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.. _nddata_slicing:
Slicing and Indexing NDData
===========================
Introduction
------------
This page only deals with peculiarities applying to
`~astropy.nddata.NDData`-like classes. For a tutorial about slicing/indexing see the
`python documentation <https://docs.python.org/tutorial/introduction.html#lists>`_
and `numpy documentation <http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html>`_.
.. warning::
`~astropy.nddata.NDData` and `~astropy.nddata.NDDataRef` enforce almost no
restrictions on the properties so it might happen that some **valid but
unusual** combination of properties always results in an IndexError or
incorrect results. In this case see :ref:`nddata_subclassing` on how to
customize slicing for a particular property.
Slicing NDDataRef
-----------------
Unlike `~astropy.nddata.NDData` the class `~astropy.nddata.NDDataRef`
implements slicing or indexing. The result will be wrapped inside the same
class as the sliced object.
Getting one element::
>>> import numpy as np
>>> from astropy.nddata import NDDataRef
>>> data = np.array([1, 2, 3, 4])
>>> ndd = NDDataRef(data)
>>> ndd[1]
NDDataRef(2)
Getting a sliced portion of the original::
>>> ndd[1:3] # Get element 1 (inclusive) to 3 (exclusive)
NDDataRef([2, 3])
This will return a reference (and as such **not a copy**) of the original
properties so changing a slice will affect the original::
>>> ndd_sliced = ndd[1:3]
>>> ndd_sliced.data[0] = 5
>>> ndd_sliced
NDDataRef([5, 3])
>>> ndd
NDDataRef([1, 5, 3, 4])
except you indexed only one element (for example ``ndd_sliced = ndd[1]``). Then
the element is a scalar and changes will not propagate to the original.
Slicing NDDataRef including attributes
--------------------------------------
In case a ``wcs``, ``mask`` or ``uncertainty`` is present this attribute will
be sliced too::
>>> from astropy.nddata import StdDevUncertainty
>>> data = np.array([1, 2, 3, 4])
>>> mask = data > 2
>>> uncertainty = StdDevUncertainty(np.sqrt(data))
>>> wcs = np.ones(4)
>>> ndd = NDDataRef(data, mask=mask, uncertainty=uncertainty, wcs=wcs)
>>> ndd_sliced = ndd[1:3]
>>> ndd_sliced.data
array([2, 3])
>>> ndd_sliced.mask
array([False, True], dtype=bool)
>>> ndd_sliced.uncertainty
StdDevUncertainty([ 1.41421356, 1.73205081])
>>> ndd_sliced.wcs
array([ 1., 1.])
but ``unit`` and ``meta`` will be unaffected.
If any of the attributes is set but doesn't implement slicing an info will be
printed and the property will be kept as is::
>>> data = np.array([1, 2, 3, 4])
>>> mask = False
>>> uncertainty = StdDevUncertainty(0)
>>> wcs = {'a': 5}
>>> ndd = NDDataRef(data, mask=mask, uncertainty=uncertainty, wcs=wcs)
>>> ndd_sliced = ndd[1:3]
INFO: uncertainty cannot be sliced. [astropy.nddata.mixins.ndslicing]
INFO: mask cannot be sliced. [astropy.nddata.mixins.ndslicing]
INFO: wcs cannot be sliced. [astropy.nddata.mixins.ndslicing]
>>> ndd_sliced.mask
False
Example: Remove masked data
---------------------------
.. warning::
If you are using a `~astropy.wcs.WCS` object as ``wcs`` this will **NOT**
be possible. But you could work around it, i.e. set it to ``None`` before
slicing.
By convention the ``mask`` attribute indicates if a point is valid or invalid.
So we are able to get all valid data points by slicing with the mask::
>>> data = np.array([[1,2,3],[4,5,6],[7,8,9]])
>>> mask = np.array([[0,1,0],[1,1,1],[0,0,1]], dtype=bool)
>>> uncertainty = StdDevUncertainty(np.sqrt(data))
>>> ndd = NDDataRef(data, mask=mask, uncertainty=uncertainty)
>>> # don't forget that ~ or you'll get the invalid points
>>> ndd_sliced = ndd[~ndd.mask]
>>> ndd_sliced
NDDataRef([1, 3, 7, 8])
>>> ndd_sliced.mask
array([False, False, False, False], dtype=bool)
>>> ndd_sliced.uncertainty
StdDevUncertainty([ 1. , 1.73205081, 2.64575131, 2.82842712])
or all invalid points::
>>> ndd_sliced = ndd[ndd.mask] # without the ~ now!
>>> ndd_sliced
NDDataRef([2, 4, 5, 6, 9])
>>> ndd_sliced.mask
array([ True, True, True, True, True], dtype=bool)
>>> ndd_sliced.uncertainty
StdDevUncertainty([ 1.41421356, 2. , 2.23606798, 2.44948974, 3. ])
.. note::
The result of this kind of indexing (boolean indexing) will always be
one-dimensional!
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