temperatures = 10**np.linspace(5.7, 7.1, num=nt + 1)
logtemps = np.linspace(5.7, 7.1, num=nt + 1)
tresp = read_csv('tresp.csv').to_numpy()
# print(tresp_logt.keys())
# data=np.ones([nx,ny,nf])
# edata=np.ones([nx,ny,nf])/10
# dem_norm=np.ones([nx,ny,nt])
data = np.array([3.4, 13.8, 184, 338, 219.55, 12.22])
edata = np.array([0.2, 0.43, 7.83, 12.9, 5.80, 0.23])
dem_norm = np.array([
0.082588151, 0.18005607, 0.30832890, 0.47582966, 0.66201794, 0.83059740,
0.93994260, 0.95951378, 0.88358527, 0.73393929, 0.54981130, 0.37136465,
0.22609001, 0.11025056
])
correction_table = get_correction_table()
wavenum = ['94', '131', '171', '193', '211', '335']
channels = []
for i in np.arange(len(wavenum)):
channels.append(float(wavenum[i]) * u.angstrom)
time_calibration = time.Time('2014-01-01T00:00:00', scale='utc')
time_test = time.Time('2014-01-01T00:00:00', scale='utc')
# deg_calibration = {}
deg_calibration = np.zeros([len(channels)])
# deg = {}
deg = np.zeros([len(channels)])
for i, c in enumerate(channels):
def __init__(self, do_deconv=False, do_degrad=True):
self.do_deconv = do_deconv
self.do_degrad = do_degrad
if self.do_degrad :
self.correction_table = get_correction_table()
def create_iron18(dir_094=None,
dir_171=None,
dir_211=None,
outdir=None,
tr_degradation_corr=[True, '2018-09-09T12:00:00'],
needing_prepped=False):
"""Takes the 94, 171, 211 channels from SDO/AIA to create an iron18 emission proxy (Del Zanna 2013).
Parameters
----------
dir_*** : str
The string of the directory with the 94A (dir_094), 171A (dir_171), and 211A (dir_211) files.
Default: None
outdir : str
Directory for the output iron18 files.
Default: None
tr_degradation_corr : list [bool, str]
Do you want the iron18 to be created where 94, 171, 211 have been corrected for the instrument degredation?
Also provide a time for the data-set of the form "YYYY-MM-DDTHH:mm:ss".
(This should always really be True with the right time.)
Default: [True, '2018-09-09T12:00:00']
needing_prepped : bool
Is the AIA data in dir_*** needing prepped?
Default: False
Returns
-------
Filenames of the iron 18 files.
"""
files_094_list = list(os.listdir(dir_094))
files_094 = []
files_094 = [f for f in files_094_list if f.endswith('.fits')]
files_094.sort()
files_171_list = list(os.listdir(dir_171))
files_171 = []
files_171 = [f for f in files_171_list if f.endswith('.fits')]
files_171.sort()
files_211_list = list(os.listdir(dir_211))
files_211 = []
files_211 = [f for f in files_211_list if f.endswith('.fits')]
files_211.sort()
degs = [1, 1, 1]
if tr_degradation_corr[0] is True:
import warnings
warnings.simplefilter('ignore')
from aiapy.calibrate import degradation
from aiapy.calibrate.util import get_correction_table, CALIBRATION_VERSION
import astropy.units as u
from astropy import time
correction_table = get_correction_table()
time_obs = time.Time(tr_degradation_corr[1], scale='utc')
channels = [94, 171, 211] * u.angstrom
for i in range(3):
deg = degradation(channels[i],
time_obs,
correction_table=correction_table)
degs[i] = deg.value
co_094 = []
co_171 = []
co_211 = []
output = []
for fn094 in files_094:
if needing_prepped:
time_094 = data_handling.getTimeFromFormat(
fn094[-39:-20], custom_fmt='%Y_%m_%dt%H_%M_%S')
else:
time_094 = data_handling.getTimeFromFormat(
fn094[3:18]
) #datetime.datetime.strptime(fn094[3:18], '%Y%m%d_%H%M%S')
for fn171 in files_171:
if needing_prepped:
time_171 = data_handling.getTimeFromFormat(
fn171[-39:-20], custom_fmt='%Y_%m_%dt%H_%M_%S')
else:
