def spec_extract(observation, region_file, region_num, min_counts):
infile = "{data_clean}[sky=region({region_file})]".format(
data_clean=observation.cluster.acisI_clean_obs(observation.id),
region_file=region_file
)
outroot = "{outdir}/acisI_region_{obsid}_{region_num}".format(
outdir=observation.cluster.super_comp_dir,
region_num=region_num,
obsid=observation.id
)
print("Starting specextract on {}".format(infile))
rt.specextract(infile=infile,
outroot=outroot,
weight='yes',
correct='no',
asp='@{}'.format(observation.pcad_asol),
combine='no',
mskfile=observation.acis_mask_sc,
# bkgfile=observation.back,
bkgresp="no",
badpixfile=observation.bad_pixel_file,
binspec=1,
clobber=True
)
io.append_to_file(observation.cluster.spec_lis(region_num), "{}.pi\n".format(outroot))
def extract_multi(info):
info = info.split('_')
obs = info[2]
reg = info[0] + '_' + info[1] + '.reg'
root = info[0] + '_' + info[1] + '_' + info[2]
evt2_file = '_' + info[3] + '_' + info[4]
wght = info[5]
reg_total = len(glob.glob('xaf_*.reg'))
reg_done = len(glob.glob('xaf_*_%s.pi') % str(obs))
print('Completed %s/%s regions' % (reg_done, reg_total))
if os.path.isfile(root + '.pi') is False:
evt2_filter = '../reprojected_data/' + obs + evt2_file + '[sky=region(%s)]' % reg # evt file for obsid with xaf.reg filter
bkg_reg = '../reprojected_data/background.reg'
bkg_filter = '../reprojected_data/' + obs + evt2_file + '[sky=region(%s)]' % bkg_reg # using the reproj_evt file with the background region
asp_file = '../reprojected_data/' + obs + '.asol'
bpix_file = '../reprojected_data/' + obs + '.bpix'
msk_file = '../reprojected_data/' + obs + '.mask'
os.system('punlearn specextract')
# Extract spectra
specextract(infile=evt2_filter, outroot=root, bkgfile=bkg_filter, asp=asp_file, badpixfile=bpix_file, mskfile=msk_file, weight=wght)
else:
print('File %s.pi found' % root)
def extract_multi(info):
info = info.split('_')
obs = info[2]
reg = info[0] + '_' + info[1] + '.reg'
root = info[0] + '_' + info[1] + '_' + info[2]
evt2_file = '_' + info[3] + '_' + info[4]
wght = info[5]
reg_total = len(glob.glob('xaf_*.reg'))
reg_done = len(glob.glob('xaf_*_%s.pi') % str(obs))
print('Completed %s/%s regions' % (reg_done, reg_total))
if os.path.isfile(root + '.pi') is False:
evt2_filter = '../reprojected_data/' + obs + evt2_file + '[sky=region(%s)]' % reg # evt file for obsid with xaf.reg filter
bkg_reg = '../reprojected_data/background.reg'
bkg_filter = '../reprojected_data/' + obs + evt2_file + '[sky=region(%s)]' % bkg_reg # using the reproj_evt file with the background region
asp_file = '../reprojected_data/' + obs + '.asol'
bpix_file = '../reprojected_data/' + obs + '.bpix'
msk_file = '../reprojected_data/' + obs + '.mask'
os.system('punlearn specextract')
# Extract spectra
specextract(infile=evt2_filter,
outroot=root,
bkgfile=bkg_filter,
asp=asp_file,
badpixfile=bpix_file,
mskfile=msk_file,
weight=wght)
else:
print('File %s.pi found' % root)
def getSpectrum(evt2,
srcReg,
nametag,
rtag,
hkfiles,
obsID,
dataPath,
bkgReg=''):
rt.specextract.punlearn()
[asol1, asol1lis, msk1, bpix] = hkfiles
inEvtFile = evt2 + "[sky=region(" + srcReg + ")]"
root = dataPath + str(obsID) + "_" + nametag + "_" + rtag
if bkgReg == '':
bkg = ''
else:
bkg = evt2 + "[sky=region(" + bkgReg + ")]"
rt.specextract.punlearn()
#print( inEvtFile)
#print( asol1)
#print( root)
rt.specextract(infile=inEvtFile,
outroot=root,
bkgfile=bkg,
asp=asol1,
mskfile=msk1,
badpixfile=bpix,
weight='no',
correctpsf='yes',
clobber='yes')
def extract_spec(obsid_list, regions, evt2_file, wght):
for reg in regions:
for obs in obsid_list:
root = reg[:-4] + '_' + obs
# Check if spectra has been extracted for the region and obsid #
if os.path.isfile(root + '.pi') is False:
print(
'Extracting spectra for obsid %s/%s: Completed %s/%s regions...'
