AIA Carrington map

Import required functions

import astropy.units as u
import matplotlib.pyplot as plt
from astropy.time import Time

from solarsynoptic.data import aia_start_of_day_map
from solarsynoptic.reprojection import reproject_carrington

Define the output of the shape map. This is number of pixels in latitude and longitude.

shape_out = [180, 360]

Fetch a single AIA map. This will download the first map observed on the given day.

map_in = aia_start_of_day_map(Time.now(), 193 * u.Angstrom)
INFO: Downloading AIA 193 near real time map to /home/docs/sunpy/data/aia_193_20230201_nrt.fits [solarsynoptic.data.aia]

Files Downloaded:   0%|          | 0/1 [00:00<?, ?file/s]

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aia_193_20230201_nrt.fits:   5%|4         | 57.8k/1.25M [00:00<00:04, 290kB/s]

aia_193_20230201_nrt.fits:  16%|#6        | 201k/1.25M [00:00<00:01, 718kB/s]

aia_193_20230201_nrt.fits:  43%|####2     | 536k/1.25M [00:00<00:00, 1.57MB/s]

aia_193_20230201_nrt.fits:  96%|#########6| 1.20M/1.25M [00:00<00:00, 2.92MB/s]


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Reproject the map into a Carrington frame of reference, and show the result.

map_out = reproject_carrington(map_in, shape_out)
fig = plt.figure()
map_out.plot()
$193 \; \mathrm{\mathring{A}}$ 2023-02-01 00:00:04
INFO: Overwriting rsun_ref with standard photospheric radius [solarsynoptic.reprojection.reprojection]
INFO: Reprojecting AIA 193.0 Angstrom 2023-02-01 00:00:04 [solarsynoptic.reprojection.reprojection]

<matplotlib.image.AxesImage object at 0x7f65cbb36250>

By default reproject_carrington uses a Carée projection, where the latitude is evenly sampled. We can change this to an equal area (CEA) projection, where sin(latitude) is evenly sampled instead.

map_out = reproject_carrington(map_in, shape_out, latitude_projection='CEA')
fig = plt.figure()
map_out.plot()

plt.show()
$193 \; \mathrm{\mathring{A}}$ 2023-02-01 00:00:04
INFO: Reprojecting AIA 193.0 Angstrom 2023-02-01 00:00:04 [solarsynoptic.reprojection.reprojection]

Total running time of the script: ( 0 minutes 2.826 seconds)

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