def get_cluster_info_from_user(self):
self.name = io.get_user_input("Enter the cluster name: ", "cluster name")
self.data_directory = os.path.normpath(config.data_directory())
self.observation_ids = get_observation_ids()
self.observations = [Observation(obsid=x, cluster=self) for x in self.observation_ids]
print()
get_fitting_values = \
io.check_yes_no("Enter values for fitting (nH, z, abundance) now? [y/n]")
if get_fitting_values:
self.hydrogen_column_density = io.get_user_input(
"Enter the hydrogen column density for {} (on order of 10^22, e.g. 0.052 for 5.2e20): ".format(self.name),
"hydrogen column density")
self.redshift = io.get_user_input("Enter the redshift of {}: ".format(self.name), "redshift")
self.abundance = io.get_user_input("Enter the abundance: ", "abundance")
else:
print("Before completing the ACB portion of the pypeline, you need "
"to edit the configuration file ({config}) "
"and update the values for hydrogen column density, redshift, "
"and abundance.".format(config=self.configuration_filename))
self.hydrogen_column_density = "Update me! (on order of 10^22 e.g. 0.052 for 5.2e20)"
self.redshift = "Update me! (e.g. 0.192)"
self.abundance = "Update me! (e.g. 0.2)"
self._last_step_completed = 1
return
fits.writeto(clstr.entropy_map_filename, norm_K, header=clstr.temperature_map_header, overwrite=True)
if __name__ == '__main__':
args, parser = get_arguments()
if args.cluster_config is not None:
clstr = cluster.load_cluster(args.cluster_config)
if args.commands:
make_commands_lis(clstr, args.resolution)
if args.eff_times_fits:
eff_times_to_fits(clstr)
elif args.temperature_map:
print("Creating temperature map.")
make_temperature_map(clstr, args.resolution)
elif args.pressure:
make_pressure_map(clstr)
elif args.entropy:
make_entropy_map(clstr)
elif args.shock:
import shockfinder
if io.check_yes_no("This is likely not going to work. Continue?"):
shockfinder.find_shock_in(clstr)
else:
fitting_preparation(clstr, args)
else:
parser.print_help()
def lightcurves_with_exclusion(cluster):
for observation in cluster.observations:
# data_nosrc_hiEfilter = "{}/acisI_nosrc_fullE.fits".format(obs_analysis_dir)
data_nosrc_hiEfilter = "{}/acisI_nosrc_hiEfilter.fits".format(observation.analysis_directory)
print("Creating the image with sources removed")
data = observation.acis_nosrc_filename
image_nosrc = "{}/img_acisI_nosrc_fullE.fits".format(observation.analysis_directory)
if io.file_exists(observation.exclude_file):
print("Removing sources from event file to be used in lightcurve")
infile = "{}[exclude sky=region({})]".format(data_nosrc_hiEfilter, observation.exclude)
outfile = "{}/acisI_lcurve.fits".format(observation.analysis_directory)
clobber = True
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
data_lcurve = "{}/acisI_lcurve.fits".format(observation.analysis_directory)
else:
yes_or_no = io.check_yes_no(
"Are there sources to be excluded from observation {} while making the lightcurve? ".format(observation.id))
if yes_or_no: # yes_or_no == True
print("Create the a region file with the region to be excluded and save it as {}".format(observation.exclude_file))
else:
data_lcurve = data_nosrc_hiEfilter
backbin = 259.28
echo = True
tstart = rt.dmkeypar(infile=data_nosrc_hiEfilter, keyword="TSTART", echo=echo)
tstop = rt.dmkeypar(infile=data_nosrc_hiEfilter, keyword="TSTOP", echo=echo)
print("Creating lightcurve from the events list with dmextract")
infile = "{}[bin time={}:{}:{}]".format(data_lcurve, tstart, tstop, backbin)
outfile = "{}/acisI_lcurve.lc".format(observation.analysis_directory)
opt = "ltc1"
rt.dmextract.punlearn()
rt.dmextract(infile=infile, outfile=outfile, opt=opt, clobber=clobber)
lcurve = outfile
print("Cleaning the lightcurve by removing flares with deflare. Press enter to continue.")
rt.deflare.punlearn()
infile = lcurve
outfile = "{}/acisI_gti.gti".format(observation.analysis_directory)
method = "clean"
save = "{}/acisI_lcurve".format(observation.analysis_directory)
rt.deflare(infile=infile, outfile=outfile, method=method, save=save)
gti = outfile
print("filtering the event list using GTI info just obtained.")
infile = "{}[@{}]".format(data_nosrc_hiEfilter, gti)
outfile = observation.clean
clobber = True
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
data_clean = outfile
print("Don't forget to check the light curves!")