time_171 = data_handling.getTimeFromFormat(
fn171[3:18]
) #datetime.datetime.strptime(fn171[3:18], '%Y%m%d_%H%M%S')
if time_094 <= time_171 < time_094 + timedelta(seconds=12):
for fn211 in files_211:
if needing_prepped:
time_211 = data_handling.getTimeFromFormat(
fn211[-39:-20], custom_fmt='%Y_%m_%dt%H_%M_%S')
else:
time_211 = data_handling.getTimeFromFormat(
fn211[3:18]
) #datetime.datetime.strptime(fn211[3:18], '%Y%m%d_%H%M%S')
if time_094 <= time_211 < time_094 + timedelta(seconds=12):
co_094.append(fn094)
co_171.append(fn171)
co_211.append(fn211)
break
break
files_094 = co_094
files_171 = co_171
files_211 = co_211
d = 1
d_total = len(files_094)
for f094, f171, f211 in zip(files_094, files_171, files_211):
aia_map_094 = sunpy.map.Map(dir_094 + f094)
if needing_prepped:
aia_map_094 = prep(aia_map_094)
data_094 = aia_map_094.data / aia_map_094.meta['exptime']
data_094[data_094 < 0] = 0
aia_map_171 = sunpy.map.Map(dir_171 + f171)
if needing_prepped:
aia_map_171 = prep(aia_map_171)
data_171 = aia_map_171.data / aia_map_171.meta['exptime']
data_171[data_171 < 0] = 0
aia_map_211 = sunpy.map.Map(dir_211 + f211)
if needing_prepped:
aia_map_211 = prep(aia_map_211)
data_211 = aia_map_211.data / aia_map_211.meta['exptime']
data_211[data_211 < 0] = 0
Iron_18 = data_094 / degs[0] - data_211 / (
120 * degs[2]) - data_171 / (450 * degs[1])
Iron_18[Iron_18 < 0] = 0
aia_map_Fe18 = sunpy.map.Map(Iron_18, aia_map_094.meta)
del aia_map_094
del aia_map_171
del aia_map_211
file = fits.open(dir_094 + f094)
hdr = file[0].header
file.close()
primary_hdu = fits.PrimaryHDU(data=Iron_18, header=hdr)
hdul = fits.HDUList([primary_hdu])
if needing_prepped:
f094 = "AIA" + f094[-39:-35] + f094[-34:-32] + f094[
-31:-29] + "_" + f094[-28:-26] + f094[-25:-23] + f094[
-22:-20] + "_0094.fits"
hdul.writeto(outdir + f094[:18] + '_FeXVIII.fits', overwrite=True)
#names the file as 'AIAYYYYMMDD_HHmmss_FeXVIII.fits'
#change header info
fits.setval(outdir + f094[:18] + '_FeXVIII.fits',
'pixlunit',
value='DN/s')
fits.setval(outdir + f094[:18] + '_FeXVIII.fits', 'exptime', value=1)
#add in info that this is iron 18 apart from just the filename
fits.setval(outdir + f094[:18] + '_FeXVIII.fits',
'iron_channel',
value='iron18')
fits.setval(outdir + f094[:18] + '_FeXVIII.fits',
'file094',
value=f094)
fits.setval(outdir + f094[:18] + '_FeXVIII.fits',
'file171',
value=f171)
fits.setval(outdir + f094[:18] + '_FeXVIII.fits',
'file211',
value=f211)
if tr_degradation_corr[0] is True:
fits.setval(outdir + f094[:18] + '_FeXVIII.fits',
'temp_resp_info',
value='atLaunch')
deg_94_str, deg_171_str, deg_211_str, cal_ver_str = str(
degs[0]), str(degs[1]), str(degs[2]), str(CALIBRATION_VERSION)
fits.setval(outdir + f094[:18] + '_FeXVIII.fits',
'cal_ver',
value=cal_ver_str)
fits.setval(outdir + f094[:18] + '_FeXVIII.fits',
'deg_94',
value=deg_94_str)
fits.setval(outdir + f094[:18] + '_FeXVIII.fits',
'deg_171',
value=deg_171_str)
fits.setval(outdir + f094[:18] + '_FeXVIII.fits',
'deg_211',
value=deg_211_str)
output.append(f094[:18] + '_FeXVIII.fits')
print(
f'\r[function: {sys._getframe().f_code.co_name}] Saved {d} submap(s) of {d_total}. ',
end='')
d += 1
return output
def batch_dem_jp2(t_start,
cadence,
nobs,
fits_dir,
jp2_dir,
get_fits=0,
serr_per=10,
min_snr=2,
fe_min=2,
use_fe=False,
sat_lvl=1.5e4,
mk_jp2=False,
plot_out=False,
plot_loci=False,
mk_fits=False,
xp=370,
yp=750):
"""
batch script for loading (or downloading) synoptic data from jsoc, setting up the AIA degradation and temperature response etc.
