Creating a visualization with ArrayAnimatorWCS

This example shows how to create a simple visualization using 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

Traceback (most recent call last):
  File "/build/python-mpl-animators/src/mpl_animators-1.2.4/examples/arrayanimatorwcs.py", line 15, in <module>
    import sunpy.map
  File "/usr/lib/python3.14/site-packages/sunpy/map/__init__.py", line 10, in <module>
    from sunpy.map.mapbase import *
  File "/usr/lib/python3.14/site-packages/sunpy/map/mapbase.py", line 20, in <module>
    import reproject
ModuleNotFoundError: No module named 'reproject'

To showcase how to visualize a sequence of 2D images using 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 WCS header. The easiest method for the array is to create a 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 WCS header that 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. 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. ArrayAnimatorWCS provides a way of setting some display properties of the 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()