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| author | Marten van Kerkwijk <mhvk@astro.utoronto.ca> | 2018-05-26 21:37:30 -0400 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2018-05-26 21:37:30 -0400 |
| commit | 09e108dc3453cbca17a80e75f5ec2b941e5b8137 (patch) | |
| tree | bfa8455af77b0a0846f54e9e4efe511f80333b4c /numpy/ma | |
| parent | 69503ff29819727df6d81847e6646c5fe8dc1a95 (diff) | |
| parent | 5c1e7ae50d6242ce1f04d1a6482a85f443b6bc1e (diff) | |
| download | numpy-09e108dc3453cbca17a80e75f5ec2b941e5b8137.tar.gz | |
Merge pull request #11120 from eric-wieser/simplify-ma-ctor
MAINT: remove redundant code in MaskedArray.__new__
Diffstat (limited to 'numpy/ma')
| -rw-r--r-- | numpy/ma/core.py | 15 |
1 files changed, 4 insertions, 11 deletions
diff --git a/numpy/ma/core.py b/numpy/ma/core.py index c0dda6f31..17682d13f 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -2799,13 +2799,8 @@ class MaskedArray(ndarray): # FIXME _sharedmask is never used. _sharedmask = True # Process mask. - # Number of named fields (or zero if none) - names_ = _data.dtype.names or () # Type of the mask - if names_: - mdtype = make_mask_descr(_data.dtype) - else: - mdtype = MaskType + mdtype = make_mask_descr(_data.dtype) if mask is nomask: # Case 1. : no mask in input. @@ -2831,14 +2826,12 @@ class MaskedArray(ndarray): _data._mask = mask _data._sharedmask = False else: + _data._sharedmask = not copy if copy: _data._mask = _data._mask.copy() - _data._sharedmask = False # Reset the shape of the original mask if getmask(data) is not nomask: data._mask.shape = data.shape - else: - _data._sharedmask = True else: # Case 2. : With a mask in input. # If mask is boolean, create an array of True or False @@ -2875,7 +2868,7 @@ class MaskedArray(ndarray): _data._mask = mask _data._sharedmask = not copy else: - if names_: + if _data.dtype.names: def _recursive_or(a, b): "do a|=b on each field of a, recursively" for name in a.dtype.names: @@ -2884,7 +2877,7 @@ class MaskedArray(ndarray): _recursive_or(af, bf) else: af |= bf - return + _recursive_or(_data._mask, mask) else: _data._mask = np.logical_or(mask, _data._mask) |