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
WARNING: SunpyUserWarning: Importing sunpy.map without its extra dependencies may result in errors.
The following packages are not installed:
['mpl-animators>=1.0.0', 'reproject>=0.9.0']
To install sunpy with these dependencies use `pip install sunpy[map]` or `pip install sunpy[all]` for all extras.
If you installed sunpy via conda, please report this to the community channel: https://matrix.to/#/#sunpy:openastronomy.org [sunpy.util.sysinfo]
WARNING: SunpyUserWarning: Importing sunpy.visualization without its extra dependencies may result in errors.
The following packages are not installed:
['mpl-animators>=1.0.0']
To install sunpy with these dependencies use `pip install sunpy[visualization]` or `pip install sunpy[all]` for all extras.
If you installed sunpy via conda, please report this to the community channel: https://matrix.to/#/#sunpy:openastronomy.org [sunpy.util.sysinfo]

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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)
WCS Keywords

Number of WCS axes: 3
CTYPE : 'TIME' 'HPLN-TAN' 'HPLT-TAN'
CRVAL : np.float64(0.0) np.float64(3.223099507700556) np.float64(1.385781353025793)
CRPIX : np.float64(0.0) np.float64(511.5) np.float64(511.5)
PC1_1 PC1_2 PC1_3  : np.float64(1.0) np.float64(0.0) np.float64(0.0)
PC2_1 PC2_2 PC2_3  : np.float64(0.0) np.float64(1.0) np.float64(0.0)
PC3_1 PC3_2 PC3_3  : np.float64(0.0) np.float64(0.0) np.float64(1.0)
CDELT : np.float64(5.0700000000020395) np.float64(2.402792) np.float64(2.402792)
NAXIS : 0  0

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()

Total running time of the script: (0 minutes 3.128 seconds)

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