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Diffstat (limited to 'doc/source/reference/maskedarray.generic.rst')
| -rw-r--r-- | doc/source/reference/maskedarray.generic.rst | 38 |
1 files changed, 18 insertions, 20 deletions
diff --git a/doc/source/reference/maskedarray.generic.rst b/doc/source/reference/maskedarray.generic.rst index 580c8a3de..7c998ae63 100644 --- a/doc/source/reference/maskedarray.generic.rst +++ b/doc/source/reference/maskedarray.generic.rst @@ -30,6 +30,7 @@ The package ensures that masked entries are not used in computations. As an illustration, let's consider the following dataset:: >>> import numpy as np + >>> import numpy.ma as ma >>> x = np.array([1, 2, 3, -1, 5]) We wish to mark the fourth entry as invalid. The easiest is to create a masked @@ -135,7 +136,7 @@ There are several ways to construct a masked array. Accessing the data ------------------ -The underlying data of a masked array can be accessed through several ways: +The underlying data of a masked array can be accessed in several ways: * through the :attr:`~MaskedArray.data` attribute. The output is a view of the array as a :class:`numpy.ndarray` or one of its subclasses, depending on the type @@ -148,8 +149,7 @@ The underlying data of a masked array can be accessed through several ways: * by using the :func:`getdata` function. -None of these methods is completely satisfactory if some entries have been marked as invalid. As a general rule, invalid data should not be relied on. -If a representation of the array is needed without any masked entries, it is recommended to fill the array with the :meth:`filled` method. +None of these methods is completely satisfactory if some entries have been marked as invalid. As a general rule, where a representation of the array is required without any masked entries, it is recommended to fill the array with the :meth:`filled` method. @@ -264,14 +264,14 @@ To unmask one or several specific entries, we can just assign one or several new fill_value = 999999) .. note:: - Unmasking an entry by direct assignment will not work if the masked array - has a *hard* mask, as shown by the :attr:`~MaskedArray.hardmask` attribute. - This feature was introduced to prevent the overwriting of the mask. - To force the unmasking of an entry in such circumstance, the mask has first - to be softened with the :meth:`soften_mask` method before the allocation, - and then re-hardened with :meth:`harden_mask`:: - - >>> x = ma.array([1, 2, 3], mask=[0, 0, 1]) + Unmasking an entry by direct assignment will silently fail if the masked array + has a *hard* mask, as shown by the :attr:`hardmask` attribute. + This feature was introduced to prevent overwriting the mask. + To force the unmasking of an entry where the array has a hard mask, the mask must first + to be softened using the :meth:`soften_mask` method before the allocation. It can be re-hardened + with :meth:`harden_mask`:: + + >>> x = ma.array([1, 2, 3], mask=[0, 0, 1], hard_mask=True) >>> x masked_array(data = [1 2 --], mask = [False False True], @@ -287,17 +287,17 @@ To unmask one or several specific entries, we can just assign one or several new masked_array(data = [1 2 --], mask = [False False True], fill_value = 999999) - >>> x.soften_mask() + >>> x.harden_mask() -To unmask all masked entries of a masked array, the simplest solution is to assign the constant :attr:`nomask` to the mask:: +To unmask all masked entries of a masked array (provided the mask isn't a hard mask), the simplest solution is to assign the constant :attr:`nomask` to the mask:: >>> x = ma.array([1, 2, 3], mask=[0, 0, 1]) >>> x masked_array(data = [1 2 --], mask = [False False True], fill_value = 999999) - >>> x.mask = nomask + >>> x.mask = ma.nomask >>> x masked_array(data = [1 2 3], mask = [False False False], @@ -364,14 +364,12 @@ Operations on masked arrays --------------------------- Arithmetic and comparison operations are supported by masked arrays. -As much as possible, invalid entries of a masked array are not processed, -meaning that the corresponding :attr:`~MaskedArray.data` entries *should* be -the same before and after the operation. +As much as possible, invalid entries of a masked array are not processed, meaning that the +corresponding :attr:`data` entries *should* be the same before and after the operation. .. warning:: - We need to stress that this behavior may not be systematic, that invalid - data may actually be affected by the operation in some cases and once again - that invalid data should not be relied on. + We need to stress that this behavior may not be systematic, that masked data may be affected + by the operation in some cases and therefore users should not rely on this data remaining unchanged. The :mod:`numpy.ma` module comes with a specific implementation of most ufuncs. |