Reprojecting Images to Different Observers#

This example demonstrates how you can reproject images to the view from different observers. We use data from these two instruments:

  • AIA on SDO, which is in orbit around Earth

  • EUVI on STEREO A, which is in orbit around the Sun away from the Earth

You will need reproject v0.6 or higher installed.

import matplotlib.pyplot as plt

import astropy.units as u
from astropy.coordinates import SkyCoord

from sunpy.coordinates import get_body_heliographic_stonyhurst
from import AIA_193_JUN2012, STEREO_A_195_JUN2012

In this example we are going to make a lot of side by side figures, so let’s change the default figure size.

plt.rcParams['figure.figsize'] = (16, 8)

Create a map for each image, after making sure to sort by the appropriate name attribute (i.e., “AIA” and “EUVI”) so that the order is reliable.

map_list =[AIA_193_JUN2012, STEREO_A_195_JUN2012])
map_list.sort(key=lambda m: m.detector)
map_aia, map_euvi = map_list

# We downsample these maps to reduce memory consumption, but you can
# comment this out.
out_shape = (512, 512)
map_aia = map_aia.resample(out_shape * u.pix)
map_euvi = map_euvi.resample(out_shape * u.pix)

Plot the two maps, with the solar limb as seen by each observatory overlaid on both plots.

fig = plt.figure()

ax1 = fig.add_subplot(121, projection=map_aia)
map_aia.draw_limb(axes=ax1, color='white')
map_euvi.draw_limb(axes=ax1, color='red')

ax2 = fig.add_subplot(122, projection=map_euvi)
limb_aia = map_aia.draw_limb(axes=ax2, color='white')
limb_euvi = map_euvi.draw_limb(axes=ax2, color='red')

plt.legend([limb_aia[0], limb_euvi[0]],
           ['Limb as seen by AIA', 'Limb as seen by EUVI A'])
AIA $193 \; \mathrm{\mathring{A}}$ 2012-06-01 00:00:07, EUVI-A $195 \; \mathrm{\mathring{A}}$ 2012-06-01 00:05:30
INFO: Missing metadata for solar radius: assuming the standard radius of the photosphere. []
INFO: Missing metadata for solar radius: assuming the standard radius of the photosphere. []
INFO: Missing metadata for solar radius: assuming the standard radius of the photosphere. []
INFO: Missing metadata for solar radius: assuming the standard radius of the photosphere. []

<matplotlib.legend.Legend object at 0x7fbf5dc5f6d0>

Data providers can set the radius at which emission in the map is assumed to have come from. Most maps use a default value for photospheric radius (including EUVI maps), but some maps (including AIA maps) are set to a slightly different value. A mismatch in solar radius means a reprojection will not work correctly on pixels near the limb. This can be prevented by modifying the values for rsun on one map to match the other.

map_euvi.meta['rsun_ref'] = map_aia.meta['rsun_ref']

We can reproject the EUVI map to the AIA observer wcs using reproject_to(). This method defaults to using the fast reproject.reproject_interp() algorithm, but a different algorithm can be specified (e.g., reproject.reproject_adaptive()).

outmap = map_euvi.reproject_to(map_aia.wcs)

We can now plot the STEREO/EUVI image as seen from the position of SDO, next to the AIA image.

fig = plt.figure()
ax1 = fig.add_subplot(121, projection=map_aia)
ax2 = fig.add_subplot(122, projection=outmap)
outmap.plot(axes=ax2, title='EUVI image as seen from SDO')

# Set the HPC grid color to black as the background is white
ax2.coords[0].grid_lines_kwargs['edgecolor'] = 'k'
ax2.coords[1].grid_lines_kwargs['edgecolor'] = 'k'
AIA $193 \; \mathrm{\mathring{A}}$ 2012-06-01 00:00:07, EUVI image as seen from SDO

AIA as Seen from Mars#

The new observer coordinate doesn’t have to be associated with an existing Map. sunpy provides a function which can get the location coordinate for any known body. In this example, we use Mars.

mars = get_body_heliographic_stonyhurst('mars',

Without a target Map wcs, we can generate our own for an arbitrary observer. First, we need an appropriate reference coordinate. This will be similar to the one contained in map_aia, except with the observer placed at Mars.

mars_ref_coord = SkyCoord(0*u.arcsec, 0*u.arcsec,

We now need to construct our output WCS; we build a custom header using using the map_aia properties and our new, mars-based reference coordinate.

mars_header =

We generate the output map and plot it next to the original image.

outmap = map_aia.reproject_to(mars_header)

fig = plt.figure()

ax1 = fig.add_subplot(121, projection=map_aia)

ax2 = fig.add_subplot(122, projection=outmap)
outmap.plot(axes=ax2, title='AIA observation as seen from Mars')
AIA $193 \; \mathrm{\mathring{A}}$ 2012-06-01 00:00:07, AIA observation as seen from Mars

Total running time of the script: (0 minutes 3.049 seconds)

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