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
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
|
"""
==============================================
Creating a visualization with ArrayAnimatorWCS
==============================================
This example shows how to create a simple visualization using
`~mpl_animators.ArrayAnimatorWCS`.
"""
import matplotlib.pyplot as plt
import astropy.units as u
import astropy.wcs
from astropy.visualization import AsinhStretch, ImageNormalize
import sunpy.map
from sunpy.data.sample import AIA_171_IMAGE, AIA_193_IMAGE
from sunpy.time import parse_time
from mpl_animators import ArrayAnimatorWCS
################################################################################
# To showcase how to visualize a sequence of 2D images using
# `~mpl_animators.ArrayAnimatorWCS`, we will use images from
# our sample data. The problem with this is that they are not part of
# a continuous dataset. To overcome this we will do two things.
# Create a stacked array of the images and create a `~astropy.wcs.WCS` header.
# The easiest method for the array is to create a `~sunpy.map.MapSequence`.
# Here we only use two files but you could pass in a larger selection of files.
map_sequence = sunpy.map.Map(AIA_171_IMAGE, AIA_193_IMAGE, sequence=True)
# Now we can just cast the sequence away into a NumPy array.
sequence_array = map_sequence.as_array()
# We'll also define a common normalization to use in the animations
norm = ImageNormalize(vmin=0, vmax=3e4, stretch=AsinhStretch(0.01))
###############################################################################
# Now we need to create the `~astropy.wcs.WCS` header that
# `~mpl_animators.ArrayAnimatorWCS` will need.
# To create the new header we can use the stored meta information from the
# ``map_sequence``.
# Now we need to get the time difference between the two observations.
t0, t1 = map(parse_time, [k["date-obs"] for k in map_sequence.all_meta()])
time_diff = (t1 - t0).to(u.s)
m = map_sequence[0]
wcs = astropy.wcs.WCS(naxis=3)
wcs.wcs.crpix = u.Quantity([0 * u.pix, *list(m.reference_pixel)])
wcs.wcs.cdelt = [time_diff.value, *list(u.Quantity(m.scale).value)]
wcs.wcs.crval = [0, m._reference_longitude.value, m._reference_latitude.value]
wcs.wcs.ctype = ["TIME", *list(m.coordinate_system)]
wcs.wcs.cunit = ["s", *list(m.spatial_units)]
wcs.wcs.aux.rsun_ref = m.rsun_meters.to_value(u.m)
# Now the resulting WCS object will look like:
print(wcs)
###############################################################################
# Now we can create the animation.
# `~mpl_animators.ArrayAnimatorWCS` requires you to select which
# axes you want to plot on the image. All other axes should have a ``0`` and
# sliders will be created to control the value for this axis.
wcs_anim = ArrayAnimatorWCS(sequence_array, wcs, [0, "x", "y"], norm=norm).get_animation()
plt.show()
###############################################################################
# You might notice that the animation could do with having the axes look
# neater. `~mpl_animators.ArrayAnimatorWCS` provides a way of setting
# some display properties of the `~astropy.visualization.wcsaxes.WCSAxes`
# object on every frame of the animation via use of the ``coord_params`` dict.
# They keys of the ``coord_params`` dict are either the first half of the
# ``CTYPE`` key, the whole ``CTYPE`` key or the entries in
# ``wcs.world_axis_physical_types`` here we use the short ctype identifiers for
# the latitude and longitude axes.
coord_params = {
"hpln": {"axislabel": "Helioprojective Longitude", "ticks": {"spacing": 10 * u.arcmin, "color": "black"}},
"hplt": {"axislabel": "Helioprojective Latitude", "ticks": {"spacing": 10 * u.arcmin, "color": "black"}},
}
# We have to recreate the visualization since we displayed it earlier.
wcs_anim = ArrayAnimatorWCS(sequence_array, wcs, [0, "x", "y"], norm=norm, coord_params=coord_params).get_animation()
plt.show()
|
