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.. _nddata_slicing:
Slicing and Indexing NDData
***************************
Introduction
============
This page only deals with peculiarities that apply to
`~astropy.nddata.NDData`-like classes. For a tutorial about slicing/indexing see the
`python documentation <https://docs.python.org/3/tutorial/introduction.html#lists>`_
and `numpy documentation <https://numpy.org/doc/stable/reference/routines.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** combinations of properties always result 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])
But only the one element that was indexed is affected (for example,
``ndd_sliced = ndd[1]``). The element is a scalar and changes will not
propagate to the original.
Slicing NDDataRef Including Attributes
======================================
In the case that a ``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))
>>> ndd = NDDataRef(data, mask=mask, uncertainty=uncertainty)
>>> ndd_sliced = ndd[1:3]
>>> ndd_sliced.data
array([2, 3])
>>> ndd_sliced.mask
array([False, True]...)
>>> ndd_sliced.uncertainty # doctest: +FLOAT_CMP
StdDevUncertainty([1.41421356, 1.73205081])
``unit`` and ``meta``, however, will be unaffected.
If any of the attributes are set but do not 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)
>>> ndd = NDDataRef(data, mask=mask, uncertainty=uncertainty)
>>> ndd_sliced = ndd[1:3]
INFO: uncertainty cannot be sliced. [astropy.nddata.mixins.ndslicing]
INFO: mask cannot be sliced. [astropy.nddata.mixins.ndslicing]
>>> ndd_sliced.mask
False
Slicing NDData with World Coordinates
-------------------------------------
If ``wcs`` is set, it must be either implement
`~astropy.wcs.wcsapi.BaseLowLevelWCS` or `~astropy.wcs.wcsapi.BaseHighLevelWCS`.
This means that only integer or range slices without a step are supported. So
slices like ``[::10]`` or array or boolean based slices will not work.
If you want to slice an ``NDData`` object called ``ndd`` without the WCS you can remove the
WCS from the ``NDData`` object by running:
>>> ndd.wcs = None
Removing Masked Data
--------------------
.. warning::
If ``wcs`` is set this will **NOT** be possible. But you can work around
this by setting the wcs attribute to `None` with ``ndd.wcs = 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.
Examples
^^^^^^^^
..
EXAMPLE START
Removing Masked Data in NDDataRef
To get all of the 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]...)
>>> ndd_sliced.uncertainty # doctest: +FLOAT_CMP
StdDevUncertainty([1. , 1.73205081, 2.64575131, 2.82842712])
Or all invalid points::
>>> ndd_sliced = ndd[ndd.mask] # without the ~ now!
>>> ndd_sliced
NDDataRef([—, —, —, —, —])
>>> ndd_sliced.mask
array([ True, True, True, True, True]...)
>>> ndd_sliced.uncertainty # doctest: +FLOAT_CMP
StdDevUncertainty([1.41421356, 2. , 2.23606798, 2.44948974, 3. ])
.. note::
The result of this kind of indexing (boolean indexing) will always be
one-dimensional!
..
EXAMPLE END
|
