op but conjugation works # For 2D arrays conjugate it is the matlab's elementwise conjugation op .' # and transpose is acting like expected # For 3D arrays conjugate it is the matlab's elementwise conjugation op .' # but transpose is the reversing all dims just like matlab's permute() # with static dimorder. and so on. Maybe we can try to identify all the use cases and the quirks before we can make design the solution. Because these are a bit more involved and I don't even know if this is exhaustive. On Mon, Jun 24, 2019 at 8:21 PM Marten van Kerkwijk < m.h.vankerkw...@gmail.com> wrote: > Hi Stephan, > > Yes, the complex conjugate dtype would make things a lot faster, but I > don't quite see why we would wait for that with introducing the `.H` > property. > > I do agree that `.H` is the correct name, giving most immediate clarity > (i.e., people who know what conjugate transpose is, will recognize it, > while likely having to look up `.CT`, while people who do not know will > have to look up regardless). But at the same time agree that the docstring > and other documentation should start with "Conjugate tranpose" - good to > try to avoid using names of people where you have to be in the "in crowd" > to know what it means. > > The above said, if we were going with the initial suggestion of `.MT` for > matrix transpose, then I'd prefer `.CT` over `.HT` as its conjugate version. > > But it seems there is little interest in that suggestion, although sadly a > clear path forward has not yet emerged either. > > All the best, > > Marten > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