running demregpy to produce 2-d DEM maps. Finally the code has optional very basic plotting routines and an optional call to
dem2jp2 to make jpeg2000 greyscale, bytescaled 8 bit images for hv.
"""
version_number = 1.2
contact_email = '*****@*****.**'
location = 'University of Glasgow A+A'
#we only want optically thin coronal wavelengths
wavenum = ['94', '131', '171', '193', '211', '335']
t_start = Time(t_start)
print(t_start)
for obs in np.arange(nobs):
t_obs = (t_start + TimeDelta(obs * cadence, format='sec'))
print(t_obs)
t_obs.precision = 0
#convert our string into a datetime object
# t=dateutil.parser.parse(TimeString(t_obs))
t = t_obs.to_datetime()
#deconstruct the datetime object into a synoptica data filename
file_str = [('AIA' + str(t.year).zfill(4) + str(t.month).zfill(2) +
str(t.day).zfill(2) + '_' + str(t.hour).zfill(2) +
str(t.minute).zfill(2) + '_' +
"{}".format(wave.zfill(4)) + '.fits')
for j, wave in enumerate(wavenum)]
dir_str = str(t.year).zfill(4) + '/' + str(
t.month).zfill(2) + '/' + str(t.day).zfill(2) + '/'
syn_url = 'http://jsoc.stanford.edu/data/aia/synoptic/'
nf = len(file_str)
cklist = []
for file in file_str:
cklist.append(os.path.isfile(fits_dir + dir_str + file))
if not os.path.isdir(fits_dir + dir_str):
os.makedirs(fits_dir + dir_str)
if not all(cklist):
# not all the files exist.
print('Downloading synoptic data')
url = [(syn_url + dir_str + 'H' + str(t.hour).zfill(2) + '00' +
'/' + file_str[jj]) for jj, c in enumerate(file_str)]
for jj, c in enumerate(file_str):
# h = httplib2.Http()
# open the webpage
# resp = h.request(url[jj], 'HEAD')
request = requests.get(url[jj])
if request.status_code < 400:
# webpage exists so download
wget.download(url[jj], fits_dir + dir_str)
cklist = []
for file in file_str:
# check for files again
cklist.append(os.path.isfile(fits_dir + dir_str + file))
if not all(cklist):
#skipping this observation
print('\n Missing synoptioc data for ' + str(t) +
'...Skipping to next...')
continue
#find the files in their directory
fits_files = [
fits_dir + dir_str + file_str[j] for j in np.arange(len(file_str))
]
#load the fits with sunpy
aia = Map(fits_files)
if aia[0].meta['percentd'] < 95.0:
#check if the percentage data > 95 otherwise skip next observation
print('\n PERCENTAGE GOOD DATA BELOW THRESHOLD = ' +
str(aia[0].meta['percentd']) + ' at ' + str(t) +
'\n...Skipping to next...')
dem = Dem()
continue
if aia[2].meta['datamax'] < 1e3:
#check if the data is there, sometimes synoptic outputs maps of noise only if the observation not taken, we just threshold aia 171 at 1k DN
print(
'\n Image looks like noise, possibly broken synoptic maps? Datamax = '
+ str(aia[2].meta['datamax']) + ' at ' + str(t) +
'\n...Skipping to next...')
