Note
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Auto-Aligning AIA and HMI Data During Plotting#
This example shows how a map is autoaligned when it is plotted on a different reference frame.
See Aligning AIA and HMI Data with Reproject for an alternate approach to image alignment, where one of the maps is modified prior to plotting, and thus is available for purposes other than plotting.
import matplotlib.pyplot as plt
import astropy.units as u
import sunpy.data.sample
import sunpy.map
We use the AIA image and HMI image from the sample data. For the HMI map, we use the special HMI color map, which expects the plotted range to be -1500 to 1500.
map_aia = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE)
map_hmi = sunpy.map.Map(sunpy.data.sample.HMI_LOS_IMAGE)
map_hmi.plot_settings['cmap'] = "hmimag"
map_hmi.plot_settings['norm'] = plt.Normalize(-1500, 1500)
Plot both images side by side. Note by the tick labels that the HMI image is oriented “upside down” relative to the AIA image.
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_subplot(121, projection=map_aia)
map_aia.plot(axes=ax1, clip_interval=(1, 99.9)*u.percent)
ax2 = fig.add_subplot(122, projection=map_hmi)
map_hmi.plot(axes=ax2)

<matplotlib.image.AxesImage object at 0x7964c143c190>
Now let us intentionally set the projection
for the right panel
to be map_aia
instead of map_hmi
. This time, plotting the
HMI image onto axes defined by the AIA reference frame will trigger
“autoalignment” functionality where each map pixel is individually
drawn. The HMI image now has the same orientation as the AIA image.
Note that off-disk HMI data are not retained by default because an
additional assumption is required to define the location of the HMI
emission in 3D space. We can use SphericalScreen
to retain the off-disk HMI data. See
Reprojecting Using a Spherical Screen
for more reference.
fig = plt.figure(figsize=(12, 5))
ax1 = fig.add_subplot(121, projection=map_aia)
map_aia.plot(axes=ax1, clip_interval=(1, 99.9)*u.percent)
ax2 = fig.add_subplot(122, projection=map_aia)
map_hmi.plot(axes=ax2, title='HMI image in AIA reference frame')

INFO: Using mesh-based autoalignment [sunpy.map.mapbase]
<matplotlib.collections.QuadMesh object at 0x7964c22a5090>
We can directly plot them over one another, by setting the transparency of the HMI plot.
fig = plt.figure()
ax1 = fig.add_subplot(projection=map_aia)
map_aia.plot(axes=ax1, clip_interval=(1, 99.9)*u.percent)
map_hmi.plot(axes=ax1, alpha=0.5)
ax1.set_title('HMI overlaid on AIA')
plt.show()

INFO: Using mesh-based autoalignment [sunpy.map.mapbase]
Total running time of the script: (0 minutes 4.219 seconds)