def ccd_sort(cluster):
print("Running ccd_sort on {}.".format(cluster.name))
for observation in cluster.observations:
print("Working on {}/{}".format(cluster.name, observation.id))
analysis_path = observation.analysis_directory
os.chdir(analysis_path)
evt1_filename = io.get_path("{}/{}".format(analysis_path,
io.get_filename_matching("acis*evt1.fits")[0]))
evt2_filename = io.get_path("{}/{}".format(analysis_path,
io.get_filename_matching("evt2.fits")[0]))
detname = rt.dmkeypar(infile=evt1_filename, keyword="DETNAM", echo=True)
print("evt1 : {}\nevt2 : {}\ndetname : {}".format(evt1_filename,
evt2_filename,
detname))
assert not isinstance(detname, type(None)), "detname returned nothing!"
detnums = [int(x) for x in detname.split('-')[-1]]
for acis_id in detnums:
print("{cluster}/{observation}: Making level 2 event file for ACIS Chip id: {acis_id}".format(
cluster=cluster.name,
observation=observation.id,
acis_id=acis_id))
rt.dmcopy(infile="{evt2_file}[ccd_id={acis_id}]".format(evt2_file=evt2_filename,
acis_id=acis_id),
outfile="acis_ccd{acis_id}.fits".format(acis_id=acis_id),
clobber=True)
acisI_list = io.get_filename_matching("acis_ccd[0-3].fits")
for i in range(len(acisI_list)):
acisI_list[i] = io.get_path("{obs_analysis_dir}/{file}".format(obs_analysis_dir=observation.analysis_directory,
file=acisI_list[i]))
io.write_contents_to_file("\n".join(acisI_list), observation.ccd_merge_list, binary=False)
merge_data_and_backgrounds(cluster, acisI_list)
return
def chandra_repro(indir="./", outdir="./repro", set_ardlib=False, clobber=True):
indir = io.get_path(indir)
outdir = io.get_path(outdir)
kwargs = {'indir': indir,
'outdir': outdir,
'set_ardlib': set_ardlib,
'clobber': clobber}
rt.chandra_repro.punlearn()
rt.chandra_repro(**kwargs)
return
def eff_times_to_fits(clstr: cluster.ClusterObj):
for observation in clstr.observations:
effbt = observation.effective_background_time
effdt = observation.effective_data_time
temp = fits.open(clstr.scale_map_file)
temp[0].data = effbt
print(io.get_path("writing {}/{}_{}_eff_bkg_time.fits".format(clstr.directory, clstr.name, observation.id)))
temp.writeto(io.get_path('{}/{}_{}_eff_bkg_time.fits'.format(clstr.directory, clstr.name, observation.id)))
temp[0].data = effdt
print(io.get_path('writing {}/{}_{}_eff_data_time.fits'.format(clstr.directory, clstr.name, observation.id)))
temp.writeto(io.get_path('{}/{}_{}_eff_data_time.fits'.format(clstr.directory, clstr.name, observation.id)))
def copy_event_files(source_dir, destination_dir):
os.chdir(source_dir)
evt2_filename = io.get_filename_matching("{source_dir}/acis*repro_evt2.fits".format(source_dir=source_dir))
if isinstance(evt2_filename, list):
evt2_filename = evt2_filename[-1]
bpix1_filename = io.get_filename_matching("{source_dir}/*repro_bpix1.fits".format(source_dir=source_dir))
if isinstance(bpix1_filename, list):
bpix1_filename = bpix1_filename[-1]
io.copy(evt2_filename, io.get_path("{}/evt2.fits".format(destination_dir)))
io.copy(bpix1_filename, io.get_path("{}/bpix1_new.fits".format(destination_dir)))
print("Copied level 2 event files")
return None
def actually_merge_observations_from(cluster):
print("Merging observations from {}.".format(cluster.name))
merged_directory = io.get_path('{}/merged_obs_evt2/'.format(
cluster.directory))
io.make_directory(merged_directory)
os.chdir(merged_directory)
merged_observations = []
for observation in cluster.observation_list:
evt2_file = "{}/{}/analysis/evt2.fits".format(cluster.directory,
observation)
merged_observations.append(evt2_file)
merged_lis = "{}/merged_obs.lis".format(merged_directory)
io.write_contents_to_file("\n".join(merged_observations),
merged_lis,
binary=False)
outroot = io.get_path("{}/{}".format(cluster.directory, cluster.name))
infile = "@{infile}[ccd_id=0:3]".format(infile=merged_lis) # for ACIS-I
# infile = "@{infile}".format(infile=merged_lis) # for ACIS-I & ACIS-S
xygrid = "1500:6500:4,1500:6500:4"
if len(merged_observations) == 1:
rt.fluximage.punlearn()
rt.fluximage(infile=infile,
outroot=outroot,
xygrid=xygrid,
clobber=True)
print("Only single observation, flux image created.")
