1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
|
Visualization
=============
Spectral-cube is not primarily a visualization package, but it has several
tools for visualizing subsets of the data.
All lower-dimensional subsets,
`~spectral_cube.lower_dimensional_structures.OneDSpectrum`, and
`~spectral_cube.lower_dimensional_structures.Projection`, have their own
``quicklook`` methods
(`~spectral_cube.lower_dimensional_structures.OneDSpectrum.quicklook` and
`~spectral_cube.lower_dimensional_structures.Projection.quicklook`,
respectively). These methods will plot the data with somewhat properly labeled
axes.
The two-dimensional viewers default to using `aplpy <http://aplpy.github.io/>`_.
Because of quirks of how aplpy sets up its plotting window, these methods will
create their own figures. If ``use_aplpy`` is set to ``False``, and similarly
if you use the ``OneDSpectrum`` quicklook, the data will be overplotted in the
latest used plot window.
In principle, one can also simply plot the data. For example, if you have a cube,
you could do::
>>> plt.plot(cube[:,0,0]) # doctest: +SKIP
to plot a spectrum sliced out of the cube or::
>>> plt.imshow(cube[0,:,:]) # doctest: +SKIP
to plot an image slice.
.. warning::
There are known incompatibilities with the above plotting approach:
matplotlib versions ``<2.1`` will crash, and you will have to clear the plot
window to reset it.
Other Visualization Tools
=========================
To visualize the cubes directly, you can use some of the other tools we provide
for pushing cube data into external viewers.
See :doc:`yt_example` for using yt as a visualization tool.
The `spectral_cube.SpectralCube.to_glue` and
`spectral_cube.SpectralCube.to_ds9` methods will send the whole cube to glue
and ds9. This approach generally requires loading the whole cube into memory.
|
