.. currentmodule:: numpy.ma
.. _maskedarray.generic:
The :mod:`numpy.ma` module
==========================
Rationale
---------
Masked arrays are arrays that may have missing or invalid entries.
The :mod:`numpy.ma` module provides a nearly work-alike replacement for numpy
that supports data arrays with masks.
What is a masked array?
-----------------------
In many circumstances, datasets can be incomplete or tainted by the presence
of invalid data. For example, a sensor may have failed to record a data, or
recorded an invalid value. The :mod:`numpy.ma` module provides a convenient
way to address this issue, by introducing masked arrays.
A masked array is the combination of a standard :class:`numpy.ndarray` and a
mask. A mask is either :attr:`nomask`, indicating that no value of the
associated array is invalid, or an array of booleans that determines for each
element of the associated array whether the value is valid or not. When an
element of the mask is ``False``, the corresponding element of the associated
array is valid and is said to be unmasked. When an element of the mask is
``True``, the corresponding element of the associated array is said to be
masked (invalid).
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
array::
>>> mx = ma.masked_array(x, mask=[0, 0, 0, 1, 0])
We can now compute the mean of the dataset, without taking the invalid data
into account::
>>> mx.mean()
2.75
The :mod:`numpy.ma` module
--------------------------
The main feature of the :mod:`numpy.ma` module is the :class:`MaskedArray`
class, which is a subclass of :class:`numpy.ndarray`. The class, its
attributes and methods are described in more details in the
:ref:`MaskedArray class ` section.
The :mod:`numpy.ma` module can be used as an addition to :mod:`numpy`: ::
>>> import numpy as np
>>> import numpy.ma as ma
To create an array with the second element invalid, we would do::
>>> y = ma.array([1, 2, 3], mask = [0, 1, 0])
To create a masked array where all values close to 1.e20 are invalid, we would
do::
>>> z = masked_values([1.0, 1.e20, 3.0, 4.0], 1.e20)
For a complete discussion of creation methods for masked arrays please see
section :ref:`Constructing masked arrays `.
Using numpy.ma
==============
.. _maskedarray.generic.constructing:
Constructing masked arrays
--------------------------
There are several ways to construct a masked array.
* A first possibility is to directly invoke the :class:`MaskedArray` class.
* A second possibility is to use the two masked array constructors,
:func:`array` and :func:`masked_array`.
.. autosummary::
:toctree: generated/
array
masked_array
* A third option is to take the view of an existing array. In that case, the
mask of the view is set to :attr:`nomask` if the array has no named fields,
or an array of boolean with the same structure as the array otherwise.
>>> x = np.array([1, 2, 3])
>>> x.view(ma.MaskedArray)
masked_array(data = [1 2 3],
mask = False,
fill_value = 999999)
>>> x = np.array([(1, 1.), (2, 2.)], dtype=[('a',int), ('b', float)])
>>> x.view(ma.MaskedArray)
masked_array(data = [(1, 1.0) (2, 2.0)],
mask = [(False, False) (False, False)],
fill_value = (999999, 1e+20),
dtype = [('a', '>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
>>> x[~x.mask]
masked_array(data = [1 4],
mask = [False False],
fill_value = 999999)
Another way to retrieve the valid data is to use the :meth:`compressed`
method, which returns a one-dimensional :class:`~numpy.ndarray` (or one of its
subclasses, depending on the value of the :attr:`~MaskedArray.baseclass`
attribute)::
>>> x.compressed()
array([1, 4])
Note that the output of :meth:`compressed` is always 1D.
Modifying the mask
------------------
Masking an entry
~~~~~~~~~~~~~~~~
The recommended way to mark one or several specific entries of a masked array
as invalid is to assign the special value :attr:`masked` to them::
>>> x = ma.array([1, 2, 3])
>>> x[0] = ma.masked
>>> x
masked_array(data = [-- 2 3],
mask = [ True False False],
fill_value = 999999)
>>> y = ma.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> y[(0, 1, 2), (1, 2, 0)] = ma.masked
>>> y
masked_array(data =
[[1 -- 3]
[4 5 --]
[-- 8 9]],
mask =
[[False True False]
[False False True]
[ True False False]],
fill_value = 999999)
>>> z = ma.array([1, 2, 3, 4])
>>> z[:-2] = ma.masked
>>> z
masked_array(data = [-- -- 3 4],
mask = [ True True False False],
fill_value = 999999)
A second possibility is to modify the :attr:`~MaskedArray.mask` directly,
but this usage is discouraged.
.. note::
When creating a new masked array with a simple, non-structured datatype,
the mask is initially set to the special value :attr:`nomask`, that
corresponds roughly to the boolean ``False``. Trying to set an element of
:attr:`nomask` will fail with a :exc:`TypeError` exception, as a boolean
does not support item assignment.