elif len(merged_observations) > 1:
rt.merge_obs.punlearn()
rt.merge_obs(infiles=infile,
outroot=outroot,
xygrid=xygrid,
clobber=True,
parallel=True,
nproc=12)
def make_response_files(cluster):
for obsid in cluster.observation_ids:
print("Making response files for observation {}".format(obsid))
obs_analysis_dir = cluster.obs_analysis_directory(obsid)
region_file = io.get_path("{}/acisI_region_0.reg".format(obs_analysis_dir))
if (not io.file_exists(region_file)) or (io.file_size(region_file) == 0):
print("Region file {} does not exist.".format(region_file))
print("When DS9 opens, draw a small circle that covers a piece of each ACIS-I chip (~20 pixels) and save it as:\n" \
"{}".format(region_file))
print("Opening SAO DS9")
io.write_contents_to_file("", region_file, False)
ds9_arguments = "ds9 -regions system physical -regions shape circle -regions format ciao -zoom 0.5 " \
"-bin factor 4 {}/acisI_clean.fits".format(obs_analysis_dir)
subprocess.run([ds9_arguments], shell=True)
print('Creating global response file.')
create_global_response_file_for(cluster, obsid, region_file)
def sherpa_save_dir(self):
return io.get_path("{super_comp_dir}/sherpa/".format(
super_comp_dir=self.super_comp_dir
))
def sherpa_save_region(self, region_num):
return io.get_path("{sherpa}/{region_num}.p".format(
sherpa=self.sherpa_save_dir,
region_num=region_num
))
def effdtime_file_obs(self, observation_id):
return io.get_path("{super_comp_dir}/effdtime-{obsid}_circle.dat".format(
super_comp_dir=self.super_comp_dir,
obsid=observation_id
))
def bad_fits_file(self):
return io.get_path("{super_comp_dir}/{cluster_name}_worst_spectral_fits.csv".format(
super_comp_dir=self.super_comp_dir,
cluster_name=self.name
))
def super_comp_cluster_config(self):
return io.get_path("{super_comp_dir}/{name}_pypeline_config.ini".format(
super_comp_dir=self.super_comp_dir,
name=self.name
))
def spec_log_directory(self):
return io.get_path("{acb_dir}/log_files/".format(
acb_dir=self.acb_dir
))
def scale_map_file(self):
return io.get_path("{acb_dir}/{cluster_name}_pype_scale_map.fits".format(
acb_dir=self.acb_dir,
cluster_name=self.name
))
def acb_dir(self):
return io.get_path("{dir}/acb/".format(dir=self.directory))
def voronoi_region_file(self):
return io.get_path("{wvt_dir}/voronoi.reg".format(wvt_dir=self.wvt_directory))
def spec_lis(self, region_number):
return io.get_path("{super_comp_dir}/spec_{region_number}.lis".format(
super_comp_dir=self.super_comp_dir,
region_number=region_number
))
def pi_directory(self):
return io.get_path("{acb_dir}/pi_files/".format(
acb_dir=self.acb_dir
))
def scale_map_region_file(self):
return io.get_path("{acb_dir}/{cluster_name}_scale_map_region_index.fits".format(
acb_dir=self.acb_dir,
cluster_name=self.name
))
def super_comp_dir(self):
return io.get_path("{acb_dir}".format( #/super_computer/".format(
acb_dir=self.acb_dir
))
def sn_map(self):
return io.get_path("{acb_dir}/{cluster_name}_pype_SN_map.fits".format(
acb_dir=self.acb_dir,
cluster_name=self.name
))
def command_lis(self):
return io.get_path("{super_comp_dir}/commands_{name}.lis".format(
super_comp_dir=self.super_comp_dir,
name=self.name
))
def region_list(self):
return io.get_path("{acb_dir}/{cluster_name}_bin3_regionlist.list".format(
acb_dir=self.acb_dir,
cluster_name=self.name
))
def backI_clean_obs(self, observation_id):
return io.get_path("{super_comp_dir}/backI_clean_{obsid}.fits".format(
super_comp_dir=self.super_comp_dir,
obsid=observation_id
))
def filtered_region_list(self):
return io.get_path("{acb_dir}/{cluster_name}_filtered_regions.list".format(
acb_dir=self.acb_dir,
cluster_name=self.name
))
def scalemap_regionlist_file_obs(self, observation_id):
return io.get_path("{super_comp_dir}/{name}_scalemap_regionlist_phys_{obsid}.reg".format(
super_comp_dir=self.super_comp_dir,
name=self.name,
obsid=observation_id
))
def region_to_index(self):
return io.get_path("{acb_dir}/{cluster_name}_region_to_index.fits".format(
acb_dir=self.acb_dir,
cluster_name=self.name
))
def directory(self):
return io.get_path("{cluster_dir}/{obsid}/".format(cluster_dir=self.cluster.directory,
obsid=self.id))
def sources_file(self):
return io.get_path("{cluster_dir}/sources.reg".format(cluster_dir=self.directory))
def pressure_map_filename(self):
return io.get_path('{output_dir}/{name}_pressure.fits'.format(
output_dir=self.output_dir,
name=self.name
))
def exclude_file(self):
return io.get_path("{cluster_dir}/exclude.reg".format(cluster_dir=self.directory))
