Question: Solar AIA and XRT computation and diagnostics: - * * * * Please only answer in terms of IDL software on bladerunner server using a

Solar AIA and XRT computation and diagnostics: -
****Please only answer in terms of IDL software on bladerunner server using a termux/terminal interface.****
My goal is to perform the diagnostics for each AIA channel and then the final AIA data with the XRT data for comparisons and accuracy (how similar or same they are - how well do they agree with each other).
To that end, I am trying to get the temperature of the bright spot(s)on the bottom left "corner" coronal loop of the sun in AIA and XRT and compare them to check how well they agree with each other. I need to use IDL in bladerunner server on terminal/command screen. I have the .sav file (idl compabilbe)which contains Be thin and Be Med responsefunctions for XRT.to access this i use the command restore,'filename.sav' to get the arrays as structure. I also have the AIA data which may be recalled using ssw_service_get_data,"ssw_service_241010_102951_47353",/loud
My code so far (in terms of IDL on terminal): -
For the INCORRECT portion of the sun: -
read_sdo, 'ssw_cutout_20240408_172959_AIA_94_.fts', index94, data94
read_sdo, 'ssw_cutout_20240408_173006_AIA_131_.fts', index131, data131
read_sdo, 'ssw_cutout_20240408_173009_AIA_171_.fts', index171, data171
read_sdo, 'ssw_cutout_20240408_173004_AIA_193_.fts', index193, data193
read_sdo, 'ssw_cutout_20240408_172957_AIA_211_.fts', index211, data211
read_sdo, 'ssw_cutout_20240408_173000_AIA_335_.fts', index335, data335
help, index94,/str
help, data94
data94= data94[3300:3800,1600:2100]
data131= data131[3300:3800,1600:2100]
data171= data171[3300:3800,1600:2100]
data193= data193[3300:3800,1600:2100]
data211= data211[3300:3800,1600:2100]
data335= data335[3300:3800,1600:2100]
plot_image, data94
plot_image, data131
plot_image, data171
nx = n_elements(data94[*,0])
ny = n_elements(data94[0,*])
img_cube = dblarr(nx, ny,6)
img_cube[*,*,0]= data94
img_cube[*,*,1]= data131
img_cube[*,*,2]= data171
img_cube[*,*,3]= data193
img_cube[*,*,4]= data211
img_cube[*,*,5]= data335
help, img_cube
lgTmin =5.6
dlgT =0.1
nlgT =17
aia_sparse_em_init, timedepend='2024-Apr-08',/evenorm, $
use_lgtaxis=findgen(nlgT)* dlgT + lgTmin, $
bases_sigmas=[0.1,0.2,0.3,0.4]
tolfunc = 'aia_bp_estimate_error(y*[1,1,1,1,1,1],[94,131,171,193,211,335])'
aia_sparse_em_solve, img_cube[*,*,0:5], tolfunc=tolfunc, tolfac=1.4, $
oem=emcube, status=status, coeff=coeff, adaptive_tolfac=adaptive_tolfac_aia, $
tolmap=tolmap_aia
aia_sparse_dem_inspect, coeff, emcube, status, /ylog, img=aia_mag_cube
plot_image, emcube[*,*,5]
plot_image, emcube[*,*,10]
logt = findgen(nlgT)* dlgT + lgTmin
print, 10^logt[10]
exposure times for each channel:
IDL> print, index94.EXPTIME ; Should return exposure time for 94
2.9020310
IDL> print, index131.EXPTIME ; Should return exposure time for 131
2.9013330
IDL> print, index171.EXPTIME ; Should return exposure time for 171
2.0001760
IDL> print, index193.EXPTIME ; Should return exposure time for 193
1.9996150
IDL> print, index211.EXPTIME ; Should return exposure time for 211
2.9011910
IDL> print, index335.EXPTIME ; Should return exposure time for 335
2.9007950
aia_exptime =[2.9020310,2.9013330,2.0001760,1.9996150,2.9011910,2.9007950]
code that worked for the correct region: -
mreadfits, 'ssw_cutout_20240408_172959_AIA_94_.fts', index94, data94
mreadfits, 'ssw_cutout_20240408_173006_AIA_131_.fts', index131, data131
mreadfits, 'ssw_cutout_20240408_173009_AIA_171_.fts', index171, data171
mreadfits, 'ssw_cutout_20240408_173004_AIA_193_.fts', index193, data193
mreadfits, 'ssw_cutout_20240408_172957_AIA_211_.fts', index211, data211
mreadfits, 'ssw_cutout_20240408_173000_AIA_335_.fts', index335, data335
help, data94, data131, data171, data193, data211, data335
data94= data94[<

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