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Fix block_replicate promoting float16/float32/complex64 inputs to float64 - #20364
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larrybradley merged 2 commits intoSep 11, 2026
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@meeseeksdev backport to v7.2.x @meeseeksdev backport to v8.0.x |
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…364-on-v7.2.x Backport PR #20364 on branch v7.2.x (Fix block_replicate promoting float16/float32/complex64 inputs to float64)
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…364-on-v8.0.x Backport PR #20364 on branch v8.0.x (Fix block_replicate promoting float16/float32/complex64 inputs to float64)
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Description
Fixes #20360.
Under NumPy NEP 50, dividing a
float32orfloat16array by anint64scalar (whichnp.prod(block_size)produces becauseblock_sizeis anintndarray) promotes the array tofloat64(andcomplex64promotes tocomplex128). Consequently,block_replicate(..., conserve_sum=True)unexpectedly promoted these dtypes to 64-bit precision, doubling the memory footprint forfloat32astronomical imagery.By converting
np.prod(block_size)to a Pythonint(int(np.prod(block_size))), NumPy retains the floating point / complex precision of the input array under NEP 50 rules, while integer arrays continue to convert tofloat64under Python true division as expected.Checklist for package maintainer(s)
np.float16,np.float32,np.float64,np.complex64,np.complex128, andnp.int32inputs.astropy.nddatatest suite passed (1324/1324 tests passed).docs/changes/nddata/20360.bugfix.rst.