Note
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Auto-Aligning AIA and HMI Data During Plotting¶
This example shows how to auto-align two images with different reference frames during plotting.
Here we use the optional keyword autoalign
when calling Map’s
plot()
method. The reference frames are defined by
the respective World Coordinate System (WCS) information.
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 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 0x7faabe6e65c0>
Setting autoalign=True
allows plotting the HMI image onto axes
defined by the AIA reference frame. In contrast to the above code
block, we intentionally set the projection
for the axes to be
the AIA map # instead of the HMI map. We also need to manually set
the plot limits because Matplotlib gets confused by the off-disk
parts of the image. Note that the HMI image now has the same
orientation as 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_aia)
map_hmi.plot(axes=ax2, autoalign=True, title='HMI image in AIA reference frame')
ax2.axis(ax1.axis())

(-0.5, 1023.5, -0.5, 1023.5)
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, autoalign=True, alpha=0.5)
ax1.set_title('HMI overlaid on AIA')
plt.show()

Total running time of the script: ( 0 minutes 10.056 seconds)