dem = Dem()
continue
correction_table = get_correction_table()
# correction_table=get_correction_table('aia_V8_20171210_050627_response_table.txt') #for use as a hardcoded response file
cal_ver = 10
#correct the images for degradation
aia = [
correct_degradation(m,
correction_table=correction_table,
calibration_version=cal_ver) for m in aia
]
# aia = [update_pointing(m) for m in aia]
channels = [aia[i].wavelength for i in range(nf)]
nt = 32
t_space = 0.05
t_min = 5.6
logtemps = np.linspace(t_min, t_min + t_space * nt, num=nt + 1)
temperatures = 10**logtemps
logt_bin = np.zeros(nt)
for i in np.arange(nt):
logt_bin[i] = (logtemps[i] + logtemps[i + 1]) / 2
tren = io.readsav('aia_trespv9_en.dat')
tresp_logt = tren.logt
tresp_calibrated = np.zeros([tresp_logt.shape[0], nf + 1])
tresp_calibrated[:, :-1] = tren.tr.T
#initialise structure
dem = Dem()
dem.bitpix = 8
nx = aia[0].meta['naxis1']
ny = aia[0].meta['naxis2']
dem.naxis1 = nx
dem.naxis2 = ny
dem.crota2 = 0
dem.crval1 = aia[0].meta['crval1']
dem.crval2 = aia[0].meta['crval2']
dem.crpix1 = 512.5
dem.crpix2 = 512.5
dem.cdelt1 = aia[0].meta['cdelt1'] * 4.0
dem.cdelt2 = aia[0].meta['cdelt2'] * 4.0
dem.cunit1 = aia[0].meta['cunit1']
dem.cunit2 = aia[0].meta['cunit2']
dem.dsun_obs = aia[0].meta['dsun_obs']
dem.crlt_obs = aia[0].meta['crlt_obs']
dem.crln_obs = aia[0].meta['crln_obs']
dem.hglt_obs = B0(t_obs).value
dem.hgln_obs = 0
dem.temperatures = temperatures
dem.minTemp = logtemps[0]
dem.maxTemp = logtemps[-1]
dem.t_obs = t_obs
dem.filt_use = 6
dem.rsun_ref = 6.957E+08
dem.rsun_obs = angular_radius(t_obs).value
dem.hv_zero = np.log10(dem.dem_min)
dem.hv_scale = (np.log10(dem.dem_max) - np.log10(dem.dem_min)) / 255
dem.contact = contact_email
dem.produced = 'Produced at ' + location + ' on: ' + datetime.today(
).strftime('%Y-%m-%d')
dem.dem_ver = version_number
dem1 = Dem()
dem1.bitpix = 8
dem1.naxis1 = nx
dem1.naxis2 = ny
dem1.crota2 = aia[0].meta['crota2']
dem1.crval1 = aia[0].meta['crval1']
dem1.crval2 = aia[0].meta['crval2']
dem1.crpix1 = 512.5
dem1.crpix2 = 512.5
dem1.cdelt1 = aia[0].meta['cdelt1'] * 4.0
dem1.cdelt2 = aia[0].meta['cdelt2'] * 4.0
dem1.cunit1 = aia[0].meta['cunit1']
dem1.cunit2 = aia[0].meta['cunit2']
dem1.dsun_obs = aia[0].meta['dsun_obs']
dem1.crlt_obs = aia[0].meta['crlt_obs']
dem1.crln_obs = aia[0].meta['crln_obs']
dem1.hglt_obs = B0(t_obs).value
dem1.hgln_obs = 0
dem1.minTemp = logtemps[0]
dem1.maxTemp = logtemps[-1]
dem1.filt_use = 7
dem1.rsun_ref = 6.957E+08
dem1.rsun_obs = np.rad2deg(np.arctan2(dem1.rsun_ref,
dem1.dsun_obs)) * 3600
dem1.hv_zero = np.log10(dem1.dem_min)
dem1.hv_scale = (np.log10(dem1.dem_max) - np.log10(dem1.dem_min)) / 255
