Masked Values (astropy.utils.masked
)¶
Often, data sets are incomplete or corrupted and it would be handy to be able
to mask certain values. Astropy provides a Masked
class to help represent
such data sets.
Warning
Masked
is experimental! While we hope basic usage will remain
similar, we are not yet sure whether it will not be necessary to change it
to make things work throughout Astropy. This also means that comments and
suggestions for improvements are especially welcome!
Note
Masked
is similar to Numpy’s MaskedArray
,
but it supports subclasses much better and also has some important
differences in behaviour.
As a result, the behaviour of functions inside numpy.ma
is poorly
defined, and one should instead use regular numpy
functions, which
are overridden to work properly with masks (with nonobvious
choices documented in astropy.utils.masked.function_helpers
; please
report numpy functions that do not work properly with Masked
values!).
Usage¶
Astropy Masked
instances behave like ndarray
or subclasses such as
Quantity
but with a mask associated, which is propagated in operations such
as addition, etc.:
>>> import numpy as np
>>> from astropy import units as u
>>> from astropy.utils.masked import Masked
>>> ma = Masked([1., 2., 3.], mask=[False, False, True])
>>> ma
MaskedNDArray([1., 2., ——])
>>> mq = ma * u.m
>>> mq + 25 * u.cm
<MaskedQuantity [1.25, 2.25, ———] m>
You can get the values without the mask using
unmasked
, or, if you need to control what
should be substituted for any masked values, with
filled()
:
>>> mq.unmasked
<Quantity [1., 2., 3.] m>
>>> mq.filled(fill_value=75*u.cm)
<Quantity [ 1. , 2. , 0.75] m>
For reductions such as sums, the mask propagates as if the sum was done directly:
>>> ma = Masked([[0., 1.], [2., 3.]], mask=[[False, True], [False, False]])
>>> ma.sum(axis=1)
MaskedNDArray([——, 5.])
>>> ma.sum()
MaskedNDArray(——)
You might wonder why masked elements are propagated, instead of just being
skipped (as is done in MaskedArray
; see below). The rationale is that this leaves a
sum which is generally not useful unless one knows the number of masked
elements. In contrast, for sample properties such as the mean, for which the
number of elements are counted, it seems natural to simply omit the masked
elements from the calculation:
>> ma.mean(1)
MaskedNDArray([0.0, 2.5])
Differences from numpy.ma.MaskedArray
¶
Masked
differs from MaskedArray
in a number of ways. In usage,
a major difference is that most operations act on the masked values, i.e., no
effort is made to preserve values. For instance, compare:
>>> np_ma = np.ma.MaskedArray([1., 2., 3.], mask=[False, True, False])
>>> (np_ma + 1).data
array([2., 2., 4.])
>>> (Masked(np_ma) + 1).unmasked
array([2., 3., 4.])
The main reason for this decision is that for some masked subclasses, like
masked Quantity
, keeping the original value makes no sense (e.g., consider
dividing a length by a time: if the unit of a masked quantity is changing, why
should its value not change?). But it also helps to keep the implementation
considerably simpler, as the Masked
class now primarily has to deal with
propagating the mask rather than deciding what to do with values.
A second difference is that for reductions, the mask propagates as it would have if the operations were done on the individual elements:
>>> np_ma.prod()
3.0
>>> np_ma[0] * np_ma[1] * np_ma[2]
masked
>>> Masked(np_ma).prod()
MaskedNDArray(——)
The rationale for this becomes clear again by thinking about subclasses like a
masked Quantity
. For instance, consider an array s
of lengths with
shape (N, 3)
, in which the last axis represents width, height, and depth.
With this, you could compute corresponding volumes by taking the product of
the values in the last axis, s.prod(axis=1)
. But if masked elements were
skipped, the physical dimension of entries in the result would depend how many
elements were masked, which is something Quantity
could not represent (and
would be rather surprising!). As noted above, however, masked elements are
skipped for operations for which this is well defined, such as for getting the
mean and other sample properties such as the variance and standard deviation.
A third difference is more conceptual. For MaskedArray
, the
instance that is created is a masked version of the unmasked instance, i.e.,
MaskedArray
remembers that is has wrapped a subclass like
Quantity
, but does not share any of its methods. Hence, even though the
resulting class looks reasonable at first glance, it does not work as expected:
>>> q = [1., 2.] * u.m
>>> np_mq = np.ma.MaskedArray(q, mask=[False, True])
>>> np_mq
masked_Quantity(data=[1.0, ],
mask=[False, True],
fill_value=1e+20)
>>> np_mq.unit
Traceback (most recent call last):
...
AttributeError: 'MaskedArray' object has no attribute 'unit'
>>> np_mq / u.s
<Quantity [1., 2.] 1 / s>
In contrast, Masked
is always wrapped around the data properly, i.e., a
MaskedQuantity
is a quantity which has masked values, but with a unit that
is never masked. Indeed, one can see this from the class hierarchy:
>>> mq.__class__.__mro__
(<class 'astropy.utils.masked.core.MaskedQuantity'>,
<class 'astropy.units.quantity.Quantity'>,
<class 'astropy.utils.masked.core.MaskedNDArray'>,
<class 'astropy.utils.masked.core.Masked'>,
<class 'astropy.utils.shapes.NDArrayShapeMethods'>,
<class 'numpy.ndarray'>,
<class 'object'>)
This choice has made the implementation much simpler: Masked
only has to
worry about how to deal with masked values, while Quantity
can worry just
about unit propagation, etc. Indeed, an experiment showed that applying
Masked
to Column
(which is a subclass of ndarray
),
the result is a new MaskedColumn
that “just works”, with no need for the
overrides and specialcasing that were needed to make MaskedArray
work with Column
. (Because the behaviour does change
somewhat, however, we chose not to replace the existing implementation.)
In some respects, rather than think of Masked
as similar to
MaskedArray
, it may be more useful to think of Masked
as similar
to marking bad elements in arrays with NaN (notanumber). Like those NaN,
the mask just propagates, except that for some operations like taking the mean
the equivalence of nanmean
is used.
Reference/API¶
astropy.utils.masked Package¶
Builtin mask mixin class.
The design uses Masked
as a factory class which automatically
generates new subclasses for any data class that is itself a
subclass of a predefined masked class, with MaskedNDArray
providing such a predefined class for ndarray
.
Classes¶

