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| author | Marten van Kerkwijk <mhvk@astro.utoronto.ca> | 2014-04-04 11:47:40 -0400 |
|---|---|---|
| committer | Marten van Kerkwijk <mhvk@astro.utoronto.ca> | 2015-04-22 19:06:52 -0400 |
| commit | 7a84c5660539bb210746ba6b9b8e38d82d9fd330 (patch) | |
| tree | a7d02e08b4a9ae144addeb051845782ff60e6fa8 /numpy/ma/core.py | |
| parent | 02b858326dac217607a83ed0bf4d7d51d5bfbfbe (diff) | |
| download | numpy-7a84c5660539bb210746ba6b9b8e38d82d9fd330.tar.gz | |
ENH: Let MaskedArray getter, setter respect baseclass overrides
Diffstat (limited to 'numpy/ma/core.py')
| -rw-r--r-- | numpy/ma/core.py | 27 |
1 files changed, 14 insertions, 13 deletions
diff --git a/numpy/ma/core.py b/numpy/ma/core.py index 51e9f0f28..ee56e97e2 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -3043,8 +3043,7 @@ class MaskedArray(ndarray): # if getmask(indx) is not nomask: # msg = "Masked arrays must be filled before they can be used as indices!" # raise IndexError(msg) - _data = ndarray.view(self, ndarray) - dout = ndarray.__getitem__(_data, indx) + dout = self.data[indx] # We could directly use ndarray.__getitem__ on self... # But then we would have to modify __array_finalize__ to prevent the # mask of being reshaped if it hasn't been set up properly yet... @@ -3074,6 +3073,8 @@ class MaskedArray(ndarray): # Update the mask if needed if _mask is not nomask: dout._mask = _mask[indx] + # set shape to match that of data; this is needed for matrices + dout._mask.shape = dout.shape dout._sharedmask = True # Note: Don't try to check for m.any(), that'll take too long... return dout @@ -3091,16 +3092,16 @@ class MaskedArray(ndarray): # if getmask(indx) is not nomask: # msg = "Masked arrays must be filled before they can be used as indices!" # raise IndexError(msg) - _data = ndarray.view(self, ndarray.__getattribute__(self, '_baseclass')) - _mask = ndarray.__getattribute__(self, '_mask') + _data = self._data + _mask = self._mask if isinstance(indx, basestring): - ndarray.__setitem__(_data, indx, value) + _data[indx] = value if _mask is nomask: self._mask = _mask = make_mask_none(self.shape, self.dtype) _mask[indx] = getmask(value) return #........................................ - _dtype = ndarray.__getattribute__(_data, 'dtype') + _dtype = _data.dtype nbfields = len(_dtype.names or ()) #........................................ if value is masked: @@ -3124,21 +3125,21 @@ class MaskedArray(ndarray): mval = tuple([False] * nbfields) if _mask is nomask: # Set the data, then the mask - ndarray.__setitem__(_data, indx, dval) + _data[indx] = dval if mval is not nomask: _mask = self._mask = make_mask_none(self.shape, _dtype) - ndarray.__setitem__(_mask, indx, mval) + _mask[indx] = mval elif not self._hardmask: # Unshare the mask if necessary to avoid propagation if not self._isfield: self.unshare_mask() - _mask = ndarray.__getattribute__(self, '_mask') + _mask = self._mask # Set the data, then the mask - ndarray.__setitem__(_data, indx, dval) - ndarray.__setitem__(_mask, indx, mval) + _data[indx] = dval + _mask[indx] = mval elif hasattr(indx, 'dtype') and (indx.dtype == MaskType): indx = indx * umath.logical_not(_mask) - ndarray.__setitem__(_data, indx, dval) + _data[indx] = dval else: if nbfields: err_msg = "Flexible 'hard' masks are not yet supported..." @@ -3149,7 +3150,7 @@ class MaskedArray(ndarray): np.copyto(dindx, dval, where=~mindx) elif mindx is nomask: dindx = dval - ndarray.__setitem__(_data, indx, dindx) + _data[indx] = dindx _mask[indx] = mindx return |