dem1.contact = contact_email
dem1.produced = 'Produced at ' + location + ' on: ' + datetime.today(
).strftime('%Y-%m-%d')
dem1.dem_ver = version_number
data = np.zeros([nx, ny, nf + 1])
#errors in dn/px
npix = 4096.**2 / (nx * ny)
edata = np.zeros([nx, ny, nf + 1])
gains = np.array([18.3, 17.6, 17.7, 18.3, 18.3, 17.6])
dn2ph = gains * [94, 131, 171, 193, 211, 335] / 3397.0
rdnse = 1.15 * np.sqrt(npix) / npix
drknse = 0.17
qntnse = 0.288819 * np.sqrt(npix) / npix
for f in range(nf):
#convert from our list to an array of data
data[:, :, f] = aia[f].data
data[data < 0.0] = 0.0
for j in np.arange(nf):
shotnoise = (dn2ph[j] * data[:, :, j])**0.5 / dn2ph[j]
esys = serr_per / 100.0 * data[:, :, j]
etemp = np.sqrt(rdnse**2. + drknse**2. + qntnse**2. + shotnoise**2)
edata[:, :, j] = np.sqrt(esys**2 + etemp**2)
for f in range(nf):
#convert to values per second
data[:, :, f] = data[:, :, f] / aia[f].exposure_time.to(u.s).value
edata[:, :,
f] = edata[:, :, f] / aia[f].exposure_time.to(u.s).value
#calculate the hot component of aia 94
a94_fe18 = np.zeros([nx, ny])
a94_warm = np.zeros([nx, ny])
a94_fe18[:, :] = data[:, :,
0] - data[:, :, 4] / 120.0 - data[:, :,
2] / 450.0
a94_warm = data[:, :, 0] - a94_fe18[:, :]
#threshold of fe_min for the hot component
a94_fe18[a94_fe18 <= 0] = 0.0001
a94_warm[a94_warm <= 0] = 0.0001
data[:, :, 6] = a94_fe18
#now we need fe18 temp response in a94
trfe = (tresp_calibrated[:, 0] - tresp_calibrated[:, 4] / 120.0 -
tresp_calibrated[:, 2] / 450.0)
trfe[tresp_logt <= 6.4] = 1e-38
#remove low peak
tresp_calibrated[:, 6] = trfe + 1e-3 * tresp_calibrated[:, 0]
#errors on fe18 are a little arbitary, percentage error and a flat of 2...
edata[:, :, 6] = serr_per / 100 * data[:, :, 6] + 2.0
plt.rcParams.update({'font.size': 10})
# dem,edem,elogt,chisq,dn_reg=dn2dem_pos(data[x1:x2,y1:y2,:filt_use],edata[x1:x2,y1:y2,:filt_use],tresp_calibrated[:,:filt_use],tresp_logt,temperatures,dem_norm0=dem_norm0[x1:x2,y1:y2,:],max_iter=10)
x1 = 0
x2 = nx
y1 = 0
y2 = ny
filt_use = 6
norm_mean = 6.35
norm_std = 0.35
dem_norm0 = np.zeros([nx, ny, nt])
dem_norm = gaussian(logt_bin, norm_mean, norm_std)
dem_norm0[:, :, :] = dem_norm[:]
dem1.data, dem1.edem, dem1.elogt, dem1.chisq, dem1.dn_reg = dn2dem_pos(
data[x1:x2, y1:y2, :filt_use],
edata[x1:x2, y1:y2, :filt_use],
tresp_calibrated[:, :filt_use],
tresp_logt,
dem.temperatures,
max_iter=15,
dem_norm0=dem_norm0)
if plot_out == True:
aia_col = ['#c2c3c0', '#g0r0r0']
fig = plt.figure(figsize=(8, 7))
for j in range(int(nt / 2)):
fig = plt.subplot(4, 4, j + 1)
em_loci = data[xp, yp, :] / tresp_calibrated
plt.errorbar(logt_bin,
dem1.data[xp, yp + j * 5, :],
color='c',