A scalar value or array of values with associated mask. 

Masked version of ndarray. 
Class Inheritance Diagram¶
astropy.utils.masked.function_helpers Module¶
Helpers for letting numpy functions interact with Masked arrays.
The module supplies helper routines for numpy functions that propagate
masks appropriately., for use in the __array_function__
implementation of MaskedNDArray
. They are not
very useful on their own, but the ones with docstrings are included in
the documentation so that there is a place to find out how the mask is
interpreted.
Functions¶

Count number of occurrences of each value in array of nonnegative ints. 

Broadcast arrays to a common shape. 

Broadcast array to the given shape. 

Construct an array from an index array and a set of arrays to choose from. 

Copies values from one array to another, broadcasting as necessary. 

Counts the number of nonzero values in the array 

Return a new array with the same shape and type as a given array. 

Return a full array with the same shape and type as a given array. 

Insert values along the given axis before the given indices. 

Onedimensional linear interpolation. 

Perform an indirect stable sort using a sequence of keys. 

Like 

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Return an array of ones with the same shape and type as a given array. 

Evaluate a piecewisedefined function. 

Change elements of an array based on conditional and input values. 

Replaces specified elements of an array with given values. 

Changes elements of an array based on conditional and input values. 

Return an array drawn from elements in choicelist, depending on conditions. 

Return an array of zeros with the same shape and type as a given array. 