% (obsid_list.index(obs) + 1, len(obsid_list),
regions.index(reg) + 1, len(regions)))
evt2 = glob.glob('../reprojected_data/' + str(obs) +
evt2_file) # evt file for obsid
evt2_filter = evt2[
0] + '[sky=region(%s)]' % reg # add xaf.reg filter
bkg_reg = glob.glob('../reprojected_data/background.reg')
bkg_filter = evt2[0] + '[sky=region(%s)]' % bkg_reg[
0] # using the reproj_evt file with the background region
asp_file = glob.glob('../reprojected_data/' + str(obs) +
'.asol')
asp_file = asp_file[0] # Turn into string from glob list
bpix_file = glob.glob('../reprojected_data/' + str(obs) +
'.bpix')
bpix_file = bpix_file[0]
msk_file = glob.glob('../reprojected_data/' + str(obs) +
'.mask')
msk_file = msk_file[0]
os.system('punlearn specextract') # Reset specextract
# Extract spectra
specextract(infile=evt2_filter,
outroot=root,
bkgfile=bkg_filter,
asp=asp_file,
badpixfile=bpix_file,
mskfile=msk_file,
weight=wght)
else:
print(
'Already extracted spectra for %s/%s obsids in %s/%s regions'
% (obsid_list.index(obs) + 1, len(obsid_list),
regions.index(reg) + 1, len(regions)))
def extract_spec(obsid_list, regions, evt2_file, wght):
for reg in regions:
for obs in obsid_list:
root = reg[:-4] + '_' + obs
# Check if spectra has been extracted for the region and obsid #
if os.path.isfile(root + '.pi') is False:
print('Extracting spectra for obsid %s/%s: Completed %s/%s regions...' % (obsid_list.index(obs)+1, len(obsid_list), regions.index(reg)+1, len(regions)))
evt2 = glob.glob('../reprojected_data/' + str(obs) + evt2_file) # evt file for obsid
evt2_filter = evt2[0] + '[sky=region(%s)]' % reg # add xaf.reg filter
bkg_reg = glob.glob('../reprojected_data/background.reg')
bkg_filter = evt2[0] + '[sky=region(%s)]' % bkg_reg[0] # using the reproj_evt file with the background region
asp_file = glob.glob('../reprojected_data/' + str(obs) + '.asol')
asp_file = asp_file[0] # Turn into string from glob list
bpix_file = glob.glob('../reprojected_data/' + str(obs) + '.bpix')
bpix_file = bpix_file[0]
msk_file = glob.glob('../reprojected_data/' + str(obs) + '.mask')
msk_file = msk_file[0]
os.system('punlearn specextract') # Reset specextract
# Extract spectra
specextract(infile=evt2_filter, outroot=root, bkgfile=bkg_filter, asp=asp_file, badpixfile=bpix_file, mskfile=msk_file, weight=wght)
else:
print('Already extracted spectra for %s/%s obsids in %s/%s regions' % (obsid_list.index(obs)+1, len(obsid_list), regions.index(reg)+1, len(regions)))
def create_global_response_file_for(cluster, obsid, region_file):
observation = cluster.observation(obsid)
#min_counts = 525
obs_analysis_dir = observation.analysis_directory
global_response_dir = "{}/globalresponse/".format(obs_analysis_dir)
io.make_directory(global_response_dir)
clean = observation.clean
back = observation.back
pbk0 = io.get_filename_matching("{}/acis*pbk0*.fits".format(obs_analysis_dir))[0]
bad_pixel_file = io.get_filename_matching("{}/bpix1_new.fits".format(obs_analysis_dir))[0]
rt.ardlib.punlearn()
rt.acis_set_ardlib(badpixfile=bad_pixel_file)
mask_file = io.get_filename_matching("{}/*msk1.fits".format(obs_analysis_dir))
make_pcad_lis(cluster, obsid)
infile = "{}[sky=region({})]".format(clean, region_file)
outroot = "{}/acisI_region_0".format(global_response_dir)
weight = True
correct_psf = False
pcad = "@{}/pcad_asol1.lis".format(obs_analysis_dir)
combine = False
bkg_file = ""
bkg_resp = False
group_type = "NUM_CTS"
binspec = 1
clobber = True
rt.specextract(infile=infile, outroot=outroot, weight=weight, correctpsf=correct_psf,
asp=pcad, combine=combine, mskfile=mask_file, bkgfile=bkg_file, bkgresp=bkg_resp,
badpixfile=bad_pixel_file, grouptype=group_type, binspec=binspec, clobber=clobber)