xerr=dem1.elogt[xp, yp + j * 5, :],
yerr=dem1.edem[xp, yp + j * 5, :],
fmt='or',
ecolor='gray',
elinewidth=3,
capsize=0)
for i in range(7):
em_loci[:-1, i] = em_loci[:-1, i] / (10**tresp_logt[1:] -
10**tresp_logt[:-1])
if plot_loci == True:
plt.plot(tresp_logt[:-1], em_loci[:-1, :6])
ax = plt.gca()
plt.ylim([1e19, 1e23])
plt.xlim([5.7, 7.3])
plt.xlabel('$\mathrm{\log_{10}T\;[K]}$')
plt.ylabel('$\mathrm{DEM\;[cm^{-5}\;K^{-1}]}$')
plt.yscale('log')
ax.label_outer()
plt.gcf().suptitle("6 Filter", fontsize=14)
plt.gcf().tight_layout(pad=2.0)
fig = plt.figure(figsize=(8, 7))
for j in range(int(nt / 2)):
fig = plt.subplot(4, 4, j + 1)
plt.imshow(np.log10(dem1.data[:, :, 2 * j] + 1),
'inferno',
vmin=19,
vmax=24,
origin='lower')
ax = plt.gca()
ax.set_title('%.1f' % (t_min + 2 * j * 0.05))
plt.gcf().suptitle("dem1", fontsize=14)
if use_fe == True:
filt_use = 7
dem_norm0 = np.zeros([nx, ny, nt])
mxdem = np.max(dem1.data)
for ii in np.arange(nx):
for jj in np.arange(ny):
dem_norm0[ii,
jj, :] = (np.convolve(dem1.data[ii, jj, 1:-1],
np.ones(5) / 5))[1:-1] / mxdem
dem_norm0[dem_norm0 <= 1e-8] = 1e-8
if plot_out == True:
aia_col = ['#c2c3c0', '#g0r0r0']
fig = plt.figure(figsize=(8, 7))
for j in range(int(nt / 2)):
fig = plt.subplot(4, 4, j + 1)
em_loci = data[xp, yp, :] / tresp_calibrated
plt.errorbar(logt_bin,
dem_norm0[xp, yp + j * 5, :] * mxdem,
color='c',
xerr=dem1.elogt[xp, yp + j * 5, :],
yerr=dem1.edem[xp, yp + j * 5, :],
fmt='or',
ecolor='gray',
elinewidth=3,
capsize=0)
for i in range(7):
em_loci[:-1, i] = em_loci[:-1, i] / (10**tresp_logt[1:] -
10**tresp_logt[:-1])
if plot_loci == True:
plt.plot(tresp_logt[:-1], em_loci[:-1, :6])
ax = plt.gca()
plt.ylim([1e19, 1e23])
plt.xlim([5.7, 7.3])
plt.xlabel('$\mathrm{\log_{10}T\;[K]}$')
plt.ylabel('$\mathrm{DEM\;[cm^{-5}\;K^{-1}]}$')
plt.yscale('log')
ax.label_outer()
plt.gcf().suptitle("DEM NORM", fontsize=14)
plt.gcf().tight_layout(pad=2.0)
if use_fe == True:
data[a94_fe18 < fe_min, :] = 0
dem.data, dem.edem, dem.elogt, dem.chisq, dem.dn_reg = dn2dem_pos(
data[x1:x2, y1:y2, :filt_use],
edata[x1:x2, y1:y2, :filt_use],
tresp_calibrated[:, :filt_use],
tresp_logt,
temperatures,
dem_norm0=dem_norm0[x1:x2, y1:y2, :],
max_iter=15)
if use_fe == True:
dem.data[a94_fe18 < fe_min, :] = dem1.data[a94_fe18 < fe_min, :]
dem.data[dem.data <= 0] = 1
if plot_out == True:
aia_col = ['#c2c3c0', '#g0r0r0']
fig = plt.figure(figsize=(8, 7))
for j in range(int(np.floor(nt / 2))):
fig = plt.subplot(4, 4, j + 1)
em_loci = data[xp, yp, :] / tresp_calibrated
plt.errorbar(logt_bin,
dem.data[xp, yp + j * 5, :],
color='c',
xerr=dem.elogt[xp, yp + j * 5, :],
yerr=dem.edem[xp, yp + j * 5, :],
fmt='or',
ecolor='gray',
elinewidth=3,