All the entries of an array can be masked at once by assigning ``True`` to the
mask::
>>> x = ma.array([1, 2, 3], mask=[0, 0, 1])
>>> x.mask = True
>>> x
masked_array(data = [-- -- --],
mask = [ True True True],
fill_value = 999999)
Finally, specific entries can be masked and/or unmasked by assigning to the
mask a sequence of booleans::
>>> x = ma.array([1, 2, 3])
>>> x.mask = [0, 1, 0]
>>> x
masked_array(data = [1 -- 3],
mask = [False True False],
fill_value = 999999)
Unmasking an entry
~~~~~~~~~~~~~~~~~~
To unmask one or several specific entries, we can just assign one or several
new valid values to them::
>>> x = ma.array([1, 2, 3], mask=[0, 0, 1])
>>> x
masked_array(data = [1 2 --],
mask = [False False True],
fill_value = 999999)
>>> x[-1] = 5
>>> x
masked_array(data = [1 2 5],
mask = [False False False],
fill_value = 999999)
.. note::
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],
fill_value = 999999)
>>> x[-1] = 5
>>> x
masked_array(data = [1 2 --],
mask = [False False True],
fill_value = 999999)
>>> x.soften_mask()
>>> x[-1] = 5
>>> x
masked_array(data = [1 2 --],
mask = [False False True],
fill_value = 999999)
>>> x.harden_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 = ma.nomask
>>> x
masked_array(data = [1 2 3],
mask = [False False False],
fill_value = 999999)
Indexing and slicing
--------------------
As a :class:`MaskedArray` is a subclass of :class:`numpy.ndarray`, it inherits
its mechanisms for indexing and slicing.
When accessing a single entry of a masked array with no named fields, the
output is either a scalar (if the corresponding entry of the mask is
``False``) or the special value :attr:`masked` (if the corresponding entry of
the mask is ``True``)::
>>> x = ma.array([1, 2, 3], mask=[0, 0, 1])
>>> x[0]
1
>>> x[-1]
masked_array(data = --,
mask = True,
fill_value = 1e+20)
>>> x[-1] is ma.masked
True
If the masked array has named fields, accessing a single entry returns a
:class:`numpy.void` object if none of the fields are masked, or a 0d masked
array with the same dtype as the initial array if at least one of the fields
is masked.
>>> y = ma.masked_array([(1,2), (3, 4)],
... mask=[(0, 0), (0, 1)],
... dtype=[('a', int), ('b', int)])
>>> y[0]
(1, 2)
>>> y[-1]
masked_array(data = (3, --),
mask = (False, True),
fill_value = (999999, 999999),
dtype = [('a', '>> x = ma.array([1, 2, 3, 4, 5], mask=[0, 1, 0, 0, 1])
>>> mx = x[:3]
>>> mx
masked_array(data = [1 -- 3],
mask = [False True False],
fill_value = 999999)
>>> mx[1] = -1
>>> mx
masked_array(data = [1 -1 3],
mask = [False True False],
fill_value = 999999)
>>> x.mask
array([False, True, False, False, True], dtype=bool)
>>> x.data
array([ 1, -1, 3, 4, 5])
Accessing a field of a masked array with structured datatype returns a
:class:`MaskedArray`.
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:`data` entries *should* be the same
before and after the operation.
.. warning::
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. Unary and binary functions that have a validity domain (such as
:func:`~numpy.log` or :func:`~numpy.divide`) return the :data:`masked`
constant whenever the input is masked or falls outside the validity domain::
>>> ma.log([-1, 0, 1, 2])
masked_array(data = [-- -- 0.0 0.69314718056],
mask = [ True True False False],
fill_value = 1e+20)
Masked arrays also support standard numpy ufuncs. The output is then a masked
array. The result of a unary ufunc is masked wherever the input is masked. The
result of a binary ufunc is masked wherever any of the input is masked. If the
ufunc also returns the optional context output (a 3-element tuple containing
the name of the ufunc, its arguments and its domain), the context is processed
and entries of the output masked array are masked wherever the corresponding
input fall outside the validity domain::
>>> x = ma.array([-1, 1, 0, 2, 3], mask=[0, 0, 0, 0, 1])
>>> np.log(x)
masked_array(data = [-- -- 0.0 0.69314718056 --],
mask = [ True True False False True],
fill_value = 1e+20)
Examples
========
Data with a given value representing missing data
-------------------------------------------------
Let's consider a list of elements, ``x``, where values of -9999. represent
missing data. We wish to compute the average value of the data and the vector
of anomalies (deviations from the average)::
>>> import numpy.ma as ma
>>> x = [0.,1.,-9999.,3.,4.]
>>> mx = ma.masked_values (x, -9999.)
>>> print mx.mean()
2.0
>>> print mx - mx.mean()
[-2.0 -1.0 -- 1.0 2.0]
>>> print mx.anom()
[-2.0 -1.0 -- 1.0 2.0]
Filling in the missing data
---------------------------
Suppose now that we wish to print that same data, but with the missing values
replaced by the average value.
>>> print mx.filled(mx.mean())
[ 0. 1. 2. 3. 4.]
Numerical operations
--------------------
Numerical operations can be easily performed without worrying about missing
values, dividing by zero, square roots of negative numbers, etc.::
>>> import numpy as np, numpy.ma as ma
>>> x = ma.array([1., -1., 3., 4., 5., 6.], mask=[0,0,0,0,1,0])
>>> y = ma.array([1., 2., 0., 4., 5., 6.], mask=[0,0,0,0,0,1])
>>> print np.sqrt(x/y)
[1.0 -- -- 1.0 -- --]
Four values of the output are invalid: the first one comes from taking the
square root of a negative number, the second from the division by zero, and
the last two where the inputs were masked.
Ignoring extreme values
-----------------------
Let's consider an array ``d`` of random floats between 0 and 1. We wish to
compute the average of the values of ``d`` while ignoring any data outside
the range ``[0.1, 0.9]``::
>>> print ma.masked_outside(d, 0.1, 0.9).mean()