infile = "{}[sky=region({})][bin pi]".format(back, region_file)
outfile = "{}/acisI_back_region_0.pi".format(global_response_dir)
clobber = True
rt.dmextract.punlearn()
print("Running: dmextract infile={}, outfile={}, clobber={}".format(infile, outfile, clobber))
rt.dmextract(infile=infile, outfile=outfile, clobber=clobber)
rt.dmhedit.punlearn()
infile = "{}/acisI_region_0.pi".format(global_response_dir)
filelist = ""
operation = "add"
key = "BACKFILE"
value = outfile
rt.dmhedit(infile=infile, filelist=filelist, operation=operation, key=key, value=value)
observation = cluster.observation(obsid)
aux_response_file = '{global_response_directory}/acisI_region_0.arf'.format(
global_response_directory=observation.global_response_directory)
redist_matrix_file = '{global_response_directory}/acisI_region_0.rmf'.format(
global_response_directory=observation.global_response_directory)
io.copy(aux_response_file, observation.aux_response_file)
io.copy(redist_matrix_file, observation.redistribution_matrix_file)
def extract_and_fit_spectra(evt_file: str,
img_file: str,
reg_file: str,
bkg_file: str,
extract_spectra: bool = True,
fit_spectra: bool = True,
**kwargs) -> Optional[FluxObj]:
"""
Extract the spectrum from a specified region and fit it
:param evt_file: Path to the events file
:param img_file: Path to the image file
:param reg_file: Path to the region file that will be used to extract the spectrum
:param bkg_file: Path to the background region file
:param extract_spectra: Set this to True to extract the spectrum
:param fit_spectra: Set this to fit and save the model spectrum
...
:return: If the number of counts inside is greater than 40, an absorbed powerlaw will be fit. If not, a representative flux and energy will be chosen. The flux can be interactively scaled run time
:rtype: Tuple[float,float] or None
"""
dmstat.punlearn()
dmstat(f"{img_file}[sky=region({reg_file})]", centroid=False)
use_flux = False
photon_flux = None
monoenergy = None
if float(dmstat.out_sum) < 40:
print(
f"The counts in the core {dmstat.out_sum} are too small for spectral fitting"
)
print(f"Flux at a monoenergy will be used for the PSF simulation")
use_flux = True
# extract the spectrum
if extract_spectra:
specextract.punlearn()
specextract(
infile="{0}[sky=region({1})]".format(evt_file, reg_file),
outroot="core_spectrum",
bkgfile="{0}[sky=region({1})]".format(evt_file, bkg_file),
clobber=True,
)
if fit_spectra and not use_flux:
SpecUtils.prepare_spectra(**kwargs)
return None
# get the ra dec from sky coords
ra_dec = CoordUtils.get_centroid(evt_file, reg_file)
if use_flux:
# calculate the monoenergy
# Using case 2 in https://cxc.cfa.harvard.edu/ciao/why/monochromatic_energy.html
dmtcalc.punlearn()
dmtcalc(
"core_spectrum.arf",
"arf_weights",
"mid_energy=(energ_lo+energ_hi)/2.0;weights=(mid_energy*specresp)",
clobber=True,
)
dmstat.punlearn()
dmstat("arf_weights[mid_energy=2.0:7.0][cols weights,specresp]",
verbose="0")
out_sum = dmstat.out_sum
out_sum = [float(val) for val in out_sum.split(",")]
monoenergy = out_sum[0] / out_sum[1]
srcflux.punlearn()
srcflux(
evt_file,
",".join(ra_dec),
"flux",
bands=f"0.5:7.0:{monoenergy}",
psfmethod="quick",
verbose="0",
clobber=True,
)
dmkeypar.punlearn()
dmkeypar("flux_0.5-7.0.flux", "net_photflux_aper")
photon_flux = dmkeypar.rval
dmkeypar.punlearn()
dmkeypar("flux_0.5-7.0.flux", "net_rate")
print(f"Net count rate: {dmkeypar.rval}")
print(
f"Monoenergy: {monoenergy} keV, photon flux: {photon_flux} photons/s/cm^2"
)
scale_fac: Union[str,
float] = input("Scaling for photon flux (1): ")
if scale_fac == "":
scale_fac = 1
photon_flux *= float(scale_fac)
return FluxObj(photon_flux, monoenergy)
return None