diff options
| -rw-r--r-- | numpy/ma/core.py | 50 |
1 files changed, 27 insertions, 23 deletions
diff --git a/numpy/ma/core.py b/numpy/ma/core.py index d7fff25ff..e546e5bbe 100644 --- a/numpy/ma/core.py +++ b/numpy/ma/core.py @@ -277,9 +277,9 @@ def filled(a, value = None): # Should we check for contiguity ? and a.flags['CONTIGUOUS']: return a elif isinstance(a, dict): - return narray(a, 'O') + return np.array(a, 'O') else: - return narray(a) + return np.array(a) #####-------------------------------------------------------------------------- def get_masked_subclass(*arrays): @@ -730,15 +730,15 @@ def get_mask(a): return getattr(a, '_mask', nomask) getmask = get_mask -def getmaskarray(a): - """Return the mask of a, if any, or a boolean array of the shape +def getmaskarray(arr): + """Return the mask of arr, if any, or a boolean array of the shape of a, full of False. """ - m = getmask(a) - if m is nomask: - m = make_mask_none(np.shape(a)) - return m + mask = getmask(arr) + if mask is nomask: + mask = make_mask_none(np.shape(arr), getdata(arr).dtype.names) + return mask def is_mask(m): """Return True if m is a legal mask. @@ -790,18 +790,21 @@ def make_mask(m, copy=False, shrink=True, flag=None): else: return result -def make_mask_none(newshape): +def make_mask_none(newshape, fields=None): """Return a mask of shape s, filled with False. Parameters ---------- news : tuple A tuple indicating the shape of the final mask. - fieldnames: {None, string sequence}, optional + fields: {None, string sequence}, optional A list of field names, if needed. """ - result = np.zeros(newshape, dtype=MaskType) + if not fields: + result = np.zeros(newshape, dtype=MaskType) + else: + result = np.zeros(newshape, dtype=[(n, MaskType) for n in fields]) return result def mask_or (m1, m2, copy=False, shrink=True): @@ -817,9 +820,9 @@ def mask_or (m1, m2, copy=False, shrink=True): First mask. m2 : array_like Second mask - copy : bool + copy : {False, True}, optional Whether to return a copy. - shrink : bool + shrink : {True, False}, optional Whether to shrink m to nomask if all its values are False. """ @@ -2865,6 +2868,7 @@ masked_%(name)s(data = %(data)s, out -= self.min(axis=axis, fill_value=fill_value) return out + # Array methods --------------------------------------- copy = _arraymethod('copy') diagonal = _arraymethod('diagonal') @@ -3501,21 +3505,21 @@ def where (condition, x=None, y=None): yv = getdata(y) if x is masked: ndtype = yv.dtype -# xm = np.ones(fc.shape, dtype=MaskType) elif y is masked: ndtype = xv.dtype -# ym = np.ones(fc.shape, dtype=MaskType) else: ndtype = np.max([xv.dtype, yv.dtype]) -# xm = getmask(x) + # Construct an empty array and fill it d = np.empty(fc.shape, dtype=ndtype).view(MaskedArray) - np.putmask(d._data, fc, xv.astype(ndtype)) - np.putmask(d._data, notfc, yv.astype(ndtype)) - d._mask = np.zeros(fc.shape, dtype=MaskType) - np.putmask(d._mask, fc, getmask(x)) - np.putmask(d._mask, notfc, getmask(y)) - d._mask |= getmaskarray(condition) - if not d._mask.any(): + _data = d._data + np.putmask(_data, fc, xv.astype(ndtype)) + np.putmask(_data, notfc, yv.astype(ndtype)) + # Create an empty mask and fill it + _mask = d._mask = np.zeros(fc.shape, dtype=MaskType) + np.putmask(_mask, fc, getmask(x)) + np.putmask(_mask, notfc, getmask(y)) + _mask |= getmaskarray(condition) + if not _mask.any(): d._mask = nomask return d |