Translation of SOHO/EIT-to-SDO/AIA observations

This example shows how to intercalibrate SOHO observations to SDO observations.

# from itipy.evaluation.util import *
# import glob
# from itipy.translate import *
# from matplotlib.colors import Normalize
# from datetime import timedelta, datetime
#
# base_path = os.getcwd()
#
# ############################################################################################################################################################################
# # For the translation of SOHO/EIT observations we make use of the multi-channel representation and translate all four EUV channels as well as the magnetogram channel simultaneous. For this application we use half of the SDO resolution as reference for our enchancement.
#
# ############################################################################################################################################################################
# # We provide a publicly available dataset which allows the users to play around with a subset of the data available without downloading the entire database.
# #
# # This dataset contains `.fits` files from **PROBA2/SWAP**, **SDO/AIA**, **Solar Orbiter/EUI (FSI and HRI)**, **SOHO/EIT** and **STEREO/EUVI**.
# #
# # In addition 3 trained models are stored with:
# # 1. PROBA2/SWAP to SDO/AIA
# # 2. Solar Orbiter/EUI FSI to SDO/AIA and
# # 3. SDO/AIA to Solar Orbiter/EUI HRI
# #
# # to perform the translation.
#
# download_gcp_bucket('iti-dataset', base_path+'/iti-testset/')
#
# ############################################################################################################################################################################
# # We load the previously downloaded SOHO files. The translator requires a list of the four aligned FITS files for each translation. We use a patch factor of 2 to save memory.
#
# soho_files = sorted(glob.glob(base_path+'/iti-testset/soho/*/*.fits', recursive=True))
# soho_maps = [Map(f).rotate() for f in soho_files] # rotate north up
#
# translator = SOHOToSDO(patch_factor=2)
# iti_soho_maps = list(translator.translate([[f] for f in soho_files]))[0]
#
# ############################################################################################################################################################################
# # We set the magnetograms to a fixed value range [-1000, 1000]
#
# iti_soho_maps[-1].plot_settings['norm'] = Normalize(vmin=-1000, vmax=1000)
# soho_maps[-1].plot_settings['norm'] = Normalize(vmin=-1000, vmax=1000)
#
# ############################################################################################################################################################################
# # Now we plot the translated maps side by side with the original SOHO observations.
#
# fig, axs = plt.subplots(2, 5, subplot_kw={'projection': soho_maps[0]}, figsize=(40, 20), dpi=100)
# for i, (soho_map, iti_map) in enumerate(zip(soho_maps, iti_soho_maps)):
#     soho_map.plot(axes=axs[0, i])
#     iti_map.plot(axes=axs[1, i])
#     axs[0, i].set_title('SOHO/EIT', fontsize=30)
#     axs[1, i].set_title('ITI', fontsize=30)
# plt.show()