Slicing and Indexing NDData¶
Introduction¶
This page only deals with peculiarities applying to
NDData
-like classes. For a tutorial about slicing/indexing see the
python documentation
and numpy documentation.
Warning
NDData
and 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 Subclassing on how to
customize slicing for a particular property.
Slicing NDDataRef¶
Unlike NDData
the class 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]...)
>>> 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 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]...)
>>> 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]...)
>>> 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!