"""
===============================
Open the IRIS Aligned AIA Cubes
===============================

In this example we will show how ``irispy`` handles the AIA cubes provided by
the IRIS instrument team. These cubes are aligned to the IRIS observation and
are 50" larger than the IRIS FOV.

They have the same format as IRIS SJI files, so you can read them via ``irispy``.
"""

import matplotlib.pyplot as plt
import pooch

from irispy.io import read_files

###############################################################################
# `We start with getting data from the IRIS data archive <https://www.lmsal.com/hek/hcr?cmd=view-event&event-id=ivo%3A%2F%2Fsot.lmsal.com%2FVOEvent%23VOEvent_IRIS_20250519_165924_3640107442_2025-05-19T16%3A59%3A242025-05-19T16%3A59%3A24.xml>`__.
#
# In this case, we will use ``pooch`` to keep this example self-contained
# but you can download the data manually using your browser as well.
#
# You will need to update the path to the data in the next section if you do that.

sdo_aia_file = pooch.retrieve(
    "https://www.lmsal.com/solarsoft/irisa/data/level2_compressed/2025/05/19/20250519_165924_3640107442/iris_l2_20250519_165924_3640107442_SDO.tar.gz",
    known_hash="b77d693fa328a96aec78b4a4aa420d5b167f8f670719fce815836627ed567f42",
)

###############################################################################
# We will now open the AIA dataset.
#
# It is provided as a compressed archive, with each AIA wavelength as a separate
# file.
#
# In this example, they are:
#
# - aia_l2_20250519_165924_3640107442_171.fits
# - aia_l2_20250519_165924_3640107442_94.fits
# - aia_l2_20250519_165924_3640107442_304.fits
# - aia_l2_20250519_165924_3640107442_193.fits
# - aia_l2_20250519_165924_3640107442_335.fits
# - aia_l2_20250519_165924_3640107442_211.fits
# - aia_l2_20250519_165924_3640107442_1700.fits
# - aia_l2_20250519_165924_3640107442_131.fits
# - aia_l2_20250519_165924_3640107442_1600.fits

# This will return a list of the AIA cubes.
aia_collection = read_files(sdo_aia_file)

###############################################################################
# Let us look at the first collection returned of the AIA cube.

print(aia_collection)

###############################################################################
# We will then select the 304 bandpass cube.

print(aia_collection["304_THIN"])

###############################################################################
# We will now plot the AIA data in the same manner as the SJI data.
#
# You can also change the axis labels and ticks if you so desire.
# `WCSAxes provides us an API we can use. <https://docs.astropy.org/en/stable/visualization/wcsaxes/index.html>`__

fig = plt.figure()
aia_collection["304_THIN"].plot(fig=fig)

plt.show()