capsize=0)
for i in range(7):
em_loci[:-1, i] = em_loci[:-1, i] / (10**tresp_logt[1:] -
10**tresp_logt[:-1])
if plot_loci == True:
plt.plot(tresp_logt[:-1], em_loci[:-1, :6])
ax = plt.gca()
plt.ylim([1e19, 1e23])
plt.xlim([5.7, 7.3])
plt.xlabel('$\mathrm{\log_{10}T\;[K]}$')
plt.ylabel('$\mathrm{DEM\;[cm^{-5}\;K^{-1}]}$')
plt.yscale('log')
ax.label_outer()
plt.gcf().suptitle("7", fontsize=14)
plt.gcf().tight_layout(pad=2.0)
fig = plt.figure(figsize=(8, 7))
for j in range(int(nt / 2)):
fig = plt.subplot(4, 4, j + 1)
plt.imshow(np.log10(dem.data[:, :, j * 2] + 1),
'inferno',
vmin=19,
vmax=24,
origin='lower')
ax = plt.gca()
ax.set_title('%.1f' % (t_min + j * 2 * 0.05))
plt.gcf().suptitle("7", fontsize=14)
plt.gcf().tight_layout(pad=2.0)
if plot_out == True:
aia_col = ['#c2c3c0', '#g0r0r0']
fig = plt.figure(figsize=(8, 7))
for j in range(int(np.floor(nt / 2))):
fig = plt.subplot(4, 4, j + 1)
em_loci = data[xp, yp, :] / tresp_calibrated
plt.errorbar(logt_bin,
dem.data[xp, yp + j * 5, :],
color='c',
xerr=dem.elogt[xp, yp + j * 5, :],
yerr=dem.edem[xp, yp + j * 5, :],
fmt='or',
ecolor='gray',
elinewidth=3,
capsize=0)
for i in range(7):
em_loci[:-1, i] = em_loci[:-1, i] / (10**tresp_logt[1:] -
10**tresp_logt[:-1])
if plot_loci == True:
plt.plot(tresp_logt[:-1], em_loci[:-1, :6])
ax = plt.gca()
plt.ylim([1e19, 1e23])
plt.xlim([5.7, 7.3])
plt.xlabel('$\mathrm{\log_{10}T\;[K]}$')
plt.ylabel('$\mathrm{DEM\;[cm^{-5}\;K^{-1}]}$')
plt.yscale('log')
ax.label_outer()
plt.gcf().suptitle("Combo", fontsize=14)
plt.gcf().tight_layout(pad=2.0)
fig = plt.figure(figsize=(8, 7))
for j in range(int(nt / 2)):
fig = plt.subplot(4, 4, j + 1)
plt.imshow(np.log10(dem.data[:, :, j * 2] + 1),
'inferno',
vmin=19,
vmax=24,
origin='lower')
ax = plt.gca()
ax.set_title('%.1f' % (t_min + j * 2 * 0.05))
plt.gcf().suptitle("Combo", fontsize=14)
plt.gcf().tight_layout(pad=2.0)
aia[0].peek()
plt.show()
dem.nimg = int(np.floor(nt / 4))
if mk_jp2 == True:
if not os.path.isdir(jp2_dir + dir_str):
os.makedirs(jp2_dir + dir_str)
for i in range(dem.nimg):
img_data = (dem.data[:, :, i * 2] + dem.data[:, :, i * 2 + 1] +
dem.data[:, :, i * 2 + 2] +
dem.data[:, :, i * 2 + 3]) / 4
jp2_fname = (str(t.year).zfill(4) + '_' +
str(t.month).zfill(2) + '_' +
str(t.day).zfill(2) + '__' +
str(t.hour).zfill(2) + '_' +
str(t.minute).zfill(2) + '_' +
str(t.second).zfill(2) + '_00' +
'__DEM_REGINV_T_' + '%.2f_%.2f' %
(logtemps[i * 4], logtemps[i * 4 + 4]))
tmin = logtemps[i * 4]
tmax = logtemps[(i + 1) * 4]
print('writing ' + jp2_fname + ' img ' + str(i + 1) + ' of ' +
str(dem.nimg))
dem2jp2(img_data,
dem,
jp2_dir + dir_str + jp2_fname,
i,
tmin,
tmax,
mk_fits=mk_fits)
return dem