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@@            Coverage Diff             @@##             main    #3345      +/-   ##==========================================- Coverage   61.86%   61.85%   -0.01%==========================================  Files          85       85                Lines       10111    10112       +1     ==========================================  Hits         6255     6255- Misses       3856     3857       +1

to test this, could we perhaps patchnp.full andnp.zeros to raise exceptions, that you would then catch in the test? I'm thinking there would be 2 test functions, 1 testing the various inputs that should hit thenp.full branch, and another testing the various inputs that should hit thenp.zeros branch. This could also work for a single test function that internally checks which numpy routine the input scalar should trigger

I've done some digging into this topic, I'll post in a bit but I would like to hold off on merging for a bit.

Ok apologies, I know that Tom's PR supercedes this in many ways but for anyone curious, my worry was about how memory is allocated.

I learned thatzeros is only faster thanfull because it doesn't allocate memory when requested, lazily doing so later when needed (seehttps://stackoverflow.com/questions/70055063/how-is-memory-handled-once-touched-for-the-first-time-in-numpy-zeros/70056188#70056188). In benchmarks, I see the times of subsequent operations on the matrix, whether something likesum that should touch all elements or__setitem__ to be identical independent of initialization so I definitely think this should be merged!

importnumpyasnpbuf=np.arange(100_000,dtype=np.float64)# cell 1%%timeitfull=np.full((1_000_000,),0,dtype=np.float64)full[900_000:]=buf# cell 2%%timeitzeros=np.zeros((1_000_000,),dtype=np.float64)zeros[900_000:]=buf# cell 3%%timeitfull=np.full((1_000_000,),0,dtype=np.float64)full.sum()# cell 4%%timeitzeros=np.zeros((1_000_000,),dtype=np.float64)zeros.sum()